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The World Saving Data Base

Norman Loayza, Humberto Lopez, Klaus Schmidt-Hebbel and Luis Serven1

January, 1998

Abstract

This paper describes the new World Saving Data Base of country time series onconsumption -saving flows and their main determinants assembled for the World Bank project onSaving Across the World. The dataset improves upon existing publicly available datasets onseveral grounds. First, it extends coverage to a broader set of developing and industrial economies.Second, it corrects data inconsistencies in country time series in existing data bases. Third, itunifies definitions regarding the composition of the public sector. Fourth, it provides saving dataadjusted for capital gains and losses from inflation and real exchange rate devaluation. Fifth, thenew data base includes a set of standard saving determinants. The paper also summarizes thecoverage of the data base. The data have been constructed from careful analysis of the major databases publicly available and from inspection of some 2,000 country-specific documents.

1-. Introduction 1 Loayza, Lopez and Serven: DECRG, The World Bank; Schmidt-Hebbel: Central Bank of Chile. We are highly

indebted to M. Chakraborty and N. Sulikosky for their invaluable contribution to the construction of the data base.We would also like to thank C. Calderon and H. Franken for able assistance, and Jong-Woo Park (The WorldBank) and W. Roncada (UN) for their generous collaboration. We also acknowledge the contribution of IMF andWorld Bank staff during the construction of the data base. Correspondence, Humberto LOPEZ, The World Bank,1818 H Street, NW, Washington, DC, USA. Tel.: +1 202 473 4909; e-mail: hlopez@worldbank.org.

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The purpose of the World Bank research project on saving is to address three broadquestions:

i) Why do saving rates differ so much across countries and time periods?ii) How much do higher saving rates contribute to raising growth?iii) What policy measures are the most effective to raise national saving?

In order to address these questions, the availability of a large cross-country data set builtwith consistent criteria is an essential pre-requisite. Empirical consumption and saving studies forboth industrial and developing countries are often criticized because of their reliance on inadequateaggregate data. The criticisms stem partly from conceptual and empirical shortcomings of existingaggregate data and partly from inadequate use of the data in applied research.

The new World Saving Data Base of country time series on consumption -saving flowsand their main determinants presents several improvements on existing publicly available databases on several grounds. First, it extends coverage to a broader set of developing and industrialeconomies. Second, it corrects data inconsistencies in country time series in existing data bases.Third, it unifies definitions regarding the composition of the public sector: the data base includespublic saving for a consolidated central government definition of the public and for a generalgovernment or non-financial public sector definition of the public sector, separately. Fourth,saving data are presented both in raw and adjusted forms: the adjustment process corrects forcapital gains and losses from inflation and real exchange rate devaluation. Fifth, the new data baseincludes a set of saving determinants.

The Data Base consists of five modules. Module 1 contains corrected National Accountsdata used to compute Gross National Saving, Gross National Disposable Income, etc. Publicsaving figures corresponding to a consolidated central government definition of the public sectorare in Module 2. Module 3 extends the public sector coverage of Module 2 to a generalgovernment or, alternatively, non-financial public sector definition of the public sector. Unlike inModule 2 where the definition of the public sector is uniform, the concept of public sector used inModule 3 corresponds in each country to the broadest definition (above that of consolidated centralgovernment) for which information could be found. Variables that are generally considered asmajor saving determinants are included in Module 4. Finally, Module 5 contains data on savingand investment dissagregated at a household, firm and general government level for a limitednumber of economies.

This paper summarizes the sources used in the construction of the Data Base as well as theproblems encountered and the solutions adopted. More extensive information regarding the data ona country by country and year by year basis is available from the authors upon request. The restof this paper is organized as follows. Section 2 describes the construction of the corrected nationalaccounts data. Section 3 discusses the construction of the consolidated central government savingdata. Section 4 extends the public sector coverage of the previous section to a general governmentor non-financial public sector depending on data availability. Section 5 discusses the correction ofsaving data for capital gains and losses. Section 6 describes the saving determinants and Section 7the dissagregated data on saving and investment. Finally, Section 8 concludes.

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2-. National Accounts (Module 1)

National Accounts data were drawn mainly from existing cross-country data bases,complemented in a few instances with information from country-specific sources. The majorsources are listed in Table 1:

Table 1Source Release Coverage

WDI World Development Indicators 1997 70-95WSD World Stars Data base 1995 60-93IEC World Bank data accessible only internally 1997 65-95OECD OECD National Accounts 1997 60-95UNNA UN National Accounts 1997 50-94ADB Asian Development Bank April 1997 75-96

Some key features of these sources are summarized below:

World Bank sources: WDI, WSD and IEC The first three sources are from the World Bank. WSD and WDI are published datasetsand as such underwent extensive consistency checks prior to publication. By contrast, the IECdataset is internal and updated on an continuous basis, but checked for consistency less frequently. Both WSD and WDI were constructed from IEC data. The IEC dataset was “frozen” andcleaned up at different points in time for that purpose. Finally, WDI contains data for 1995, butmost of the observations are estimates. The three sources report constant-price accounting in base 1987. However, the WDIdataset does not report directly the constant-price statistical discrepancy in the GDP expenditureidentity, but instead it adds the discrepancy with private consumption, into the variable privateconsumption etc. WDI does report the growth rate of real per-capita private consumption,exclusive of the statistical discrepancy. To identify the discrepancy for those observations takenfrom WDI, we first computed the level of real per capita private consumption using the 1987figure for real private consumption (1987 is the base year, and the constant-price expenditureidentity holds exactly) and the growth rate just mentioned. We then multiplied this variable by thepopulation figures provided by WDI, to arrive at real private consumption.

OECD National Accounts

For OECD countries, we used as primary source the national accounts data published bythe OECD (National Accounts, 1997 ed., vol. I). Unlike the World Bank sources, however, the

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OECD uses 1990 as the base year for its constant-price series. Thus, to facilitate comparabilitybetween both sets of data, the OECD constant-price figures were re-based to 1987.

Rebasing can generate large discrepancies in the constant-price GDP expenditure identitywhenever there are large relative price changes between the old and new base years. For a coupleof countries, the rebased series showed very large discrepancies reaching as high as 127 percent ofconstant-price GDP! This could be traced to anomalous values of the 1990-based deflator of theIncrease in Stocks in the year 1987, and raises doubts on the accuracy of the original OECD data.

These relative price changes could reflect an incorrect calculation at the source of thedeflator of the Increase in Stocks (likely erroneously computed by deflating the change in the valueof, rather than the value of the change in, stocks). Hence, for a set of problematic countries weused an alternative formula to rebase the constant-price series from 1990 to 1987. Specifically, werebased gross domestic investment (GDI) instead of rebasing its individual components2. The re-computed statistical discrepancy clearly decreased when we use this alternative procedure. Aftercalculating the new residual, we proceeded to obtain the new constant-price Increase in Stocks bysubtracting the rebased Fixed Capital Formation from the rebased GDI.

Finally, two other countries (Greece and Mexico) present relatively sizable statisticaldiscrepancies in their re-based OECD accounts, which are not due to specifically to changes in therelative price of inventories across base years, but rather to variation in all relative prices in acontext of relatively high inflation. Because of these discrepancies between GDP and itsexpenditure, the re-based national accounts figures for these two countries should be taken withcaution.

The OECD does not report GNP nor Net Factor Income (NFY) at constant prices. Toobtain them, we followed the practice used in the World Bank datasets, namely deflating current-price net factor income with the total expenditure deflator. The steps were as follows:

1) calculate expenditure as Consumption + Investment in 1990 based constant prices;2) rebase real expenditure thus computed to 1987;3) divide current-price net factor income (defined as GNP-GDP) by the 1987 basedexpenditure deflator, and add the result to constant-price GDP.

United Nations National Accounts (UNNA)

The UN data base offers extensive country and time coverage but differs from the sourcesdescribed so far in that for most countries it does not provide linked series spanning the full periodof data coverage. Base years vary not only across countries, but also over time, reflecting changesin the base years used by the underlying national sources. As a result, for some countries and yearsthe UN provides more than one observation per variable, reflecting the overlap between seriesusing “old” and “new” base years.

Asian Development Bank (ADB)

2 GDI = Fixed Capital Formation + Increase in Stocks

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For Asian and Pacific countries, the Key Indicators of the ADB offer informationregarding their national accounts. The ADB reports both current and constant-price figures for themain national accounts aggregates; however, as in the OECD case, the base year is not 1987.Hence we proceeded to rebase the data and to check for problems resulting from the rebasing.

2.1 Data selection

A preliminary step was to check the internal consistency of each source, by checking thebasic macroeconomic identities:

• GNP = GDP + NFY• GDP = PrivCons + GGCons + Inv + Exp - Imp + Discr• PrivCons + GGCons = TotCons

Observations not satisfying these identities were discarded.

To ensure the consistency of our data, all the income and expenditure figures for a givencountry and year were taken from the same source. With this rule, it is enough to establish apattern of selection among sources for any one given aggregate, and the selection pattern for allothers is exactly the same.

As our key variable we took GDP. In view of the considerations given above on thealternative sources, we established the selection routing among them as follows:

1) Use OECD NA whenever possible -- in effect, for virtually all industrial-countryobservations.2) Use WDI whenever possible (except for 1995, since for that year WDI data areestimates). In effect, this makes WDI our basic source of developing country data for1970-94.3) Complete remaining gaps with WSD and IEC data when the connection with the abovesources is smooth3. In effect, these two sources become the leading ones for developingcountry data prior to 1970.4) Complete remaining gaps with UNNA and ADB when the connection is smooth, asabove.

To these four steps ranking cross-country data sources we added:

5) Complete remaining gaps (and also replace clearly erroneous information) withnational sources, under the same conditions as in the preceding point.

2.2 Construction of Saving Variables (at current prices)

To compute aggregate saving, we followed convention and used the residual-inclusiveconsumption figures (Consumption etc). The key saving and saving-related measures were derivedas follows:

3 A smooth connection between source A and B means that (A-B)/A is smaller than 1.5% for the years in common.

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Gross Domestic Saving (GDS)This variable is calculated as GDP - Total Consumption etc (i.e. including the statistical

discrepancy).

Gross National Disposable Income (GNDI)This variable is calculated as GNP + Total Net Transfers from Abroad. Construction of

the transfer data is described below.

Gross National Saving (GNS)This variable is calculated as GNDI - Total Consumption etc.

2.3 Transfers from abroad

In order to calculate Gross National Disposable Income (GNDI) and Gross NationalSaving as just described, we need information on Net Current Transfers from Abroad. However,available sources of data on external transfers provide abundant information on total externaltransfers, but little on their breakdown into current and capital. Only one source (the IMF Balanceof Payments 5th Edition --IMF BOP5--, described below) contains systematic data of this kind,and only since the mid-70s. Further, the country coverage of the data on capital transfers is ratherlimited prior to the mid-1980s.

Apart from data availability, two other considerations cast further doubt on thecurrent/capital disaggregation of external transfers. First, empirical relevance: according to theavailable data, net capital transfers from abroad are virtually insignificant except for a handful ofvery small economies (more details below). Second, the available data reveal a strong negativeassociation between current and capital transfers from abroad. This suggests either substitutionbetween both kinds of transfers, or accounting inconsistency in their recording.

These considerations indicate that the current-capital distinction of external transfers is oflimited practical relevance, at least in the available data. For this reason, we opted for using TotalTransfers (Capital + Current) in the calculation of Gross National Saving. While this proceduredeparts from conventional accounting practice, in the light of the available data we believe that it isof very limited consequence in terms of measurement error. Its key advantage is that it allows adramatic extension of the available samples.

For Total Transfers, seven different sources with international coverage were initiallyconsidered. Some of their main features are listed in Table 2:

Table 2

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Source Coverage Currency Latest RevisionWorld Bank IEC 1965-95 US$ 1997OECD 1960-95 Local Currency 1997IMF BOP5 1970-95 US$ 1996UN 1950-94 Local Currency 1997World Bank WDI 1970-95 Local Currency 1997World Bank WSD 1965-93 Local Currency 1995IMF BOP4 1965-94 US$ 1991

The first six of these sources report some disaggregation between current and capitaltransfers. However, with the exception of the IMF’s BOP 5 the underlying criteria for thedisaggregation are unclear. For most countries, the OECD and UN sources classify all transfers ascurrent, and report capital transfers for very few.

In turn, the World Bank sources typically consider all private transfers as current and allofficial transfers as capital. They also pose other problems:

• The transfer figures implicit in the Gross National Saving (GNS) data reported bythese sources did not equal the transfers reported separately by the source. This meansthat the calculation of GNS makes use of (unreported) transfer data, different fromthe reported figures.

• The exchange rates used to convert the data from US$ to local currency (the so- called“IEC conversion factor”) are for some countries extremely different from the officialexchange rates reported by the IMF, in an attempt to correct for the existence ofparallel foreign exchange markets and other distortions. However, these alternativerates are available only for a limited number of countries and years, typically in the1980s only, and there is no documentation of the procedure followed to compute them.This renders the conversion factors arbitrary.

• The transfer data reported by IEC and WDI were qualified by the World Bank staffthat assembled them as not reliable.

For these reasons, only the sources shown in bold in Table 2 above were retained. We next

consider some features of sources used in the construction of the series.

BOP5The IMF publishes the balance of payments data that member countries report to the Fund.

In the 1995 Yearbook, these data were published for the first time according to the methodologyand format of the fifth edition of the Balance of Payments Manual. This new edition distinguishesbetween current and capital transfers, based on guidelines established in the 1993 SNA. Bycontrast, the 4th edition reports only total transfers.

Of the available sources of transfer data, BOP5 is the best one because:• It has the most comprehensive breakdown (current vs. capital, private vs. government, and

debit vs. credit).• It is the most recently updated source.

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Although BOP5 reports Debt forgiveness and Nonproduced nonfinancial assets as part oftransfers, for our analysis we excluded them. They are therefore subtracted from the BOP5 seriesdescribed below. Such transactions are not considered transfers in BOP4 either.

The US$ series from BOP5 were converted to local currency using the year-averageofficial exchange rate reported in the IFS. In order to combine these data with the NationalAccounts information, both need to refer to the same time period. BOP5 reports in Calendar Year(CY) except for Gambia and Iran, which are in Fiscal Year (FY). For some countries, however,National Accounts data refer to FY rather than CY, or even to a third time unit different from bothFY and CY.

To convert BOP5 to National Accounts periods we first choose the time notation asfollows:The fiscal year including months in calendar years t1 and t2 will be located under heading:• t1 if the fiscal year ends BEFORE June 30,• t2 if the fiscal year ends ON or AFTER June 30.

With this convention, we then used a simple proportional adjustment to translate BOP calendaryear data to the new time period. For example:

Year Ending 3/31 1970 = 3/12 * 1970 + 9/12 * 1971Year Ending 6/30 1970 = 6/12 * 1969 + 6/12 * 1970Year Ending 7/7 1970 = 5/12 * 1969 + 7/12 * 1970

While current transfers are numerically important for virtually all countries, a preliminary checkshows that in most cases capital transfers are quantitatively insignificant, except for a handful ofvery small economies.

Only 19 countries possess any annual observation with capital transfers from abroad(positive or negative) at or above 2 percent of GNP4. Of the above total, 90 percent of theobservations correspond to the period after 1985. In fact, only 7 countries possess any observationswith capital transfers exceeding 2 percent of GNP before that date. This fact suggests either achange over time in the nature of transfers or, more likely, a possible methodological inconsistencyin the classification of external transfers between the periods before and after 1985.

In addition, in most of the above countries there is a clear negative association betweencurrent and capital transfers from abroad as percentages of GNP, which strongly suggests a sort ofsubstitutability (whether true or artifact of accounting methodology) between both categories oftransfers. Formally, the contemporaneous correlation between current and capital transfers aspercent of GNP is negative and large (above .60 in absolute value) in 9 of the 19 countriespossessing at least one observation with capital transfers above 2 percent of GNP. The correlationis also negative, but more moderate, in another set of 3 countries. Only in 3 countries bothcategories of transfers tend to move together. For the remaining 4 of those 19 countries there aretoo few nonzero observations on capital transfers to detect any significant association. 4 The countries are: Antigua and Barbuda, Bahamas, Fiji, Dominica, Grenada, Guinea-Bissau, Israel, Kiribati,

Madagascar, Mali, Malta, New Zealand, Niger, St. Kitts and Nevis, St. Lucia, St. Vincent, Tanzania, Vanuatu andZimbabwe.

Years in the source table (in calendar year)

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The reduced magnitude of capital transfers, and the strong evidence of substitutability -- orperhaps erratic accounting -- between both categories of transfers, indicates that there is little tolose analytically or numerically from combining all external transfers into one single magnitude,and add the latter to GNP to arrive at gross national disposable income (GNDI). This has theenormous advantage of allowing the computation of GNDI, and hence national saving, for the verylarge number of observations for which the current-capital breakdown is unavailable -- virtually allthe years prior to 1975 for all countries, plus many more observations after that date.

While this procedure departs from convention, the above evidence implies that the resultingdisposable income and national saving figures will be in the vast majority of cases numericallyvery similar to, and in some cases analytically more accurate than, the ones that would be obtainedusing only current external transfers. Some inaccuracies might result but, from the earlierdiscussion, they are likely to be confined to just a small handful of countries.

BOP4As mentioned earlier, the breakdown of transfers into current and capital does not exist in

BOP4. Although as explained above, BOP5 is the best source for developing countries available tous, its time coverage is limited (it starts from the 1970s), while BOP4 starts from the 60s. Thelogical procedure is to conciliate first BOP4 with BOP5, because BOP5 is mostly a rearrangementof BOP4.

UNNAThe United Nations National Accounts report both Current and Capital Transfers. The

advantage of this source is that it is already in local currency and covers from 1960 to 1995. Itwas primarily used to fill gaps in BOP5&4.

OECDThe OECD National Accounts report Current and Capital Transfers. The advantage of

this source is that it is already in local currency and it covers from 1960 to 1995. It was primarilyused for industrial countries.

Consolidation of BOP5&4, OECD NA and UNNAThe last step was to put together all the sources. To combine them, the procedure

used was the following:

1. We used OECD NA for most industrial countries.2. BOP5&4 were the basic source for developing countries.3. UNNA were used to fill gaps.

Finally, to complete gaps in the general pattern above, we used other complementarysources. The main ones were the World Bank’s Africa Development Indicators (ADI), published in1996, which includes data for countries in that continent spanning the years 1970-1995, and theAsian Development Bank’s (ADB) country files, which span the years 1975-95. In addition, we

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also used national sources as well as internal IMF documents (Recent Economic Developmentsand Article IV consultations).3-. Consolidated Central Government Saving (Module 2)

Module 2 contains data on public sector saving. The precise definition of the public sectorused in Module 2 is that of consolidated central government (CCG): budgetary centralgovernment plus extrabudgetary central government plus social security agencies. Notice thatthis definition of the central government is equivalent to that of general government minus local andregional governments. Thus, the consolidated central government is equivalent to the generalgovernment in those countries without local and regional governments or where the accounts of thelocal and regional governments are under a particular central government unit.

Like in the national accounts case described above, the public saving figures of theconsolidated central government are defined in the data base as inclusive of all net transfers fromabroad. This choice is dictated by the unavailability of information on the disaggregation of foreigngrants between current and capital, and by the relative minor magnitude of capital transfers exceptfor a handful small economies. Private saving is defined as the residual difference between grossnational saving and public saving. Observe that by construction, private saving in Module 2 willinclude the saving of the local governments and the public enterprises.

Sources

Public saving figures were drawn from several sources and complemented in some caseswith information from country-specific sources. The major sources are listed in Table 3.

Table 3Source Release Coverage

UNNA United Nations National Accounts 1997 50-94GFS Government Finance Statistics 1996 70-96RED Recent Economic Developments Several

Issues60-96

ADI World Bank African Development Indicators 1996 70-94ADB Asian Development Bank 1997 75-96

Some key features of these sources are summarized below.

United Nations National Accounts

UNNA do not report data for the consolidated central government definition of the publicsector. However, the subsectoring that comes under the UNNA ‘s definition of the generalgovernment consists of central, state or provincial, local governments and the social security funds.Hence, the public saving figures corresponding to the consolidated central government werecomputed as gross saving of the central government plus gross saving of the social security plus

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net capital transfers from abroad. The data obtained from the UNNA are on a commitment basisand on a national accounts year.

GFS

GFS report fiscal data on a fiscal year basis (which for several countries differs from thenational accounts year) for most of the member countries of the IMF starting in 1970. Thepublished data refer to two different definitions or levels of the central government: theconsolidated central government and a narrower definition, the budgetary central government, thatexcludes the social security and other extra-budgetary units. The countries for which the datacorrespond to this narrower definition of central government are 205. Of these, apart from NewZealand, that usually reports data for a definition of central government that includes publicenterprises, the remaining 19 countries are developing countries where the main extrabudgetaryaccount (the social security) is not very important. In fact, information provided by IMF staffindicates that the revenues of the social security in several of these countries would be below 2percent of the GDP. Therefore, although for some countries the definition of central governmentdiffers, it is not unreasonable to treat all the countries in Module 2 as if they were fullycomparable.

Gross public saving figures based on GFS data are computed as current revenue minuscurrent expenditure plus total grants minus capital transfers abroad. Observe that as noted above,both current and capital grants are considered in the revenue side.

There is an important remark to make regarding the accounting valuation of UNNA andGFS figures. UNNA data are recorded on a commitment basis whereas GFS data are recorded on acash basis. These differences in valuation might produce some problems when dealing withcountries with a significant amount of arrears. Given the limited information in the originalsources, no attempt has been made to adjust the figures from cash to commitment.

Another issue to take into account is the fact that GFS report data on a fiscal year basis.All figures from GFS were converted to a national accounts year using the method outlined inSection 2.

UNNA and GFS information would cover around 125 countries, although for many ofthem the time coverage is very limited: the percentage of available observations for the period 70-94 with respect to the 150 countries universe would be less than 50 percent.

Recent Economic Developments (RED)

The IMF publishes a country RED with a frequency that ranges from 1 to 3 years. Thetypical fiscal page reports (among other indicators) current revenues, current expenditures andgrants. Hence the calculation of gross saving figures is similar to those based on GFS information.

5 The countries are: Bangladesh , Belize, Botswana, Fiji, Gambia, Ghana, Guinea-Bissau, Jordan, Kenya, Malawi,

New Zealand, Oman, Papua New Guinea, Philippines, Sierra Leone, Solomon Islands, St. Vincent and theGrenadines, Sudan, Swaziland, Trinidad and Tobago and United Arab Emirates.

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Besides GFS data are based on REDs data and so the use of REDs seems a natural choice toincrease the coverage of the data base.

There are however some comments to make regarding the problems found when collectingthe data from the REDs. The main difficulties and the tentative solutions with this source are listedbelow.

• The information regarding cash-commitment valuations is very limited. A typical fiscal pageof the REDs reports the government balance on a commitment basis as the difference betweenrevenues and expenditures. Therefore, one would be tempted to assume that both revenues andexpenditures are presented on a commitment basis (i.e. public saving figures based on REDswould be commitment valued). However, it is also possible that some items are commitmentvalued and some other cash valued. Notice that if a country running arrears wants to present afiscal page with a lower than actual deficit it can do so by presenting the revenues on acommitment basis and the expenditures on a cash basis. Since the amount actually financed(the cash deficit) remains unchanged, the effects of such creative accounting would be anartificial reduction of the arrears (usually a performance criterion of IMF programs). As withGFS data, given limited information, no attempt has been made to adjust the data.

• Inconsistency between revenue and financing items. In general REDs consider grants on therevenue side of the fiscal page (above the line). However, in several cases (mainly in the early70s) grants were considered as a financing item (below the line). The problem was solved byadding to the reported current revenue the grants item when applicable.

• Lack of revised figures. The REDs fiscal page usually report no more than 5 years of data,some of which are estimates and some are preliminary. Every revision of data that involvesmore than the reported 5 years cannot be captured. In order to minimize the problem with therevisions, the relevant figures were always taken from the most recent RED.

• Government definition unclear. In many cases the REDs refer to the public sector or to thecentral government without any further reference to the government units included in thedefinition. In general, this problem has been solved by looking for overlapping periods wherefor at least one of the periods the information is available. As a last resort, and in absence ofany other information, we maintained the definition used in GFS.

• Insufficient information on the revenue side. As noted above, one of the elements used tocompute public saving is current revenue. While the expenditure side is in most of the casesdisaggregated into current and capital expenditures, there are several cases where the onlyreported item on the revenue side is total revenue. This is usually the case when capital revenueis negligible. Besides, the average difference between total revenue and current revenue is lessthan 1 percent of total revenue. In those cases where current revenue was not available, totalrevenue has been used in its place.

• Year Definition. REDs report fiscal data on a fiscal year basis that in several countries differsfrom the national accounts year. The figures were thus converted to a national accounts yearusing the method described in the previous section for “Transfers from abroad”.

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• Data consistency. Whenever an observation was not considered consistent with the definitionof central government used in Module 2 (apart from the budgetary central governmentcountries listed above), the observation was not included in the data base. Hence, dataconsistency has been given a higher priority than coverage.

ADI

An additional source used is the ADI of the WB. ADI report government surplus/deficit(including net lending and grants) and capital expenditure (including net lending) for a set ofAfrican countries. ADI fiscal data were themselves collected from GFS (among other sources)and, therefore, most of the data contained in ADI were already included in the data base. There are,however, several countries for which ADI contains WB-based information that is not included inGFS. That additional information has been used to increase the coverage. In this case, gross savingis computed as government balance after grants plus capital expenditure minus capital transfersabroad. Notice that, as noted above, since total revenue is almost equivalent to current revenue inmost countries, public saving figures computed in this way are consistent with the definition usedin the previous sources. Like with GFS and RED data, figures in a fiscal year other than thenational accounts year were adjusted.

ADB

A final source is the key indicators of the Asian Development Bank (ADB). The sourcehas proved useful since it has allowed to increase the number of countries in the data base. Thestructure of the information in the ADB key indicators is similar to the GFS, and hence the samestrategy has been followed (i.e. gross public saving figures are computed as current revenue minuscurrent expenditure plus total grants minus capital transfers abroad).

Finally, country documents were also examined for those countries where observationswere still missing.

The sources for capital transfers abroad are just two. For those countries for which publicsaving was computed with UNNA data, capital transfers abroad were extracted from UNNA.Otherwise GFS was used. Due to the limited number of observations, whenever none of the sourcesprovided information, capital transfers abroad were assumed to be zero. This assumption is likelycorrect for most developing countries.

Consolidation of sources

To combine the sources described above the procedure employed was the following:

1. We used United Nations national accounts as our preferred source. However, most of thecountries for which UNNA report data are industrial countries.

2. In those cases where either UNNA did not report data or the time coverage was very limited,we used GFS.

3. REDs were used to increase both the time coverage and the countries included in the data base.4. ADI data were used to fill gaps in African countries.

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5. Finally, ADB data were used to fill remaining gaps in Asian countries.

4-. Public Sector Saving (Module 3)

In the previous section we have reviewed the construction of the public saving figurescorresponding to the consolidated central government definition of the public sector. Unlike inModule 2, where the public sector is uniformly defined across countries, the concept of publicsector used in Module 3 corresponds in each country to the broadest definition for whichinformation could be found other than the consolidated central government. The three alternativesare:

• the central government, budgetary or consolidated (CCG), plus state-owned enterprises(SOE).

• the general government (GG), defined as the consolidated central government (budgetarycentral government, extrabudgetary central government and social security agencies) plusstate, local and regional governments.

• the non-financial public sector (PS), defined as the general government plus non-financial public enterprises

For those countries where information is unavailable for all these public sector definitions,Module 3 reports no data. Figures for the consolidated central government are reported in Module2 of the data base.

As above, and due to the almost complete unavailability of information on thedisaggregation of foreign grants between current and capital, public saving is defined as inclusiveof all net transfers from abroad.

Sources

The public saving figures contained in module 3 were drawn from several data bases andcomplemented with country-specific information. Table 4 summarizes the main sources.

Table 4Source Release Coverage

UNNA United Nations National Accounts 1997 50-94WSDM2

World Saving Data Base, Module 2 1997 60-95

BB Bureaucrats in Business 1995 65-95RED Recent Economic Developments Several

Issues60-96

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United Nations National Accounts

• The UNNA report gross saving of the general government derived as the difference between itsdisposable income and final consumption expenditure, from the SNA income and outlayaccount. In some cases, UNNA report net rather than gross saving of the general government,in which case consumption of fixed capital has been added to arrive at a measure of grosssaving. The gross saving and net saving figures reported in the UNNA are inclusive of netcurrent transfers. In order to be consistent with the definition of gross saving used in our database, net capital transfers to the general government have been added.

• The subsectoring that comes under the UNNA ‘s definition of the general government consists

of central, state or provincial, local governments and the social security funds. There is oneexception (Malta) where public enterprises are included in general government.

• The UNNA data are on a commitment basis. The UNNA reports the saving figures on a

national accounts year basis even for countries where the national accounts year is differentfrom the fiscal year.

WSD Module 2

In a number of countries6 without local and regional governments, or where the accountsof the local and regional governments are under a particular central government unit, the concept ofgeneral government is equivalent to that of the consolidated central government, and hence thepublic saving figures reported by Modules 2 and 3 are the same.

Bureaucrats in Business: The Economics and Politics of Government Ownership

Bureaucrats in Business is a World Bank Policy Research Report published in 1995. Themain features of this source are as follows:

• Data refers to the current account balance of state-owned enterprises. SOEs are defined hereas government-owned or controlled economic entities that generate the bulk of their revenuefrom selling goods and services.

• SOE saving (or current account balance) are obtained as the sum of net operating andnonoperating revenues. Unless otherwise noted, net operating revenue (or operating surplus orbalance) refers to gross operating profits, or operating revenue minus the costs of intermediateinputs, wages, factor rentals, and depreciation. The sum of operating surplus and netnonoperating revenue is SOE saving (or current account balance). Whereas the SOE savingdata published in Bureaucrats in Business exclude all transfers to and from the SOEs, such assubsidies and dividends, the gross saving of the SOEs used in computing the public sector

6 The countries are: Bahamas, Kuwait, Maldives, Oman, St. Kitts and Nevis, Seychelles, Singapore, Tonga, Republic

of Yemen and Zaire.

16

saving figures include net transfers to and from SOEs. The latter are obtained from the rawdata underlying the data published in the above mentioned source.

• It is not clear from the source whether SOE saving is reported on a cash or commitment basis.

The saving figures in module 2 of the consolidated central government or the budgetarycentral government are added to the current account balance of the SOEs to arrive at a broadermeasure7. Of course, by definition, this measure excludes the saving figures of state, local andregional governments.

The source of savings of the relevant definition of the central government is as described inmodule 2 and that for the SOEs is the raw data underlying the data published in Bureaucrats inBusiness.

Recent Economic Developments

As in Module 2, the Recent Economic Developments of the IMF were used to complementthe data from the sources outlined above. The list of countries for which data on non-financialpublic sector saving is available from the REDs is the following: Antigua and Barbuda, Argentina,Barbados, Belize, Bolivia, Chile, Costa Rica, Dominica, Dominican Republic, Ecuador, ElSalvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Malaysia, Nicaragua,Pakistan, Panama, Paraguay, Peru, Saudi Arabia, St. Lucia, St. Vincent and the Grenadines,Thailand, Trinidad and Tobago, Uruguay, Venezuela. The problems and solutions described in theprecedent section are fully applicable to this section.

Consolidation of sources

To combine the sources described above the procedure was the following:

1. We used United Nations national accounts data for 33 countries8.2. Bureaucrats in Business combined with data from Module 2 was the main source for the

central government plus SOE definition of the public sector (14 countries).3. Finally, RED were used to increase both the time coverage and the countries included in the

data base (29 countries).

5-. Adjusted Saving 7 The countries for which the data in Module 3 refer to central government plus SOE are: Bangladesh, Congo,

Ethiopia, Gambia, Malawi, Mali, Myanmar, Nepal, Niger, Rwanda, Senegal, Sierra Leone, Tanzania and Tunisia.8 The countries are: Australia, Austria, Belgium, Botswana, Cameroon, Cote d’ Ivoire, Canada, Denmark, Finland,France, Gabon, Germany, Former Federal Republic of, Iceland, Ireland, Israel, Italy, Japan, Luxembourg, Malta,Namibia, Netherlands, Norway, Philippines, Portugal, South Africa, Spain, Sudan, Sweden, United Kingdom, UnitedStates, Zambia, Zimbabwe.

17

One of the improvements of the World Saving Data Base over preceding comparabledatasets is the inclusion of saving data adjusted for capital gains and losses. This section describesthe adjustment procedure.

The logic underlying the adjustments can be described as follows. Consider an agent thatissues domestic-currency and foreign-currency financial liabilities. The agent’s (nominal) flow ofsaving over period t, as conventionally measured, equals the decline in her net domestic-currencyliabilities NDD and net foreign-currency liabilities NFD:

( )s NDD NDD e NFD NFDt t t t t t = − − − −− −( ) ,1 1

where e is the nominal exchange rate, and a bar over a variable indicates its period-average. It iseasy to see, however, that saving measured in this way does not equal “true” saving -- i.e., thenominal value of the change in the agent’s real wealth over period t. The latter is given by:

( ) ( )s NDD p NDD p p e NFD p e NFD p pta

t t t t t t t t t t t t= − − − −− − − − −/ / / /1 1 1 1 1

where the superscript a denotes adjusted saving, pt is the domestic price level and, as before, priceand stock variables without bars denote their end-of-period values.

The difference between adjusted and unadjusted saving is the agent’s net capital gains ncg.Formally:

( ) ( )( ) ( )

ncg p p NDD p p NDD

p p e e e NFD p p e e e NFDt t t t t t t

t t t t t t t t t t

=

.t t

− − + −− − + −

− −

− − − −

/ /

/ / / /

1 11 1

1 1 1 1

The first line in the right-hand side of this expression captures the capital gain on netdomestic-currency liabilities, while the second line measures that on net foreign-currency liabilities.

5.1 Practical Implementation

This section describes the practical implementation of the above expressions. Theprocedure employed focuses on the computation of net capital gains ncg. Once these have beencomputed, adjusted saving follows immediately as sa =s+ncg.

Adjustment of saving for capital gains and losses on domestic-currency liabilities has beenattempted in a number of occasions in the literature. However, the adjustment related to foreign-currency liabilities is much less frequently done. For this reason, we proceed in two stages,introducing first the adjustment for capital gains on domestic-currency liabilities and then addingthat on foreign-currency liabilities.

One major difficulty in the calculation of capital gains is the unavailability of adequateend-of-period domestic prices. For the vast majority of countries in the data base we can onlyobserve year-average prices from the National Accounts. One possible alternative is the consumerprice index (CPI), which in general is computed on a monthly basis. However, it also poses major

18

problems: first, CPIs are not available for all developing countries; second, in many countries theircoverage is limited to an arbitrarily-chosen set of goods, often very different from the actualconsumption basket; third, the geographical coverage of the information underlying the CPI isoften limited to a few (or even a single) urban areas.

For these reasons, we have chosen instead to approximate the end-of-period price level attime t as a (geometric) average of the period-average price levels during periods t and t+1:

p p pt t t = α α+−1

1

where α (1-α ) is the weight given to the current period (next-period) average prices. Under thisassumption, net capital gains can be reformulated in terms of observable prices as

( )( ) ( )( )( )( ) ( )( )

ncg NDD NDD

e e e NFD e e e NFD

t t t t t

t t t t t t t t t t

=

+

1 1 1 1

1 1

11

1

11

1 1 1

− + − − + +

− + − − +

+−

−

+−

− − −

π π

π π

α α

α α/ /

where π t t tp p = −−/ 1 1 .

The above formulation has been used to compute the net capital gains (losses) of the publicsector and the private sector. To do so, we need (in addition to prices and exchange rates)information on the private and public sector’s net domestic and foreign-currency liabilities. Thus,in principle we would need data on four different asset (liability) stocks.

Information on public sector liabilities can be constructed along the lines described later.For this purpose, we consolidate the Central Government with the Central Bank, and hence theirjoint net domestic liabilities include the money base (and exclude any net government debt held bythe Central Bank).

However the main difficulty lies in identifying the private sector’s net liabilities, on whichvery limited information exists. To reduce the scale of the problem, we follow convention inassuming that (i) the public sector’s net domestic-currency liabilities equal the private sector’s netdomestic-currency assets (i.e., NDDg = -NDDp , where the subscripts p and g refer to the privateand public sector, respectively); and (ii) the public sector’s net foreign liabilities are fully held bynon-residents. The first assumption means that non-residents do not hold domestic-currency publicdebt. The second implies that residents do not hold foreign-currency public debt; hence thecountry’s (net) external debt equals the sum of NFDp + NFDg .

We are well aware that these assumptions are not literally true, particularly in industrialcountries. To dispense with them, however, we would need a 2x2 breakdown of net public debtstocks by currency and holder, which is unavailable except for a few observations on a handful ofindustrial countries.

With the above assumptions, the capital gains/losses of the domestic private sector on itsholdings of public debt depend only on domestic inflation, and not on exchange rate movements.Similarly, the capital gains/losses of the nation as a whole (private plus public sectors) relate only

19

to the country’s net foreign-currency debt, and not to the domestic-currency debt owed toforeigners.

One final but very important caution relates to the lack of reliable measures of privateforeign-currency assets held abroad -- capital flight. The very limited availability of this keyinformation makes our estimates of net capital gains on foreign-currency net liabilities approximateat best. For this reason, and others mentioned below, those estimates should be taken with a verylarge grain of salt.

The final unobservable ingredient needed to implement numerically the above formulationis an estimate of α (the weight of current-period average prices in end-of-period prices). Weexperimented with a few alternative values: .5, 1 and 0, as well as a simple average of the lattertwo specifications. Values of 1 and 0 yielded somewhat erratic results (they imply that the end ofperiod price level at t is equal to either the average price level during t or the average price levelduring t+1).

5.2 Results: capital gains on domestic-currency assets

The numerical results concerning capital gains related to domestic-currency assets were onthe whole relatively insensitive to the choice of α, with the exception of a few countriesexperiencing episodes of extreme inflation. They are listed below:

Argentina, Bolivia, Brazil, Guinea-Bissau, Guyana, Israel, Myanmar, Malaysia, Nicaragua,Peru, Surinam, Zaire and Zambia.

In these cases, the timing and magnitude of the results are highly sensitive to the choice ofα , and therefore we followed a more precise procedure. Specifically, for these countries weestimatedα comparing end-of-period and period-average consumer price indices (and constrainingthe estimate of α to lie between 0 and 1). Such estimation produces different values of α for eachcountry and each year. For the remaining countries, we report the results obtained with α = .5.

The results of the adjustment for capital gains on foreign-currency assets are much lessencouraging. For countries and periods of high inflation and large devaluations, the results yieldhuge estimated capital gains (or losses), and their timing and magnitude are quite sensitive to thechoice of α . A change in the value used can turn a very large capital gain into a very large capitalloss. Further, for a considerable number of countries the resulting capital gains figures displaymarked volatility from year to year.

There are several reasons for these disappointing results. First, we are clearlyunderestimating private and aggregate foreign assets (due to the lack of information on flightcapital), and in many instances overestimating foreign liabilities by measuring them at face value --rather than at the theoretically-correct (but mostly unobservable) market value. The two factscombined likely lead to a substantial overstatement of the open liability position of manydeveloping countries, and hence to an exaggeration of the capital gains and losses associated withprice and/or exchange rate movements.

20

Second, the calculations use official exchange rates, which can diverge greatly from truemarket rates. Because of this divergence, huge depreciations of the official rate may actuallyinvolve little change (even none) in the true market rate, which would be the theoretically correctone to value agents’ foreign-currency assets and liabilities. Alternatively, we have attempted toapproximate the market-determined rate using the parallel market exchange rate. However, ourcomputations suggest that using parallel rates in the adjustment do not significantly improve theresults. Apart from other considerations, however, for consistency a complete experiment wouldhave required the use of the same rate in the conversion of external transfers to domestic currency,something we have not attempted.

Third, notwithstanding the above reservations, the numerical results likely reflect also thefact that real exchange rates can display very large movements in the short term -- for example incountries experiencing high inflation and devaluing periodically a fixed nominal exchange rate.This pattern of overvaluation followed by maxidevaluation would result in large capital gainsalternating with large losses. It is also possible that this oscillating pattern could be amplified byour crude approximation to end-of-period prices.

Taken together, all these considerations suggest that for a sizable number of countries ourestimated capital gains on foreign-currency assets are likely to be very unreliable. Only forcountries with moderate inflation and relatively small exchange depreciations can they be viewedwith some confidence. Outside such countries, the resulting adjusted-saving figures should be usedwith great caution.

5.3 Data Sources

Apart from the inflation (GNP deflator inflation rate) and exchange rate series that weredrawn from Module 1 and IFS respectively, the adjustment procedure requires stocks of public andprivate sectors net domestic and net foreign liabilities. Unfortunately, for most of the countries,there is a complete lack of information regarding the net financial positions of both the public andthe private sectors. Moreover, in many cases we have found that the lack of information alsoextends to the gross positions. Given these limitations we proceeded as follows in the computationof the required series:

Gross domestic liabilities of the public sector are defined as gross domestic debt of therelevant definition of the public sector plus the money base. As noted above, we consolidate thecentral government with the central bank and hence we consider their joint liabilities which includethe money base and exclude the government debt held by the central bank.

We have found two main problems with gross domestic debt figures. First, gross domesticdebt figures for definitions of the public sector broader than the consolidated central governmentare not generally available. Second, the reliability of the available gross debt figures as a measureof gross domestic liabilities may be limited. In particular, for some countries the gross debt figuresof the central government and the total claims on the central government follow divergent paths,with the total claims being significantly larger than the gross debt.

A possible reason for this finding is that governments replace bonded debt by non-bondeddebt and the only the former is reported as a liability. A second reason may be that the sources forthe data are different. Debt figures reported in GFS and IFS (the main sources used to computegross domestic liabilities of the public sector) are typically obtained from the ministry of finance of

21

the respective country, whereas the claims on the central government are obtained from the centralbank and the banking system. A final reason could be that some of the figures are reported oncommitment basis whereas others are reported on a cash basis.

Specifically, we checked on a country by country basis for the coverage of both grossdomestic debt figures of the consolidated central government and claims on the central government.When the number of observations of one of the series was significantly larger than in the other, theseries was chosen to compute our measure of gross domestic liabilities (i.e. to be added to themoney base). In those cases where the coverage of both series was similar we checked for majordiscrepancies. Whenever the claims on the central government were significantly larger than thegross debt figures, the former was used in the computation of the gross domestic liabilities figures.

For a broader definition than the consolidated central government, gross domesticliabilities were computed by adding the gross domestic liabilities of the CCG and the claims ofdeposit money banks and nonbank financial institutions on the remaining units of the relevantpublic sector definition.

Gross domestic assets of the public sector are defined as public sector’s claims on depositmoney banks and nonbank financial institutions.

There are some exceptions to the above descriptions. For ISRAEL and BRAZIL, given thelarge proportion of indexed net debt outstanding, the only component used in the computation ofthe net domestic liabilities of the public sector was the monetary base. Another exception isCHILE: the period 78-94 takes into account the indexed instruments (i.e. capital gains arecomputed on non-indexed debt). For the period 70-77, indexed debt has been estimated using theproportion of indexed debt in 1978. Finally, public gross domestic debt of OECD countrieswas computed as general government total debt minus public sector foreign debt.

Gross foreign liabilities of the public sector are defined in the adjustment procedure asgross foreign debt of the public sector. Data on external debt were mainly drawn from the WorldBank’s World Debt Tables. However, the World Debt Tables do not report external debt for theOECD countries. As an alternative we used gross foreign debt figures of the consolidated centralgovernment.

Gross foreign assets of the public sector are defined as foreign assets of the central bank.

Gross foreign liabilities of the private sector are defined as gross foreign debt of the privatesector. Whenever the World Debt Tables do not include data on private sector external debt, theexternal liabilities of deposit money banks and nonbank financial institutions were used.

Gross foreign assets of the private sector are defined as foreign assets of deposit moneybanks and nonbank financial institutions.

6-. Saving Determinants (Module 4)

We next consider the variables incorporated into the World Saving Data Base as savingdeterminants. The data are classified into five different categories:

22

Family and demographic structure variables.Financial development variables.Poverty and inequality variables.Social security variables.External variables.

Sources

Most of the saving determinants data were drawn from World Bank and IMF data bases.The main sources are the following. The International Economics Department (IEC) database of the World Bank (WB). The International Financial Statistics (IFS) of the InternationalMonetary Fund (IMF). The World Development Indicators (WDI) of the World Bank. Capitalcontrols data have been kindly provided by G.M. Milesi-Ferreti of the IMF. Table 5 summarizesthese sources.

Table 5Source Release Coverage

IEC World Bank data accessible only internally (viaQuery4)

1997 60-95

WDI World Development Indicators 1997 70-95IFS International Financial Statistics 1997 60-96

Unless otherwise indicated, the data refer to the Western calendar year. Thevariables included are the following.

Family and demographic structure variables

Variable Source CoverageTotal Population IEC 65-94Population Younger than 15 IEC 65-94Population Older than 65 IEC 65-94Population Between 15 and 65 IEC 65-94Life Expectancy at Birth IEC 65-94Urban Population IEC 60-94Number Of Persons Per Family IEC 65-94Human Capital Stock IEC 65-87Human Capital Stock, Primary IEC 65-87Net Enrollment Ratio, Primary IEC 75-94

The annual observations of the variables “Population younger than 15”, “Population olderand 65” and “Population between 15 and 65” are interpolated. We have run a consistency check asto see whether the sum of these three variables equals Total Population. For those cases where the

23

discrepancy was larger than 2 percent of “Total Population”, we adjusted “Population youngerthan 15”, “Population older and 65” and “Population between 15 and 65” using their shares ofTotal Population.

The variables on human capital are in fact estimates of educational attainment. Theirestimation procedure is thoroughly described in Nehru, Swanson and Dubey (1993, The WorldBank, International Economics Department Working Paper, WPS 1124). In addition, variablesrelated to educational attainment are provided by the Barro and Lee data base, which can beaccessed from the World Bank Research Department’s growth web page.

Related variables to the stock of human capital can also be found in

http://www.worldbank.org/html/prdmg/grthweb/growth_t.htm

Upon reaching the site, go to “datasets” and then to the Barro and Lee data base.

Financial development variables

Variable Source CoverageM1, Stock End of Period IFS 60-94Money plus Quasi-Money IFS 60-94Spread Between Average Depositand Lending Rates (%)

WDI 70-94

Deposit Rate IFS 65-94Treasury Bill Rate IFS 65-94Money Market Rate IFS 65-94Discount Rate IFS 70-94Total domestic Credit, End ofPeriod

IFS 60-94

Total Credit to the Private Sector,End of Period

IFS 60-94

Capital Controls, MultipleExchange Rates

IMF, G.M. Milesi-Ferreti

66-94

Capital Controls, Current AccountRestrictions

IMF, G.M. Milesi-Ferreti

66-94

Capital Controls, Capital AccountRestrictions

IMF, G.M. Milesi-Ferreti

66-94

Capital Controls, MandatorySurrender of Export Proceeds

IMF, G.M. Milesi-Ferreti

66-94

Poverty and inequality variables

24

The data related to this category can be found in the Deininger-Squire data base of annualcountry data for Gini coefficients and quintile shares of income/expenditure. The data base can beaccessed and downloaded from

http://www.worldbank.org/html/prdmg/grthweb/growth_t.htm

Upon reaching the site, go to “datasets” and finally to the Deininger-Squire data base.

Social security variables

Variable Source CoverageSocial Security, % of PublicExpenditure on SS

World Tables 70-94

Social Security, % of Working agepopulation covered

IEC 70-94

External sector variables

Four variables have been included in this section:

Variable Source CoverageTERMS OF TRADE (1987=100) WDI 80-94Total Aid Flows Own Calculations 70-93Total Official Transfers Own Calculations 70-94Atlas Conversion Factors Own Calculations 60-94

The World Bank Atlas Conversion factor for any year is the average of the real exchangerate for that year and for the two preceding years, adjusted for current relative prices between thatcountry and the U.S. Hence, it is a nominal variable. For example, nominal GNP in domesticcurrency over the Atlas conversion factor would yield GNP in current U.S. dollars.

7. Personal and Firm Saving and Investment (Module 7)

Finally, this section reviews Module 5 of the World Saving Data Base, which containsdisaggregated data at a personal, firm and general government levels. The module reportsdisaggregated Gross Saving (GS) and Gross Fixed Capital Formation (GFCF). The series spanthe period 1970 to 1994. Personal saving and investment include both households andunincorporated enterprises. The number of countries for which disaggregated data are available is22.

25

Data on Consumption of Fixed Capital (CFC) are also available. Therefore, it is possibleto compute both Net Saving and Net Fixed Capital Formation. However, the number of countriesfor which disaggregated CFC is available is smaller than those for which GS is available.Additionally, disaggregated consumption (current prices) is also included. These series aredisaggregated between general government consumption and personal consumption.

The sources used in the compilation of module 5 are the OECD and the United NationsNational Accounts.

The module also reports data on net saving from the United Nations National Accounts,spanning the period 1960 to 1993. The number of countries for which disaggregated net saving isavailable is 44.

In principle, gross saving in module 5 is defined by the sources as inclusive of only currenttransfers rather than all external transfers (i.e. both current and capital), although in practice thesources classify all external transfers as current in several of the countries considered.

Notice also that the sources of the data in this module differs from those used in modules1 to 3. In any event, the implication is that the resulting gross saving figures may differ somewhatfrom those presented in modules 1 to 3.

7. Conclusions and Further Steps

This paper has described the main aspects of the World Saving Data Base assembled forthe World Bank research project on saving. As noted in Section 1, the data of the World SavingData Base improves upon existing data bases on several grounds.

First, its coverage is larger than in any previous available dataset. Apart from thepublished datasets described above (UNNA, OECD, ADI, ADB, GFS, etc), we have examinedaround 1,500 REDs of the IMF, and around 500 World Bank and country specific documents.Some data have also been kindly provided by IMF and World Bank staff and by several CentralBanks upon request. The Data Appendix briefly summarizes some aspects regarding the coverageof the variables.

Second, some data inconsistencies in country time series have been corrected. The previoussections have described some of the checks that were performed on the data. Apart from that, wealso tested for the presence of outliers from a purely statistical viewpoint. The information of thesetests was used to double check the collected data. However, further work ahead will try to improveupon the existing version on this ground.

Third, the public sector is defined consistently in the dataset. Although in both module 2and module 3 we have used more than a single definition of the public sector, the data of eachmodule is, to our view, largely comparable across countries. Improving this aspect of the data baseseems difficult given the lack of available data.

26

Fourth, the World Saving Data Base includes saving data adjusted for inflation andexchange rate depreciation related capital gains.

Data Appendix

27

Table 6. Coverage of selected variables

CURRENTPRICES

CONSTANTPRICES

GNP 4748 4515GDP 4788 4562Total Consumption 4360 3581Total Consumption etc 4420 3728Private Consumption 4381 3565Private Consumption etc 3404 3503GG Consumption 4375 3556Gross Domestic Investment 4437 3812Gross Fixed Domestic Investment 3786 2936Net Factor Income 4719 4502Imports GNFS 4463 3772Exports GNFS 4464 3772Total Transfers 4088Gross National Disposable Income (GNDI) 3933Gross National Saving (GNS) 3797Gross Domestic Saving (GDS) 4420GNS/GNDI 3797CCG Saving 3060Private Saving CCG def. 2831GG-PS Saving 2200Private Saving GG-PS def. 2103CCG Saving/GNDI 2899Private Saving/GNDI CCG def. 2831GG-PS Saving/GNDI 2166Private Saving/GNDI GG-PS def. 2103Adjusted CCG Saving/GNDI (domestic capital gains) 2593Adjusted Private Saving/GNDI CCG def. (domestic capital gains) 2543Adjusted GG-PS Saving/GNDI (domestic capital gains) 1966Adjusted Private Saving/GNDI GG-PS def. (domestic capital gains) 1956Adjusted GNS/GNDI 2859Adjusted CCG Saving/GNDI 2535Adjusted Private Saving/GNDI CCG def. 2489Adjusted GG-PS Saving/GNDI 1619Adjusted Private Saving/GNDI GG-PS def. 1583

GG: General Government.CCG: Consolidated Central Government.GG-PS: General Government or Public Sector or Central Government plus SOE.

28

Table 7, Country coverage of selected variables.

Code Country Name Region V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12

ATG Antigua and Barbuda LAC 5 11 18 5 5 0 0 0 0 4 0 0

ARG Argentina LAC 30 25 29 25 29 24 29 24 29 24 24 22

BHS Bahamas, The LAC 11 22 22 11 11 22 22 11 11 11 22 22

BRB Barbados LAC 29 21 19 21 19 21 19 21 19 23 21 19

BLZ Belize LAC 15 17 14 15 13 17 13 15 13 15 17 13

BOL Bolivia LAC 35 25 33 25 33 24 33 24 33 24 24 24

BRA Brazil LAC 31 20 30 20 30 20 30 20 30 24 20 24

CHL Chile LAC 31 25 28 25 28 24 27 24 27 24 24 24

COL Colombia LAC 29 25 27 25 27 23 27 23 27 24 23 23

CRI Costa Rica LAC 36 23 28 23 28 23 28 23 28 24 23 24

DMA Dominica LAC 11 19 19 11 11 19 19 11 11 11 19 19

DOM Dominican Republic LAC 28 23 28 23 28 23 27 23 27 24 23 24

ECU Ecuador LAC 36 25 31 25 31 24 29 24 29 24 24 24

SLV El Salvador LAC 31 25 27 25 27 24 27 24 27 24 24 23

GRD Grenada LAC 15 15 15 15 15 13 13 13 13 13 13 13

GTM Guatemala LAC 35 25 29 25 28 24 28 24 28 24 24 24

GUY Guyana LAC 27 21 26 21 26 19 25 19 25 20 19 20

HTI Haiti LAC 34 21 25 21 24 21 24 21 24 23 21 23

HND Honduras LAC 36 25 35 25 35 24 34 24 34 24 24 24

JAM Jamaica LAC 30 24 30 24 30 24 30 24 30 24 24 24

MEX Mexico LAC 35 23 25 23 25 22 24 22 24 24 22 23

NIC Nicaragua LAC 30 25 30 25 29 24 29 24 29 24 24 24

PAN Panama LAC 35 25 29 25 29 23 28 23 28 23 23 23

PRY Paraguay LAC 35 25 31 25 31 24 31 24 31 24 24 24

PER Peru LAC 29 25 26 25 26 24 26 24 26 24 24 24

KNA St. Kitts and Nevis LAC 12 10 10 7 7 9 9 7 7 12 9 9

LCA St. Lucia LAC 14 16 16 14 14 9 9 8 8 8 9 9

VCT St. Vincent and the Grenad. LAC 11 17 17 11 11 7 7 2 2 11 7 7

SUR Suriname LAC 29 24 0 23 0 24 0 23 0 23 8 0

TTO Trinidad and Tobago LAC 31 25 25 25 25 24 25 24 25 24 24 21

URY Uruguay LAC 35 22 22 22 22 21 21 21 21 23 21 21

VEN Venezuela LAC 29 25 27 25 27 24 26 24 26 24 24 24

V1: GNS/GNDIV2: CCG Saving /GNDIV3: CCG def. Private Saving/ GNDIV4: GG-PS Saving/GNDIV5: GG-PS def. Saving/GNDIV6: Adjusted CCG Saving /GNDI (domestic capital gains)V7: Adjusted Private Saving /GNDI CCG def. (domestic capital gains)V8: Adjusted GG-PS Saving /GNDI (domestic capital gains)V9: Adjusted Private Saving /GNDI GG-PS def. (domestic capital gains)V10: Adjusted GNS/GNDIV11: Adjusted CCG Saving /GNDIV12: Adjusted GG-PS Saving /GNDI

29

Table 8, Country coverage of selected variables.

Code Country Name Region V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12

BRN Brunei EA&P 0 0 0 0 0 0 0 0 0 0 0 0

CHN China EA&P 31 17 0 17 0 9 0 9 0 24 9 0

COK Cook Island EA&P 0 0 0 0 0 0 0 0 0 0 0 0

FJI Fiji EA&P 30 24 0 24 0 24 0 24 0 24 24 0

HKG Hong Kong EA&P 30 25 0 25 0 0 0 0 0 23 0 0

IDN Indonesia EA&P 30 24 20 24 20 23 19 23 19 24 23 12

KIR Kiribati EA&P 14 16 0 14 0 2 0 2 0 14 2 0

KOR Korea, Republic of EA&P 35 25 25 25 24 23 23 23 23 23 23 23

MYS Malaysia EA&P 35 25 35 25 35 24 34 24 34 24 24 24

PNG Papua New Guinea EA&P 26 20 0 20 0 20 0 20 0 24 20 0

PHL Philippines EA&P 36 23 36 23 36 22 34 22 34 24 22 24

SGP Singapore EA&P 31 24 25 24 25 24 25 24 25 24 24 24

SLB Solomon islands EA&P 11 14 0 9 0 10 0 9 0 11 10 0

TWN Taiwan, China EA&P 31 25 31 25 31 21 26 21 26 0 0 0

THA Thailand EA&P 36 25 28 25 28 24 28 24 28 24 24 24

TON Tonga EA&P 18 19 19 18 18 10 10 9 9 9 10 0

VUT Vanuatu EA&P 11 13 0 11 0 13 0 11 0 11 13 0

WSM Western Samoa EA&P 11 0 0 0 0 0 0 0 0 11 0 0

Table 9, Country coverage of selected variables.

Code Country Name Region V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12

AUS Australia IND 35 25 35 25 35 23 33 23 33 23 23 23

AUT Austria IND 35 25 35 25 35 24 34 24 34 24 24 24

BEL Belgium IND 35 25 35 25 35 24 34 24 34 24 24 24

CAN Canada IND 35 25 35 25 35 24 34 24 34 24 24 24

DNK Denmark IND 35 25 24 25 24 24 24 24 24 24 22 24

FIN Finland IND 35 25 35 25 35 24 34 24 34 24 23 24

FRA France IND 35 25 25 25 25 24 25 24 25 24 24 24

DEU Germany IND 5 4 0 4 0 0 0 0 0 0 0 0

DFA Germany, Former Fed. Rep. IND 35 25 35 25 35 23 33 23 33 23 23 23

GRC Greece IND 35 23 25 23 25 23 0 23 0 24 23 0

ISL Iceland IND 35 25 25 25 25 22 22 22 22 24 22 22

IRL Ireland IND 35 25 24 25 24 24 24 24 24 24 20 23

ITA Italy IND 35 25 35 25 35 24 32 24 32 24 24 24

JPN Japan IND 35 24 35 24 35 23 33 23 33 24 23 23

LUX Luxembourg IND 35 25 17 25 17 20 12 20 12 24 20 3

NLD Netherlands IND 35 25 26 25 26 24 26 24 26 24 21 24

NZL New Zealand IND 35 25 0 25 0 20 0 20 0 23 20 0

NOR Norway IND 35 25 33 25 33 24 30 24 30 24 23 21

PRT Portugal IND 35 25 25 25 25 24 20 24 20 24 24 17

ESP Spain IND 35 25 20 25 20 24 20 24 20 24 24 20

SWE Sweden IND 35 25 25 25 25 24 25 24 25 24 24 24

30

CHE Switzerland IND 35 25 0 25 0 24 0 24 0 24 24 0

GBR United Kingdom IND 35 25 32 25 32 24 32 24 32 24 24 24

USA United States IND 35 25 34 25 34 24 33 24 33 24 24 23

Table 10, Country coverage of selected variables.

Code Country Name Region V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12

DZA Algeria MENA 29 0 0 0 0 0 0 0 0 24 0 0

BHR Bahrain MENA 15 15 0 15 0 13 0 13 0 13 13 0

CYP Cyprus MENA 35 25 0 25 0 18 0 18 0 18 18 0

EGY Egypt, Arab Rep. MENA 36 20 32 20 32 20 30 20 30 23 20 20

IRN Iran, Islamic Republic of MENA 28 25 0 25 0 15 0 15 0 19 9 0

IRQ Iraq MENA 0 0 0 0 0 0 0 0 0 0 0 0

ISR Israel MENA 35 24 15 24 15 23 14 23 14 23 23 14

JOR Jordan MENA 35 25 0 25 0 11 0 11 0 11 11 0

KWT Kuwait MENA 24 23 24 23 24 21 22 21 22 21 21 21

LBN Lebanon MENA 0 0 0 0 0 0 0 0 0 0 0 0

MLT Malta MENA 29 23 29 23 29 22 28 22 28 22 22 22

MAR Morocco MENA 35 25 0 25 0 23 0 23 0 23 23 0

OMN Oman MENA 21 21 21 21 21 20 20 20 20 20 20 20

QAT Qatar MENA 0 0 0 0 0 0 0 0 0 0 0 0

SAU Saudi Arabia MENA 28 0 22 0 22 0 20 0 20 23 0 0

SYR Syrian Arab Rep. MENA 24 23 0 20 0 22 0 20 0 21 22 0

TUN Tunisia MENA 31 23 7 23 7 23 7 23 7 24 23 7

TUR Turkey MENA 29 25 29 25 29 24 28 24 28 24 24 24

ARE United Arab Emirates MENA 18 18 18 18 18 17 17 17 17 17 17 0

YAR Yemen, Former Arab Rep. MENA 12 0 0 0 0 0 0 0 0 0 0 0

YMD Yemen, Former People Rep. MENA 11 0 0 0 0 0 0 0 0 0 0 0

YEM Yemen, Republic of MENA 4 4 4 4 4 0 0 0 0 0 0 0

Table 11, Country coverage of selected variables.

Code Country Name Region V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12

BGD Bangladesh SA 23 22 4 22 4 20 4 20 4 22 20 4

BTN Bhutan SA 15 15 0 15 0 10 0 10 0 14 10 0

IND India SA 35 25 35 25 35 23 33 23 33 23 23 23

MDV Maldives SA 0 9 9 0 0 8 8 0 0 0 8 8

MMR Myanmar SA 27 19 14 19 14 16 12 16 12 21 16 12

NPL Nepal SA 20 20 6 20 6 20 6 20 6 20 20 6

PAK Pakistan SA 27 25 24 25 24 22 24 22 24 23 22 22

LKA Sri Lanka SA 31 25 0 25 0 24 0 24 0 24 24 0

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Table 12, Country coverage of selected variables.

Code Country Name Region V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12AGO Angola SSA 10 10 0 10 0 0 0 0 0 9 0 0BEN Benin SSA 30 20 0 20 0 20 0 20 0 24 20 0BWA Botswana SSA 25 23 15 23 15 21 14 21 14 23 21 10BFA Burkina Faso SSA 27 22 0 22 0 22 0 22 0 24 22 0BDI Burundi SSA 10 10 0 10 0 9 0 9 0 9 9 0CMR Cameroon SSA 23 19 12 19 12 19 12 19 12 22 19 12CPV Cape Verde SSA 17 0 0 0 0 0 0 0 0 17 0 0CAF Central African Republic SSA 27 16 0 16 0 16 0 16 0 24 16 0TCD Chad SSA 23 16 0 15 0 16 0 15 0 20 16 0COM Comoros SSA 15 15 0 15 0 13 0 13 0 13 13 0COG Congo SSA 25 25 5 25 5 23 5 23 5 23 23 5CIV Cote d'ivoire SSA 31 25 10 25 10 24 10 24 10 24 24 9DJI Djibouti SSA 2 2 0 2 0 0 0 0 0 0 0 0GNQ Equatorial Guinea SSA 14 0 0 0 0 0 0 0 0 8 0 0ETH Ethiopia SSA 15 14 9 14 9 11 8 11 8 11 11 8GAB Gabon SSA 27 22 5 22 5 20 5 20 5 22 20 5GMB Gambia, The SSA 25 25 12 25 12 22 12 22 12 22 22 12GHA Ghana SSA 28 23 0 23 0 23 0 23 0 24 23 0GIN Guinea SSA 10 9 0 9 0 5 0 5 0 8 5 0GNB Guinea-Bissau SSA 14 13 0 13 0 8 0 8 0 13 8 0KEN Kenya SSA 28 23 0 23 0 22 0 22 0 23 22 0LSO Lesotho SSA 22 22 0 22 0 20 0 20 0 22 20 0LBR Liberia SSA 17 18 0 17 0 16 0 16 0 16 16 0MDG Madagascar SSA 27 23 0 23 0 23 0 23 0 24 23 0MWI Malawi SSA 28 25 12 25 12 24 12 24 12 24 24 12MLI Mali SSA 27 25 15 25 15 24 15 24 15 24 24 15MRT Mauritania SSA 28 22 0 22 0 22 0 22 0 24 22 0MUS Mauritius SSA 30 25 0 25 0 17 0 17 0 18 17 0MOZ Mozambique, Rep. of SSA 15 15 0 15 0 10 0 10 0 14 10 0NAM Namibia SSA 15 10 16 10 15 4 4 4 4 14 4 4NER Niger SSA 27 19 12 19 12 19 12 19 12 24 19 12NGA Nigeria SSA 28 18 0 18 0 17 0 17 0 23 17 0RWA Rwanda SSA 27 24 7 24 7 23 7 23 7 23 23 7STP Sao Tome and Principe SSA 21 9 0 9 0 9 0 9 0 21 9 0SEN Senegal SSA 27 25 7 25 7 23 7 23 7 23 23 7SYC Seychelles SSA 19 22 22 19 19 21 21 18 18 18 17 17SLE Sierra Leone SSA 25 21 10 21 10 20 10 20 10 22 20 10SOM Somalia SSA 23 7 0 7 0 7 0 7 0 19 7 0ZAF South Africa SSA 36 22 36 22 36 22 34 22 34 24 22 24SDN Sudan SSA 24 9 5 9 5 9 5 9 5 20 9 5SWZ Swaziland SSA 24 23 0 23 0 18 0 18 0 22 18 0TZA Tanzania SSA 14 14 10 14 10 11 7 11 7 11 11 7TGO Togo SSA 30 18 0 18 0 18 0 18 0 24 18 0UGA Uganda SSA 28 16 0 16 0 11 0 11 0 11 11 0ZAR Zaire SSA 19 20 20 19 19 20 20 19 19 19 20 20ZMB Zambia SSA 27 23 13 23 13 23 13 23 13 24 23 12

32

ZWE Zimbabwe SSA 20 19 15 18 15 18 12 17 12 20 18 12