pure.tue.nl · SUMMARY This thesis provides complete revised and consistent time series of nominal and real manufacturing value added for different branches of Tanzanian

Eindhoven University of Technology

MASTER

Manufacturing statistics : reconstructing Tanzanian Manufacturing Value Added 1965-1995

Prins, I.M.

Award date:1997

Link to publication

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TECHNOLOGY AND DEVELOPMENT STIJDIES

Facu1ty of Teclmo1ogy Management

Eindhoven University of Teclmo1ogy

M.Sc. Theses Series TDS 1997.16

MANuFACTIJRING STATISTICS

SUPERVISORS:

Prof.dr. A Szinnai Prof.dr. C.A.A.M.Withagen Drs. M.F. Timmer

Reconstructing Tanzanian Manufacturing Value Added 1965-1995

Menno Prins, August 1997

MANUFACTURING STATISTICS

RECONSTRUCTING T ANZANIAN MANUFACTURING

V ALUE ADDED 1965-1995

Eindhoven, A11gust 1997.

SUPERVISORS:

prof.dr. A. Szirmai prof.dr. C.A.A.M. Withagen drs. M.P Timmer

Technology and Development Studies Faculty of Technology Management Eindhoven University of Technology

(M. Sc. Thesis)

MennoPrins

SUMMARY

This thesis provides complete revised and consistent time series of nominal and real manufacturing value added for different branches of Tanzanian manufacturing for the time span 1965-1995. The focus of the thesis is on reconstructing statistics of medium & large scale manufacturing industries ( covering establishments with tenor more persons engaged). This will enable a more accurate monitoring of industrial performance. We have assessed the quality of the primary data underpinning the manufacturing statistics and examined the compilation of final series of nominal and real value added within the context of national accounting. The intemationally standardised system of national accounts is the most comprehensive representation of the national economy and has been the guiding framework of our study.

In case of manufacturing value added at current prices it was found that shortfans in the coverage of manufacturing industries, flaws in making estimates for non-response and improp er identification of value added in the questionnaire used to inquire manufacturing establishments, caused huge biases in the value added figures derived at. After an in-depth analysis of the statistics, adjustments have been made. As a re sult manufacturing value added increased for the entire period, varying from 3% in 1978 to 127% in 1988. Level adjustments have been different for various manufacturing branches, resulting in considerable adjustments in the structure of manufacturing over time.

The core of calculating real value added for manufacturing is the calculation of an index of industrial production (liP). The liP is the ratio ofvalue added in a current year to the value added in a base year, with the effect of price changes filtered out. Two approaches to filter the effect of price changes will be discussed: (1) the indirect approach and (2) the direct approach. Although in theory the indirect approach has preferenee for the construction of an liP, for Tanzania we pref er the direct approach, mainly because no appropriate price index is available to deflate nominal value added series.

Since no liP has been publisbed for the years 1965-1985, we have constructed an liP, based on quantity data of 32 manufacturing commodities for this period. For the years 1985-1995 an liP has been published, however, the weights used to calculate the index were found to be inappropriate. Therefore, we have reweighted the liP 1985-1995, using 1989 census weights.

The adjustments to nominal and real value added have yielded the following new insights in the Tanzanian manufacturing performance:

1) The level of manufacturing bas been substantially higher according to the adjusted value added series at current prices.

2) Based on our adjusted value added figures, the structural changes in manufacturing between 1966 and 1989 have been less pronounced than was indicated by the unadjusted data. The textile sector remained a major contributor to manufacturing value added in the late eighties. The share of food and textiles in manufacturing value added has been around 50% for the entire period (1965-1995).

3) The adjusted real value added series clearly show tuming points in the processof industrialisation as differences in growth rates are more pronounced than the unadjusted series. Growth is higher in the sixties and seventies. The adjusted figures reveal a collap se of the manufacturing sector between 1978-1987. Recovery takes place between 1987-1990, but a renewed stagnation is identified in the nineties.

This thesis can be considered as a step forward in gaining insight in the performance of Tanzanian manufacturing and a step forward toward further improverneut of the national accounts of Tanzania.

CONTENTS

SUMMARY

PREFACE v

ABBREVIATIONS vii

1. INTRODUCTION 1

2. TANZANIAN MANUFACTURING PERFORMANCE: AN ORIENTATION IN THE FIELD OF RESEARCH3

2.1 BACKGROUND 3

2.1.1 Industry as Field of Interest 3 2.1.2 Tanzanian Industrial Performance in Brief 4 2.1. 3 Tanzanian Industrial Stafistics 5

2.2 RESEARCH FRAMEWORK 5 2.2.1 Research Question and~.E!:E!\[) 2.2.2 Scope and Limitations 6 2.2.3 Objectives and Significanee 6

3. DEALING WITH MANUFACTURING STATISTICS: A RESEARCH METHODOLOGY 7

3.1 METIIODOLOGICAL ISSUES 7 3.2 ASSESSMENT CRITERIA 8

3.2.1 Frameworkfor Data Col/eetion 9 3.2.2 Data Col/eetion 9 3.2.3 Data Interpreta/ion 9 3. 2.4 Data Campi/ation JO 3.2.5 Data Presenta/ion JO

4. THE MEASUREMENT OF VALUE ADDED AT CONSTANT PRICES: A THEORETICAL DISCOURSE 11

4.1 INTRODUCTION TO INDEX NUMBERS 11 4.2 THEORETICAL APPROACHES TO INDEX NUMBERS 13

4.2.1 Functional Approach 13 4.2.2 Statistica! Approach 15

4.3 SOMEINDEXNUMBERFORMULAE 16 4.3.1 Laspeyres, Paasche and Fisher Index Numbers 16 4.3.2 Base-Weightedversus Chain-weighted Index Numbers 18 4.3.3 Practical Implementation of Index Numbersl8

4.4 INDEX OF INDUSTRIAL PRODUCTION 20 4. 4.1 Economie Contents of the Index of In dustrial Production 20 4.4.2 Approximating the true Index ofindustrial Production 21 4.4.3 Direct Quantity Approach versus Indirect Price Dejlator Approach 22 4.4.4 The injluence of Deficiencies in Weights 25

4.4.5 FINDINGS AND CONCLUf.IONS 29

5. CDRRENT STATISTICAL PRACTICE FOR MANUFACTURING 31

5.1 MANUFACTIJRING IN THE NATION AL ACCOUNTS 31

5.J.J Constructing Manufacturing Value Added Series 3J 5.J.2 Manufacturing Series in the National Accounts 32

5.2 DATA SOURCES FOR MANUFACTIJRING 34

5. 2.J Annual Survey and Censuses of Industrial Production 34 5. 2. 2 Quarterly Survey of In dustrial Production 35 5.2.3 Input-Output Tables 35 5.2.4 Consumer and Producer Price Index Numbers 36

5.3 ASSESSMENT OF MANUFACTIJRING VALUE ADDED DATA 36

5.3.J MVA Estimates of Smal! Scale and Informal Sector Manufacturing 37 5.3.2 Nomina/ MVA of Medium & Large Sca/e Manufacturing 39 5.3.3 Rea/ iYJVA of Medium & Large Scale Manufacturing 42

5.4 FINDINGS AND CONCLUSIONS 45

6. ADJUSTMENTS TO THE STATISTICS 47

6.1 1989 CENSUSANALYSIS47

6.J.J Various Improvements 47 6.J.2 Reclassi.fication ofthe cost category 'All other Casts' 48

6.2 ANALYSIS OF 10+ MANUFACTIJRING VALUE ADDED 50

6.2.J Coverage Assessment of JO+ Manufacturing Establishments 50 6.2.2Adjustmentsto JO+ Nomina/MVA J965-J990 52 6.2.3 Adjustments to JO+ Rea/MVA J965-J990 55 6.2.4 Estimation of Nomina/ MVA J99J-J995 57

6.3 FINDINGS AND CONCLUSIONS 57

7. NEW INSIGHTS IN TANZANIAN MANUFACTURING PERFORMANCE 59

7.1 LEVELADWSTMENTSINNOMINAL VALUEADDED59

7.2 STRUCTIJRAL CHANGES IN MANUFACTIJRING 61

7.3 TRENDS INREALÜROWTH62

8. CONCLUSIONS 67

9. REFERENCES 69

APPENDIXES

A: IN-DEPTHANALYSISOFTHE 1989 CENSUS 75

BI: IN-DEPTHANALYSISOFNOMINALMANUFACTIJRING VALUEADDED 1978-1990 83

B2: ADruSTMENTS TO NO MIN AL MANUFACTIJRING V ALUE ADDED 1978-1990 93

C: ADWSTMENTS TO NO MIN AL MANUFACTIJRING V ALUE ADDED 1965-1978 105

D: ASSESSMENT OF THE DIRECTORY OF INDUSTRIAL EsT ABUSHMENTS 115

E: EsTIMATINGNOMINALMANUFACTIJRING VALUEADDED 1991-1995 119

F: ÜVERALLADWSTMENTSTONOMINALMANUFACTIJRINGVALUEADDED 1965-1995 123

G: CONSTRUCTING AN INDEX OF INDUSTRIAL PRODUCTION 1965-1985 125

G: REBASING THE INDEX OF INDUSTRIAL PRODUCTION 1985-1995 135

1: OVERALL ADruSTMENTS TO REAL MANUFACTIJRING V ALUE ADDED 1965-1995 147

ii

FIGURES AND TABLES

FIGURE 1-1 Schematised Contents of the Thesis 2

TABLE 4-1 Production ofTimber 1985-1990 12 FIGURE 4-1 Real Output as a Measure ofProductive Capacity 14 T ABLE 4-2 Quantity Index Numbers for Wood & Wood Products 17 T ABLE 4-3 Spiicing Runs of Index Numbers 19 T ABLE 4-4 Rebasing and Switching Runs of Index Numbers 19 T ABLE 4-5 Deviations in Weights applied for the PPI 28 TABLE 4-6 The liP compared to deflated value added indexes 1985-1990 28

FIGURE 5-1 Compilation of Manufacturing Value Added for the National Accounts 32 TABLE 5-1 Comparison National Accounts and ASIP MVA 33 T ABLE 5-2 Manufacturing Value Added from the 1976 Input-Output Table 38 T ABLE 5-3 Vale added shares for 14 selected Cammodities 43 T ABLE 5-4 Examination of weights used in the QSI 44

FIGURE 6-1 Cost Structure 1989 Census 48 T ABLE 6-1 Adjustments to value added in the 1989 census 49 TABLE 6-2 Number of 10+ Establishments in the DIE (1965-1990) 51 T ABLE 6-3 Results of the Sample: Response Rates of the ASIP and the censuses, 1978-1989 52 TABLE 6-4 Level adjustments to Nominal MVA 1965-1990 54 T ABLE 6-5 Unadjusted and Adjusted Index of Industrial Production 1965-1995 56

FIGURE 7-1 Unadjusted & Adjusted Nominal Value Added for 10+ Manufacturing, 1965-1990 60 TABLE 7-1 Structural Changes in Tanzanian 10+ Manufacturing 63 FIGURE 7-2 Value Added Shares of 10+ Manufacturing Branchesbasedon Unadjusted data for the years

1966, 1978 and 1989 64 FIGURE 7-3 Value Added Shares of 10+ Manufacturing Branchesbasedon Adjusted data for the years 1966,

1978 and 1989 64 FIGURE 7-4 Unadjusted and Adjusted Index ofReal Value Added 1965-1994 of 10+ Manufacturing 65 T ABLE 7-2 Annual Trend Rat es of Growth in Tanzanian 1 0+ Manufacturing for Three-year Periods, 1965-

1994.65 FIGURE 7-5 Growth Pattems for six 10+ Manufacturing Branches 1965-1995 66 FIGURE 7-6 Published and Reweighted Index of lndustrial Production for nine 10+ Manufacturing Branches

1985-1995 67

iii

PREFACE

I believe prefaces are meant to reveal some of what is called the story behind the paper it is prefacing. The contents ofthis thesis may be neat and well-ordered (at least I have tried to do so), the hidden story behind covers painstaking (and sometimes chaotic) number crunching, sleepless nights, a number of 'reflection breaks', times of de-motivation and re-motivation, but most of all it covers the story of a breath-taking existential African adventure. Looking back, I have discovered that some rather interesting paradoxes regarding my final research project have been apparent during the last years, ofwhich I feel compelled to share some.

lt may sound somewhat strange, but three years ago I would probably have been very unhappy with this thesis. At that time I had done some reflections on 'my place in this world' and had been trying to convince myselfthat I was not the right person to get involved in any kind oftechnology-supportingjob abroad. Workingin a developing country required an expert and an invitation and I lacked both at that time. So, as I carried on reflecting, I should quit (and I did forsome time) my Techno/ogy and Development studies and ensue student life in some other subject. How could all this happen? The answer should be a plain I don 't like technology, but although this seems a simple truth, I needed a few years to discover it and it has been only very recently that I have used it to explain the whole lot.

The attraction ofbeing a persistent student though, had lost its strengthafter some time. I got tired of slaloming around different studies and I wanted some real, new and exciting to happen. It was Mr. Szirrnai who ( starting a bout two years a go) successfully tickled my sense of curiosity and challenged me to get involved in economie statistics. Meanwhile, I had, more or less, come to terrns with my place in this technology-driven world and, last year I decided that statistics would be a perfect seamless way of maturing from someone who is 'absorbed' in technology as, to borrow a popular expression, a supernurd (yes, in the dim past I have been a true technician, indeed, an electronic engineer!) to someone who stays out of it and strictly confin es himself to numeric descriptions. Eventually, if maturing would carry on, I could become a philosopher in a later stage or even write poetry some day ...

For the time being I enjoy the stage oftrying to besomesort ofstatistician and it has been truly exciting to live it at the Bureau of Statistks_in_Dar.eS-Salaanrfrom April to October 1996. Three years ago I thought my place would not be found in Tanzania or any developing country (whatever that might be). The people of Tanzania, however, have granted me this place. I have never received so many welcomes (Karibu Tanzania) and invitations, as I did during this half-year-stay in Tanzania. How I have enjoyed my time in Tanzania among those who shared their Jives with me! One ofthem, Eric Ibrahim, has given me the best compliment I could dream of, brilliantly expressing how I have experienced my time in Tanzania and (what he didn't know) meanwhile 'metaphoring' the paradoxes which have remained characteristic for the story behind this thesis: You are white in face, but African by blood.

The completion ofthis thesis would nothave been possible without the con tribution ofmany. First of all, I would like to thank all the memhers of the Bureau of Statistics Tanzania for their kind co-operation and for perrnitting me to examine many unpublished sources. I am grateful to Mr. Komba and Mr. Freeman (World Bank), my field supervisors, fortheir instructive assistance and guidance. Mr. Freeman should be mentioned for his ideas to tackle particular problems in the industrial statistics ofwhich some have been the basis for adjustments presented in this thesis. A special word of gratitude deserves Elide Mwanri, who assisted me in various matters and really did a superb job of collecting data from old and dusty records. Furtherrnore, I would like to thank the memhers ofthe Industrial Section for providing numerous statistics, in formation on methodological issues and for kindly sharing their culture with me; memhers of the National Accounts Section for ideas, co-operation, air-conditioning, tea and titbits; Mr. Mahimbo Muhimu fora good time working together with our 'friend' Access; Mr. Kennedy (World Bank) for his

V

assistance on research methodology and Mr. Kent (Statistics Sweden) for supporting my attempts to match industrial registers.

I wish to thank Ezekiel Mwakajwana & Magdalena and Robert & Naomi Mhamba for providing a home for me in a magnificent way. In being so hospitabie and caring you have been of more help than you realise you have given me. Thanks Robert for taking me around in Dar es Salaam and the University. I am also grateful for Mr. Mjema (University of Dar es Salaam) who always seemed prepared to helpand provide me with more information on various matters.

I would like to express my gratitude to Mr. Szirmai for persuading me to return and round offmy studies, for his persistenee in motivating and challenging me and for keeping me on the right track throughout the course of my final research project. His countless guiding remarks, comments and advice as my first supervisor on the subject matters, deserve to be mentioned here. I am also grateful to Marcel Timmer for always being prepared to provide me with more ofhis excellent guiding remarks until the latest possible moments. Thanks are due to Mr. Withagen for helpful comments and stimulating me to get the general line clear.

My family and friends have been a true support for me in many ways. They financially aided me and expressed confidence in this project for which I am really grateful. Thanks are due to the VGSEi for giving me the opportunity to anticipate in the Indexcammiss ie. Thank Joehem for reading the final draft and providing useful comments.

Finally, I would like to thank Christ Jesus forsharing some ofhis splendid pieces of grace by making it possible for me to finish my studies.

Menno Prins,

Eindhoven, 15 August 1997.

vi

ASIP BoS BoT CPI CRE DIE GDP GO 11 liP IRQ ISIC MVA PPI QSIP RPI SIDO SNA Takwim u TSh. UN VA VETA

ABBREVIATIONS

Armual Survey of Industrial Production Bureau of Statistics Bank of Tanzania Consumer Price Index Central Register of Establishments Directory of Industrial Establishments Gross Dornestic Product Gross Output Intermediate Inputs Index of Industrial Production Index of Real Output International Standard Industrial Classification of All Economie Activities Manufacturing Value Added Producer Price Index Quarterly Survey of Industrial Production Retail Price Index Small Scale Industries Development Organisation System of National Accounts Bureau of Statistics Tanzanian Shilling United Nations Value Added Vocational Education and Training Authority

In the text we will refer to books, artiel es and statistica! sourees as they arise in a notation which relates to the References at the end of the text. However, in the text we will use abbreviations instead of full text. For example the statistica! souree Bureau of Statistics (July 1993) given in the References is referred to in the text as BoS (July 1993). When referring toa particularpage number (e.g. page 23), we will denote this as follows: BoS (July 1993: 23).

vii

1. INTRODUCTION

Within the faculty of Technology Management at the Eindhoven University of Technology the research group Technology and Development Studies (TDS) focuses on the analysis of technologkal development and the process of industrialisation in developing countries. The

research group TDS has developed a number of research activities to perform in-depth research on the role of industrialisation in the development of Tanzania. Along with other perspectives, TDS is interested in charting the pattem of industrial development. In this respect, the performance of the Tanzanian manufacturing sector and manufacturing branches are a primary field of interest.

The Bureau of Statistics (Takwimu), located in Dar es Salaam, provides all kinds of industrial statistics, which are continuously being updated and revised. The crux of the ongoing process of revising industrial statistics is embedded in the objective ofimproving the representation ofthe Tanzanian industry within the system of national accounts. The need for further improvements of the industrial statistics and the goal of gaining insight in the industrialisation process, has led to the formulation of a research assignment on industrial statistics fora TDS-master's student at Takwimu. The result of fieldresearch carried out at Takwimu from April till October 1996 are embodied in this thesis, which focuses on the analysis and improverneut of the manufacture of statistics representing the Tanzanian manufacturing industry.

For the sake of clarity we have presented the conten1 s of this thesis in three main parts in tigure 1. The contents of the frrst part, chapters 2 and 3, relate to tb.e design of research described in this thesis. The second part regards the examina/ion of statistica! theory and practice that has been carried out (presented in chapters 4 and 5). At last, chapter 6 and 7 (constituting the part adjustments) deal with the revisions that have been applied to the manufacturing statistics, ensuing from the fmdings of the examination performed in the previous chapters.

Chapter 2 provides an orientation in the field of research. It is plainly ciarifled what will be the subject of research. The backgrounds and problems related to our research subject are sketched and the research framework is presented. It is concluded that we will dedicate ourselves to the reconstruction of series of medium & large manufacturing value added at current and constant prices for different manufacturing branches. In Chapter 3 various methodological issues are dealt with, resulting in criteria on how to improve existing time series ofvalue added.

In chapter 4 an outline of index number theory is given and theoretica/ considerations are discussed in the search for an index of industrial production which fits best for Tanzania. Chapter 5 describes the current statistica! practice regarding manufacturing in Tanzania. W eaknesses in primary data and deficiencies in the construction ofvalue added series are reviewed. Chapter 6 and 7 describe

INTRODUCTION

adjustments that have been made to the manufacturing statistics resulting in revised time series of manufacturing value added at current prices and in an index of industrial production from 1965-1995.

DESIGN

EXAMINATION

REVISIONS

Chapter 2: Background & Research Framework

Chapter 3: Research Methodology

Chapter4: Measuring Value Added at Constant Prices

Chapter5: Current Statistica! Practice

Chapter6: Adjustments to the Stafistics

Chapter7: Resulting Revised Series

FIGURE 1-1 Schematised Contents of the Thesis

The detailed examination of statistica! sourees and the discussion of the various adjustments to the statistics are presented in Appendixes A to I. Appendix A describes the in-depth analysis of the census ofindustrial production carried out in 1989. The analysis ofthe statistics between 1978-1990 is presented in Appendix B. Appendix C provides a description of adjustments to the manufacturing statistics for the period 1965-1978. In Appendix D the directory of industrial establishments, which serves as a framework for collection primary data for the manufacturing industry, is being assessed. An estimate for manufacturing value added for the years 1990-1995 is given in Appendix E and the overall adjustments to manufacturing value added at current prices are summarised in Appendix F. Appendixes G, H and I deal with the construction and improverneut of the index of industrial production between 1965 and 1995.

2

2. TANZANIAN MANUFACTURING PERFORMANCE:

AN ORIENTATION IN THE FIELD OF RESEARCH

This chapter aims at giving a description of the background and the framework of research carried out. Some remarks about the relevanee of industry as field of interest are given, a brief overview of the history of the Tanzanian industry and the underlying statistles are presented and subsequently the

research framework is presented.

2.1 BACKGROUND

2.1.1 INDUSTRY AS FIELD OF INTEREST

Scientists and policy makers from all kind of backgrounds agree on the leading role of industrialisation in the development of the economie performance in the developed countries. Many developing countries have adapted development strategies with an emphasis on industrialisation. Tanzania is one of those developing countries striving to establish an industrial base since independenee was achieved in 1961 (Kuuya 1980: 69).

Throughout the existence of Tanzania as a nation, the Tanzanian government has attempted to achleve a better performing industrial sector. The contribution ofthe manufacturing sector to the gross dornestic product (GDP) ofthe national economy has remained small, but still it is recognised that it is of great importallee to have detailed knowledge of the industrial performance. It is even recognised that " ... industry generates new phenomena and it is therefore more important in the functioning of an economy than its share in GDP suggests." (BoS 1993). In addition, industrial statistics form the keystone for policy oriented economie decisions and policy evaluation. It is therefore indisputable that the basis for economie policy in a developing country will be improved by broadening insight in the performance of its manufacturing sector.

Insight in the performance of the manufacturing sector can be achieved by examination of m~in characteristics of the industry. The development of intemationally standardised concepts is an ongoing process, but there is a

TANZANIAN MANUFACTURING PERFORMANCE

widespread agreement about the importance of certain indicators descrihing the perfonnance of industry. These characteristics can be found within the industria1 statistics of the national accounts. The nationa1 accounts are the most comprehensive representation of an economy and are intemationally standardised in the System of Nationa1 Accounts (UN 1968 & UN 1993). With respect to concepts and definitions regarding performance indicators, this system of nationa1 accounting fonns the guiding framework for this study.

2.1.2 T ANZANIAN INDUSTRIAL PERFORMANCE IN BRIEF

At the time of independence, attained in 1961, the Tanzanian economie structure was oriented to the export of raw materia1s. Tanzania had virtually no industrial base, re1ying on imports for manufacturing products. Despite the fact that Tanzania was the 1argest memher of the East African Community and had manifold natura! resources like U ganda and Kenya " ... she was relegated to a position of secondary importance whenever the British investors set up manufacturing and processing p1ants" (Kuuya 1980: 74). Compared to Kenya, the infrastructure in Tanzania was less developed and the tariff policy in Kenya attracted more industries. Furthennore, Tanzania was lacking capita!.

The beginning ofTanzanian industrialisation somehow coincides with the end ofWor1d War 11. The manufacturing industry experienced a remarkable growth after the independence. In tenns of number of establishments, a high rate of growth can be observed in the post-independenee period. Before 1946, on1y a few establishments with 10 or more persons engaged were set up. Half of the tota1 establishments in 1965 was created between 1946 and 1960 and more than one third was set up between 1961 and 1965 (Rweyemamu 1973).

In tenns ofthe share ofva1ue added in tota1 va1ue added in the national economy, industrial growth between 1961 and 1973 was considerab1e. The share increased from 3.6% to 10.1% oftota1 GDP. The re1ative increase of manufacturing value added took p1ace hand in hand with a process of import substitution; the share of dornestic production in total supply increased from 32% (1961) to 45% (1973). The condusion ho1ds that impressive industrial growth took p1ace in the first two decades after independence, as many authors have concluded before (Skarstein & Wangwe 1984, Skarstein 1986, Bank of Tanzania 1981).

A landmark in the history of Tanzania was the Arusha declamtion (1967), where a policy of nationalisation and se1f-reliance was fonnulated. A new course of development was charted by this declaration, bringing the major means of production into public ownership and causing new investments mainly to take place in this new public sector. As Skarstein (1986) puts forward, no economie imperatives required the extensive nationalisation of the manufacturing industry. However, according to president Nyerere, the economie development of Tanzania up to 1967 had resulted in an increase of inequality between citizens, leading towards attitudes of social inequality (p. 82, 83).

Analyses of many authors again harmonise at the turn the industrial deve1opment takes in the late seventies. Although the exact years of reversal may be difficult to identify, it is observed that the contribution of manufacturing industry to GDP stagnated between 1972 and 1974 and started to decline in the following years. Although employment and manufacturing investment increased between 1973 and 1983, Skarstein (1986) conclude-; that this increase was 'largely independent of actual prodw tion' (p. 88). The share of manufacturing value added in total GDP decreased from 14.0% in 1:173 to 6.3% in U82.

With the turn of industrial growht to stagnation and even decline in the seventies and eighties, the industrial policy tended to a high level of control in the manufacturing industry, strict import controls, discouragement of production for the export market and further stimulation of the dornestic market In this period the economy faced a shortage of foreign exchange and strong devaluation of the Tanzanian Shilling (Van Engelen 1996).

From 1984 onwards, the govemment started liberalising the economy and the elimate for the manufacturing sector changed. The manufacturing output stopped its downward trend in 1987. Wangwe (1990) explains that this growth recovery has been facilitated by the relatively greater inflow of foreign exchange (p.34). In recent years the industry shows a continuing improvement, ascribed to various refonns initiated by the govemment. The govemment recently has launched measures to promote the entry of the private sector into virtually all branches ofthe economy, that were fonnerly state monopolies and carried out various trade refonns to promote foreign direct investment in the country (Board ofExtemal Trade 1996). Although the Board ofExtemal Trade concludes that the manufacturing sector still is 'at its infancy stage', an ongoing recovery of the manufacturing

4

AN ORIENTA TION IN THE FIELD OF RESEARCH

sector is identified and the National Investment Act (1990) is recognised as a milestone for the Tanzanian industry, signaling a shift away from the public to the private sector.

2.1.3 TANZANIAN INDUSTRIAL STATISTICS

In the previous paragraph the history of the Tanzanian industry has been briefly outlined. The text is based on what different researchers have concluded on the available industrial statistics, provided by the Bureau of Statistics in Tanzania (Takwimu). Some authors have noticed weaknesses in the industrial statistics (e.g., Mbelle 1990, Van Engelen 1996) and Coulson (1982) declares that it is 'not easy' to interpret the data forthe industrial sector. Others specifically refer to the underrecorded activities of the informal sector in the official statistics (e.g. Maliyamkano & Bagachwa 1990, Bagachwa & Naho 1995). The need for ongoing revision of industrial stalistics is fully recognised at Takwimu. In the latest revision of the national accounts, the problem of improp er representation of the informal sector is identified (Stäglin & Komba 1992) and the sometimes serious backlog in the pubHeation of industrial statistics is acknowledged as a serious problem (alo. Redeby 1988).

Industrial statistics for Tanzania can be grouped as follows:

1) Statistics of indicators of manufacturing performance such as gross output, value added, depreciation & number of persons employed ofthe medium & large scale industries1 basedon the annual survey of industrial production and the censuses of industrial production (available since 1965);

2) The quarterly survey of in dustrial production, containing quantity data of the production of manufacturing cammodities and an index of industrial production2 (available since 1985);

3) Time series of manufacturing value added at current and constant prices, presented in all publications of the Tanzanian national accounts (the series date back to 1961);

4) Data on manufacturing performance found in input-output tables and severallabour statistics;

5) The producer price index and informal sector surveys which have been publisbed more recently.

A quick examination of the available statistics, reveals that time series of manufacturing value added for different manufacturing branches over a longer time span are not available. Most recent revisions ofvalue added series at current prices go back to 1976 (e.g. Stäglin & Komba 1992). The value added series at constant prices have been presented at 1966 and 1976 prices and havenotbeen rebased for more recent years. A recurrent issue is the frequent lack of methodological descriptions that should accompany the numbers presented in different publications.

It would be premature to draw any substantive conclusions basedon this brief examination ofthe industrial statistics. Nevertheless, it can be concluded that the need for improverneut ofthe industrial statistics is widely recognised. We will pursue the examininatin ofthe quality ofthe industrial statistics insection 5.

2.2 RESEARCH FRAMEWORK

2.2.1 RESEARCH QUESTION AND GoAL OF RESEARCH

The wider field of interest in this study is the development of the Tanzanian industry since independenee from an economie point of view. Therefore, the underlying aim is to gain insight in the developments that have taken place in the structure and performance of the Tanzanian industry since independence. To gain insight we will particularly ask ourselves the following question: do the available stafistics rejlect the reality ofthe industrial performance over time? Accordingly, the focus ofthis thesis is on assessing and, where possible, improving the

1 We de fine medium & large scale industries as industries with I 0 or more persons engaged (I 0+ establishments). Small scale industries are defined to comprises establishments with 5 to 9 persons engaged (5-9 estab;ishments) and the informal sector covers 1-4 establishments. 2 The purpose ofthe index of in dustrial production is to show in a series the movement ofthe volume of output in the industrial sector. In section 4 (paragraph 4.4) we will extensively deal with the index of in dustrial production.

5

TANZANIAN MANUFACTURING PERFORMANCE

quality of the published statistics on manufacturing performance, aiming at improving the monitoring of industrial performance.

This study should be situated among ongoing efforts to improve the national accounts of Tanzania. The revision of 1992 (Stäglin & Komba 1992) is the latest materialisation of these efforts. During the fieldwork carried out for this thesis, the project Strengthening of National Accounts was being executed by Takwimu in co-operation with the World Bank. The fieldwork has been carried out in close co-operation with this project. Our goal of research is depicted as follows: make a contribution to the impravement ofthe quality ofthe industrial stafistics in the context ofthe national accounts of Tanzania.

2.2.2 SCOPE AND LIMITATIONS

Hitherto we have, without preference, used the terms industry as well as manufacturing. Sirree 'broad' and 'narrow' defmitions of industry exist, covering or excluding certain activities, weneed to be more specific. In this study we will focus on the manufacturing sector only, excluding mining, construction and electricity generation.

The focus of this study will be on constructing time series of manufacturing value added at current and constant prices. Where value added in current prices (nomina! value added) is used for analysis of structural changes within the manufacturing sector, the latter, often captured under real value added, is widely recognised as a key to understanding the production process (e.g. Cassing, 1996). Productivity analysis and the study of teehuical change all begin with analysing real value added (obviously in conjunction with otherperformance indicators such as employment)3

.

We will focus on the manufacturing sector of Tanzania mainland. Another limitation of this study regards the restricted coverage of the manufacturing sector. Although some attention will be paid to the way estimates have been made for the small scale and informal sector, the focus of this thesis is fully restricted to the improverneut of statistics regarding medium & large scale manufacturing (for definition see footnote 1). Due to lacking reliable statistics of medium & large scale manufacturing from 1961 to 1964, we willlimit our focus to the period 1965-1995.

2.2.3 ÜBJECTIVES AND SIGNIFICANCE

The objective of this study is to contribute to an improverneut of the empirica! economie information concerning the structure and performance of Tanzanian manufacturing. The following obj ectives are aimed at:

1) Improving the quality of basic data underpinning the manufacturing statistics;

2) Contributing to the improverneut of the compilation of industrial statistics within the context of national accounting;

3) Generating time series of manufacturing value added at current prices for the manufacturing sector as a whole and for selected manufacturing branches.

4) Constructing an index of industlial production for the entire period under consideration.

5) Some analyses of structural change at current and constant prices using new data to reassess trends in growth, performance and structural change in Tanzanian manufacturing.

6) Providing a better basis for further research on the manufacturing performance over time;

The significanee of this study lies in the provision of an improved set of empirica! data and the construction of a comprehensive set of time series of manufacturing value added, which can serve as a base for further research and policy-oriented evaluations.

3 Initially, our intention was to construct time series for gross output and employment as wel!. Due tothefact that the revisions of time series for manufacturing value added were very time-consuming, we have limited ourselves to value added.

6

3. DEALING WITH MANUFACTURING STATISTICS

A RESEARCH METHODOLOGY

In this chapter we will deal with methodological issues involved in the assessment of time series of manufacturing value added. The central question is how totranslate our research objectives in manageable research tools. Since our objective is to assess, improve and (eventually) expand an

existing set of statistics, we will develop criteria for the proper examination and evaluation of the industrial statistics1

.

3.1 METHODOLOGICAL ISSUES

The basic approach of this study is to analyse the quality of the raw empirica! data, which form the basis for the time series of manufacturing value added in the framework of the system of national accounts. In addition we need to examine how fmal value added figures are derived fmm the basic data. In this context, S. Kuznets2 has distinguished three levels in research on economie indicators:

1) primary data: data of several parameters of households and enterprises (e.g. price and quantity data of certain products);

2) indicators: translation ofprimary data into indicators such as gross output and value added, which are not directly observable anymore;

3) economie research, which involves developing economie theory, interpreting economie representations and forecasting trends.

In statistica! research three aspects ofthe construction of indicators have been distinguished: (1} the ideal measure, (2) the translation of ideal measure into a statistica! measure and (3) the estimation of this statistica! measure from a sample (Dalén 1992). Keeping in mind that the methodology presented

1 Consirlering the character of research, which will mainly consist of analysing, reconstructing and interpreting data, the type of

research is a data analysttype (D.C. Miller, 1991). 2 Borrowed from lecture notes of A Szirmai.

DEALING WITH MANUFACTURING STATISTICS

here should facilitate criteria on how to imprave existing time series of value add~d in the national accounts, we distinguish five stages of data manipulation. These stages can be considered as an extension ofthe frrst two levels ofthe methodology trilogy developed by Kuznets:

1) Frameworkfor Data Colleefion The framework for data collection is the register of industrial establishments that Takwimu uses to collect data. The main criteria here are the coverage of the manufacturing establishments in the register and the quality of the register over time.

2) Data Colleefion Central questions relating to data collection are: how are data collected and how reliable is it done? An important issue is to examine in what way non-response is accounted for.

3) Data Interpretation Data interpretation deals with how the collected data are translated in statistie-economie concepts. Important are the concepts and defmitions used for construction of the different indicators of manufacturing performance.

4) Data Campi/ation This stage relates to the compilation of of fmal series of manufacturing value added. Questions to be answered here are: for which years are estimates available for small scale and informal sector manufacturing and how are these estimates extrapolated for other years? How do value added series at current prices relate to value added series at constant prices?

5) Data Presentation The manufacturing data presented can be grouped in different ways and the presentation over time requires certain assumptions and methodology. Questions to be posed are: what system of grouping should be used and what are the data requirements for this grouping? How will data be presenled over time?

It can be observed that stages 1 and 2 deal with primary data, while stages 3 to 5 deal with the translation of primary data into economie indicators. The ultimate aim of the data manipulation is to obtain time series of manufacturing value added for the national accounts.

In the practice of Tanzaniaan exhaustive list of medium & large scale establishments (the so-called register of industrial establishment) farms the basis for sending questionnaires to these establishments (stage 1). In case of response, establishments send back a filled-in questionnaire or in case of nonresponse Takwimu makes an estimate for the non-responding establishment (stage 2). The questionnaires are sent and colleeled on an annual base for the annual survey of industrial production. In a some years a census instead of a survey has been carried out. In the census years data is collected in the same way as is done for the survey, however, efforts are intenslied to get a high response-rate and the coverage of the census is better than the coverage of the survey. Small scale and sametimes even informal establishments are inquired as well.

In the next stage (stage 3), indicators such as value added, gross output, etc. are determined from the primary data collected through the questionnair~.:s. The results of this stage of data interpretation are publisbed in the annual survey and censuses of industrial production. The filled-in questionnaires of the responded establishments are stared and saved in files for each establishment.

To compile time series for the national accounts (stage 4), the publisbed value added figures for the years covered in the time series, are put tagether and completed with estimates for the smal1 scale and informal sector. Since only for some beneh-mark years estimates have been made forthese sectors, this processing of data involves a procedure of extrapolating beneh-mark estimates to form a time series. In this stage value added at constant prices is being compiled as well. Finally, the series is presented in national accounts publications conform UN-guidelines (stage 5).

3.2 ASSESSMENT CRITERIA

Each of the five distinguished stages, distinguised in the previous paragraph, can be seperate1y studied, which will be done in the following paragraphs.

8

A RESEARCH METHODOLOGY

3 .2.1 FRAMEWORK FOR DATA COLLECTION

To find out how much of real industrial activity is covered by the register of industries used by Takwimu, different approaches can be used. First of all, the directory can be linked with other possible existing directories of manufacturing activity in order to find out how many items match and how many do not. Another possibility is to compare different data sourees on manufacturing employment at Takwim u.

A re1ated subject is that of changing coverage over time. For examp1e, in a certain year of intense data collection activity, many new establishments might be discovered and added to the directory. These establishments may have existed quite some time and account for undercoverage in previous years3

• To get insight in the phenomenon of changing coverage over time, data on the number of establishments and data on employment need to be examined.

Summarising: the following steps could be taken to find out about the coverage of the directory of industries over time:

1) cross-checking the directory with alternative available registers on manufacturing;

2) examining data on employment from alternative sources;

3) examining coverage gains in terms of number of establishment or emp1oyment over time.

Another topic close1y re1ated to assessing the framework for data co1lection is the coverage of the smal/ scale and informal sector. Only fora few beneh-mark years data have been collected on the performance of small-sca1e and informa1 sector industries. In Tanzania, the informa1 sector has been of pronounced interest only since a decade. Recently (in 1991 and 1995), two surveys have been undertaken to identify the structure and performance of the informa1 sector for different industria1 branches. We will need to examine whether estimates made in the nationa1 accounts for the small scale and informa1 sector are robust enough and we need to find out whether it is possible to come up with an estimate for the entire manufacturing performance, including the medium & large scale, sma11 scale and informa1 sector.

3.2.2 DATA COLLECTION

As Takwimu is collecting 1ots of data from the manufacturing sector on an annual basis, it is important to get insight in how these data are collected and how non-response is accounted for. Considering the fact that publisbed reports do not a1ways give sufficient information on response rates and the methodo1ogy of making estimates for non-response, weneed to:

1) interview the persons responsible for datacollection at Takwimu to get insight in the methodology used;

2) go back to the primary data (e.g. filled-in questionnaires for the annua1 survey of industrial production) stored in files for each establishmeht4 in case there are no records kept on non-response rates and when there is need for quantification of non-response.

3 .2.3 DATA INTERPRET A TI ON

As mentioned in chapter 2, we follow the measurement of manufacturing performance based on the standardised measuring rods of the system of nationa1 accounts (SNA). The indicators constructed from the data collected in the field have to be determined according to ru1es of this system. In many cases however, there are discrepancies between the national accounts concept ofvalue added and va1ue added calculated in the industrial statistics. For example, the figures usua11y derived in an analysis of a census

3 An excellent artiele on this subject is written by A. Korns (1993), revealing significant undercoverage in the Indonesian directory of industries. 4 For the data before 1978 information is available on how is estimated for non-response. After the industrial census of 1978, no data on non-response rates are available.

9

DEALING WITH MANUFACTURING STATISTICS

of industrial production does not coincides with the value added concept of the SNA, because it includes business services such as advertising and insurance (UN, 1961: 8).

In finding out how the data have been intetpreted, the original questionnaires used for the different industrial surveys and censuses have to be examined. To be more specific, the working definition of value added needs to be examined and related to SNA-concepts.

3 .2.4 DATA COMPILATION

As we will try to construct indicators according to SNA, a lot of 'number crunching' has to be done, to co me up with the final figures, fitting the mould of national accounting. These fmal figures have to be compiled, basedon the collected dataandon estimates and imputations. For example, figures ofvalue added will be put in series of constant and current values. For that pUipose, estimates have to be made of the ra te of inflation and one has to choose, which year' s prices to take as a base for time series.

Another issue related to data processing is the estimation of parts of economie activity for which no direct data collection has been carried out, or where only poor or incomplete data are available. This issue can, to a large extent, be regarcled as a coverage problem and is therefore addressed under framework for data collection. What should be addressed here is the way estimates are compiled. For example, how are estimates derived for the years where no survey data on the informal sector are available?

The main topic treated under data campi/ation is the way value added at constant prices is calculated. The calculation of real value added is also a standardised concept within the SNA and it directly touches upon the theory of index numbers. Index numbers form an integral part of the constant price calculations of indicators of manufacturing performance such as value added. We will deal with index number theory in the next chapter. The index of industrial production plays a special role in this and will bedealt with separately. Among others, the following questions will be discussed insection 4:

1) What kind of index numbers are available from literature, what are their properties and what are their data requirements;

2) How should an index of industrial production be constructed;

3) Given the nature and availability of the Tanzanian industrial statistics, which index numbers are appropriate for calculating constant price figures?

3.2.5 DATAPRESENTATION

In presenting the performance of the manufacturing industry since independence, we will construct time series for value added for the nine main branches and for manufacturing as a whole, as defined in the International System of Industrial Classification (ISIC, UN 1990). We will stick to the second revision of ISIC, because allindustrial statistics since 1971 have been presented conform this standard. Data regarding manufacturing performance prior to 1971 is publisbed conform to the frrst revision of IS C. They need to be converted to the second revision.

The issue of presenting indicators in time is partly addressed in the methodology of data compilation. In the fmal presentation of the data, different questions may rise dealing with the background of the long term trend of the industrial performance. Researchers have distinguished slightly different periods and so-called turning points in the industrial performance of Tanzania. Therefore, a proper distinction of certain periods in which time series are presented, should also be considered here.

The distinguished stages of data manipulation will be extensively utilised in chapter 5, where we will examine the current statistica! practice for manufacturing in Tanzania. Theoretica! and practical considerations regarding the measurement ofvalue added at constant prices is pursued in the next chapter.

10

4. THE MEASUREMENT OF VALUE ADDED AT

CONSTANT PRICES

A THEORETICAL DISCOURSE

The classica! scholar of index theory, Irving Fisher, extensively dealt with problems regarding index formulae. He also deemed it necessary to treat in detail the role of errors in data (Köves 1983: 9). The problems of errors in data is pursued in section 5 and 6. In this chapter we will

deal with theory regarding the measurement ofvalue added at constant prices. Besides the fact that the concept of value added makes economie sense and that value added is a strictly defmed concept in the system of national accounts, the issue of measuring value added at constant prices is closely related to index number theory. Therefore, a terse introduetion to index number theory will be given in the following paragraph. We will discuss some theoretica! approaches and briefly mention some wellknown index formulae. A specific application ofthe index number theory, often utilised in the system ofnational accounts, the index ofindustrial production, will be subsequently dealt with. This index will be of particular relevanee for us since we aim at construcûng an index of industrial production for Tanzanian manufacturing. In the paragraphs that follow we will focus on how to get an accurate estimation ofvalue added at constant prices utilising the index of industrial production. For this purpose a comparison is made between the use of a price index as a deflator to get an index of real production and the use of quantity indexes to construct a real index. The influence of deticiencies in weights used in index numbers are elaborated and various (practical and theoretica!) considerations are taken into account in assessing the best way to construct an index of industrial production for Tanzania.

4.1 INTRODUCTION TO INDEX NUMBERS

In cernparing absolute levels and dynamics of (economie) development, index numbers have an outstanding role. For example the Consumer Price Index (CPI) is a world-wide used economie indicator

THE MEASUREMENT OF VALUE ADDED AT CONSTANT PRICES

to measure the changes in prices of a given basket of consumer goods and services. Other well-known index numbers are financial indexes such as the Dow Jones Index.

An index number is an expression of change for a certain variabie from one situation to another. Situations may be time, spatial or groups of individuals. Using index numbers is oftenamatter of convenience, because it simplilles the e.x:pression of change, movement or comparison within and between variables1

. For example, a singular index number (Q) of the individual quantity change from year 0 to tof commodity kis given as follows:

where qlcO and qkt represent the quantity of the kth commodity in years 0 and t. In the same way an index number for individual price change can be constructed, exchanging the quantities in equation (I) with the corresponding prices of commodity k, Pko and Pkt·· In the following table a quantity index is constructed for the commodity Timber for 1985-1990. Index numbers are expressed with one selected situation as 1 (in general represented as a percentage: 100). The selected situation is called the reference base or comparison base ofthe series of index numbers which is in our case is the year 1985. Values ofthe index for all years, are related to this reference base.

TABLE 4-1 Production of Tirober 1985-1990

Timber Cubic lli1eters Index

1985 52000 1 00 1986 69000 132 1987 95000 182 1988 1 00000 191 1989 1 05000 200 1990 118000 226

Source: QSIP 1995:3

Index numbers as given in Table 4-1 (actually output relatives) can be regarded as a standard sealing of the magnitude of a variable. This is not the crux of index number theory. In economics (where index numbers are extensively utilised) one is interested in a general price level, an overall fmancial index and suchlike. This involves that a set of items has to be averaged to a so-called aggregated index number. The method of averaging items to an index-number, is the essence of index number theory and boils down to selection of an index number formula. Ragnar Frisch puts it this way: "The index-number problem arises whenever we want a quantitative expression for a complex that is made up of individual measurements for which no common physical unit exists. The d :sire to unite such measurements and the fact that this cannot be done by using physical or teehuical I rinciples of comparison only, constitute the essence of the index-number problem and all the difficulties center bere. "2 Now it becomes clear why index number theory is related to the desire of measuring value added at constant prices: it is an aggregate of which no physical unit exists throughout time. Before turning to the index of industrial production, we will deal with some widely used index number formulae in paragraph 4.3. To begin with, we will frrst take a look at different theoretica! approaches to index numbers.

1 Besides simplifying expressionsof change, an index number also leaves out 'information' of a certain variable. In other words, an index number abstracts out of various features the change in its magnitude. An index number is thus '1imited' to the measure of change in the magnitude from one situation toanother (Allen, 1975). 2 Frisch, Ragner. (1936). The problem oflndex Numbers. Econometrica 4, 1-38, page 1, cited by Allen (1975: 5).

12


4.2 THEORETICAL APPROACHES TO INDEX NUMBERS

Some widely used index numbers appear in several approaches (categorised under different names) to index numbers. Allen (1975) states that most writers have distinguished two approaches (stochastic and aggregated approach). Diewert (1981) categorises three approaches (statistica!, test and functional approach) and Selvanathan & Prasada Rao (1994) deal with with a number alternative approaches, but regroups them to two approaches (the functional and atomistic approach). We will very briefly deal with the different approaches where we follow the dichotomy used by Allen and Selvanathan & Prasado Rao, but we have captured the different approaches under the following names:

1) Functional Approach (also known as preferenee field approach, economie theory approach, and aggregated approach);

2) Statistica! Approach (also known as stochastic approach, test approach, alomistic approach, axiomatic approach, and probabi/istic approach).

4.2.1 FUNCTIONAL APPROACH

The premise in the functional approach is that the price and quantity data observed in the economical reality are functionally re1ated. This approach has its foundation in the standard micro-economie theoretic approach to the construction of index numbers. Many authors outline the construction of a socalled constant-utility price index, of which the economie basis has to be sought in the theory of consumer behaviour. Allen (1975) and Usher (1980) extensively deal with theory of consumer behaviour, but also deal with the supply side approach to indexnumber construction (Allen 1975: 72-75 and Usher 1980: 53-64). Basedon these texts, we will concentrate on the output (supply) side ofthe market, to develop an index of real output. For expository purposes, we have illustrated a twocommodity industry of an economy in Figure 4-1 with the products timber and plywood of which the quantities are indicated on the vertical and horizontal axes of the figure.

The highest outputs producable in 1965 and 1995 are indicated by the production possibility curves T65

and T95 . What can be got out of technology is confmed within these production possibility curves, where the letter T is nmemonic for the level of technology or the productive capacity of the economy. We assume that economie (read: industrial) growth has taken place between 1965 and 1995, which implies that the production possibility curve has shifted outward between these years ( otherwise the quantities produced would have remained the same year after year). lt is due to the process of technica! change that growth takes place. It pushes the production possibility frontier away from the origin from one year to another.

The behaviour of output-maxiruising producers and utility maxiruising consumers on a common market will yield in a particular point of equilibrium at which producers will choose to produce partienlar amounts of timber and plywood. This is the point where the production possibility curve will be tangent to an indifference curve, which represents the demand side ofthe economl. In the diagram the broken lines ( U65 and U95) represent the indifference curves for 1965 and 199 5 and the equilibrium points are given at points (65) and (95). Although we cannot depiet money output in the figure directly, we will represent output in terms of units of timber. For this purpose one can assume the price of timber to be bound at 1 Tsh. per unit and the price of plywood is expressed re1ative to the timber price. Money output for 1965 and 1995 is designated as 0 65 and 0 95 . Let (ptim, Pply) be the corresponding prices of quantities (q1;m, qp1y) in 1965 at point (65), then money output in 1965 is given as:

In terms of units of timber, money output for 1965 is given as:

(3)

3 A representative consumer equally evaluates each combination of purchases of plywood and timber on an indifference curve. Each indifference curve corresponds toa particular level of utility (Allen 1975: 65-66).

13


We willuse the same notation 0 65 for money output in units ot timber, which can be presentedon the diagram as the projection of point (65) onto the vertical axis by a line of slope pp1)Ptïm· The intersection ofthis line with the vertical axis is given as 0 65 in the figure. For 1995 a similarprojection of point (95) yields an equivalent money income 0 95.

Timber

095

py

FIGURE 4-1 Real Output as a Measure of Productive Capacity

Plywood

Turning to our plll}Jose of de" reloping an index of real output, we could indicate real output as reflecting the productive capacity of an economy at constant prices, i.e. 1965 prices or 1995 prices. Real output in 1995 compared to 1965 can be defmed as the maximum output that wou1d be produced if producers in 1995 were faced with 1965 prices for timber and plywood in accordance with the production possibility in 1995 (T95). The point where the tangent of the production possibility cUIVe T95 with slope pp1)Pnm (at prices prevalent in 1965) through point N intersects the vertical axis is given as 0 95(65) in the figure, representing output in 1995 which would be producedat 1965 prices. Another comparison we cou1d make is evaluating output in 1965 at 1995 prices. The point oftangency ofthe 1ine with slope Ppz)Pnm (at prices prevalent in 1995) to the production possibility cUIVe T65 is given at point M. Output at constant 1995 prices for 1965 is indicated on the vertical axis as 0 65(95). An index ofreal output (IRO) can now be defined as a ratio of outputs in either 1965 prices or 1995 prices:

(4) IR0(65) = 095(65)

and 065

(5) IR0(95) = 095

065(95)

14


However, in economie reality we can only abserve the producers' quantity choices at points (65) and (95) and not the hypothetical quantity choices at points Mand N. Therefore an exact measure of a true index of real output cannot be made. As revealed in a variety of studies, the following step in the economie approach to index numbers is to find approximations in so-called up per and lower bounds (e.g. Diewert 1981: 166; Usher 1980: 170). Turning to Figure 4-1 once more, we have drawn a dotted line through point (65) with slope Ppt/Ptim (in 1995) to findan approximation for 0 65(95) designated by 8'65(95). The same is done for 0 95(65) deriving 095(65). From the figure it can easily beseen that:

(6) 095(65) s;

095(65) = IR0(65) and

065 065

(7) 095

~ 095

= IR0(95) 065(95) 065(95)

In this way we have found a lower bound for the index of real output at 1965 prices in inequality (6) and an upper bound in inequality (7) for the index of real output at 1995 prices. An important point to recognise here is that the bounds are valid for true iudexes at different output levels. We will not pursue this matter in this paper. lt is enough to refer to a common argument that there should be a 'strong presumption' that any true index could be pinned down between both bounds (Allen 1975: 70).

The first argument of (6) is known as the base-weighted approximation of the index of real output (utilising base year prices, which in our example was 1965) and the frrst argument of (7) is known as the current-weighted approximation of the index of real output (utilising current year prices, which in our example was 1995). The index numbers for the two-situation case can easily be extended (N;-;::3) toa N-commodity economy. Let p.wand qlcû be the price and quantity of commodity kin base year 0, Pkt and qkt be price and quantity of commodity kin current year tand assume that an IRO at constant prices (irrespective which year is taken) is to be found between the base-weighted and current-weighted index number bounds conform inequalities (6) and (7), then fora N-commodity economy the following inequality holds:

N N

LPtoql.:l LP!.:tql.:l (8) k-1 s; IRO s; k-1

N N

LPtoqko LP!.:tqkO k=1 k=1

Discussion of the validity of this inequality is best postporred until paragraph 4.3 .1, where we will calculate index numbers fora partienlar branch of the Tanzanian industry.

4.2.2 STATISTICAL APPROACH

Although many different approaches are captured under the statistica/ approach, the central point of view is nottaken in economie theory, but lies in the science of statistics, where axioms and features of probability play a prominent role. We willjust very briefly touch on the stochastic and test approach.

The general assumption in the stochastic approach is that all prices are affected proportionately by the expansion of the money supply. The existence of a common price index makes it indifferent what price index will be used, as long as a sufficient number of price ratios are used to construct the index number. In other words, each relative is taken to be equal to the underlying price index, which measures the overall price changes between the current and base years. Other components, which have a random or a non-random character are also included in the measurement, which causes the price relatives to deviate from the overall price index. Hence, the index number problem under the stochastic approach can be viewed as a signa/ extraction problem (Selvanathan & Prasada Rao 1994: 48-67).

In the process of signal extraction, many well-known index number formulae can be constructed, which appear under the functional approach as well. Although it is said that the stochastic approach makes one loose touch with economics, the advantage of using statistica! aspects of index numbers is that (for the same index numbers which appear under the functional approach) standard errors can be obtained. Selvanathan & Prasada Rao (1994) extensively deal with proof for the existence of index numbers from the stochastic approach and quantifying the standard errors.

15


Toselect index formulae using statistica! axiom's, Irving Fisher's approach is the introduetion of a number of tests to be used to narrow the choice of the formulae in practice. In this test approach, one can search for ani deal index, using certain statistica! criteria (we will present the so-called i deal Fisher index in the next paragraph). These well-known tests can be found in any text book on index numbers. We will only give three of the most common tests. We define Q 01 and P 01 as a quantity and price index number, where the subscripts 0 and t signify the years of comparison, respectively the base year and current year t.

1) The TimeReversa/Test concerns the transitive property an index number should have: 1

Qo, =-Q ' tO

i.e., if quantity data for the periods 0 and t are interchanged, then the resulting quantity index should be equal to the reciprocal of the original index.

2) The Circular Test can be considered as an extension of the time reversaltest for the case when more than two years are involved:

Qos x Qst = Qo. (s * t, s and t= 0, 1, 2, ... )

3) The Factor-reversalTest concerns the relation between corresponding price and quantity index numbers. Let V01 be the change in nominal aggregated value from year 0 to year t for a N-

commodity economy, given by 'L~; 1p~aq~a/'f .. ~; 1 Pkoqk0 , then the factorreversal test reads:

Qo,xPo, =Va,' which means that when applying a particular index number formula for a price index and a quantity index, the product of these indexes should account for the value change.

4.3 SOME INDEX NUMBER FORMULAE

4.3 .1 LASPEYRES, P AASCHE AND FISHER INDEX NUMBERS

From the functional approach we found upper and lower bounds for real output iudexes in (8). These index formulae are the famous index number formulae of Laspeyres and Paasche. The Laspeyres and Paasche index numbers are given as price index numbers and quantity index numbers, but in this section we will mainly concentrate on quantity index numbers, in this way preparing ourselves for some theoretica! exercises to be undertaken later on. The Laspeyres base-weighted quantity index number (Q01L) and price index (P01L)number are given by:

N N

LPkOq/a LP!aqkO (9) Q L _ ..::k.::..;1:...._ __ and p,L = k;1 _

Ot-N Ot N LPkoqko LPtoqko k;1 k;1

The Paasche current-weighted index formulae (Q0{' and P0{') are given by:

N N

LP!aq/a LP!aq/a (10) QP _ ""'k-""'-1 __ and p,P = ~ki'i-';1~_ Ot-N Ot N

LPiaqko LPkoqia k;1 k;1

This way of writing down the index formula is called the changing-cost, or ratio-of-aggregates. By defming wk as:

(11)

16


we can rewrite the Laspeyres index into aso-called weighted average form. We aud the subscript 0 to denote base weights (w.w). For the quantity index we get:

(12)

For the Paasche quantity index we should use wkt (current weights). However, for the Paasche form the substitution of definition (11) into (9) does not yield a very clear-cut formula and a better result is obtained from the redprocal of P 01 :

(13)

As we have seen in paragraph 4.2.1, a true output index must be found somewhere between the Laspeyres and Paasche quantity index numbers, which can be considered to be upper and lower bounds of the true index. When the Laspeyres and Paasche quantity index numbers are checked against the tests developed by Fisher, both fail to pass the tests mentioned in paragraph 4.2.2. However, taking their geometrie mean, the combined Laspeyres-Paasche index numberpasses the tests (Allen 1975: 61). The thus constructed index number is known in literature as the Fisher !deal Index, of which the quantity form is given by:

(14) QPisher = ~QL Qp Ot Ot OI

In Table 4-2 we have constructed Paasche, Laspeyres and Fisher aggregated index numbers for the manufacturing branch wood and wood produels (ISIC 33). Only data for three commodities in this sector are available and the quantities are given as output relatives (as defined in (1)). The weights calculated according to (11) should be the shares in value of output. However, ahead ofthe analysis of the index of industrial production in paragraph 4.4, we have used value added weights of the three commodities in ISIC 33 given as percentages in Table 4-2 for the years 1985 and 1990.

TABLE4-2 Quantity Index Numbers for Wood & Wood Products

Timber Plywood Wooded Wood & Wood Products Crates Laspeyres Paasche Fische

1986 132 122 83 127 104 115 1987 182 132 123 175 146 160 1988 191 142 106 181 138 158 1989 200 106 87 186 121 150 1990 226 96 104 210 138 170

The Laspeyres series are calculated using formula (12), the Paasche series app1ying formula (13) and the Fisher series (according to (14)) is calculated taking the geometrie mean ofboth series. For 1987, e.g., the Laspeyres index is determined adding 0.87xl82 + 0.04x132 + 0.09x123 = 175, the Paasche index is calculated as l/(0.47x100/182 + 0.09x100/132 + 0.44x100/123) = 146 and the Fisher index is the square root of 175x146 = 160.

Glancing over the data in table 4-2 reveals that for all years the Laspeyres index is larger than the Paasche index. This violates the inequality given in (8) where it says that the Laspeyres quantity index is a lower bound and the Paasche an upper bound of the true quantity index. The reason for this contradiction is to be found in the underlying assumptions of the economie analysis of the supply side ofthe economy as foliowed in paragraph 4.2.1. It is assumed that equilibrium points of production and consumption are found along to the production possibility curves. Since the production possibility curve is concave from the origin, a Laspeyres index number is always a lower bound of the true index. When

17


approa~hing the index number problem from the demand side, it is assumed that equilibrium levels of consumption and production are found along the utility functions, which are convex from the origin. In that case, a Laspeyres index number will always form an upper bound of the true index. Hence, the matter boils down to the question when the equilibrium of the market will move along a production possibility curve and when will it move in accordance with utility functions. The answer is evident (Allen 1975): in a supply-dominated market, the index number bounds arrived at from the supply approach will be valid and in a demand-oriented market the index number bounds developed from the demand approach will hold. We can therefore conclude that for Tanzania the wood and wood products market is a demand-oriented market

4.3.2 BASE-WEIGHIEDVERSUS CHAIN-WEIGHIED INDEXNUMBERS

In our last example, we have applied index number formulae for more than one year, to be specific, we have applied the formulae for 5 years: 1986-1990. Allen (1975) describes this application of index number formulae as binary comparisons, which means that quantities in each successive year are compared to the base year. Another way of camparing price and quantity data, utilising data for all years, is to make rolling comparisons. Instead of fixing a reference base year, iudexes are calculated for each year with reference to the preceding year, after which consecutive values of the indexes obtained for each year are multiplied to form an index for the total of the years. This way of index calculation is known as /inking andthe result is a chain index4 (Al et all990: 12). The procedure of binary camparisans is also called the fixed application of an index number formulae. A chain index (regardless what index number formula is used) for the Laspeyres quantity index can be written as:

(15) Q;, = Q;l x Q{; x Qt x ... x Qf;-l)t

It should be noted here that the choice between index formulae has a different character than the choice between afixed (i.e. base-weighted or current-weighted index numbers) application of an index formula and the chain approach. A chain index can be calculated with any index number formula. According to Al et al (1990) "the choice of the most appropriate index number formula concerns the way in which simultaneons changes in values, quantities and prices must be aggregated. The choice between the direct application of index number formulae and the chain approach concerns the question how consecutive changes must be treated to form a time series". Therefore, the choice for a Laspeyres formula implies the choice for aggregating quantities, using base year value weights. The question whether to use a fixed approach or a chain approach depends on how an aggregated index in year t should be calculated. In formula form the question implies the choice between:

1·1

(16) I(O,t) or TI J('t ;r + 1) , <=0

where I (x,y) stands for an index calculated for year y, with comparison year x. Discussion of theoretica! arguments to make a choice between these approaches would lead too far. The condusion of Al et al (1990) is that there can be no theoretica! and practical objections found to use a chain index app1 oach. The use of chain indexes only poses problems when data is entirely lacking or incomplete. We v il1 see that in case of Tanzania, data requirements cannot even be fulfilled for an indirect approach of measuring real value added (see paragraph 4.4.3). We will neither be able to utilise a chain index number approach since data requirements are evenly high for this type of index number. Considering these practical confmements, we willleave the matter of chain index numbers at this point.

4.3 .3 PRACTICAL IMPLE:tvlENT A TI ON OF INDEX NUMBERS

In this paragraph we will deal with some practical aspects of using index numbers. We will describe the procedure of switching the reference year, spiicing runs of index numbers and rebasing a series. These practical implementations of index numbers will be extensively utilised in due course.

In Table 4-3 two series of index numbers for wood & wood products are calculated, using the Laspeyres formula in a fixed approach. The first series is basedon 1985 weights and runs from 1985-1990. The

4 In index number theory, the concept of rolling comparisons is known as the Divisia Integral Index (Allen 1975). Elaborating this

falls beyond the scope ofthis text.

18


second series is basedon 1990 weights and runs from 1990-1994. When one is interested in a series for the entire period, 1985-1994, weneed to splice together the two series. This spiicing is done in a year that both series have in common, that is in our case 1990. The spliced series for the years 1991-1994 (t), defining year 1990 as T, is calculated as follows:

(17) Isplicea (O,t) = l(O,T) x l(T,t)

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994

TABLE 4-3 Spiicing Runs of Index Numbers

Wood & Wood Products Basedon '85

1985=100 100 127 175 181 186 210

Based on '90 Spliced Series 1990=100 1985=100

100 127 175 181 186

100 210 90 188 68 143 71 149 65 137

Source: QSIP 1995:3 & table4.2 for the weights. Notes: The Laspeyres formula is applied in a direct approach.

For 1991 we get 90 x 210 = 189 (in percentages), for 1992 multiplying 68 x 210 = 143, etc. The results of this arithmetic procedure of multiplying can be found in Table 4-3. Spiicing of index numbers is not theoretically justified, but rests on the idea that although the different runs are based on different weights, they are approximations of change in a given and continuing, but non-observable magnitude (Allen 1975: 32). This magnitude in our example is the index of industrial production for wood and wood products.

TABLE4-4 Rebasing and Switching Runs of Index Numbers

Wood & Wood Products Based on '85 Rebased on'90 Rebased on '90

Switched to '85. 1985=100 1990=100 1985=100

1985 100 72 100 1986 127 74 102 1987 175 102 141 1988 181 98 135 1989 186 88 122 1990 210 100 138

Source: QSIP 1995:3 & Table 4-2 tor the weights. Notes: The Laspeyres formula is applied in a direct approach;

For the '85 series in forward form, and in the rebased series in backward form.

In Table 4-4 we have rebased the volume index for wood and wood products. lnstead of using the weights of 1985, we have used 1990 weights for the 3 commodities. As can be seen, rebasing the index requires a complete recalculation of the index number series. We have used the same index number formula, only this time the Laspeyres formula is applied in a backward run. That is to say, the reference base is 100 in 1990 and the index is calculated backwarcts up to 1985.

Another practical tooi for dealing with index numbr.rs is switching. Where rebasing implies complete recalculation, the procedure of switching the original base year to some other year, is a simple rescaling procedure. The relative magnitudes of the switched index numbers remain unaltered. We have switched the rebased index from 1990 to 1985 as reference year, to make it comparable with the original series.

19


As can be seen from the table, the impact of rebasing an index number can be very significant. We will deal with issues regarding weighting in Paragraph 4.4.4.

4.4 INDEX OF lNDUSTRIAL PRODUCTION

The index of industrial production is a particular application of the index number concept. The index of industrial production (liP) has economie theoretica! grounds, in which typical index number problems show up. We will first deal with the economie contents ofthe liP, after which we will deal the typical index problems related to the liP.

4. 4. 1 ECONOMIC CONTENTS OF 1HE INDEX OF INDUSlRIAL PRODUCTION

The concept of national product provides value added as an indicator of the value of 'work done' in the various sectors ofthe economy. In evaluating the national economy from time to time, one is interested in the re al development of value added, that is, the development of value added with the impact of changes of prices eliminated. Since taxation and subsidies are considered as distmbing factors, the factor co st concept is preferred. In sum this is called the measurement of output at constant factor cast. The concept of the index of industrial production (liP) corresponds to this approach of measuring real output. Albeit that the scope ofthe liP is limited to the output ofnon-agricultural commodities, the liP has particular relevanee in any analysis of economie changes, since industrial production is one of the more dynamic elements of an economy (UN 1961: 5).

The purpose of an index of industrial production is to show in a series the movement in the volume of output in the industrial sector. In concept the liP measures the aggregated amount of 'work done' for each industry that is covered in the liP. 'Work done' should be interpreted as the difference between the gross output of products and the input of materials, products and services provided by other industries ( Central Statistica! Office 1976). The measure of 'work done' in current prices is value added. To eliminate the influence of price changes, the value change is broken down in a quantity change and a price change and the quantity movement is isolated. This procedure has been foliowed in Figure 4-1 for the value of output for two commodities. As is the case with the index of real output (paragraph 4.2.1), a true index of industrial production cannot be found.

First, let us consicter the process of transforming several inputs into fmal products. In general, the proportions in which materials are used to produce various products will vary according to changes in relative prices of matenals used and will depend on technologica1 conditions. Keeping the prices of matenals and products constant, will not imply that the margin betweenthem is also held constant, because of these dynamic changes that take place. Therefore any statistica! formula that could be suggested only represents an approach to our concept of changes in the volume of '\vork done' (UN 1961: 21).

Second, the concept of 'work done' always relate to aggregat ~sof work done to produce various products. The terms quantities and prices cease to be clear, since quantities and prices of different products cannot be added. This so-called aggregation problem adds on the mentioned impossibility of a true application of constant prices. The consequences are that changes in value added, cannot be entirely decomposed in a price and a quantity component. For the index calculated in Table 4-2 (Laspeyres formula), we cannot say that there bas been a change of 27% in quantity of wood & wood products from 1985 to 1986. Account mustbetaken of the existence of what is called the structura/ change component (Al et al 1990: 7). Production of timber bas grown, while wooden crates production dropped in 1986. lncluding the structural change component into the quantity measure is referred to as volume. There is no equivalent for including the structural change component into the price measure.

In sum, from economie theoretica! point of view, there is no true index of the index of industrial production. Besides dynarnic changes that take place in prices and technological circumstances from one year to another, the aggregation problem yields a structural change component, which makes it impossible to develop a true measure of production in constant prices.

20


4.4.2 APPROXIMATING THE TRUE INDEX OF INDUSTRIAL PRODUCTION.

For the sake of international comparability and the aim of a prompt and frequent computation of the index of industrial production, a simple and single formula should be used, according to the United Nations (1961). The nature ofthe formula should be well-known and should be in a fixed-weighted form (conform to the constant-prices concept). Both, the Laspeyres and Paasche formulae are reviewed (and it is observed as well that they form upper and lowerbounds ofthe true index). Since current weights are only available after some time, the Laspeyres formula is to be preferred to the Paasche form. Index number runs are said to be valid only for a short period and regular rebasing the index is necessary, where a period offive year is suggested (UN 1961: 16).

Assuming that all goods (N) of the economy can be sampled and assume that the value added of a given good k in year 0 is defrned as

N N

(18) VAko = LPfoqfo- LP~oq~o' k=l k=l

where the superscripts 0 and I signify output and input. A direct translation ofthe value added at constant prices concept in a Laspeyres index formula is derived by Geary and also known as the method of double dejlation5

:

(19)

In practice it is very difficult to fulfil data requirements6 of the formula. Price and quantity series are required for both inputs and outputs. Focusing on inputs only, the character of inputs is less homogeneons than outputs. Substitute series of consumption of energy or material inputs could be used, but no quantity measure for services provided by other industries can be applied. However, even when data requirements can be somehow be fulfilled, in cases of large relative price changes, the nominator ordenominator can yield a negative value (UN 1961: 21). Therefore, in general a proxy indicator (physical output) for value added is weighted with value added shares in the base year (Centra! Statistica! Office 1976). In formula form this is:

(20) N o

liPDirect = Lw kO q: and k=l q kO

(21)

where (qokiqo AO) is the ratio ofphysical output indicator and wAO is the value added weight for cammodities kin the base year. Since value added in general is not available at product level, the value added weight is provided by the industry which a partienlar product represents7

. Usually the proxy indicator is the production ( or sales of delivery) of physical quantities covered in the index. Sametimes when no physical quantity series are availab1e fora certain industry, indicators like material inputs, labour inputs or energy consumed can be used. In the case of the United Kingdom 97% of the index is calculated using physical output indicators and for the remaining of the index, other indicators were used. In the case of Tanzanian index of industrial production 1985-1995, only output physical indicators whereused.

The main task of the construction of an index of industrial production is to determine the weights to be attached to the various industries in the base year. Difficulties may rise when it is impossible to

5 Translation ofthe constant prices concept in the Geary index number actually yields a ratio ofvalue added at constant prices. Wh en using the terms value added at constant prices ( or real value added) we refer to the index of industrial production, also reffered to as the re al value added index or the re al value added ,·atio. 6 In UN (1961) it is concluded that the complete datanecessary for an accurate index are unlikelyto be available, even from a census of production (p. 27) 7 Several products may represent a certain industry. In this case the product value added is estimated using relative values of output, which normally are available at product level in contrast with value added figures.

21

THE MEASUREMENT OF V ALUE ADDED AT CONSTANT PRICES

quéllttify production in certain industries8

. In this case industry weights can be imputed toother industries when it is reasonable to expect a parallel movement of industries. In table 4-2 the volume index for wood and wood produels was calculated from production relatives of three commodities data only. Various weights of industries (e.g. furniture making) were applied to the industries represented by the commodities.

Besides selecting quanturn measures, it is possible to follow another approach of measuring real movements in production: measuring value added at current prices with the use of a price deflator to eliminate price changes. In the United Kingdom, real output indicators for the largest portion ofthe index of industrial production are obtained by deflating value added series (Centra! Statistica! Office 1976). For Tanzania the index of industrial production constructed from 1985 onwards is entirely based on the first type of series, i.e. weighting quantity series with value added weights. Besides calculating deflated value added series for industries, it is also possible to deflate value added for total manufacturing with an appropriate price index. In paragraph 4.4.3 we will deal with differences between the method of deflating value added with a price deflator and using base year value added weighted volume series.

4.4.3 DIRECT QUANTITY APPROACH VERSUS INDIRECT PRICE DEFLATOR APPROACJ/

For comparison of the direct approach of weighting physical quantity relatives versus the indirect approach of deflating current value added, we consider two iudexes of industrial production. The first liP is constructed entirely from quantity series of products covered and the second liP is a deflated value added ratio, using an appropriate price index. Assuming that the Geary formula (19) would in theory the best approach for constructing an liP (see UN 1961), we will elaborate under which conditions the proxy output index and the deflated value added index will be the same as the Geary index, which we will consider to be the re a/ index of industrial production.

Conditions under which the direct and indirect approaches equal the IIPGeary

Utilising (18) and (21), we can rewrite the direct approach as given in (20) to:

(22) N 0

IJpDirecl = LW kO q ~ k~l q kO

Substituting (18) in (22) yields:

(23)

Comparing (23) with the Geary formula (19), it can beseen thatJJpdirect is the same as the Geary approach when for all k the following is true:

(24) 0

!!.JLqr 0 kO

qkO

q~ = -T-

qkO

0 = qkO -T-

qkO

The ratio q0 /q' (where q is the vector of quantities of inputs or outputs in a partienlar year) is often defmed as A., that is, the vector of technica! input -output relations (UN 1961: 54 ), also known as the productivity of inputs. When A.0 = Îl.t, which means that the productivity of inputs remain the same between the years 0 and t, the resulting index from a direct approach to the concept of the liP will coincides the Geary approach.

8 For example for fumiture making no quantity measure could be found in Tanzania. No consistent unit of measure for chairs, tab les, desks, etc. could properly be defmed. 9 This paragraph is based on a draft version of The difference between a re al value added index and a dejlated current value added index, written by Marcel Timmer (1996).

22


In case of deilating current value added to construct an liP, we assume the samebaskets of goods from which a price index is constructed and assume that an appropriate price index is available as a deflator. The value added ratio in current prices can be written as:

N N

(25) VA1 = VAo

L p~q~ - L p~q~ k=l k=l

and we define the price index as:

(26) ]Jejlator =

where q ·k. denotes quantity for commodity k for either the base year 0, or current year tand the suffix * represents the possibility that the price and quantity series are either inputs or outputs. The indirect approach to construct an liP is then defmed as:

(27) VA~

]]pindirect = V Ao = ]Jef/ator

The liPindirect is the same as the Geary approach, if the first and secoud term of the numerator of (27),

L~=t p ~ q ~ and L~=t p~ q~ , multiplied with the deflator, are the same as the left and right terms of the

numerator ofthe Geary formula (19). Therefore the following should be true:

N N N

LP;oq;_ LP:q; L o o N N PJuqb LP~q~ x k~l L o o <=> k=l = k=l and: = PkoqJu N N k=l LP:q; k=l LP;oq; L o o Pkoqb

(28) k=1 k=l k=l

N N N

N LP;oq; N LP:q; LP~q~ LP~q~ x k;} = L I I <=> k=1 = k=1 PkoqJu N N k=l LP:q;_ k=l LP;oq; L I I PkoqJu

k=l k=1 k=l

For the deflator we can state that it should be a current weighted price index (Paasche formula). Furthermore, it can be seen that the indirect approach yields the same results as applying the Geary formula if the Paasche input price index equals the Paasche output price index.

Let us assume that an appropriate Paasche price index of inputs or outputs is ava;lable. The conditions for the direct and indirect approach boil down to constant productivity of inputs -rersus Paasche input price index equals Paasche output price index. The question is which ofthe conditions creates the highest bias (compared to Geary) in case ofviolation of these conditions in reality. It is to be expected that the productivity of inputs would increase over a period of time, when technological development moves on and will result in, e.g., better machines yielding a higher efficiency for raw materials used. We do not have data on input productivity for Tanzania. When the liPdirect will be rebased every 5 year, the bias will involve changes in input productivity over a period of 5 years, of which we do not know how significant productivity changes are.

What we do know is that the liPdirect will understate the real (Geary) index of industrial production when input productivity increases, because (see (24)):

(29)

Therefore the numerator of (23) will be smaller than the real index.

23


The condition for the liP;, direct given in (28) implies that the prices of inputs and products should not change in proportion. Unfortunately, we lack price series for inputs and outputs in manufacturing for Tanzania. Inputs are only partly rellected in output prices. Changes in input prices are rellected in changes of output prices (although it may takesome time), but output prices arealso determined by factor prices (labour and capital). In case the input prices would rise more quickly than output prices

and our dellator is a Paasche output price index (denoted by L~=l p~q~ /L~= 1 pf0q~ ), the right-hand

term in the numerator offormula (27) becomes L~=1 p~q~ x 1/ (L~=l p~q~ I L~=1 pf0q~) and would

be smaller than the real index, thus yielding a higher value for the index of industrial production than the real (Geary) index would do.

Taking into account the fact that we lack data to determine the duferences in the direct and indirect approach, we ex-peet both approaches to deviate since it is reasonable to assume that the productivity of inputs is likely to change over time and that it is possible that input and output prices will change in proporti on. Based on analysis of conditions of which both approaches to the liP are equal to the IIPGeary does not enable us to give preferenee to one of the approaches, but it does give directions for future research.

Other considerations regarding direct and indirect approaches to the liP

Other considerations regarding the direct and indirect approach to constructing an index of industrial production have to do with the question to what extent the approaches meet the putpose ofthe liP. The concept of the liP is that it should measure changes in the volume of production and that it should take account of (UN 1961: 20) the follO'\.ving:

1) varlation in types and qualities of goods;

2) changes in work ofprogress;

3) changes to the technica! input-output relations.

When obtaining output quantity series, it is clear that changes in the quality of a product do not show up in the quantity component. The series is only partially sensitive for the amount of workin progress when products produced (not delivered) are taken. At last, (as we have seen) the direct approach assumes constant input-output relations. In sum, none ofthe aspects a true liP can be fully met by the direct approach of the liP.

When dellating value added in current prices, the advantage is that the entire output of an industry is covered. U sing value added figures also automatically includes new products and changes of input productivity ( Central Statistica! Office 1976). In principle, quality changes are not incorporated in a price index, but it is a common argument (e.g. U sher 1980) that it is easier to adjust a price index for quality changes, than is the case for a quantity index. In short, dellating value added with a price index incorporates for the least some aspects of what an appropriate index of industrial production should measure.

Although we should pref er a dellated series based on these arguments, there are practical arguments, which weaken the arguments fora dellated value added series. For, the crux for the liPindirect is to obtain an appropriate dellator. It is essential to use a price index number for dellation that is calculated at the appropriate stage in the production process and weighted with the appropriate value added weights. For Tanzania we do nothave such indexes at our disposal. From 1992 onwards a producer price index is available, but forthese years, no accurate value added series in current prices are available. From 1970 onwards a consumer price index is available and a retail price index for Dar es Salaam is available since 1963. For using the wholesale price index, there are considerable differences between the concept ofthe liP and that ofthe wholesale price index (UN 1961: 46-47):

1) the wholesale price index is basedon a different industry grouping;

2) the wholesale price index also covers prices of imported goods;

3) the liP is weighted with value added (net output) weights, while the wholesale price index is weighted with gross output values.

24


The use of the consumer price index for deflating value added figures does even raise more difficulties. The consumer price index is intended to give an approximation the cost of living index. In this way we completely loose touch with the concept ofthe liP. Therefore deficiencies will appear:

1) the consumer price index will use weights based on consumption of an average consumer in Tanzania, which will differ from the value added weights derived from the manufacturing sector. The coverage of the consumer price index will be the entire economy;

2) the consumer price index covers imported goods;

3) no appropriate industry grouping is being utilised.

In sum, no appropriate index is availab1e for Tanzania to deflate current va1ue added figures. Even when assumed that the wholesale price index or the consumer price index is a good enough approximation of a true deflator, the biases can be considerable, since the index number runs are rebased irregular and after long runs (every 10/15 year).

Another disadvantage of using the indirect approach is that an good estimate of value added is needed annually, thus driving up data requirements for the liP. In contrast, for the liPdirect, data requirements are much less. What is needed, is a regular estimate of value added weights for an appropriate bundie of manufacturing goods. Of course, for some goods it is difficult, if not impossible, to identify a quantity component. Ho wever, the same counts for constructing a price index.

It also should be mentioned here that, since we cannot sample all products N in manufacturing, it is more reasonable to assume that prices of omitted products move in the same way as prices of products included, than to assume that output moves together. In other word, a quantity index would be more vulnerable for non-representativity than a price index. The quality of a sample would thus be better in case of a price index. To get to the bottorn of the matter, we should take a look at the weights applied, when using a certain ( wholesale or consumer) price index as deflator. Where in the direct approach value added weights can be derived relatively easily and accurate, in the indirect approach a bias in weights is more likely to occur in case of Tanzania (as we will see in the next paragraph). Any bias in the weights used for the price index will have impact on the quality of the liP. We therefore need to take a closer look at the influence of deficiencies in weights on the magnitude of an index number formula.

4. 4. 4 THE INFLUENCE OF DEFICIENCIES lN WEIGHTS

We have seen that the indirect approach has several advantages above the direct approach. However, due to lacking the appropriate price index to be used as a deflator, we expect problems in the correctness of the applied weights. Therefore, the sensitivity of the magnitude of an index number to changes in the weights needs to be assessed. We therefore will rewrite the indirect approach formula in the form of the direct formula. For that reason, we assume that for both approaches the same sample of products is taken from all manufacturing comuodities (we will just keep on using N to denote the number of products covered in the liP). Furthermore, we assume the best possible case for Tanzania, namely, the availability of an output price index in Paasche form10

• Furthermore it is assumed that price

and corresponding quantity data for all k for both, current and base year ( p f0q ~0 , and p Z q Z ) can be

identified. Since value added at current prices is a necessity for the indirect approach, it is reasonable to

holdon this assumption. We furthermore defme c, =VA, fGO, , where GOt, stands for aggregated gross

output in year t, which we assume corresponds with argument L~;J p Z q Z , from the output price

formula. First let us now rewrite the indirect formula (27) to a plain consistent form:

10 This is almost true ofthe producer price index; the index is constructed from manufacturing output prices using the Laspeyres formula.

25


JJpindirect VA~

_ VA0 - Jdej/ator

(30)

It can be seen that the indirect approach is written in the fonn of a base weighted quantity index. Nevertheless, the index number is dependent on data in current prices as well. The value added gross output ratio' s ( c) for the base and current year influence the index number and should be interpreled as follows: when input productivity will increase, then (assuming input and output prices change in the same proportion) c in year t will be higher than in year 0 and consequently the index of industrial production will expand in the same extent. It should be noted here that the quantity relatives are not used in reality as is explicitly written down in the fonnula. The quantity series have an implied character. The combination of value added in current prices and output prices, makes it possible to derive these quantity relatives11

.

For the direct approach we have (see (20)):

In fonnula (30) w00.t is thesetof weights derived from gross output figures in the base year and wok in

fonnula (31) is thesetof weights derived from value added figures in the base year. For the direct approach we have printed the production relatives in bold fonn, so to distinguish between the indirect approach. The index fonnula of the indirect approach written in this equivalent fonn is detennined by the production relatives weighted by gross output from base year and multiplied by the proportion of the value added- gross output ratio of current and base year. Compared to the fonnula of the direct approach, it can be seen that for the indirect approach, more data inputs detennine the index. Thus, the index is more sensitive for weaknesses in the data. This upholds our fmdings of the previous paragraph.

Turning to the weights, we see that the difference liesintheuse of gross output versus value added12•

To detennine the influence ofvariations in thesetof weights to the index number, we shortly elaborate findings ofDe Wolff(l957).

A formula for the effect of variances in weights

We denote the relative difference of the weights applied to commodity k in the fonnula compared to the appropriate weights as ~wk fw.t . Since we know that the weights wk all add up to I (hence, :L~w.t=O)

we know that the weighted average is nulland the weighted coefficient ofvariation is given by:

(32)

The relative deviation from the production relatives to the weighted index nurnber (J) is given by ~ik = ik -I , where i .t is the partial production index of cornmodity k. Since the weighted average of ~i .t is 0 (:Lw.tMk = 0), the weighted coefficient ofvariation is defmed as follows:

(33)

To develop a simp Ie fonnula, De Wolff utilises the correlation coefficient between ~ik and ~ w k / w k •

This coefficient is given by:

11 Since the quantity relatives have an implied form, the advantages of an price index (e.g. incorporation of quality change) are

expressed in the quantity series. 12

This is due to the assumption ofthe availability of an output index, which is weighted with gross output weights. In reality the conditions for the price index are even weaker for Tanzania.

26


(34) = =

If we denote the changes in the aggregated index caused by changes in the set of weights with M, than it follows:

(35)

Substituting the definition of 11ik into (35), we get:

(36) N N N

M = L~wk(I +~ik) = IL~wk + L~wk~ik k=l k=l k=l

which coincides with the numerator of (34). Therefore (36) can be rewritten as:

Ifwe divide Mby L this leads to:

(38) M

I

where V; is a measure ofthe weighted varlation of ~iJl.

The resulting formula (38) shows that the relative change of an index number is equal to the product of the weighted varlation of the partial production indexes, the weighted varlation of the relative changes of the weights and the weighted coefficient of correlation of the ratios and of the relative change.

Influence of variations in the weights for the direct and indirect approach

We will now apply the formula found to assess the differences between the direct and indirect formulae for an index of industrial production. The correlation coefficient Pïw is difficult to fmd, but we know

that I p ;w I :::;; I , so that

(39) ~~~ :::;; cr )~ There is no reasou to assume that V; for the indirect and direct formula differ significantly. For the direct approach, it is clear that deviations between the applied weights and the value added weights will be moderate, since the direct approach uses value added shares as weights. In case a variety of products in a certain industry grouping are weighted together, value added weights can in general not be found. In this case, an approximation ofvalue added is used, by applying gross output values. Therefore, there will alway!. be some deviation between the weights applied and the re a/ value added weights. For the indirect approach we expect crw to be bigger, since gross output, in stead ofvalue added weights are used. From industry to industry the value added gross output ratios differ, causing a bias between applied gross output weights and true value added weights. To quantify crw we have taken data from the producer price index, as constructed for Tanzanian manufacturing from 1992 onwards. The varianee between the weights used for the main industry grouping (ISIC 31-39) and the value added weights of 1989

13 are calculated. In table 4-5 the weights are given and the values for (~wv2/wk are calculated.

13 No value added data for 1992 is available. For 1990 a survey ofindustrial production is available, but the 1989 value added

shares are considered to be the more reliable, since in 1989 a census insteadof a survey was carried out For this case, we assume that structural changes in the manufacturing sector bringing about differences in value added weights between 1989-1992, were far less significant than deficiencies in weights caused by improper weighting.

27


TABLE4-5 Deviations in Weights applied for the PPI

I SIC Output PPI-weight VA share '89 (~wY Iw;

31 64786674 41.4% 42.2% U.UUUl:l

32 30391211 19.4% 7.9% 0.06822 33 1277280 0.8% 4.2% 0.14214 34 4363170 2.8% 6.3% 0.04428 35 12980282 8.3% 17.3% 0.09810 36 20043300 12.8% 5.0% 0.04788 37 15307304 9.8% 4.5% 0.02875 38/39 7359751 4.7% 12.6% 0.13356

TOT AL 156508972 100.0% 100.0% 0.56308

cr., = 0.75

Source: data files of the Producer Price Index Source: Census of lndustrial Production 1989.

The thus calculated crw of 0.75 is very significant. Assuming that crw for the direct approach is notmore than 0.2 and that Vi for both approaches is 0.2, we can see that14

:

IMJ ldir.ct

~ 0.04 and

I~J indirect

Tl ~ 0.15

The relative error (under certain conditions) for the direct approach bas a maximum of 4%, while for the indirect approach this is 15%. Of course, it may be stated that the coefficient of correlation Pïw

might be small, however, we expect that, e.g., a decline in production within a certain industry will have impact on the amount ofvalue added by this industry and consequently on the weight calculated for this industry. We therefore have no reasons to assume that Pïw is close to null.

TABLE 4-6 The liP compared to deflated value added indexes 1985-1990

Manufacturing RPI 1 CPI RPI-deflated 2 CPI-deflated Index of lndustrial Value Added VA index VA index Production

(1985=100) 1985 5111606 100 100 100 100 100 1986 6412236 136 133 92 95 97 1987 11062008 172 172 126 126 107 1988 11357863 224 227 99 98 115 1989 21474018 293 295 143 142 117 1990 23955853 377 401 124 117 114

Source: Annual Survey of lndustrial Production for manufacturing value added; for the Retail Price Index (RPI), and the Consumer Price Index (CPI): Bank of Tanzania, Economie Bulletin 31/12/95. Index of industrial production: Quarterly Survey of lndustial Production 1995:3.

Notes: (1) base years of the RPI, and CPI are nol originally set to 1985, but series are switched to 1 (2) Deflatedindexes are calculated deviding value added by the RPI (CPI) price index, and rewriting the thus derived value in constant prices series, as a series of index numbers.

Taken into account the high error levels, it is to be expected that the index of industria1 production as it is calculated for Tanzanian manufacturing 1985-1995, will deviate from the deflated value added index, using a price index. Unfortunately no producer price index is avai1able for 1985-1990 and no value added figures after 1990. Therefore, for 1985-1990, the liP 1985-1990 is listed together with deflated

14 We cannot detennine the value of cr., for the direct approach, but we are inclined to assume that it is very unlikely that it wil!

exceed 0.2. The value for V; is choosen arbitrary.

28


value addt.J indexes in Table 4-6 using the consumer and retail price index (resp. CPI and RPI). As can be seen from the table, the deflated value added index runs differ considerable from the liP calculated for this period (most pronounced in 1988), which supports our hypothesis.

4.4.5 FINDINGS & CONCLUSIONS

Taking a look at the theoretica! conditions for which the direct and indirect measure of an index of industrial production coincides with the real, that is, the Geary formula, we could not draw any conclusions on which approach is better. We have seen that the indirect approach better approximates the concept of an index of industrial production. Considering the data requirements for an indirect approach of the liP, it must be concluded that these are far more pronounced than is the case for the direct approach. These requirements cannot even be met for Tanzania.

Furthermore, price deflators available are constructed from a different concept than the liP. We have focussed on the incluence ofvariations in the set ofweights to the liP. Comparison ofthe weights applied in the direct formula with the weights applied in the indirect formula, revealed that biases in the liP are significantly higher for the indirect approach.

Regarding the choice between the direct and indirect approach we conclude that for the purpose of measuring value added at constant prices in Tanzania, a Laspeyres quantity index is the most appropriate approximation to an index of industrial production and thus for measuring value added at constant prices.

29

5. CDRRENT STATISTICAL PRACTICE FOR

MANUFACTURING

I n this chapter we will reflect on the state of the art of the statistles on manufacturing performance. Series for manufacturing value added (MVA) have been constructed for the national accounts throughout the history of Tanzania. We will examine how these series are compiled and which data

sourees are utilised. The following questions will be answered:

1) What primary data sourees are available for manufacturing?

2) In what way is primary data translated into indicators of manufacturing performance, i.e. MVA in current and constant prices compiled for the national accounts?

3) What are the problems regarding the basic data and the compilation procedure arriving at time series for manufacturing performance?

Paragraph 5-1 presentshow of manufacturing series are constructed for the national accounts. We will separately discuss the data sourees for manufacturing in paragraph 5.2. The quality of and problems regarding the data sourees and the compilation of manufacturing series for the national accounts are dealt with in paragraphs 5.3. This chapter ends with conclusions on the assessment of manufacturing data and suggests adjustments to improve the quality of the primary data and the series of manufacturing value added.

5.1 MANUFACTURING IN THE NATIONAL ACCOUNTS

5. 1.1 CONSlRUCTING MANuF ACTURING V ALUE ADDED SERIES

We have presented the procedure of constructing national accounts estimates for manufacturing value added in figure 5-1. The basis for this figure is the methodology which is partly described in the sourees and methods P' Iblications of 1985 (BoS May 1985) and which has partly been revealed in discussions with national accounts memhers ofTakwimu. Forthe sake of clarity, we have simplilled the real world,

CDRRENT STATISTICAL PRACTICE FOR MANUFACTURING

so to get clear insight in the way different sourees are combined to produce value added series for manufacturing suited for the national accounts.

FIGURE 5-1 Compilation of Manufacturing Value Added for the National Accounts

.................... ::.:-::-::::::::::::·· .................

ASIP& ·• censllses üöf)

·· · nr• •···

___..,COMPILE Nomina! VA

IESTIMATE SSI & IS (1-9)1 1+---

1 (QSlP) _.,COMPILE Real VA

(s9~>~

____ .,.

· • • riominal < < • ·•M:vArJr·······

téälMVA ••·• fodhe. ................

• riàtîölial. ......................

äcëöünt.s ··• ...................

(1)

(2)

(3)

Notes: DIE= Directory ofindustrial Establishments, ASIP = Annual Survey oflndustrial Production, IIP =Index of Industrial Production, QSIP = Quarterly Survey oflndustrial Production, (M)V A= Manufacturing Value Added, Pop. Census= Population Census, IS-surveys= Informal Sector Surveys, HHB survey= Household Budget Survey, E&E surveys. = Employment and Earnings Surveys. SSI = Small Scale Industries, IS = Informal Sector. (1) SSI estimated at 32 million TSh for realand nomina! value added series forthe years 1976 onwards. IS is estimated as l/3 of 1 0+ manufacturing. (2) Index of 50+ applied to 1 0+ manufacturing.

The meaning of some abbreviations have not yet been introduced, but will soon be clarified. For the time being it is enough to briefly explain the structure ofthe diagram. On the one hand the statistics presented in the QSIP, ASIP and censuses of industrial production are based on the directory of industrial establishments (DIE) serving as a framework for data collection. These statistics intheir turn are core inputs for compiling manufacturing value added for the national accounts. On the other hand, various statistics (provided in the population census, household budget survey, etc.) are used to come up with an estimate for small scale and informal manufacturing. Put together the estimates for medium & large scale, small scale and informal sector manufacturing, national accounts compatible value added is obtained1

•

Another look at the diagram tells us that the estimates for current and constant prices series are more or less independent. The compilation of the constant price series are mainly based on a (provisional) index of industrial production2

• The estimation procedure of the small scale and informal sector is the more less the same for current and constant prices.

5.1.2 MANuFACTIJRING SERIES IN 1HE NATIONAL ACCOUNTS

The history of national accounting of Tanzania, starts with the frrst comprehensive inquiry into the national income of Tanganyika, performed by Peacock and Dosser in 1956. Their work was publisbed in National Income ofTanganyika, 1952-1954. It was the frrst of many publications on national

1 In the tigure it is refrained from the problem of compiling time series when aru LUal data is lacking and beneh-mark value added needs to be extrapolated with the use of certain price ( an/or quantity) indexes. In particular this is the case for estimates for small scale and informal manufacturing. 2 The national accountants have constructed a (unpublished) provisional index of industrial production for national accounts purposes only. In the quarterly survey (QSIP) an index of industrial production has been published for 1985-1995.

32

CURRENT STATISTICAL PRACTICE FOR MANUFACTURING

accounts that followed3. From the very beginning of national accounting, the ongoing efforts in

improving the quality of the national accounts yielded publications of revised series every now and then. The compilation practice adopted in the Tanzanian national accounts is a combination of production and income approach. For manufacturing the product approach has been in use throughout the time span 1961-1995. Albeit that Stäglin & Komba (1992) report revisions ofthe national accounts for the expenditure si de, the contents of this report do not give rise to any practical implications for GDP calculations regarding manufacturing

4•

TABLE 5-1 Comparison N ational Accounts and ASIP MVA

ASIP National Accounts Ratio Series 1971 1 Series 1976 Series 1981 Series 1985 Series 1993 Nat. Acc./ ASIP

(10+) (0+)

(Jv!VA in current prices in millions Tsh)

1960 109 202 1961 139 258 1962 154 288 1963 156 292 1964 194 371 1965 267 234 429 161% 1966 295 415 525 178% 1967 319 477 571 179% 1968 378 519 648 171% 1969 475 597 742 156% 1970 561 828 828 148% 1971 643 937 947 147% 1972 806 1106 1144 142% 1973 914 1227 1260 138% 1974 1155 1482 128% 1975 1774 1976 1572 3 2047 2811 2811 135% 1977 2424 3287 3287 1978 2815 3 2860 3859 4091 145% 1979 2927 3277 3868 4368 149% 1980 2891 3262 4097 4477 155% 1981 3108 4501 4671 150% 1982 3204 4361 4861 152% 1983 3620 4527 4969 137% 1984 4405 4630 5932 135% 1985 5129 7763 151% 1986 6022 9839 163% 1987 11062 14761 133% 1988 11358 20725 182% 1989 21646 3 46064 213% 1990 23956 59961 250%

Sources: ASIP: Annual Survey of lndustrial Production, several issues, except for: 1976 (input-output table, 10+), 1978 (Census, 10+) and 1989 (Census, 10+); Series 1971: OECD (1971), based on, among others, BoS (Jvlarch Series 1976: World Bank (1976), basedon revisions publishad in 1972 & more recent years; Series 1981: BoS (Sept. 1981); Series 1985: BoS (Jvlay 19 l5); Series 1993: BoS (April1993).

Notes: (1) OECD and World Bank series are g.ven, because they are backdated to 1960. (2) The ratio ofthe most recent national accounts tigure toASIP values is taken. (3) Censuses and 1/0 table recalculated to 10+ coverage.

In Table 5-1 the several revised series of national accounts nominal manufacturing value added are presented together with value added taken from the annual survey of industrial production (which will be discussed in paragraph 5.2.1). In this table we estimate the ratio of ASIP manufacturing value added

3 The Tanganyika Unit ofthe East African Statistica! Department published national account estimates forthe years 1954-1957. The flrst national accounts series publisbed by the 'United Republic ofTanganyika and Zanzibar' was The National Accounts of Tanganyika, 1960-1962 (May 1964). In March 1970, the Bureau of Statistics of Tanzania brought about National Accounts of Tanzania, 1966 to 1968. 4 When camparing gross output flgures presented in the national accounts report of Stäglin & Komba with ASIP flgures, it appears that the ASIP must have forrned the core for GDP calculations for manufacturing. Taken 1985 as an example, only forthebranch Jood, beverages and tobacco a considerable level adjustment had been applied, which is in line with 1976 estimates for small scale and informal sector manufacturing. The branch gross output for non-metallic mineral produels was for some (not specilled) reason adjusted downward. For other branches gross output is in line with the ASIP estimates. We therefore conclude that the manufacturing series presented in the revised series arebasedon the production (instead ofthe expenditure) approach

33


to national accounts manufacturing value added, using the latest revision of national accounts available for each given year. This will give us a feel of the magnitude of the estimates that have been made for small scale and informal sector manufacturing. It can be seen that estimates made for those industries not covered in the surveys and the censuses ofindustrial production vary from 28% in 1974 to 150% in 1990. We will take a closer look at the way these estimates are calculated in paragraph 5.3.

5.2 DATA SOURCES FOR MANUFACTURING

Data sourees for manufacturing are available in the industrial statistics, the input-output tables and in several publications ofprice index numbers. The industrial statistics comprise 1) the annua1 survey, 2) the censuses ofindustrial production and 3) the quarterly survey. We will deal these statistics in the following paragraphs. In actdition we will discuss available input-output tables and deal with some price index numbers.

5.2.1 ANNuAL SURVEY AND CENSUSES OF lNDUSTRIAL PRODUCTION

The annual survey of industrial production and the censuses of industrial production are conducted to collect detailed datafora wide range of variables concerning the performance and structure of industries5

. Data on employment, gross output, intermediate consumption, value added, expenditure on fixed assets, etc. are being collected fora number of industrial sub industries. The main objective of the annual survey of industrial production and the censuses is to provide data needed for the compilation of the national accounts and to provide useful information to planners and decision makers on the structure and performance ofthe industrial sector. Industrial censuses have been conducted only three times in the history ofthe industrial statistics of Tanzania: in 1961, 1978 and in 1989. The ASIP is basedon limited coverage in termsof establishment size (i.e. establishments with 10 or more persons engaged), where the censuses have an extended coverage (the 1978 census covered establishments with 5 or more · persons engaged and the 1961 and 1989 census covered 1+ establishments).

The frrst industrial survey was carried out in 1955/1956 requesting data for Dar es Salaam for 1954, covering establishments with 5 persons or more engaged (5+ ). Two surveys of industrial production foliowed (1956 and 1958) both having an extension in scope of industrial activity. The latter was the first survey to cover all industrial5+ establishments throughout the country (Central Statistica! Bureau, August 1964:1). In 1961 a census of industrial production was carried out, covering allindustrial establishments, irrespective of size (1+). All the surveys (and the 1961 census) that have been carried out before 1965 are characterised by poor response rates, differences in coverage and concepts and by inconsistency ofthe defmitions used. The 1965 survey (Central Statistica! Bureau 1967)is considered as the first comprehensive and consistent survey of industrial production, where basic industrial data are collected according to the international standardised system of national accounts. It is said that the survey should not be considered comparable to earlier inquiries (Central Statistica! Bureau, 1967:3). In this report, we will therefore focus on the annual survey carried out from 1965 onwards (as· ndicated in Paragraph 2.2.2).

In the years 1966-197 4 Takwimu conducted surveys of industrial production annually ( Central Statistkal Bureau 1967, 1969 & BoS 1970 et al). By and large, the ASIP has had the samedesign and coverage over time. Establishments with 10 persons or more engaged were covered (10+ establishments). However, some differences in non-response treatment and coverage of industrial activities emerged during examination ofthe ASIP (see later).

In 1978 a census ofindustrial production (data publisbed in BoS 1983) was conducted collecting the samedata as the ASIP, but covering more establishments: establishments with 5 persons ore more engaged (5+). From 1979 to 1988 another series of ASIP followed, again reflecting 10+ industrial activity. These surveys provide no information on the treatment of non-response. No significant changes in methodology occurred during these years.

5 The focus ofthis report is manufacturing only, but the surveys and censuses cover more industrial activities. Since 1967, the

survey of industrial production also covered industries engaged in mining, quarrying and electricity.

34


The 1989 census of industrial production (u oS aprill994) covered allindustrial establishments (1 +), irrespective of size (establishments with 9 or less persons engaged on a sample basis). In 1990 an industrial survey was conducted (10+). For more recent years, data for the ASIP have been collected, but due to budget cuts no new survey has been publisbed yet.

5.2.2 QUARTERLY SURVEY OF lNDUSTRIAL PRODUCTION

The index of industrial production 1985-1995 is based on data collected in the quarterly survey of industrial production (QSIP), publisbed as lndustrial Cammodities reports since 1985 (BoS Aprill988 et al). The putpose of the survey is to measure the quantitative production of main industrial commodities and to provide weighted production volume indexes of different industrial activities. It covers establishments with 50 and more persons engaged in manufacturing (50+ establishments). Nevertheless some activities have been excluded: garment manufacturing, furniture making, printing and publishing and pharmaceutical products. The reasons for omitting these activities are (1) difficulties in assessing quantitative units and (2) the relatively small part of total production of excluded actlvities in 50+ establishments. In the 1978 census, the 50+ establishments cover 90% of manufacturing gross output in most of the branches covered in the quarterly survey and about 80% of total manufacturing (Redeby, 1986).

In the previous chapter it was made clear what role the index of industrial production plays in estimating real value added. In the case of the Tanzanian manufacturing sector, the index is provided in the QSIP. The index is constructed using gross output6 of main commodities as a substitute for the value that is added by a certain industry (Redeby, 1986: 4). The index formula is a Laspeyres index and is given as follows:

N 0

(1) JIP.QSIP "" 0 q kJ I = LJ W k85 ----a- '

k=l q kO

where wk85° is the gross output weight in 1985, calculated at market prices and qkt 0 is quantity output of commodity k in year t.

5.2.3 INPUT-ÜUfPUT TABLES

The basis for constructing an input-outputtableis the relationship between inputs of raw matenals and outputs of fmal products. By distinguishing subindustries and in tracing the product flows between these industries, an input-output table can be constructed fora partienlar year. For Tanzania the history of input-output tables dates back to 1954 when the frrst input-output table was constructed, distinguishing 14 sectors. For the years 1961, 1969, 1970 and 1976 new input-output tables were constructedeach having improved quality with re gard to previous versions. In view of the quality and availability of the input-output table, we will focus on the 1976 input-output table only7 (BoS 1986).

In the 19'16 input-output table, 32 sectors were distinguished for manufacturing. Data sourees for determining inputs and outputsforthese sectors were questionnaires ofthe ASIP of 19768 (10+). Due to a large non-response rate, estimates had to be made for non-responding establishments. In terms of gross output, a fmal estimate was determined for non-response, adding 35% of gross output of the responding establishments. About 30% of gross output was estimated for informal and small scale establishments using other data sources, such as the household budget survey, annual trade reports and estimates based on the fmal demand side9

•

6 As the 1985 ASIP results were not yet available, Takwimu used estimates of commodity gross output, the souree of which is not specified, as weights. 7

We only have the 1976 input-output table with sourees and methods report at our disposal. While the 1976 input-outputtableis incorporated in the national accounts manufacturing calculations, we have not discovered any references in the national accounts methodologyto input-output tables of 1961, 1979 or 1970. We therefore assume we can ignore earliertables within the scope ofthis study. 8

The Survey oflndustrial Production for 1976 has never been published. 9 Calculated from the sourees and methods part ofthe Input-Output table 1976 (BoS 1986:7-12). Out of32 subsectors, for 2 subindustries it is not clear how the total output (responding and not responding establishments) is derived at. In case of 6 sub sectors, data were taken from other sourees (not ASIP) and for the remaining 24 subsectors, response form the ASIP was used, adding estimates for non-response.

35


5 .2.4 CONSUMER AND PRODUCER PRICE INDEX NUMBERS

In the practice of Tanzanian manufacturing statistics, price index numbers have been used to dejlate nominal value added and for injlating real value added. We will briefly discuss consumer and producer

. . d b 10 pnce m ex num ers .

Consumer Price Index Numbers

Price index numbers in the sphere of consumption, have been published throughout the history of Tanzania. Since 1970, the National Consumer Prince Index (CPI) has been published (BoT June 1970 etc), providing a general index, as wellasso-called group index numbers, e.g. index numbers for food, drinks & tobacco, etcu. For the years 1963-1969, a Costof Living Index is available, basedon a family expenditure survey conducted in 1963 (BoT June 1973: 86).

Before the introduetion ofthe Producer Price Index (PPI) in 1993, Takwimu was dependent on the consumer price index number for constructing deflators for the national accounts. For the 1976-1984 manufacturing value added series at constant 1976 prices, the co st of living index of dothing and footwear consumed by urban dwellers was used to deflate nominal value added (BoS May 1985: 27). In addition, in the revision of the national accounts, currently undertaken, the CPI is being used to deflate nominal informal sector value added series. Another application of the CPI for industrial statistics is forecasting trends for years were no complete data set is available. For example, the latest issue of the annual survey had reference to 1990, while constant price estimates have been available up tilll994. To co me up with estimates for value added at current prices for 1991-1994, real value added is inflated with the CPI.

The Producer Price Index

Producer Price Index (PPI) series were first introduced in Tanzania in 1993 (BoS January 1996). The main obj ective was to provide a deflator for the system of national accounts to calculate value added at constant prices. For the time being only the manufacturing sector is covered. Data until the last quarter of 199 5 have been collected and processed, but some problems encountered in terros of commodity drop out, dosure of base year establishments and broad commodity definitions12

•

The PPI is going to be used for current price estimates (inflating real value added) for years where data are lacking. The use of the PPI instead of the CPI can be regarded as a substantial improvement. In the previous chapter we have seen how discrepancies in the weights used in the index bias the aggregated index. 1t is to be expected that the weights (based on manufacturing output) applied in the PPI yield an index which comes closer to an appropriate price index than is the case with the CPI13

•

5.3 ASSESSMENT OF MANUFACTURING VALUE ADDED DATA

Let us concentrate on figure 5-1 once more. The more or less independent procedure to derive at nominal value added (illustrated as layer 1 in the figure) and real value added series (illustrated as layer 3 in the figure) for manufacturing allow us to separately discuss these issues. The estimates for small scale and informal sector manufacturing will be separately dealt with as well (illustrated as layer 2 in the figure) in paragraph 5. 3 .1. The stages of data manipulation as presented in Chapter 3 will be subsequently dealt with, to asses the nominal value added series for medium & large scale manufacturing (paragraph 5.3.2). For the assessment ofreal value added we discuss several important issues regarding the quality of the series in paragraph 5.3.3.

10 We only have a retail price index (RPI) available over a contiguous time span (see e.g. annex table Bl-4).

11 Although the CPI is recognised as accurate and comparable to international standards, a workshop on CPI recommended various

improvements: the consumption basket should be enlarged, coverage of social groups extended and the market basket and weights revised (BoS February 1995: 9-11). 12

lnformation derived form a draft version of consultancy on producer price index, Terms of Reference, by S.M. Mbaruku. 13

In the previous chapter it is shown that for purposes of deflating nomina! manufacturing value added, the index weights used in the PPI are not sufficient. Still we expect the PPI to be a better indicator for the trend of general price level in manufacturing than the CPI (utilising weights based on a consumption approach).

36


5.3 .1 .liSTIMATES FOR SMALL SCALE AND INFORMAL SECTOR MANUF ACTURING

Efforts to estimate value added generated by Small Scale Industries

Havnevik et al (1985) have identified serious problems regarding data on small sca1e industries. They assessed availab1e statistics on industrial performance of small scale industries, by examining the surveys ofthe SIDO (Small Scale Industries Development Organisation) carried out for 1975 and 1977/78 and compared these statistics with the small scale data available in the 1978 census of industrial production. It occurred that there were substantial problems in comparing the SIDO statistics with figures presented in the 1978 census. It is even concluded that 'at present there are no statistics which can be used to present a reliab1e picture of development trends in Tanzanian crafts and small scale industries." (p.114)

This condusion has been affrrmed by Takwimu (BoS May 1985) stating that there are no reliable data available for manufacturing activities carried out on a small scale by households 'such as beer brewing, tailoring, mat and basket making, footwear, saw milling, wood carving, etc.' No surveys of small scale industries have been utilised in the national accounts before the results of the census of ioclustrial production in 1978 came available. Estimates made for small scale manufacturing prior to 1978, have been based on the surveys of employment and earnings. The survey of employment and earnings covered data provided by 5+ establishments. Estimates for value added were based on the difference between aggregated earnings in the ASIP and the employment and earnings surveys14 (BoS March 1971).

Estimates for small scale industries sirree 1976 are based on the results of the 1978 census of industrial production (BoS May 1985i5

, 16

. In the revision ofthe national accounts which is currently being undertaken, estimates will also be based on data provided in the 1989 census.

Efforts to estimate value added generated by the Informal Sector

The 1989 census was the first datacollection of Takwimu, where informal sector establishments were covered. Nevertheless, only establishments have been covered of which the owner indicated that it was set up as a prime means of income. In this way informal sector enterprises were omitted which were run as a secondary or third activity17

• It is likely that most typical informal sector activity must have been overlooked, as it has been recognised that many public sector employees have resorted to alternative informal means of economie 'survival' (Bagachwa 1995). Therefore it isjustified to assert that no framework for covering the informal manufacturing sector existed before a comprehensive informal sector survey was carried out in 1991.

In the practice of national accounting, estimates for informal sector manufacturing have been based on many different sources, that is, whatever was available that could serve as a framework. In the oldest report on sourees and methods, an estimate is made for value added by household and cottage industries, which was defmed by aU "residual establishments engaged in manufacturing or processing activities which are generally undcrtaken on a household or cottage industry basis" (BoS March 1971:37). The basic approach was to estimate the tota1 number of persons engaged in manufacturing for 1966 ( estimate based on the population census 1967) and subtracting persons engaged in medium & large scale (based on the ASIP 1966) manufacturing and small scale manufacturing industries (based on the emp1oyment and earnings survey 1966). The resulting aggregated residual number of employees

14 BoS (March 1971) reports that a sample of 44 smali-scale manufacturing establishments had been taken to make an estimate for the ratio of value of eamings and total value added for small scale industries. It is not elaborated in detail how the fmal estimate is calculated. 15 In the sourees and methods publication (BoS May 1985) the coverage ofthe 1976 input-output tab ie is considered to be comparable with the 1978 census. It is notmade clear why this should be true. In our opinion the core ofthe 1976 input-output table is constructed basedon 1976 questionnaires ofthe ASIP and is thus comparable with medium & large scale manufacturing. Indeed, estimates have been made for small scale and informal industries, but this does notmake the 1976 input-output tab ie comparable with the 1978 census, for, the 1978 census only covers establishments employing 5 persons or more. 16 Estimates for 1976 arebasedon the input-output tab1e (see previous footnote). Estimates for 1977 and years succeeding 1978, are based on the 1978 census. We will touch up on the methodology of making estimates for the smal! scale industries for other years than 1978 in paragraph 5.3.2. 17 Based on interviews with persons responsible for the industrial statistics at Takwim u.

37

CURRENT STA TISTICAL PRACTICE POR MANUP ACTDRING

was assumed to be the number of persons engaged in household and cottage industries18. An estimate

for value added was calculated by taking the number of persons engaged in informal sector manufacturing and multiplying it with an estimate of net output per person19

.

The second sourees and methods pubHeation (regarding 1976-1984 series, BoS May 1985) reaffirm that there is lack of reliable information relating to manufacturing establishments where less than five persons are engaged. The assumption is made that the contribution of such informal establishments (i.e. 1-4 establishments) bas a value comparable with one-third of the estimated manufacturing value added for medium & large scale industriesbasedon the ASIP. Though it is not clear why this one-third estimate has been chosen, we believe that it must have beenbasedon the 1976 input-output table. In table 5-2 the value added ofthe input-outputtableis split between value added generated by medium & large scale manufacturing establishments and value added estimated for small scale and informal sector.

TABLE 5-2 Manufacturing Value Added from the 1976 Input-Output Table

Value Added

(million TSh) (%) Value added by establ. covered in the ASIP

Estimated value added by small scale and informal industries 1

Total Manufacturing Value Added

Source: input-output table 1976 Notes (1) small scale and informal sector industries comprise: 'Local Brewing',

'Tailoring', 'Other SSI', and 'Cotton Giming'.

1572 100%

517 33%

2os9 133%

It can beseen that intheinput-output table, value added ofthe small scale and informal sector equals 3 3% of value added of medium & large scale establishments. Though this one-third estimate for the 1976 input-output table includes small scale industries, we suspect this to be the basis for the one-third estimate utilised for the informal sector in the national accounts practice20

•

Recent surveys undertaken in 1991 and 1994/95 to collect comprehensive data on the informal sector performance, make it possible to improve estimates for informal and small scale manufacturing. At present, Takwimu undertakes efforts to incorporate these informal sector survey results into improved national accounts estimates for informal manufacturing.

Preliminary Examination of Small Scale and Informal Sector Manufacturing

Until the mid eighties, the Tanzanian govemment viewed the informal sector activities as a passing phenomenon, lacking the potentlal to recreate and sustain itself. Fora number of reasons21 the second economy wasnotsubject to stimulating policies, instead, attempts were made to counter the growth of the second economy (Maliyamkono & Bagachwa 1990: 30-31). This was one of the reasoos why the informal sector did no1 expand until the mid-eighties in reaction to a declining formal economy in the late seventies and early eighties. According to Bagachwa (1995: 271-272) there is enough evidence of rapid growth of the informal sector after the mid-eighties, in contrast with the pattem of the official GDP.

18 We assume that these industries can be captured under infonnal sector manufacturing, which in our defmition comprises establishments with 1-4 persons engaged 19 Por national accounts purposes only, a sample of262 informal sector industries has been carried out to estimate average value added per person (BoS March 1971). These estimate have been revised when the results ofthe 1969 Household Budget Survey became available. We do nothave a sourees and methods reports at our disposal which deals with this revision. It is assumed that the methodology for determining informal sector value added has remairred the same. 20

This can be defended, as from draft notes it has been identified that estimates for the smal) scale based on the 1978 census, were added after adding this one-third estimate for the informal sector. Purthermore, the smal) scale sector tumed out to he small compared to the estimate made in 1976 for the value added by both, the informal and small scale industries. 21

Maliyamkano and Bagachwa distinguish 5 existing negative attitudes towards the informal sector: (I) the second economy does not add to productivity, (2) the second economy distorts societal objectives, (3) a second economy threatens Tanzanian's desire for a classless society, ( 4) second economy activities interfere with country's Ie gal system and (5) the informal sector system fosters centre-periphery types of relationships.

38


The evidence fora separate development of the informal and formal economy, especially after the mideighties, really undermines the robustness of the one third estimate of the informal sector for manufacturing. The one-third estimate can be considered as untenable. Although initially our aim was to make an improved estimate for small scale & informal sector manufacturing, it has become apparent it is tentative and difficulties exercise. Due to time constraints, we therefore have refrained from further assessment ofthe informal sector and focused on large scale manufacturing only. We believe that it will contribute to the clarity of the statistics to make separate estimates for large scale and small scale & informal sector manufacturing. This counts for nomina! value added estimates as well as for estimates of real value added.

In the particular case of constructing an index of industrial production, it is recommended that only when there is reason to expect a parallel movements of industries, the coverage of an index of industrial production should be extended with the informal and small scale industries. It is to be expected that the character and growth trends of the small scale and informal sector correspond. It is therefore desirabie and in line with UN recommendations, to separately construct an index of industrial production, covering medium & large scale establishments and an index covering small scale & informal establishments should be constructed (see UN 1961: 12-13). Due to constraints of time and data the task of constructing an index for small scale & informal manufacturing will not be tackled in this thesis.

5.3.2 NOMINALMVA OF MEDIUM& LARGE SCALEMANuFACTIJRJNG

Frameworkof Data Collection

The framework for data collection for the annual surveys of industrial production for the years 1965-1977 was formed by the directory of industrial establishments (DIE) maintained at the industrial section of Takwimu (see Figure 5-1). This directory of industries had been prepared for the first survey carried out in 1965, basedon the records kept by the Ministry of Labour on basis ofthe Factory Ordinance. The directory of industrial establishments since then was updated using certain sources: (1) new factory registration certificates issued by Registrar of Industries22

, (2) the Inspeetor of Factorles (Ministry of Labour) and (3) the directory ofbusinesses maintained at the Labour section ofTakwimu.

In 1978 a census (5+) was carried outbasedon a framework which had a broader coverage than the ASIP in previous years. Besides the incorporation of small scale establishments, also for medium & large scale establishments the coverage was improved (Havnevik et al1985: 89). The reason for this coverage gain lies in the upgrading of the previous framework through a canvassing procedure carried out by Takwimu, yielding a list of industries, among which were many unregistered establishments.

The 1989 census ofindustrial production covered establishments of all sizes (1+). The medium & large scale establishments were covered by a take-all procedure (as is the case for the ASIP) and 1-9 establishments by sampling. The sampling framework was provided by a canvassing procedure. For the data regarding 10+ manufacturing, the same goes as for the 1978 census: a lot more industrial activity is covered than the annual survey does in preceding years.

Though we cannot rely on any published report regarding the coverage of the annual surveys between 1978 and 1989, analysis of data on the number of establishments (10+ establishments) suggests that certain establishments must have beenleftout in the annual survey. The number of 10+ establishments covered in both censuses (1978 and 1989), were considerably higher than those for the inter-censalyears. After discussion with memhers of the industrial section of Takwimu, it turned out that certain activities covered in the census years were not taken into account in the annual surveys:

• non-factory type of activities like furniture and tailoring, to be found in the 10-19 size class;

• manufacturing output of training institutes, women's organisations, generating value added, but for frequently changing activities.

22 Every industry has to register at the Ministry ofTrade and Industries (MTI) in the so called Registrar oflndustries. New industries have to apply at MTI for either an industrial certificate or an industriallicense. For updating the Directory oflndustrial Establishments at Takwirnu, newly distributed certificates or licenses can be used. (Based on an interview with the Registrar of Industries and discussions with memhers ofthe industrial section ofTakwirnu).

39


No adjustment for undercoverage was made in the annual reports, because the contribution to MVA of the omitted establishments were assumed to be insignificant.

It can be concluded that the coverage of the Directory of Industrial Establishments has been different throughout time and has been somehow 'insensitive' for industrial growth23

. We will need to investigate the impact these coverage issues in more detail.

Data Collection

The publications ofthe ASIP tagether with the censuses form the basis for current price series ofvalue added for manufacturing. The publications span a period from 1965 to 1990. The quality ofthe data presented in the surveys and the availability of methodology justifying the numbers, differ throughout the years. Different periods can be distinguished:

1) 1965-1971: the numbers in the ASIP reflect responding establishments only. No adjustment for nonresponse is being made.

2) 1972-1974: the numbers in the ASIP represent the coverage ofthe DIE. Extensive information on response rat es and methodology on treatment of non-response accompany the numbers.

3) 1978-1990: ASIP-numbers represent the coverage of DIE, but no information on response rates and methodology of adjusting for non-response, is available.

In the period 1965-1971, the national accounts had to make adjustments for non-response. For the years 1972-1974 the ASIP incorporated non-response. The response rates in terms ofnumber of establishments forthese years were respectively, 86%, 77% and 79%. In the period where no ASIP or census has been publisbed (1975-1977), an input-output table (1976) is available. This tableis basedon the 1976 ASIP questionnaire data. As is discussed in paragraph 5.2.3 in termsof gross output, 35% is added for non-response24

•

For the years 1978-1989 no clear methodology is available. An important issue here is how nonresponse is treated. Based on interviews with memhers of the industrial section of Takwimu, for the census of 1989 a response-rate of about 95% was achieved. The target response for the annual surveys has been set to 85%, but it is not clear whether this target was truly met in all surveys. The methodology foliowed to estimate for non-response was as follows: in case of non-response take last year figures ofthe establishmene5

• This method of simpte repetition doesnottake into account changes in prices, thus causing a downward bias for value added estimated for the non-responding establishments. Since no information on response rates is available, we will have to collect information on non-response and the occurrence of simple repetition, so as to be able to assess the quality of the estimates in the ASIP.

Data Interpretation

Our aim is to produce time series that are consistent over time and that are well-defmed within the framework ofthe system ofnational accounts (SNA). For this purpose the industrial data shou1d be carefully examined whether any 'distortion' is caused by the use of non SNA-concepts and defmitions.

The manufacturing value added series presented in the national accounts are 'in strict conformity with' (BoS 1981) or 'toa large extent conform' (BoS 1995) the latest United Nations Recommendations. Although the words 'to a large extent' may call forth the question: 'to what extent?', we will not elaborate on this point in this paper.

23 Although the DIE was said to be updated using other sources, the revision ofthe DIE for the 1978 census made clear that the DIE had been insensitive towards unregistered establishments. 24 Souree forthese numbers is the ASIP and the input-output tab ie 1976. We were not able to present non-response figures in the same terms ofreference (e.g. MVA) due to dilTerences in presenting the response rates. 25 In the years 1972-1974 three methods were used to estimate for non-response: (I) use last year questionnaire data without modification (simpte repetition), (2) substitute figures of a comparable establishments, or (3) scale down larger establishment data. (ASIP of 1972). It is not clear in what proportion the different methods are incorporated.

40


A change in the questionnaire design, gradually introduced from 1980 onwa.rds, gave rise to flaws in calculating manufacturing value added. These flaws were discovered during in-depth analysis of the 1989 census where the same questionnaire design was used. The shortcomings mainly refer to properly defining and measuring intermediate inputs and subsequently manufacturing value added. The impact ofthe use ofthis 'new' questionnaire in the annual survey 1980-1988 and 1990 needs to be assessed.

Data Compilation

It has been elaborated in paragraph 5.3.1 how estimates have been made for small scale and informal sector manufacturing. From the sourees and methods reports two sets of assumptions have been used in the compilation of fmal figures for manufacturing value added. The methodology for the latest national accounts figures (presented in the 1993 series, Table 5-1) is not clear for the years 1985 onward. The first set of assumptions we have identified, refers to the nomina! and real value added series 1966-1976 (with 1966 as the base year for real value added) and the second set refers to the 1976-1990 series (with 1976 as the base year for real value added).

1) In the series prior to 1976 the manufacturing series is presented as manufacturing and handicrafts. The first step for calculating the series was to determine value added figures for 1966 (bench-mark year). An estimate fortotal employment (E) was derived from the population census (1967) and together with a crude estimate for manufacturing value added per person (MVAperson) basedon the Household Budget Survey (carried out for 1969 and deflated to get a 1966 estimate), value added for total manufacturing was estimated as follows (BoS March 1971):

(2) MVA1966,1+ = E1966,1+ x MVAf~~~: ,

where MV A1,x is manufacturing value added in year t, covering establishments with x persons engaged.

The value added figure thus derived was independent of the ASIP results. However, extrapolation of the 1966 figure was separately being carried outforASIP (1 0+) data, for the small scale estimates and for informal sector estimates26

. The estimate for small scale industries (5-9) was obtained from the (annual) employment & earnings surveys. Extrapolation of the 1966 estimate for the informal sector was carried out with the use of a (not specified) price index (P 1). The ASIP has been available annually, providing an annual estimate for the 10+ establishments. Hence, the manufacturing series at current prices in formula form can be written as:

(3) MVA,,1+ = MVA,,1o+ + MVA,,s-9 + MVA1966,1-4 x P/,,1-4

For value added series at constant prices, we assume that an index of industrial production based on a selected number of commodities (produced by 10+ establishments) has been constructed for national accounts putposes and used to extrapolate 1966 manufacturing value added27

:

(4) MVA,,1+ = MVA1969,1+ x IIP,,1o+

2) The procedures used in Tanzanian national accounting for the 1976 value added series are the same for current and constant prices (see Figure 5-1). The very core of calculating the final value added estimate is the value added series deriv~d from the annual surveys and censuses of industrial production (10+). In case ofreal value added, the MVA series as generated in (to be discussed in paragraph 5. 3. 3) equation ( 6) is used. Constant and current prices series of manufacturing value added have been compiled as follows28

:

(5) MVA,,1+ = 1 t MVA,,to+ + MVA191s,s-9

26 The sourees & methods report of 1971 (BoS 1971) simply extrapolates the 1966 estimate with a constructed quantity index (for constant prices series) and a price index (for current prices series). This because the ASIP results for 1967 and later were not available at that time. It is notified that ASIP results should be used in the future. We therefore assume that in later revisions ofthe 1966 series (of which we do nothave a more recent sourees and methods report) the ASIP results have been utilised as presented here. 27 From the sourees and method reports it is not clear how series at constant prices are calculated, but the descriptions strongly impress upon a straightforward extrapolation ofthe 1966 beneh-mark estimate with the use of a (for national accounts purposes) constructed index of industrial production based on 1 0+ establishments. 28 Derived from data files of national accounts calculations.

41


The weakness in the first set of assumptions for the 1966 series is main1y the questionab1e quality of the crude estimate for total manufacturing value added, which does not utilise the data collected in the annual survey of industrial production. The one-third estimate taken for the informa1 sector for the 1976 series, assumes that the informal sector is moving in the same direction as the format sector. As is examined in paragraph 5.3.1, it is observed that in case of a declining forma1 economy, the informa1 sector would expand, thus moving in an opposite direction. Consictering these weaknesses, it is indisputable that the construction of an independent time series for nomina! and real value added for medium & large scale manufacturing will improve the quality of the manufacturing statistics.

Data Presentation

Informal sector manufacturing has only been estimated for the manufacturing sector as a whole only, due to lack of insight in the structure of the informal sector. No national accounts compatible series of manufacturing value added for a set of manufacturing branches have been compiled up till now. The following remarks can be made, taking a look at the current and constant price series available:

1) There is a split in the current price series in 1976. The latest revisions date back to 1976 and do refer to earliertime series. This causes ajump in the MVA series(see table 5-1): 2047 million (old revised series) to 2811 million Tsh. (new revised series).

2) The revised !SIC classification which came into use in Tanzania from 1971 onwards does not influence the aggregated time series. However, for subindustries we may need to examine the influence of redefinition of certain manufacturing sub industries.

To construct time series for different branches, value added data for each branch are required. Since very limited data on small scale and informal sector manufacturing is available, presentation of medium & large scale manufacturing is a good alternative.

5.3.3 REALMVA OF MEDIUM& LARGE SCALEMANUFACTURING

The manufacturing series at constant prices presented up till now, can be divided in the 1966 series, the 1976 series and the latest revised preliminary series with (probably) 1985 as base year. Conform UNregulations (UN 1961) these base years are too far apart. lt should be examined whether it is possible to construct a series which is more regularly rebased.

As we have noted in section 3 (set aside the theoretically preferabie method of double deflation) we can determine real value added by deflating value added at current prices or by moving base year value added figures with an output index of physical quantities. Both methods have been applied in the practice of national accounting in Tanzania, but predominantly the latter method. According to the sourees and methods report (BoS May 1985), the 1976-1984 series of manufacturing value added at current prices were deflated with the cost of living index of dothing and footwear (see also paragraph 5.1.4). Basedon other sourees and data files ofnational accounts calculations, we consider application of this indirect approach to determine real value added as e,xceptional for Tanzania. From 1985 onwards an liP is available in the QSIP where a direct approach to the liP has been followed. Befc re 1985 a provisional index of industrial production has been constructed by the national accountants. We will deal with both indexes later on.

Turning to the QSIP we have examined some data collection issues. The response rates of the quarterly survey are much better than the response rates for the ASIP29

. The most recent pubHeation of the quarterly survey reported a response rate of 90% (QSIP 1995:3). The response rate for the second quarter of 1993 was 93%30

. For other issues ofthe QSIP, no information is available forthe exact response rates. As aresult ofinterviewing Takwimu members, it occurred that within the QSIP, no straightforward method is applied for making estimates for non-response. Either the average of last year figures is taken as a substitute for non-response, or the quarterly returns of last year is used. lt is assumed the influence of either methods on the fmal index numbers can be regarded as insignificant

29 According to Industrial Statistics, draft notes on contents ofthe industrial statistics as produced by the industrial section of

Takwim u. 30 from draft notes, see note 29.

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How data is compiled in series for the national accounts is elaborated in the previous paragraph. 1t was concluded that an independent series for medium & large scale real value added would improve the quality ofthe statistics. Therefore, we will now turn to the liP as it has been constructed for national accounts purposes and discuss the liP available in the QSIP.

The Index of lndustrial Production constructed for National Accounts purposes

Before the index of in dustrial production became available for the nationa1 accounts of Tanzania, a 'provisional' (as such not pub1ished) index of industria1 production was constructed basedon a se1ected number of commodities weighted with base year va1ue added shares (1966 or 1976). For the series with 1966 as a base year (up till 1976), no sourees & methods report giving clear insight in the way constant price series are compiled is available. However, in a UN-report (1979) it says 'various production re1atives' are used to extrapo1ate 1966 figures. We assume that these 'production re1atives' were used to construct a quantity index (based on selected commodities) in the same way as is done for the 1976 series.

For the series with 1976 as base year, an indexbasedon 14 selected commodities was adopted as an index of in dustrial production (no use was made ofthe index of in dustrial production, available since 1985). The index was constructed app1ying 1976 industry weights to the 14 commodities. The commodities represent branches ofwhich va1ue added weights are used. Let the va1ue added weights in the base year ofthe commodity k be wkO then,

(6) 14 q

JJP/rovisional = L Wko _!!_

k=l q kO

These 14 commodities were assumed to reflect the deve1opment ofthe whole manufacturing sector. Commodities, ISIC code and weights ofthe 14 commodities are given in the table 1-3.

TABLE 5-3 Vale added shares for 14 selected Commodities

Commodity I SIC Weight

Konyagi 3131 6% Beer 3133 6% Cigarettes 3140 4% Textiles 3211,3213 21% Fertilizer 3512 2% Sisal Ropes 3515 21% Paints 3221 6% Petroleum 3529/30 6% Cement 3692 3% Rolled Steel 3710 8% Iron Sheets 3720 8% Aluminum 370 8% Radio 3832 2% Dry Cells 3839 2%

100% Source: Draft notes on manufacturing

trom the National Accounts Section,

Bureau of Statistics.

The constructed index (IIPtrovisional) was used to estimate aggregated series of manufacturing va1ue added at constant prices. Va1ue added has been presented in the prices ofthe base years (the year in which the weights are calcu1ated) by straightforwardly moving base year value added (VA 0) with the index of industria1 production, hence:

(7) VA =VA x IIPProvisional I 0 I

lt has not become clear why on1y 14 commodities have been utilised. More quantity data were availab1e and shou1d in our opinion be utilised for constructing an index of industria1 production. Another point

43


of impravement could betheuse of 1978 census data instcad of 1976 data from the input-output table. The coverage gain reached at in 1978 and the even better response rate make it a more reliable souree for calculating value added shares for an index of industrial production. In addition, as is mentioned, more base years should be utilised when an liP for 1965-1985 is being constructed.

The Index of Industrial Production available in the QSIP

An index of industrial production (produced in the quarterly survey) is available since 1985, based on quantity data of about 120 commodities. The quarterly survey of industrial production provides gross output weighted production volume indexes of manufacturing branches for the years 1985-1994. For certain weights considerable discrepancies exist between the 1985 industrial survey data and the weights used in the index. The most pronounced differences are given in table 5-4.

TABLES-4 Examination of weights used in the QSIP

QSIP1 ASIP2

Branch GO-shares GO-shares VA-shares

(shares in%) (shares in%)

Beverage Industries 13.1 6.0 7.2

Tobacco Industries 5.6 3.0 5.4

Petroleum Refineries 14.9 0.7 2.0

Source: Note on Manufacturing Series, draft note by Russen Freeman

Notes: (1) QSIP shares are calculated from gross output at market prices. (2) the annual survey uses the factor prices concept.

Memhers of the industrial section of Takwimu indicate that the duferences are mainly caused by the value of sales taxes that are included in the gross output weights of tobacco and beverages and by taking the gross output of petroleum products (instead ofvalue added for refining the oil).

Besides discrepancies in the weights used to compile the index, it is observed that the sample of establishments and cammodities included in the quarterly survey has remained the same since the quarterly survey was launched in 1985 (Redeby, 1989). This implies that the index is only sensitive to new developments in the manufacturing sector represented by the covered establishments. Industrial growth caused by an increase of industrial establishments is not identified. Apart from this, some issues should be taken into account when the index is going to be used for national accounts pmposes:

• Real growth rates for the selection of 50+ establishments should be representative for the entire 10+ manufacturing sector. Understatement of growth will occur if companies reach an optimum level of production below the employment level of 50. Overstatement of growth will occur in case a few large scale establishments display high growth rates, wher• as large numbers of medium scale establishments experience stagnation.

• Applying the index to total manufacturing, including small scale manufacturing and the informal sector, may cause considerable problems. As the 1989 census states (BoS June 1993) for Dar es Salaam, 85% ofthe small scale activities are found in three activities: grain milling (ISIC 3116), tailoring (ISIC 3320) and furniture making (ISIC 3320). Because ofthe distinct value-added structure for small scale and informal manufacturing, the index based on 50+ establishments is not suitable to use for the entire manufacturing sector.

1t is very difficult to estimate a possible bias caused by 'statie' framework used for datacollection for the quarterly survey. What lies within our possibilities and is an absolute necessity, is rebasing the index applying appropriate industry weights to the production relatives available.

In sum, the index of industrial production available in the QSIP should be incorporated in the constant price calcu1ations of manufacturing performance. Notwithstanding the fact that in the current revisions of the national accounts this will be done, the remarks made above on the limitations of the index call

44


for fmther assessment and improvement of the index of industrial production of which we attempt to take a frrst step in the chapter to co me.

5.4 FINDINGS AND CONCLUSIONS

It is shown in this chapter that the process of calculating value added figures for the national accounts of Tanzania, a number ofproblems have been encountered. lt is recognised that a shift is currently taking place in the way figures were determined before and the way they will be calculated in current revisions ofthe national accounts. Nonetheless, there is need for further examination ofthe following issues in order to improve estimates for manufacturing value added over time:

1) For small sca1e and informal sector manufacturing, no consistent framework exists, a1beit that new studies have come available in the last decade. lt is observed that it is likely that the informal sector (and small scale industries) moves in an opposite direction to medium & large scale manufacturing. The methodology basedon takinga portion of medium & large scale manufacturing as a representation of informal sector value added in the national accounts should therefore be rejected. Due to time and data constraints we will not further pursue assessment of small scale and informal manufacturing in this thesis.

2) The coverage of the basic framework for data collection for medium & large scale manufacturing has been different throughout time and does not adequately reflect industrial growth. In this respect it is important to conduct careful analysis of the quality of the directory of industrial establishments (DIE).

3) The methodology of estimating for non-response in medium & large scale manufacturing varles considerably over time. In some years no adjustment is made, for other years a method of simple repetitionis applied. Due topoor methodology justification, response rates need to be estimated, as well as the bias of simple repetition in the value added figures.

4) Conceptually, the series in the national accounts are conform the international recommendations. Nevertheless, the introduetion of a new questionnaire in the annual survey resulted in an incorrect concept of manufacturing value added. The impact of the use of a new questionnaire on the manufacturing value added series needs to be assessed.

5) W eaknesses have been identified in the compilation of final series for nomina! and real value added in the national accounts. The construction of an independent time series for nomina! and real value added for medium & large scale manufacturing will improve the quality of the manufacturing statistics.

6) For nominal value added series, the latest revisions date back to 1976 only, showing a break in the series in that year. Examination is needed whether it is possible to backdate revised figures prior to 1976. Furthermore value added series for medium & large scale manufacturing value added could be constructed for some particular manufacturing branches.

7) For determining real value added for 1965-19&5, a better index of industrial production can be constructed with a better coverage of manufacturing commodities. In addition, a reassessment of the choice of base years should be conducted.

8) It was discovered that the index of industrial production available for 1985-1995 has unrealistic weights forsome commodities. For the index of industrial production available in the quarterly survey (1985-1995) it is therefore concluded that rebasing the index, applying proper value added weights, is indispensable.

45

6. ADJUSTMENTS TO THE STATISTICS

In the previous chapter we have dealt with the state-of-the-art statistics on manufacturing performance. We have identified several shortcomings in the data underlying the national accounts series of manufacturing value added. In this chapter we will outline the results of research carried

out to improve the quality of the manufacturing value added series.

Several improvements are based on the results of in-depth analysis of the 1989 census of industrial production. We will therefore separately discuss the analysis of the census frrst. Thereafter, we will discuss the improvements made in the value added series of medium and large scale industries. The quality of the directory of industrial establishments is dealt with and we will discuss severa1 improvements in the nomina! value added series 1965-1990. We will construct an index of industrial production for 1965-1985 andrebase the 1985-1995 index to arrive at improved real value added series. Finally, an estimate for nomina! value added for the years 1991-1995 is presented.

6.1 1989 CENSUS ANALYSIS

Access to the 1989 census database allowed us to assess the basic data at t-stablishment level for 10+ establishments. First of all it was discovered that the data were not fully edited before they were published. Several data quality improvements and adjustments have been carried out which are described in Appendix A We have adjusted the basic data as aresult of data screening, recalculation of aggregates and setting right the errors in value added calculations, due to shortcomings in the questionnaire design.

6.1.1 V ARIOUS IMPROVEMENTS

Data screening has been carried out by removing double counts, and adjusting spurious data. The questionnaire used in the 1989 census covered income categoties which should not be part of income as defined in system of national accounts. These categories, Subsidies Received, and Profit from Safe of Fixed Assets should be omitted in gross output calculations, not fortheleast because the census aimed at making estimates at factor costs. The costs of intermediate inputs has been adjusted by recalculating the aggregate total production costs, omitting tot al indirect taxespa id. The results of data screening and redefming aggregates are presented in Table A-5 (Appendix A). As aresult of data screening and redefluition of gross output and intermediate inputs, value added dropped from 21.4 billion to 20.3

ADJUSTMENTS TO THE STATISTICS

hiliion Tsh. A sho.tcoming in the questionnaire which had far more impact on manufacturing value added in 1989 is the not properly defined cost category all ather casts.

6.1.2 RECLASSIFICATION OF TIIE COST CATEGORY 'ALL OTHER Cosrs'

The major impravement in the census data has been achieved by reclassifying the cost category all ather casts. Examination ofthe cost structure of 10+ establishments in the census revealed that some establishments have enormous amounts of costs allocated to the all ather cast category. In particular they have allocated large sums of interest payments to this category, where they do not belong1

• This condusion is basedon the following argument. The questionnaire has 16 categones forproduction costs. One of these categones is bank charges and insurance paid, which explicitly excludes interest costs. However, altl10ugh tl1e instructions for the category all ather casts say not to include labour cost, sales tax, corporation tax, excise duty and depreciation, it does not instruct to exclude interest payments in this category. In Figure 6-1 we can see that among the different cost categories, all ather casts takes a very large share (after raw materials, even the largest!). The category all ot11er costs can be considered as a rest category, so, tl1ere is enough reason to test tl1e hypothesis that a large part of all other costs camprises interest costs.

FIGURE 6-1 Cost Structure 1989 Census

Furthermore it was identified that besides interests costs some other costs of non-intermediate inputs could have been included in all ather casts because they were not identified in the questionnaire. These categones are bad debts, directars Jees, and danatians.

Since the instructions of the questionnaire do not instruct to leave out these costs of non-intermediate inputs, we believe that establishments are inclined to allocate these costs to the cost category all ather casts. Our hypothesis is that the reason for the blow-up of all ather casts is the inclusion ofvarious costs of non-intermediate inputs of which interest costs probably takes the largest part

2• To test this

1 Intermediate inputs or intermediale consumption consists ofthe value ofthe goods and services consumed as inputs by a process ofproduction. (UN 1993: 143). Cost categones such as interests costs, directors fees, and donations are not intermediate inputs. 2 The results as presented in Tab1e A-3 reveal that interest costs bears about 97% of all non-intermediate inputs.

48


hypo thesis, we have identified the contents of the co st category all ather wsts to detennine the bias of possible non-intennediate inputs in this category. We have summarised the methodology and the results of identifying the bias of inclusion of non-intem1ediate inputs in Appendix A. Our analysis revealed that the value of non-intennediate inputs that has been allocated to all ather casts was 11 hiliion Tsh. As a result, the total value of intennediate inputs has been adjusted from 99 to 87 hiliion (see Table A-5), consequently blowing up value added.

The fina1 adjustments due to data screening, redefluition of value added and reclassifying all ather casts are summarised in Table 6-1 (for details see Table A-5). Reclassification of the co st category all ather casts contributes most to the adjustments to value added in tl1e census.

TABLE 6-1 Adjustments to value added in the 1989 census

VA VA change VA-shares Unedited Adjusted lncrease Unedifed Adjusfed

31 Food, Beverages and Tobacco 9056273 11993660 32.4% 42.2% 37.9% 311,2 Food Processing 3780350 5630907 49.0% 17.6% 17.8% 313 Beverages 2499740 3003066 20.1% 11.6% 9.5% 314 Tabacco & Cigarettes 2776183 3359687 21.0% 12.9% 10.6%

32 Textiles and Leather 1698375 5763344 239.3% 7.9% 18.2% 321 Textile 1420016 5256427 270.2% 6.6% 16.6% 322 Wearing Apparel 39431 105844 168.4% 0.2% 0.3% 323 Leather and Produels 105488 203614 93.0% 0.5% 0.6% 324 Footwear 133440 197460 48.0% 0.6% 0.6%

33 Wood and Wood Produels 906628 1115385 23.0% 4.2% 3.5% 331 Wood Produels 489491 665871 36.0% 2.3% 2.1% 332 Furniture and Fixtures 417137 449514 7.8% 1.9% 1.4%

34 Paper and Paper Products 1353161 1865700 37.9% 6.3% 5.9% 341 Paper Produels 692643 1087127 57.0% 3.2% 3.4% 342 Printing and Publishing 660518 778572 17.9% 3.1% 2.5%

35 Chemicals, Petroleum and Plastic Prod. 3717945 4469785 20.2% 17.3% 14.1% 351 lndustrial Chemieals 1616812 1343461 -16.9% 7.5% 4.3% 352,3 Other Chem. and Petroleum Ref. 1640071 2172372 32.5% 7.6% 6.9% 355 Rubber Produels 220807 482826 118.7% 1.0% 1.5% 356 Plastic Produels 240255 471126 96.1% 1.1% 1.5%

36 Non-Metallic Produels 1068483 1413674 32.3% 5.0% 4.5%

361, 2, Non-Metallic Produels 1068483 1413674 32.3% 5.0% 4.5%

37 Basic Metallndustries 961512 1391481 44.7% 4.5% 4.4% 371,2 Basic Metallndustries 961512 1391481 44.7% 4.5% 4.4%

38 Fabr. Metal Prod., Machinery and Equipm. 2437497 3318483 36.1% 11.4% 10.5% 381 Metal Produels 758959 84C121 10.7% 3.5% 2.7% 382 Machinery (Exc. Electr) 157091 165508 5.4% 0.7% 0.5%

383 Electrical Machinery 582344 710035 21.9% 2.7% 2.2% 384 Transport Equipment 939103 1602818 70.7% 4.4% 5.1%

39 Other Industries 274144 278939 1.7% 1.3% 0.9%

385/ 39 other Industries 274144 278939 1.7% 1.3% 0.9%

3 Total Manufacturing 21474018 31610450 47.2% 100.0% 100.0%

Source: Appendix A, Table A·5.

As the table reveals, huge adjustments have been made in the 1989 census for all branches (except branch 39). An 47% overall iocrcase ofva1ue added has been arrived at. The most remarkable change is the enonnous 240% value added increase for ISIC 32, textile and 1eather, raising their part in manufacturing value added from 7.9% to 18.2%!

49


6.2 ANALYSIS OF 1 0+ MANUFACTURING V ALUE ADDED

6.2.1 COVERAGE ASSESSMENT OF 1 0+ MANuFACTURING ESTABLISHMENTS

The quality of the directory of industrial establishments (DIE) has been assessed from several perspectives. First of all it was attempted to match the DIE with other sourees of manufacturing activity to get an estimate for possible undercoverage. Although Takwimu has co-operated with other institutions who maintain registers of industries, there is a broad agreement that all registers suffer from deficiencies, and that the registers have never been matebed together. First of all the DIE as it was used for the 1989 census and the 1990 survey has been matebed with the Central Register of Establishments, maintained at Takwimu. The matching procedure and results are discussed in Appendix D.

As is stated in the Appendix, the results do not allow us to make a reliable estimate for undercoverage of the DIE. lt was also attempted to match the DIE with another register (VET A), but it proved to be very difficult and laborious to match establishments ofboth registers. Therefore the matching procedure has notbeen completed. Notwithstanding difficulties encountered, we strongly recommend undertaking further efforts to match industrial registers to improve the framework of data collection.

From another perspective, we have been able to assess the quality of the DIE in the period 1965-1990. We have listed the coverage (in terms of 10+ establishments) ofthe DIE from 1965-1990 in Table 6-2. Some remarkable differences exist between the coverage ofthe census years (1978 and 1989) and the survey-years. In 1978 the DIE covered 1282 10+ establishments, while before (in survey year 1974) only 499 10+ establishments were covered. It can beseen as well, that a coverage cut-backtook place after 1978 in the survey years. Again, in 1989 the number of establishments in the DIE increased significantly. In 1978 tl1e higher amount of establishments is found intheISIC sectors 32, 33, 36 & 38. In 1989 the most ofthe increase in coverage can be found in the activities 33 and 38.

We should note here that for censuses as wellas for the annual survey, the coverage levels we have compared regard medium & large scale establishments (10+). However, in discussion with members of Takwimuit was found that in the annual surveys, establishments with frequently changing output (e.g. generated by training institutes) areleftout (see paragraph 5.3.2). The contribution of these omitted activities was assumed to be insignificant and no adjustment was performed. Other omitted industries in the annual survey are found in the non-factory type of production, furniture making and tailoring, of which most activities fall in the 10-19 size class.

Hence, in comparing the coverage of different sources, we should distinguish between establishment conscious1y excluded from data collection (such as non-factory type of production in the surveys) and the degree of success in covering categories of establishments intended to be covered. We capture the coverage aimed at with intended coverage. lt has become clear that the intended coverages of surveys and censuses differed. Besides these differences in intended coverage, however, the degree of success of co vering 1 0+ activities has been higher for the 1978 census. The surveys up till 197 4 were based on the register of industries and/or the inspeetor of factorles (Havnevik et. al., 1985: 89). 1 he 1978 Census was based on field work of listing industries carried out by Takwimu. This listing cont.lined many unregistered establishments, where the listing of industries before 1978 was limited to forma1 registered establishments, supplied by ministries.

The higher number of establishments in the sector 33 for 1978 and 1989 can be explained in terms of intended coverage differences between censuses and the annual survey. The same counts for the increased number of establishments covered for ISIC 32 and 36 in 1978. However, the coverage gain of ISIC 38 in 1978 and 1989 cannot be ascribed to differences in intended coverage. The inclusion of unregistered establishments in the register has improved the degree of success covering as many 1 0+ manufacturing establishments as possible. This is reflected in the number of establishments covered in the annual survey after 1978. The number of establishments remains stabie around 700 establishments between 1979-1988, whereas before 1978, around 500 10+ establishments were being covered (1969-1974). consirlering the number of establishments covered in 1990, the 1989 coverage gain seems to be consolidated in the anrual survey as well.

50


TABLE 6-2 Number of 10+ Establishments in the DIE (1965-1990)

ISIC (rev.2)

Year 31 32 33 34 35 36 37 38 39 Sub- Not resp. & not Total total process. 3

1965 2 139 66 90 22 23 9 19 3 6 377 71 448

1966 134 86 81 33 34 13 19 24 14 438 41 479

1967 134 92 77 27 36 10 18 24 13 431 38 469

1968

1969 135 81 79 34 34 13 15 28 11 430 66 496

1970 144 77 83 37 37 16 15 31 12 452 78 530

1971 153 90 89 40 41 21 3 57 8 502

1972 155 94 77 40 40 18 3 55 6 488

1973 153 95 82 43 46 15 4 57 8 503

1974 151 96 79 46 47 16 4 54 6 499

1978 191 313 433 54 66 70 5 115 35 1282

1979 174 167 127 57 60 20 5 95 14 719

1980 173 168 130 57 55 21 5 92 13 714

1981 161 167 132 57 56 23 5 91 13 705

1982 174 168 128 57 57 16 5 80 15 700

1983 165 175 134 57 62 19 5 95 13 725

1984 152 169 120 56 54 12 6 76 12 657

1985 159 163 112 52 59 14 6 87 13 665

1986 155 166 114 58 63 16 6 87 12 677

1987 160 163 119 60 66 18 6 96 12 700

1988 165 163 122 62 67 19 6 96 11 711

1989 168 158 214 62 66 22 6 166 24 886

1990 170 157 213 61 66 21 6 169 20 883

Sources: ASIP 1965-1974 (except 1968 where no survey was at our disposal), 1979-1988, and 1990. Censuses 1978, and (adjusted) census 1989. Notes (1) data for 1965-1970 are originally tabuialed for rev .1, but converled to rev .2. (2) Data for ISIC 32 & 38 for 1965 are adjusted numbers (for details see ASIP 1966).

(3) Data Tabuialed for 1965-1970 re presenled responding establishments only. The number of not responding establishments are given in the ASIP reports. In actdition data fora number of government establishments we re not processed

Consequences of coverage gains in terms of number of establishments for performance indicators as value added, leads us to consicter how establishments of different size, contribute to value added of gross output. For the annual survey of industrial production for the years 1979 till 1988, no data on the distrioution of indicators over size classes are available. For the census years, we ob serve that establishments with at least 50 persons engaged, contribute for about 90% of the gross output and value added generated by 10+ establishments. lt is said that the coverage ofthe 50+ category bas been better than the 10-49 category, because the character ofthe smaller establishments is less stabie and more erratic.

To adjust value added figures for (among others) undercoverage, we have carried out research on the quality of the manufacturing value added estimates for the years 1978-1990. The results of this analysis are discussed in Appendix B 1. In brief, we have attempted to trace 50+ establishments which were not covered before 1989, to make an estimate for undercoverage for the years 1978-1988.

We have notbeen able to carry out similar research for the years 1965-1977, due to lack of data. However, for 1978 we have gross output figures based on the directory of industrial establishments before the coverage gain was arrived ae. Together with census gross output v.re can determine what

3 The Economie Survey 1981, shows gross output data forthe years 1977, and 1978. For the year 1979 the gross output data more or less corresponds with annual survey data, while for 1978 data seems to he based on old coverage. The number of establishments

51


consequences the coverage gain (in tenns of establishments) had for gross output4• We have been able to backdate the coverage gain of 1978 in tenns of gross output and value added for the years 1965-1977. The results of backdating the coverage gain of 1978 are presented tagether with other adjustments to 1 0+ manufacturing value added in Appendix C.

6.2.2 ADJUSTMENTS TO 1 0+ NüMINAL MVA 1965-1990

For the medium & large scale manufacturing value added series (10+ series) in the period 1978-1990, tirree sourees of distartion were identified: ( 1) inclusion of non-intermediate inputs caused by impraper questionnaire design, (2) underestimation ofvalue added by non-responding establishments and (3) undercoverage indicated by the coverage gain in 1989 (see paragraph 6.1). For the 10+ series from 1965-197 4 the biases in value added data camprise (1) no estimation for non-responding establishments forsome years, (2) omitted value added because certain gaverument establishments have nat been covered and (3) undercoverage indicated by the coverage gain in 1978 (see previous paragraph).

TABLE 6-3 Results of the Sample: Response Rates of the ASIP and the censuses, 1978-1989

50-99 persons engaged

Responded

Not Responded

Not Covered

Total

Response-rate •

100-499 persons engaged

Responded

Not Responded

Not Covered

Total

Response-rate

500 or more persons engaged

Responded

Not Responded

Not Covered

Total

Response-rate

(number of establishments)

1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

21

3

0

24

19 12 13 17 20 14 22 21 20 14 35

6 13 12 10 10 18 11 12 13 19

0 3 4 5 4 3 2 3 3 3 0

25 28 29 32 34 35 35 36 36 36 36

88% 76% 48% 52% 63% 67% 44% 67% 64% 61% 42% 97%

31

2 2

35

25 23

9 12

4 3

38 38

18 15 22 22 25 28 30

17 20 13 14 12 12 11

4 7 7 7 6 4 3

39 42 42 43 43 44 44

19

23

2

44

44

0

0

44

94% 74% 66% 51% 43% 63% 61% 68% 70% 73% 45% 100%

13

2

0

15

15 9

0 7

2

16 18

8

9

3

20

9

9

4

22

14 16

6 7

4

24 24

15 19 14

8 5 8

1 0 0

24 24 22

9

13

0

22

21

0

22 87% 100% 56% 17% 50% 70% 70% 65% 79% 64% 41% 95%

Source: Table 61-10, Appendix B1.

Notes: (•) Response rate is calculated dividing responding units by (total minus nol covered units)

Improvements in Nomina! MVA 1978-1990

To imprave the MVA series for 1978-1990, we have carried out a sample of the questionnaires compiled for the annual survey and censuses for the period 1978-1990. We have sampled 50+ establishments of which original survey and censuses questionnaires where kept in separate files (see

given for 1978 are 560, and for 1979 890. We assume that during preparation ofthe 1978 census (ofwhich the first figures were published in the statistica! abstract of 1982), the directory was not directly updated. Therefore, an old version ofthe directory of industrial establishments. was used to prepare data for 1978. 4 For 1975-1977 no annual survey ofindustrial production was published. However, questionnaires were collected forthese years, which (we assume) are used for determining manufacturing data as presented in the Economie Survey. We have tables derived from the economie surveys (BoS 1977 & 1982), were only gross output, establishment, and employment data are presented.

52


Appendix B 1 for methodo1ogy). This enabled us to write down for each establishment for which years a tilled-in survey-questionnaire was kept in the file. Also, the first year where a 'new' surveyquestionnaire was used (the same as used in the 1989 census) could be traced. In addition it was identified since when the establishment had been in production. Collecting data from more than 100 establishments allowed us to make estimates of understatement of not responding establishments caused by simpte repetition (see Table B 1-6). Besiel es this, an estimate for the adoption ra te of the new questionnaire (Table B 1-8) and the relative coverage rate could be estimated (Table B 1-9).

The results of the sample, and adjustment estimates for value added 1978-1990 are presenteel in Appendix B 1. To prepare for the significant level adjustments that are going to be applied, we have presenteel estimated response rates for 1978-1990 in table 6-2. The table shows that the censal years (1978 and 1989) have the highest ra te of response for all size classes, especially in 1989. For the intercensal years, the responserateis low, even down to 43% (for size class 100-499) in 1983. Generally spoken, there is no significant difference in rates of response between the size classes. On average the response rates for the entire perioei are 64% (50-100), 67% (100-499), 69% (500+). For 1988 the response rate are very low. It is assumed that resources at Takwimu did not allow to make follow-ups in the field, but that they were set aside for prepara ti ons of the 1989 census.

An previously encountered problem shows up again: for the non-responding establishments (a vast number!) previous year data are repeated. The downward bias, even when data are only repeated for 1 year is considerable, taken into account the price changes (up to 35%). For example, at a response rate of 50%, and a price change of 30%, the underestimation is 15%5

. The results ofthe sample show (see Table B 1-10 & B 1-11 of Appendix BI) that data are not only repeated for one year, but in some cases even for eight years. Data repetition for more than one year is prevalent, thus causing huge understated estimations for non-response.

Backdating adjustments to the 1989 census (i.e. biases caused by inclusion ofnon-intermediate inputs due to questionnaire design), the adjustments due to undercoverage, and adjustments to the biases caused by treatment of non-response for the years 1978-1990 are presenteel in Appendix B2.

Improvements in Nomina! MVA 1965-1978

It has been, relatively speaking, much easier to make improvements to the series derived from the annual survey in the perioei 1965-1974 than for 1978-1990. The record-keeping regarding the annual survey enabled us to make estimates for non-response and omitted industries. These omitted industries re gard government owned industries of which it was difficult to obtain reliable data during the midsixties6. The adjustments applied for non-response and omitted industries are presenteel in Appendix C. In this Appendix the results ofbackdating the coverage gain of 1978 has been incorporated as well (see paragraph 6.2.1).

Overall Adjustments in Nominal MVA 1965-1990

The in-depth analysis of the 1989 census, the annual survey and coverage assessment have led to very significant level adjustments of medium & large scale nomina! manufacturing value added. The overall adjustments to value added from 1965-1990 have been summarised in Appendix F. To get a clear insight in the contribution of the different adjustment to the overalllevel adjustment, we have listeel step-by-step adjustments fortotal manufacturing in Table 6-4.

The adjustments to the 1989 census (89-adj.) as presenteel in Appendix A and summarised in Table 6-1, have been extrapolated and backcasteel for the perioei 1978-1990 as described in Appendix B2. Adjustments for non-response (NR) for 1965-1971 were based on the sourees at Takwimu as outlined in Appendix C. For 1978-1990 adjustments for non-response refer to the underestimation of value added of non-responding establishments (see Appendix BI & B2). In the years 1965-1968 several government owned establishments have consciously been omitted from the smveys. Estimates forthese omitted establishments (OE) have been made as dealt with in Appendix C. Estimates for undercoverage (UC) for the perioei 1978-1988 are based on our own analysis as presenteel in Appendix B 1. At last the

5 For assumption underlying these calculations, see Appendix Bl. 6 These difficulties were caused by the centralised management ofEast African Community Industries unable to provide data for Tanzania only, and changing management shortly after the Arusha Declaration.

53


coverage gain attained in the census year 1978 has been backcasted for pre~ious survey years according to methodology described in Appendix C Remember, that the 1978 census figures have also been slightly adjusted based on undercoverage analysis of 1978-1990 value added (see Appendix B 1). These adjusted data has been used to backcast the coverage gain prior to 1978.

TABLE 6-4 Level adjustments to Nominal MVA 1965-1990

Adjustments to MVA Change of MVA Unadj. 89-adj. NR-adj. OE-adj. U C-a dj. 89-adj. NR-adj. OE-adj. UC-adj. TOT AL

(Value Added in Millions TSh.) (Change in%)

1965 267 282 323 514 6% 14% 59% 93% 1966 295 339 508 15% 50% 72% 1967 319 323 343 490 1% 6% 43% 54% 1968 378 390 401 544 3% 3% 36% 44% 1969 475 497 497 685 5% 0% 38% 44% 1970 561 571 571 801 2% 0% 40% 43% 1971 643 657 657 879 2% 0% 34% 37% 1972 806 806 806 1097 0% 0% 36% 36% 1973 914 914 914 1271 0% 0% 39% 39% 1974 1157 1157 1157 1546 0% 0% 34% 34% 1975 1246 1676 35% 35% 1976 1480 1997 35% 35% 1977 2075 2842 37% 37% 1978 2186 2926 34% 34%

1978 2842 2842 2842 2926 0% 0% 3% 3% 1979 2927 2927 2985 3238 0% 2% 8% 11% 1980 2891 2900 3278 3621 0% 13% 10% 25% 1981 3108 3144 3979 4550 1% 27% 14% 46% 1982 3204 3323 4848 5856 4% 46% 21% 83% 1983 3620 3963 5661 6759 9% 43% 19% 87% 1984 4417 5179 7117 7956 17% 37% 12% 80% 1985 5112 6244 8734 9613 22% 40% 10% 88% 1986 6412 8338 11458 12090 30% 37% 6% 89% 1987 11062 14185 20276 21144 28% 43% 4% 91% 1988 11358 15382 25060 25829 35% 63% 3% 127% 1989 21474 31610 31815 31815 47% 1% 0% 48% 1990 23956 36406 37576 37576 52% 3% 0% 57%

Source: own analysis, as presenled in Appendixes A-F. Notes: 89-adj. = 1989 Census based adjustment; NR-adj. = Non-Response based adjustment:

OE-adj. = Omitted Establishments based adjustment: UC-adj. = undercoverage based adjustment.

The adjustments given as a percentage in the right -hand columns of Table 6-4 are calculated relative to previous adjustments. E.g. undercoverage adjustment in 1988 is calculated as the level adjustment of manufacturing value added adjusted for non-response (NR-adj. value added), which in 1988 is 25,060 million TSh. The overalllevel adjustments are striking for the entire period. Only in the census year 1978 there has been little or no adjustments. The maximum adjustment of 127% is made to value added in 1988.

We consider the level adjustments applied before 1978 to be less reliable than adjustments applied for the 1978-1990 series. This statement is mainly motivated for the less reliable adjustments for undercoverage for 1965-1978. Forthese years we have applied growth rates ofva1ue added figures before the coverage gain, and backdated va1ue added data using these growth rates, starting with the year 1978, for which a coverage gain was reached. The disadvantage of applying thls method is that growth rates are subject to undercoverage as well. Fora gain in coverage such as happened for 1978 and 1989, we expect that in previous years, new establishments were set up from time to time, but that they were not covered until field work for the 1978 and 1989 census revealed their existence. In this way growth of manufacturing performance was gradually understated. This phenomenon is known as drifting.

For the period 1978-1989 we have used our own sample data, which estimated undercoverage based on information ofthe first year of production and the first year of coverage in the DIE. Adjustments based on this data do not suffer from the bias of drifting, and are assumed more reliab1e.

54


6.2.3 ADJUSTMENTSTC 10+REALMVA 1965-1995

In the previous section ( chapter 5) it appeared that for determining series of real value added, the National Accounts Section of Takwimu constructed an index of industrial production based on a selected amount of commodities. More quantity data for manufacturing commodities are available, thus enabling us to construct an improved index of industrial production before 1985. In addition, we will try to regularly rebase the index for the period 1965-1985.

In chapter 5 we have dealt with the quality ofthe index ofindustrial production 1985-1995. The frrst improverneut to be made is rebasing the index. In the following paragraphs we deal with the following:

1) Construct an liP for 1965-1985, and

2) Reweight the liP for 1985-1995.

Finally the index numbers are spliced together to obtain an index of industrial production for 1965-1995.

Constructing an Index of Industrial Production for 1965-1985

For constructing an improved index of industrial production as much quantity data as possible were collected for 1965-1985. In total quantity data for 32 commodities could be traced. Appendix G presents the construction of the index of industrial production for the years 1965-1985. In Table G-1 we have listed quantity data for these commodities. To construct an liP for a large time span as in our case, it is necessary to regularly rebase the index. The considerations for choosing base years were mainly to maximise the amount of quantity information on commodities that could be covered for a given period. For example when choosing 1965 as a base year, only 9 commodities can be covered, compared to 16 if taking 1966 as a base year. A secoud consideration availability ofvalue added data for 10+ establishments in the surveys and censuses. We have chosen to take the following base years:

1) 1966; the coverage is 16 products (instead of 9 for 65), and quality of the 1966 survey is much better than the survey carried out in 65.

2) 1970; the number of covered productsis 19. The same coverage could be reached in 1969, 1971, and 1972, but for 1970 we had 4-digit data at our disposal, while for 1971, and 1972 we only had value added at 3 digit. Another advantage taking 1970 as a base year is the pattem of periodical rebasing (note the following base years).

3) 1975; in total25 commodities can be covered (in stead of 24 in 1974). For 1975 no survey was available, but we could use branch weights of 1974, and industry weights from the input-output table of 1976.

4) 1980; for 1985, 30 commodities could be covered (instead of 25 for 1979). Since the quality ofthe annua1 survey data for the 80s, was assumed to be insufficient, we aimed at using 1978 census data. Choosing 1980 as a base year, was compromise between good coverage and reliability of data.

How value added weights are calculated forthese base years is discussec in Appendix G. Besides determining an liP fortotal manufacturing we aim at presenting real value added for some manufacturing branches. Since a limited number of commodities is available, we have selected 6 branches, hereby joining the ISIC branches 33 with 34, and 37 with 38 and 39. We have calculated iudexes forthese branches for each period. The thus calculated iudexes for branches and total manufacturing are listed in Table G-4. The runs of index numbers for each subperiod (i.e. 1965-1970, 1970-1975, 1975-1980, and 1980-1985) are spliced and the reference base is switched to 1976 to make the runs of index numbers for the entire period comparable with previous real va1ue added publications (with 1976 as a base year).

55


TABLE 6-5 Unadjusted and Adjusted Index of Industrial Production 1965-1995

Adjusted Unadjusted IS IC 31 32 33/34 35 36 37/38139 3 Branch Food, Textiles Wood Pr. Chemicals, Non- (Basic) Metal Pr., Total Total

Beverages & Furn. & Fixt. Petroleum, Metallic Mach. & Equipm., Manufac- Manufac-& Leather Paper Pr. Rubber& Mineral & OtherMan. turing turing

Tobacco Print. & Publ. Plastic Prod. Produels Industries (1976=100) (1976=100)

1965 55 90 1966 61 25 118 63 84 21 42 49 1967 65 28 101 87 98 21 45 55 1968 61 40 123 72 114 22 49 59 1969 62 55 121 73 128 23 56 65 1970 69 68 139 62 129 29 65 67 1971 83 74 139 87 131 37 76 74 1972 84 81 141 101 129 50 83 80 1973 86 92 159 87 150 79 92 83 1974 81 93 142 86 106 87 90 85 1975 78 93 123 99 115 89 90 85 1976 100 100 100 100 100 100 100 100 1977 92 106 151 96 115 111 103 94 1978 104 96 124 105 105 126 107 97 1979 90 102 100 83 111 129 98 100 1980 77 91 108 80 85 129 90 95 1981 61 79 97 76 108 109 79 85 1982 56 75 91 68 93 128 74 82 1983 59 59 68 67 69 111 65 75 1984 53 56 58 108 103 118 69 77 1985 50 56 62 71 105 139 66 74 1986 47 58 71 66 123 153 67 72 1987 42 74 98 69 128 117 65 79 1988 50 83 89 66 134 104 69 85 1989 48 76 85 75 137 112 68 86 1990 54 77 79 85 159 144 77 84 1991 55 73 78 86 216 136 77 86 1992 53 65 61 80 156 102 68 81 1993 56 68 159 76 166 89 75 81 1994 57 67 91 85 141 126 75 74 1995 60 58 74 81 155 71 68

Source: Annex Table 1-2.

Rebasing the Index of Industrial Production 1985-1995

The Industrial Section used 1985 output data to weight the liP from 1985 onwards. As we have seen severa1 weights applied to commodities had significant discrepancies with value added data for 1985. Moreover, the analysis of the annual survey made clear that the reliability of the data between around the mid-eighties is questionable. In our opinion, the most reliable souree for weighting the liP 1985-1995 is adjusted data of the 1989 census of industrial production.

We have reweighted the index of industrial production utilising (adjusted) 1989 census value added as base year weights in Appendix H. Table H-2 describes in which way the weights are determined for the 98 commodities covered in the index. In Table H-1 we have listed the quantity indexes (with a switched reference base of 1989) for the covered commodities. As is done for the period 1965-1985 our index of industrial production is constructed for each branch and fortotal manufacturing (see Table H-3). In actdition we have calculated the index of industrial production using the original 1985 weights. The rebased liP together with the published and recalculated index of industrial production are presented in TableH-4.

Revised Real Manufacturing Value Added Series 1965-1995

In Appendix I the liP 1965-1985 and liP 1985-1995 have been constructed for six branches of manufacturing. The series are spliced in 1985 and the reference year is switched to 1976. In Table 6-5

56


the new series are presented. The index fortotal manufacturing is compared with the most recently publisbed real value added index. We will analyse the major differences between our series and previous series in chapter 7.

6.2.4 ESTIMATIONOFNOMINALMVA 1991-1995

Because no annual survey has been publisbed since the 1990 survey, we use our index of industrial production plus a price index to arrive at estimates in current values for the years 1991-1995. In Appendix E estimates have been made for manufacturing value added for 1991-1990. The methodology applied is simply moving branches with a price and quantity index. A price index has been constructed partly from the CPI and PPI7 in table E-1. The quantity index (table E-2) is a the rebased index of industrial production, which will be discussed in paragraph 6.5.2. Let PI denote the constructed price index for manufacturing, value added in year t for each branch {j) is estimated as follows:

(1) VA1,, = VA1.1990 x PI1,, x IIP1.,

The results are presented in table E-3, Appendix E.

6.3 FINDINGS AND CONCLUSIONS

The data improvements discussed and presented in this chapter involve vast changes in the quantitative description of the manufacturing performance. In-depth analysis of the 1989 census revealed some striking flaws in the value added calculations resulting in an overall adjustment of about 45% to manufacturing value added for 1989. Adjusted data caused structural shifts in the distribution ofvalue added over branches, most pronounced for Textiles and Leather (ISIC 32).

In the directory of industrial establishments, coverage gains in 1978 and 1989 were discovered in terms of number of establishments. For the previous years we have backdated estimates of these coverage gains in terms of 1 0+ manufacturing value added. In addition, for the period 1978-1989, adjustments have been made for underestimation of non-response and the 1989 census adjustments have been backdated. For the period 1965-1978 we have adjusted 1 0+ value added for omitted establishments and non-response.

We reweighted the index, applying 1989 weights, derived from adjusted census data. For 1965-1985 no index of in dustrial production (UP) was available, although a selected amount of cammodities was used by the National Accounts Section ofTakwimu to estimate value added in constant prices. We constructed an liP for 6 branches, and fortotal manufacturing for 1965-1995. The coverage of cammodities was enlarged, and the index has been regularly rebased.

In sum, improvements in the manufacturing value added over time camprises revisions of value added at current and at constant prices. In addition, nomina! and real value added series have been constructed at branch level for an extensive period of time, which never have been presented in existing series of manufacturing value added. The imprcved data set, thus achieved, allows us to examine manufacturing performance over time for different branches in a better way.

7 The construction of a price index in this way is borrowed from Mr. R. Freeman who has prepared a similar estimate in draft data files which he has kindly sent me.

57

7. NEW lNSIGHTS IN T ANZANIAN

MANUFACTURING PERFORMANCE

I n the previous chapter it has become clear that the adjustments of the value added series have been much greater than marginal. Very substantive adjustments have been made in the statistles of medium & large scale manufacturing value added. In this chapter it is analysed whether and to what

extent the improvements in the statistics result in new insights concerning Tanzanian manufacturing performance.

7.1 LEVEL ADJUSTMENTS IN NOMINAL VALUE ADDED

It is beyond doubt that the level of medium & large scale manufacturing petformanee has been underestimated in the publisbed statistles of Tanzania. In figures 7-1 and 7-2 ourlevel adjustments are graphically presented for the series 1965-1978 and 1978-1990. Manufacturing value added increased for the entire period, but most pronounced adjustments appeared between 1982 and 1990. Value added inc~·eased with 83% in 1982, 127% in 1988 to 57% in 1990. Between 1965 and 1978, value added increased on average with 40%. Though the adjusted levels are much higher, it can be seen that for 1965-1978 the adjusted nominal trend closely follows the unadjusted trend, while this is not true for 1978-1990. Especially between 1986 and 1990 the levels adjustments are more pronounced than is the case for previous years.

NEW INSIGHTS IN TANZANIAN MANUFACTURING PERFORMANCE

FIGURE 7-1 Unadjusted & Adjusted Nominal Value Added for 10+ Manufacturing, 1965-1990

60


7.2 STRUCTURAL CHANGES IN MANUFACTURING

After the 1989 census analysis, it became apparent that the adjusted statistics did not only imply level adjustments for total manufacturing, but also involved changes in the structure of manufacturing performance. In Table 7-1 structural changes are presented as the value added shares of nine branches calculated from the unadjusted and adjusted series for the years 1966. 1978 and 1989. For 1978 there is no difference between the unadjusted and adjusted structure as expressed by the value added shares of the manufacturing branches. Forthebranch Basic Metal Products the adjusted share in 1966 is considerable higher, while for Food, Beverages & Tobacco a reversed adjustment took place: the value added share for this branch decreased significantly from 42% to 26%. For 1989 the major structural change involved Textiles and Leather, where the value added share enormously increased from 8% to 18%.

TABLE 7-1 Structural Changes in Tanzanian 10+ Manufacturing

1966 1978 1989 ISIC Branch Unadj. Adj. Unadj. Adj. Unadj. Adj.

31 Food, Beverages & Tobacco 42% 26% 24% 24% 42% 38% 32 Textiles & Leather 25% 30% 30% 30% 8% 18% 33 Wood Products, Fumiture & Fixtures 7% 8% 3% 3% 4% 4% 34 Paper Products, Printing & Publishing 4% 3% 7% 7% 6% 6% 35 Chemicals, Petroleum, Rubber & Plastic Product 8% 5% 12% 12% 17% 14% 36 Non-rretallic Mineral Products 3% 2% 3% 3% 5% 4% 37 Basic Metal Products 6% 19% 8% 8% 4% 4% 38 Metal Products, Machinery & Equipment 4% 6% 11% 11% 11% 10% 39 other Manufacturing Industries 1% 2% 2% 2% 1% 1% 3 T otal Manufacturing 100% 100% 100% 100% 100% 100%

Source: calculated from Table F-1 & F-2.

We have graphically presented the structural changes in Tanzanian manufacturing sector over time as indicated by the unadjusted and adjusted data on manufacturing value added in figures 7-2 and 7-3. It can be clearly seen that, based on our revised data, the structural shifts in manufacturing have been less pronounced than was indicated by the unadjusted data. ThP- textile sector remained a major contributor to manufacturing value added in the late eighties. Furthermore, the share of food and textiles in manufacturing value added bas remained 50% between 1965 and 1995.

61


Structurál Changes In Tanzariian Manufaè:turlnirbased on Uriàdjusited ···· Data · · ··· ·· ·············· ·· ··· ·····

FIGURE 7-2 Value Added Shares of 10+ Manufacturing Branchesbasedon Unadjusted data for the years 1966,

1978 and 1989

Structural Changes In Tanzanian Manufacturlng basedon · · · ·· · Adjustecl Data. ····

FIGURE 7-3 Value Added Shares of 10+ Manufacturing Branchesbasedon Adjusted data for the years 1966,

1978 and 1989

7.3 TRENDS IN REAL GROWTH

In figure 7-5 the unadjusted and adjusted real value added iudexes (the index of industrial production) are graphically presented for 1965-1994. A quick view at the figure reveals that real value added in 1965 is slightly lower for the adjusted series and that unadjusted and adjusted series have the same level in 1994. Different periods of growth and stagnation can be better distinguished in the adjusted series. Main turning points in the industrialisation pattem for Tanzania are the years 1978 and 1983.

62


Real Value Added Index

--Unadjusted (1976=1 00)

-Adjusted

FIGURE 7-4 Unadjusted and Adjusted Index ofReal Value Added 1965-1994 of 10+ Manufacturing

We have presented average annual trend rates of growth for three-year periods in Table 7-2. In general terrns the adjusted growth rates are more pronounced for the entire period. The adjusted figures show more growth in the sixties and seventies, especially between 1965-1972, when growth rates doubled. The new figures also bring out the collapse ofindustrial production between 1978-1983. Between 1978-1981 growth rates were adjusted from -4% to -10%. Renewed stagnation is identified in the nineties. The adjusted figures show a recovery from 1987-1990, foliowed by renewed stagnation in the nineties.

TABLE 7-2 Annual Trend Rates of Growth in Tanzanian 10+ Manufacturing

for Three-year Periods, 1965-1994.

Annual Trend Rates of Growth Unadjusted Adjusted

1966-1969 9% 10% 1969-1972 7% 14% 1972-1975 2% 3% 1975-1978 5% 6% 1978-1981 -4% -10% 1981-1984 -3% -4% 1984-1987 1% -2% 1987-1990 2% 5% 1990-1994 -3% -1 o/o

Sou ree: calculated trom table 6-5.

An important outcome of this research on manufacturing performance is the estimate of growth rates for different manufacturing branches from 1965-1985. No such series were available before. The real growth pattems for six branches are presented in Figures 7-5.

Growth pattems ofthe branches food, beverages & tobacco and textiles and leather more or less correspond with the pattem fortotal manufacturing as presented in Figure 7-4. The trend ofthe branch wood and paper (ISIC 34 and 35) is irregular and show some decline. Chemicals, petroleum, rubber and

63


plastic show neitherpronounced growth, norpronounced decline between 1965 and 1995. Mineral products show growth up ti111973, decline between 1973-1983 andrecoveryup til11995. Real value added of metal and machinery (ISIC 37, 38 & 39) grew up til11978 after which a period of stagnation followed, with decline in recent years.

FIGURE 7-5 Growth Patterns for six 10+ Manufacturing Branches 1965-1995

64


For 1985-1994 we are able to compare unadjusted and adjusted real value added iudexes at branch level. The index of industrial production as it has been published in the quarterly survey and the reweighted index of industrial production are compared for nine branches in figures 7-6. It can beseen that growth trends for individual branches mainly differ fortheISIC branches 31, 33, 34 and 38. Most pronounced differences are revealed in total manufacturing, where different directions in the trends for the unadjusted and adjusted indexes can be identified. E.g., between 1989-1990 the manufacturing industry declined according to the published index, while we identify considerable growth in our reweighted index.

FIGURE 7-6 Publisbed and Reweighted Index of In dustrial Production for nine

10+ Manufacturing Branches 1985-1995

65

8. CONCLUSIONS

By perfonning in-depth analysis of several sourees of the manufacturing statistics and evaluating the index of industrial production for Tanzania, this study has reconstructed nominal as well as real manufacturing value added series for medium & large scale manufacturing for the years 1965-1995.

In case of manufacturing value added at current prices it was found that shortfalls in the coverage of manufacturing industries, flaws in making estimates for non-response and impraper identification of value added in the questionnaire used to inquire manufacturing establishments, caused huge biases in the value added figures derived at. After an in-depth analysis ofthe statistics, adjustments have been made. As aresult manufacturing value added increased for the entire period, varying from 3% in 1978 to 127% in 1988. Level adjustments have been different for various manufacturing branches, resulting in considerable adjustments in the structure of manufacturing over time.

We have extensively discussed the care of calculating real value added for manufacturing, the index of industrial production (liP). Although in theory the indirect approach to the liP should be preferred, for Tanzania we pref er the direct approach, mainly because no appropriate price index is available to deflate nominal value added series. Since no liP has been publisbed for the years 1965-1985, we have constructed an liP, based on quantity data of over 30 manufacturing commodities. For the years 1985-1995 an liP has been published, however, the weights used to calculate the index were found to be irappropriate. Therefore, we have reweighted the liP 1985-1995, using 1989 census weights.

The results of research can be summarised in the following points:

1) The level of manufacturing has been substantially higher according to the adjusted value added series at current prices.

2) Based on our adjusted value added figures, the structural changes in manufacturing between 1966 and 1989 have been less pronounced than was indicated by the unadjusted data.

3) The adjusted real value added series clearly show turning points in the process of industrialisation as duferences in growth rates are more pronounced than the unadjusted series.

4) New aspectsof research found in this thesis are the provision of a complete smoothed set ofvalue added series in current and constant prices.

5) Real value added series for different manufacturing branches have been constructed for 1965-1995. No such series have been publisbed befare for Tanzania.

This thesis can be considered as a step forward in gaining insight in the performance of Tanzanian manufacturing and a step forward toward forther impravement of the national accounts of Tanzania.

9. REPERENCES

Al, P.G., Balk, B.M., Boer, S. de & Bakker, G. P. den (1990). The use ofChain Indicesjor dejlating the National Accounts. National Accounts, occassional paper, nr. NA-005. Voorburg: Netherlands Central Bureau of Statistics.

Allen, R.G.D. (1975). Index Numbers in Theory and Practice. Chicago: Aldine Publishing Company.

Bagachwa, M.S.D. (1995). The Informal Sector under Adjustment in Tanzania. In L.A. Msambichaka, AAL. Kilindo & G.D.Mjema (eds.), Beyond Structural Adjustment Program in Tanzania, Successes, Failures and New Perspectives. Dar es Salaam: Economie Research Bureau.

Bagachwa, M.S.D. & Naho, A. (1995). Estimating the Second Economy in Tanzania. World Development, 23, 8, pp. 1387-1399.

Bank of Tanzania. (1981). Tanzania: Twenty Years of Independenee (1961-1981), A Review ofPolitical and Economie Performance. Dar es Salaam: Bank of Tanzania.

Board of ExternalTrade (1996). 20th Dar es Salaam Internatonal Trade Fair (Catalogue).

Central Statistkal Office (1976). The measurement of changes in production. Studies in Official Statistics, No.25. London: Her Majesty's Stationery Office.

Cassing, S. (1996). Correctly Measuring Real Value Added. Review of Income & Wealth 42 (2). pp. 195-206.

Coulson, A. (1982). The State and Industrialization in Tanzania. In: M.Fransman, Industry and Accumulation in Africa, pp. 60-76. London: Heinemann.

Dal én, J. (1992). Computing Elementary Aggregates in the Swedish Consumer Price Index. Joumal of Official Stafistics 8 (2), pp. 129-147.

Diewert, W.E. (1981). The economie theory of index numbers: a survey. In Angus Deaton (ed.), Essays in the theory and measurement of consumer behaviour, in honour of Sir Richard Stone. Cambridge, etc.: Cambridge University Press.

Engelen, D. van. (Aprill996). Pub ie Policy Making and the Rise and the Fa// ofthe Tanzanian Manufacturing Sector (M.Sc. Thesis). Eindhoven: Eindhoven Univerity of Technology.

Havnevik, K.J., Skarstein, R. & Wangwe, S.M. (1985). Smal/Scale Industrial Sector Study- Tanzania, Review of experiences and recommendations for the future. A Report presented to the Millistry of Industries and Trade, October 1985.

REPERENCES

Korns (1993). A Workable System for Updating lndonesia 's Manufacturing Directory. Paper for delivery at International Conference on Establishment Surveys, Buffalo NY, June 27-30, 1993.

Köves, P. (1983). Index Theory and Economie Reality. Budapest: Akadémiai Kiadó.

Kuuya, Masette. (1980). Import Substituion as an Jndustrial Strategy: The Tanzanian Case. In J.F. Rweyemamu, Industialisation asd Iocome Distribution in Africa. Dakar: Codesria.

Maliyamkono, T.L. & Bagachwa, M.S.D. (1990). The Second Economy in Tanzania. Dar es Salaam: ESAURP.

Mbelle, A. (1990). Efficiency Performance Under Structural Adjustment Programmes: The Case of Textile Manufacturing in Tanzania.

Miller, Delbert C. (1991). Handbaak of Research Design and Social Measurement. London: Sage.

Redeby, J. (1986). Reportfrom a short-term mission to Dar es Sa/aam, Tanzania, Oct. 7- Nov. 7, 1986 on The Quarterly Survey of Jndustrial Production. Dar es Salaam: Statistics Sweden.

Redeby, J. (1988). Report from a short-term mission to Tanzania, August 15 - September 10, 1988. Dar es Salaam: Statistics Sweden.

Redeby, J. (1989). Reportfrom a short-term mission to Dar es Sa/aam, Tanzania, Nov. 27- Dec. 16, 1989. Dar es Salaam: Statistics Sweden.

Rweyemamu, J. (1973). Underdevelopment and Jndustrialization in Tanzania,a Study of Perverse Capitalist Industrial Development. Nairobi: Oxford University Press.

Selvanathan, E.A. & Prasada Rao, D.S. (1994). Index Numbers, a Stochastical Approach. Houndmills, etc.: MacMillan.

Skarstein, R. (1986). Growth and Crisis in the Manufacturing Sector. In J. Boesen, K.J. Havnevik, J. Koponen & R. Odgaard, Tanzania, Crisis and Struggle for Survival. Uppsala: Scandanavian Institute of African Studies.

Skarstein, R. & Wangwe, S.M. (1986). Jndustrial Development in Tanzania: Same Critica/ Issues. Uppsala/ Dar es Salaam: Scadinvian Institute of African Studies & Tanzanian Publishing House.

Timmer, M. (1996) The difference between a real value added index and a dejlated current value added index (draft paper).

Usher, D. (1980). The Measurement of Economie Growth. Oxford: Basil Blackwell.

United Nations (1961). Index Numbers of In dustrial Production. Studies in Methods, series F., No.l. New York: United Nations.

United Nations (1968). A System ofNational Accounts, (Studies in Methods, Series F, No.2, Rev.3). New York: United Nations.

United Nations (1990). International Standard In dustrial C/assification of all Economie Activities, third revisie n. Statistica! Papers, Series M, No.4, rev.3. New York: United Nations.

United Nations (1993). System ofNational Accounts (Studies in Methods, Series F, no.2, Rev.4). Brussels/ Luxembourg, etc.: Office for Official Publications ofth European Communities, etc.

Wangwe, S.M. (1990). Economie Development and Adjustment after the jirst ERP; Jndustrial evelopment in Tanzania: Are Infant Industries Maturing? Paper presented at the 6th Economie Policy Workshop. Dar es Salaam, 2nd- 4th January 1990.

Wolff, P. de (1957). De invloed van gewichten op de hoogte van een gewogen indexcijfer, Statistica Neerlandica XI (1), pp. 17-22.

70

REPERENCES

Statistica! Sourees

Bank of Tanzania. (June 1970 [1973, 1977, 1978, 1981, 1982]). Economie and Operations Report June 1982 [1973, 1977, 1978, 1981, 1982]. Dar es Salaam: Bank of Tanzania.

Bank of Tanzania. (March 1975). Economie Bulletin March 1975, Vol. VII, No.1. Dar es Salaam: Bank of Tanzania.

Bank of Tanzania. (June 1989). Economie and Operations Report for the Year ended 30th June 1989. Dar es Salaam: Bank of Tanzania.

Bank of Tanzania. (1996). Economie Bul/etinfor the Quarter ended 31st December, 1995 (vol. XXIV No.4). Dar es Salaam: Bank of Tanzania.

Bureau of Statisties. (1970 [et.al.]). Survey ofindustral Production 1967 [1969-1974, 1979-1988, 1990]. Dar es Salaam: Bureau of Statistics, Millistry of Economie Mfairs & Development Planning.

Bureau of Statisties. (March 1971). National AccountsofTanzania 1966 to 1968, Sourees and Methods. Dar es Salaam: Bureau of Statistics, Ministry of Economie Mfairs & Development Planning.

Bureau of Statistics (1972). The Economie Survey 1971-1972. Dar es Salaam: Government Printer.

Bureau of Statistics (1977). The Economie Survey 1975-1976. Dar es Salaam: Government Printer.

Bureau of Statistics (September 1981). National AccountsofTanzania 1966-1980. Dar es Salaam: Bureau of Statistics, Ministry of Economie Mfairs & Development Planning.

Bureau of Statistics (1982). The Economie Survey 1981. Dar es Salaam: Government Printer.

Bureau of Statisties (1983). Statistica/ Absract 1982. Dar es Salaam: Bureau of Statistics, Ministry of Economie Mfairs & Development Planning.

Bureau of Statistics (May 1985). National Accounts ofTanzania: 1976-1984, Sourees and Methods.

Bureau of Statistics (June 1985). Ha/i ya Uchumi wa Taifa katika Uvaka 1984 [The Economie Survey 1984], Dar es Salaam: Government Printer.

Bureau of Statistics. (1986). Input-Output Table ofTanzania, 1976, a Background on Sourees and Methods. Dar es Salaam: Bureau of Statistics, Ministry of Finance, Eonomic Mfairs and Planning.

Bureau of Statisties (June 1986). Ha/i ya Uchumi wa Taifa katika Uvaka 1985 [The Economie Survey 1985]. Dar es Salaam: Government Printer.

Bureau of Statistics. (Aprill988, [et.al]). Industrial Commodities, Quarter/y Report 87-1 [1985:4, 1986:1-4, 1987:1-4, 1988:1-2, 1989:1-4, 1990:1-2, 1990:1-4, 1992: 1-2, 1991:1-4, 1992:1-4, 1993:1, 1993:3, 1993:4, 1994:1, 1995:3]. Dar es Salaam: Bur"au of Statistics, Millistry of Finance, Economie Affairs and Planning.

Bureau of Statisties. (April 1993). National Accounts ofTanzania, 1976-1990. Dar es Salaam: Bureau of Statistics, President's Office, Planning Commission.

Bureau of Statisties. (July 1993). Census of In dustrial Production, Volume V. Methodology Report. Dar es Salaam: Bureau of Statistics, lndustria1 Section.

Bureau of Statistics. (April1994). Census ofindustria/ Production (1989), Volume IV (Part 1), Comprehensive Report 1 0+. Dar es Salaam: Bureau of Statistics, President' s Office, Planning Commission.

Bureau of Statistics. (February 1995). Workshop on Consumer Price Index, 6 to 8 February 1995, .Mkonge Hotel, Tanga.

Bureau of Statistles (March 1995). Selected Statistica/ Series 1951-1992. Dar es Salaam: Bureau of Statistics, President's Office, Planning Commission.

71

REFERENCES

Bureau of Matistics. (August 1995). National Accounts ofTanzania, 1976-1994. Dar es Salaam: Bureau of Statistics, President's Office, Planning Commission.

Bureau of Statistics. (January 1996). Producer Price Index (Manufacturing Sector), Quarterly Report 1995:3. Dar es Salaam: Bureau of Statistics, President's Office, Planning Commission.

Central Statistic Bureau. (August 1964). Census oflndustrial Production in Tangayika 1961. Dar es Salaam: Central Statistic Bureau.

Central Statistic Bureau (1965). Survey of Industries 1965, Memorandum on Deflnitions, Concepts and Procedures. Dar es Salaam: Central Statistic Bureau, Ministry of Economie Affairs & Development Planning.

Central Statistic Bureau. (1967 [1969]). Survey of Industries 1965 [1966]. Dar es Salaam: Central Statistic Bureau, Ministry of Economie Affairs & Development Planning.

Organisation for Economie Co-opemtion and Development (february 1971). Latest lnformation on National Accounts ofLess Deve/oped Countries, F/8, No.5. Paris: OECD.

Stäglin, R. & Komba, J.M. (ed.). (1992). Revised National Accounts ofTanzania, data on the expenditure side 1976-1985. Berlin, etc.: Statistica! Office ofthe European Communities.

United Nations. (1979). National Accounting Practices in Seventy Countries, (Studies in Methods, Series F. No. 26, Volume III). New York: United Nations.

World Bank (1976). World Tables 1976. Baltimore/ London: Johns Hopkins University Press.

72

ÁPPENDIXES

Appendix A IN DEPTH ANAL YSIS OF THE 1989 CENSUS

BACKGROUND

In-depth analysis of the 1989 census was carried out after it was discovered that no profound analysis had been performed before. During the process of analysing data (which was available at establishment level), a number of duplicate establishments were found in the directory of industries used for the census. Some data screening was necessary. Besides this recalculation of aggregates gave some smprising results. However, as is made clear in section 5, the main focus of the analysis was identifying the bias of inclusion of non-intermediate inputs in the census. In this appendix, we will describe various improvements made as a result of data screening, and adjustments made to the intermediate inputs, of which research design, methodology and results are presented.

V ARIOUS lMPROVEMENTS

.Data screening

In the directory of industries used for the 1989 census, six duplicate establishments were found and removed. Some extreme values of variables of establishments were traeed and adjusted. Nine establishments were traeed with nul-values for gross output. Eight o these establishments could nottraeed and the one which could be traeed was regrouped in the 5-9 size class. We decided to omit these establishments in our analysis, assuming they were either regrouped or out of production. The total number of manufacturing establishments in the directory of industries in 1989 was consequently corrected to 886 (the original number was 900). We adjusted data as aresult of data screening foranother two establishments. For one establishment we, more or less, coincidentally found out that a typing error was made and for the other establishment showed negative gross output. The value of gross output for this establishment was estimated by averaging gross output values of establishments of same size and industry1

.

Calculation of the Aggregates

We recalculated the aggregates captured under tata! production cast, tata! persons engaged, and Iabour casts. Although, for example, for production costs, 169 records showed discrepancies with the manually calculated aggregates (as was done for the unadjusted census data), only for the aggregate tata! persons engaged substantia1 differences were identified (for the other aggregates, the differences were around 1 %). In Table A-1 the recalculated totals for each branch are compared with the unadjusted tota1s.

TABLEA-1 Comparison of Unadjusted, and Recalculated Aggregate Total Persons Engaged

lndustrial Activity

31 Food, Beverages and Tobacco

32 Textiles and Leather

33 Wood and Wood Products

34 Paper and Paper Products

Total Persons Engaged

Unadjusted Reca/culated

42231

37083

13283

7:?25

43003

39128

19975

7256

1 We could not extrapolate figures of other years, because the original file for this establishment was not available.

75

lndustrial Activity

35 Chemicals, Petroleum and Plastic Pred.

36 Non-Metallic Produels

37 Basic Metal Industries

38 Fabr. Metal Pred., Machinery and Equipm.

39 Other Industries

3 Total Manufacturing

Redefining Gross Output

Total Persons Engaged

Unadjusted

6694

5067

1373

11663

1288

126007

Reca/cu/ated

8905

5049

1373

12099

5541

142329

The questionnaire used in the census covers an income category which should not be part of income as defrned in the system of national accounts. This income category is Profit from Safe of Fixed Assets. Another category Subsidies Received should also be left out in the calculations. This because in calculations for the total production costs (intermediate inputs) for the co st catagories Tota/Indirect Taxes Paid were left out. The census results are presented in factor costs concept, therefore the calculations should fit this concept. We have labeled the recalculation of gross output omitting the two categones just mentioned as redeflned GO. The results are presented in Table A-5 along with with adjustments as aresult of data screening and adjustment to the co st category all other casts. We will deal with the latter adjustments in the next paragraph.

ADJUSTING INTERMEDIATE INPUTS

N on-intermediate Inputs

As is explained in section5, due to shortfans in the questionnaire used for the 1989 census, we strongly suspect non-intermediale inputs (interest costs) to have been included in the intermediale inputs, thus causing a downward bias to manufacturing value added. Because the cost category all other casts is not carefully specified, several cost items, such as interest costs, are possibly included. We therefore will test the hypothesis that a large part of all other costs camprises interests costs and other non-intermediale inputs.

76

1989 share

FIGUREA-1

0.0

0.0

mMaterials . ·····•••······••· . . ... . . ....... ... ............. ........ ...... .

•• aFÜël, Lubr., E.leëtr, 8c W.ate ·· t:JIIlc!!Jstrlal s~rviêês •.••· ····· ·

....... . .. ·················· ......... .

g~öst of Rësätës •• •••· · aO ther c östs

Comparison of the Structure of Production Costs in the 1978 & 1989 Census of Industrial Production (10+)

Thç analysis ofthe contents ofthe item all ather casts in the 1989 questionnaire was set in with the examination of establishments, which contributed remarkable negatively to value added. It is possible, but unlikely, that so many establishments contributed negatively to value added. Negative profits are more likely to occur than negative value added. In trying to find those establishments which possibly distorted the total picture for aggregated value added, the contents of the production co st catagories was examined, and the category all ather casts was identified as overporportionated as can beseen in Figure 6-1 (section 6). The 1978 census of industrial production also tabulates detailed data on the structure of production costs. Although a different categorisation was used in the 1978 census, a comparison of the cost structure of both censuses could be made, by restructuring the categones for 1989 according to 1978 standards. The result of this comparison is presented in Figure A-1 (note that ather casts is conform 1978 definitions, and is thus not comparable with the contents of the category all ather casts). The tigure clearly shows us that the share of other costs in the total production costs is much higher in 1989 than the share in costs of production in 1978.

The cost catagories interest costs, directors fees, bad debts, and donations are not identified in the questionnaire and could all have been allocated to all ather casts. In discussion with memhers of the industrial section it was agreed that due to misunderstanding interest costs (and the other non-intermediate inputs) might have been tabulated under all ather cast, although it was recognised that interest costs is not an intermediate input. We will need to trace determining whether, and to what extent the co st catagories mentioned are allocated in all ather casts (OC).

Limitations

lt would have been desirabie to perform a sample and trace the actual other costs (without allocated nonintermediate inputs) of all establishments. However, the data available put on some constraints. First of all , no additional information in the census could be used to trace, whether interest costs, bad depths or directors fees were included in oe. Second, the questionnaire design used in the 1989 census was in use since 1983 for the annual survey of industrial production. This made it hardly impossible to extrapo1ate oe figures from other years. Third, no detailed data (such as balance sheet data) at establishment level for the year 1989 were available for all covered establishments.

The only way to co me up with a reliable estimate of the contents of the other costs category, was to obtain the actual figures again. However, no framework for sampling was available. Only fora limited number of parastatels, balance sheet (balance sheets for most manufacturing parastatels could not be obtained) were available. Due to these practicallimitation, we assumed that data forthese parastatels were representative for the entire manufacturing sector, whether private or pub lic. The balance sheets of the manufacturing parastatels available at Takwimu were thus used, to make an estimate of the portion of actual other costs2 in the all ather casts category.

Research Design

The strategy of identifying the actual other costs started by isolating those establishments, which contributed the most to the category all ather castl. Out ofthe 886 establishments in manufacturing, establishments were selected, contributing more than 500.000 Tsh. In Table A-2 it is described that the 175 who passed this criteria, (out of 886) took a share of 96% in the aggregate all ather casts. The establishments were dividt:d in categories, according to their contribution to the aggregated other costs.

2 By actual other costs, we mean the other costs, without the supposedly included non-intermediate inputs.

3 Obviously we focus here on manufacturing only. N evertheless, a relatively large portion of all other costs tot the total intermediate inputs

was observed for Mining and Quarrying (!SIC 2) and Electricity (!SIC 4). We regard this observation notper se as remarkable, because the questionnaire was primarily designed for manufacturing.

77

TABLEA-2 Distribution of all other costs over size classes

oe category Establishm. Parastatels Balance sheets Other Costs Share Availab/e

Group (bil/ion Tsh) (number) (number) (number) (000 Tsh) (%) 1 >=5 5 4 4 5130548 32 2 >=1.5 and < 5 23 16 10 5039776 31 3 >=0.5 and <1.5 35 23 8 3156447 20 4 <0.5 (and > 0.05) 112 47 15 2760002 17

TOT AL 175 108 34 16086773 100

A quick look at the table tells us that only 5 establishments contribute for 32%, and only 23 establishments for another 30% of the aggregated other costs. Out of the 5 establishments in group 1, 4 were parastatels, but only 1 balance sheet was available. We decided to treat the 4 parastatels of group 1 as a special case and collected data at the Tanzania Auditing eompany. In this way, we were able to get holdon all balance sheet data ofthe 4 parastatels in group 1. As can be seen from the table, for the group 2, 10 balance sheets were traeed and used forrecalculation ofthe other costs, for group 3, 8 balance sheets, and for group 4, 15 balance sheets.

In genera!, the research design was borrowed from sampling with probability proportionate to size (pps sampling), apart from the fact that no sample could be taken, but that we were restricted to the data available. By selecting 5 establishments who contribute more than 30% to all ather casts, a stratum of which the size is very small, but very important, is isolated. In a sample design with pps, one would do a 'take all' (instead of sampling) for this stratum. In pps sampling, strata contributing more are given a higher probability of selection than strata contributing less to a certain variabie (in our case all ather casts). Since we could not apply a sampling procedure, we could only work with what was available. Nevertheless, wetried to divide the data· available as appropriate as possible over the defined size classes.

Methodology

For each size class, the 'actual all other costs' (actual OC), and the 'presumed all other costs' (presumed OC) are calculated, using the balance sheets available in each size class. Presumed oe are determined by actding interest costs, directors fees, bad debts, an donations (in fact all the possible non-intermediale inputs items which could have been included) to the actua/ oe. Under the assumption that these non-intermediale inputs have been included in the co st category all ather casts (unadjusted Oe), presumed oe should be equal to unadjusted oe as tabulated in the census. In this way the hypothesis can be tested, that in the category all ather casts also non-intermediale inputs were included.

After calculation of actual, presumed, and unadjusted oe, we will frrst evaluate presumed and unadjusted oe for each size c ass. Based on whether presumed and unadjusted oe are comp:rrable, we can calculate an adjustment raLo for each size class. We define this adjustment factor as follows:

(1) actualOei

't i = , where i stands for size class. unadjusted oei

We assume that besides the selected 170 establishments (responsible for 96% of all ather casts), no adjustment is needed for inclusion of non-intermediale inputs for the other establishments. We also assume that cost structures of parastatels and non-parastatels are identical (no data for non-parastatels could be obtained). We will apply for each of the 170 establishment an adjustment factor to the category all ather casts, as follows:

(2) adjusted oe = 't i x unadjusted oe

We are able to make an exception for size group 1. Because individual establishments within this size group have such a hu ge impact on performance of the respective branches, we will use the in1 lividual valnes for

78

actual oe (as calculated from the balance sheets) for the 4 parastatels, and apply the (weighted) average ratio of actual oe to unadjusted oe of group 1 to remaining establishment in group 1.

Results

In A-3 the results of data coneetion for the 37 parastatels are tabulated for each size class. eomparing the presumed oe and the unadjusted oe, we can see that for group 1, 2, and 3, the presumed oe are slightly lower than the unadjusted oe. Only for group 4, the presumed oe is higher than the unadjusted Oe. Although the approach we have chosenis definitely subject to error, it is smptising that fora specific cost category diserepan ei es exist if one takes in mind that the balance sheets for the parastatels must have been the basis for filling in the 1989 census questionnaires! We also have compared our calculations ofthe cost category Bank Charges & Insurance with the tabulated values in the census, and here one can notice the same tendency (see A-3).

The cause of this discrepancy can be searched for in the contents of other categoties listed in the questionnaire. We therefore have selected 5 establishments, to see whether discrepancies occur in certain cost catagoties of the production costs. We have selected establishments in group 1 and group 2, were discrepancies between presumed and unadjusted oe were most pronounced. The results of categotising all costs from the balance sheets under the categoties as given in the census are given in A-4. The table shows that although differences exist between the values of particular production co st categoties, the total production costs minus oe are more or less the same, except for one establishment (unit 11 ). In other words, forthese 5 establishments none of the production co st categoties is responsible for discrepancies in the presumed and unadjusted Oe. For unit 11, the cost categoties Raw Materials, Packing Materials, eost of Accountancy, and Office Supplies differ significantly. For this unit we will rely on our own calculations (based on balance sheets), and enter these data into the database ofthe 1989 census.

Although duferences exist between the presumed Oe and unadjusted Oe, the results do not proofthe hypothesis that in the category all ather casts also non-intermediale inputs are included, should be rejected. We are inclined to state that the data underpins the hypothesis that value of all other casts is far too large, and that non-intermediale inputs are allocated to this catagory. We therefore decide to ignore the discrepancies between the presumed and unadjusted oe, and stick to determining the adjustment factor as given in (1) for each size class (based on our data collection) and thus removing the bias of non-intermediate inputs by applying formula (2).

We have adjusted the all other casts category for the 170 establishments as follows:

• For group 1 (unadjusted oe > =5 billion), the data for actual other casts detived from the balance sheet were used. This was the case for the 4 parastatels in this group. For the remaining establishments we have applied the (weighted) average ratio of actual oe to unadjusted oe of group 1.

• For group 2 (unadjusted oe between 1.5 and 5 billion) the adjustment factor was applied to all the 23 establishments in this group. For one establishment (unit 11) all data on production costs found in the balance sheet were entered into the database (see notes above).

• For group 3 and 4 the same approach has been foliowed as for group 2.

The results of adjustment to intermediate consumption (other costs), and results from other adjustment to variables ofthe census ofindustrial production, are listed in A-5. Takinga look at the selected establishments for editing, 2 ofthe 5 establishments in group 1, are found in activity 3211, and contributed for 2.9 biBion to the aggregated other costs. Allalysis ofbalance sheet data proved that 95% ofthis 2.9 billion was incorrectly included in 'all other costs', explaining about 170% ofthe 240% increase fortextile and leather. For ISie 37, where a 45% increase was observed, only 1 establishment out of group 1, explained 50% ofthis increase.

79

(all numbers in Tsh)

Group Establishm.

un~ 2

unit3 unit4

Group Total

2 unitS un~ 6 unit 7 un~8

un~9

unit 10 unit 11 unit 12 unit 13 un~14

Group Total

3 unit 15 unit 16 un~ 17 un~ 18 un~ 19 un~20

unit21 unit 22

Group Total

4 unit 23 unit24 unit25 un~26

unit27 unit28 unit29 unit30 un~31

un~32

unit33 unit 34 un~35

unit36

un~37

Group Total

Grand Total

actual oe (i)

266.976.036

34.536.141 4.681.668

316.768.012

44.124.089 162.935.352 27.616.567 24.390.358 95.724.405

3.144.980 3.261.753

221.897.775 39.912.000 19.389.249

642.396.528

46.209.019 44.770.344

133.067.000 32.636.702 56.837.833

4.553.712

3.475.998 54.376.000

375.926.608

10.184.074 34.664.103

10.078.000 1.031.231

25.839.122 14.470.429 9.444.432

40.814.420

27.386.966 7.780.358 7.244.759

40.907.394

750.446 3.269.870

8.262.992 242.128.595

1.577.219.743

TABLEA-3 Identifying Actual Other Costs, Presumed Other Costs, and Adjustment Factor

Interest

Casts

(i i)

252.225.652 375.919.660 504.520.469

3.210.085.859

182.306.835

91.090.374 36.040.029 17.352.877

178.033

25.829.054 22.391.477

175.765.501 9.224.000

188.034.160 748.212.340

73.988.669 26.043.016 97.449.000

914.925 2.676.056 1.334.869

12.941.858 4.254.000

219.602.393

0 0 0

88.528.211 0

13.959.387

76.515.672 30.887.000 53.700.566 40.637.809

0

47.967.129 0

51.572.267

2.106.185 405.874.226

4.583.774.818

Ba la nee Sheet Data

Directers

Bad Debts Fees

(iii) (iv)

6.061.449

0 0

56.231 6.117.680

0 8.540.227 1.871.718

847.600 2.768.537

207.689 1.337.587

0 3.141.000

0 18.714.358

0 49.076.824 32.934.000

4.505.256

618.478 0

16.351 0

87.150.909

148.310 1.359.849

166.000 0

8.305.672 3.080.988

173.244 884.871

0 0

208.490

105.583

0 586.991

4.672 15.024.670

127.007.617

196.000 226.016 482.016

1.082.731 0

275.000 570.088

42.000 114.982 151.054

2.149.076 120.000

0 4.504.931

0 105.000

2.039.000 288.750 140.000

1.177.436

57.491 442.000

4.249.677

60.000 0

100.000 328.800

30.000 120.000 52.427

419.877 60.000

0 100.000

25.000

0 261.331

16.426 1.573.861

10.810.485

Donations

(v)

559.654 438.227

1.540.881

478.650 529.500 409.811 548.300

1.047.278 279.157

1.867.234 0

277.000 45.850

5A82.780

712.164 1.025.250 5.435.000

441.460 0

144.560

829.293 912.000

9.499.727

118.500 46.000

0 79.500

252.800

34.500 333.308 222.020 241.725

38.000

266.514

70.385 36.000

1.697.424

.18.293 3.454.969

19.978.357

Presumed

oe (vii) = (i)+(ii)+

509.922.611 3.534.994.448

227.992.305 263.095.453

66.213.125 43.709.223 99.760.253 29.575.862 29.009.105

399.812.352 52.674.000

207.469.259 1A19.310.937

120.909.852 121.020.434 270.924.000

38.787.093 60.272.367

7.210.577 17.320.991 59.984.000

696.429.314

10.510.884 36.069.952 10.344.000 89.967.742 34.427.594 31.665.304

86.519.083 73.228.188 81.389.257

48.456.167 7.819.763

89.075.491 786.446

57.387.883 10.408.568

668.056.321

6.318.791.020

Census Data

Unadjusted

oe (viii)

832.000.000 511.603.000

4.308.886.000

256.404.000 196.128.000 157.459.000

200.613.000 204.265.000 328.283.000 193.053.000 205.980.000

2.046.927.000

136.454.000

61.108.000 136.809.000 76.196.000

61.803.000 59.371.000

784.751.000

47.047.000 43.620.000 13.696.000 10.694.000 37.684.000 15.113.000 19.436.000 31.539.000 43.646.000 45.378.000 20.337.000

444.644.000

7.585.208.000

(i)/(viii)

Ba la nee

Sheet Data Census Data

Bank Charges & lnsurance

5.990.846 231.735

37.173.782

6.984.928

0 8.121.479 8.578 037 4.600.841 1.860.819

519.630 0

3.042.000 0

33.707.734

11.598.465 392.985

30.054.000 1.883.289 2.138.119

811.512

1.801.971 3.459.000

52.139.341

3.497.979 5.522.574

477.000 668.048

5.347.139 1.617.254

3.652.514 12.534.214 13.792.124

1.177.921 1.567.082

35.446.437 168.020

6.646.571

2.490.931 94.605.808

217.626.665

12.132.000 628.000

46.874.000

84.538.000 91.090.000 2.370.000

498.000 4.778.000 4.506.000

520.000 24.519.000

3.042.000 3~.510.000

215.861.000

9.868.000 12.283.000 5.870.000 2.494.000 1.986.000 2.570.000 4.809.000 2.115.000

41.995.000

4.515.000 5.523.000

477.000

4.198.000 5.347.000

1.617.000 2.285.000

12.534.000 13.351.000

2.927.000 3.833.000

35.447.000 2.164.000

49.355.000 1.800.000

145.373.000

450.103.000

TABLEA-4 Comparison of Balance Sheet and Census Data for all Cost Categories

Estab Souree Cast Catagory

R M te . I Ch . Pack. Fuels & Et ~· 'ty & Costof Rents for Bank Transport

Communie Costof Office

OwnAcc. Costof · a rta ~mica Material Lubricant e~ ICI lndustrial hire of fixed Charges & a ti on Con st. Resale ISubTotal lunadj.

s s a• cost Accountanc Supplies s s Services assets lnsurance Services Materia Is

unit2 Balance sheet data 1388345 0 0 0 84 5022 0 23874 70933 2190 0 2462 0 0 1492910 510 89 Census data 1388345 1097 16566 28466 14842 0 0 23018 44883 2248 0 4331 0 0 1523796 835

unit3 Balance sheet data 87236 136937 3505 86019 112164 4860 0 5991 28025 2382 8390 4595 36747 0 516849 388 89 Census data 849n 139195 3505 54152 112164 12226 0 12132 32304 2382 900 4595 0 0 458532 832

unit 10 Balance sheet data 375630 0 4786 38343 37592 14010 131 1861 11042 3052 12792 2155 0 0 501394 29 89 Census data 354908 1017 4538 38343 37592 0 0 4506 2731 3052 9045 2154 0 2128 460014 200



Source: own sample.

TABLE A-5 Results of in-depth Analysis Census

In dustrial Activity

31 Food, Saverages and Tobacco 311, 2 Food Processing 313 Beverages 314 Tobacco & Cigarettes

32 Texliles and Leather 321 Textile 322 Wearlng Apparel 323 Leather and Produels 324 Footwear

33 Wood and Wood Produels 331 Wood Produels 332 FurnHure and Flxtures

34 Paper and Paper Produels 341 Paper Produels 342 Prlntlng and Publishing

35 Chemie als, Petroleum and Plastic Prod.

351 lnduslrlal Chemieals 352, 3 Olher Chem. and Petroleum Ref. 355 Rubber Produels 356 Plastic Produels

38 Non-Metallic Product& 361, 2, 9 Non-Metallic Produels

37 Basic Metallnduslriaa 371, 2 Basic Metallnduslries

38 Fabr. Melal Prod., Machinery and Equipm. 381 Metal Produels 382 Machlnery (Exc. Electr) 383 Electrlcal Machlnery 384 Transport Equipment

39 Other Industries 385/ 390 Other Industries

3 Total Manufacluring

No of Est

Unadj. Edited '

172 150

19 3

161 83 57

9 12

219 82

137

62 9

53

66

12 39

7 8

23 23

6 6

167 89 31

6 41

24 24

900

168 146

19 3

158 83 54

9 12

214 80

134

62

53

66 12 39

8

22 22

166 88 31

41

24 2~

8861

Pers Eng

Unadj. Edited

42231 34589

4969 2673

37083 30665

2108 1275 3035

13283 5587 7696

7325 4398 2927

6694 1799 3169

945 781

5067 5067

1373 1373

11663 5815 2041

527 3280

1288 1288

126007

42733 35021

5039 2673

38957 31014

2632 2275 3036

19975 9236

10739

7256 4398 2858

8905 1793 5044

941 1127

5049 5049

1373 1373

12099 6252 2035

527 3285

5541 5541

141888

Sou rees: for unadjusted num ber: data files of 1he Census of lnduslrlal Production

Unadj.

40919651 25514163

8974792 6430696

20211955 17732474

594643 956718 928120

4477978 2859033 1618945

7426765 5104493 2322272

16881144 4251848 7723208 3307048 1599040

5609989 5609989

9108224 9108224

14869635 4475633 1038355 2304581 7051066

1128663 1128663

120634004

Forthe edHed, redefined, and OC-adjusted numbers: Own analysls,l.e. adjustments to origlnal numbers in Census.

Notes: Unadjusted numbers, are numbers as they we re orlglnally published In the 1989 census.

Gross Output

Edited Redefined 2

40114398 24708910

8974792 6430696

20138278 17732474

520966 956718 928120

4432519 2829687 1602832

7426765 5104493 2322272

16993776 4251848 7835840 3307048 1599040

5609989 5609989

9108224 9108224

14869635 4475633 1038355 2304581 7051066

1128663 1128663

119822247

39728305 24531575

8959562 6237168

20103716 17701027

519464 955413 927812

4399697 2808994 1590703

7411796 5095407 2316389

16580975 3846063 7834241 3306681 1593990

5608126 5608126

9108194 9108194

14840528 4474990 1015911 2303391 7046236

1126405 1126405

118907742

(1) EdHed numbers result from datascreenlng. I.e., removlng dupllcates, typlng errors, and making estimates for outliers. (2) Redefined Gross Output are recalculated aggregates. dropping lncome catagorles "Subsidies Received", and "Profil from Sale of Fixed As sets". (3) OC-adjusted Intermediale Inputs are recalculaled lntennediate Inputs, whereby the catagory "all other costs" is adjusted wlht adjustment factor (4) Redefined Inteon edlate inputs are the recalculated production costs, om lttlng the catagory 'totallndirect taxes paid'.

lntermediate Inputs

Unadj. Redefined. oe adjusted ' IUnadj.

31863378 21733813

6475052 3654513

18513580 16312458

555212 851230 794680

3571350 2369542 1201808

6073604 4411850 1661754

13163199 2635036 6083137 3086241 1358785

4541506 4541506

8146712 8146712

12432138 3716674

881264 1722237 6111963

854519 854519

99159986

31184344 21054779

6475052 3654513

18376659 16312458

418291 851230 794680

3534420 2346132 1188288

6073604 4411850 1661754

13163199 2635036 6083137 3086241 1358785

4540856 4540856

8622695 8622695

12232138 3716674

881264 1722237 5911963

854519 854519

98582434

27734645 18900668

5956496 2877481

14340372 12444600

413620 751799 730352

3284312 2143123 1141189

5546096 4008280 1537817

12111190 2502602 5661869 2823855 1122864

4194452 4194452

7716713 7716713

11522045 3634869

850403 1593356 5443418

847467 847467

87297292

9056273 3780350 2499740 2776183

1698375 1420016

39431 105488 133440

906628 489491 417137

1353161 692643 660518

3717945 1616812 1640071

220807 240255

1068483 1068483

961512 961512

2437497 758959 157091 582344 939103

274144 274144

21474018

Value Added'

Edited Redefined

8930054 3654131 2499740 2776183

1761619 1420016

102675 105488 133440

898099 483555 414544

1353161 692643 660518

3830577 1616812 1752703 220807 240255

1069133 1069133

485529 485529

2637497 758959 157091 582344

1139103

274144 274144

21239813

8543961 3476796 2484510 2582655

1727057 1388569

101173 104183 133132

865277 462862 402415

1338192 683557 654635

3417776 1211027 1751104

220440 235205

1067270 1067270

485499 485499

2608390 758316 134647 581154

1134273

271886 271886

20325308

(5) EdHed value added (VA) is calculated taklng edited gross output (GO) minus unadjusted Intermediale Inputs (11); Redefined VA= Redefined GO- Redefined 11; oe adjusted VA= Redefined GO- oe adjusted 11.

oe adjusted

11993660 5630907 3003066 3359687

5763344 5256427

105844 203614 197460

1115385 665871 449514

1865700 1087127 778572

4469785 1343461 2172372

482826 471126

1413674 1413674

1391481 1391481

3318483 840121 165508 710035

1602818

278939 278939

31610450

Appendix B 1 IN-DEPTH ANALYSIS OF NOMINAL MANDPACTDRING

VALDE ADED 1978-1990

BACKGROUND

During the in-depth analysis ofthe 1989 census, a (non-random) test-sample of 15 files (corresponding with 15 establishments) was taken, containing questionnaires from the census, and the annual survey of industrial production. Going through the records, which contained questionnaires from 1970 onwards, it occurred that the response rate1 forthese establishments intheinter censal years was on average very low. It was noticed as well that units did not respond for several contiguous years.

Taken into consideration that estimates for non-responding establishments are based on previous year figures, it appeared that this methodology in combination with low response rates would cause huge understatement for performance indicators as value added and gross output (in current prices), since neither price increases nor real growth is allowed for in this methodology. Especially when an establishments does not respond for several successive years, the data set for this particular establishment is repeated for several years, thus causing a huge understatement of performance indicators in current prices.

To co me up with a reliable estimate of understatement of value added figures in current prices, within the annual survey of industrial production, a sample design has been set up to identify the influence of repeating data sets. Although the sample was designed in the first place to identify understatement ofvalue added due to treatment of non-response, information on the adoption rate ofthe so called 'new questionnaire'2 was necessary, so as to be able to make an estimate for the overstatement of non-intermediate inputs due to the use of this new questionnaire in the annual survey.

Sampling the original files of establishments included in the annual survey of industrial production, made it also possible to get insight in how many years it takes for an establishment to get into the directory of the annual survey. This, because the questionnaire comprises the question: 'producing since?'. Assuming the frrst questionnaire in a file to correspond with the first year of inclusion in the directory of industrial estabishments, an estimate for the number of establishments not covered can be made confronting this frrst year with the answer on the 'producing since' question. In this way we can assess the coverage ofthe directory of industries over time.

SAMPLE DESIGN

TJ,e only sample framework we have at our disposal is the directory of industries used for the 1989 census of industrial production. We have taken a sample from the establishments with 50 or more persons engaged. In total339 establishments in this category of establishments, contribute over 90% to gross output in the census years. We have performed a stratified sample in order to discriminate between industries and size classes. Although we expect more varlation of the response rate among size classes, than among branches, we have initially designed our sample to get insight in response rates for both, size classes, and manufacturing branches. Through stratifying the sample we filter the varlation among the subindustries (I SIC 31 to 39) and size classes.

1 Assuming that the preserree of a filled in questionnaire in a file, represented response, and lacking a filled in questionnaire for a certain year meant non-response. This assumption was confirmed to be valid, after discussion with memhers ofthe industrial section ofTakwirnu. 2 As aresult of in-depth analysis ofthe 1989 census, it appeared that the 'new questionnaire' (gradually introduced in the annual survey since 1981 and used in the 1989 census as wel!) not properly identified costs belonging to the cost category 'all other costs', but allowed non-intermediate inputs (alo. interests paid, directors fees) to be included in this category, thus overstating intermediate inputs, and consequently understating value added.

83

The sample taken from the 1989 censu:>, might be less appropriate for estimates for the late seventies, and early eighties due todrop outs, and newcomers in the 1989 directory. However, it is assumed that the sample is big enough to incorporate the difference in coverage between the directory kept in the late seventies and 1989.

The following tables give an outline of the sample procedure. The first table shows the size of the sample for each so called stratum size. The sample size is 153 out of a population of 339. A stratum is a selected number of industries in a certain size class and industry. The second table shows the resulting sample for each branch.

TABLE Bl-1 Sample Size per Stratum

No of Est in stratum Sample per stratum

<=5 take all

6-10 5 11-20 7 21-30 10 30+ 13

Tata I

TABLE Bl-2

Cumm. Sample

25 20 42 40 26

153

Number of Establishments per Size Class and Industry for the Population and Sample

Size C/ass

I SIC 50-99 100499 500+ Total

Popuiatien Sample Popuiatien Sample Popuiatien Sample Popuiatien Sample

31 30 10 38 13 20 7 88 30 32 18 7 51 13 14 7 83 27 33 27 10 9 5 4 4 40 19 34 6 5 11 7 2 2 19 14 35 13 7 12 7 4 4 29 18 36 8 5 3 3 4 4 15 12 37 6 5 6 5 38 22 10 29 4 4 55 24 39 2 2 1 1 1 1 4 4

Tata I 126 56 160 64 53 3 339 153

We have applied a random number to each establishment in a particular stratum, and selected the frrst i establishments, according to sample sizes as g ven in TableB-I and B-2.

Questionnaire & Data Collection

Tomeet the goals of our sample, namely (1) to measure the influence of repeating data sets, (2) to measure the adoption rate of the so called 'new questionnaire', and (3) to measure the coverage of the directory of industries over time, we developed a simple questionnaire with the following questions:

1) Which year is the first year a new questionnaire is used for an establishment?

2) Since when is the establishment in production (this can be derived from the questionnaires used in the annual survey and the censuses)?

3) For which years are questionnaires available in the record ofthe establishment (a questionnaire available is equivalent with response)?

During the data collection, it occurred that many establishments could not be traced. In some cases the data was found to be storedunder different ISIC classes, but by no means a overall check on these flaws could be

84

performed. The number of establishments that could be traeed are 108 ofwhich 38 in class 50-99, 46 in class 100-499 and 24 in class 500+. It is striking that about 45 establishments could not be traced. It is assumed that the dataforthese establishments are available, but that either the ISIC code has changed, or the file is kept under a wrong ISIC code. Other causes of non-tracibility could berecent drop outs (though the tendency is to keep files of drop outs) or some files were processed for other purposes during the time the sample was taken.

The sample of 108 is too small to differentiate between branches. Forsome branches relatively more establishments could not be traeed than for other branches. Therefore the results presented in the following pages, only distinguish size classes. It is assumed that the initially stratified sample for branches, and the 45 not traceable units do not bias the random character of the sample. The results of the sample for each size class, are enumerated in Table B 1-9.

DETERMINING UNDERESTIMATION CAUSED BY REPEATING VALUE ADDED

FIGURES IN CASE OF NON-RESPONSE.

For 1990 we were able to make an estimate based on matching value added figures with 1989 value added figures3

. It is relatively easy to make an estimate for this year, because the estimated value added for nonresponse can be isolated and corrected with the trend ofvalue added for the responding establishments. The results can be found in Table B1-3.

TABLE Bl-3 Estimation of Value Added for Non-response in 1990

Value Added Value Added Under- Adjustment to

Respondents Non-Respondenis estimation 1 total Value Added 2

1989

1990

Growth '89-90

16391490 18873325

15%

5082528

5082528

0%

769545 3.2%

Source: ow n analysis of datafiles of the 1989 census and the industrial survey 1990.

Notes (1) Underestimation is calculated taking applying !he 15% grow th in value added

of the responding establishments, to value added of non-responding establishments

(2) adjustment is calculated deviding underestimation by total value added in 1990.

For other years it was much more difficult to quantify underestimation, since no computer data files were available. We assume that the methodology of repeating previous year value added figures in case of nonresponse did notchange for the period 1978-1989, and that this methodology was applied even in case of iDeessant non-response patterns. For determining the level ofunderestimation due to repeating data sets, we assume:

1) null-growth for the whole period (1978-1990) for each establishment,

2) value of output moves with the va1ue of input, and,

3) deve1opment ofproducerprices move with the consumerprice index (CPI).

The choice of the CPI has to do with lacking a Producer Price Index for the period under examination. Table B 1-4 presents the available iudexes for price changes. It is recognised that the CPI is nota proper measuring rod for inflating producer values, but as can be seen from the table there is no proper alternative.

3 We had access to the computer data files ofthe 1990 ASIP and the 1989 census, which made it possible to match value added figures at establishment level. In case value added had exactly the same value, it was assumed that this was an estimate for non-response.

85

TABLE B1-4 Indicators for Price Changes 1978-1989

1977=100

1977 1978 1978 1ffil 1931 1932 1$3 1ffi4 m 19ll 1937 19ll 1im

CFl 1000 1CE6 1:!13 1:67 1939 2)19 3226 4!!2 Effi4 7i52 1007.4 1321.6 1721.5

Chr'g! i% 13'/o 3J'/o 23'/o :<9'/o 2i% :F/o Flo 32'/o 3J'Io 31% 3J'/o

CEMrelzil piceirrll< 1<DO ra 441.8 5'1:16 8J24 1013.1 1317.6 17216

Chr'g! F/o :F/o 23'/o 3J'/o 31%

CEMCtEt á L.Mrg lrrll< 1<DO 12l2 147.1 1766 732.7 2:125 E7 4526 631.5 911.4 11~.8 1!'91.6 aJ74.9

Chr'g! 23'/o 1i% ZP/o 32'/o 23'/o 24% 2i% 43'/o :Fio 31% Flo JJ>/o

S:trcE Bn<áTam-ia, Ei:xn:n'c BJISil\ \aiasis;us.

Ntes CEMOr esSjam RtJl pice irrll< isl:!m::tcnwg;!earssin Dres Sjam

Cb;t á L.Mrg lrrll< is l:!m::tcn Md::lallrr:mt>~in DresSjam

Quantifying the Level of Underestimation

Tak.ing into account these assumptions, we defme the totallevel of underestimation ( UE) for a size class for manufacturing value added in year tas follows (level ofunderestimation in%):

(1)

where, N1 is the number of establishments in the sample,

tnr t is the total number of non-respondeuts in the sample in year t,

S1 the maximum number of years value added is repeated for a certain establishment in year t,

nr1 (t-i) is the number of non-respondeuts of which value added has been repeated since year t-i.

CP/1 is the consumer price index in year t.

We can express UEt also in terms of a sum of underestimations caused by data sets, which are repeated i times. Hence we get:

s,

(2) UE1 = L uet,i , i=l

tnr1 nr1 (t-i) CP/1 where, ue

1. = ___ _..:.....;___:.... __ ___:._

·' N 1 tnr1 CP I t-i

In Table Bl-11, the ue1,; for each year (t), each data set (i) and for each sizeclass has been calculated. As can be seen, sor te data sets which are repeated for a number of years have a large uet,i. We have called the number of years (i) a certain data set is repeated, the ith order underestimation. The sum of these ith order underestimations yields UE1 for each size class.

Since we have designed a stratified sample for each size class, the underestimation for each size class can be weighted according to the value added weights of each size class. In TableB 1-3 the value added weights for each size class, and for each year are listed.

86

TABLE B1-5 Distri bution of Value Added over Size Classes in 1978 & 1989

Size class Census Years Annual Survey

1978 1989 1979-1988

numb. employed 000 TSh share 000 TSh share share

50-99 183315 7% 2985596 10% 9%

100-499 1288704 50% 14109000 49% 49%

500 and more 1113857 43% 11625040 40% 42%

Total(50 and more) 2585876 100% 28719636 100% 100%

Source: 1978 and 1989 census of industrial production

Notes: (*) No information on contribution of value added over size classes is available from the annual surveys.

Therefore (since the shares of the censuses don't differ considerable) the average of the census years is taken.

The weighted level of underestimation for manufacturing, due to repeating value added data sets figures in case of non-response are given in the Table B 1-6.

TABLE B1-6 Weighted Underestimation, due to repeating Data Sets

Size Class\Year 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

CP/-change 7% 13% 30% 26% 29% 27% 36% 33% 32% 30% 31% 30%

Underestimation

50-99 0% 3% 19% 32% 43% 51% 83% 65% 71% 99% 111% 1% 100-499 0% 3% 12% 25% 49% 49% 31% 27% 28% 32% 44% 0% 500+ 0% 0% 13% 27% 43% 34% 35°Á> 50% 41% 44% 75% 1% Total* 0% 2% 13% 27% 46% 43% 37% 40% 37% 43% 63% 1%

Source: Table 81-3 & 81-11

Notes: (*) Weighed average of the underestimation per size class, according to the VA-shares.

For 1978 the shares for sizeclasses 5, 6 and 7 are resp. 7, 50 and 43%. For 1989, 9, 49 and 42%

For the between-census years, the average of the census years is taken (9, 49 and 42%)

Comparison of Assumed and Real Effects of Repeating Data Sets

We have assumed the CPI as a good measure for the price increases for manufacturing. Nevertheless, correcting repeated data sets for a number of years only in terms of price changes, and not taldng into account the real movement may bias the figures. We have assumed null-growth over a long period, and, in addition, the CPI may not be a good estimate for price movement in manufacturing. We therefore have selected some establishments in the sample, which had a very irregular pattem of responding, that is to say, we selected some extreme 'stubborn' -kind of non-responding establishments. This in order to get a feel the actual movement of value added forthese establishments.

In Table B 1-7 the value added figures forthese selected establishments are given for each year it responded. In case of non-response, "nr" is inserted. As can be seen from the table, when an establishment (unit) does not respond for several years, the next value of the response really booms the level of the latest response. The ratio ofnew responsetolast response data is defmed as the effect. The actual effect is compared with the effect we would have applied in case the establishment would still not have responded.

87

1990

3",(,

TABLE Bl-7 Some examples of Data repetition

1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 effect

(1977=100) Price Change

CPI 106.6 120.3 156.7 196.9 253.9 322.6 439.2 585 775.2 1007 1321.6 1721.5 Rea/ (1)

Estim.( 2)

CP I-change

Sample

unit 1

unit 2

unit 3

unit 4

unit 5

unit 6

unit7

unit 8

7% 32% 30% 33% 32% 33% 33% 34% 34% 34% 35%

(VA in 000'000 TSh)

7444 nr nr nr nr nr nr nr nr

39841 nr nr nr nr nr

953 nr nr nr nr nr nr nr nr nr

6854 nr nr nr nr nr

4092 nr nr nr nr nr nr nr nr nr nr

68632 nr nr nr nr nr 72204

1203 nr nr nr nr nr nr nr nr nr nr

1249 nr nr nr nr nr nr nr nr nr

Total (1989)

Overall Effect (3) Arithmetic Mean

35%

75692

82200

1774

30415

12528

50302

18900

271811

10

2

2

4

3

1

42

15

10

11

5 14

5 16

4 16

14

11

Weighted Mean '89 13 1 0 weights (4)

Sou ree: own sample; selected sample of 8 units having long range of non-response years.

Notes: nr = non-response. Blank eelt means no information, or out of scope for the sample.

(1) Real Effect is determined as the ratio ofvalue added of latest response to the previous response of a certain unit.

(2) Assumed effect is based on the ratio of the CPI in the two years of response.

(3) Assumed effect might be a bit overstated, although in case ofweighted mean ofthe effect, !he assumed effect understates real effect of simpte repetition. (4) Weight of unit 6 wasleftout for the weighting, thus understating the assumed effect.

Comparing the actual and assumed effect does not incentives to assume that the overall effect of assumed and actual movements should not be more or less the same. Though the data on individuallevel show discrepancies, at an aggregate level these duferences balance each other out.

DETERMINING THE ADOPTION RATE OF THE 'NEW QUESTIONNAIRE'.

As is said, the sample taken from the directory of the annual survey of industrial production, also providedus with information to what extent the 'new questionnaire' (the same as used in the 1989 census of industrial production) was adopted in each year. From the sample results we have calculated the adoption rate for each year. Note, that not for each establishment information on introduetion of new questionnaire is available, since some establishments were only inquired with the new questionnaires, because they have been in production since the mid-eighties. Table B 1-8 presents the results of the sample.

88

TABLE B1-8 Adoption Rate of "New Questionnaire"

Year Number Share Adeption Ra te

1980 1 1% 1%

1981 3 3% 5%

1982 9 10% 15%

1983 20 23% 38%

1984 26 30% 69%

1985 11 13% 81%

1986 7 8% 90%

1987 1% 91%

1988 1 1% 92%

1989 7 8% 100%

Sample Size 86

Source: calculated from Table 81-10

DETERMINING THE LEVEL OF UNDERCOVERAGE

When an establishment was covered since year t, and is said to be in production since year t-i, than (in case i> 0), we can assume that for the years t-1, t-2 .... !-i, the directory of industries suffered undercoverage. Although it is unlikely that an industry started production at the same level it produced in 1989, we simply assume that the establishment had the same level of production throughout the scope of the sample (1978-1989). Of course this oversimplifies the real situation. However, an additional remark to be made here is that for the smaller size classes we expect the coverage rate to be much lower, but these size classes are not covered in the sample. Reason to believe that the rate of coverage is lower for smaller size classes, is the drop in coverage after 1978, as is dealt with insection 6 (paragraph 6.2.1). This drop in termsof number of establishments mainly involved small scale establishments. Besides this, we do not incotporate the overall decline in manufacturing performance in the early eighties. In sum, for 1978-1989 we assume the samelevel ofproduction, and nullgrowth for each establishment. Hence, the rate of coverage (CR) fora size class can be defmed as follows:

CR= Nt-net t N

t

(3)

where nc1 is the number of not covered establishments.

As is the case with the determination of the total underestimation due to repeating value added figures, we need to weight the coverage rates for each size class, in order to come up with an estimate for the total rate of undercoverage. In Table B 1-9, the results of determining the coverage ra te for each size class and the weighted total is summarised.

TABLE B1-9 Coverage of the DIE from 1978 to 1989 as a percentage of the Coverage of the DIE in 1989

1978 1979 1!Bl 1001 1!132 1!1!3 1!1!4 1!1!6 1!1!6 1!117 1938 1!119 en.rn 10J% 10J% Wlo 63'/o 84% 88% 91% 94% 92% 92% 92'% 10J% 1CD4Il 94% Wlo 92% !D'/o 83'/o 83'/o 84% 63'/o 91% Wlo 9:1'/o 10J% 5Th 10J% 94% !9'/o ffi'/o 82% 83'/o 9:1'/o 9:1'/o 10J% 1CU'/o 1CU'/o 1CU'/o Wigta:!~ 97% @Jo 91% 87% @Jo 84'/o W/o 91% W/o 93>/o 97% 1W'/o

S::uce caaJaEdfrcrnta::lelOOC

Nies: (i IJI.toig1Ed wrg vàu= a:tl3:j sta'es fcr ~ rcte fcr EBtl si<E class.

Fa 1978 !te sta'es fcr si2e:;lasses 5, 6 a-d 7 ël'e resp 7, 00 a-d 43'/o Fa 1SB:\ Q 43 a-d 42% Fa !te irtB' C8"E;<j ya:rs, !te ëM'fGge d !te Q3"El6E5 is tael (Q 43 ëJ'd 42"/~

We should be aware that the percentages given here are based on the number of establishments which should be covered in the directory of industrial establishments in course of time. The degree of succes is determined

89

by measuring how fast an industry gets in the directory of inudstrial establiehsments. Therefore for 19~9 we ob serve a 100% coverage rate, because for more recent years there is no up-to-data directory to check whether new establishments emerged which were in production in 1989, but were not covered that year. Therefore, the percentages should be interpreted as follows: 13% ofvalue was added in 1983 by establishments which were not covered in the directory of industries, although they were in production in 1983.

An interesting ob servation to be made here is, that although in 1978 a census was performed, which had a much better coverage than surveys undertaken before (due to expanding the directory of industries with establishments, which had an 'unofficial status'), still establishments, even in the larger size classes, are not discovered. Therefore it is very likely that the 1989 census suffers undercoverage as well. Appendix D deals with the assessment of the directory of industries used for the 1989 census.

90

10 I

17

" 29 34 40 05 56 58 59 63 64 65 73 89 94 96 97 98

102 104 105 115 118 119 129 133 135 137 144 145

Produclng slnce

1980

1936

1976

1974

1982

1979 1972

1980 1974 1980 1969 1982 1952 1975 1983 1975 1964

1949 1971 1962 1981 1966 1975

1973 1965 1962 1974 1978

1973 1984 1986 195 ..

U6 1983 147 1978 148 1965 153 1978

Responded Nol Responded Nol Covered Tol al

8 11 12

" 26 33 36

" " 43 44 46 47

48 52 53 66 77 84 85

•• 90 93 99

101 106 107 108 110 116 121 122 124 125 \30 13\ \32 \36 \39 \40 \41 142 143

1978 1982 1940 1934 1974 1982 1979 1976 19'56 1936 1974 1974 1974 1978

1958 1967 1964 1963 1982 1974 1962 1966 1969 1950 1967 1965 1963 1962 1966 1966 1981 1971 1972 1963 1962 1967 1978 1965 1!il79 1957 1979 1984 1968 1975

46 .(6 Responded Nol Responded Net Covered Total

Quesllonn;alre slnce year

1985

1986

1983

1985 1984 1983

1981 1983 1983

1982 1983 1985

1984 1984 1982 1982 1984 1989 1982 1983 1985 1989 1985 1983

1982

1986

1989 1986 1988

1983 1989 1983 1961 1986

1983 1984 1984 1984 1985 1983 1983 1986 1984 1982 1984 1984

1989 1984 1984 1985 1983 1983 1984 1985 1985 ~ 98 4 1989

1964 1984 198.( 1984 1984

Hl62

1986

15185

46

~

~

~

~

~

~

~

~

~

~

~

"'

~

~

~

~

~

"' ~

~

~

~

35

36

~

~

' .,

~

~ ., ~

~

44

0 0 ..

~

~

"' ~

~

~

~

"' . ~

~

14 19 3

36

lil

.,

~

~

19 23 2 ..

TABLE Bl-10 Outcome of Sample per Size Class

~

~

~

~

~

~

~

"' ~

~

~

~

~

"' ~ . ~

20 13

36

~ .,

., ~

~

~

~

36 \1 3 ..

~

~

~

~

~ . ~

"' ~

~

~

"'

~

21 12

36

.,

.,

28 12

• 44

~

~

~

~

~ . ~

~

"' ~

~

~

~

"' ~

22 \1

35

~ .,

.,

D ~

2!)

12 6 43

~

~

~

"' ~

~

~

"' ~

14 \8 3

35

.,

~ .,

D ~

22 \4 7 43

~

. ~

~

~

"' ~

~

~

~

"' ~

20 10 4

31

Response

. ~ . ~

. ~

~ .,

~ ., . ~

. ~

D ~

22 13

. ~

~

"' ~ ~

~ . ~

~

~

~

"' ~

17 10

32

. ~ . ~

~ .,

~ ., . ~

. ~

~

c ~

15 20 7 42

D

. ~

~

"' ~ ~

~ . ~

"'

13 12 4

29

. ~

' .,

., . ~

D ~

\8 17

• 39

D ~

~ . ~

~

~

"' ~ ~

~

~

"'

12 13

28

~

~ .,

~ .,

. ~

~

~

D

23 12 3 38

~

D

~

~

~

~

~

~

"' ~ ~

~

~

~

~

~

"'

~

19

0 25

~

' .,

~ .,

~

D ~

25

• • 38

~

D

~

~

"' ~

~

~

~

~

~

"'

~

21

24

' .,

~ ., ~

~

. ~

~

D ~

J1 2 2 35

91

SIZE CLASS 7 (100 or more penons •ngag•dl

ID

10 21 23 31 32

35 37 .. 57 61 67 80

92 95

100 103 109 112 113

114 126

127 134 138

Produclng slnce

1982 1974 1936 1977 1962 1968

1962 1967 1978 1976 1965

1959 1972

1965 1971 1966 1983 1980 1966 1969

1982 1981 1979

Resonded Nol Responded Nol Covered Total

New Q uesllonnalre

slnce year

198(

1980

1989 1982 1982 1987 1983

1984 1983 1986 1984 1984

1985 1983

1984 1983 1981

1983

1983 1984

1984

Source: Own Sample.

D .I

21

22

D

13

22

D .I

" • 22

19 5

Notes: (1) An ldenllllcallon Number (IC) Is asslgned lo lhe establishments In the sample. (2) I he response symbols used re present the tollowlng·.

-.1' Eslabtllshm ent responded

15

1

24

0 Out of Scope. I.e. the eslabllshm ent posslbly responded before lhe perlod covered In lhe sample.

16 7 " 6

D

• 22

D

20

Nol Covered: year In whlch no response was nollced, bul establishment was In production accordlng to the yeartllled In at 'produclng slnce' questlon. Nol In Producllon: lnformatlon derlved trom the questionnaires used In the annual survey.

IEl Nol Usable: only one response Is glven In I he en lire perlod.lheretore we cannot make any estlmates.

TABLE Bl-11 Underestimation (UEJ for each Size Class and Year

1978

Establishments employing 5()...99 persons

TOTAL 0%

1st order

2nd order

3rd order

4th order

Sth order

6th order

7th order

8th order

9th order

10th order

Establishments employlng 100-499 persons

1979

3%

0.03

TOTAL 0% 3'4

1st order 0.03

2nd order

3rd order

4th order

5th order

61h order

7th order

8th order

9th order

Establishments employing 500 and more

TOTAL 0%

1st order

2nd order

3rd order

4th order

5111 order 61h order

7th order

8th order

Source: Table 81-10 & 81-4 (lor lle CPI).

1980

19%

0.10

0.09

12'.4

0.08

0.04

13%

0.13

1981

32%

0.01

0.18

0.14

25% 0.05

0.13

0.07

27%

0.05

0.22

1982

43%

0.00

0.02

0.21

0.20

49%

om 0.12

0.22

0.12

43%

0.02

0.10

0.31

Notes: Formula (2) has been applied to come up with the estimates (UE 1) presenled in this table.

92

1983

51%

0.02

0.00

0.00

0.22

0.27

49%

0.02

0.02

0.06

0.34

0.06

34%

0.01

0.00

0.16

0.17

1984

83%

0.11

0.00

0.00

0.00

0.33

0.39

31%

0.09

0.04

0.03

0.00

0.15

35%

0.05

0.03

0.00

0.16

0.12

1985

65%

0.03

0.10

0.00

0.00

0.00

0.12

0.41

27%

0.06

0.07

0.04

0.00

0.00

0.10

50%

0.06

0.04

0.00

0.00

0.24

0.17

1986

71%

0.06

0.02

0.09

0.00

0.00

0.00

0.18

0.38

28%

0.05

0.10

0.00

0.00

0.00

0.00

0.14

41%

0.03

0.00

0.06

0.00

0.00

0.33

1987

89%

0.04

0.09

0.00

0.13

0.00

0.00

0.00

0.22

0.51

32%

0.04

0.07

0.03

0.00

0.00

0.00

0.00

0.18

44%

0.07

0.03

0.00

0.10

0.00

0.00

0.25

D

D .I

18

1988

111%

0.09

0.06

0.08

0.00

0.19

0.00 Q_QQ

0.00

0.00

0.69

44%

0.14

0.03

0.03

0.00

0.00

0.00

0.00

0.00

0.24

7&%

0.09

0.13

0.06

0.00

0.14

0.00

0.00

0.34

D

D D .I

15

16

1989_

1%

0.01

0%

0.00

1%

0.01

D

D D .I

13

15

Appendix B 2 ADJUSTMENTS TO NOMINAL MANUFACTURING VALUE

ADDED 1978-1990

In Appendix B 1 in depth analysis of the manufacturing value added has been elaborated. In this appendix all suggested adjustments are applied in correct order to the unadjusted value added figures. In sum, the following adjustments need to be applied:

1) Extrapolation of 1989 census adjustment (alo. the inclusion ofnon-intermediate inputs in the 'all other cost' category);

2) Adjustments for underestimation of non-response;

3) Adjustments for undercoverage.

lNCLUSION OF NON-INTERMEDIATE INPUTS IN THE ASIP

The first adjustment ofnon-intermediate inputs is basedon the 1989 census analysis. The questionnaire used for the 1989 census, was gradually introduced for the annual survey in the early eighties. We have seen in Appendix A that the co st category all other casts, was not properly defined, and therefore included a lot of non-intermediate inputs. It is likely that the same has happened in the annual survey, in the years the questionnaire was in use. It would have been easy to test this hypothesis if we had data on all other casts category for all years. However, the only information that is available to estimate the magnitude of the all other casts is the information reported in the official publications. In this publications, intermediate inputs are splitted up in material inputs, and non-material inputs. Hence, the only estimate for all other costs is the value of non-material inputs. Assuming that the distribution of the production costs over other categones included in non-material inputs will notchange significantly over a period of 10 years, our hypothesis is that the amplitude of the aggregate non-material production costs should increase after the introduetion of he new questionnaire. Note here that the questionnaire is introduced gradually, and that full adoption took place in 1989 (see Table B1-8, appendix B). In 1985 the adoption rateis around 80%, and unti11982, only 15% ofthe new questionnaires were introduced. Our assumption is that data up till1982, and from 1985 onwards will discriminate regarding non-material inputs.

TABLE B2-1 Materialand Non-material Inputs for the periods 1978-1989

1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990

(000'000 Tsh)

Materia Is 4.67 4.95 5.14 5.37 5.46 5.92 3.93 8.31 13.01 20.67 25.35 58.82 63.79

Non-Materials 1.79 1.95 2.33 2.35 2.37 3.23 8.59 6.86 8.75 11.79 15.05 40.34 56.57

TOT AL 6.46 6.90 7.47 7.72 7.83 9.15 12.53 15.17 21.76 32.46 40.40 99.16 120.36

%

Materials 72% 72% 69% 70% 70% 65% 31% 55% 60% 64% 63% 59% 53%

Non-Materia Is 28% 28% 31% 30% 30% 35% 69% 45% 40% 36% 37% 41% 47%

TOT AL 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Average 78-82 Average 85-90

Materials 70% 59%

Non-Materia Is 30% 41%

TOT AL 100% 100%

Source: ASIP 1979-1988, and 1990. Censuses 1978, and 1989.

93

It is recognised that the development of interest casts is also subject to the development ofthe economie scene. Especially for the parastatels, one should take into account that the interest being paid may have increased a lot to the end of the 80s, because the government more or less secured their existence, until the privatisation move in the 90s. On aggregate level, the trend of the co st groups materials and non-materials, are given in Table B2-l.

As the table clearly shows, there is a significant difference between the share of non-material inputs for the periods 1978-1982, and 1985-1990. The valnes for materialand non-material inputs for the year 1984 show extreme values. We consider this as an outlier, with a 69% share for non-material inputs. The cause for this outlier is unknown, but possibly caused by errors in processing the data. The highest ratio for non-materials is in 1990 (47%), which by no means comes close to the 69% of 1983. Another observation is that the census year 1989 doesn't seem to be a irregular year in the reach, instead non-materials take the 1985-1990-average of41%.

If we take a look at the valnes of the shares for the periods 1978-1982, and 1985-1990, we can see a clear difference. The low shares in the period of 1978-1982 for non-materials do not come close to the shares for 1985-1990. The lowest share for 1985-1990 (36% in 1987) still is significantly higher than the highest share for 1978-1982 (31% in 1980). Basedon these observations we consider the hypothesis proved1 that the share of non-material inputs in the total intermediate inputs is higher for 1985-1990, than for 1978-1982. Therefore, we assume that adjustments for non-intermediate inputs applied for 1989 can be applied to the years where the questionnaire was used as well.

The strengtil of the condusion we have drawn, depends on the assumption that the category all other casts is responsible for varlation in the non-material inputs. If we take a look at the 1989 census, we see that all other casts takes about 55% of the non-material inputs. The other cost categoties are: industrial services, transport & cornmunications services, casts of resale, bank charges, and insurance paid, and professional services. There is no reason to assume that the character of these co st categoties is dynamic, rather we expect it to be statie. We expect the cost category interest casts much more dynamic, and much more dependent on the fmancial, and macro-economie circumstances. We see this affirmed in the numbers: the varianee ofthe non-material category between 1978 and 1982 is much less than the varianee between 1985 and 1990 (respectively 0.18 E-3, and 1.88 E-3).

To adjust for the inclusion of non-intermediate consumption, insight in the actual introduetion of the new questionnaire is obtained from the sample, described in Appendix B. A quick look at the Table B1-8 tells us that introduetion really started in 1982 (though only 15% was introduced) and that full utilisation took place in 1983, 1984 and 1985. The very late introduetion for some units can be as cribed to non-response for several successive years.

To adjust value added data, we will adjust annual survey data the same way as is done in 1989 (an 47% overall adjustment in terrus of value added), corrected with the adoption rate of the new questionnaire. To apply any results ofthe 1989 census at branch level, should be a very tentative exercise, because the most influenced sector, IS. C 32, has an increasing share in manufacturing value added, when going back in time, causing a even up to 70% increase of the manufacturing value added. It should be put into consideration that the textile sector was a priority sector under the Economie Recovery Progranune (ERP) in launched in 1986, airning at improving capacity utilisation, and that the actual production for the sector has dropped from 1981 onwards2

•

Another point, at least as important, is that the payments of interest (the largest portion of non-intermediate consumption in the other co st category) in the period under consideration for each branch can be influenced by a lot of factors, which will not be discussed in this study.

1 Smali-sample test of hypothesis abut the means (with HO: mean share non-matenals 1978-1982 is equal to mean share non-matenals 1985-1990, and Ha: mean share non-matenals 1978-1982 is lowerthan mean share non-matenals 1985-1990) yielded rejection of HO. 2 Mbelle (1990), who discusses the efficiency performance ofthe textile industry before and after ERP.

94

APPL YING LEVEL ADJUSTMENTS

In Appendix BI level adjustments are being suggested for non-response and undercoverage for 1978-1990 nomina! manufacturing value added. The adjustment factors are determined fortotal manufacturing only. Under the assumption that the calculated adjustment factors can be applied at each branch level, it does not matter in which order the adjustments are being applied.

To adjust value added figures as accurate as possible the gross output adjustment applied to the 1989 census will be used for the annual survey as well

3. The adjustments suggested for the inclusion of non-intermediate

inputs is best applied to non-material inputs. However, given the fluctuative character of non-material inputs as reported in Table B2-l, we will apply adjustments tototal intermediate inputs. In sum, adjustments to both gross output and intermediate inputs will be applied conform 1989 census adjustments, and corrected with the adoption rate of the new questionnaire.

In the tables B2-1-B2-3 adjustments have been stepwise applied to unadjusted manufacturing value added. It can be seen that the totallevel adjustment is very substantial.

3 These adjustments were also needed because ofthe questionnaire design. Earlier questionnaires used in the annual survey properly defined

gross output.

95

TABLE 82-2 (1 of3)

Level Adjustments To Gross Output at current prices of 10+ Establishments 1978-1990

Unadjusted Gross Output tor 3-digit branches, 1978-1990


311,2 Food Processing

313 Boveragos

314 Tabacco & Clgarettes

32 Toxtllos and Loathor

321 Toxtlle

322 Woanng Apparol

323 Loother and Produels

324 Footwear

33 Wood and Wood Produels

331 Wood Produels

332 Fumiture end Flxtures


341 Paper Produels

342 Printing and Publishing

35 Chemlcals, Petroleum and Plastic Prod.

351 lnduslrlal Chemieals

352, 3 Other Chem. and Petroleum Ref.

355 Rubber Produels

356 Plastic Produels

36 NoMiolalllc Produels

361, 2, Non-Metallic Produels

37 Basic Motal Industries

371,2 Basic Melallnduslrles

38 Fabr. Molal Prod., Machlnory and Equipm.

381 Melal Produels

382 Machlnery (Exc. Electr)

383 Eleclrlcal Machlnery

384 Transport Equlpmenl

38 Olhor Industries

385139 Other Industries

3 Tolal Manufaçturing

1976

2.393.033

1.722.845

445.844

224.344

2.635.630

1.859.240

402.122

91.817

282.451

216.864

132.018

84.846

421.345

143.656

277.689

1.246.206

307.163

574.707

258.369

105.967

262.483

262.483

390.690

390.690

1.502.650

394.314

258.107

258.368

591.861

64.715

84.715

8.153.616

1979

2.586.599

1.934.410

395.440

256.749

2.665.033

1.836.485

421.207

149.106

258.235

297.016

163.396

133.620

472.158

150.137

322.021

1.333.965

351.140

575.672

267.805

139.348

304.999

304.999

353.365

353.365

1.695.927

528.416

140.986

272.289

754.236

122.794

122.794

8.831.858

1980

2.702.788

2.073.380

380.758

248.650

2.913.634

2.079.197

400.326

174.036

260.075

299.438

160.599

138.839

492.937

156.418

336.519

1.415.200

378.603

615.671

264.291

156.635

328.920

328.920

326.770

326.770

1.758.862

546.136

72.235

305.826

834.665

138.799

138.799

10.377.348

1981

2.985.588

2.265.826

464.682

255.080

2.933.781

2.053.897

424.330

174.068

281.486

345.157

207.197

137.960

502.344

156.849

345.495

1.436.786

422.413

621.301

263.187

129.885

440.124

440.124

369.892

369.892

1.695.268

493.434

68.201

261.287

872.346

119.060

119.060

10.828.000

Source: Annual Surveyoflnduslrlal Production, issues 1979-1990, and Censuses oflndustrlal Produelion 1978, and 1989.

Adoption Rate New Questionnalre1

1978 1979 1980 1981

3 Total Manufacturing 0% 0% 1% 5%

Source: own anatysls of annuel survey, see Table 81·8.

1982

3.483.802

2.305.541

927.774

250.487

2.690.505

1.734.760

466.527

176.281

312.937

324.482

187.384

137.098

561.403

174.312

387.091

1.413.463

363.262

665.906

267.683

116.612

310.972

310.972

364.203

364.203

1.775.717

549.347

73.456

217.261

935.653

113.653

113.653

11.038.200

1982

15%

1983

4.018.497

2.588.924

877.687

551.886

3.089.070

1.990.725

480.062

203.836

414.447

360.272

210.644

149.628

624.385

174.170

450.215

1.561.657

438.208

724.763

280.337

118.349

632.668

632.668

376.034

376.034

1.996.717

680.692

64.650

381.107

870.268

108.190

108.190

12.767.490

1983

38%

1984

5.328.390

3.830.339

946.165

551.886

3.543.620

2.252.665

530.970

221.344

538.641

432.291

284.858

147.433

882.218

286.178

596.040

2.445.648

857.480

1.111.117

354.271

122.780

805.675

805.675

1.201.701

1.201.701

2.175.968

781.620

117.461

457.735

819.152

128.747

128.747

16.944.258

1984

69%

Notes: (1) In the 'new' questionnaire, gross output ts calculated in market prlces, end one of the lncome catagories defined In the questionnaire Is notconform SNA definitions.

1985

6.420.995

4.593.115

1.223.689

604.191

3.980.953

2.761.961

480.985

213.662

524.345

489.732

316.339

173.393

1.166.864

307.820

859.044

2.936.198

1.047.196

1.085.567

674.391

129.044

1.170.088

1.170.088

1.122.159

1.122.159

2.853.824

851.422

204.730

504.868

1.292.804

145.303

145.303

20.286.116

1985

81%

1986

7.417.980

4.911.576

1.330.333

1.176.071

5.458.387

4.080.744

448.963

297.506

631.174

649.817

531.745

318.072

1.539.628

589.836

949.792

3.987.564

1.327.749

1.696.319

673.686

289.830

2.497.137

2.497.137

1.579.603

1.579.603

4.664.641

1.063.895

211.706

670.260

2.718.980

175.603

175.603

28.170.580

1986

90%

1987

9.995.078

6.036.599

2.177.076

1.671.403

10.620.494

9.128.239

439.956

441.288

611.011

1.043.792

688.466

355.326

2.006.431

843.312

1.163.119

7.717.192

2.982.266

2.901.676

1.520.484

312.766

2.849.091

2.849.091

3.846.505

3.846.505

5.370.704

1.472.843

278.729

728.996

2.890.136

182.011

182.011

43.521.298

1987

91%

1988

13.604.036

7.403.877

2.197.366

4.002.793

12.117.502

10.637.634

447.814

421.043

611.011

1.184.479

785.699

398.780

2.880.994

1.569.738

1.311.256

9.097.681

3.522.937

3.354.970

1.897.008

312.766

3.000.407

3.000.407

4.023.861

4.023.861

5.678.799

1.665.110

280.108

711.451

3.022.130

181.176

181.176

51.758.935

1988

92%

1989

40.919.651

25.514.163

8.974.792

6.430.696

20.211.955

17.732.474

594.643

956.718

928.120

4.477.978

2.859.033

1.618.945

7.426.765

5.104.493

2.322.272

16.881.144

4.251.848

7.723.208

3.307.048

1.599.040

5.609.989

5.609.989

9.108.224

9.108.224

14.869.635

4.475.633

1.038.355

2.304.581

7.051.066

1.128.663

1.128.663

120.634.004

1989

100%

1990

51.902.487

29.411.910

12.152.762

10.337.815

21.945.329

19.452.669

1.046.157

582.604

863.899

4.103.196

2.473.155

1.630.041

8.590.001

6.043.510

2.546.491

20.416.272

5.121.909

9.100.738

4.547.486

1.646.139

5.702.433

5.702.433

11.082.895

11.082.895

19.529.152

4.740.054

1.531.565

2.742.387

10.515.146

1.046.654

1.046.654

144.318.419

1990

100%

Adjustments to Gross Output In Beneh-mark Year 19891

31 Food, Saverages and Tobacco


313 Beverages

314 Tabacco & Cigarettes

32 Textiles and Laather

321 Textile

322 Wearlng Apparel

323 leather and Produels

324 Foolwear


331 Wood Produels

332 Fumiture and Flxtures


341 Paper Produels

342 PrlnHng and Publishing

35 Chemlcals, Petroleum and Plastic Prod. 351 lndustrlal Chemieals


355 Rubber Produels




37 Basic Motallnduslr1es

371,2 Basic Metallndustrles

38 Fabr. Metal Procl., Machinery and Equlpm.

381 Metal Produels


383 Electrlcal Machlnery

384 Tnonsport Equlpment

39 Other Industries

385/39 Olher Industries

3 Total Manulacturing

1989

-2,91h

-3,9%

-0,2%

-3,0%

-0,5%

-0,2%

-12,6%

.0,1%

0,0%

-1,7%

-1,8%

-1.7%

-0,2%

.0,2%

-0,3%

-1,8%

-9,5%

1,4%

0,0%

-o,3%

0,0%

0,0%

0,0%

0,0%

-0,2%

0,0%

-2,2%

-0,1%

.0,1%

-0,2% -0,2",(,

-1,4%

Sou ree: own enelysis of 1989 census of industrie I production; calculated from table A-5.

Notes: (1) a4usbnents camprises data screening, end redefinitien of gross output conform SNA

TABLE 82-2 (2 of 3) Level Adjustments To Gross Output at current prices of 10+ Establishments 1978-1990

TABLE 82-2 (3 of 3)

Level Adjustments To Gross Output at current prices of 10+ Establishments 1978-1990

Adjusted Gross Output for 3-digit branches, 1978-1990

31 Food, Boverages and Tobacco


313 Boverages

314 Tabacco & CigareHes

32 Textilas and Lealher

321 Textile

322 Weartng Apparei

323 Leethor and Produels

324 Fooiweer


331 Wood Produels

332 Fumlture and Fixtures

34 Paper and Paper Produels

341 Paper Produels

342 Printing end Publishing


351 Industrie! Chemieals

352, 3 Olher Chem. and Petroleum Ref.

355 Rubber Produels


38 Non-Molallic Produels


37 Basic Melal Industries

371,2 Basic Metallnduslries

38 Fabr. Melal Prod., Machlnery and Equipm.

381 Metal Produels

382 Machlnery (Exc. Eieclr)

383 Eleclrlcal Machinery

384 Transport Equlpmenl

38 O!her Industries

385/39 Olher lnduslrles

3 Tolal Manufacturlng

1978

2.393.033

1.722.845

445.644

224.344

2.635.630

1.859.240

402.122

91.617

282.451

216.864

132.018

84.846

42"1.345

143.656

277.689

1.248.206

307.163

574.707

258.369

105.967

282.483

262.483

390.690

390.690

1.502.650

394.314

258.107

258.368

591.861

64.715

84.715

8.153.616

1979

2.586.599

1.934.410

395.440

256.749

2.665.033

1.836.485

421.207

149.106

258.235

297.016

163.396

133.620

472.158

150.137

322.021

1.333.965

351.140

575.672

267.805

139.348

304.999

304.999

353.365

353.365

1.695.927

528.416

140.96°

272.269

754.236

122.784

122.794

8.831.858

1960

2.701.765

2.072.452

360.750

248.563

2.912.999

2.079.154

399.737

174.033

260.074

299.377

160.566

138.811

492.924

156.415

336.509

1.414.877

378.183

615.774

264.291

156.629

328.919

328.919

326.770

326.770

1.758.834

546.135

72.217

305.824

834.658

138.796

138.796

10.375.260

1961

2.981.136

2.261.767

464.645

254.723

2.931.101

2.053.728

421.835

174.057

281.482

344.876

207.028

137.848

502.290

156.836

345.454

1.435.306

420.538

621.716

263.186

129.866

440.117

440.117

369.892

369.892

1.695.162

493.431

68.132

261.281

872.316

119.049

119.049

10.818.929

1982

3.469.003

2.292.119

927.536

249.347

2.681.072

1.734.295

457.611

176.245

312.921

323.625

166.666

136.736

561.208

174.265

366.943

1.409.609

356.021

667.353

267.679

116.556

310.956

310.956

364.203

364.203

1.775.351

549.335

73.216

217.244

935.556

113.619

113.619

11.008.646

1983

3.973.294

2.550.666

677.115

545.513

3.064.267

1.969.370

456.773

203.729

414.394

357.856

209.229

146.626

623.828

174.051

449.777

1.549.452

422.160

726.761

260.325

118.206

632.587

632.587

376.034

376.034

1.995.839

660.654

64.114

361.031

870.039

108.107

108.107

12.681.264

Notes: (1) adjustments to gross output are extrapolated backwards to 1978, and forward to 1990, correcting foradeption ra te of new questionnaire for each branch j:

GO_.. - GO....._.."" 111 00 "") Jl - Jl x~+'tJR!I'rfl

The adjusbnents are made under the followlng assumptions:

- The levet-adjustment made tor gross output In 1989 account for all years uslng the same questionnaire;

• The adoptJon ra te of the new questionnaire reflects the rate at whlch adjustments to gross output In 1989 have to be applied for other years.

1984

5.214.694

3.729.139

945.063

540.492

3.494.496

2.249.924

484.916

221.137

538.518

427.106

281.438

145.669

880.833

285.829

595.004

2.400.171

801.337

1.122.076

354.244

122.514

805.491

805.491

1.201.698

1.201.698

2.173.602

781.543

115.719

457.573

818.767

128.570

128.570

16.726.662

1965

6.260.527

4.449.137

1.221.999

589.391

3.927.091

2.757.974

431.489

213.425

524.203

482.763

311.832

170.931

1.164.647

307.374

857.273

2.867.161

965.848

1.098.270

674.330

128.712

1.169.772

1.169.772

1.122.156

1.122.156

2.849.189

851.322

201.128

504.656

1.292.083

145.068

145.066

19.988.372

1986

7.214.912

4.742.219

1.328.312

1.144.382

5.400.536

4.074.264

398.142

297.143

630.966

836.516

523.412

313.104

1.536.534

568.896

947.638

3.895.077

1.214.293

1.718.154

673.619

289.010

2.496.395

2.496.395

1.579.598

1.579.598

4.658.630

1.063.758

207.609

669.950

2.717.312

175.288

175.288

27.793.486

1987

9.625.254

5.825.747

2.173.725

1.625.782

10.554.634

9.113.557

389.508

440.742

610.827

1.027.241

677.537

349.704

2.002.397

841.951

1.160.447

7.495.835

2.724.123

2.939.511

1.520.331

311.870

2.848.233

2.848.233

3.846.494

3.846.494

5.362.911

1.472.651

273.265

728.655

2.888.340

181.881

181.681

42.944.679

1986

13.228.028

7.141.951

2.193.941

3.892.136

12.047.451

10.620.305

395.806

420.515

610.825

1.165.457

773.067

392.390

2.875.376

1.567.171

1.308.205

8.822.034

3.214.084

3.399.277

1.896.815

311.859

2.999.492

2.999.492

4.023.849

4.023.649

5.670.778

1.664.890

274.546

711.114

3.020.228

180.843

180.843

51.013.308

1969

39.728.305

24.531.575

8.959.562

6.237.168

20.103.718

17.701.027

519.464

955.413

927.812

4.399.697

2.808.994

1.590.703

7.411.796

5.095.407

2.316.389

16.580.975

3.846.063

7.834.241

3.306.681

1.593.990

5.608.126

5.608.126

9.108.194

9.108.194

14.840.528

4.474.990

1.015.911

2.303.391

7.046.236

1.126.405

1.126.405

118.907.742

1990

50.438.058

28.279.214

12.132.139

10.026.705

21.777.487

19.418.171

913.894

581.809

863.612

4.031.475

2.429.870

1.601.605

8.572.793

6.032.753

2.540.040

20.052.584

4.633.088

9.231.575

4.546.981

1.640.940

5.700.539

5.700.539

11.082.858

11.082.858

19.488.747

4.739.373

1.498.460

2.740.971

10.507.943

1.044.560

1.044.560

142.187.102

TABLE 82-3 (1 of3)

Level Adjustments To lntermediate Inputs at current prices of 10+ Establishments 1978-1990

Unadjusted lntermedlate Inputs' for 3-dlgit bra:::nc.:.;c:..:.h:..:e;.:sc..;1c;:9~7.;;.8-'-'1;.:9.;;.9.;;.0_-:-----:-:-:-:----:-:-:-----:-:-:-:---~:-:-:-----:-::-:--------=-----:-:-:-----:-:----------------1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989


311, 2 Food Processing

313 Beverages



321 Textile

322 Weartng Appanel

323 Leather and Produels

324 Footwear


331 Wood Produels

332 Fumlture and Flxtures


341 Paper Produels

342 Prln~ng and Publishing

35 Chemicala, Petroleum and Plastic Prod.

351 lndustrial Chemieals


355 Rubber Produels

356 Pla~ Produels

36 Non-Metallic Product•

361, 2. Non-Metallic Products

37 Basic Metallndustriea

371,2 Basic Metallndustrtes

38 Fabr. Metal Prod., Maehinery and Equipm.

381 Metal Produels


383 Electrtcal Machlnery

384 Transport Equlpment

39 Other Industries

385/39 Other Industries

1.718.173

1.337.932

266.623

113.618

1.768.950

1.235.100

292.838

54.602

186.410

123.566

65.744

57.822

235.419

73.241

162.178

908.614

235.602

395.286

206.181

71.545

168.066

168.066

176.741

176.741

1.176.339

282.711

187.583

208.731

497.314

35.432

35.432

1.862.451

1.450.540

258.122

153.789

1.787.159

1.132.254

351.083

111.919

191.903

180.406

97.686

82.720

296.463

89.159

207.304

907.489

241.672

403.551

174.373

87.893

214.982

214.982

253.173

253.173

1.301.250

369.656

96.479

205.662

629.453

101.150

101.150

2.085.111

1.599.476

333.123

152.512

1.934.648

1.301.794

318.685

119.449

194.720

194.673

102.408

92.265

310.103

91.865

218.238

969.178

288.055

415.858

170.382

94.883

232.445

232.445

278.265

278.265

1.362.827

378.756

45.638

258.890

679.543

119.201

119.201

2.211.286

1.705.166

355.242

150.878

2.021.689

1.323.980

342.922

136.640

218.147

199.181

107.053

92.128

308.380

90.349

218.031

972.745

333.617

392.337

168.344

78.447

310.487

310.487

269.053

269.053

1.334.028

356.271

41.890

226.403

709.464

92.945

92.945

3 Total Manufacturing 6.311.300 6.904.523 7.486.451 7.719.794

Source: Annual Survey of lndustrtal Production, Issues 1979-1990, and Censuses of lndustrtal Production 1978, and 1989.

Notes: (1) Intermediale Inputs are In Tanzania lndlcated as production costs.

Adoption Rate New Questionnaire' 1978 1979 1980 1981

3 Total Manufacturjng O% 0% 1% 5%

Source: own analysls ofannual survey, see Table 81-8.

Notes: (1) the use of a 'new' questionnaire caused a significant bias In the calculallons of Intermediale Inputs (see appendix A).

2.366.250

1.620.834

597.326

148.090

2.069.008

1.341.626

382.280

123.340

221.762

224.158

119.970

104.188

384.579

108.946

275.633

867.356

219.077

404.803

172.782

70.694

173.133

173.133

347.880

347.880

1.311.835

383.014

45.005

164.377

719.439

90.169

90.169

7.834.368

1982

15%

2.915.400

1.950.716

691.016

273.668

2.178.740

1.342.369

379.192

159.062

298.117

275.903

164.481

111.422

404.905

103.535

301.370

1.074.799

281.586

492.555

223.823

76.835

454.297

454.297

264.691

264.691

1.498.449

465.754

38.318

323.466

670.911

80.546

80.546

9.147.730

1983

38%

3.903.169

2.931.450

698.051

273.668

2.536.928

1.497.133

429.577

167.175

443.043

319.103

209.605

109.498

542.514

158.942

383.572

1.737.866

667.859

730.545

262.188

77.274

732.730

732.730

1.057.728

1.057.728

1.589.676

537.547

55.059

356.746

640.324

107.325

107.325

12.527.039

1984

69%

4.746.751

3.559.730

856.499

330 522

2.992.777

2.016.015

412.326

146.058

418.378

337.618

215.619

121.999

820.685

178.780

641.905

2.161.725

894.992

693.270

475.760

97.703

980.893

980.893

939.704

939.704

2.078.691

582.565

135.624

395.135

965.367

115.666

115.666

15.174.510

1985

81%

5.815.062

4.352.284

927.816

534.962

4.484.925

3.307.015

386.335

277.877

513.698

574.746

340.593

234.153

1.162.723

477.965

684.758

2.665.409

976.584

1.139.311

396.017

153.497

2.000.784

2.000.784

1.313.258

1.313.258

3.596.167

705.866

164.440

294.216

2.431.645

145.270

145.270

21.758.344

1986

90%

7.092.640

4.896.508

1.311.435

884.697

8.769.259

7.525.905

364.193

377.300

501.861

694.783

415.486

279.297

1.458.569

611.811

846.758

4.779.970

986.579

2.280.708

1.333.732

178.951

2.168.484

2.168.484

3.364.562

3.364.562

3.981.864

989.944

178.750

553.436

2.259.734

149.159

149.159

32.459.290

1987

91%

10.178.669

6.000.184

1.421.886

2.756.599

10.062.716

8.862.551

370.978

327.326

501.861

825.317

511.956

313.361

2.234.130

1.218.347

1.015.783

6.600.844

1.947.861

2.715.836

1.758.196

178.951

2.354.938

2.354.938

3.629.449

3.629.449

4.366.537

1.136.031

184.742

581.336

2.464.428

148.472

148.472

40.401.072

1988

92%

31.863.378

21.733.813

6.475.052

3.654.513

18.513.580

16.312.458

555.212

851.230

794.680

3.571.350

2.369.542

1.201.808

6.073.604

4.411.850

1.661.754

13.163.199

2.635.036

6.083.137

3.086.241

1.358.785

4.541.506

4.541.506

8.146.712

8.146.712

12.432.138

3.716.674

881.264

1.722.237

6.111.963

854.519

854.519

99.159.986

1989

100%

1~90

39.578.858

24.085.007

8.978.885

6.514.966

21.269.457

19.046.808

988.644

515.ot2

718.993

2.998.630

1.820.291

1.178.339

6.808.129

4.949.260

1.858.869

16.594.203

3.058.465

7.608.566

4.363.005

1.564.167

4.938.140

4.938.140

10.055.038

10.055.038

17.297.673

4.068.320

1.063.022

2.003.085

10.163.246

822.438

822.438

120.362.566

1990

100%

TABLE 82-3 (2 of3)


Adjustments to Intermediale Inputs In Beneh-mark Year 19891

1989



313

314

Beverages

Tabacco & Clgarettes


321 Textile

322

323

324

Wearing Apparel

Leather and Produels

Footwear


33 t Wood Produels

332 Fumlture and Fixtures


34 t Paper Produels

342 Printing and Publishing

35 Chemicals, Petroleum and Plastic Prod.



355 Rubber Produels




37 Basic Metallndustriea

371, 2 Basic Metallndustries

38 Fabr. Metal Prod., Machinery and Equipm.

381 Metal Produels


383

384

Electrlcal Machlnery

Transport Equlpment

39 Other Industries


.. 13,0%

-13,0%

-8,0%

-21,3%

-22,5%

-23,7%

-25,5%

-11,7%

-8,1%

-8,0%

-9,6%

-5,0%

-8,7%

-9,1%

-7,5%

-8,0%

-5,0%

-6,9%

-8,5%

-17.4%

-7,6%

-7,6%

-5,3%

-5,3%

-7,3%

-2,2%

-3,5%

-7,5%

-10.9%

..0,8%

-0,8%

3 Total Manufacturing -12,0%

Source: own analysls of 1989 census of lndustriai production; calcuiated trom tab Ie A-5.

Notes: (1) adjustments camprises data screening and removlng non-Intermediale Inputs trom the all olher casts category (see Appendix xxx for details).

TABLE 82-3 (3 of 3)


Adjusted lntermediate Inputs for 3-digit branches, 1978-1990

31 Food, Bevarages and Tobacco


313 Boverages

314 Tabacco & Gigarelles

32 Textilea and Leather

321 Textlle

322 Wearlng Apparel

323 Leather and Produels

324 Footwear


331 Wood Produels

332 Furnlture and FIXtures


341 Paper Produels

342 Prlnting and Publishing




355 Rubber Produels




37 Basic Metallnduatrieo

371,2 Basic Metallndustrles

38 Fabr. Metal Pred., Machinery and Equipm.

381 Metal Produels


383 Electrlcal Machlnery


39 other Industries


3 Total Manu1acturing

1978

1.718.173

1.337.932

266.623

113.618

1.768.950

1.235.100

292.838

54.602

186.410

123.566

65.744

57.822

235.419

73.241

162.178

908.614

235.602

395.286

206.181

71.545

168.066

168.066

176.741

176.741

1.176.339

282.711

187.583

208.731

497.314

35.432

35.432

6.311.300

1979

1.862.451

1.450.540

258.122

153.789

1.787.159

1.132.254

351.083

111.919

191.903

180.406

97.686

82.720

296.463

89.159

207.304

907.489

241.672

403.551

174.373

87.893

214.982

214.982

253.173

253.173

1.301.250

369.656

96.479

205.662

629.453

101.150

101.150

6.904.523

1980

2.081.999

1.597.052

332.813

152.135

1.929.768

1.298.205

317.740

119.287

194.537

194.505

102.294

92.211

309.816

91.767

218.049

968.315

287.887

415.523

170.214

94.691

232.238

232.238

278.094

278.094

1.361.622

378.659

45.619

258.665

678.679

119.190

119.190

7.475.548

1981

2.198.132

1.694.827

353.919

149.386

2.001.456

1.309.379

338.854

135.898

217.326

198.489

106.577

91.912

307.239

89.965

217.275

969.402

332.837

391.073

167.678

77.813

309.383

309.383

268.392

268.392

1.329.198

355.906

41.822

225.615

705.855

92.909

92.909

7.674.602

1982

2.322.320

1.588.895

590.095

143.330

2.001.293

1.293.539

367.543

121.162

219.048

221.631

118.237

103.394

379.965

107.440

272.526

857.378

217.413

400.565

170.561

68.839

171.133

171.133

345.104

345.104

1.296.567

381.740

44.767

162.518

707.543

90.057

90.057

7.685.448

1983

2.774.261

1.853.140

669.781

251.340

2.003.109

1.220.234

342.085

151.933

288.857

267.716

158.450

109.265

392.646

99.901

292.745

1.043.859

276.155

479.466

216.521

71.716

440.976

440.976

259.330

259.330

1.456.552

461.820

37.803

314.178

642.751

80.291

80.291

8.718.739

1984

3.562.735

2.669.288

659.699

233.748

2.180.232

1.253.596

354.419

153.778

418.439

301.573

195.864

105.709

512.913

148.968

363.946

1.655.633

644.831

695.837

246.896

68.069

694.315

694.315

1.019.427

1.019.427

1.513.870

529.430

53.736

338.431

592.273

106.717

106.717

11.547.417

Notes: (1) adjustments to lntennediate Inputs In beneh-mark year 1989, are extrapolated backwards to 1978, and to 1990, correcting lor adeption rate of new questionnaire:

II""pU - II-" x IJ + 't u . rM2) jt - jt ~ JFS Jf

The aaJustments are maae unaer me rouow1ng assumpttons:

- The corrections lor ü1e ether casts category made In 1989, account lor all years uslng the same questionnaire;

- The adjustment to lntennediate Inputs made In 198915 a goed approxination ofadjustments made totheether costs catagory of intennedlate Inputs;

- The adeption rate ofthe new questionnaire reflectthe rate at whlch adjustments to lntennediate Inputs In 1989 have to be applied lor otheryears;

- the bias to 'all ether costs' ofthe new questionnaire does nol change lor the years 1980-1990 lor netther ofthe manufacturlng branches.

1985

4.256.016

3.182.028

800.668

273.320

2.476.650

1.626.930

326.736

132.171

390.812

315.839

198.849

116.990

768.406

165.469

602.937

2.039.303

858.379

654.192

442.837

83.895

919.880

919.880

899.332

899.332

1.954.371

572.128

131.758

371.067

879.418

114.889

114.889

13.744.687

1986

5.138.717

3.844.309

861.288

433.121

3.628.350

2.604.946

298.121

248.815

476.467

535.032

311.454

223.578

1.077.851

438.819

639.032

2.496.814

932.638

1.068.669

365.872

129.635

1.863.888

1.863.888

1.251.196

1.251.196

3.319.242

691.956

159.284

274.503

2.193.500

144.197

144.197

19.455.287

1987

6.247.859

4.317.592

1.21ö.178

714.089

6.989.728

5.907.430

279.955

337.328

465.016

645.998

379.478

266.520

1.350.532

561.052

789.480

4.460.724

941.607

2.137.457

1.230.889

150.771

2.018.187

2.018.187

3.203.494

3.203.494

3.694.678

970.182

173.073

515.873

2.035.551

148.042

148.042

28.759.242

1988

8.817.159

5.281.685

1.317.283

2.218.191

7.973.007

6.932.190

284.070

292.204

464.543

765.860

467.018

298.842

2.062.161

1.115.971

946.190

6.172.294

1.857.932

2.543.068

1.620.884

150.409

2.189.625

2.189.625

3.453.473

3.453.473

4.050.037

1.113.062

178.799

541.373

2.216.803

147.346

147.346

35.630.963

1989

27.734.645

18.900.668

5.956.496

2.877.481

14.340.372

12.444.600

413.620

751.799

730.352

3.284.312

2.143.123

1.141.189

5.546.096

4.008.280

1.537.817

12.111.190

2.502.602

5.661.869

2.823.855

1.122.864

4.194.452

4.194.452

7.716.713

7.716.713

11.522.045

3.634.869

850.403

1.593.356

5.443.418

847.467

847.467

87.297.292

1990

34.334.915

20.945.368

8.259.809

5.129.737

16.382.769

14.530.607

736.516

454.854

660.792

2.765.259

1.646.355

1.118.904

6.216.761

4.496.530

1.720.231

15.271.066

2.904.750

7.081.660

3.992.071

1.292.586

4.560.775

4.560.775

9.524.314

9.524.314

15.909.317

3.978.775

1.025.796

1.853.187

9.051.560

815.650

815.650

1 05.780.828

TABLE 82-4 (1 of 3)

Level Adjustments To Value Added at current prices of 10+ Establishments 1978-1990

Unadjusted Value Added for 3-dlglt branches, 1978-1990.



313 Beven~ges

314 Tobacco & CigareHes

32 Taxtlles and Looihor

321 Textile

322 Wearing Apparel

323 Lealher and Produels

324 Footwear


331 Wood Produels

332 Fumiture end Fixtures

34 Paper and Paper Products 341 Paper Produels

342 Prinflng end Publishing

35 Chomlcals, Petroleum and Plastic Prod.

351 lndustrlal Chemieals

352, 3 Other Chem. and Petroleum Rel.

355 Rubber Produels




37 Basic Motal Industries

371, 2 Basic Metallnduslries

38 Fabr. Ilotal Prod., Machlnery and Equipm.

381 Metal Produels


383 Eleclrlcal Mechlnery

384 Tn1nsport t:qulpment

38 Olher Industries


3 Tolal Manu1aeluring

1978

874.860

384.913

179.221

110.726

866.680

624.140

109.284

37.215

96.041

83.298

66.274

27.024

185.928

70.415

115.511

337.592

71.561

179.421

52.188

34.422

84.417

94.417

213.849

213.949

328.311

111.603

70.524

49.637

94.547

49.283

49.283

2.842.318

1979

724.148

483.870

137.318

102.960

877.874

704.231

70.124

37.187

66.332

118.810

65.710

50.900

175.695

60.978

114.717

428.476

109.468

172.121

93.432

51.455

80.017

90.017

100.192

100.192

384.677

158.760

44.507

66.627

124.783

21.644

21.644

2.927.333

1980

617.677

473.904

47.635

96.138

978.986

777.403

81.641

54.587

65.355

104.765

58.191

46.574

182.834

64.553

118.281

446.022

90.548

199.813

93.909

61.752

96.475

96.475

48.505

48.505

396.035

167.380

26.597

46.936

155.122

19.598

19.598

2.890.897

1981

774.302

560.660

109.440

104.202

912.092

729.917

81.408

37.428

63.339

145.976

100.144

45.832

193.984

66.500

127.464

484.041

88.796

228.964

94.843

51.438

129.637

129.637

100.839

100.839

381.240

137.163

26.311

34.884

162.882

26.115

26.115

3.108.206

Source: Annual Survey ollndustrlal Production, Issues 1979-1990, and Censuses ollndustrial Production 1978, and 1989.

1982

1.117.552

684.707

330.448

102.397

621.497

393.134

84.247

52.941

91.175

100.324

67.414

32.910

176.824

65.366

111.458

548.107

144.185

261.103

94.901

45.918

137.839

137.839

18.323

16.323

463.882

166.333

28.451

52.884

216.214

23.484

23.484

3.203.832

1983

1.103.097

638.208

186.671

278.218

910.330

648.356

100.870

44.774

116.330

84.369

46.163

38.206

219.480

70.635

148.845

486.858

156.622

232.208

56.514

41.514

178.371

178.371

111.343

111.343

498.268

214.938

26.332

57.641

199.357

27.644

27.644

3.619.760

1984

1.425.221

898.889

248.114

278.218

1.006.692

755.532

101.393

54.169

95.598

113.188

75.253

37.935

339.704

127.236

212.468

707.782

189.621

380.572

92.083

45.506

72.945

72.945

143.973

143.973

586.292

244.073

62.402

100.989

178.828

21.422

21.422

4.417.219

1985

1.674.244

1.033.385

367.190

273.669

988.176

745.946

68.659

67.604

105.967

152.114

100.720

51.394

346.179

129.040

217.139

774.473

152.204

392.297

198.631

31.341

189.195

189.195

182.455

182.455

775.133

268.857

69.106

109.733

327.437

29.837

29.637

5.111.606

1986

1.602.918

559.292

402.517

641.109

973.462

773.729

62.628

19.629

117.476

275.071

191.152

83.919

376.905

111.871

265.034

1.322.175

351.165

557.008

277.669

136.333

496.353

496.353

266.345

266.345

1.068.674

358.029

47.266

376.044

287.335

30.333

30.333

6.412.236

1987

2.792.438

1.140.091

865.641

786.706

1.851.235

1.602.334

75.763

63.988

109.150

349.009

272.980

76.029

547.862

231.501

316.361

2.937.222

1.995.687

620.968

186.752

133.815

680.607

680.607

481.943

481.943

1.388.840

482.899

99.979

175.560

630.402

32.852

32.852

11.062.008

1988

3.425.387

1.403.693

775.480

1.246.194

2.054.786

1.775.083

76.836

93.717

109.150

359.162

273.743

85.419

846.884

351.391

295.473

2.486.837

1.575.076

639.134

138.812

133.815

845.469

645.469

394.412

394.412

1.312.282

529.079

95.366

130.115

557.702

32.704

32.704

11.357.863

1989

9.056.273

3.780.350

2.499.740

2.776.183

1.698.375

1.420.016

39.431

105.488

133.440

906.628

489.491

417.137

1.353.181

692.643

660.518

3.717.945

1.616.812

1.640.071

220.807

240.255

1.068.483

1.068.483

981.512

961.512

2.437.497

758.959

157.091

582.344

939.103

274.144

274.144

21.474.018

1990

12.323.629

5.326.903

3.173.877

3.822.849

675.872

405.861

57.513

67.592

144.906

1.104.588

652.864

451.702

1.781.872

1.094.250

687.622

3.822.069

2.063.444

1.492.172

184.481

81.972

784.293

764.293

1.027.857

1.027.857

2.231.479

671.734

468.543

739.302

351.900

224.218

224.216

23.955.853

TABLE 82-4 (2 of3)


Value Added': Adjusted Gross Output minus Adjusted lntermediate Inputs for 3-digit branches, 1978-1990



313 Beversges

314 Tebecco & ClgereHes


321 Texlilo

322 Weering Apperel

323 Leethor end Produels

324 Fooiweer


331 Wood Produels

332 Furnlturo end Flxlures


341 Peper Produels

342 Prlntlng and Publishing


351 lndustrlal Chemieals

352, 3 Olher Chom. and Petroleum Ref.

355 Rubber Produels


36 Non-Mataille Produels


37 Basic Met•llnduslries

371, 2 Basic Metallnduslries

38 Fabr. Metal Prod., Machlnery and Equlpm.

381 Motel Produels

382 Machlnery (Exc. Eleclr)

383 Electrlcal Machlnory


39 Olher Industries

385/39 Othor Industries

3 Total Manufaelur1ng

Source: Celculated from !he tables B2-2 end 82-3.

1978

674.860

384.913

179.221

110.726

866.680

624.140

109.284

37.215

96.041

83.298

66.274

27.024

185.826

70.415

115.511

337.592

71.561

179.421

52.188

34.422

84.417

94.417

213.849

213.949

326.311

111.603

70.524

49.637

94.547

48.283

49.283

2.842.316

1979

724.148

483.870

137.318

102.960

877.874

704.231

70.124

37.187

66.332

116.610

65.710

50.900

175.695

60.978

114.717

426.476

109.468

172.121

93.432

51.455

90.017

90.017

100.1,2.

100.192

384.677

158.760

44.507

66.627

124.783

21.644

21.644

2.827.333

1980

618.766

475.400

47.938

96.428

883.231

780.949

81.998

54.746

65.537

104.872

58.272

46.600

183.108

64.647

118.460

446.562

90.296

200.251

94.077

61.938

86.680

96.680

48.676

48.676

397.213

167.476

26.597

47.159

155.980

19.606

19.606

2.898.713

1981

783.004

566.940

110.727

105.337

928.645

744.349

82.980

38.159

64.156

146.387

100.451

45.936

195.051

66.871

128.180

465.904

87.701

230.643

95.507

52.052

130.734

130.734

101.499

101.499

365.965

137.524

26.311

35.666

166.464

26.140

26.140

3.144.328

Notes: Valuo Addod lor eech branch U) Is celculeted sublractlng !he edjusted lntennedlato Inpuls from tho edjusled Gross Output:

VA;.= ao;:-" -II':"''"

Welghted Underestlmatlon, due to Repeating Data Sets In case of Non-Response 1978-1989 1978 1979 1980 1981

3 Total Manufacturlng 0% 2% 13% 27%

Source: own analysis ofthe annual survey (s~e Table 81·6, p.78).

Notes: (1) In case of non·reponse, value added figures of the prevlous year was used, without correction for price changes or growth.

1982

1.146.682

703.224

337.441

106.017

679.779

440.756

90.068

55.082

93.873

101.994

68.651

33.343

161.243

66.826

114.417

552.231

140.609

266.788

97.117

47.718

139.823

139.823

19.098

19.098

478.764

167.595

28.449

54.726

228.013

23.562

23.562

3.323.198

1982

46%

1983

1.199.033

697.526

207.335

294.173

1.061.158

769.136

114.688

51.797

125.537

90.140

50.779

39.361

231.182

74.150

157.032

505.593

146.005

249.295

63.804

46.490

191.612

191.612

116.703

116.703

539.287

218.834

26.311

66.854

227.288

27.816

27.816

3.962.525

1983

43%

In our anatysis we have made an estimate for the bias in manufacturlng value added, caused by this methad of adjusting for non-response (see Appendix 81).

A weighted rate of underestlmation Is calculated welghting coverage rates for each size class with value added weights.

1984

1.651.959

1.059.850

285.365

306.743

1.314.264

996.328

130.498

67.359

120.079

125.533

85.573

39.960

367.919

136.861

231.058

744.538

156.506

426.239

107.348

54.445

111.176

111.176

162.272

182.272

659.732

252.113

61.983

119.142

226.494

21.853

21.853

5.179.245

1984

37%

1985

2.004.511

1.267.109

421.331

316.071

1.450.442

1.131.044

104.753

81.253

133.391

166.924

112.984

53.941

396.241

141.905

254.335

827.857

107.469

444.078

231.493

44.817

249.891

249.891

222.824

222.824

884.819

279.194

69.370

133.589

412.665

30.177

30.177

6.243.685

1985

40%

1986

2.076.195

897.910

467.024

711.261

1.772.186

1.469.318

100.021

48.327

154.519

301.484

211.958

89.526

458.683

150.077

308.606

1.398.263

281.655

649.485

307.747

159.376

632.506

632.506

328.403

328.403

1.339.387

371.803

48.325

395.447

523.813

31.092

31.092

8.338.199

1986

37%

1987

3.377.395

1.508.154

957.547

911.693

3.564.905

3.206.127

109.553

103.414

145.812

381.244

298.060

83.184

651.865

280.898

370.967

3.035.111

1.782.516

802.054

289.442

161.099

830.046

830.046

643.000

643.000

1.668.233

502.469

100.192

212.782

852.790

33.638

33.638

14.185.437

1987

43%

1988

4.410.870

1.860.266

876.658

1.673.945

4.074.444

3.688.114

111.736

128.312

146.282

399.596

306.049

93.548

813.215

451.200

362.014

2.649.740

1.356.152

856.209

275.930

161.449

809.866

809.866

570.376

570.376

1.620.741

551.828

95.747

169.740

803.425

33.497

33.497

15.382.345

1988

63%

1989

11.993.660

5.630.907

3.003.066

3.359.687

5.763.344

5.256.427

105.844

203.614

197.460

1.115.385

665.871

449.514

1.865.700

1.087.127

778.572

4.469.785

1.343.461

2.172.372

482.826

471.126

1.413.674

1.413.674

1.391.481

1.391.481

3.318.483

840.121

165.508

710.035

1.602.818

278.939

278.939

31.610.450

1989

1%

1990

16.103.142

7.333.846

3.872.330

4.896.967

5.394.718

4.887.565

177.379

126.955

202.820

1.266.216

783.515

482.701

2.356.031

1.536.222

819.809

4.781.518

1.728.338

2.149.915

554.910

348.354

1.139.764

1.139.764

1.558.545

1.558.545

3.577.430

760.598

472.664

887.784

1.456.383

228.910

228.910

36.406.275

1990

3%

TABLE 82-4 (3 of 3)


Welghted Undercoverage Rate 1978-19891

1978 1979 1980 1981 1982 1983

3 Total Manufacturing 3% 8% 10% 14% 21% 19%

Source: own analysls of the annuel survey; calculated trom table 81-9.

Notes: (1) as a result of a na lysis of the annuel survey, an estimate could be made for the coverage ra te {in terms of establishments) for each year, compared to 1989.

Aweighted undercoverage ratets calculated weighting coverage rates for each slze class with value added weights (see Appendix 81).

Adjusted Value Added tor 3-diglt branches, 1978-19901



313 Beversges


32 Taxilies and Laather

321 Textile

322 Weering Apparel

323 Lesther and Produels

324 Fooiweer


331 Wood Produels

332 Fumilure end Flxlures


341 Paper Produels

342 Prlntlng end Publishing

35 Chornlcals, Potr~:>um and Plastic Prod.

351 lnduslrlal Chemieals


355 Rubber Products

356 Plastlc Produels

36 Non-Mataille Produels


37 Basic Motallndustrios

371, 2 Basic Metallndustrles

38 Fabr. Motat Prod., Machlnery and Equipm.

381 Metal Produels

382 Machinery (Exc. Electr)

383 ElectMcal Machinery

384 Transport Equipment

39 other Industries



1978

694.642

396196

184474

113972

892085

642435

112487

38306

98856

96033

68217

27816

191376

72479

118897

347488

73659

184680

53718

35431

97185

97185

220220

220220

335876

114874

72591

51092

97318

50728

50728

2.925.632

1979

800.919

535168

151876

113875

870942

778891

77558

41129

73364

128972

72676

56296

194321

67443

126879

471689

121073

190369

103337

56910

89560

99560

110814

110814

436518

175591

49225

73691

138012

23839

23939

3.237.676

1980

774.008

593713

59868

120426

1227928

975305

102404

68371

81848

130972

72774

58197

228678

80736

147942

557698

112768

250087

117490

77352

120741

120741

60790

60790

496067

209156

33217

58896

194799

24486

24486

3.621.368

1981

1.133.023

820374

160224

152425

1345216

1077089

120074

55217

92835

211826

145355

66471

282243

96764

185479

674173

126905

333746

138201

75321

189175

189175

146872

146872

529558

199001

38072

51609

240876

37825

37825

4.549.909

1982

2.020.531

1239128

594594

186809

1197817

776642

158706

97059

165410

179720

120968

58752

319362

117751

201611

973069

247762

470098

171127

84082

246378

246378

33653

33653

643649

295314

50129

96432

401774

41518

41518

5.855.698

Notes: (1) Adjustments to value added camprise adjustments for non--response, and undercoverage, end Is calculated for each branch U> as follows:

VA;:""""= VA;. (I+ ~;")(1 + ~;"') We herebyessumo that

1) A4ustment ra te tor trestment of non-response tor total manufacturtng 1978-1990 are equal la adjustment rates at branch level.

2) Adjustment rate for undercoverage fortotal manufacturlng 1978-1990 Is equal to undercoverage rates at branch level.

1983

2.045.164

1189766

353650

501769

1810011

1311911

195623

88349

214128

153752

86614

67138

394326

126478

267849

662368

249040

425221

108830

79297

326831

326831

199060

199060

919859

373264

44878

114032

387684

47446

47446

6.758.857

1984

12%

1984

2.537.525

1628005

438341

471179

2018602

1530431

200453

103468

184450

192827

131446

61381

565150

210228

354922

1143663

240404

654733

164895

83631

170774

170774

279982

279982

1013395

387263

95210

183010

347911

33568

33568

7.955.665

1985

10%

1985

3.086.159

1950849

648684

486625

2233110

1741363

161278

125098

205370

256998

173950

83047

610055

218478

391577

1274575

165460

683706

356408

69001

364734

384734

343061

343061

1377669

429849

106802

205674

635342

46461

46461

9.612.820

1986

6%

1986

3.010.267

1301876

677136

1031255

2569485

2130358

145020

70069

224037

437120

307318

129803

665042

217596

447446

2027336

408370

941686

446201

231078

917068

917068

476149

476149

1941972

539075

70066

573357

759474

45080

45080

12.089.519

1987

4%

1987

5.034.089

2247941

1427247

1358901

5313578

4778810

163291

154141

217336

568253

444265

123988

971621

418686

552935

4523907

2656883

1195481

431421

240123

1237203

1237203

956407

958407

2486541

748943

149339

317156

1271104

50139

50139

21.143.739

1988

3%

1988

7.406.440

3123636

1472026

2810778

6641536

6192837

187620

215453

245626

670976

513897

157079

1365496

757626

607871

4449268

2277161

1437689

463324

271095

1359874

1359874

957738

957738

2721441

926594

160772

285016

1349059

56245

56245

25.829.015

1989

0%

1989

12.071.381

5667396

3022526

3381458

5800692

5290489

106530

204933

198739

1122613

670186

452426

1877790

1094172

783617

4498750

1352167

2186449

485955

474179

1422835

1422835

1400498

1400498

3339987

845566

166580

714637

1613204

280748

280746

31.815.292

1990

0%

1990

16.620.431

7569434

3996722

5054275

5568015

5044570

183077

131033

209335

1306891

808684

498207

2431715

1585571

846145

4935117

1783858

2218978

572736

359545

1176377

1176377

1608610

1608610

3692350

785031

487848

916303

1503168

236263

236263

37.575.771

Appendix C ÁDJDSTMENTS TO NOMINAL MANDPACTDRING VALDE

ADDED 1965-1978

BACKGROUND

In section 5 it is revealed that for the years 1965-1971, the numbers presented in the ASIP reflect the responding units only. For the years 1972-1974, account was made for non-response. Furthermore, it appeared that for the years 1965-1968 several establishments were omitted in the annual SlllVey. At the national accounts section estimates were made for non-response and omitted value added for the year 1966, which we will use to make estimates for other years.

Another issue is the coverage gain (in terms of establishments in the DIE) reached for the 1978 census. We will focus on backcasting this coverage gain for previous years, after making an estimate for non-response, and omitted value added.

DETERMINING NON-RESPONSE RATES 1965-1971

Basedon the reports ofthe annual SlllVey 1965-1971, and on a methodology report ofthe national accounts (BoS, March 1971) we have been able to make an estimate for non-response forthethese years. Utilising all data available a weighted non-response rate has been determined and presented in Table C-12. The unadjusted series for medium & large scale manufacturing value added are given in Table C-1. The resulting weighted non-response rate are summarised in Table C-2.

TABLE C-1 Unadjusted Nominal Manufacturing Value Added 1965-1978

I SIC 31 32 33 34 35 36 37 38 39 3 Branch Food, rexbles Wood Paper chêm~cals, Non- Bas1c Mefal öthêr Tofal

Beverages & Products, Products, Petroleum, Metallic Metal Products, Manufac- Manufac-& Leather Furniture & Printing & Rubber & Mineral Products achinery turing turing

Tobacco Fixtures Publishing lastic Prod Products Equipment Industries

1965 117,893 44,194 23,284 8,676 21,345 4,104 36,437 6,845 3,923 266,701 1966 125,312 72,324 21,693 12,306 24,525 8,794 16,885 10,450 2,873 295,162 1967 110,074 79,122 22,971 14,029 48,416 16,013 13,965 8,491 5,544 318,625 1968 164,420 73,206 23,607 15,360 47,707 16,987 15,625 16,873 4,502 378,286 1969 182,666 100,064 26,783 18,584 55,004 20,713 20,501 48,164 2,932 475,411 1970 218,669 147,857 38,046 21,500 58,852 22,107 23,628 27,452 2,505 560,616 1971 263,712 159,685 26,678 26,344 74,407 23,956 12,466 51,399 4,224 642,871 1972 293,794 199,317 24,585 27,958 110,998 37,151 19,834 79,622 13,069 806,328 1973 315,618 242,624 33,092 43,503 120,529 30,704 28,870 89,563 9,824 914,327 1974 368,793 259,988 31,550 73,955 228,142 36,294 32,739 109,649 15,542 1,156,652

Source: Annual Survey of lndustrial Production, issues 1965-1974, except for 1968 (no survey available).

105

TABLE C-2 Summary Table of Estimates for Non-response

Non-response Value Added of Non- Distribution over

Rates 1 respond. Establishm 2 Branches known?

1965 5.9% 15716 Yes

1966

1967 1.4% 4527 No

1968 3 3.0% 11224 No

1969 4.5% 21457 No

1970 1.9% 10518 No

1971 2.2% 14267 Yes

1972

1973

1974 Source: Table C-10

Notes: (1) from 1972 onwards, the annual survey makes estimates for notrasponding establishrrents

(2) Non-response rates are assurred to be appropriate to adjust value added (w hich is reasonable, since the

rates are w eighted w ith gross output.

(3) Non-response estimate for 1968 is basedon average of non-response rates of 1967 and 1969.

As can beseen only for 1965 and 1971 the distribution of non-response overbranches is known. As can be seen only for 1965 and 1971 the distribution of non-response over branches is known and used to calculate adjustments for non-response at branch level. For the years 1967-1970 estimates for branches are calculated, assuming that adjustment rates fortotal manufacturing are equal to adjustment rates at branch level. The results of adjustments for non-response can be found in table C-4.

TABLE C-3 Value Added 1965-1974 adjusted for Non-response1

IS IC 31 32 33 34 35 36 37 38 39 3 Branch Food, Textdes Wood Päper Chemcals, Non- BaSIC Metal other lotal

Beverages & A"oducts, A"oducts, R:!troleum Metallic Metal A"oducts, Manufac- Manufac-

& Leather Furniture & A"inting & Rubber & Mineral A"oducts achinery turing turing

Tobacco FD<tures F\lblishing lastic A"od A"oducts Equiprrent Industries

(value Added 1n Thousands I sh.)

1965 121,495 46,159 26,558 10,641 22301 4288 38069 8,810 4099 282,417

1966

1967 111,638 80,246 23,297 14,228 49,104 16,240 14,163 8,612 5,623 323,152 1968 169,298 75,378 24,308 15,816 49,122 17,491 16,088 17,374 4,636 389,510 1969 190,910 104,580 27,992 19,423 57,486 21,648 21,426 50,338 3,064 496,868 1970 222,772 150,631 38,760 21,903 59,956 22,522 24,071 27,967 2,552 571,134

1971 269,564 163,229 27,270 26,929 76,058 24,488 12,743 52,540 4,318 657,138 1972 293,794 199,317 24,585 27,958 110,998 37,151 19.834 79,622 13,069 806,328 1973 315,618 242,624 33,092 43,503 120,529 30,704 28 870 89,563 9,824 914,327 1974 368,793 259,988 31,550 73,955 228,142 36,294 3~.739 109,649 15,542 1,156,652

Source: Table C-1 and C-10.

Notes: (1) Adjustrrents for non-response for the years 1967-1970, is calculated for each branch, assuning that adjustrnent rates

non-response fortotal manufacturing 1965-197 4 are equal to adjustrnent rates at branch level. For 1965, and 1970 the distributio

of non-responding establishrrents over branches was used to calculate adjustrrents for non-response at branch level.

ADJUSTMENTS FOR ÜMITTED ESTABLISHMENTS 1965-1968

It appeared that besides there was not accounted for not responding establishments, several government owned industries were omitted in the surveys for several reasons for the years 1965-1968. An estimate is made for the value added which has been omitted by not covered government establishments. These estimates are mainly based on national accounts estimates for 1966 (BoS, March 1971) and forecasted under particular assumptions, which can be found in Table C-13. Adjustments to value added for omitted establishments are calculated for

106

each branch assuming that the uistribution of omitted value added in 1965, 1967-1968 is the same as for 1966 (which is known and listed in table C-13). The results of these adjustments are listed in table C-5.

TABLE C-4 Value Added 1965-1974 adjusted for Non-Response & Omitted Establishments1

I SIC 31 32 33 34 35 36 37 38 39 3

Branch Food, Textiles Wood Paper Chemica Is, Non- Basic Me tal Other Total Beverages & Products, Products, Petroleum, Metallic Me tal Products, Manufac- Manufac-

& Leather Furniture & Printing & Rubber & Mineral Produels Machinery & turing turing Tobacco Fixtures Publishin!flastic Prod.Products Equipment Industries

(Value Added in Thousands TSh.) 1965 128,534 52,271 29,586 11,276 23,651 4,476 50,077 16,638 6,046 322,555 1966 133,000 79,000 25,000 13,000 26,000 9,000 30,000 19,000 5,000 339,000

1967 115,081 83,236 24,778 14,539 49,764 16,333 20,037 12,441 6,575 342,785 1968 171,291 77,109 25,165 15,996 49,505 17,544 19,489 19,591 5,187 400,876 1969 190,910 104,580 27,992 19,423 57,486 21,648 21,426 50,338 3,064 496,868 1970 222,772 150,631 38,760 21,903 59,956 22,522 24,071 27,967 2,552 571 '134 1971 269,564 163,229 27,270 26,929 76,058 24,488 12,743 52,540 4,318 657,138 1972 293,794 199,317 24,585 27,958 110,998 37,151 19,834 79,622 13,069 806,328

1973 315,618 242,624 33,092 43,503 120,529 30,704 28,870 89,563 9,824 914,327 1974 368,793 259,988 31,550 73,955 228,142 36,294 32,739 109,649 15,542 1,156,652

Source: Basedon Table C-3 and Table C-11. Notes: (1) Adjustments for omitted establishments for the years 1965, and 1966-1968, are calculated for each branch,

assuming that the distri bution of omitted value added in 1965, 1967-1968 is the sa me as for 1966 (see table C-11 ).

BACKCASTING 1978 COVERAGE GAIN

Insection 5, we have dealt with the quality ofthe DIE, used as a framework for data collection for the annual survey carried out since 1965, and the censuses (1978 and 1989). As we have seen considerable coverage gains in the DIE were attained in the coverage years. Many establishments since 1978 included in the DIE, were in production before 1978, as is the case for the establishments discovered in 1989. We will deal with estimating the contribution of those establishments discovered in the 1978 census, and examine how we can adjust value added figures before the coverage gains.

Although no ASIP is available for 1974-1977, data on gross output is publisbed in the economie survey. Furthermore, we have value added figures for tata/ manufacturing value added at our disposal. We have listed adjusted value added ( derived from tab1e C-4 ), and gross output data at branch level for the period 1965-1978 in Table C-5. As can beseen for 1978 gross output figures basedon the old DIE, and census gross output (with coverage gain) are listed. With this data we can determine what consequences the coverage gain in terms of establishments had for gross output. We can see that due to coverage gain, total gross output has increased from 6807 to 9153 million Tsh: (a gain of 34%).

107

TABLE C-5 Adjusted Value Added 1965-1974 and (unadjusted) Gross Output 1974-1978

IS IC 31 32 33 34 35 36 37 38 39 3 Branch Food, lex!Jies Vl/öod Päper ChemcaiS, Non· Bas1c Mëlal öther 1otäl

Beverages & A'oducts, A'oducts, Petreleurn lli1etallic lli1etal A'oducts, lli1anufac- lli1anufac-

& Leather Furniture & A-inting & Rubber & Mneral A'oducts lli1achinery & turing turing

Tobacco FD<tures AJblishing Aastic A'od. A'oducts Equiperent Industries

(Value Addëd 1n 1 housands I sh.)

1965 1 128534 52271 29586 11276 23651 4476 50077 16638 6046 322555 1966 133000 79000 25000 13000 26000 9000 30000 19000 5000 339000 1967 115081 83236 24778 14539 49764 16333 20037 12441 6575 342785 1968 171291 77109 25165 15996 49505 17544 19489 19591 5187 400876 1969 190910 104580 27992 19423 57486 21648 21426 50338 3064 496868 1970 222772 150631 38760 21903 59956 22522 24071 27967 2552 571134 1971 269564 163229 27270 26929 76058 24488 12743 52540 4318 657138 1972 293794 199317 24585 27958 110998 37151 19834 79622 13069 806328 1973 315618 242624 33092 43503 120529 30704 28870 89563 9824 914327 1974 368793 259988 31550 73955 228142 36294 32739 109649 15542 1156652

(Gross Output in Thousands TSh.)

1974 1266546 826856 92534 173997 661021 92632 338403 295967 108680 3856636 1975 1328665 961211 108136 202714 816700 191552 436749 371326 128547 4545600 1976 1574307 1133754 127551 239111 963268 119784 515182 428558 151625 5253140 1977 2067443 1511866 158489 281826 968646 124617 583709 672310 155320 6524226

1978 2 2258096 1389386 160762 385270 1043608 196433 655430 553802 164317 6807104

1978 3 2393033 2635630 216864 421345 1246206 262483 390690 1502650 84715 9153616

Source: Table C-4; Econome Survey 1981 for Gross Output data 1974-1978 Notes: (1) 1965 is recalculated, i.e. redefined according to 1966 definitions

(2) Data for 1978 is assurred to be based on old coverage of llrectory of hdustries.

(3) Gross Output data fromthe 1978 census of industrial production (with coverage gain).

To backdate the coverage gain of 1978, we have followed the following procedure:

1) Growth rates at branch level are determined using value added and gross output data.

2) to get value added figures for the years 1974-1978 (were no value added data is available at branch level) we have forecast the data using growth rat es found under 1 ), and have adjusted these figures with the actual values for total manufacturing.

3) We have determine adjusted growth rates for 1974-1978 basedon adjusted value added forecasts found under 2).

The results of calculated (unadjusted) growth rates are found in Table C-6. Growth rates for each year and branch have been calcu1ated for 1974 to 1978 as follows1

:

(1)

and for 1965-1974 (utilisingvalue added data):

(2) r. = VA, -1· I VA,_I

1 The growth rate for 1977 is determined using 'low coverage' gross output data for 1978 and 1977.

108

TABLE C-6 Growth Rates Manufacturing Branches 1965-1978

IS IC 31 32 33 34 35 36 37 38 39 3 Branch Food, Texbles Wöod Päper Chemcais, Non- BasiC Metal Other lotäi

Beverages & Ftoducts, Ftoducts, Petroleum, Metallic Metal Ftoducts, Manufac- Manufac-& Leather Furniture & Ftinting & Rubber& Mineral Ftoducts Machinery & turing turing

Tobacco Frxtures R.Jblishing Aastic Ftod. Ftoducts Equiprrent Industries

Year (Ännual Growth Rates)

1965 3,5% 51,1% -15,5% 15,3% 9,9% 101,1% -40,1% 14,2% -17,3% 5,1%

1966 -13,5% 5,4% -0,9% 11,8% 91,4% 81,5% -33,2% -34,5% 31,5% 1,1%

1967 48,8% -7,4% 1,6% 10,0% -0,5% 7,4% -2,7% 57,5% -21,1% 16,9%

1968 11,5% 35,6% 11,2% 21,4% 16,1% 23,4% 9,9% 156,9% -40,9% 23,9%

1969 16,7% 44,0% 38,5% 12,8% 4,3% 4,0% 12,3% -44,4% -16,7% 14,9%

1970 21,0% 8,4% -29,6% 22,9% 26,9% 8,7% -47,1% 87,9% 69,2% 15,1%

1971 9,0% 22,1% -9,8% 3,8% 45,9% 51,7% 55,7% 51,5% 202,7% 22,7%

1972 7,4% 21,7% 34,6% 55,6% 8,6% -17,4% 45,6% 12,5% -24,8% 13,4% 1973 16,8% 7,2% -4,7% 70,0% 89,3% 18,2% 13,4% 22,4% 58,2% 26,5%

1974 4,9% 16,2% 16,9% 16,5% 23,6% 106,8% 29,1% 25,5% 18,3% 17,9%

1975 18,5% 18,0% 18,0% 18,0% 17,9% -37,5% 18,0% 15,4% 18,0% 15,6%

1976 31,3% 33,4% 24,3% 17,9% 0,6% 4,0% 13,3% 56,9% 2,4% 24,2%

1977 9,2% -8,1% 1,4% 36,7% 7,7% 57,6% 12,3% -17,6% 5,8% 4,3% 1978

Source: Calculated from Table C-5.

Notes: Annual growth rates 1966-1974 are calculated fromvalue added data, and rates for 1975-1978 are calculated

from gross output data.

The calculated growth rat es have been used to estimate value added at branch level for 197 5-1978. The difference between the values fortotal manufacturing derived, and the actua/ value added figures fortotal manufacturing has been determined, and all branches have been equally adjusted. The results are given in Table C-7. The adjusted growth rat es, calculated from these adjusted value added figures, are listed in Table C-8.

TABLE C-7 Forecasted Value Added 1975-1978

I SIC 31 32 33 34 35 36 37 38 39 3 Branch Food, Textiles Wood Paper Chemie als, Non- Basic Metal Other Total

Beverages & Products, Products, Petroleum, Metallic Metal Producls, Manufac- Manufac-& Leather Furniture & Printing & Rubber & Mineral Produels Machinery & turing turing

Tobacco Fixtures Publishing Plastic Prod. Produels Equipment Industries Year (Value Added in Thousands TSh.)

1965 128534 52271 29586 11276 23651 4476 50077 16638 6046 322555 1966 133000 79000 25000 13000 26000 9000 30000 19000 5000 339000 1967 115081 83236 24778 14539 49764 16333 20037 12441 6575 342785 1968 171291 77109 25165 15996 49505 17544 19489 19591 5187 400876 1969 190910 104580 27992 19423 57486 21648 21426 50338 3064 496868 1970 222772 150631 38760 21903 59956 22522 24071 27967 2552 571134 1971 269564 163229 27270 26929 76058 24488 12743 52540 4318 657138 1972 293794 199317 24585 27958 110998 37151 19834 79622 13069 806328 1973 315618 242624 33092 43503 120529 30704 28870 89563 9824 914327 1974 368793 259988 31550 73955 228142 36294 32739 109649 15542 1156652 1975 352459 275342 33589 78495 256793 68374 38494 125328 16748 1245622 1976 432312 336193 41014 95846 313533 44261 47004 149733 20449 1480345 1977 636564 502671 57141 126664 353510 51630 59714 263377 23487 2074758 1978 703901 467686 58680 175307 385598 82394 67884 219646 25156 2186252

Source: Calculated from Table C-5 & Table C-6 Notes: 1975-1978 data is forecasted using 1974 value added data, and applying annual growth rates from table xxx.

for the years 1975-1978, all forecasts are multiplied with an adjustment factor c which is defined as fellows:

actual MVA C=

forecasted MVA

109

TABLE C-8 Adjusted Growth Rates Manufacturing Branches 1965-1978

IS IC Branch

1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978

31 Food,

Beverages &

Tobacco

3,5% -13,5% 48,8% 11,5% 16,7% 21,0% 9,0% 7,4%

16,8% -4,4% 22,7% 47,2% 10,6%

32 Textiles

&

33 34 Wood Paper

Products, Products, Leather Furn~ure & Printing &

Fixtures Publishing

51,1% 5,4%

-7,4% 35,6% 44,0%

8,4% 22,1% 21,7%

7,2% 5,9%

22,1% 49,5% -7,0%

-15,5% -0,9% 1,6%

11,2% 38,5% -29,6%

-9,8% 34,6% -4,7% 6,5%

22,1% 39,3%

2,7%

15,3% 11,8% 10,0% 21,4% 12,8% 22,9% 3,8%

55,6% 70,0%

6,1% 22,1% 32,2% 38,4%

Source: Calculated from table C-7.

35 36 Chernicals, Non-Petroleum, Metallic Rubber & Mineral

Plastic Prod. Produels (Annual Growth Rates)

9,9% 101,1% 91,4% 81,5% -0,5% 7,4% 16,1% 23,4% 4,3% 4,0%

26,9% 45,9% 8,6%

89,3% 12,6% 22,1% 12,8% 9,1%

8,7% 51,7%

-17,4% 18,2% 88,4%

-35,3% 16,6% 59,6%

37 38 Basic Metal Metal Products,

Produels Machinery &

Equipment

-40,1% -33,2%

-2,7% 9,9%

12,3% -47,1% 55,7% 45,6% 13,4% 17,6% 22,1% 27,0% 13,7%

14,2% -34,5% 57,5%

156,9% -44,4% 87,9% 51,5% 12,5% 22,4% 14,3% 19,5% 75,9% -16,6%

Notes: Annual growth rates 1965-1977 are calculated from forecasted value added data (see table C-7)

39 3 Other Total

Manufac- Manufac-turing turing

Industries

-17,3% 31,5%

-21,1% -40,9% -16,7% 69,2%

202,7% -24,8% 58,2% 7,8%

22,1% 14,9% 7,1%

5,1% 1,1%

16,9% 23,9% 14,9% 15,1% 22,7% 13,4% 26,5% 7,7%

18,8% 40,2%

5,4%

Now appropriate growth rates based on value added figures prior to the 1978 coverage gain have been found, the coverage gain of 1978 should be backdated. This is carried out by taking the (adjusted) census value added data of 1978, and calculating previous years by applying growth rates for each branch (j):

(3) ' ' Tin 1

VA18_ . =VA18 . x ~ ) n,J ,J 1 i=l + '18-i,j

The results ofbackdating 1978 value added are numerated in Table C-9.

110

TABLE C-9 Results Backcasting 1978 Coverage Gain, applying Adjusted Annual Growth Rates

I SIC Branch

1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978

31 32 33 Food, Textiles Wood

Beverages & Products, & Leather Furniture &

Tobacco Fixtures

126843 99704 48419 131250 150688 40914 113567 158768 169038 147081 188399 199481 219841 287321 266018 311350 289929 380186 311466 462792 363942 495913 347822 525201 426625 641270 628191 958817 694642 892085

40551 41184 45810 63433 44629 40235 54157 51633 54971 67121 93514 96033

34 35 36 37 38 Paper Chemicals, Non- Basic Metal

Products, Petroleum, Metallic Metal Products, Printing & Rubber & Mineral Publishing Plastic Pred. Produels

Produels Machinery & Equipment

(Value Added in Thousands TSh.) 12310 21314 5280 162453 14192 15872 17462 21203 23911 29397 30521 47491 80734 85690

104631 138275 191376

23430 44846 44612 51805 54030 68541

100028 108617 205594 231413 282545 318571 347488

10616 97323 19265 65002 20694 63224 25534 69509 26565 78089 28883 41338 43820 64343 36216 93657 42809 106208 80648 124878 52206 152486 60898 193717 97185 220220

25442 29054 19024 29958 76975 42766 80342

121755 136957 167672 191648 228967 402748 335876

Source: For 1978 value added: Table B2-4, Annex B2, for 1965-1977 calculations: Table C-8.

39 3 Other Total

M anufac- M anufac-turing turing

Industries

12192 513956 10082 507549 13259 490154 10460 543711 6179 684895 5146 801103 8707 879205

26354 1097171 19810 1271162 31340 1545845 33771 1676041 41236 1997087 47362 2842092 50728 2925632

Notes: Data is backcasled using actjusled 1978 value added data (w~h coverage gain), and applying (adjusted) annual growth rates trom table C-8 to calculated previous years.

The difference between these backcasted value added figures and value added figures before coverage adjustment are listed for each year in table C-1 0. In Table C-11 the adjustments to value added and gross output caused by the coverage gainare listed for 1978. Forsome branches a drop in coverage is observed (e.g. ISIC 31, and 35 in value added terms). We assume that these coverage drops were caused by reallocating establishments to other branches. Smprising differences are found in the coverage gains forthebranches ISIC 37, and 39. Where in termsof gross output a coverage drop is observed, in terms ofvalue added huge coverage gains are derived at. We can not exp1ain these discrepancies. We assume that the explanation could be found in inclusion ofnew covered establishments for ISIC 37 and 39 with a high value-added-gross output-ratios, and reallocation of covered establishments (with much worse value-added-gross output-ratios) to other branches.

TABLE C-10 Level Adjustments to Value Added 1965-1978 due to Backdating Coverage Gain of 1978

Before adjustment After adjustm. Level for undercoverage for undercoverage Adjustment

(Value Added in Thousands TSh.) (%change)

1965 322555 513956 59,3% 1966 339000 507549 49,7% 1967 342785 490154 43,0% 1968 400876 543711 35,6% 1969 496868 684895 37,8% 1970 571134 801103 40,3% 1971 657138 879205 33,8% 1972 806328 1097171 36,1% 1973 914327 1271162 39,0% 1974 1156652 1545845 33,6% 1975 1245622 1676041 34,6% 1976 1480345 1997087 34,9% 1977 2074758 2842092 37,0% 1978 2186252 2925632 33,8%

Source: calculated from table C-7, and C-9.

TABLE C-11 Coverage Gain 1978 in terrns ofValue Added and Gross Output for each Manufacturing Branch

ISIC Branch 31 Food, Beverages & Tobacco 32 Textiles & Leather 33 Wood Products, Furniture & Fixtures 34 Paper Products, Printing & Publishing 35 Chemica Is, Petroieum, Rubber & Plastic Produels 36 Non-metallic Mineral Products 37 Basic Metal Products 38 Metal Products, Machinery & Equipment 39 Other Manufacturing Industries


Source: Calculated from table C-5.

in terms of in terms of Value Added Gross Output

(Change in %) -1,3% 90,7% 63,7%

9,2% -9,9% 18,0%

224,4% 52,9%

101,6% 33,8%

6,0% 89,7% 34,9%

9,4% 19,4% 33,6%

-40,4% 171,3% -48,4% 34,5%

ll1

TABLE C-12 (1 of2)

Estimating Weighted Non-Response Rates 1965-1971

Estimation of Weighted Rate of Non-Response in 1965

Rasponding Establ. Not Rasponding Establ. Gross Output Weighted Non-

Size Clas Incl. 2310&384 1 Excl. 2310&384 2 Incl. 2310&384 J Excl. 2310&384. in'66 5 Response R ate 6

(Nu mb) (Nu mb) (Nu mb) (Nu mb) (Share)

10-19 147 100 31 21 5% 1,0%

20-49 175 120 24 16 14% 1,9%

50-99 121 83 13 9 17% 1,8%

100-499 11g 81 3 2 46% 1,2%

500+ 7 5 18% 0,0%

Total 569 389 71 48 100% 5,9%

Source: Survey of lndustrial Production 1965. Notes: (1) Number of rasponding establishments inclusive the industries I SIC (rev.1) 2310, Sisal decortication & processing, and I SIC (rev.1) 384: Motor vehicle repair. These industries should be lelt out in value added calculations, according to SNA guidelines.

(2) Only the the lola/ number of responding estabishments exc/usive industries 2310 and 384 is known. Therefore, the distri bution of establishments (exclusive these industries) over size classes is estimated applying the distribution over size classes of responding establishments inclusive industries 2310, and 384.

(3) Number of notrasponding establishments inclusive the industries ISIC 2310 and 384. (4) Analegeus to (2) the the distribution of notrasponding establishments exclusive 2310 and 34 over size classes is estimated. (5) No data tor distribution of value added or gross output (GO) over size classes are available tor 1965. Oistribution of gross output

(net value added) over size classes is available in 1966.

(6) Wh en N'esp denotes number of rasponding establishments, N001

resp number of net responding establishments, w Go the gross output weight of the size class, and suffix s denotes the sizeclass, then the weighted non-response (NR), in termsof percentage of gross output, is calculated as fellows:

_..."

NRWeJghUd = L Ns"""' w~ 'N,

Distribution of Non-Response in 1965 over Manufacturing Branches

Not Responding Establishments IC (rev.2) (number) (share)

31 11 23%

32 6 13% 33 10 21% 34 6 13%

38 6 13%

Others 9 19%

TOT AL 48 100%

Source: Survey of lndustrial Production 1965.

Distribution of Gross Output over Size Classes in 1967

Size Clas Gross Output

(Mill. Tsh) (Share) 10-1g 61 4,7% 20-49 198 15,1% 50-99 244 18,6% 100-499 575 43,7% 500+ 236 18,0% TOT AL 1315 100,0%

Estimation of Rate of Non-Response in 1967

Responding Establishm. 2

ISIC (rev.1), and Description 1 Numb. of Estab. Gross Output

20 Food Industries

25 Wood Produels ecept Furniture

26 Fumiture and Fixtures TOT AL


(Number) (Million TSh)

63 94

32 12

26 13 121 119

Not Responding Establishm.

Nu mb. of Estab. Gross Output 3

(Number) (Million TSh) (Share)

19 18,7 1,4%

Notes (1) The only intermation on non-response is that ~is said that alllarge and important establishments' returns were compiled.

Furthermore, the 19 non-respondents are mainly from the lood and wood working industries. We have assumed all non-respondents

to be in the industries 20, 25 and 26, and size class 10-49.

(2) Number of establishments, and gross output (value added figures lor size classes not available) of response in industries ISIC (rev.1)

20, 25, and 26 and in size class 10-49.

(3) An estimate of contri bution of not rasponding establishments to gross output is detenmined calculating 19/121, and multiplying this

with 119 (gross output of responding establishments). The share of the outcome tototal gross output is calculated dividing the outcome

with 1351 (total manufacturing gross output in 1967)

TABLE C-12 (2 of2)

Estimating Weighted Non-Response Rates 1965-1971

Es ti mation of Rate of Non-Response in 1969

Size Clas

10-19

20-99

TOT AL

Responding Establishm. 2

Numb. of Estab.

(Number)

109

219

328

Gross Output

(Share)

5%

34%

38%

No! Responding Establishm.

Numb. of Estab. 3 Estimated Gross Output •

(Number) (Share)

48

16

64

2,1%

2,5%

4,5%

Source: numb. of establ. Survey of lndustrial Production 1969; Gross Output Sharesbasedon Table "Distr. of GO over Size Cl. in 1967". Notes (1) We assume (based on ASIP 1969, p.5) !ha! 75% of non-respondenis is to be found in size class 10-19,

and !he remaining 25% in size class 20-99. (2) Number of responding establishments tor size classes 10-19, and 20-99. The gross output shares of these size classes is based on 1967 gross output figures

(3) Only !he total number of non-respondenis is given (64). Distribution among size classes is estimated under !he assumptions given under (1 ).

(4) N"'sp denales number of responding establishments, Nnot,.sp denales !he number of nat responding

establishments, w Go denotes !he gross output weight of !he size class, and suffix s denales !he sizeclass, !hen !he weighted non-response (NR), in terms of percentage of gross output, is calculated as fellows:


I Responding Establishm. No! Responding Establishm.

Size Class 1 Nu mb. of Estab. 2 Gross Output 3 Nu mb. of Estab. 3 Es!imated Gross Output •

(Share) (Number) (Share) (Number) (Share)

10-19

20-99

TOT AL

25%

51%

76%

115

230

345

5%

34%

38%

21

7 28

0,9%

1,0%

1,9%

Source: numb. of establ. Survey of lndustrial Production 1970; Gross output sharesbasedon Table"Distr. of GO over Size Cl. in 1967".

Notes (1) We state !he sa me assumption as tor 1969, thus: 75% of non-respondenis is to be found in size class 10-19,

and the remaining 25% in size class 20-99.

(2) Number of responding establishments over size classes is nat known tor 1970. Therefore, !he shares of 1969 are used to estimate

the number of responding establishments tor size classes 10-19, and 20-99. The total tabuialed (responded) number of establishments

is 452. Hence, !he number of establishments lor size class 10-19 is estimated taking 25% of 452, which is 115.

(3) The gross output shares of size classes is based on 1967 gross output figures

(4) Only the total number of non-respondenis (28) is known. Distri bution over size classes is estimated under !he given assumptions (note 1).

(5) The weighted rate of non-resonse is calculated as is explained under note (4) of previous table.


Size Clas

10-19

20-99

TOT AL

Responding Establishm.

Numb. of Estab. 2 Gross Output 2

(Number) (Share)

113

235

348

5%

34%

38%

No! Responding Establishm.

Numb. of Estab. 3 Estimated Gross Output •

(Number) (Share)

26

8

34

1,1%

1,1%

2,2%

Source: numb. of establ. Survey of lndustrial Production 1971; Gross output sharesbasedon Table "Distr. of GO over Size Cl. in 1967". Notes (1) We state !he same assumption as lor 1969 and 1970. (2) The gross output shares of size classes is '>asec on 1967 gross outputfigures. (3) The total number of non-respondents, 34, :s distributed over size classes under given assumptions (note 1). (4) The weighted rate of non-resonse is calculated as is explained under note (5) of previous table.

Distribution of Non-Response in 1971 over Manufacturing Branches

No! Responding Establishments IC (rev.2) (number) (share)

31 10 29% 32 4 12% 33 12 35% 34 0% 35 3% 36 3 9% 37 0% 38 2 6% 39 2 6%

TOT AL 34 100%


TABLE C-13 (1 of1)

Adjustments for Undercoverage (UC) for the years 1965-1968.

Omitted Establishments 1965-1968

Govemm. er EAC 1

Establishments 2 (Number)

Omitted Value Added 3 (Mill TSh)

1965 1966·

47

40

33

31

1967

19

20

Source: Annual Survey of lndustrial Production 1965, 1966 & 1967.

1968

10

11

Net es: (1) Establishments of E.A. Community er Govemment of which no reliable data were available omitted (net estimated) in the stalistics of

the annual survey. Estimates forthese omitted establishments for 1966 are given in BoS (March, 1971), but these estimates includes adjustments

for 'professional' smal! scale (5-9) establishments, and a lso includes estimates for non-response. A total estimate of 69 million TSh is derived at.

(2) The number of establishments omitted in the surveys of industrial production. In the survey of 1969 (and later issues), nothing is said

about omitted establishments, we assume these are covered since 1969. For 1968 no survey is at our disposal. We have assumed that

downward trend of net covered establishmentscontinuesin 1968, and that only 10 establishments were omitted in 1968.

(3) Estimation of the contribut ion of omitted establishments is determined as fellows:

- the total wagebill for omitted establisments in 1966 is 20 milhen Tsh (ASIP 1966, p. 4).; compared te a total wage bill for manufacturing

(123 Million), this is 16.3%. Aassuming this number te be a goed represesentive for the percentage of value added by omitted establishments

te total value added by responded establishments, we estimate the contribution of omitted establishments in 1966: 16% x 295 = 48 million TSh.

-We need te adjust this value, because establishments from industry 384 ( repair of machinery) take a large part in the total adjustment: 25 against

44 million shilling for ether industries. Therefore, we have calculated 44/69 times 48 = 31 milhen shilling te be representive for omitted value added.

- for 1965, and 1967-1968, the contribution of omitted establishments is estimated by extrapolating 1966 omitted value added.

Extrapolation is based on the number of establishments omitted, and calculated as fellows:

Let N 1 omlled denotes the number of omitted establishments in year t, VA 1 omlled denotes the value added by omitted establishments

in year t, and r1,1966 denotes growth rate of value added in current prices from year t te beneh-mark year 1966, than

Nomitied VAomitted =VAomil.,d_t __ (l+r. )

t 1966 N;~~ted t,l966

We have assumed an annual growth rate of 10%, basedon the growth in manufacturing value added (in current prices) from 1966-1967.

Distribution of Omitted Value Added over Branches in 1966.

IS IC (rev.2) 31 32 33 34 35 36 37 38 39 3

Branch Food, Textiles Wood Paper he mica I Non- Basic Metal Other Total

Beverages & Produels Products, etroleu Metallic Metal Products, Manufac- Manufac-

& Leather umiture Printing Rubber Mineral Produels achinery turing turing

Tobacco Fixtures ublishin astic Pro Produels Equipmen Industries

(Value Added in Milhen TSh.)

Unadjusted (i) 125 72 22 12 25 9 17 10 3 295

Adjusted (i i) 133 79 25 13 26 9 30 19 5 339

Adjustment 1 (ii) = (ii)- 8 7 3 0 13 9 2 44

Share 2 (iv) 18% 15% 8% 2% 3% 0% 30% 20% 5% 100%

Source: Annual Survey of lndustrial Production 1966, and BoS (March, 1971).

Net es: (1) Adjustments te value added cernprise adjustments for omitted establishments, estimates for 'professional' smal! scale (5-9) establishments,

and also includes estimates for non-response. A total estimate of 69 million TSh is derived at. Of this 69 million shilling, 25 falls in the industry

384 (IS IC, rev.1), which is repair of machinery. This industry is leftout in value added calculations, according te SNA definitions.

(2) the share of adjustments teeach manufacturing branch in the total adjustment (44 million TSh.) in 1966.

Appendix D ASSESSMENT OF THE DIRECTORY OF INDUSTRIAL

ESTABLISHMENTS

EXISTING REGISTERS OF MANUFACTURING ESTABLISHMENTS

At the time the 1989 census was carried out, Takwimu hadjust recently set up a Central Registry of Establishments (CRE). Up till now, the directory of industrial establishments (DIE) in use for the 1989 census and the annual surveys, maintained at the industrial section has notbeen matched with the CRE. In the context of aiming at estimating the coverage of the 1989 census and improving the directory kept at the lndustrial Section of Takwimu, a matching procedure of the CRE directory and the 1989 directory has been carried out1

The directory for the 1989 census and the annual survey for 1 0+ establishments maintained at Takwimu should, ideally, be comparable with other sourees of registration, in order to co me up with possib1e industrial activity which had not yet been covered by Takwimu. Legal requirements for setting up business, stipulate on the acquisition of an industriallicense (for enterprises with investments in buildings and machinery above 10 million Tsh). Besides this re gistration at the National Provident Fund is needed for the emp1oyer' s liability for national insurance. Another souree of re gistration is the activity of levying income tax. In the past the Safety and Labour inspeetor used to send information to Takwimu on new establishments, which was cross checked in the field by the regional offices. However, a register was not maintained at the Safety and Labour department

Unfortunately, none ofthe mentioned registers is suitable fora matching procedure. Though the Minister of Trade and Industries has a (not computerised) register of licenses, no information on closures of units is available. The institution which would have the highest coverage of industrial activity is the Income Tax Department This department has just recently started to workunder the Tanzania Revenue Authority, and is not expected to have any data sufficient for the purpose of matching the manufacturing directory kept at the industrial section. A register ofthe National Provident Fund (NPF) is not available (despite the fact that this register is said to be partly based on an old version of the register of the CRE at Takwimu).

A register that is available for matching against the directory maintained at Takwimu is the VET A register, originating from the National Provident Fund. VETA (Vocational Education and Training Authority) was came into operation January 1995. Although the number of establishments registered in VETA exceeds that of Takwimu, this register has only very limited data on employment and/or size class, which makes it very difficult to perform reliable matching2

..

Summarising: besides matching procedures that can be performed between the directory of industrial establishments (DIE) andregisters of the CRE and VET A, one can say that no fully reliable souree of manufacturing activity is available. In sum there are two cross-check tasks to perform: one is to match the DIE against the CRE directory, to come up with feedback for the CRE and to improve the corporation between different department within Takwimu and the second is to perform matching against a part of VET A.

MATCHING THE DIE AND THE CRE

The results of the matching procedure of the CRE and the DIE, both maintained at Takwimu are given in Table D-1. In the presentation of the matching resu1ts, advantage is taken of the experiences with the CRE database during fieldwork carried out in the Survey of Construction, Trade & Transport (SCTT) 1994. The

1 A paper on remaining problems with the data input systemfor the industrial census puts it in this way: " ... the data should be possible to

match against the CRE, used to update the CE, and so on. We should beat least able to co-operate within Takwimu". 2

For matching, we cannot rely on camparing the narnes only. Resisters have different type convictions regarding names. Therefore, information on in dustrial activity, size class, etc. is needed to make computerised matching possible.

115

goal ofthis survt.y was not only to provide data on certain fieldsof economie activity, but it also aimed at providing feedback to the CRE, 'indicating deficiencies found within the sample taken from the CRE records3

.

TABLE D-1 Results of Matching Establishments in the CRE and the DIE

Database

CRE

Adjusted Total Establishments (i+ii-iii-iv)

(i) unadjusted tata!

(ii) wrong IS IC, should be !SIC 3

(iii) should nat be IS IC 3

(iv) duplicates

DIE:

Total Establishments (v-vi)

(v) unadjusted tata!

(vi) duplicates

Matches of records in CRE with DIE (vii+viii-ix)

(vii) computerised matching

(viii) manual matching

(ix) trash matching

Share of Matched part of CRE to tata! CRE

Share of DIE which is matched with CRE

Maximum Combined Register as percentage of DIE1

Source: own analysis.

Notes: (i) Totals as given in the original CRE database.

Size Class (numb. of pers. engaged)

10-19

371

327

76

12

20

328

331

3

159

147

25

13

43%

48%

165%

20-49 50-99

269 150

254 140

40 16

10 3

15 3

227 125

230 126

3

175 109

134 85

49 27

8 3

65% 73%

77% 87%

141% 133%

100-499

170

162

14

3

3

157

160

3

140

116

29

5

82%

89%

119%

500+

56

50

8

51

53

2

51

48

6

3

91%

100%

110%

TOT AL

1016

933

154

29

42

888

900

12

634

530

136

32

62%

71%

143%

(ii) The portion of total establishments that should be in I SIC 3 (but has wrong I SIC code), is estimated using the portion of establishments that was matched with the DIE, and had wrong I SIC code in the CRE register. (iii) Estimate of number of establishments, which had I SIC 3 code, but should have ether I SIC code is based on the result of the Survey of Construction, Transport & Trade (SCTT), see table xxx. For 50+ we used percentage of 'wrong I SIC' to the total of the larger establishments. For 10-50 establishments, we used percentage of smaller establishments. (iv) Estimate of duplicates is basedon the results ofthe SCTT (see table xxx). As under (iii) we used percentage of 'duplicates' to tata! number of establishments of the SCTT, applying the percentage of larger establishments to 50+, and percentage •f smaller establishments to 10-50 establishments. (v) based on original totals from the 198 3 census database.

(vi) duplicates found during in-depth analysis of the 1989 census

(vii) Matching results from computerised linking and queering the DIE and CRE databases.

(viii) based on a rough estimation made by 'insiders' who went through the non-matching list

(ix) trash matching, camprises matches of duplicate establishments in the CRE with establishments in DIE, and/or matches of duplicate establishments in DIE with establishments in CRE. 1 Maximum Combined Register as percentage of DIE is number of establishments of the DIE compieled with the non-matched part ofthe CRE, divided by the total number of establishments in the DIE.

For both registers, the number of establishments within each size class are presented in unadjusted, and adjusted totals. With use of the SCTT, and our own findings, the original numbers had to be adjusted. The results of matching the establishments in both registers are given at the bottorn of the table. The frrst step of

3 Strengthening of National Accounts Project, Survey ofConstruction, Trade & Transport, 1994, [nstructions for listing Business

Establishments (17 and 29 Ju1y 1996).

116

matching was performed by using computer files. The second step was performed by 'insiders' (those who are familiar with the narnes of the manufacturing establishments) at the Industrial Section, going through the nonmatching CRE listand indicating ofwhich they knew were in the list ofthe DIE. The third step consistedof data screening, i.e. identifying and removing matches with duplicated establishments and not correctly applied matches.

The final result of the matching is that in total 71% of the establishments in the 1989 DIE are matebed with establishments in the CRE directory. Out of these 71% (634 records), about 90% matching is taken by the three size classes, containing establishments with 50 or more persons engaged. These size classes account for about 90% of economie activity in manufacturing in terms ofvalue added and gross output. So, in these terms of economie activity, a 80% match has been reached. However, in terms of establishments, it is estimated that there are 382 establishments in the CRE directorynotmatching with the Census DIE, while in the DIE 254 records are left unmatched. If we add these unmatched establishments of both registers to the matebed number of establishments we get the so-called Maximum Combined Register. The number of establishments in this Maximum Combined Register is 1270. Comparing this number with the 1989 DIE, we ob serve that this (hypothetical) register covers 43% more establishments.

However, before drawing any condusion on this matching results, weneed to take a look at the results of the SCTT, presented in the following table. The economie activity of Construction, Trade and Transport has a different character than manufacturing and the establishments tend on average to be smaller and less stabie especially intrade and transport. Taking this into consideration, the results of the SCTT covering 5+ establishments are compared with the census files, covering 10+ establishments. In Table D-2 the results of overall e:x:perience of the SCTT regarding the quality of the CRE are presented. The number of establishments of which the CRE had correct data, the number that were not found, or were closed down, and the number of duplicates are presented for smaller establishments, larger establishments, and for the total number of establishments results are presented.

TABLE D-2 Some Statistics Regarding the Quality of the CRE; Results of the Sun-ey of

Construction, Trade& Transport 1994

Nat Found, Sample CRE Data Closed down,

Size Correct 1 Toa Small wrong IS IC 2 Duplicates

TOT AL 1807 1096 575 55 81

% 100% 61% 32% 3% 4%

Small Establ. 1148 755 281 43 69

% 100% 66% 24% 4% 6%

Large Establ. 659 341 294 12 12

% 100% 52% 45% 2% 2%

Source: preliminary results of the Survey of Construction, Trade & Transport, 1994.

Notes: 1 CRE Data correct, does nat imply that all parameters ofthe CRE are correct forthese establishments, but that according to field checks, establishments 'should have data' for the survey. V\nlile the sen excluded parastatals, and government owned enterprises, we have included these in the category 'CRE data Correct'. 2 wrong ISIC implies that contrary to CRE information, establishments had such ISIC codes, that they should nat have been included in the sample

A quick look tells us, that the overall quality of the CRE for Construction, Trade and Transport was poor. The CRE had a rough 60% of 'useful' establishments forthese activities, while 32% was 'Not Found, Closedor Too Small'. From the results ofmatchingthe CRE with the DIE, we can see that 62% ofthe CRE could be matebed with the DIE. However, comparing this percentage with the 60% 'useful' establishments would not be correct, since we have incorporated duplicates and wrong ISIC in the adjusted number of CRE. In our

117

results we have 58% ofthe unadjusted CRE matching with the DIE4• Ifwe assume t.he quality ofthe CRE for

Construction, Trade and Transport can be generalised to manufacturing the majority of the remaining unmatched 42% of manufacturing establishments in the CRE cannot be found, is closed down, or is too small, has wrong ISIC or is a duplicate.

It should be noted that the manufacturing directory of the CRE is ex1Jected to have a better quality5 than the directory for construction, trade and transport. However, taken alone the results of matching CRE and DIE, these are not appropriate providing any profound base for making an estimate for coverage.

MATCHING THE DIE AND VETA

Since January 1995, an independent souree of data on industrial activity is the Vocationa1 Education and Training Authority (VET A). The provisions of the Act of Parlement entitles VET A to collect a levy of the Gross monthly emoluments paid by employers, employing 4 or more persons. For this purpose VET A has appointed the NPF as its collecting agent. VET A The VETA directory allows for matching with the DIE, thus providing another framework for estimating the coverage of the DIE.

For the establishments covered in VETA (besides the name) are no additiona1 parameters available. The data have a poor quality and are outdated. The reliability for using the entire VET A directory to get insight in the coverage of the 1989 census is questionable. A matching procedure between the entire 1 0+ register, and the VET A register is not considered fruitful (and has yielded nothing). Nevertheless, the establishments which have data on employment, are verified to be establishments which are in production. Therefore these establishmentscan be used for matching with the directory of industries. Nevertheless, we have attempted to match VETA with DIE, but due to lack of parameters (such as name, size c1ass, activity, emp1oyment, etc.) it proved to be very difficult and laborious to match establishments of bath registers. The matching procedure has therefore notbeen completed.

4 Calculated as follows: 96 matched establishments were found to have a wrong !SIC code (and should have ISIC 3) in the CRE register. To make a fair comparison with the SCTT results, we should subtract these establishments from the matched total of 634, yielding 538 matched establishments. 538 establishments takes 58% in the (unadjusted) total of933 manufacturing establishments in CRE. 5 It is recognised that the manufacturing directory ofthe CRE is thought to be better than the C<.nstruction and Trade & Transport (C&TT)

register. Unless the unmatched records ofthe CRE register are foliowed up in the field, no quantitative statement can be made on the quality ofthe CRE for manufacturing in comparison with that ofthe C&TT.

118

Appendix E ESTIMATING NOMINAL MANUFACTURING VALUE ADDED

1991-1995

TABELE-l Manufacturing Price Index 1989-1995

1989 1990 1991 1992 1993 1994 1995 (1992=100)

31 Food, Beverages and Tobacco 311, 2 Food Processing 313 Beverages 314 Tabacco & Cigarettes

32 Textiles and Leather 321 Textile 322 Wearing Apparel 323 Leather and Produels 324 Footwear

33 Wood and Wood Products

46 50 50

50 50 47 47

63 69 69

68 68 64 64

82 77 77

81 81 82 82

100 114 100 114 100 117

100 104 100 116 100 116 100 116

152 204 148 167 179 206

119 196 152 192 152 192 152 192

331 Wood Produels 47 64 82 100 147 156 189

332 Fumiture and Fixtures 1 46 66 82 100 147 156 189

34 Paper and Paper Produels 341 Paper Produels

342 Printing and Publishing2


47

47

351 lndustrial Chemieals 47

352,3 Other Chem. and Petroleum Ref. 47 355 Rubber Produels 47 356 Plastic Produels 47

36 Non-Metallic Products

64

64

64 64 64 64

82

82

82

82 82 82

100 126

100 126

100 101

100 119 100 130 100 111

163 240

163 240

155 186

157 181 122 137 138 175

361, 2 Non-Metallic Produels 47 64 82 100 126 170 210

37 Basic Metallndustries 371, 2 Basic Metallndustries 47 64 82 100 119 154 190

38 Fabr. Metal Prod., Machinery and Equipm. 381 Metal Produels 47 382 Machinery (Exc. Electr) 47 383 Electrical Machinery 47 384 Transport Equipment 47

39 Other Industries

64 64 64 64

82 82 82 82

100 118 100 105 100 110 100 115

158 231 120 145 132 160 140 160

385/3 Other Industries 4 47 64 82 100 116 152 192

3 Total Manufacturlng 47 64 82 100 116 152 192

Sou ree: Data files of BoS.

1989 1990 1991 1992 1993 1994 1995 (1990=100 *)

74 100 132 72 100 111 72 100 111

73 73 74 74

100 121 100 121 100 129 100 129

160 144 144

148 148 157 157

182 243 326 164 213 240 168 258 296

154 176 290 172 225 284 182 238 301 182 238 301

74 100 129 157 231 245 296

69 100 125 152 223 237 287

74

74

100 129

100 129

157

157

74 100 129 157

74 100 129 157 74 100 129 157 74 100 129 157

198 256 376

198 256 376

158 243 292 187 246 284 204 191 215 174 216 274

74 100 129 157 198 267 329

74 100 129 157 187 241 298

74 74 74 74

100 129 100 129 100 129 100 129

157 157 157 157

185 248 362 165 188 227 172 207 251 180 220 251

74 100 129 157 182 238 301

74 100 129 157 182 238 301

Notes: for 1990-1992 the CPI is used. Food Manufacturing is backdated with the Food Index, Beverages & Tobacco with Beverages and Tobacco, Textiles and Apparel with the Clothing and Footwear Index and Fumiture with Furniture and Utensils. All others with the Total index.

(*) Series originally with 1992 as base year is switched to 1990. (1) PPI is not availale for 332 is not available. Index is moved with 331. (2) No PPI for 342 is available. 342 is moved with 341. (3) the PPI has an index for 352 and 353. We have taken the (unweighted) average of both indexes for our 352,3. (4) No PPI for other manufacturing is available. The indexfortotal manufacturing is used.

119

TABELE-2 Reweighted Index of In dustrial Production 1989-1995

31 Food, Beverages and Tobacco 311, 2 Food Processing 313 Beverages 314 Tabacco & Gigareties

32 Textiles and Leather 321 Textile 322 Wearing Apparel 323 Leather and Produels 324 Footwear

33 Wood and Wood Products 331 Wood Produels 332 Furniture and Fixtures

34 Paper and Paper Products 341 Paper Produels 342 Printing and Publishing

35 Chemicals, Petroleum and Plastic Prod. 351 lndustrial Chemieals 352,3 Other Chem. and Petroleum Ref. 355 Rubber Produels 356 Plastic Produels

36 Non-Metallic Products 361, 2, Non-Metallic Produels

37 Basic Metal Industries 371, 2 Bas ie Met al Industries

1989 1990 1991 1992 1993 1994 1995 (1989=100)

100 104 109 101 98 100 93 100 116 107 112 121 136 188 100 126 137 128 147 137 124

100 102 98 89 97 100 84 78 58 17 100 84 78 58 17

91 74 91 139 91 139

100 94 79 45 23 20 63

100 114 102 78 82 76 71 100 114 102 78 82 76 71

100 82 87 89 240 123 96 100 82 87

100 100 100 100

116 108 111 120 97 86

132 130

69 240 123 96

97 106 73

164

75 110

88 148

69 112 83

270

39 144 88

149

100 116 158 114 121 103 113

100 101 97 98 111 96 54

38 Fabr. Metal Prod., Machinery and Equipm. 381 Metal Produels 100 382 Machinery (Exc. Electr) 100 383 Electrical Machinery 100 384 Transport Equipment 100

105 129 690 578 91 117

132 98

39 Other lnd ustries 385139 Other Industries 100 112 123


Source: revised index of industrial production (Appendix H, table H-3) Notes: (*) Series with 1989 as base year is switched te 1990.

120

167 60 57 31 105 378 1601 488 129 125 100 106 39 13 25 32

10 22 24 30

1989 1990 1991 1992 1993 1994 1995 (1990=100 *)

96 100 104 97 94 96 89 86 100 92 96 104 117 162 79 100 108 102 116 109 98

98 100 96 87 119 100 92 69 119 100 92 69 106 100 84 48

95 89 72 21 108 165 21 108 165 24 21 67

88 100 69 88 72 67 62 88 100 89 88 72 67 62

122 100 105 122 100 105

86 90

103 76

100 93 100 109 100 89 100 99

84 291 150 117 84 291 150 117

83 96 75

124

84 100 91

112

59 102 85

204

33 130 91

113

86 100 136 98 104 89 97

99 100 96 97 111 96 53

96 14

110 76

100 123 100 84 100 129 100 75

89 100 110

159 15

142 30

58 55 55 232

138 110 10 19

9 20 21

30 71

117 25

27

TABELE-3 Extrapolated Nomina! Manufacturing Value Added 1990-1995

1990 1991 1992 1993 1994 1995 (Value Added in Thousands TSh.)

31 Food, Beverages and Tobacco 16.620.431 20.632.866 24.681.947 29.734.101 41.772.098 62.322.721 311,2 Food Processing 7.569.434 10.402.540 11.748.793 12.989.873 17.636.758 22.078.352 313 Saverages 3.996.722 4.071.845 5.546.977 6.845.540 9.994.852 15.586.854 314 Tabacco & Gigarelles 5.054.275 6.058.471 7.386.177 9.898.688 14.140.488 14.657.516

32 Textiles and Leather 6.668.016 6.416.197 7.000.707 7.642.798 8.742.718 12.440.171 321 Textile 5.044.570 5.830.674 6.513.628 7.335.991 7.858.067 10.511A02 322 Wearing Apparel 183.077 203.766 187.872 65.301 444.089 856.024 323 Lealher and Produels 131.033 155.736 142.589 49.561 337.051 649.697 324 Footwear 209.335 225.021 156.619 91.944 103.511 423.048

33 Wood and Wood Products 1.306.891 1.487.842 1.377.601 2.143.704 2.106.268 2.373.631 331 Wood Produels 808.684 930.343 862.384 1.342.064 1.317.995 1.486.010 332 Furn~ure and Fixlures 498.207 557.500 515.118 801.640 787.263 887.621

34 Paper and Paper Products 2.431.716 3.296.681 3.218.837 13.996.366 9.307.782 10.668.642 341 Paper Produels 1.585.571 2.149.496 2.098.804 9.126.162 6.069.028 6.956.360 342 Printing and Publishing 846.145 1.147.085 1.120.033 4.870.203 3.238.754 3.712.282

36 Chemica Is, Petroleum and Plastic Pred. 4.936.117 6.347.722 7.038.396 7.709.009 10.642.969 12.163.708 351 lnduslrial Chemieals 1.783.858 2.134.528 2.328.361 1.818.262 2.570.701 1.732.248 352,3 Other Chem. and Petroleum Ref 2.218.978 3.102.093 3.332.040 4.124.213 5.546.011 8.185.155 355 Rubber Produels 572.736 654.420 677.772 1.063.289 935.612 1.118.741 356 Plastic Produels 359.545 456.681 700.223 703.244 1.590.645 1.117.564

36 Non-Metallic Products 1.176.377 2.069.176 1.809.982 2A27.974 2.781.764 3.770.136 361,2,9 Non-Metallic Produels 1.176.377 2.059.175 1.809.982 2.427.974 2.781.754 3.770.135

37 Basic Metallndustrles 1.608.610 1.993.350 2.453.480 3.318.191 3.710.155 2.651.419 371,2 Basic Metallndustries 1.608.610 1.993.350 2.453.480 3.318.191 3.710.155 2.551.419

38 Fabr. Metal Pred., Machinery and Equipm 3.692.360 4.741.762 4.818.466 3.720.164 6.917.330 6.234.181 381 Metal Produels 785.031 1.247.694 1.960.485 835.415 1.063.714 844.820 382 Machinery (Exc. Eleclr) 487.848 525.669 116.739 439.668 2.129.217 784.013 383 Electrical Machinery 916.303 1.523.188 2.043.436 2.180.204 2.087.275 2.678.392 384 Transport Equipmenl 1.503.168 1.445.211 697.795 264.868 637.124 926.955

39 Other Industries 236.263 333.082 31.896 83.916 119.163 190.323 385/390 Other Industries 236.263 333.082 31.895 83.916 119.163 190.323

3 Total Manufacturing 37.676.771 47.207.667 62.431.202 70.676.211 86.099.226 101.704.931

Source:values added for 1990 from adjusted series, Appendix B2, Table 62-5. Other years calculaled from lable E-1 and E-2. Notes: branch value added is extrapolated muHiplying 1990 values with !he appropriale price and quanltty index (see tables E-1 and E-2).

121

Appendix F OVERALL ADJUSTMENTS TO NOMINAL MANUFACTURING

VALUEADDED 1965-1995

TABEL F-1 Unadjusted Nominal Value Added 1965-1990

I SIC 31 32 33 34 35 36 37 38 39 3 Branch Food, Textiles Wood Paper Chemicals, Non- Basic Me tal Other Total

Beverages & Products, Products, Petroleum, Metallic Metal Products, Manufac- Manufac-& Leather Furniture & Printing & Rubber& Mineral Produels Machinery & turing turing

Tobacco Fixtures Publishing Plastic Prod. Produels Equipment Industries

(Value Added in Millions TSh.) 1965 118 44 23 9 21 4 36 7 4 267 1966 125 72 22 12 25 9 17 10 3 295 1967 110 79 23 14 48 16 14 8 6 319 1968 164 73 24 15 48 17 16 17 5 378 1969 183 100 27 19 55 21 21 48 3 475 1970 219 148 38 22 59 22 24 27 3 561 1971 264 160 27 26 74 24 12 51 4 643 1972 294 199 25 28 111 37 20 80 13 806 1973 316 243 33 44 121 31 29 90 10 914 1974 369 260 32 74 228 36 33 110 16 1157 1975 1246 1976 1480 1977 2075 1978 675 867 93 186 338 94 214 326 49 2842 1979 724 878 117 176 426 90 100 395 22 2927 1980 618 979 105 183 446 96 49 396 20 2891 1981 774 912 146 194 464 130 101 361 26 3108 1982 1118 621 100 177 546 138 16 464 23 3204 1983 1103 910 84 219 487 178 111 498 28 3620 1984 1425 1007 113 340 708 73 144 586 21 4417 1985 1674 988 152 346 774 189 182 775 30 5112 1986 1603 973 275 377 1322 496 266 1069 30 6412 1987 2792 1851 349 548 2937 681 482 1389 33 11062 1988 3425 2055 359 647 2487 645 394 1312 33 11358 1989 9056 1698 907 1353 3718 1068 962 2437 274 21474 1990 12324 676 1105 1782 3822 764 1028 2231 224 23956

Source: ASIP (various issues) & Economie Su!Vey 1975-1976 for value added data for 1975-1977.

123

TABELF-2 Adjusted Nominal Value Added 1965-1995

I SIC 31 Branch Food,

Beverages &

Tobacco

1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995

127 131 114 169 188 220 266 290 311 364 348 427 628 695 801 774

1133 2021 2045 2538 3086 3010 5034 7406

12071 16620 20533 24682 29734 41772 52323

32 Textiles

& Leather

100 151 159 147 199 287 311 380 463 496 525 641 959 892 971

1228 1345 1198 1810 2019 2233 2569 5314 6842 5801 5568 6415 7001 7543 8743

12440

33 34 35 36 37 Wood Paper Chemieals Non- Basic

Products, Products, Petroleum, Metallic Metal Furniture & Printing & Rubber & Mineral Produels

Fixtures Publishing lastic Pro Produels

48 41 41 41 46 63 45 40 54 52 55 67 94 96

129 131 212 180 154 193 257 437 568 671

1123 1307 1488 1378 2144 2105 2374

(Value Added in Millions TSh.)

12 14 16 17 21 24 29 31 47 81 86

105 138 191 194 229 282 319 394 565 610 665 972

1365 1878 2432 3297 3219

13996 9308

10669

21 23 45 45 52 54 69

100 109 206 231 283 319 347 472 558 674 973 862

1144 1275 2027 4524 4449 4499 4935 6348 7038 7709

10643 12154

5 11 19 21 26 27 29 44 36 43 81 52 61 97

100 121 189 246 327 171 385 917

1237 1360 1423 1176 2059 1810 2428 2782 3770

162 97 65 63 70 78 41 64 94

106 125 152 194 220 111

61 147 34

199 280 343 476 958 958

1400 1609 1993 2453 3318 3710 2551

Sources: Tables B2-4, Table C-9, and Table E-3.

124

38 39 3 Metal Other Total

Products, Manufac- Manufac-achinery turing turing

Equipment Industries

25 29 19 30 77 43 80

122 137 168 192 229 403 336 437 496 530 844 920

1013 1378 1942 2487 2721 3340 3692 4742 4818 3720 5917 5234

12 514 10 508 13 490 10 544 6 685 5 801 9 879

26 1097 20 1271 31 1546 34 1676 41 1997 47 2842 51 2926 24 3238 24 3621 38 4550 42 5856 47 6759 34 7956 46 9613 45 12090 50 21144 56 25829

281 31815 236 37576 333 47208

32 52431 84 70676

119 85099 190 101705

Appendix G CONSTRUCTING AN INDEX OF INDUSTRIAL PRODUCTION

1965-1985

This Appendix presents the construction of an index of industrial production for 1965-1985 (liP 1965-1985). Although for national accounts pmposes an liP has been constructed for some time series within this period, the coverage of cammodities in the index has been poor, and the choice of base years need to be reassessed (see the concluding remarks of chapter 5).

INDEX WEIGHTS

In paragraph 6.2.3 it is explained why is chosen for base years 1966, 1970, 1975 and 1980. Table G-1 shows the production volumes, and Table G-2 presents the quantity relatives of the 32 cammodities covered (with 1966, 1970, 1975, and 1980 as reference years). The following step in determining the liP is to obtain value added weights for each commodity for each base year. The following weights are determined:

1) In a few cases where there are more cammodities representing an manufacturing industry, so-called intraindustry weights are needed. The problem with intra-industry weights is that (usually) no value added data at product level are available. The same problem raised in reweighting the liP 1985-1995. We have used the gross-output valnes of cammodities within an industry, to come up with estimates for the intra-industry weights (see also Appendix H). For the years 1965-1985 there are no value data availab1e at product level. For that reason we have made one estimate for intra-industry weights for the entire period, based on 1985 and 1989 data. Table G-31 provides qualitative comments and notes to clarify the choices of the weights.

2) When there are severa1 industries per branch, industry value added for each industry is needed. Value added shares for several industries are imputed when none of the covered cammodities represents these industries. Tab1e G-3 gives comments on the imputation applied. Industry indexes are weighted by their value added to get a branch index.

3) W eights for each branch are needed to weight branch index es to arrive at an index fortotal manufacturing. Forsome branches no production relative is available to represent the branch, e.g. ISIC 384, Transport Equipment in 1966, 1970, and 1975. In the example of ISIC 384, only one otherbranch is represented in ISIC 38 (Electrical Machinery) which gets the full share ofiSIC 38 in 1970 and 1975. In 1966 nobranch within ISIC 38 is represented by a commodity. In this case the weight of ISIC 37 is imputed to ISIC 38 (see table G-3).

In Table G-3 the value added weight for each commodity2 is determined by multiplying branch weight with industry weight, and, eventually, multiplying this with the intra-industry weight. Note here that weights are given in percentages

3. E.g. for Chibi.Jku (locally brewed beer) we calculate the weight in 1975 a~ follows: 5%

(branch weight) times 90% (industry weight) times 10% (intra-industry weight) yields the 0.5% weight for Chibuku in manufacturing value added.

Some difficulties arose for determining the 1975 and 1980 value added weights. For 1975 we have combined 1974 survey data (ofwhich we had value added at 3-digit level), and data available intheinput-output table (used todetermine the industry weights). For 1980 we assumed that value added weights calculated from the 1978 census could be used.

We have applied the Laspeyres formula to calculate the index (see formula 20 in chapter 4). Let q0k be

quantity output of commodity k (k=l, 2, ... , n), and let wk,B be the value added weight applied to this

1 The way we deal with, and clarify our method of constructing an liP is inspired by Harry X. Wu (1997), Reconstructing Chinese GDP

According to the National Accounts Concepts ofValue Added: The Industrial Sector 1949-1994. 2

Strictly speaking, we cannot calculate value added shares for each commodity, since some cammodities have intra-industry shares based on gross output. 3 The branch weights are given as a percentage ofMV A, while the industry weights are listed as a percentage of branch value added. Intraindustry weights are given as a percentage of industry value added.

125

commodit/, than the index of incmstrial production (liP) for year t (t = B, B+ 1, ... , B+T; where B is a base year, and T is the lengthof a subperiod) is given by

The results of the constructed index is presented for each subperiod in Table G-4 for six selected manufacturing branches. The index runs are spliced in 1965, 1970, 1975 and 1980 and switched to 1976 as reference year. The index calculated for total manufacturing is compared with the published index of rea1 value added5

•

DISCUSSION

For 1966, 1970 and 1975 we have utilised unadjusted value added weights to construct the liP. As is indicated in paragraph 6.2.2 (p.54) we are inclined to doubt the reliability of our adjustments to the nomina! value added figures at branch level, mainly caused by the phenomenon of drifting. Furthermore, the adjusted figures have only reference to the nine main ISIC branches. To construct an liP, weneed value added weights at the lowest possible level. For the 1985-1995 series we have been able to develop reliable adjustments for the 1989 census at establishment level, allowing us to reweight the liP for this period.

The structural shifts in manufacturing due to adjustments in the data are considerable for 1965-1985. Regarding 1966 as a base year, the structural shifts due to level adjustments are less pronounced for the years 1967-1970 than they are for 1966 and mainly involve branch 37, ofwhich we only have three quantity series available. In sum, we consider the basis of the adjusted nominal value added series for 1965-1985 (as presentedinAppendix C) not reliable enough to incorporate them as base year weights for our liP.

Based on these arguments, we have not applied adjusted value added shares for the construction of the liP 1965-1985, but used the published ASIP value added shares. We strongly recommend further research on this matter and, specifically, on constructing price index numbers in order to be able to construct a series from the indirect approach. On the one hand such a series co mes closer to the concept of an liP. On the other hand, a second series makes it possible to assess the quality of liP arrived at following the direct approach.

4 Note that not the value added weight ofthe commodity is taken, but the value added ofthe industry orbranch it represents. s Actually, the publisbed series are presented in 1976 prices (BoS March 1995), but we have replaced this series with a real value added index (with 1976 value added fixed lOO).

126

TABLE G-1 (1 of2)

Production Volumes for the liP 1965-1985

Production Volumes of 34 commodities for the period 1961-1985

Canned Wheat

Meat Fleur

(Tonn.) (Tonn.)

1961

1962

1963

1964

1965 11287

1966 9310

1967 9673

1968 6824

1969 6988

1970 7980

1971 8362

1972 4878

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1401

4740

2193

1650

580

764

1004

1078

736

333

176

169

129

28378

30297

39050

40351

41820

42916

42075

43119

50002

47459

50802

34194

35185

72690

80975

87940

54280

31588

26099

27282

48346

35363

38909

Biscuits Beer Chibuku Konya9i

(Tonn.) (Th. Liters) (Th. Liters) (Cases)

795

438

839

561

757

946

678

425

278

228

194

142

4341

5009

7849

10244

12151

18816

23193

24612

25689

31200

46621

56129

62234

63658

64264

66409

74108

81458

75155

63828

64252

64189

65592

68750

75750

82

6573

7451

7401

7295

8694

7087

6203

4361

11421

13178

15226

15224

18636

15596

15644

18453

15292

11376

56127

42500

52200

52966

60333

50233

47907

51752

Cigarr. Textiles Blankets Carpets

Sisal

Ropes

(Th. Liters) (Mill. Piec.) (Th. sq.m.) (Th. sq.m.) (Th. Piec.) (Th. sq.m.) (Tonn.)

1343

383

470

477

543

452

431

485

510

766

837

872

682

1377

1144

1535

1869

2049

2044

2137

2336

2599

2923

3285

3451

3652

3511

3678

4013

4292

4153

4735

3865

4693

3841

3600

2666

5360

7467

10139

14315

14497

28871

46260

58412

67010

74136

80764

86399

87435

82716

78869

72932

85070

93123

96133

86275

59656

57300

56200

3444

3584

3577

3644

4154

4077

4533

5476

2686

4309

3674

3514

2706

2549

1827 791

751

713

612

514

667

112

56

65

109

108

658

5865

10332

15126

16718

18724

20404

23135

22575

25354

29496

25492

42377

36535

31423

37911

31616

13246

20595

17320

22367

14486

Packaging

Fishnets Leather Shoes Plywood Containers Material

(Metr.T.) (Mil!. Sq.Ft.) (Th. Pairs) (Th. sq.m.) (Mill. Piec.) (Tonn.)

109

108

127

148

303

286

229

524

463

210

248

528

470

532

211

177

113

121

67

96

13,0

10,9

10,4

10,2

9,5

5,2

1490

2200

2100

1600

2457

2320

2800

2700

3689

6331

6363

4310

4178

2523

2201

1948

1979

1955

186

611

724

952

818

990

975

1122

1119

1139

1285

1147

988

806

1217

1003

804

874

712

770

432

473

550

75,4

82,5

69,9

53,8

39,9

41,8

16674

12828

11606

11178

8892

8922

TABLE G-1 (2 of2)

Production Volumes for the liP 1965-1985

Tyres & Bye Tyres Enamel-

Fertilizer Paints Soap Pyrethrum Petroleum Tubes & Tubes ware Cement

(Tonn.) (Th. Liters) (Tonn.) (Metr.T.) (Th. Tonn.) (Metr.T.) (Th. Piec.) (Th. Piec.) (Th. Piec.) (Th. Tonn.)

1961

1962

1963

1964

1965

1966

1967

1968

1969

1970

1971

1972

1973 32594

1974 58778

1975 59327

1976 42146

1977 36886

1978

1979

1980

1981

1982

1983

1984

1985

44443

45384

50852

69029

13662

31211

51565

41403

443

440

739

1005

1248

1445

1605

1648

1754

2208

2844

3014

2872

2983

3047

4682

2512

1364

1474

1139

713

624

600

6662

6863

10408

10213

23572

13004

99

107

177

203

291

190

177

110

177

204

155

148

189

138

99

72

52

47

39

36

45

32

39

463

642

637

626

684

717

763

731

772

719

746

621

535

561

588

481

505

455

604

395

4788

4212

5514

5967

5783

4624

4902 372

313

231

186

199

173

779

363

672

676

415

331

5306

3841

4881

5608

5436

5541

4267

4150

1378

2657

2183

2838

2331

2153

875

362

50

147

156

168

177

179

237

314

296

266

244

247

250

299

306

390

334

247

369

376

Sourees lor Canned Meat, Wheat Fleur, Biscuits, Beer, Chibuku, Konyagi, Cigarettes, Textiles, Carpets, Sisal Ropes, Plywood, Fertilizer, Paints, Pyrethrum, Petroleum, Cement, Rolled Steel, Iron Sheets, Aluminium, Radios, and Dry Cells:

'65-'70: Economie Survey 1971-72

'71-'74: Economie Survey 1975-76

'75-'79: Economie Survey 1981

'80-'81: Economie Survey 1984

'82-'85: Economie Survey 1985 Sourees lor Blankets, Shoes, Leather, Enamelware, Batteries, Battenes f. M.Vehicles, Soap, Tyres and Tubes, Radiators, Packaging Material, Containers, Bicycle Tyres and Tubes

'65-'72: Bank of Tanzania, Tanzania:Twenty Years of lndependence.

'73-'79: Bank of Tanzania, Economie and Operatiens Report, June 1982.

'80-'85: Bank of Tanzania, Economie and Operatien Report lor the year ended 30th June 1989.

Rolled

Steel

(Metr.T.)

4776

8591

9298

10500

11912

16423

17950

18414

16473

12104

12104

9445

11297

Iron

Sheets Aluminium Radios

(Metr.T.) (Metr.T.) (Th. Piec.)

11987

13265

13261

13516

17484

21869

20800

20800

26000

25617

25943

27506

30183

29985

17322

10105

16044

16044

22661

21672

2666

1524

2073

2323

2701

3427

3602

3332

3660

3247

3446

4005

4048

4030

4010

4460

3132

3031

1790

2407

200

240

257

235

247

223

155

110

48

40

69

Dry

Batteries Cells

Auto

Batteries

(Th. Piec.) (Th. Piec.) (Pieces)

5543

11278

15026

24012

45049

48001

50301

57870

64664

70914

70436

79248

77350

59464

47400

41390

44077

48100

50301

57870

64664

70914

71436

79248

78006

73227

47385

37438

44110

29976

21082

29599

14192

13963

Radiators

(Pieces)

5394

4029

6109

3913

5810

8215

lndexes of 31 cammodities for the perlod 1966-1986

1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

1965

121 100 104 73 75 86

1966 100 1967 99 1968 117 1969 136

Canned

Mea1

100 105 61 18 59 27 100

75 26 35 46 49

Fishnets

1970 278 100 1971 94

76 173 153 69 100

118 251 224 253

100 68 31 16 16 12

1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

100 100 84 54 57 32 45

97 100 104 106 104 107

VIlheat Flour

100 116 110 118 79 82 100

207 230 250 154 90

Leather

100 84 80 78 73 40

100 83 86

153 112 123

100 76

117 110 133

Shoes

129 100 137 234 236 160

Biscuits

100 192 128 173 216 155

155 100 60 53 47 47 47

100 63 41 34 29 21

65 100 123 131 137 166

100 86

104 102

Beer

100 149 180 199 204 206 100

103 115 127 117 99

Plywood

118 100 100 102 115 102 88 100

82 123 102 81

100 101 101 103 108 119

88 100 81 88 49 54 63

TABLE G-2 (1 of 2)

Production lndexes for the liP 1965-1985

Chibuku Konyagi 1 Cigarr. Textiles 2 Blankets

Sisal

Rop es

100 99

117 96 84 59 100

262 302 349 349 427 100

84 84 99 82 61

100 101 116 96 92

103

Packaging Containers Material

100 109 93 71 53 55

100 77 70 67 53 54

100 105 158 173 180 141

39 100 100 104 114 127 100

112 126 133 141 135

Fertillizer

100 71 62 75 76 86 100

136 27 61

101 81

100 105 114 122 118 135

100 124 144 160

100 82 99 81 76 56

Paints

164 100 106 134 173 183 174 100

104 106 163 87

100 101 202 323 408 100

115 127 138 148 150

47 100 108 84 52 46 44

100 104 104 106 121 100

98 109 132 65

100 104 100 95 85 90 82 83 63 97 59

107 100

Soap

103 93 64 62 60

100 143 94 87

Pyrethrum

54 100 161 185 141 135 172 100

73 52 38 28

100 146 162 181 197 100

113 111 124 145 125 100

166 143 123 149

100 124 100 95 42 90 65 77 55 65 84

100 139 138 135

Petroleum

148 100 105 112 107 113 105 100

104 86 74 78

71 46

100 103 156 153 354 195

25 100 82 100 83 77 96 68 83

82 86 77

103 67

TABLE G-2 (2 of 2)

Production lndexes for the liP 1965-1985

Tyres& BycTyres Enamel- Rolled

TuhAs & Tubes ware Cement steel

1965 1966 100 100 1967 72 294 1968 92 312 1969 106 336 1970 102 100 354 100

1971 102 101 1972 78 134

1973 76 177 1974 25 167 1975 100 49 100 150 100 100 1976 131 82 92 113 1977 142 107 93 128 1978 137 88 94 177 1979 110 81 112 193

1980 116 100 100 33 115 100 198 100 1981 84 47 127 89 1982 62 86 109 66 1983 50 87 81 66 1984 53 53 121 51 1985 47 42 123 61

Source: Production Volumes, Table G-1. Notes: (1) For konyagiiHers and cases quantities were available. LHers Is taken, were avallab Ie. I! not, the quantHy cases was taken.

(2) Textiles are assumed to be woven tabrics. (3) Data tor Carpets Is omltted, because series staris aner 1980

Iron

Sheets

100 111

111 113 146 100

125 119 119

149 147 100

101 107

118 117 68 100

58

93 93

131 125

(4) Dry cells correspond wHh batterles (aHhough trom different sources). Batterles (BoT) Is choosen, because H provides data tor a larger time span.

(5) Data tor Motor vehlcle batterles is omltted, because of Incomplete series.

Aluminium

100 57

78 87

101 100

127 133 123

136 120 100

106 123 125 124

123 100 111 78 76 45 60

Radios Batteries 4 , Radiators

100 133

213 399 426

100 446 100 120 115 129 129 118 141 124 140 112 100 158 100 100

70 98 75 49 75 113 22 60 73 18 52 108 31 56 152

TABLE G-3 (1 of2)

Constructing an Index of lndustrial Production for 1965-1985

Branch weights, (lntra-)industry-weights, and qualitative comments

I SIC & Branch/ Com modlty Descrlptlon Branch Welghts

(Share in MVA)

1966 1970 1975 1 1980 2

~t~Htl!liifiilM~liW~Milfltlifllfffffllfflj~MlmtmmD~ffli!~WftfD.% 311 Food Processing 29% 21% 20% 14%

3111 Canned meat 3116 Wheatflour 3117 Biscuits

313 Beverages 6% 9% 5-J. 6%

3131 4 Spirits

3133 4 Boltled beer Chibuku

314 Tabacco & Clgarettes I 7% 9% 7% 4% 3140 Cigarettes

*l!~Wt~,~~Bitifiiitli!tl!lititjMm!,~Htil~~~l~H~t~~tMI~!' 3211 Woven Fabrics (textiles) 3212 Blankets 3215 Sisal ropesltwines

Fishnet&prod.

323 Leather and Produels I 1-Jo

3233 Leather

324 Footwear I 2% 2o/o 3% 3240 Shoes

mmii,tlJ!!!~!f!iiiiiEilitl!lllliifiim;.~mt~lll~t~lllllm~:mmllif~ 3311 Plywood

UMI5!if:J!il5!il:~~l1111Ifl11ll1~l~fl!!!Il111!M111fl!!!!!!!!fl1'fl!Glflml 341 Paper Produels f 7%

3412 Containers Packing Material

HM:!:l@ii!Mi!il1M1f~J.~WJ.@!î!g~lf:f®.Mll~I~HM:f4lM~tlM~!!illt®.tlMil~l 351 lndustrlal Chemieals 5% 7% 7% 3%

3511 Phyrethrum extract 3512 Fertilizers

352 Other Chemieals

3521 Paints 3523 Soap

353 • Petroleum Reflnerles••• 3530 Petroleum fuels

355 • Rubber Produels•••

3%

1%

I

2% 4% 3%1

2% 4-t. 3%

6% 3o/o

I

lndustry Welghts

(Share in Branch)

1966 1970 1975 1980 2

46% 23% 49% 35% 54% 77% 38% 24%

13% 41%

10% 20%

100% 100% 90% 80%

100% 100% 100% 100%

43% 67% 65% 50% 32% 13% 15% 32% 26% 20% 20% 18%

100%

100% 100% 100%

100% 100% 100% 100%

10ci%

100% 100% 30% 30% 70% 70%

100% 100% 100% 24% 76%

100% 100% 100% 100%

lntra-lnd.

welghts

(Share in lnd.)

Estlmated 3

90% 10%

80% 20%

5o% 50%

Welght per Product

(Share in MVA)

1966 1970 1975 1980

13% 5% 10% 5% 16% 16% 7% 3%

3% 6%

1% 1%

6% 8% 4% 5% 1% 0% 1%

7% 9% 7% 4%

10% 17% 13% 13% 8% 3% 3% 8% 5% 4% 3% 4% 1% 1% 1% 1%

1%

2% 2% 3%

12% 11% 9% 3%

3% 3%

5% 7% 2% 1% 5% 2%

3% 2% 4% 1% 2%

1% 2% 4% 3%

Qualltatlve Comments

I SIC 312 is weighted togetherwith 311 For '80: Weights of 3111-3115 applied to Canned Meat &

weights of 3117-3119 applied to Biscuits For '66 & '70: mutual weights of grain mill products, and canned

meat/fruit applied to canned meat and wheat fleur. For '75: industrial shares taken trom '76 input-output table

321 gets weight of 322 and 323 (lor 1966, 1970 and 1975)

and 324 in 1966. For '66&'70: Weights of 2312 (I SIC rev.1) applied to textiles,

243/44 (I SIC, rev.1) applied to blankets, and weight of 233 (I SIC, rev.1) applied to 3215 (I SIC, rev.1)

For '75: in dustry shares estimated, based on 1976 110 table For '80: 3212 takes weight of 3213/4, and 3215 that of 3219

For '66, '70 and '75 ISIC 33 gets weight of 34, because no quantity data lor 34 is available lor these years

For '75: data lor '80 used, since no 4-digit data lor 3511/3512 is lacking lor the years 197 4, 1975 or 1976.

3522 is weighted logether with 3523 in the 79 survey

For '66&'70: 353 gets weight of 355

For '75&'80: 355 gets weight of 356

TABLE G-3 (2 of2)

Constructing an Index of lndustrial Production for 1965-1985

3551 Auto tyres& Tubes Bicycle Tyres&Tubes

100%

~r:~g:n;:;~~~~~~~~~~~!~:~:~:~:~:~:~:~r:~m:~r:~:~:lmm:~:~m:~:~:~m;~mr:~;:~~:~rr:~:~:~:~r~~ 1

OO%

1

OO%

1

OO%

369' Clay & Ceramlc Products

3692 Cement 1-J. 2% 2% a•;.

1UtH!I.~i!lä!~mB~MH:~:~i:I~!lfiilHfiiiHM:l~!f.4fii=:~:aJH:it:iHtM:iH:ti!tJ.: 371 7 Iron & Steel Basic Industries 8% 3% 5% 10%

100% 100% 100%

3710 Rolled steel I I 100% Iron Sheets 100% 100%

I 372 7 Non-terrous Metal Basic lnd. 3% 1% 2% 3%

:~Mltf~H~~ww~MMJ!ii!i~rt!®.~:~ww.w:rn~m:n:~:~:~:n:=r:~mnn~:mMt~tM*

3720 Alumrnoum I 383 Electrlcal Machlnery 5% 7% 3%

3832 8 Radios

3839 8 Dry cells

384 Transport Equlpment 3843 Radiators

4•t.

100% 100% 100%

100%

20%

80%

100%

100%

100%

100%

100%

20%

80%

100%

90%

10%

60% 40%

2%

1%

8%

3%

2%

2%

3%

1%

5%

6%

2%

2%

3%

2%

2%

1%

6%

H!!itm!!!ll#.#.W!Mf!tHMtHliilliUII!:UH~w!ffN!!!M1Hl!!®i.1ttM!N! 100% 100% 100%

Sources: ASIP 1966, 1970, 1972, 1979, 19761npuVOutput table and the 1978 Census.

Notes: (1) The industry-weights lor 1975 are estimated using data trom the 1974 ASIP. and trom the 19761npuVOutput table. This because no lndustrial Survey was launched lor 1975.

(2) The branch weights lor 1980, are based on the 1978 census, since 1980 A SIP is considered not reliable enough due to several adjustments. The industryweights lor 1980 are currentiy basedon the 1979 Survey, since VA at 4-digit lor 1978 is notatour disposal. As soon as 1978 on 4-digit comes available, these should be used.

(3) The estimates lor intra-industry shares are based on the 1989 census of industrial production. Only 1 estimate is made lor the entire period (1965-1985). Some adjustrnents were madebasedon common sense.E.g.lor Chibuku. the contribution within 3131 was assumed to be lower than lor 1989, since production started later, and shows larger growth On terms of quantity) than beer. Th is assumption is somehow underlined by 1985 data, where Chibuku had a lower share within 3131, than it had in 1989.

(4) No data on shares lor 3131 and 3133 are available lor 1975 and 1980. Estimate forthese industries is basedon 1989 census data. The assumption is made that growth (in terrns of VA) lor industry 3131 was slightiy higher than 3133 between 1975 and 1980. Therefore we estimate the share lor 3131 within 313 in 1975 to be 10%, and in 1980 20%.

(5) 353 is tabuialed logether with 352 in both 1975 and 1980. We have estimated (based on 1979, 1985 and 1989 data) that both have a 50% share in the value added tabulated under 3521353.

(6) Since 362 is tabuialed with 369 for1966, 1970 and 1975, we estimated shares as fellows:

For 1975, we used 1976 1/0 table data, revealing 50% share tor 362 and 369 within 36, non-metallic products. The largest contributor lor 369 is cement, and given the volume increase between 1966 and 1975, we assumed share to increase trom 0.2 in 1966 to 0.4 in 1970. Production of Enamelware drastically decreases trom 1966 onwards. Contri bution of 362 therefore is considered to take a 0.8 share in 1966, dropping to 0.6 in 1970, and 0.5 in 1975. In 1978, census data reveals that the share of 362 dropped down to 0.15.

(7) 371 and 372 are tabuialed tagether in '66, '70, '75 and '80. The estimate lor both contributions to MVA, arebasedon 1989 census data. The assumption rs made that mutual shares didn't change considerable. We have fixed the shares on 0.25 lor 371 and 0. 75 lor 372

(8) No data on shares of 3832 and 3839 is available. Basedon 1989 census data. 3832 is assumed to take 0.2 share, and 3839 a share of 0.8. We have assumed that no change in mutual contribution to VA within 383 occurred between 1975 and 1980.

3% 0%

Weights of 3610 and 3620 applied to 3620( Enamelware) 0% For '80: Shares of 362 and 369 based on '79 survey

3%

Because within branch 38, only quantity data lor 383 and 384

are available, 37 gels weight of branches 381 and 382.

6% (weight of 381/382 equally applied to 371 and 372) 4%

3%

1%

2%

4%

100%

38 gets weight of 383+384 and 39

Weight of 39 equally applied to 383 and 384

TABLE G-4 Index of lndustrial Production 1965-1985

lndexes for 6 branches and total manufacturing based on 31 cammodities

1965 1966 1967

1968 1969 1970

1971 1972 1973 1974

1975 1976 1977 1978 1979 1980 1981 1982

1983 1984 1985

1965

1966

1967

1968

1969

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

Coverage (Numbof

Products)

4

5

7

31 Food and Boverages 1966 1970 1975 1980 TOTAL

=100 =100 =100 =100 1976=100

90 55 100

100 w 1~

112 100

121 1~

1~

118 113 100

1~

119 1~

115 w 100

80 73 76 69 65

~

~

61

~

~

~

M ~

~

n 100 ~

1~

00 77 ~

~

~

~

w

35 Chemlcals, Petroleum & Plastic

Coverage (Numbof Products)

3

3

5

7

1966 1970 1975 1980 TOTAL =100 =100 =100 =100 1976=100

100

137

114

115

98 100

140

162

140

139

159 100

101

97

106

84

81 100

95

85

84

135

89

~

~

n 73

~

~

1~

~

~

w 100

~

100

~

00

n ~

~

100

n



32 Textlles & Leather

1966 1970 1975 1980 TOTAL =100 =100 =100 =100 1976=100

100

111 158

215 5 267 100

6

109 120 135 136 136 100

108

114 104 110 98 100

87 82 65 62 62


1966 1970 1975 1980 TOTAL

~

~

~

~

~

~

~

~

ro ro

100

100 ~

W2 91 N ~

~

~

~

=100 =100 =100 =100 1976=100

2 100

116,7

136

151,8

152,8 100

101,6

100.7

116,8

82

2 89 100

87

100

91

97

74 100

127

109

81

121

123

M 00

114

1~

1~

1~

1~

1W

100

115

100

115

100

111

M 100

ro ~

1ro W5

33/34 Wood, Paper and Produels Coverage 1966 1970 1975 1980 TOTAL

(Numb of =100 =100 =100 =100 1976=100

Products)

3

76 90

100

86 104 102

118 100

100 1~

115 1~

~ 100

~

1~

1~

81 ~ 100

89 83 63 53 57

118

101 1~

121 1~

1~

141 1~

1G

1~

100 1~

1~

100 100 ~

~

~

~

~

37/38/39 (basic) Metal, mach.& equlpm. & other

Coverage 1966 1970 1975 1980 TOTAL (Numb of =100 =100 =100 =100 1976=100

Products)

2

3

6

100

97

102

106

135 100

130

173

276

302

311 100

112

125

141

145

144

21

21

22

23

29

37

50

79

87

89

100

111

126

129

100 129

84 109

83 128

n 111

77

91

118

139

Source: Table G-2 lor quantity lndexes, Table G-3 lor welghts, and BoS (March '95) "Selected Statistica I Serles 1951-1992" lor published index.

Coverage (Numb of Products)

3 Total Manufacturlng

1966 1970 1975 =100 =100 =100

16

19

25

30

100

107

117

132

153 100

118

128

143

139

139 100

111

114

118

108

100

1980 TOTAL Published =100 1976=100 (Calculated from

MVA In '76 prices)

100

87

82

n 76

74

G e G ~

~

n ~

~

00

00

100

W3

1~

00

00

N ~

~

~

~

G

~

~

~

~

~

00

~

M M

100

~

~

100

~

M ~

~

77

~

Appendix H REBASING THE INDEX OF lNDUSTRIAL PRODUCTION

1985-1995

In chapter 5 it was concluded that rebasing the index of industrial production (liP) available in the quarterly survey of industrial production (QSIP) would be a substantial improvement. In this appendix it is described how this rebasing has been carried out.

1989 V ALUE ADDED WEIGHTS

The most reliable value added figures in the period 1985-1995 are the adjusted 1989 census figures. For this reason we will reweight the index of industrial production with 1989 as the new base year. To calculate the index from 1985-1995, the following Laspeyres formula (see equation 20, chapter 4) is applied:

(1) n qo

IIP, = L: w k,l989 --c!- , k=l qk,l989

with k (k=1, 2, ... , n), and wk. 1989 the value added weight applied to commodity k, and the last term representing the ratio of quantity outputs of commodity kin the current and base year. The QSIP reports present quantity relatives of 92 cammodities covering almost the entire manufacturing sector. For 6 more cammodities we have estimated the quantity relatives as will be dealt with in the next paragraph. The 98 cammodities covered in our rebased index are presented in table H -1. The reference year has been switched from 1985 to 1989.

The crux of rebasing the liP is determining the 1989 value added weights applied in formula (1). The following steps have been foliowed to weight the index:

1) Ifthere are more cammodities per industry, then the quantity relatives are weighted with their gross output values in the base year to get an industry index.

2) When there are several industries per branch industry indexes are weighted by their value added to get a branch index.

3) Branch indexes are weighted with their value added to derive an index fortotal manufacturing.

To deterrnine the gross output values for cammodities in step 1, we have used data files ofthe 1989 census1

and determined the relative gross output weights of all cammodities in one partienlar industry. The weights arrived at are called intra-industry weights. The weights usecl for step 2 and 3 are derived from adjusted census value added figures (see Table A-5). Table H-2 extensively t;Ummarises the intra-industry weights, and the industry and branch value added weights used in the former 1985 weighted index, and in our 1989 reweighted index. Besides this, the narnes ofthe products included todetermine intra-industry weights, and qualitative comments on cammodities used are presentedinseparate columns. For several industries where no quantity data are avai1ab1e, we have imputed weights toother industries (e.g. furniture making is imputed to wood products). Forsome intra-industry weights we have carried out some data screening. Information on imputation ofweights and data-screening is given in Tab1e H-2 as well.

1 We have used 1989 census data files (Eindhoven version). No such value-quantity data was available at Takwimu. All data containing value information (even in case no quantity information was available) for cammodities were used. Nevertheless, some data screening was necessary. Double counted products were dropped, and in a few cases data were adjusted when it was clearthat a typing error was made. In the few cases typing errors we re assumed, this is mentioned in the 'qualitative comments' column of annex table H-2.

135

Es TIMATION QUANTITY RELATIVES FOR 6 COMMODITIES

In total 92 commodities are listed in the publications of the quarterly survey of industrial production (the so called commodity reports). However, from the data files one can see that the actual coverage lies around 120 commodities. For 22 commodities no quantity data are available, because weights forthese commodities were null (or approximately null) in 1985. Due to insufficient data sourees we havenotbeen able to cover these commodities. For six commodities we have derived quantity relatives from the branch index (where the commodity is grouped in) and the other commodities inthebranch (j). The quantity index for the unknown commodity x in year t is given by:

(2) Q . = ..!!.!!.!_ Xj,l

q -9',1985

Assuming we know all other quantity iudexes of commodities k (k=l, 2, ... , n-1) ofthe n commodities in branchj, then the unknown quantity relative Qxj, can be determined for each year (t),

n-1

L W.yQ.y,t

(3) Q l p k=1 xj,t = I j,l --"---'----

Wxj

We have calculated quantity relatives with (4) for the commodities paper products, catton yarn, leather goods, beach sandals, ceramics, and made an estimate for ISIC 3813 covering the two commodities Steel structures and Louvres. The quantity relatives calculated in this way are presented along with the quantity relatives derived from the QSIP reports in Table H-1.

Table H-3 presents the reweighted liP 1985-1995. In Table H-4 the reweighted index is compared with the pubished and recalculated index.

136

31 Food and Beverages

1985 1986

1987 1988 1989

1990 1991

1992 1993 1994 1995

Canned Me at

370 315 378

119 100

74

37

Standard. Butter Milk

132 303 129 104

99

100 78 51 46

54 28

17

227

185

142 100 148 152

33 27 12 6

32 Textlles and Leather

Woven

Fabrlcs 1985 117 1986 103

1987 128 1988 95 1989 100 1990 98 1991 83

1992 1993 1994 1995

106 88 52 20

Catton Catton Unt Yam•

43 30 66 29 79 89

109 111 100 100 100 109 88 107 83 93 85

110

76 101 109 68

34 Paperand Paper Produels

1985 1988 1987

1988 1989

1990 1991 1992 1993 1994 1995

Hard- Chip-Board Board Paper

70 98 24 106 100

96 100 114 110 98 84 63 80

109 94

180

100 90

124 80

161 66 29

53 95 87

100

75 54 42 25 33

34

Ghee

1000 950

690 230

100 260 530 130 190 280 105

Canvas

53 71 76

113 100 109 95

102 66 55 28

Paper Prod. •

98 100 138 108 100

79 101

81 440 208 151

TABLE H-1 (1 of2) Quantity lndexes of Cammodities Covered in the liP 1965-1995

Baby Cann. Fr. Food & Veget. -

Text Bags

67 133

84 136

144 100

43

70 85

111 108 100

75 75

106 75 42

60

155

179 108 100

71

77 116 111

93 167

Blankets 137 115 111 115 100 108 119 85 90 85 92

Vegetabl.

Oild-Fats•• 83

64 83

127

100 166 244 188

145 82 41

Maize

Flour

217 113

124 104 100

72 67 83

65 122

45

Knitted Soeks &

Fabrics Stocking 55 199 67 187 73 118 97 135

100 100 88 98 78 86

115 97

131 99

98 110

72 23

Wheat

Flour 185 189 106 144

100 57 15

81 35 67 67

Polished

Rice

23 17 42

99 100 56

62 43

Biscuits

& Pasta 75

156

108 150

100 92 56 38

29 18 14

Refined

Sugar

122 109

23

107 100 117

133 128 116 122 110

Sisal S. Ropes Fishnets Tanned

Carpet & Twines & prod. Leather 357 78 58 213 204 103 75 149

68 88 97 151 150 100 141 100 100 100 100 100 121 113 92 100

93 114 91 57

71 11

54 16

117 135 115

99

58 56 74 62

47 13

35 Chemicals, Petroleum & Plastic Produels

Wattte Ex1r.

Pyrelhr. lnd. & Med. Fertilizer Powd.lns. Extr. gasses & Pest. ••

116 97 76 152 139

67 81 78

100 128 120

94

80 102

30

101 102 110 100 125 112 121

88 61

52

~

99 1ro 100 1~

1W

94 1~

rn 142

173 71

21 100 64 76

158 165

76 100 106 78 51 47 16 7

Uquid Ins.

&Pest. ••

348 108 185

178 100 217 128 110

39 23 6

Paints 64 77

111 97

100 102 118

110 99 94

166

Btack

Tea 93 79 84

86 100 108 115

110 123 136 136

pr. Hides

& Skins 141 111

96 110

100 66 59 52 21

Soap & Deterg.

ro n 94 1~

100 117 1~

W3

1U 102 1~

Blended

Tea

145 120

125 168 100

111 78 71 80 88 74

Leath.

Goods •

266 290 273 152 100 157 171 93

14 21

Safety

Match.

115 120

90 51

100 172 209 128 171 185 234

Coffee

Beans

99 102 94

109 100 64 54

60 98 69 71

Shoes 294 279 135 126 100 103

74 38 12 21 78

Mosq.

Coils

353 101 188 213 100 132

88 68 63 52 25

Instant Animal

Coffee Feeds

92 238 ffl3 113

1~

100 ~

1D 1V 1U ~

U3

Be ach

Sand. •

244 95 61

105 100

68 95 66

57 17 17

Adh.&lnd.

deterg. ••

133

77 111 87

100 85 80

159 103 176 167

195 169 117

100 79 58 36 12 11

Petrol. Fuels

96 90 90

107 100 82 82 87 85 83 92

Distilled

Spirits

73 72 72 89

100 91

114

142 143 149 185

Vlnne 213 262

226 43

100 343 183 204

72 91 20

Beer

141 121 110

99 100

83 93 92

106 106 217

33 Wood and Wood Product

Sawn nmber

50 66 91 96

100 113 85 71 54 42 40

Petrol. Resid.

96 81 67 87

100 62

131

105 86 90

126

Plywood

94 115 125 134 100

91 97 69 40 28 17

Au1o

Tyres

53 65 93 88

100 98 87 74

89 83 89

Wooden

Crates

115 95

141 122 100 120 121 87

121 122

Bicyc.

Tyres

793 233 321 289 100 252

79 90

Chibuku

67 80

76 94

100 85

95 81 91 66 70

Au1o

Tubes

45 83 80 84

100 84

75 59 83 86 84

Soft

Orinks

Bicyc.

Tubes

~

~

94 119 100

1~

1~

1~

1~

~0

1~

192 100 106 233 100

39

CigareH.

93 96 93

97 100 131

136 133 136 119 130

Rubber

Artides

39 45 50

118 100 123

71 53 13 15 16

Cured

Tobacc.

110 109

125 110 100

114 140 116 176 188 106

Plastic

Artides

68 73 73 63

100 132 130 164 148 270 149

36 Non-metallic Produels

Glass-Ceram.• wares Cement

1985 260 87 63 1986 259 122 73 1987 230 103 84 1988 92 96 99 1989 100 100 100 1990 220 112 112 1991 127 118 172 1992 91 122 114 1993 116 110 126 1994 105 64 115 1995 83 88 123

Ume

79 109 116 107 100 107 95 32 27

45

Asb. Sheets

109 37 79 72

100 116 96

51 57 32 42

38 Fabricated Metal Products, Machlnery and Elecrical

Razor St. Struc. Hoes P1oughs Blades & Louvr. •

1985 91 4 177 140 1986 108 3 107 148 1987 125 142 174 113 1988 129 0 128 208 1989 100 100 100 100 1990 94 141 25 201 1991 68 69 7 454 1992 1993 1994 1995

24 22 14

B6 21 35

6B1 54 19 1B

lource: Quarterty Survey of lndustrial Production 1995:3.

Metal Alum. Cont. Wares

154 172 231 1B3 80 134 85 BB

100 100 95 54 95 52

142 B2

91 117 157 84

Rolled Steel

TABLE H-1 (2 of 2)

Quantity lndexes of Cammodities Covered in the liP 1965-1995

37 Basic Metallndustries

76 76 64 67

100 60 51 41 4B 47 17

Corrug. Iron Sh.

107 42 82 73

100 107 116 119 128 113 100

Steel Sh.&Bill.

96

54 78 67

100 112 95 96

122 95 69

Gatvan.

Pipes 114 55

124 65

100 106 73

128 10B 61 26

Alum.

Sh.&Circ. 162 100 179 128 100 170 172 191 21B 179

75

Wire lnd. Mach.Tis Electr. Trans- Electr. Switch Dry Prod. Mach. &Equipm • Motors formers Cook. Gears • Radios •• Cells

16 1B7 663 173 63 2B5 22 123 124 13 23B 1B57 143 71 379 5 85 76 33 267 456 119 76 378 47 128 74

11B 16B 226 121 131 136 27 183 68 100 100 100 100 100 100 100 100 100 103 70 804 125 114 80 51 128 60 106 84 66B 11B 93 152 13 182 124 55 37 32 11

73 90

107 81

111 431

1B75 563

226 494

2B 42

108 48 53 80

122 161 138 93

70 115

193 170 96

117

131 148 162 164

~otes: Last quarterfor 1995 is estimated following last quarterty pattem of 1994 (except lor series marked with ••). • Series is not publishad in the quarter1y survey, but detennined camparing publishad branch series with publishad commodity series of a certain branch.

E.g., Cotton yam is estimated by subtrading the contribution of woven fabrics, eetton lint, canvas, etc. in the publishad series lor I SIC 321. For the years 1993-1995, the numbers are taken from the data files of the QSIP.

- Last yea(s quarterty pattem can't be followed, due to inregularity or Jack of data. The last quarter lor 1995 is estimated following the unweighted (geometrie) mean of the quarterty pattem (from the 3rd to the 4th quarter) lor 1985-1994.

• Adjusted number. The original quantity (or the quarterty part of it) had and extremely unusual value. Therefore, a type error was assumed, and the value (or the quarterty part of it) was adjusted basedon common sense.

- Cammodities with nil-production. Establishments producing this comm. temporarily dosed or not in eperation during reference period.

Auto Cables Lamps Buses & Radia-

Batter. & VVires & Bulbs lorries Tractors tors

48 57 7B 102 219 233 79 B3 210 75 79 1B9 B7 113 64 71 121 160 75 102 127 73 32 139

100 100 100 100 100 100 70 121 48 136 29 105 73 139 43 102 18 72 41 30 22 16

164 149 153 107

82 15

36

24

75 64 41 52

39 Other Manunfact,

Polish.

Oiamonds

135 147 10B 94

100 112 123

10 22 24 30

TABLE H-2 (1 of4)

Reweighting the liP 1985-1995

Branch-weights & intraindustry weights (for 1985 and 1989), and qualitatvie comments.

ISIC & Branch/ Commodlty Descrlptlon Branch Welghts (Share in MVA)

1985 1989

tmJBB~iDt'ltls!•WI •• II l!iiJPdS 311 Food Processing 14% 9%

3111.2 Canned maat Standard. milk

Butter Ghee Baby lood & milk

3113 Canned fruits/juic.

3115 Veg. oils and fats

3116 Maize fleur VVheatflour Rice

3117 Biscuits & pasta

3118,9 Sugar Sugar Confectionery

312 Food Produels 4% 9%

3121 Cashew kemels BlackTea Blended tea Colfee beans, cured Instant Catree

Afrlcafe lnst

Honey Beewax

3122 Animal feeds

313 Beverages 13% 10%

3131 Spirits

3132 VVIne

3133 BotUed beer Chibuku

3134 Soft drinks

314 Tabacco & Clgarettes 6% 11% 3140 Cigarettes

Tobacco, cured

321 Textlle 17% 17%

3211 Woven fabrics Catton lint Cattonyam

lnd.-Welghts (Share in Branch)

1985 1989

13% 10%

3% 1%

19% 10%

34% 23%

3% 7%

28% 48%

75% 99%

25% 1%

8% 7%

1% 1%

78% 61%

13% 31%

100% 100%

84% 66%

lntralnd. welghts (Share in lndustry)

1985 1989

6% 0,02%

79% 97%

7% 3%

2% 0,1% 7% #

49% 6% 46% 19%

5% 75%

99% 87%

1% 13%

. 78% 79%

7% 17% 8% 3%

8% 1%

.

98% 80%

2% 20%

73% # 27% 100%

50% 19% 42% 24%

3% 56%

Na mes of produels lncluded lor delermlnlng the lntralndustry welghls lor 1989

Canned Produels Standardized Milk

Butter Ghee ?

Maize Fleur, Maize VVheat Fleur, Fleur Rice

Sugar, white sugar Jaggery, Sweets, China ball sweets

kemels blaek tea madetea Clean catree & Parchment colfee instant colfee

africafe

honey beewax

Beer Kibuku Beer

? Tabacco

Woven Fabries, Woven fabric, (grey) sheets, bed sheets, Catton Lint, Lint Sales Catton Yam, Yam

Commenls on produels & reweighling procedure

VVIthin 31, no estimates tor production indexes lor cammodities

nat lisled in the QSIP reports, could be made.

Because of the low share and incomplete series, canned meat is lelt out in the reweighted index.

Baby lood & milk is exduded in reweighting;

na such product could be traeed within 1989 census data files, and series is incomplete

Sugar Confectionery s notlisled in the QSIP reports, and therefore lelt out in the reweighted index

Cashew Kemels has a nuli-share in '85, and is nat lisled in the QSIP reports, and in reweighted index this product is lelt out as welf.

Africafe Ins!, Honey and Bee wax are not lisled in the QSIP reports,

and are therefore nat covered in the reweighted index .

Original values of produels On '89) Beer and Chibuku are adjusted because of (assumed) typing enrors

No share traceable lor "89, therefore the 1985~ntra-industry shares are used tor reweighting,

Original values ('89) of produels 'bed sheets' and 'grey sheeting' Were assumed a factor 1000 toa high, and divided by 1000. Catton yam is nat covered in QSIP reports, but series is estimated

TABLE H-2 (2 of 4)


Canvas 5% 1% Canvas Material (see table IIPO)

3212 Textile bags 4% 14% 42% 57% sisal bags Sanit. tewels # ? Sani. Towels is not covered in QSIP report (share <2% within 3212) Blankets 58% 43% (non-woven) blankets Canvas goods . Canvas goods, toot balls, mail bags Canvas goods has null share in 1985. Nol covered in QSIP reports

3213 Knilled fabr 6% 3% 99% 94% colton knilled fabrics, knilled cotton, (textile) fabrics Towels . bath towels Towels has null share in 1985. Not covered in QSIP reports Knitted garm (outw.) knit. garm., boy shorts/suits, kitenge, rnaria & chakora Knitted Garm. has 5% share in 1985, but is not covered in QSIP reports. Socks/stockings 1% 6% soeks stocking Not estimate could be made. Polybags . PPIPE bags Poly bags has null share in 1985. Not covered in QSIP reports

3214 Carpets 0% 0% Carpets has null share in 1985. Not covered in QSIP reports

3215 Sisal ropesltwines 6% 17% 95% 74% Packing twines, baler twines, nylon twines, ropes The (1989-)weight of3219 is applied to 3215 Fishnet&prod. 5% 26% Fishnets, fishing nets

323 Weartng Apparel 1% 1% Branch weight of 322 ('89) applied to 323. 3231 Tanned Laather 92% 83% 78% 9% all types laather

Dr hides/skins 22% 91% Game skins, hides & skins

Laather goods is not covered in QSIP reports, but series is estimated 3233 Laather goods 8% 17% 100% 100% lesther goods (see table IIPO)

Suilcases . # ? Suilcases has null share in 1985. Not covered in QSIP reports

324 Leather and Produets 2% 1% 3240 Shoes 100% 100% 86% 75% ladiest gents/ youthl children shoes

Beach sandals 14% 25% Rubber beach sandals Beach Sandals is not covered in QSIP, but series is estimated (table IIPO)

"~~l*~J:é1:~! ' •i!tl!1!JH!~ ~ ~ ii~ mA" ;:~, :.'Wihl I 331 Wood Products 1% 4%

3311 Timber 91% 56% 96% 85% Timber, rough timber Plywood 4% 15% Plywood Wooden poles has share of less than 1% in 3311, but is not covered in QSIP Waoden poles Pol es Series could nol be estimated

3312 Wooden eratas 9% 44% The (1989-)weight of 3319 & 3320 is applied to 3312.

341 Paper Products 2% 6% 3411 Hard boards 59% 51% 16% 22% Timber, Rough Timber

Chipboards 8% 9% chipboards Paper 76% 69% lndustrial

3412 Paper produels Notinduded 41% 49% Paper produels is nol covered in QSIP, but series is estimated (table IIPO)

. ., : • - m:ll~i&ti%""'% ; *1:' W""/ 'i 351 lndustrtal Chemieals 2% 4%

3511 Wattie extract 30% 75% 48% 85% Wattie extract For weighting 3511, the1985 intraindustry weights are used Pyrethrum extr 42% # ? si nee no data on pyrethrum is available tor 1989 Oxygen gas 10% 15% lndustrial Oxygen, Medical Oxygen

3512 Fertilizers 70% 25% 75% 79% S.A., T.SP., NPK20-10-10, NPK6-20-18 lndustry-weight of 3513 ('89) is applied to 3512. Ins & pest powder 12% 5% Kynakil, Thiodan, Actellic S.Dhst, Dust pr., wormet powder Ins & pest liquid 13% 16% Kilpest, sumith., Tembo tox, Liq. pr., milsant milvemlbenzole dr.

352 Other Chemieals 4% 5%

3521 Paints 8% 11%

3523 Soap & detergents 59% 57%1 3522 & 3523 are weighted logether in 3523 (for 1989).

TABLE H-2 (3 of 4)


3529 Safety matches 33% 32% 47% 65% Safety Matches Mosquito coils 11% 5% Mosquito coils, Aggartlatis Adhesives & ind. 42% 30% Petroleum Jelly, Cellulose

353 Petroleum Rennerles 15% 2% 3530 Petroleum fuels 100% 100% 90% 100% Petroil, Kerosine, oil, Diesel 1985 intraindustry shares are used to reweight 3530

Petroleum residue 10% #

355 Rubber Produels 2% 2% 3551 Auto tyres 97% 100% 86% 95% Tyres Bicycle tyres/ tubes are lelt out in reweighting !he index.

Auto tubes 5% 5% Tubes Bicycle tyres 7% # ?

Bicycle tubes 2% # ?

3559 Rubber articles 3% 0,4%

356 Plaz!!c Produels 1% 1% 3560 Plastic articles 100% 100%

. i4' 361 Pottery, China, Earlhenware 0% 0%

3610 Caramies 100% 100'1 Caramies is nol covered in QSIP, but series is estimated (table IIPO)

362 Glass, Glass produels 1% 1•/o 3620 Glasswares 100% 100%

369 Clay & Ceramlc Produels 4% 3% 3691.2,9 Bumt bricks 100% 100% Bumt Bricks Bumt bricks has null share in 1985. Nol covered in QSIP reports

Cement 96% 97% Cement, Portland Cement, Febre Cement Value of product 'Pac sheets' originally was placed under Lime 1% 0,1% Lime quantity heading, but considered to be value-data, since Asbestos sheets 3% 2% Pacsheets quantity-information was speelfled in the units given.

iiiS 371 Iron & Steel Basic Industries 6% 3%

3710 Rolled steel 100% 100% 17% 58% Rolled Steel colls Iron sheet - eerrug 39% 29% Steel bags Steel sheets/biflets 34% 12% Steel Biliets Steel pipes 10% 1% Flue Pipes 3'

372 Non-ferrous Metal Basic lnd. 1% 1% 3720 Aluminium 100% 100%

ill (illllifi f !t ;:v«~ • • f • > ; l1: d: << ~ ;;''{; l ~~ 381 Metal Produels 2% 3%

3811 Hoes 24% 36% 56% 70% Round eye hoes 3811 gels !he weights of 3812 (lor 1989)

Ploughs 9% 30% ploughs

Razortllades 34% # ? Razor blades nol covered in reweighted index

3813 Steel structures 15% 16% 98% 100% machine steel Steel structures and Louvres are nol covered in QSIP, but series Louvres 2% . Louvres Frames is estimated as 3813 series (see table IIPO)

3819 Metal containers 61% 48% 50% 18% Open top cans Aluminium wares 39% 63% Aluminium circles, sheets, pots Due to assumed typing error, value of product VVIre produels 11% 19% wire nails, drawn wire, fenzing wire, wire u-nails, barbed wire Bartled wire' is divided by 1000. Crown corks . crown corcs Crown corks has null share in 1985. Nol covered in QSIP reports

382 Machlnery (Exc. Eleelr) 0% 1% 3823 lnd machines 98% 16%

Sou ree:

Notes:

TABLE H-2 (4 of 4)


3829 Mach tools/equipm 2% 84% 100% 83%

Air eend, deepfr. & refrigr. . 17%

383 Electrlcal Machlnery 2% 2% 3831 Electric motors 16% 43% 8% 7%

Transfermers 57% 68%

Electric cockers 22% 9% Switchgears 12% 16%

3832 Radios & R/caseette 16% #

3839 Dry eens 68% 57% 66% 60%

Auto battenes 16% #

Electr cableslwires 14% 29%

Electr lampslbulbs 4% 10%

384 Transport Equlpment 2% 5% 3843 Assem buses &lorries 100% 95% 91% 56%

Assem tradors 1% # Motor bodies&trailers . 39%

Radiators 8% 5%

3844 Bicycles 0% 5% . 10%

Motorcycles . 90%

390 Other Industries 0% 1%

3901 Pollshad diamonds 100% 70%

3909 Brooms and brushes 0% 30%

1~1

For produd~nformation & industry weights lor 1985: Data files of !he Quarterty Survey of lndustrial Production.

For imraindustry shares: data files of quantity data of !he 1989 Census of lndustrial Production (Eindhoven version}. • = nul values; # = no products/industry given of this kind; 0% = less than 0.5% All produels are lisled which were reported In !he data files of !he quarterty survey.

var. spares. rice huiler parts. jockey pulleys, muft coupling,

br. fordoffer roller, man hole cover, spore gears. nee rollers refrigerators

Motors Transfermers Cockers Switchgears

Dry battenes ?

Power cables, PVC wirs, AAC/ACSR Fluorescent fitungs. and low voltage

buses, aches&trucks ? Trailers. Motor/Bus bodies, Radiators

forks. frames, mudguards motorcyctes

Produels willh nuli-shares in 1985 are nol covered in !he reweighted index (except these which are estimated; see table IIPO},

because no quantity intermation for !he entire period (1985-1995} is available tor these products.

NOT LISTED IN QSIP REPORTS. BUT ESTIMATED (see table IIPO}

Air eend .... has null share in 1985. Nol covered in QSIP reports

Branch 383 is weighted using 1985 share lor Radio's (3832}, and diminishing both shares of 3831 and 3839 with half of 3832 share. Hence. shares are resp.: 35%. 16%. and 49%

Switchgears is nol covered in QSIP, but series is estimated (table IIPO}

3839 is weighted with '85 intraindustry weights. No '89 weight for auto balt. is available. No big discrepancies between ether intra-ind. weights of '85189

Electr. lampslbulbs excluded due to incomplete series.

3834 is weighted using 85 intrainduslry weights, si nee no '89 intraindustry weights are available lor assembling of tractors Motor bodies&trailesr has null share in 1985. Nol covered in QSIP reports

Bicycles has null share in 1985. Nol covered in QSIP reports Motorcycles has null share in 1985. Nol covered in QSIP reports

lndustry weight of 385 is added to weight of 390 ('89}.

Brooms & brushes has null share in 1985. Nol covered in QSIP reports

T ABLE H-3 (1 of 2)

Reweighted liP 1985-1995

Calculated reweighted liP at 4-digit, 3-digit, and 2-digit level, using 1989 industrial & intra-industrial weights

31 Food and Beverages

311 3111,2.

19851 1041 138

1966

1987

1988

1989

1990

1991

1992

1993

1994

1995

98

87

104

100

113

116

112

118

120

126

133

107

100

100

80

55

46

53

27

17


• 321 3211

1985 74 50

1986 77 52

1987 98 94

1968 109 107

1989 100 100

1990

1991

1992

1993

1994

1995

101

96

86

90

88

77

34 Paper and P.Pr.

34

1985 69

1986 85

1987 116

1986 103

1969 100

1990 82

1991 87

1992 69

1993 240

1994 123

1995 96

105

96

64

96

92

69

3411

41

70

96

98

100

85

73

58

50

43

44

3113

133

155

179

106

100

71

77

116

111

93

167

3115

83

64

63

127

100

166

244

188

145

62

41

3116.

65

55

59

108

100

57

53

53

15

21

16

3117

75

156

108

150

100

92

56

36

29

16

14

3212 3213 3214 3215

99 63 357 73

98 74 204 913

111 76 66 91

111 99 150 111

100 100 100 100

89 89 121 107

94 76 93 106

97 114 71 101

62 98 11 114

61 126 54 104

74 94 16 69

3118,9

122

109

23

107

100

117

133

128

116

122

110

321

61

67

95

108

100

102

98

89

97

91

74

311

103

98

54

112

100

102

111

101

82

77

65

3121

102

87

92

101

100

107

107

102

115

125

123

3231 3233

147 266

115 290

101 273

109 152

100 100

69 157

59 171

51 93

20 3

14

21


3412

96

100

136

108

100

79

101

61

440

206

151

1985

1986

1967

1968

1989

1990

1991

1992

1993

1994

1995

351 3511 3512·

95

88

92

88

100

113

114

106

101

113

107

104

83

92

94

100

125

116

106

90

67

50

183

162

94

49

100

90

64

70

30

15

5

351

124

103

92

83

100

116

108

97

75

69

39

3122

238

195

169

117

100

79

58

36

12

11

4

323

167

144

130

116

100

84

78

58

17

91

139

312

103

88

92

101

100

107

106

102

115

124

122

3521

64

77

111

97

100

102

116

110

99

94

166

324

282

234

117

121

100

94

79

45

23

20

63

3523

70

75

94

105

100

117

121

103

112

102

143

3131 3132

73 213

72 262

72

89

100

91

114

142

143

149

185

3529

132

106

101

70

100

144

165

134

145

176

204

226

43

100

343

163

204

72

91

20

352

89

85

98

93

100

124

135

113

121

125

165

3133

126

113

103

98

100

84

93

90

103

98

186

3134

93

91

94

119

100

180

129

146

154

210

195

353

96

89

88

105

100

80

87

89

85

83

95

313

113

104

99

103

100

116

107

112

121

136

188

314

98

100

101

101

100

126

137

128

147

137

124


1965

1966

1987

1968

1989

1990

1991

1992

1993

1994

1995

33

83

83

116

110

100

114

102

78

82

76

71

3551.

53

66

92

88

100

97

86

73

89

83

88

3559

39

45

50

118

100

123

71

53

13

15

16

3311

57

74

96

101

100

110

86

70

51

39

37

355

52

66

92

88

100

97

86

73

88

83

88

3312

115

95

141

122

100

120

121

87

121

122

331.

83

83

116

110

100

114

102

78

82

76

71

356

68

73

73

63

100

132

130

164

148

270

149


1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

36 I 361

76

89

93

98

100

116

158

114

121

103

113

260

259

230

92

100

220

127

91

116

105

83

362

87

122

103

96

100

112

118

122

110

64

88

38 Fabr. Metal Prod., Machlnery and Equlpm.

38 I 3811 3813 3819

1985 I 1341 65 140 139

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

171

109

99

100

144 134

94

67

128

71

77

130

91

100

108

68

43

16

10

5

148

113

208

100

201

454

681

54

19

18

160

105

93

100

71

70

93

96

105

55

381

113

128

115

110

100

105

129

167

60

57

31

369.

64

72

84

99

100

112

170

112

124

113

121

Source: Table IIP1 lor lhe 1985-weights used; Table IIPO lor quantity indexes.

3823 3829

187 663

238 1857

267 456

168

100

70

84

73

90

107

81

226

100

804

668

111

431

1875

563

382

589

1606

427

217

100

690

578

105

378

1601

488

T ABLE H-3 (2 of 2)

Reweighted liP 1985-1995

3831 3832 3839

83 123 102

92 85 77

100 128 82

114

100

102

87

111

100

50

66

183

100

128

182

193

170

96

117

74

100

71

118

121

129

137

131

383

99

84

96

106

100

91

117

129

125

100

106

Notes • Weighted series is a spileed series. slnce a partlal series of which weighted is constructed, does nol cover the entire period

384.

114

84

78

77

100

132

98

39

13

25

32

37 Basic Metallndustries ' 37

1985 I 104

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

71

96

82

100

101

97

98

111

96

54

39 Other lnd. ' ' 39

19851 135 1986 147

1987 108

1988

1989

1990

1991

1992

1993

1994

1995

94

100

112

123

10

22

24

30

371

88

63

72

68

100

81

75

71

80

72

48

372

162

100

179

128

100

170

172

191

218

179

75

3 Manunfact.

19851 97 1986 98

1987 96

1988

1989

1990

1991

1992

1993

1994

1995

101

100

112

113

99

110

110

100

100

101

99

104

103

116

117

103

114

114

103

TABLE H-4 Comparison Unadjusted and Reweighted Index of lndustrial Production 1985-1995

Published (1985-weighted), Calculated (1985-weighted), and 1989-reweighted liP at 2-digit level

Year

Growth

Rates

Year

Growth

Rates

31

Publ. Calc. Rew.

1985 100 100 100

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1985-1990

1990-1994

19985-1994

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1985-1990

1990-1994

19985-1994

91

88

94

98

92

102

98

98

94

91

80

93

87

95

102

99

98

95

110

-1,7% -1,1%

0,5% 0,2%

-0,7% -0,5%

36

Publ. Calc.

100 100

117 117

127 127

142 142

144 144

163 163

235 235

163 163

176 176

154 154

168

94

84

100

96

109

111

107

113

115

121

1,7%

1,3%

1,5%

Rew.

100

117

122

129

131

152

207

149

159

135

148

10,3% 10,3% 8,7%

-1,4% -1,3% -3,0%

4,9% 5,0% 3,4%

32

Pub I. 100

112

132

150

142

142

126

129

128

108

7,3%

-6,6%

0,9%

37

Pub I.

100

57

85

71

100

102

94

99

112

92

0,4%

-2,5%

-0,9%

Calc. Rew.

100 100

113

130

147

139

139

123

128

124

110

112

6,8%

-5,6%

1,1%

103

132

146

134

136

130

116

121

119

103

6,3%

-3,4%

1,9%

Calc. Rew.

100 100

56 68

85 92

71 79

100 96

102 97

94 93

99 94

111 107

92 92

65 51

0,3%

-2,4%

-0,9%

-0,7%

-1,1%

-0,9%

33

Publ. Calc.

100

125

171

184

189

213

163

135

106

88

16,3%

-19,8%

-1,4%

38

100

127

175

181

186

210

161

132

104

83

80

16,0%

-20,7%

-2,0%

Rew.

100

101

141

134

121

138

123

94

99

92

86

6,7%

-9,7%

-0,9%

Publ. Calc. Rew.

100 100

97 97

119 119

102 102

123 123

134 134

128 127

118 118

67 67

59 59

6,0%

-18,5%

-5,7%

45

6,0%

-18,7%

-5,8%

100

127

81

74

75

107

100

70

50

95

53

1,4%

-2,9%

-0,5%

34

Publ. Calc. Rew.

100 100 100

160

251

228

245

192

163

129

249

160

13,9%

-4,5%

5,4%

39

160

251

228

245

192

163

129

249

160

139

13,9%

-4,4%

5,4%

Publ. Calc.

100 100

110 108

79 80

69 69

72 74

83 83

81 91

7 7 12 16

18 18

22

-3,7% -3,6%

-31,8% -32,2%

-17,3% -17,6%

123

169

149

146

120

126

101

349

179

140

3,7%

10,6%

6,7%

Rew.

100

108

80

69

74

83

91

7 16

18

22

-3,6%

-32,2%

-17,6%

Source: tor the reweighted index: Table H-3, tor the calculated 1985-weighted: own calculation trom data files QSIP, and tor the published index: QSIP 1995:3

Notes: the growth rates tor the given (sub-)periods, are average annual growth rates.

35

Publ. Calc. Rew.

100 100 100

97

100

109

110

102

106

101

100

103

0,4%

0,2%

0,3%

3

96

99

108

111

102

107

103

101

103

115

0,4%

0,2%

0,3%

Publ. Calc.

100 100

97 97

107 103

115 112

117 113

114 114

117 117

110 110

110 109

100 101

107

93

97

93

106

119

120

112

107

119

113

3,6%

-0,1%

2,0%

Rew.

100

101

99

104

103

116

117

103

114

114

103

2,7%

-3,2%

0,0%

2,7% 3,0%

-2,9% -0,5%

0,2% 1,4%

Appendix I OVERALL ADJUSTMENTS TO REAL MANUFACTURING

VALUE ADDED 1965-1995

SC 31

19E5 ffi 19E5 61 1967 ffi 1968 61 1900 62 1970 EB 1971 83 1972 84 1973 85 1974 81 1975 78 1976 100 1977 92 1978 104 1979 9J 19a:l 77 1981 61 1982 55 1983 EB 1984 53

ffi 61 ffi 61 62 EB 83 84 85 81 78

100 92

104 9J 77

61 55 EB 53

19E5 9) 100 9)

1985 94 47 1987 84 42 1988 100 9)

1963 96 48 199J 100 54 1991 111 ffi 1S92 107 53 1993 113 55 1994 115 51 1995 121 ro

&x.rce TGtlle G4 ard TGtlle H-4.

TABELI-1 Adjusted Real Value Added Index 1965-1995

32 lëXtîlëS

&

t..eahEr Tol::a;co

25 28 4J ffi 68 74 81 92 93 93

100 106 96

102 91 79 75 EB 55

25 28 4J ffi 68 74 81 92 93 93

100 106 96

102 91 79 75 EB 55

55100 55 103 58 132 74 148 83 134 76 136 77

130 73 116 ffi 121 68 119 67 103 58

Wi5ë!Ff. R .. m&Rxt.

Fàf:a' Ft. Ftirt & R.tJl

9J 118 101 123 121 139 139 141 159 142 123 100 151 124 100 108 97 91 68 58

9J 118 101 123 121 139 139 141 159 142 123 100 151 124 100 108 97 91

68 58

62100 62 115 71 158 98 144 EB 136 ffi 127 79 125 78 98 61

2ffi 159 147 91 119 74

Oîëri'êaiS, FètrdaJTI RJtter&

Rastic Ftcxi

63 67

72 73 62 87

101 67 85 99

100 96

106 83 8J 76 68 67

108

63 67

72 73 62 67

101 67 85 99

100 96

105 83 8J 76 68 67

108 71 100 71

93 ffi 97 EB 93 ffi

106 75 119 ffi 120 85 112 8J 107 76 119 ffi 113 81

84 98

114 128 129 131 129 150 106 115 100 115 105 111 ffi

108 93 EB

103

84

98 114 128 129 131 129 19)

106 115 100 115 105 111 ffi

108 93 EB

103 105 100 105

117 123 122 128 129 134 131 137 152 1EB 207 216 149 155 1EB 168 135 141 148 155

37f28139 (El3Sc) I'V'ëiäl Ff., Wa::h. & EQJiJ:m'

&ataMn. nct...stries

21 21 22 23 29 37 9)

79 67

EB 100 111 126 129 129 100 128 111 118

21 21 22 23 29 37 9)

79 67

89 100 111 126 129 129 100 128 111 118

139 100 139 110 153 84 117 75104 8J 112

103 144 98136 73102 64 89 00126 51 71

42 45

49 55 ffi 76 83 92 9J 9J

100 103 107 98 9J 79 74 ffi EB

3 iëïtà

M:n.JfacILrirg

42 45 49

55 ffi 76 83 92 9J 9J

100 103 107 98 9J 79 74 ffi EB

ffi100 ffi 101 67 99 ffi

104 EB 103 68 116 77

117 77

103 68 114 75 114 75 103 68

N::tes: Sa-ies 19ffi-1985 (!re left cdum; fcr ea;h t:rarch) a-e spieed in 1985 w~h tt-e 19ffi-1995series (tt-e col\lm in tte rridle) to gEt tte 19ffi-1995 series (ri!tt cdlJlll).

147

TABEL 1-~ Comparison ofthe Unadjusted and Adjusted Real Value Added Index

Adjusted Unadjuste

IS IC 31 32 33/34 35 36 37/38139 3 Branch Föod, rexbles Wood A'. dïemcaiS, Nön- (BasiC) lll'êtal A'., Total Total

Beverages & Furn. & F1xt. Petroleum, Metallic Mach. & Equipm, Manufac- Manufac-

& Leather Paper A". Rubber & Mneral & other Man. turing turing Tobacco A"int. & A.Jbl. Aastic A"od. A"oducts Industries

(1976-100) (1976-100) 1965 55 90 1966 61 25 118 63 84 21 42 49 1967 65 28 101 87 98 21 45 55 1968 61 40 123 72 114 22 49 59 1969 62 55 121 73 128 23 56 65 1970 69 68 139 62 129 29 65 67 1971 83 74 139 87 131 37 76 74 1972 84 81 141 101 129 50 83 80 1973 86 92 159 87 150 79 92 83 1974 81 93 142 86 106 87 90 85 1975 78 93 123 99 115 89 90 85 1976 100 100 100 100 100 100 100 100 1977 92 106 151 96 115 111 103 94 1978 104 96 124 105 105 126 107 97 1979 90 102 100 83 111 129 98 100 1980 77 91 108 80 85 129 90 95 1981 61 79 97 76 108 109 79 85 1982 56 75 91 68 93 128 74 82 1983 59 59 68 67 69 111 65 75 1984 53 56 58 108 103 118 69 77 1985 50 56 62 71 105 139 66 74 1986 47 58 71 66 123 153 67 72 1987 42 74 98 69 128 117 65 79 1988 50 83 89 66 134 104 69 85 1989 48 76 85 75 137 112 68 86 1990 54 77 79 85 159 144 77 84 1991 55 73 78 86 216 136 77 86 1992 53 65 61 80 156 102 68 81 1993 56 68 159 76 166 89 75 81 1994 57 67 91 85 141 126 75 74 1995 60 58 74 81 155 71 68

Source: Tablex 1-1, and Table G-4 and H-4 for the unadjusted index.

148

M. SC. THESES IN TECHNOLOGY AND DEVELOP:MENT STIJDIES SIN CE 1997

97.1 Jolm van Rijn; The implementation of building Teclmiques in Gedaref. The search for appropriate designs and building teclmiques for primary schools and health eentres and their implementation in

1 Gedaref (Sudan)

97.2 Marie Odile Zanders; An assessment of Dornestic Waste Water Pollution for the Lake Victoria Region

97.3 Marcel Cloo and Pjotr Ekelmans; The role ofMIC Tanzania Ltd in the development ofthe Tanzanian telecommunication sector. Tanzania in search of an appropriate telecommunication teclmology

97.4 Geert Bergman; Measuring lndustrial Efficiency inDeveloping Countries. Theory and a casestudy: bottling of Coca-Cola in Dar es Salaam, Tanzania

97.5 Francine Jansen; Allalysis ofthe housing situation in Minsk (Belarus)

97.6 Bartelt Bongenaar; Part 1) Evaluation ofthe role ofthe Tanzania Industrial Research and Development Organisation; Part 2) The satellite receiver design

97.7 Mark Pantus; Implementation of a total quality assurance system in electronics. A study of total quality in a Philippine electtonics company

97.8 Jan Buis; Productivity measurement in the Costa Rican low-income housing projects. The search for an adequate methodology

97.9 Casper Esmeijer; Production and application of lime in Tanzania; with special reference to the construction sector.'

97.10 W amer W erkhoven; Mapping the effects and impacts of refugees sites. The case of the 1994-1996 influx in the Kagera region, Tanzania.

97.11 Otto Bos; Energy Conservation in Tanzanian lndustry. Manual Energy Conservalion for Tanzanian Metal Industry.

97.12 Bas Sturkenboom: Allalysis Aeronautical Navigation Service Organisation in Tanzania. Teclmology audit Aeronautical Fixed Service provision via low-speed Aeronautical Fixed Telecommunication Network.

97.13 Gertrude Zijland Robbert Lassche: Testinga Teclmology Audit Methodology. A rapid assessment of six small and medium scale enterprises in Tanzania.

97.14 Jurgen Busink and Sander Wilson: Testinga Teclmology Audit Methodology. A rapid assessment of eleven enterprises in Costa Rica.

97.15 Riek van der Kamp: Teclmology and Human Resources in the Indonesian Textile Industry- The role of teclmological progress, education and Human Resource Development in economie performance.

97.16 Menno Prins: Manufacturing statistics. Reconstmcting Tanzanian Manufacturing Value Added 1965-1995

Ifyou would like to receive a copy of one ofthe above indicated M.Sc. theses, please contact:

Department of Technology and Development Studies Eindhoven University of Teclmology MS.c. research co-ordinator L. Robben DG 1.02 POBox513 5600 MB Eindhoven The Netherlands

Documents

pure.tue.nl · SUMMARY This thesis provides complete revised and consistent time series of nominal and real manufacturing value added for different branches of Tanzanian