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Finance & Development, March 1982
Article

Comparing government expenditures internationally: A report on a study of government expenditures in over 90 countries and a means of comparing them

Author(s):
International Monetary Fund. External Relations Dept.
Published Date:
March 1982
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Peter Heller and Alan Tait

Little work has been done to establish a framework for making intercountry comparisons of national expenditures. Officials frequently ask how expenditure patterns compare. When they do, they generally look at countries that have similar per capita incomes; but this variable is often an unsatisfactory proxy for the many underlying social, demographic, and economic factors that make countries with similar incomes so different.

This article summarizes the work on a detailed study that starts to develop more useful measures of comparison. Ninety-three countries with different economies and demographic situations were included in the study. The data collected on expenditures in both functional and economic categories provide a broad basis for comparison. Thus, predicted expenditures were estimated for the different budget categories (public service, defense, health, and so on) as well as for spending on broad economic categories such as current expenditures, goods and services, and wages and salaries. These two sets of data constituted a “norm” that could be compared with a particular country’s actual expenditures to identify whether, given its budget priorities, these differed from what might be expected. The model could also be used to forecast what a country’s expenditure patterns would be, if it followed the pattern of other countries with comparable economic and demographic characteristics.

Four caveats should be stated at the outset. First, it is entirely possible that a single measure of expenditure may not capture all government activities in a particular area and may, therefore, not reflect all its financial commitments. The sheer diversity of government expenditure may preclude comprehensiveness in such a measure; in addition, many objectives can be achieved by the use of policy instruments other than direct government expenditure—through tax expenditures, for instance, or price controls, tariffs, and so on. Second, the index cannot be used to judge the appropriateness of a country’s functional expenditure priorities. Ultimately, the public expenditure budget reflects the social and economic priorities of a country’s government and, presumably, of its population. Third, the indices reviewed are to be treated as possible starting points for discussion but they should not replace detailed country studies as a basis for actual expenditure decisions. Fourth, relations between central and local finance and expenditures differ widely between countries, and efforts to correct for these differences may not have been wholly successful.

This article is based on a longer study on the same topic. Copies of the longer study are obtainable from the authors.

Constructing the norm

The data for this cross-country study were drawn from the International Monetary Fund’s Government Finance Statistics Yearbook (GFS) and its International Financial Statistics (IFS), and also from the World Tables of the World Bank. A model for each type of expenditure was constructed and used to predict a “norm” for each country, (see box on calculation of norm). The functional expenditure model was based on six groups of factors: (1) demographic influences, (2) sociological concerns, (3) the structure of the economy, (4) the level of economic development, (5) technological factors, and (6) environmental factors.

Demographic influences are likely to be the principal underlying determinants of the demand for services. The larger the share of school-age groups in the population, for instance, the greater the likely demand for education; the higher the percentage of the elderly, the greater the demand for medical care and perhaps more elaborate public mechanisms for old age support. Other demographic variables, such as life expectancy, population growth, the proportion of population in urban areas, and infant or child mortality rates, may imply the existence of demand for certain types of services. Sociological concerns, meanwhile, may explain whether there is a need for the public sector to provide certain services. For example, social security programs are required where significant migration to the urban areas exists and extended family arrangements have broken down.

The structure of an economy may play a key role in shaping priorities for public expenditure in different economic sectors. A dominant agricultural sector may require public expenditures to complement or service private sector activities. Developing countries may also desire to change the structure of the economy by expanding public expenditure in sectors that are not currently adding significantly to total output.

Technological factors influence costs. For example, the lower the desired pupil-teacher ratio, the higher the cost of realizing a given percentage of enrollment for the population. Environmental factors may influence both the cost of providing services and the likely magnitude of underlying demand; for example, inadequate access to clean water may imply a significant demand for investment to provide drinking water as well as the likely need for medical services because of the effects of contaminated water supplies.

It may be hypothesized that the types of goods and services purchased through government expenditure—the so-called economic categories of expenditure—art also shaped by functional or sectoral priorities. The mix of labor, current consumption of other goods and services, and capital goods is likely to be different for each functional sector, so that the economic mix of expenditure will be largely determined by the functional mix.

The norm derived from the model reflects what a country would be expected to spend on a sector, given its economic, social, and demographic characteristics and given the actual expenditure of the large number of countries, both developed and developing, in the sample. Given its estimates for predicted expenditure in a particular country, their ratio with actual expenditures was computed and this formed the international expenditure comparison (IEC) index. Thus, the index represents the actual expenditure/gross domestic product (GDP) ratio as a percentage of the predicted expenditure/GDP ratio. A high value of the index (above unity) for a functional expenditure category simply indicates that a country is spending more than would be predicted, given its economic and social characteristics (or in the case of an IEC index for an economic input, given the structure of its functional expenditure). It does not indicate the actual share in GDP of a given category of expenditure; a country with a low IEC index may still be spending a higher share of GDP on a category of expenditure than a country with a higher one. The sources of a discrepancy between actual and predicted expenditures for a country may represent a conscious policy choice by the authorities to attach a different emphasis to a sector than is attached by its peer countries.

Calculating the “norm” and the international expenditure comparison (IEC) index

An example of how the expenditure norm is calculated can be shown for social security (SS) expenditure as a share of gross domestic product (GDP).

(1 An econometric model was constructed to predict SS expenditures as a share of GDP and the weights derived from the model for the variables in the equation. The following independent variables were used to predict SS: income per capita in thousands of dollars (YPERCAP), percentage of the population over age 65 (POPAGED > 65), the infant mortality rate (INFMRT), and the share of the labor force in industry (LFINDUSTRY). (For example, SS = –4.76 + .25(YPERCAP)+ .84(POPAGED > 65) + .07 (INFMRT) + .13 (LFINDUSTRY)

(2) The actual values for the independent variables of a country were inserted into the equation and a predicted value for SS calculated. This represents the share of social security expenditure in GDP that would be expected for a country with such economic and demographic characteristics. For example, for France, in 1977,

YPERCAP (in thousands) = US$7.21 INFMRT = 1 per cent

POPAGED > 65 = 13 per cent LFINDUSTRY = 41 per cent.

Substituting these values into the above equation, we obtain predicted

SS = –4.76 + (.25) (7.21) + .84 (13) + (.07) (1) + .13 (41)

Result = predicted SS of 13.4

Since actual SS for France = 16.1, the IEC index value = 16.1 ÷ 13.4 = 1.2.

Two important data problems need to be noted. First, the disaggregated public expenditure data in the GFS Yearbook relate to the consolidated central government accounts. In some countries, the role of provincial and local governments is quite prominent, particularly in the provision of certain government services, notably education. Efforts were made to include their expenditures, but these could not be obtained for all countries. Second, for some countries, the GFS Yearbook classification of expenditure obscures the ultimate intent of the expenditure. Similarly, it is often difficult to distinguish expenditure on health from expenditure on social security. When such distortions were obvious, an attempt was made to reclassify expenditure in the appropriate functional expenditure categories.

Index results

Tables 1 and 2 provide selected results on the indices for the different functional and economic categories of government expenditure. To illustrate the types of statements that may be drawn from them, the results in Table 1 suggest that the French Government spends a little more than might be expected, based on norm expenditures, on education (9 per cent more) and perhaps 20 per cent more than expected on health and social security. The Egyptian Government spends twice as much as expected on education and the United Kingdom 50 per cent more, but Greece spends 30 per cent less. Similarly from Table 2, it appears that the Government of Mali spends some 79 per cent more than might be expected on government wages and salaries and Greece spends 112 per cent more, while Korea spends 55 per cent less than predicted.

Table 1International expenditure comparison index: some functional categories of expenditure, 1977 1
CountryGeneral

public

service
DefenseEducationHealthSocial

security &

welfare
Health,

social security, &

welfare
Housing,

community

amenities
Agriculture,

forestry, &

fisheries
Mining,

manufacturing, &

construction
Electricity,

natural gas,

& water
Transportation &

communication
Australia96711301116985386172
Belgium6471132871191116829138400 *171
Egypt6768211107187163342132199147
France777510912112012093451353719
Germany, Fed. Rep. of6411492118971018951188
Greece8332571698382641214619103
Italy1093910814710412073100400 *190
Japan857550601222041488
Kenya7389104123400 *138289443231115
Mexico43138252217142163400 *57
Norway6212112092828430922712327118
Pakistan39164152327241693146102117
Sweden94851541271231228694240400 *58
Tanzania95114106138400 *1888113528617193
United Kingdom99112152110667926595131400 *112
United States 25131885717876641819
Sources: IMF, Government Finance Statistics Yearbook and International Financial Statistics, and World Bank, World Tables.—Indicates data not available.

Denotes that this particular international expenditure comparison index should be treated with care as actual expenditures were extremely small and predicted expenditures negative.

As the text explains in more detail, this index represents the actual expenditure-gross domestic product (GDP) ratio as a percentage of the predicted expenditure/GDP ratio.

1973–75.

Sources: IMF, Government Finance Statistics Yearbook and International Financial Statistics, and World Bank, World Tables.—Indicates data not available.

Denotes that this particular international expenditure comparison index should be treated with care as actual expenditures were extremely small and predicted expenditures negative.

As the text explains in more detail, this index represents the actual expenditure-gross domestic product (GDP) ratio as a percentage of the predicted expenditure/GDP ratio.

1973–75.

Table 2Examples of the international expenditure comparison index for economic categories of expenditure, 19771
Current

expenditure
Goods

and

services
Wages

and

salaries
Other goods

and

services
InterestSubsidiesCapital

expenditure
Acquisition of

capital

assets
Capital

transfer
Bolivia8086875729127817266
France83588986261108432397
Germany, Fed. Rep. of102127608999392
Greece8914521210770269611739
Kenya9590951001251438993
Korea837445974717314096155
Mali121181795961018755
Malaysia90961137515098223115224
Netherlands9247666156127114193
Sri Lanka102817984306109170128230
United Kingdom937365104114124346261
United States10313810177212400 *
Uruguay9210599733089948712
Sources: IMF, Government Finance Statistics Yearbook and International Financial Statistics, and World Bank, World Tables.

Denotes that this particular international expenditure item should be treated with care as actual expenditures were extremely small and predicted expenditures negative.

—Indicates data not available.

Data are for 1977, except for Uruguay, which refers to 1978. As the text explains in more detail, this index represents the actual expenditure-gross domestic product (GDP) ratio as a percentage of the predicted expenditure-GDP ratio.

Sources: IMF, Government Finance Statistics Yearbook and International Financial Statistics, and World Bank, World Tables.

Denotes that this particular international expenditure item should be treated with care as actual expenditures were extremely small and predicted expenditures negative.

—Indicates data not available.

Data are for 1977, except for Uruguay, which refers to 1978. As the text explains in more detail, this index represents the actual expenditure-gross domestic product (GDP) ratio as a percentage of the predicted expenditure-GDP ratio.

Without going into any detailed discussion, certain broad patterns in the IEC index for both functional and economic expenditures can be illustrated. In the functional index, actual expenditures on general public services, for instance, exhibit little variation from those predicted. Countries such as Argentina, Morocco, Suriname, The Gambia, and Uruguay seem to spend more than might be expected; Australia and the United Kingdom spend up to expectations, while Mexico, the United States, and Yugoslavia spend less. For defense spending, the country ranking confirmed the expected evaluations. The spread between predicted and observed results for expenditure on education and health is the smallest of all the functional categories, suggesting a greater unanimity and consensus among countries in relation to government expenditure on education.

Some of the results were surprising. Most of the industrial European countries spend more on health than might be expected, while the United States and Japan spend some 25–30 per cent less. Again, expenditures on social security and welfare in the United Kingdom, often considered to be a welfare state, are some 35 per cent less than would be expected. Most interesting, too, is the number of highly industrial developed countries that apparently spend more than might be expected on economic services such as mining, manufacturing, and construction. Norway spends 23 per cent more than expected; the United Kingdom, 31 per cent; Belgium, 38 per cent; France, 35 per cent; Sweden, 140 per cent; and Italy, over 300 per cent more.

Comparing predictions based on economic expenditure categories with actual expenditures also threw up some useful results. Surprisingly some large European countries (Italy, the United Kingdom, and Austria) spend less than expected on wages and salaries. Overall, Asian, African, and Latin American countries allocate less to wages and salaries than might be expected, while Middle Eastern countries tend to allocate more. The narrow dispersion of IEC indices for this category of expenditure suggests that countries tend to be more likely to spend what would be expected on wages and salaries than other categories. Meanwhile there seems to be no systematic bias across regions in expenditures on other goods and services.

Surprisingly, however, Mexico and the United States seem to offer less in subsidies and other current transfers than might be expected (Mexico almost 50 per cent less, although this is possibly because it gives aid to industries in other ways, such as tax concessions). At the same time, it would not necessarily be expected that the United Kingdom spends some 24 per cent more than predicted on subsidies, Korea 73 per cent more, the Philippines and Egypt more than twice as much, and Sudan and Pakistan over four times as much. Equally interesting, African countries spend far more than expected on subsidies and transfers, while more than half of the Latin American countries spend less than expected.

Another surprise is that a country such as the Netherlands is spending almost twice what might be expected on capital transfers, given the relative importance of its agricultural, mining, and manufacturing sectors. However, this may be explained by the sporadic nature of capital transactions, although capital transfers—often made under entitlement programs—would be expected to be less open to major fluctuations than purchases of capital assets directly by government. Across regions, countries in Africa and Latin America tend to spend less than would be expected on capital transfers.

It is often argued that countries economize on nonwage forms of current expenditure, particularly when faced with a budgetary squeeze, and that they maintain excessive current spending relative to capital expenditure. If this were true, one would expect that countries would exhibit higher IEC indices for wages relative to their indices for other purchases of goods and services—and similarly, for current expenditure relative to capital expenditure. The IEC indices were used to test these hypotheses. The study found that the wage imbalance hypothesis cannot be confirmed. With the exception of the Asian region, half the countries appear to spend more than expected on wages, relative to the amount spent on goods and services. Only in the Asian region is there a clear overemphasis on purchases of other goods and services relative to wages.

Examining next the relative balance of current and capital expenditures, the study found a more varied pattern. In Africa and the industrial countries, a clear bias was found toward spending more than expected on current relative to capital expenditure. On the other hand, almost two thirds of the Latin American and Middle Eastern countries appeared to demonstrate the opposite bias in favor of capital expenditure.

In comparing wages to subsidies and transfers, greater emphasis on subsidies was found in Africa and among the industrial countries. More than two thirds of the African countries attached a higher weight to subsidies vis-à-vis wages than would have been predicted. The reverse was true in the Latin American region.

These examples of the implications of IEC indices show how they can help the policymaker to obtain a broad sense of whether public expenditure in a given sector appears reasonable, in terms of the expenditure policies of other comparable countries. The indices avoid the misleading results that might occur if per capita income data were used to identify “comparable countries” statistically the results often show that per capita income is a weak predictor of the share in GDP of government spending in a given sector.

Yet the user of the indices must never forget that they are not normative measures and countries should not be “judged” on the basis of these data. After all, if a country is spending, say, twice as much as might be expected (given its population structure, urbanization rates, economic structure, and so on) on education, it probably has a good reason. This discrepancy signaled by the index should have one primary function: to focus attention on the question of why there are these differences. These indices are not intended to replace detailed country studies as a basis for actual expenditure decisions but they may be constructive in provoking further policy analysis and discussion of methodological questions in this area. The predictive equations can also be used to examine the likely evolution of the expenditures for a country. This data would be helpful for any medium-term financial planning exercise.

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