An assessment of the the BER's manufacturing survey in South Africa

30th CIRET Conference, New York, October 2010

The impact of weight adjustment on the accuracy of

business tendency surveys

An assessment of the manufacturing survey of South Africa

George Kershoff

Deputy Director, Bureau for Economic Research (BER), Stellenbosch University *

Abstract

Firm weights are applied to the qualitative responses of participants to calculate business

tendency survey (BTS) results. Sector weights are employed to produce higher levels of

aggregation. If a comprehensive and current business register is available, sampling weights

are utilized to provide for selection probability and significance.

What impact does weighting have on the accuracy of the BTS results? If a national

business register is unavailable and the response rate is low (i.e. the characteristics of the

reporting units are unlikely to be the same as those of the sample units), accuracy is not derived

from the application of random sampling and weighting, but is inferred if a close relationship

between the survey results and a reference series can be established.

To assess the impact of weighting on the accuracy of survey results, the relationship

between a reference series and the BTS results calculated by using only firm and sector

weights is compared to one between the same reference series and the BTS results calculated

by also ex post adjusting the weights. Weight adjustment rectifies deviations (over and under

representation) between the structure of the responses and that of the population. Non-

responses (missing data) are in effect treated by increasing the weights of those that

responded.

A visual inspection reveals little difference between the survey results based on the data

with and without weight adjustment. The correlation between the survey results based on the

adjusted data and the reference series (0.69) is lower than the one between the results based

on the unadjusted data and the reference series (0.75).

The finding that weight adjustment does not improve the accuracy of the BTS results is

comforting in so far as it backs the method applied by the BER to calculate the survey results.

Contrary to expectations, the results are therefore not highly sensitive to weighting and can

therefore be regarded as quite robust.

* BER, Private Bag X5050, Stellenbosch, 7599, South Africa. E-mail: [email protected]. This paper was finalised

on 30 June 2010.

mailto:[email protected]

The impact of weight adjustment on the accuracy of business tendency surveys

2

Key Words: Manufacturing sector, business tendency survey (BTS) method, weighting, firm weights,

sector weights, weight adjustment, South Africa

JEL Classification: C42


3

Introduction

Business tendency surveys (BTS) use questionnaires to collect data from firms. Data on the

business performance (such as sales, prices, stocks and employment) is collected. The

questionnaires are sent to only a selection of firms from the population or universe. Not all firms in the

population are quizzed, because it would be too laborious and / or expensive. The selection of firms is

called a sample and is put together in such a way as to represent the structure of the population in the

best way possible. The results obtained from surveying a sample is inferred to apply to the whole

population.

BTS are regarded as longitudinal research as the same phenomenon – the performance of

business – is studied over time. The same questionnaire and sample are used between consecutive

surveys. Given that the same sample is used, BTS resembles panel research. Tracking the same

firms increase accuracy, as it raises the response rate and likelihood that changes in the results can

be attributed to actual changes and not to the use of different samples. However, care must be taken

that the structure of the panel does not drift away from that of the population, as inferences about the population can in such cases not be made from the results obtained from the panel.

The question that immediately follows from this is what impact does the sample design have on

the accuracy of BTS results, where accuracy is defined as the closeness between the estimated value

and the (unknown) true population value. The sample design consists of various elements, which

could all affect accuracy. This paper focuses on only one aspect, namely the impact of the

representativeness of the actual responses on the accuracy of the BTS results. Inferences about the

population cannot be drawn from the results obtained from a sample if the structure of the responses deviates significantly from that of the population.

To narrow it down further, this paper focuses only on the impact of weighting on the accuracy of

BTS results. What is weighting? Each sample unit is allocated a weight that reflects its relative

importance in the population. Weighting is necessary to make the sample representative of the

population. So, the specific question arising from this is: If the response rate is low and the majority of

participants do not respond to every survey, what impact does using fixed sample weights in contrast

to variable reporting unit weights have on the accuracy of BTS results? (If the response rate is close to

100%, then the sample and reporting units would agree and the sample and reporting unit weights

would be the same.) Does adjusting the weights to provide for non-responses and the resultant over

and under representation / biases of the actual responses (as opposed to a representative sample)

before processing the results of each survey improve accuracy?

Furthermore, this study of the impact of weighting on the accuracy of BTS results is limited

further to situations where information on the structure of the population is totally unavailable or very

limited. If such information is lacking, it is impossible to establish if the structure of the sample reflects

that of the population. In such circumstances, stratified random sampling and the estimation of

sampling weights are not possible. Purposive (non-probability) sampling can be employed, but then

the margins of error of the survey results cannot be estimated and the only way to establish accuracy

is to infer it from a close relationship between the survey results and a corresponding benchmark or

reference series, assuming that such a series is accurate, i.e. accurately reflect the (unknown) true population value.

This study is an instance where the robustness of the BTS results are examined, as a sensitivity

analysis is conducted to establish how one element of the survey design – weighting – affects the

accuracy of BTS results. Furthermore, by comparing the BTS results to a benchmark, this study is also an example of where the quality of the results is monitored.


4

Using weights to calculate the survey results

The OECD (Organisation for Economic Co-operation and Development) published a handbook in

2003 to outline the international best practice to conduct BTS. In the handbook, the OECD recommends that sample and size weights be used to process BTS.

What are sample weights? Sample weights are the inverse of the probability with which each

reporting unit has been selected. The following example is provided in the handbook to explain sample

weights: “Suppose, for example, that the target universe has been divided into two groups – large and

small reporting units. If all large reporting units are selected for the sample (probability of 1) and if only

one in ten are selected from the small reporting units (probability of 0.1), the answers of the reporting

units must be multiplied by 1/1 =1 and 1/0.1 =10 respectively. Higher weights are given to the small

reporting units because they have to represent all the other small reporting units that were not

selected for the sample (OECD, 2003: 36). In practice, sample weights can only be estimated if

detailed information on the structure of the population, such as the number of firms per sector and size

class, is available. Such information is usually derived from a business register of all the firms of a

country. Stratified random sampling cannot be used and the sampling weights per reporting unit

cannot be calculated if such a business register is unavailable.

What are size weights? Given that not the whole population, but only a selection of firms is

surveyed, each reporting unit is multiplied by a size weight reflecting its relative importance in the

population. Size weights, therefore, raise the results originating from a sample to that of the

population. Two types of size weights are distinguished, namely firm and sector weights. Firm weights

are used to calculate BTS results, because it is assumed that the answers of large firms carry more

weight than those of small firms. Firm weights “are not generally required in processing answers to

quantitative questions because the answers already reflect the size of the reporting unit. Data reported

on the value of sales, tons of output, numbers employed, etc. will be in larger values, volumes or

numbers for large firms than for small ones” (OECD, 2003: 37). Number of employees and turnover

are typically used as firm weights. Sector weights are used to aggregate the results to higher levels,

such as from meat producers to all food producers and finally to all manufacturers. Variables, such as

value added, income, sales and production, can be used as sector weights. The choice depends on

the availability of data and the preferred reference series. For instance, value added can be used as

sector weights if the survey results are to closely reflect the movements in GDP. If a breakdown of

value added is unavailable, then sales, for example, can be used. The BTS results have in practice turned out to be not extremely sensitive to the choice of the weighting variable (OECD, 2003:37).

The OECD recommends that the results per sector (e.g. food, beverages and clothing) be

calculated in practice as the sum of the weights per question / variable. The weights, in turn, should be

computed as the responses (i.e. up, same or down) times the sample weights times the firm weights.

The total / aggregate is to be calculated as the sum of the weights per sector (calculated as the

responses multiplied by their sample weights) times their respective sector weights. The difference

between calculating the results per sector and the total is that firm weights are not used in the latter instance (OECD, 2003: 37-47).

The biggest difference between the method that the BER applies to calculate the survey results

and the one recommended by the OECD is that the BER does not make use of sample weights. The

reason why sample weights are not employed is that they cannot be estimated without access to the

national business register. Statistics South Africa (Stats SA, 2005) has published the findings of a

census of the manufacturing sector and publishes the total number of firms in the universe in its

monthly manufacturing production and sales statistical releases, but not a detailed breakdown of the

number of firms per sector and size class contained in the business register.


5

The BER cannot employ stratified random sampling to put together and maintain a panel as

recommended by the OECD due to the unavailability of the national business register. Instead, the

BER makes use of purposive (non-probability) sampling. However, it needs to be pointed out that if

the same selection of firms is surveyed in repeated rounds as is customary in BTS, then the sample is

no longer strictly random irrespective of the fact that random sampling was used to construct the

sample at the beginning (OECD, 2003: 21). When the BER recruits new responding units every 2 to 3

years to replace those that have become inactive, invitations are sent to all the contacts on the

purchased address lists that satisfy certain criteria. Given that the probability of selection is therefore

the same for all units during recruitment, the adverse impact of the omission of sample weights on the

accuracy of the results is likely to be smaller. Nevertheless, the quality of the results obtained from a purposive sample has to be monitored closely to ensure that the selection of firms is unbiased.

The BER makes use of size weights as recommended by the OECD. Employment is used as

firm weights. The BER distinguishes 9 employment size classes (see Table 1 below). For the

purposes of this study, they had to be reconciled with Stats SA’s 4 turnover size classes so that the

findings of the census about the structure of the population can be applied to the sample. For

instance, Stats SA classifies firms with turnovers of more than R51 million in 2005 as large. The equivalent size group in the case of the BER are firms with more than 100 employees.

Table 1 Size classes and firm weights

BER Stats SA

Size class Number of

employees

Firm weight Equivalent size

class

Turnover in Rm

in 2005

1 1 – 19 1 Micro < 5

2 20 – 49 4 Small 5 – 13

3 50 – 99 10 Medium 13 – 51

4 100 – 199 19

Large > 51

5 200 – 299 34

6 300 – 399 48

7 400 – 499 62

8 500 – 999 94

9 1 000+ 286

The BER uses the 3-digit SIC (Standard Industrial Classification) codes to distinguish 19 sectors

(see Table 3 in the Appendix). The petroleum sector is not covered. The leather, footwear and rubber

sectors are included in the total, but their results are not published. Employment is also used as sector weights. The sector weights were last updated in 1996.

The sector weights were changed to the percentages of domestic sales volumes in this study, because domestic sales were selected as reference series to monitor the quality of the survey results.

Various data sources were accessed to calculate domestic sales volumes. Stats SA publishes

monthly manufacturing sales and production statistics. The manufacturing sales figures are available

as actual and seasonally adjusted current price values per sector. The statistics on manufacturing

production volumes are published in index form. Stats SA also publishes a monthly producer price

index (PPI) for domestic output and export commodities. The South African Revenue Service (SARS)

publishes export data per HS (harmonized system) category. Fortunately Quantec Research South Africa publishes the same data also per SIC category.


6

Domestic sales volumes were calculated as the difference between total and export sales and deflated by the relevant PPI for domestic output.

The sector weights reflecting domestic sales volumes differ from those based on employment

(see Table 3 in the Appendix). Some sectors (such as food, beverages, paper and transport

equipment) are responsible for a bigger share of total sales than for employment. For example,

transport equipment represents 18% of domestic sales volumes compared to 7% of employment.

Other sectors (such as textiles, clothing, footwear, non-metal mineral products and metal products) in

turn account for a bigger share of employment than for sales.

Stats SA (2005: 19-25) publishes information on income per sector and firm size class, but not

for sales. For instance, large meat producers generated 82% of the income created by all meat

producers in 2005. In contrast, large plastic producers accounted for only 58%. In this study, it is

assumed that sales are distributed identical to income across firm size classes and that the distribution

remained stable over the period under review (2001 – 2009). It is reasonable to use income as a proxy

for sales given that sales make up the largest share of turnover by far1.

Table 2 Example of how weights are employed to calculate the survey results

Respondents per

sector

Size

class

Firm

weight 1

Sector

weight

Combi

ned

weight

Response

Up Same Down Total

Meat

A. Small 2 4 0.051 0.204 0.204

B. Medium 3 10 0.051 0.510 0.510

C. Large 6 48 0.051 2.448 2.448

D. Large 8 94 0.051 4.794 4.794

Sector: Sum of weights 0.204 2.958 4.794 7.956

Sector: Percentage 3 37 60 100

Motor vehicles

E. Medium 3 10 0.106 1.060 1.060

F. Large 8 94 0.106 9.964 9.964

G. Large 8 94 0.106 9.964 9.964

Sector: Sum of weights 11.024 0.000 9.964 20.988

Sector: Percentage 53 0 47 100

Total

Total: Sum of weights 11.228 2.958 14.758 28.944

Total: Percentage 39 10 51 100

1 See Table 1.

An example of how the BER calculates the results in practice is provided in Table 2 above. The

value of 0.204 attached to the “up” response of a small meat producer (identified as respondent “A”) is

calculated as the product of the firm weight (4) and the sector weight (0.051). The BER calculates the

results per sector as the sum of the weights per variable and per sector. In the example above, the

results for the meat sector are calculated by first computing the sums of the weights of the “up” (0.204

in the example above), “same” (2.958), “down” (4.794) and all responses (7.956). In a second step,

1 Definitions for sales and turnover are provided in the glossary of Stats SA’s monthly manufacturing data

releases.


7

these totals are transformed to percentages, namely 3% (0.204 / 7.956 * 100) “up”, 37% “same” and

60% “down”. In the final step, the net balance (+57) is calculated as the percentage “up” (60%) less

the percentage “down” (3%). The results for the total are calculated in the same manner. The sums of

weights of all the “up”, “same” and “down” responses are calculated in a first step. These are then expressed as percentages in a second step. The net balance is calculated in the final step.

Changes in the number and composition of responses

An analysis of the representativeness of a sample is of little value if the response rate is low,

because then the characteristics of the reporting units are likely to differ from those of the sample

units. In such a case, it is more useful to study the representativeness of the actual responses. From

time to time it is necessary to examine the impact of changes in the number and composition of

responses on the reliability of the survey results. It is also necessary to find out if non-responses are causing a bias that needs to be corrected.

The number of responses to the BER’s manufacturing survey varies from one quarter to the next.

Not everybody participating in a particular survey has necessarily participated in the previous one or

will participate in the next one. Currently the BER does not allow for questionnaires reaching it after

the date of return by subsequently revising the initially published results. An analysis of the number of

responses included in the published results, therefore, underestimates the actual response rate. In

addition, the BER currently does not take any additional steps (such as imputation or weight

adjustment) to account for missing data other than following up all unit non-responses once before the

date of return. It is implicitly assumed that the characteristics and responses of those that did not respond are identical to those that did respond.

Figure 1 Number of responses per size class, quarterly average

0

50

100

150

200

250

300

350

400

2001 2002 2003 2004 2005 2006 2007 2008 2009

Sector Total - Large

Sector Total - Small (<100)


8

The number of responses increased from an average of 250 per quarter in 2001 to 350 in 2002

due to the recruitment of new responding units to replace inactive ones (see Figure 1). Bar 2005, 2008

and 2009 when recruitment also took place, the number of actual responses drifted steadily downwards to an average of 220 per quarter in 2009.

The composition of responses is also of interest, because changes in the number of large firms

in important sectors, in particular, could have a big impact on representativeness. The increase in the

total number of responses between 2001 and 2005 can be attributed to a rise in the number of small

(i.e. firms with less than 100 employees) responding units (see Table 4 in the Appendix). However,

both the number of small and large responding units declined between 2005 and 2009. The textiles, clothing, paper and transport equipment sectors registered particularly large declines.

Weighting is supposed to rectify deficiencies in the representativeness of the sample or the

actual responses when the response rate is low. To establish if this happens in the case of the BER’s surveys, the sector shares of the BER’s surveys are compared to a benchmark.

To conduct the comparison, the sum of weights per sector first has to be expressed as a

percentage of the total. In the example provided earlier of how the survey results are calculated (see

Table 2), the sum of the weights of the meat sector makes up 27% (7.956 / 28.944 *100) of the total.

The share of transport equipment (73%) can be calculated in the same manner. The selected

benchmark is the composition of domestic sales volumes per sector.

The analysis reveals that the sector composition of the BER’s surveys not only deviate

noticeably from those of Stats SA, but also fluctuate significantly more from one year to the next. The

relative shares of the beverage, paper, printing, plastics, machinery, transport equipment, furniture

and other goods sectors are too low (see Table 5 in the Appendix). In contrast, the relative shares of

the textiles, clothing, non-metal mineral products and metal products sectors are too high.

Furthermore, the relative shares of the clothing, transport equipment and machinery sectors do not

respectively decline, increase and decline over the period 2001 to 2009 in the case of the BER’s

surveys as in the case of the benchmark.

The fluctuations in the sector ratios of the BER’s surveys can partly be attributed to the non-

treatment of missing data. Returning once more to the example provided earlier (see Table 2), if the

respondent marked “D” (i.e. a large meat producer) fails to respond during a particular quarter, then

the sum of weights of the meat sector declines from 7.956 to 3.162 and that of all sectors from 28.944

to 24.150. As a result, the relative share of the meat sector declines from 27% to 13% (3.162 / 24.15 *100).

Fixed weights, therefore, do not compensate sufficiently for shortcomings / weaknesses in the representativeness of the responses to the BER’s surveys.

Do weight adjustments improve accuracy?

One way of aligning the composition of the sum of weights and that of the population (in practice

represented by the reference series) is to adjust the weights of all the responses ex post. The

combined weight of each response is thus calculated as a firm weight multiplied by a sector weight

multiplied by an adjustment. The adjustment can only be done ex post, i.e. when all the available

responses can be assessed and before the results are processed for the first time. The purpose of the

adjustment is to rectify deviations (over and under representation) between the structure of the

responses and that of the population. Non-responses (missing data) are in effect treated by increasing

the weights of those that responded. Instead of applying permanent / fixed weights between

consecutive quarters, this method requires that each respondent has a unique weight every quarter.

This unique weight depends on how many units per sector and size class respond during a particular

quarter.


9

Returning to the example of how the BER computes the survey results (see Table 2), if the

benchmark indicates that the share of the meat sector is actually 35% and not 27% (i.e. meat

producers are underrepresented in the survey), then an adjustment will consist of multiplying the

weights of all meat producing respondents by 1.42 (= 11.3012 / 7.956) to align the sector shares of the

surveys with those of the benchmark. Likewise, the missing response of respondent “D” can be

handled by multiplying the weights of the remaining respondents “A”, “B” and “C” by 2.5 (7.956 / 3.162) to restore the sum of weights of the meat sector to 7.956 and its share to 27%.

The ultimate goal of this study is to find out if such weight adjustments improve the accuracy of

the BTS results. In order to do this, the BER’s survey results over the period 2001 to 2009 were

recalculated after adjusting all weights in such a manner that the sector and size structure of the

survey results agree with that of the reference series, namely domestic sales volumes. The 343

quarterly surveys produced 12 952 responses. These responses were adjusted so that the structure of

every quarter agrees with that of the benchmark classified according to 38 sectors4 and 4 size

classes5. The compositions of the sum of weights per sector and per quarter before and after the

adjustment are shown in Figure 2 and Figure 3.

Figure 2 The sector composition of the survey data without weight adjustment

2 If the share of the meat sector is increased from 27% to 35%, then the share of the motor vehicle sector declines

from 73% to 65%. The sum of weights of the total must be 32.289 (= 20.988 / 0.65) for the share of motor vehicles to be 65%. The sum of weights of the meat sector is then 11.301 (= 0.35 * 32.289).

3 No data was available for the first quarter of 2001 and fourth quarter of 2005.

4 The sectors agree with the 3-digit SIC code level in Table 3 except for a few cases where sectors were

combined. 5 See Table 1.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

01

Q2

01

Q3

01

Q4

02

Q1

02

Q2

02

Q3

02

Q4

03

Q1

03

Q2

03

Q3

03

Q4

04

Q1

04

Q2

04

q3

04

Q4

05

Q1

05

Q2

05

Q3

06

Q1

06

Q2

06

Q3

06

Q4

07

Q1

07

Q2

07

Q3

07

Q4

08

Q1

08

q2

08

Q3

08

Q4

09

Q1

09

Q2

09

Q3

09

Q4

Not publ -

Transp equip -

Elec machin -

Machinery -

Metal products -

Basic metals -

Non-metal min -

Chemicals -

Wood -

Clothing -

Textiles -

Beverages -

Food -

Plastics -

Printing -

Paper -

Furn & other -


10

Figure 3 The sector composition of the survey data with weight adjustment

Figure 2 indicates that the sector shares of the survey results deviate noticeably from those of

the reference series shown in Figure 3 and brought about by the adjustment.

To assess the impact of weight adjustment on the accuracy of the BTS results, the relationship

between a reference series and the survey results calculated by using only firm and sector weights

(i.e. the method currently employed by the BER) is compared to the one between the same reference

series and the BTS results calculated by also ex post adjusting the weights. In this case accuracy is

not derived from the application of random sampling and weighting, but is inferred from the strength of

the relationship between the BTS results and the reference series. This approach implicitly assumes

that the reference series is accurate. However, one must bear in mind that this is not always the case

in reality. The standard way of determining the strength of the relationship between two properties is to

calculate the correlation coefficient (r). The method that produces the most reliable results is therefore the one delivering the highest positive correlation coefficient between the survey and quantitative data.

The survey data is presented in the form of net balances, i.e. the difference between the

percentage “up” and percentage “down” responses. The reference series is taken as the year on year

percentage change in domestic sales volumes6. The quantitative data was transformed in this manner

to make it comparable to the survey data, which stems from the question “Did the volume of domestic

sales increase, remain the same or decrease during the current quarter compared to the same quarter of a year ago?” Both series were not adjusted for seasonality.

A visual inspection reveals little difference between the survey results based on the data with

and without weight adjustment (see Figure 4). The correlation between the survey results based on

the adjusted data and the reference series (0.69) is actually lower than the one between the results

6 See page 6 for how domestic sales volumes were calculated.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

01

Q2

01

Q3

01

Q4

02

Q1

02

Q2

02

Q3

02

Q4

03

Q1

03

Q2

03

Q3

03

Q4

04

Q1

04

Q2

04

q3

04

Q4

05

Q1

05

Q2

05

Q3

06

Q1

06

Q2

06

Q3

06

Q4

07

Q1

07

Q2

07

Q3

07

Q4

08

Q1

08

q2

08

Q3

08

Q4

09

Q1

09

Q2

09

Q3

09

Q4

Not publ -

Transp equip -

Elec machin -

Machinery -

Metal prod -

Basic metals -

Non-metal min -

Chemicals -

Wood -

Clothing -

Textiles -

Beverages -

Food -

Plastics -

Printing -

Paper -

Furn & other -


11

based on the unadjusted data and the reference series (0.75). The survey results and the reference

series must move in opposite directions during fewer quarters for the positive correlation to be higher (see Figure 5).

Figure 4 Domestic sales volumes – BER survey results based on data with and

without weight adjustment

Figure 5 Domestic sales volumes – the adjusted survey data compared to the

reference series

Conclusion

The finding that weight adjustment does not improve the accuracy of the BTS results is

comforting in so far as it backs the method applied by the BER to calculate the survey results.

Contrary to expectations, the results are therefore not highly sensitive to weighting and can therefore

-80

-60

-40

-20

0

20

40

60

80

100

Mar

-01

Sep

-01

Mar

-02

Sep

-02

Mar

-03

Sep

-03

Mar

-04

Sep

-04

Mar

-05

Sep

-05

Mar

-06

Sep

-06

Mar

-07

Sep

-07

Mar

-08

Sep

-08

Mar

-09

Sep

-09

Ne

t %

= %

up

less

% d

ow

n

Without weight adjustment (lhs) With weight adjustment (lhs)

-30

-20

-10

0

10

20

30

-100

-80

-60

-40

-20

0

20

40

60

80

100

Mar

-01

Oct

-01

May

-02

Dec

-02

Jul-

03

Feb

-04

Sep

-04

Ap

r-0

5

No

v-0

5

Jun

-06

Jan

-07

Au

g-0

7

Mar

-08

Oct

-08

May

-09

Dec

-09

Ye

ar o

n y

ear

% c

han

ge

Ne

t %

= %

up

less

% d

ow

n

With weight adjustment (lhs) Reference series (rhs)


12

be regarded as quite robust. This finding stands in contrast to that of the volume of retail sales, where

the same analysis yielded an improvement in accuracy. The correlation between the survey results

and reference series increased from 0.73 to 0.81 when weight adjusted data was used (Kershoff, 2009: 8).

Donzé et al (2004 : 29) found that three of the six European countries they studied make use of

weight adjustment. They also recommended that “sampling weights should be adjusted by constructing nonresponse weights to tackle the problem of unit nonresponse” (Donzé et al, 2004: 46).

Although this study weakens the case for the universal application of weight adjustment, it must

be kept in mind that the findings from a study of one variable of a particular sector of a specific country

over a limited time period cannot be applied unquestioningly to all questions of all sectors in all

countries over all periods.

References

Donzé L, Etter R, Sydow N & O Zellweger. (2004). Study on Sample Design for Industry Surveys. Final Report ECFIN/2003/A3-03, November 2004.

Kershoff G. (2009) What impact does the response rate and weighting have on the reliability of South Africa’s retail survey? Paper presented at the fourth joint EC-OECD workshop on business and consumer opinion surveys held in Brussels, 12-13 October 2009.

OECD (Organisation for Economic Co-operation and Development). (2003). Business Tendency Surveys. A Handbook. Paris: OECD.

Potgieter, L.J. du P., R. Nänny and S.J.J. van Zyl. (1997). A Guide to the Fifth Edition of the Industrial Classification of all Economic Activities (SIC). Research Report No. 245. Pretoria: Bureau for Market Research (BMR).

Stats SA (Statistics South Africa). Manufacturing: Production and Sales. Statistical release P3041.2.

Stats SA (Statistics South Africa).(2005). Manufacturing Industry, 2005. Report no. 30-02-02 (2005)

Stats SA (Statistics South Africa). Producer Price Index (PPI). Statistical release P0142.1


13

Appendix

Table 3 Sector classification and sector weights

SIC

codes

Description BER sectors Sector weights, %

Employm

ent 1

Domestic

sales

volume 2

301 Meat, fish, fruit, vegetables, oils

Food 13.0 15.1 302 Dairy products

303 Grain mill products

304 Other food

305 Beverages Beverages 2.6 4.7

311 Spinning, weaving, yarns Textiles 6.3 2.4

312 Other textiles

313 Knitted & crocheted articles Clothing 9.7 2.3

314-315 Wearing apparel & articles of fur

316 Leather & leather products Leather 3 0.7 0.6

317 Footwear Footwear 3 2.6 0.4

321 Sawmilling Wood 3.9 2.3

322 Wood & wood products

323 Paper & paper products Paper 3.3 4.6

324 Publishing Printing 3.5 4.0

325-326 Printing & reproduction of recorded media

331-333 Petroleum Not covered – –4

334 Chemical products Chemicals 7.7 8.7

335-336 Other chemical products

337 Rubber Rubber 3 1.4 1.2

338 Plastic Plastic 2.9 3.3

341 Glass Non-metal minerals

5.6 3.3 342 Other non-metallic mineral products

351 Basic iron & steel Basic metals 6.9 7.6

352 Precious & non-ferrous metal products

353-354 Structural metal products Metal products 9.1 5.4

355 Other fabricated metal products

356 General purpose machinery Machinery 6.0 6.0

357 Special purpose machinery


14

358 Domestic appliances

Electrical machinery

5.0 4.5

359 Office machinery, computers

361 Electrical motors, generators, transformers

362 Electricity distribution apparatus

363 Insulated wire & cables

364 Batteries

365 Electric bulbs & tubes

366 Other electrical equipment

371-373 Radio, TV & communication apparatus

374-376 Medical appliances, photographic equipment, watches

381 Motor cars

Transport equipment

7.0 18.1 382 Trailers & bodies for motor vehicles

383 Parts & accessories for motor vehicles

384-387 Other transport equipment

391 Furniture

Furniture & other 3.0 5.3 392, 395, 306

Other (incl. tobacco)

Total excluding petroleum 4 100.0 100.0

1 Employment according to the 1991 census

2 Domestic sales volume = total less foreign sales, which is then deflated by the relevant PPI for domestic output

3 Not published

4 Petroleum accounted for 5.5% of domestic sales volumes in 2005

SIC = The Standard Industrial Classification of all Economic Activities, 5th

edition. Source of codes and description:

Potgieter et al, 1997


15

Table 4 Number of responses per sector and size class, quarterly average

Small1 Large

1 Total

Food

2001 8 11 18

2005 8 10 18

2009 7 10 17

Beverages

2001 1 6 7

2005 3 3 6

2009 2 5 7

Textiles

2001 3 9 12

2005 5 7 12

2009 3 3 7

Clothing

2001 7 19 26

2005 6 14 20

2009 5 6 11

Wood

2001 5 5 10

2005 3 6 9

2009 4 7 11

Paper & products

2001 3 10 14

2005 6 18 24

2009 5 8 12

Printing & publishing

2001 4 7 11

2005 4 5 9

2009 3 4 7

Chemicals

2001 8 13 21

2005 11 17 28

2009 13 16 29

Plastics

2001 4 7 11

2005 6 6 12

2009 9 10 19

Non-metal minerals

2001 6 18 23

2005 7 19 26

2009 6 15 20


16

Small1 Large

1 Total

Basic metals

2001 2 7 9

2005 2 10 12

2009 3 7 10

Metal products

2001 9 16 25

2005 12 17 29

2009 8 18 26

Machinery

2001 3 7 9

2005 6 6 12

2009 4 9 13

Electrical machinery

2001 5 9 13

2005 7 5 13

2009 4 5 9

Transport equipment

2001 4 14 18

2005 6 15 22

2009 2 6 8

Furniture & other

2001 6 9 15

2005 5 8 13

2009 5 4 9

Not published 1

2001 3 7 10

2005 6 6 12

2009 3 4 7

Total

2001 81 172 253

2005 104 172 276

2009 85 135 220

1 Small refers to all responding units with less than 100 employees. Large

depicts units with 100 and more employees.


17

Table 5 The composition of the survey results vis-à-vis domestic sales volumes,

average percentage

S/B 2001 2002 2003 2004 2005 2006 2007 2008 2009 Ave

Food S 15.8 14.6 14.7 15.2 15.1 14.7 14.6 15.0 17.6 15.3

B 14.9 20.6 17.4 20.5 17.7 15.7 21.2 17.3 18.3 18.2

Beverages S 4.4 4.1 4.5 4.7 4.8 4.6 4.6 4.7 5.6 4.7

B 1.8 1.4 1.3 1.2 1.2 1.7 1.1 2.2 1.8 1.5

Textiles S 2.7 2.7 2.5 2.5 2.4 2.3 2.2 2.1 2.0 2.4

B 3.3 3.8 4.0 3.5 3.8 4.2 3.4 3.1 2.1 3.5

Clothing S 2.4 2.2 2.3 2.3 2.3 2.2 2.3 2.4 2.6 2.3

B 18.1 14.0 14.5 12.5 15.2 12.0 13.2 8.6 7.4 12.8

Wood S 1.9 1.9 2.0 2.1 2.3 2.3 2.4 2.3 2.2 2.2

B 1.6 1.9 1.2 2.1 1.9 2.6 1.7 4.1 4.7 2.4

Paper &products S 4.7 4.7 4.8 4.7 4.6 4.6 4.5 4.6 5.0 4.7

B 3.2 3.0 3.9 3.9 3.9 3.4 3.9 2.4 2.2 3.3

Printing & publishing S 4.1 3.8 4.0 3.9 4.0 4.0 3.9 3.7 3.9 3.9

B 1.6 0.7 1.6 0.6 1.5 2.3 1.3 1.2 1.7 1.4

Chemicals S 9.2 8.9 9.4 8.9 8.7 8.8 9.5 9.8 10.8 9.3

B 8.6 10.5 11.0 9.9 10.3 8.9 9.8 8.6 10.4 9.8

Plastics S 3.2 3.4 3.5 3.4 3.3 3.5 4.0 4.2 5.1 3.7

B 1.1 1.4 1.3 1.1 1.4 1.3 1.2 1.9 2.8 1.5

Non-metal minerals S 3.3 3.1 3.2 3.2 3.3 3.3 3.2 3.3 3.3 3.2

B 6.2 7.3 7.2 8.8 8.1 7.8 7.6 10.4 11.2 8.3

Basic metals S 9.7 10.2 9.3 8.9 7.6 8.3 8.1 7.8 5.4 8.4

B 6.5 6.5 6.0 5.0 4.5 6.4 5.8 7.3 6.8 6.1

Metal products S 5.4 5.7 6.0 5.8 5.4 5.4 5.6 5.8 5.8 5.7

B 8.5 9.2 8.7 8.2 10.8 15.0 13.8 14.7 14.5 11.5

Machinery S 5.2 5.9 6.3 6.4 6.0 5.2 4.1 2.7 3.9 5.1

B 2.7 3.0 3.1 2.8 2.8 2.1 3.0 4.4 7.4 3.5

Electrical machinery S 5.1 4.8 4.8 4.6 4.5 4.2 3.7 4.1 4.4 4.5

B 7.4 4.8 5.2 4.7 5.1 6.1 4.7 4.1 2.2 4.9

Transport equipment S 15.3 16.5 15.8 16.3 18.1 19.2 20.6 20.2 15.3 17.5

B 9.9 7.7 9.8 12.2 8.6 7.5 5.2 7.2 4.7 8.1

Furniture & other S 5.0 5.0 4.5 4.7 5.3 5.2 4.9 5.3 5.1 5.0

B 2.3 1.8 1.8 1.2 1.3 1.3 1.2 1.0 1.1 1.4

Not published S 2.6 2.5 2.4 2.4 2.2 2.0 1.9 2.0 2.1 2.2

B 2.2 2.4 2.0 1.8 1.7 1.7 1.8 1.4 0.8 1.8

Total S 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

B 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

S = Domestic sales volume, B = BER surveys

Ave = average for the period 2001 – 2009

Economy & Finance

An assessment of the the BER's manufacturing survey in South Africa