Economic Determinants of Government Subsidies

Benedict J. Clements; Hugo Rodríguez; Gerd Schwartz

doi:10.5089/9781451979664.001.A001

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Economic Determinants of Government Subsidies

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Mr. Benedict J. Clements

Mr. Benedict J. Clements
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Mr. Hugo Rodríguez

Mr. Hugo Rodríguez https://isni.org/isni/0000000404811396 International Monetary Fund

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Mr. Gerd Schwartz

Mr. Gerd Schwartz
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Contributor Notes

Authors’ E-Mail Addresses: bclements@imf.org, hugo.rodriguez@econ.upf.es, and gschwartz@imf.org

Publication Date:: 01 Dec 1998

eISBN:: 9781451979664

Language:: English

Keywords:: WP; government; subsidy equation

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The paper studies the economic determinants of government subsidies using panel data for 40 countries over 18 years (from 1975 to 1992) and finds that individual country-specific factors play a sizeable role in determining government subsidies. But it also suggests several characteristics—a small government, a small external current account deficit, and a productive structure geared more toward services and agriculture than manufacturing—may make it easier to keep subsidy expenditures down. The paper also suggests that globalization and the associated increase in openness are not impediments to reducing subsidies. In itself, an IMF-supported adjustment program is found not to be a significant determinant of government subsidy expenditures.

Abstract

I. Introduction

This paper explores the economic determinants of government subsidies across countries and over time. In the most general terms, a subsidy can be defined as any government assistance that allows consumers to purchase goods and services at prices lower than those offered by a perfectly competitive private sector, or raises producers’ incomes beyond those that would be earned without this intervention. A particular concern about extensive government subsidies is that they may easily become “unproductive” in the sense that a much lower level of government expenditure would yield the same aggregate social benefit but at a much lower cost, as discussed in Chu and others (1995). Tanzi and Schuknecht (1995, 1996), in their analyses of industrial countries, showed that over the last century, transfers to households and subsidies have expanded more rapidly than all others categories of government expenditures.² While much of this increase reflects the growth of social security expenditures, subsidy expenditures have been growing in line with the expansion of the state over the last century. Although skepticism about the ever-expanding role of government has grown stronger since the 1970s, public expenditure usually kept rising, driven, among other reasons, by continued increases in spending on the “welfare state,” which may broadly be defined to include both government subsidies and transfers to households.

While concerns about subsidization policies and their economic effects have clearly been growing, as evidenced also by the increasing number of reports and studies issued by governments and international organizations,³ few of the industrial countries surveyed by Tanzi and Schuknecht (1995, 1996) have actually shifted their policy regimes. These results are supported by Clements and others (1995) who show that in the European Union, for example, government subsidies continued to grow even after the 1970s.

Tanzi and Schuknecht (1996) contend that the failure to reign in public expenditure in industrial countries largely reflects entitlements and the power of interest groups. In the case of government subsidies, this may be explained, in part, by the fact that what constitutes a subsidy is often rather elusive (Houthakker, 1972); also, whereas for most government spending programs it is only the benefits that are often elusive and difficult to quantify, for subsidy programs it is frequently both benefits and costs (Break, 1972).

As a result of these measurement problems, relatively few papers have analyzed the determinants of government subsidies. Houthakker (1972) proposes that subsidies can be considered gifts, and he argues that “as we all know from birthdays and Christmas Eves, the exchange of gifts, even of rather useless gifts, frequently helps to stimulate good fellowship and a sense of community.” In contrast, Tait and Heller (1982) explore some of the economic determinants of government spending on subsidies and transfers. Based on data for 76 countries taken from the IMF’s Government Finance Statistics (GFS) database, they show that defense expenditures to GDP, education outlays as a share of GDP, social security and welfare expenditure to GDP, and per capita income are all significant determinants of the level of subsidies and transfers. More recently, using pooled GFS data for developing countries for 1978–86, Heller and Diamond (1990) discovered that the ratio of education spending to GDP, as well as the level of social security and welfare outlays to GDP, were the only significant determinants of government subsidies and transfers. A similar equation was estimated to assess the determinants of subsidies and transfers net of spending for social security. The results indicate that the level of economic services spending as a share of GDP, education spending as a share of GDP, government health outlays to GDP, and GDP per capita are all significant factors in explaining the share of GDP devoted to subsidies and transfers (net of social security) across countries.

Given the aggregation of subsidies and transfers in the GFS data, the few existing studies using these data offer limited guidance concerning the economic determinants of government subsidies alone. For example, while Heller and Diamond (1990) show that education and social security spending determine aggregate outlays on transfers and subsidies, it seems reasonable to assume that these mainly affect expenditures on transfers to households rather than subsidies. Thus, there is a need for an analysis of the determinants of subsidy spending alone, given the limited insights one can derive concerning subsidies from data that also include transfers. Unfortunately, this prevents using GFS data, as very few countries report separately their expenditures on subsidies. Even fewer countries report these subsidies at the level of general government. In light of these drawbacks of the GFS data on subsidies, data from the United Nations’ System of National Accounts (SNA) are used for the econometric work presented in this paper.⁴

The paper is organized as follows. The next section develops the theoretical model used as a basis to understand which variables determine subsidies in general. Section III discusses several issues regarding data collection and the definition of subsidies used in this study. The econometric results are presented in Section IV; Section V concludes.

II. An Econometric Model of Government Subsidies

The existing literature on the determinants of government subsidies has largely relied on empirical, inductive methods to assess the factors associated with high levels of subsidy expenditure. For example, both Heller and Diamond (1990) and Freinkman and Haney (1997) use factor analysis to determine the interrelationships in the data. In the case of Freinkman and Haney (1997), “a total of twenty-four unique…independent variables were examined as potential subsidy determinants in the regression analysis.” Although all variables can be hypothesized to have some effect on subsidy behavior, factor analysis is mostly useful when one has no clear idea on how the various data are related, or, when, in the words of Heller and Diamond (1990), “we are confronted by a bewildering array of possible causal influence and a large number of possible indicators, all of which appear highly correlated.”

This paper offers a somewhat different approach to the analysis of government subsidies. In particular, we borrow from models that were developed by Hewitt (1991) for military expenditure and Hewitt and Van Rijckeghem (1995) for wage expenditure and develop an explicit optimization approach to government subsidies. These models—which are rooted in public choice theory—assume that there exists a government that selects economic policies so as to maximize its own welfare, subject to constraints. The welfare function does not necessarily imply that the government only pursues its own self-interest: any consideration (such as altruism, public well-being, etc.) can enter into the model. In particular, the welfare function may include citizens’ evaluation of the government’s objectives and actions (e.g., via public opinion polls), and how these evaluations influence the government’s decisions. The model we develop suggests that there exists simultaneity in the determination of government subsidies and total government expenditures: one may think of government subsidies as being determined in the context of overall government expenditures. This can be captured through two equations that can be tested empirically.

Economic welfare (W), as perceived by the government, can be expressed by the following function:

\begin{matrix} W = W (UC, UE, UO, state variables) & (1) \end{matrix}

where

UC = utility derived from private consumption

UE = utility derived from pursuing equity objectives

UO = utility derived from pursuing other political objectives

Accordingly, the government decides where to allocate expenditure to maximize welfare. It is assumed that the government uses subsidies to pursue its efficiency and equity objectives through the following transformation or cost functions:

\begin{matrix} UC = UC (TPC; state variables) & (2) \end{matrix}

\begin{matrix} UE = UE (SE; state variables) & (3) \end{matrix}

\begin{matrix} UO = UO (OE; state variables) & (4) \end{matrix}

where

TPC = total private consumption

SE = subsidies for equity reasons

OE = other government expenditures

In general, subsidy expenditures affect the welfare function, as they lower the costs associated with each level of private consumption. TPC, which is an observable variable, is in turn a function of two unobservable components: consumption derived from private initiative or simply “private consumption” (C), and consumption derived from public intervention (CP) where CP itself is a function of subsidies that affect efficiency or are provided for efficiency reasons (SC). Under this assumption, we could express UC in equation (2) as a function of C and SC alone, so that

\begin{matrix} UC = UC (C, SC; state variables) & (2') \end{matrix}

The government must allocate expenditures subject to two equality constraints. First, total government expenditure (TE) consists of government subsidies, other government expenditure, and interest expenditure (IE). For simplicity, variable IE is assumed to be exogenously determined; in other words, IE limits what the government may spend on subsidies and other expenditures. Hence, we have:

\begin{matrix} TE = S + OE + IE & (5) \end{matrix}

Total subsidies (S) consist of efficiency and equity subsidies:

\begin{matrix} S = SC + SE & (6) \end{matrix}

Since we do not have data on SC or SE, we assume that they are functions of total subsidies (S), so that

\begin{matrix} SC = SC (S; state variables) & (7) \end{matrix}

\begin{matrix} SE = SE (S; state variables) & (8) \end{matrix}

The model assumes that none of the additional income or purchasing power that government transfers or subsidies provide to households leads to higher household savings; in other words, the macroeconomic effects of these government expenditures are similar to government outlays on goods and services. Under this assumption, total government expenditure (TE) net of interest expenditures (IE) is constrained by the country’s Gross Domestic Product (GDP), consumption from private initiative (C), and foreign savings. Higher foreign savings (i.e., a worsening of the current account balance (CA)) allows for greater government expenditure; a reduction in foreign savings (i.e., a strengthening of CA) requires lower total government expenditures.⁵ Hence we have:

\begin{matrix} TE = GDP - CA - C + IE & (9) \end{matrix}

Private investment is excluded from equation (9) as it can be thought of as deferred consumption, so that consumption from private initiative, C, would include both current and deferred private consumption.

After substituting expressions (2′), (3) and (4) into equation (1), the latter can be written as:

\begin{matrix} W = W (C, S, OE; state variables) & (10) \end{matrix}

The welfare function in (10) can be approximated by a Cobb-Douglas function, so that the government’s optimization problem can be expressed as a problem of maximizing:

\begin{matrix} A C^{α} S^{β} O E^{γ} & (11) \end{matrix}

subject to

\begin{array}{l} TE = S + OE + IE and \\ TE = GDP - CA - C + IE \end{array}

and where α, β, γ are coefficients that represent the relative value in the social welfare function of different types of expenditures. The government’s optimization problem is with respect to three variables: C, S, and OE. From the first order conditions of the Lagrangian expression:

\underset{C, S, OE}{Max W} = A C^{α} S^{β} O E^{γ} + λ (TE - S - OE - IE) + δ (TE - GDP + CA + C - IE)

(where λ and δ are the Lagrangian multipliers that reflect the relative value of the two constraints) we can derive the following recursive system:⁶

\begin{matrix} S / GDP = R 1 [(TE - IE) / GDP; state variables] & (12) \end{matrix}

\begin{matrix} TE / GDP = R 2 [IE / GDP, - CA / GDP; state variables] & (13) \end{matrix}

Equation (12) can be interpreted as saying that government subsidies increase as the size of government increases, as proxied by total government expenditure net of interest. Equation (13) can be viewed as indicating that total government expenditures increase with interest expenditures, and that a strengthening of the current balance (i.e., a higher value for CA/GDP) reduces the amount of resources available to finance government expenditures.

The state variables in the equations for subsidies (12) and total government expenditures (13) are economic variables that may affect the relative weights the government attaches to each political objective as well as the transformation functions. State variables on government expenditures fall into two categories: those that affect both the subsidy equation and government expenditure equation, and those that affect either the subsidy equation or the government expenditure equation. An overview of different state variables we use is provided in Appendix 1.

A key question addressed in this study is whether the existence of a IMF-supported adjustment program is associated with lower subsidy expenditure. The IMF’s policy advice often calls for countries to reduce unproductive spending, including generalized subsidies, as they are an inefficient means of achieving equity objectives (Chu and others, 1995). Two recent studies (International Monetary Fund, 1997; Abed and others, 1998) show that IMF-supported adjustment programs have indeed been associated with reductions in subsidies and transfers as a share of GDP. The general model above permits an empirical assessment of how IMF-supported programs might affect subsidies, either directly (in the subsidies equation) or indirectly (through the government expenditures equation). The effect of IMF programs is assessed by including a dummy variable for the existence of IMF programs in the list of state variables in equations (12) and (13).

III. The Data

Given the difficulty of finding a comprehensive measure of subsidies,⁷ the advantages and disadvantages of various data sources merit discussion. As shown in Table 1, the available data on subsidies vary not only according to their “extensiveness”—how many countries are covered and for what number of years—but also according to their “intensiveness”—how broadly they capture the different channels through which governments may provide subsidies. GFS and SNA have the most extensive coverage, but in terms of intensiveness they do not perform well, since only direct transfers to producers are counted as subsidies.

Table 1.

Summary of Coverage and Measurement of Government Subsidies in Various Databases

^¹For the purpose of this paper.

^²The net concepts used by the CEC and EFTA primarily measure subsidies as the total cost to the public sector of government subsidization activities net of any cost recoveries (e.g., repayments). For some categories of subsidies, the net cost to the government is proxied by the benefit received by the recipient. For example, the CEC measures the subsidy element of soft loans as the difference between the market rate of interest facing the recipient and the soft loan rate. See CEC (1989) for a detailed elaboration of measurement concepts used for different subsidy categories.

Table 1.

Summary of Coverage and Measurement of Government Subsidies in Various Databases

Data Base	Transactions covered	Sectoral Coverage	Measurement Basis	Institutional Coverage	Period Covered	Country Coverage
SNA	current unrequited payments to producers (accrual basis)	All	Gross cost to government	General government	1975–1992¹	UN member countries
GFS	current unrequited payments to producers (cash basis)	All	Gross cost to government	Consolidated central government	1975–1992¹	IMF member countries
CEC (1989, 1990, 1992)	current unrequited payments to producers (“cash grants,”) soft loans, guarantees, equity subsidies, tax subsidies (i.e., foregone tax revenue)	Manufacturing, agriculture, fisheries, coal, inland waterways, railways	Net grant equivalent²	General government	1981–1986 1986–1988 1988–1990	European Union member countries (Spain and Portugal excluded for some years)
EFTA (1986, 1990)	current unrequited payments to producers, soft loans, guarantees, equity subsidies	Manufacturing, energy, mining, fisheries	Net cost to government²	General government	1980–1984 1985–1989	EFTA member countries

^¹For the purpose of this paper.

Table 1.

Summary of Coverage and Measurement of Government Subsidies in Various Databases

Data Base	Transactions covered	Sectoral Coverage	Measurement Basis	Institutional Coverage	Period Covered	Country Coverage
SNA	current unrequited payments to producers (accrual basis)	All	Gross cost to government	General government	1975–1992¹	UN member countries
GFS	current unrequited payments to producers (cash basis)	All	Gross cost to government	Consolidated central government	1975–1992¹	IMF member countries
CEC (1989, 1990, 1992)	current unrequited payments to producers (“cash grants,”) soft loans, guarantees, equity subsidies, tax subsidies (i.e., foregone tax revenue)	Manufacturing, agriculture, fisheries, coal, inland waterways, railways	Net grant equivalent²	General government	1981–1986 1986–1988 1988–1990	European Union member countries (Spain and Portugal excluded for some years)
EFTA (1986, 1990)	current unrequited payments to producers, soft loans, guarantees, equity subsidies	Manufacturing, energy, mining, fisheries	Net cost to government²	General government	1980–1984 1985–1989	EFTA member countries

^¹For the purpose of this paper.

This paper utilizes SNA data for subsidies for the estimation of the econometric model. In the SNA, government subsidies are defined as “…current unrequited payments that government units, including non-resident government units, make to enterprises on the basis of their production activities or the quantities or values of the goods and services which they produce, sell or import” (Inter-Secretariat Working Group on National Accounts, 1993). Hence, the SNA data only consist of direct transfers to producers and do not include other types of subsidy-creating government interventions such as tax reductions and equity participations. As discussed in Clements and others (1995), while methodologically similar, SNA data are preferable to GFS data. First, the SNA data better reflect the macroeconomic impact of government subsidies, since they are recorded on an accrual basis (and thus also include imputed transactions that involve payments pertaining to, but not necessarily taking place in, the current period), whereas the GFS data reflect a strict cash concept. Finally, the SNA data pertain to the general government and also include transactions with supranational organizations, such as the agricultural subsidies received by European Union (EU) member countries through the Commission of the European Communities (CEC), whereas GFS data are available primarily for the central government alone. Finally, relatively few countries provide separate data on subsidies for the GFS database; for many countries, only an aggregate figure for subsidies plus transfers is available.

Despite the broad country coverage of the SNA database, the limited concept of subsidies that is utilized in the SNA raises the question as to what extent these data are representative of government subsidization policies. Fortunately, SNA subsidies appear to be a fairly good proxy for a more comprehensive measure of subsidies. We arrive at this conclusion by comparing SNA data with other, more comprehensive measures of subsidies that exist for a smaller number of countries, such as four surveys on state subsidies by the CEC (1989, 1990, 1992, 1995), and periodic surveys on government subsidies by the European Free Trade Association (EFTA, 1986, 1990).

Table 2 presents correlation coefficients between the SNA data and the CEC and EFTA data on subsidies.⁸ The results indicate a close correspondence between subsidies measured by the SNA and other sources. Overall, the correlation coefficient between the SNA and CEC measures of government subsidies is 0.87. For most of the countries there is a very strong positive correlation between the two subsidy measures, and only for two countries do the SNA data fail to track the broader CEC data (France has a coefficient of -0.38 and Luxembourg a coefficient of only 0.19). Similarly for the EFTA countries, there is also a good overall correlation of 0.67 between the two measures of government subsidies. Again, for most countries the degree of correlation is high; only for Norway it is poor (with a correlation coefficient of -0.08).

Table 2.

Correlation Coefficients Between Subsidy Data from CEC and EFTA Relative to SNA Data, 1981–86

Source: Authors’ estimates.

Table 2.

Correlation Coefficients Between Subsidy Data from CEC and EFTA Relative to SNA Data, 1981–86

Country	Correlation Coefficient
Total for EU-10	0.87
Belgium	0.59
Denmark	0.39
France	-0.38
Germany	0.75
Greece	0.85
Ireland	0.86
Italy	0.36
Luxembourg	0.19
Netherlands	0.89
United Kingdom	0.76
Total for EFTA	0.67
Austria	0.74
Finland	0.71
Iceland	0.66
Norway	-0.08
Sweden	0.34
Switzerland	0.34

Source: Authors’ estimates.

Table 2.

Correlation Coefficients Between Subsidy Data from CEC and EFTA Relative to SNA Data, 1981–86

Country	Correlation Coefficient
Total for EU-10	0.87
Belgium	0.59
Denmark	0.39
France	-0.38
Germany	0.75
Greece	0.85
Ireland	0.86
Italy	0.36
Luxembourg	0.19
Netherlands	0.89
United Kingdom	0.76
Total for EFTA	0.67
Austria	0.74
Finland	0.71
Iceland	0.66
Norway	-0.08
Sweden	0.34
Switzerland	0.34

Source: Authors’ estimates.

A look at the composition of government subsidies reveals a similar picture. For example, the CEC data for the periods 1981-86, 1986–88, and 1988–90 show that “cash grants” (current unrequited payments to producers) constitute the largest share of subsidies, averaging about 57 percent of the total during the 1981–90 time period (Figure 1). The pattern is fairly stable over time, implying that these payments to producers for current operations—which are close in definition to SNA subsidies—are approximately a fixed proportion of all subsidies in the EU.

Figure 1:

Types of Government Subsidies in the European Community, 1981–90

Citation: IMF Working Papers 1998, 166; 10.5089/9781451979664.001.A001

Source: Commission of the European Communities, different years.

From these considerations we conclude that the SNA database achieves a sizable coverage of countries and years and provides a fairly representative picture of the level of subsidies across and within countries. That is, it appears that governments that heavily rely on “cash grant” subsidies are, in general, also heavy users of other kinds of subsidies. Therefore, the SNA data may be used (and considered as an instrument) to estimate the general model for subsidies developed above.

IV. Econometric Results

In this section, we estimate the simple two-equation model outlined above. We focus mainly on the structural system, although we also look at the reduced form of the subsidy equation. The structural system allows us to establish which variables determine government subsidies and the channels through which these variables transmit their influence: directly (through the subsidies equation), or indirectly (through the total government expenditures equation). Data for all variables of the model were collected for 40 countries for the period 1975–92.⁹

The two equation system we estimate for government subsidies and total government expenditures is as follows:

\begin{array}{l} {(S / GDP)}_{i t} = α_{0} + α_{1} {(TE / GDP)}_{i t} + α_{2} {(IE / GDP)}_{i t} + α_{3} {(CA / GDP)}_{i t} + α_{4} {(MAN / GDP)}_{i t} \\ + α_{5} {(AGR / GDP)}_{i t} + α_{6} {OPEN}_{i t} + α_{7} F P_{i t} + \in_{i t} \\ {(TE / GDP)}_{i t} = β_{0} + β_{1} {(IE / GDP)}_{i t} + β_{2} {(CA / GDP)}_{i t} + β_{3} {(AGR / GDP)}_{i t} + β_{4} OPE N_{i t} \\ + β_{5} {DEP}_{i t} + β_{6} {URB}_{i t} + β_{7} {FP}_{i t} + v_{i t} \end{array}

with

(MAN/GDP) = manufacturing share of GDP

(AGR/GDP) = agriculture share of GDP

OPEN = degree of openness

DEP = dependency ratio

URB = urbanization ratio

FP = existence of an IMF-supported adjustment program (dummy variable)

It should be noted that the formulation above includes interest expenditure as a state variable in the subsidy equation. This is done to capture the fact that in financially constrained or highly indebted countries, high interest outlays could exert pressure to lower all other outlays. To capture this possibility, interest expenditures are separated from other government expenditures.

In the subsidy equation, the shares of manufacturing and agriculture in GDP are included as explanatory variables.¹⁰ For an economy that comprises manufacturing, agriculture, and services, the coefficients of these two variables indicate how much subsidies as a share of GDP change when one percent of GDP is shifted away from services to either agriculture or manufacturing.

Initial tests indicated the existence of first-order autocorrelation for a number of countries. To compute the correct standard errors for the regression coefficients, we used the autocorrelation correction described in Pindyck and Rubinfeld (1991).¹¹ As a result, the variables were appropriately transformed. From the original model we have:

Y_{i t} = α + β X_{i t} + \in_{i t}

with ∈^*_it = ρ_it ∈^*_it-1 + u_it. After computing the autocorrelation coefficients (ρ_i) from the residuals of each country individually, the model can thus be transformed into:

Y_{i t}^{*} = α (1 - ρ_{i}) + β X_{i t}^{*} + ϵ_{i t}^{*}

where

Y_{i t}^{*} = Y_{i t} - ρ_{i} Y_{i t - 1}; X_{i t}^{*} = X_{i t} - ρ_{i} X_{i t - 1}; ϵ_{i t}^{*} = ϵ_{i t} - ρ_{i} ϵ_{i t - 1}

Applying this correction left us with a total of 580 observations. All variables in the model can be expected to be stationary in the sense that they have a limited variance, as they are all expressed as ratios varying between zero and one (with the exception of the dummy for IMF-supported programs).

We then tested 2 versions of the structural model. The first version was the simple pooled model, which after the correction for first-order autocorrelation, effectively covers the period from 1976 to 1992. The pooled model attempts to explain differences in subsidy expenditure both between countries and within countries. The second version of the model we tested was the fixed effects model, which does not restrict the 40 countries to a common intercept but allows for individual country (“fixed”) effects. The fixed effect model attempts to explain variations in subsidy expenditure within countries by holding constant any between-country differences with a unique intercept term for each country. Both the pooled and the fixed effects specifications were tested using full-information maximum likelihood (FIML) estimators; the results are shown in Tables 3 and 4. An advantage of the FIML estimators is that they do not lose efficiency when the regression residuals are not normally distributed. The estimation involves applying the maximum likelihood principle to the two equations simultaneously; the estimators are consistent, asymptotically efficient, and their asymptotic frequency distribution is normal. The FIML estimators have the same asymptotic properties, including the same asymptotic variance-covariance matrix, as the Three-Stage Least Squares (3SLS) estimators. The advantages and disadvantages of the two model specifications (pooled and fixed effects) are indicated below in the discussion of the econometric results.

Table 3.

Results from Estimating the Structural Model (Pooled Data), 1975–92 ^1/