Fiscal Politics
Chapter

Chapter 7. Energy Subsidies and Public Social Spending: Theory and Evidence

Author(s):
Vitor Gaspar, Sanjeev Gupta, and Carlos Mulas-Granados
Published Date:
April 2017
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Introduction

Many studies have stressed the fiscal cost and environmental impact of energy subsidies (see, for example, IEA 2011; IMF 2013a; Parry and others 2014; and World Bank 2010, among others).1 A comprehensive assessment by Clements and others (2013) suggests that pretax energy subsidies amounted to 0.7 percent of global GDP in 2011.2 The figures are even more striking when the negative externalities from energy consumption are factored in (posttax energy subsidies were estimated to be about 2.9 percent of global GDP in 2011, equivalent to 8.5 percent of total government revenues; see Clements and others 2013). In addition, generalized energy subsidies have important distributional effects—there is wide micro-based evidence that they mostly benefit the wealthy, given their higher energy consumption.3 High energy subsidies, at least until the recent past, have been a major policy challenge, especially when energy prices were high and on the rise.

From a political economy standpoint, many governments have argued that energy subsidies help shelter the purchasing power of the poor from high energy costs. At the same time, under limited budgetary resources, energy subsidies may come at the cost of lower spending elsewhere in the budget, including priority social expenditures. A natural question is, therefore, why the poor would support energy subsidies, a form of redistribution that disproportionately benefits upper-income groups. Or put differently, under which conditions could high energy subsidies and low public social spending occur as an equilibrium outcome of a political game determining the composition of public spending?4 It could be argued that the elite exploit imperfect information to make “sneaky” transfers to their constituency (see Coate and Morris 1995). This explanation, however, seems hard to reconcile with long-lived energy subsidies, especially if they indeed crowd out categories of public spending that are relevant to the poor.

Against this backdrop, this chapter develops a simple political game between the elite and the middle class to examine the conflicting allocation of public resources between energy subsidies and public social spending. The analysis shows that high energy subsidies and low social spending may indeed emerge in equilibrium when the delivery of the public good is subject to important bottlenecks, reflecting weak domestic institutions or political ineffectiveness. Intuitively, the poor support that equilibrium because energy subsidies provide a small but certain benefit to consumption, whereas the delivery of the public good is subject to significant leakages (for example, through corruption). The elite, internalizing this, set a subsidy rate that is suboptimally high, crowding out public social spending, especially when fiscal space is narrow.5

This chapter empirically tests the above predictions of this conceptual framework, using a large cross-section of low- and middle-income countries. A key empirical challenge to identifying the crowding-out effect of energy subsidies on public social spending is that the two aggregates may be jointly determined in the budget process. A simple ordinary least squares (OLS) estimation would therefore deliver biased estimates. To address this simultaneity bias and other potential sources of endogeneity, the procedure instruments subsidies in a given country by the level of subsidies in neighboring countries. In addition, it takes into account political constraints on the executive to capture how easily politicians can implement subsidy policies.

The instrumental variable estimation results suggest that energy subsidies indeed crowd out public social spending. More specifically, the analysis finds that a 1 percentage point increase in energy subsidies relative to GDP leads, on average, to a reduction of public spending in education and health by two-thirds percentage point of GDP. The estimations also point to important nonlinearities: the crowding out is stronger in the presence of weak domestic institutions or in an environment of political ineffectiveness.

This analysis is related to the existing literature along three dimensions. On the theoretical front, very little work has modeled the impact of energy subsidies on the economy. Chapter 14 of Acemoglu (2014) surveys a number of general theories as to why inefficient forms of redistribution may occur in a political equilibrium. Energy subsidies, however, warrant separate treatment, given their peculiar features—they are “generalized,” highly regressive, and have become widespread over the recent past (see Annex Figures 7.1.1 and 7.1.2). This chapter models energy subsidies explicitly and examines how they conflict with the provision of public social services in an economy, contingent on the quality of its institutions. Plante (2014) uses an open economy dynamic general equilibrium model in which oil is used as an input into the production function of firms, and finds that fuel subsidies reduce aggregate welfare, mainly by distorting the relative price of nontradable to tradable goods. Also, Strand (2013) develops a political economy model in which two interest groups value two different types of fuel (gasoline versus kerosene). He then characterizes the conditions under which positive subsidies emerge in equilibrium for each type of fuel, in autocracies and in young democracies.

With regard to domestic institutions in public finances, Abed and Gupta (2002) present a number of analyses of the impact of governance and corruption on the composition of government expenditures (and on economic performance). Mauro (1998) finds in a cross-section of countries that corruption reduces government spending on education. The conceptual framework in this chapter complements those results by providing a channel through which weak domestic institutions affect public social spending.6

Regarding the empirical strategy, the use of subsidies in neighboring countries in identifying the causal relationship between energy subsidies and social spending (both policy choices) builds on the literature on spatial spillovers in fiscal choices. For example, Keen and Lockwood (2010) find that the adoption of the value-added tax (VAT) in a given country depends on the fraction of neighboring countries that have already adopted it. Fatás and Mihov (2013) examine how domestic institutions (constraints on the executive, in particular) affect policy volatility and hence economic growth.

The remainder of the chapter is organized as follows: The second section presents the political game and its main implications. The third section tests the predictions of the model, with emphasis on the identification strategy. The fourth section concludes and draws policy implications, with reference to recent developments in international oil prices.

The Political Game

Economic Environment

The analysis considers an economy populated by two types of agents: the rich with income yr, and the relatively poor with income yp; yp < yr. The rich can be thought of as representing the elite and the relatively poor as representing the middle class.7 Each agent derives utility from a private good, c (aggregate of energy consumption, ce, and non-energy consumption, cn) and from a public good, k. The economy is endowed with a resource rent, z, and income is subject to proportional taxation at a fixed rate, τ¯. The common pool of resources is used to subsidize energy and undertake social spending. A peculiar feature of social expenditures—potentially valued differently by the rich and the poor—is that their delivery is subject to bottlenecks (the economy incurred a deadweight loss, ζ, for each dollar of public social spending), reflecting weak domestic institutions.

The Game and Its Equilibrium

Setup and Timing of Events

The model developed here is relatively simple but provides a convenient way to examine the interplay between energy subsidies and public social spending. The utility function of agent i is given by equation (7.1):8

where the private good c is a Cobb-Douglas aggregate of the energy and non-energy goods:9

Consider the following timing of events: At the beginning of the game, nature chooses the level of bottlenecks, ζ, in the economy.10 This choice is observed by both the rich and the poor. The game then proceeds as follows: (1) the rich decide on the subsidy rate, δ; (2) the poor choose how much to spend on the public good, k (public social spending); and (3) the rich and the poor each decide how much of the subsidized private good (energy, ce) and of the nonsubsidized private good (non-energy, cn) to consume. This game structure reflects the fact that the rich benefit the most from energy subsidies (as shown below and consistent with empirical evidence) and would therefore push for them, whereas the poor are exante likely to favor social expenditures, given the high cost of market-provided services.11

Solving the Game (Backward Induction)

The political game can be solved backward as follows:

Third Stage of the Game: Each Agent Decides on the Consumption of Private Goods

For a given subsidy rate δ, and a given provision of the public good k, each agent decides the amount of the private good to consume. Showing that the Cobb-Douglas specification combined with the log utility on the aggregate private good induces each agent to devote a constant share of his or her disposable income to energy and non-energy consumption is straightforward.

The corresponding shares are θ for energy and 1 − θ for non-energy consumption:

where δ is the subsidy rate—the agent only pays a fraction 1 − δ of the international energy price, pw, normalized so that the price of the non-energy good equals unity.12Equation (7.3) sheds some light on two interesting features of the model specification. First, the constant consumption shares imply that energy consumption increases with the subsidy rate for given disposable income and international energy price. Second, the rich benefit the most from energy subsidies (to an extent that increases with the relative income, yr/yp, of the rich), consistent with micro evidence.13

Second Stage of the Game: The Poor Choose the Amount of Social Spending

Taking the subsidy rate δ as given, the poor choose the amount of public social spending. It is assumed that only a fraction, 1 − ζ, of social expenditures actually contributes to social infrastructure because of various bottlenecks, reflecting weak domestic institutions. Relatively bad institutions would translate into a high value of ζ. There are several practical interpretations of ζ. It can be viewed as the share of budgeted social expenditures that are diverted away from their intended purpose (for example, through corruption). ζ may also reflect efficiency losses in the delivery of social spending. 1 − ζ could also be thought of as the perceived return to social infrastructure such as education.14 The amount of subsidies and public social spending (the latter adjusted for the deadweight loss incurred in the delivery of the public good) is naturally constrained by the available resources:15

where λ is the fraction of rich people in the population and y = λyr + (1 − λ)yp is aggregate income. Given equation (7.3), equation (7.4) boils down to

The poor maximize up(cp, k) = ln(cp) + vp(k), subject to equation (7.5), the economy’s budget constraint. After some simple algebraic manipulations, and taking into account the results from the third stage of the game, equation (7.6) results:

where the constant αp is a nonlinear combination of parameters, including the consumption share of energy, disposable income, and the exogenous price of energy.16 Interestingly, the utility of the poor increases with the subsidy rate δ, a condition that we do not impose ex ante. An expression similar to equation (7.6) holds for the rich. We can already anticipate that the optimal choice of the public good by the poor will reflect the interplay between the utility derived from energy subsidies and that provided by the public good.

The Lagrangian of a poor agent’s maximization problem is written as follows:

The optimal choice of k is dictated by the two following conditions, in addition to the nonnegativity constraint on μp and the budget constraint:

The first condition implies that μp > 0, given that vp>0 and ζ < 1. Condition (ii) therefore yields the following optimal choice for k, denoted k*:

The optimal social spending choice has some interesting features. The first term in the second set of brackets represents the common pool of resources in the economy and could be thought of as mirroring the available fiscal space. The second term captures the cost of energy subsidies.

Given that ζ,τ¯,δ<1, one has

A higher subsidy rate lowers the amount of resources available for public social spending. This was expected given that the total amount of subsidies increases with the subsidy rate, and subsidies and social spending are the only uses of the common pool of resources in the model. Notwithstanding this negative marginal effect of subsidies on social expenditures, equation (7.7) suggests that a sizable resource endowment, or more generally, a large fiscal space, would limit the impact of energy subsidies on public social spending. This implication of the model is tested in the empirical section of the chapter.

Equation (7.7) implies that the amount of public social spending (chosen by the poor) decreases with the extent of leakages in the delivery of the public good:17

The above derivative is partial in the sense that it does not capture the indirect impact of ζ on k*, through δ. In fact, the chapter shows below that the choice of δ by the elite in the first stage of the game also depends on ζ, the bottlenecks that hamper delivery of the public good.

First Stage of the Game: The Elite Set the Subsidy Rate

Taking into account the above choices, the utility function of the rich is

where the constant αr is the counterpart of αp (see the second stage of the game above). We write k*(δ, ζ) to emphasize that the optimal level of the public good depends on the subsidy rate (and on the quality of domestic institutions), as shown above. The first-order condition (with respect to δ) for an interior solution is

Using equation (7.8), and after some algebraic manipulations, the optimal choice of δ is given by

It follows that

The bottlenecks in the delivery of the public good lower its benefit, leading to a second-best outcome of higher energy subsidies and lower public social spending. Equation (7.9) also suggests that the rich would choose a lower subsidy rate if the public good provides good quality services at the margin (that is, if vr(k*) is high).

Although we assume an exogenous tax rate, τ¯, the model provides some useful insights into the role of taxation: if the social contract were designed such that energy subsidies were contingent on a higher income tax rate (or a more progressive tax system), the process would intuitively converge toward a “higher equilibrium” of low energy subsidies (see equation (7.9)) and high priority social spending (see equations (7.7) and (7.8)).

In summary, the transmission mechanism in the model is one whereby weak domestic institutions (and to some extent low-quality public services) induce the rich to choose a high subsidy rate. This in turn crowds out public spending, especially under narrow fiscal space. Intuitively, the poor support that equilibrium because energy subsidies provide a small but certain benefit to consumption, whereas delivery of the desirable public good is subject to significant leakages. These predictions of the model are tested in the empirical section.

The model has been kept simple to develop the intuition through the lens of closed-form solutions. The model could be extended along several dimensions. First, we assume that agents’ utility is separable in public and private goods. In reality, however, they could be either strong complements or substitutes. Second, domestic institutions are assumed to be exogenous in this model. However, a large body of the political economy literature documents perverse effects of the natural resources endowment on domestic institutions. This model could therefore be extended to account for that interplay (for example, by setting ζ = ζ(z), an increasing function of z). Third, for tractability and transparency, the model assumes that income is the main source of heterogeneity between the rich and the poor. Each type of agent may, however, have different intrinsic preferences between the energy and the non-energy goods. The model developed in this chapter can be extended to account for that particular feature, for example, by allowing the share of the energy good (and of the non-energy good) to be different across the consumption baskets of the rich and the poor.

Empirical Analysis

Data Description and Stylized Facts

Data Description: Energy Subsidies and Social Spending

The data on energy subsidies are drawn from Clements, Gupta, and Nozaki (2013) and are computed based on the “price gap” approach (see Koplow 2009). The measure of energy subsidies includes subsidies on a wide range of products (petroleum products, gas, coal, and electricity) for a panel of low- and middle-income countries.18 Consumer subsidies arise when the prices paid by consumers, including both firms (intermediate consumption) and households (final consumption), are below a benchmark price, while producer subsidies arise when prices received by suppliers are above this benchmark. The benchmark price for calculating subsidies on an energy product that is internationally traded is based on the international price. When the energy product is mostly nontraded (such as electricity), the appropriate benchmark price is the cost-recovery price for the domestic producer, augmented by distribution costs and a normal return to capital. The advantage of the “price gap” approach is that it helps capture subsidies that are implicit, such as those provided by countries that supply petroleum products to their populations at prices below those prevailing on international markets. More formally, energy subsidies are computed as follows:

where e denotes the energy product potentially subsidized (petroleum products, gas, coal, or electricity), ci,te is the total domestic consumption of product e in country i in year t, pw is the benchmark price (in local currency), and p is the domestic price.19

Estimates of pretax energy subsidies based on this approach do suffer from some limitations. First, subsidies calculated here mostly reflect consumer subsidies because producer prices are not available for a large number of countries. Second, consumption and price data often come from different sources that are not necessarily comparable across countries. Third, benchmark prices by product (especially products that are traded internationally) rely on the assumption of similar transportation and distribution margins across countries. Such measurement errors in the data have implications, explored later in the text, for the choice of the econometric specification.

We obtain data on social spending from the World Bank’s World Development Indicators (2013) database. Despite some data limitations for earlier years (especially during the 1980s and the 1990s), substantial efforts have been made to record public social spending for the past decade for a large number of countries. In that vein, Clements, Gupta, and Nozaki (2013) provide a comprehensive data set on public spending on education and health covering 140 countries. Their series, however, end in 2009. Because the recent period (post-2008) during which energy subsidies have risen substantially (on the back of rising international oil prices) is key to this analysis, the World Development Indicators series is used. For the purpose of this analysis, a narrow but relatively easy-to-capture concept of public social spending is adopted, defined as the sum of public expenditure in education and health (expressed as a percentage of GDP for cross-country comparability).20 Many other control variables used in the estimations (see specification below) are also from the World Development Indicators (2013) database.

Given that we are interested in the effect of energy subsidies on social spending, which is conditional on the quality of institutions and political environment of each country, the subsidies variable is interacted with several time-varying indicators of the quality of domestic institutions and the fragility of the political environment.

The first variable interacted with energy subsidies is a synthetic index of governance quality that aggregates six dimensions of governance reported in the World Bank’s World Governance Indicators data set: voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption. To build the governance indicators used in the econometric estimations, all the original governance-quality indicators are transformed using the following formula:

where x represents an indicator of governance quality and min(x) and max(x) represent the minimum and the maximum of that indicator, respectively. This transformation ensures that G takes values between 0 and 1. G increases with deterioration of the quality of governance. To aggregate the six indicators into a normalized index, principal component analysis is used. The aggregate index of governance is the first principal component of the vector of the six indicators of governance already constructed.

The second set of indicators interacted with energy subsidies focuses on the extent of political fragility. For this, the analysis relies on indicators assembled by the Center for Systemic Peace. The first indicator measures the degree of state fragility. The fragility index scores each country on both effectiveness and legitimacy in four performance dimensions: security, political, economic, and social. Each of the indicators is rated on a four-point fragility scale: 0 for “no fragility,” 1 for “low fragility,” 2 for “medium fragility,” and 3 for “high fragility,” with the exception of the economic effectiveness indicator, which is rated on a five-point scale (including 4 for “extreme fragility”). The state fragility index, then, combines scores on the eight indicators and ranges from 0 for “no fragility” to 25 for “extreme fragility.”

Finally, to better isolate the effect of the political environment on the subsidies–social spending trade-off, the component “political ineffectiveness” is extracted from the broad state fragility index discussed above and the model is reestimated.

Stylized Facts

We start with a panel data set covering 109 low- and middle-income countries over 2000–11, the period for which energy subsidies data are available, from Clements, Gupta, and Nozaki (2013).21 The estimate of energy subsidies for each country is normalized by its GDP in current prices.

Figure 7.1 provides some insights into the question examined in the chapter. It portrays the evolution of energy subsidies and social spending between two episodes (2002–06 and 2007–11) across different regions of the globe. (Annex Figure 7.1.1 and Annex Figure 7.1.2 illustrate the intensification of energy subsidies between 2006 and 2011 around the globe and in the Middle East and North Africa region, respectively.)22 Although subsidies decreased in emerging and developing Asia between the two subperiods, they increased in sub-Saharan Africa. Strikingly, social spending moved in opposite directions across (and in) both regions, pointing to a potential trade-off between energy subsidies and social spending. In contrast, social spending did not decline in the resource-rich Middle East and North Africa (MENA) region, despite the sharp increase in energy subsidies in the region, suggesting that countries’ endowment or resource space may condition the extent of crowding out.

Figure 7.1.Change in Subsidies and Social Spending across Regions between 2002–06 and 2007–11

Sources: Clements, Gupta, and Nozaki 2013; World Development Indicators 2013; and authors’ calculations.

Note: CIS = Commonwealth of Independent States.

Figure 7.2 provides a more disaggregated picture (at the country level) and paints a quite similar story: (1) energy subsidies rose around the globe between the two identified subperiods (most countries in the sample are to the right of the vertical axis), but the evolution of social spending was uneven (countries are almost equally split below and above the horizontal axis); (2) some resource-rich countries were somewhat able to afford higher subsidies without cutting public social spending (at face value), and when social spending did decrease in resource-rich countries, the decline was much lower than the increase in subsidies (above the negative 45-degree line); and (3) in general, where social spending did increase, the increase was lower than the increase in subsidies (below the 45-degree line). Consequently, only a handful of countries went through the virtuous circle of lower energy subsidies and higher public social spending (second quadrant) during the two identified subperiods.

Figure 7.2.Energy Subsidies and Social Spending across Countries: What Has Happened?

(2007–11 average vs. 2002–06 average)

Sources: Clements, Gupta, and Nozaki 2013; World Development Indicators 2013; and authors’ calculations.

Note: Data labels in the figure use International Organization for Standardization (ISO) country codes.

Note that the patterns discussed above are based on a panel data structure, whereas the econometric analysis focuses on the cross-section dimension of the data for reasons discussed below. Nonetheless, these patterns provide useful insights into the link between subsidies and social spending. The main advantage of Figures 7.1 and 7.2 is that they illustrate how subsidies and social spending changed over the two subperiods for groups of countries (Figure 7.1) and for individual countries (Figure 7.2). Presenting similar evidence in a pure cross-section setting requires controlling for other relevant countries’ characteristics, including demographics. This is done more systematically in the econometric analysis below; the discussion also elaborates on the choice of estimation method.

Econometric Analysis

Model Specification

We propose an empirical strategy to investigate the potential impact of energy subsidies on public social spending. Identifying the causal link between these two aggregates is complicated by a number of factors discussed below. We consider the following cross-section estimation:

where Social is the sum of public spending on education and health, and Subsidies is total pretax energy subsidies as defined above (both series expressed as percentages of GDP). The vector X comprises a battery of controls that have been identified as relevant determinants of public social spending in the literature (see, for example, Baqir 2002; Clements, Gupta, and Nozaki 2013; IMF 2003; Mauro 1998 to name only a few). These controls include countries’ demographic characteristics (dependency ratio and urbanization) and macroeconomic aggregates such as initial real per capita income, government size, the degree of trade openness, and macroeconomic volatility (measured as the standard deviation of the annual growth rate of real GDP).23 Importantly, and related to the conceptual framework, the vector X also contains measures of the quality of domestic institutions.

We also assess the extent to which the crowding out, if any, depends on relevant countries’ economic and political characteristics embodied in Fi, namely the quality of domestic institutions and political effectiveness.24 It is therefore useful to consider special cases in thinking about equation (7.12). When Fi = 0 (linear case), the crowding-out coefficient is given by β1. If Fi ≠ 0 (nonlinear case), the crowding out depends on the level of the variable Fi and is given by β1 + β2Fi.

Why Adopt a Cross-Sectional Specification?

Although it is tempting to use panel estimations to exploit the full structure of the data set (time and cross-section dimensions), such an approach may not be desirable in this context for a number of technical reasons. First, the subsidy variable is likely to be contaminated with measurement errors, despite its careful derivation. These measurement errors may lead to an attenuation bias in the presence of country fixed effects. This issue is even more relevant here because most within-country variations (year to year) in the subsidies variable do not always represent a shift in the subsidy regime, but in some cases may reflect changes in the benchmark price if domestic prices are sticky across countries in the short term. We therefore run the risk of identifying the impact of shocks to energy prices on social spending rather than the crowding-out effect of subsidies on social spending, the focus of this analysis.

Second, the measure of energy subsidies used here captures implicit subsidies not necessarily reflected in the budgets. Using yearly data could be misleading, potentially leading to a stronger attenuation bias, because the estimates of the crowding-out coefficient would be biased downward (in absolute terms). This occurs because year-to-year changes in implicit subsidies are not necessarily “financed” by cutting public social spending, but might instead lead to “losses” incurred by state-owned enterprises (SOEs), financed via arrears accumulation by SOEs or public debt at large. Using within-country averages over the period of analysis would limit this bias because subsidies would eventually lead to fiscal retrenchment: SOEs cannot run losses indefinitely (without bailout from the central government) nor can debt be built up indefinitely without adjustment.

Third, the short time period (2000–11) and the strong inertia characterizing the dependent variable (public social spending) and some of its determinants (such as demographics, institutions, and natural resources dependency, which change only slowly over time) limit the information content of the time dimension of the data. This feature of the variables of interest, coupled with the short time dimension, exposes the risk that fixed effects absorb almost all the variations in the data in panel estimations.

The country-specific averages of the energy-subsidies-to-GDP ratios mostly reflect what could be referred to as “subsidy regimes” or “pricing regimes,” meaning the extent to which some countries tend to subsidize energy products more than others.25 The between-country variation is an appealing dimension of the data. Using cross-sectional estimates implies assessing questions such as do countries more prone to subsidizing energy products also have less social spending?

Identification Strategy: Addressing the Endogeneity of Subsidies

One of the main challenges associated with cross-sectional studies is how to properly control for unobservable factors to limit the risks of endogeneity bias. One could easily control for a pair of fixed effects (country and time) to obtain clean parameter estimates of the effects of subsidies in panel data estimations. The limits associated with the panel specification in the context of this analysis are discussed above.

The sources of endogeneity associated with energy subsidies may vary. First, because subsidies and social spending may be jointly determined, OLS estimates of β1 and β2 (see equation (7.12)) would suffer from a simultaneity bias. Second, the fact that cross-sectional estimations cannot control for unobservable factors that may jointly affect the subsidy regime and the level of public social spending is yet another potential source of bias.

Against this backdrop, this chapter proposes an identification strategy based on the level of subsidies in neighboring countries. The set of instruments is also enriched to include the extent of political constraints on the executive as an “exogenous” source of variation in the level of energy subsidies across countries, conditional on a battery of covariates. Two main conditions should govern the relevance of these instruments: First, they should be strongly correlated with the subsidies-to-GDP ratio in the observed country. In other words, the instruments, even after controlling for other covariates, should be significant in the first-stage equations modeling the cross-country variation in energy subsidies. The strength of the instruments is gauged using the F-stat and Shea R2 associated with the first-stage regressions. Second, the instruments should be correlated with the outcome of interest (here, the ratio of public social spending to GDP) only through their impact on the subsidies variable or through any other variable that is already controlled for in the econometric specifications. This criterion of orthogonality is tested using the Sargan overidentification test.

Another critical question is whether these instruments are economically relevant. What is the story behind their selection? The first instrumental variable records the average level of subsidies (as a percentage of GDP) in neighboring countries. The identification strategy in this analysis is based on the intuition that countries are more likely to subsidize energy products if other countries in their neighborhood are doing the same. The procedure also tests whether this could be more likely if the country of interest is a net commodity exporter, because pressures to share the pie would be higher.26 Various factors can justify the effects of neighboring subsidy regimes on countries’ behavior. One main argument is related to political economy. In the absence of informational asymmetries (we assume that citizens are aware of the policies adopted in neighboring countries over a 12-year horizon, the horizon over which the sample data are averaged), the hypothesis is that citizens are more likely to ask for energy subsidies if neighboring countries subsidize, and even more so if the home country is an oil producer. These types of spatial spillovers in fiscal choices have been used to model fiscal policy choices in developing countries in the literature (see, for example, Keen and Lockwood 2010 on VAT adoption).

The instrumental variable is constructed as the weighted average of energy-subsidies-to-GDP ratios over all neighboring countries, using the worldwide gravity database assembled by the CEPII.27 More specifically, for each country i in the sample, the energy subsidy intensity in neighboring countries (ESINCi) is evaluated as

where sj is the subsidy-to-GDP ratio in country j and

Because the instrument is built using neighboring countries’ data, it seems relatively exogenous to the level of public social spending in each country. It could be argued that the subsidy intensity in neighboring countries (the main instrument) simply captures common shocks affecting countries. Because a wide range of variables, including trade openness, are also controlled for, the latter risk is limited. The risk would be more severe if the analysis were using yearly panel data, given that a common oil shock may trigger, at least in the short term, synchronized fiscal policy responses, including decisions to partially or fully subsidize. By looking at averages of subsidy-to-GDP ratios, we are measuring to some extent subsidies or pricing regimes across countries, devoting attention to the structural component of these regimes, instead of year-over-year shifts in implicit subsidies.

The second instrument is the degree of political constraints on the executive. The assumption is that political constraints on the policy discretion of the executive are likely to limit his or her ability to develop and implement energy subsidy policies. The idea that political constraints limit fiscal policy discretion has been established empirically (for example, Fatás and Mihov 2013; Ebeke and Öçer 2013). A potential issue with this instrument, however, is that if it is correlated with energy subsidies, it may also be correlated with other expenditure categories, including public social spending. The instrument would then violate the fundamental exclusion restriction. This risk, however, is limited because the econometric specification controls for overall government size (net of subsidies), in both the first- and second-stage regressions. Moreover, public social spending is a more traditional budgetary expense than energy subsidies, and the constraints on the executive would, arguably, be more binding for the latter.

When the econometric specification involves interaction terms (subsidies crossed with some conditional variables) as is the case here, the instrumental variable approach is amended to account for the additional endogenous variables generated by the interaction terms—we therefore instrument not only the subsidy-to-GDP ratio, but also its interaction terms. The matrix of instruments thus includes the additive term of the two instruments discussed above, including their respective interaction terms with each conditional variable.

Estimation Results

Baseline Estimations (Linear Model)

Equipped with the above framework, the model is estimated using two-stage least squares with robust standard errors. Table 7.1 presents the results of the instrumental variable estimations. The second-stage estimates are reported in panel 1 of the table and the first-stage estimates are displayed in panel 2. The table displays the results of the linear effect of subsidies on public social spending, followed by nonlinear effects, conditional on relevant country characteristics, including the quality of domestic institutions and the extent of political effectiveness. The average crowding-out effect of energy subsidies on public social spending is estimated (see column (1)) to be about two-thirds (less than unity). In addition, all the significant coefficients have the expected signs. In particular, government size independently affects the level of social spending positively. The results are unchanged when government size is replaced with total tax revenues.28

Table 7.1.Energy Subsidies and Public Social Spending: The Role of Governance and Political Effectiveness
(1)(2)(3)(4)
Panel A: Dependent Variable = Public Social Spending-to-GDP (second stage)
Energy Subsidies to GDP−0.661***0.155−0.0243−0.237
(0.248)(0.713)(−0.104)(−0.870)
Energy Subsidies to GDP × Weak Governance−2.502**
(−2.236)
Energy Subsidies to GDP × State Fragility−0.0887**
(−2.072)
Energy Subsidies to GDP × Political Ineffectiveness−0.965**
(−2.561)
Weak Governance−1.215
(−0.710)
State Fragility−0.0235
(−0.284)
Political Ineffectiveness0.161
(0.445)
Log Initial per Capita Income0.6632.9572.2512.205
(2.329)(1.406)(0.970)(1.094)
Oil Rents to GDP−0.000989***−0.000967***−0.00103***
(−4.324)(−4.047)(−4.542)
Public Debt to GDP0.002−0.00696*−0.00468−0.00738*
(0.005)(−1.664)(−1.114)(−1.700)
Government Total Expenditures to GDP0.193***0.264***0.258***0.270***
(0.028)(9.115)(9.101)(9.339)
Age-Dependency Ratio0.0390.155***0.156***0.145***
(0.047)(3.671)(3.492)(3.246)
Urbanization Rate0.0160.0229*0.01700.0226*
(0.016)(1.779)(1.276)(1.690)
Trade Openness0.011*0.0158***0.0164***0.0164***
(0.007)(2.811)(2.919)(2.738)
Output Volatility−0.196***0.0122−0.0457−0.00849
(0.070)(0.186)(−0.716)(−0.125)
Military Expenditures to GDP−0.316***−0.302***−0.339***
(−4.205)(−4.152)(−4.449)
Panel B: Dependent Variable = Energy Subsidies-to-GDP (first stage)
Energy Subsidy Intensity in Neighboring Countries0.075***0.076**0.086**0.087**
(0.024)(0.037)(0.038)(0.041)
Subsidies in Neighbors × Oil Rents to GDP0.003**0.003***0.003**
(0.001)(0.001)(0.001)
Subsidies in Neighbors × Weak Governance0.305**
(0.143)
Subsidies in Neighbors × State Fragility0.015***
(0.006)
Subsidies in Neighbors × Political Ineffectiveness0.063**
(0.031)
Constraints on the Executive−0.296***−0.171−0.224**−0.208*
(0.087)(0.108)(0.100)(0.106)
Observations108104104104
R20.3280.5880.5730.556
Joint Significance of Subsidies Coefficients: p-value0.0280.0780.020
F-stat of First Stage18.3598.8959.3079.523
Hansen Overidentification Test: p-value0.0440.4080.1730.257
Source: Authors’ calculations.Note: All first-stage specifications include the full set of control variables as they appear in the second stage. Standard errors appear in parentheses.*p < .1; **p < .05; ***p < .01.
Source: Authors’ calculations.Note: All first-stage specifications include the full set of control variables as they appear in the second stage. Standard errors appear in parentheses.*p < .1; **p < .05; ***p < .01.

The first-stage regressions, which use subsidies in neighboring countries and political constraints on the executive as instruments for subsidies, show the significance of the instrumental variables, indicating a strong association, even in the presence of the full set of controls. The Hansen test of overidentification also suggests that the instruments are not strongly uncorrelated, with the residuals of the structural model at the 10 percent significance threshold. Although this level of significance may appear weak, allowing for interaction terms significantly improves the outcome of the test (see columns (2)–(4)). We can therefore conclude that public social spending tends to be lower on average in countries with higher energy subsidies (all expressed as percentages of GDP).

Is the crowding out exacerbated by weak governance and political ineffectiveness?

One central implication of this theoretical model (see “The Political Game” section) is that the quality of domestic institutions shapes the extent of the crowding-out effect of energy subsidies on public social spending. That prediction is tested here by assessing whether the crowding out is stronger in countries exhibiting high levels of institutional and political vulnerability. To do this, the baseline specification is modified to include a battery of interaction terms characterizing the institutional and political environment. The estimated interaction terms are negative (as expected) and significant. This outcome suggests that the marginal effect of energy subsidies on public social spending is larger when governance is weak or in an environment characterized by political ineffectiveness.

Conclusion and Policy Implications

This chapter examines, both conceptually and empirically, the impact of energy subsidies on public social spending. It first shows that high energy subsidies and low public social spending can emerge as the equilibrium outcome of a political game between the elite and the middle class when the delivery of the public good is subject to leakages, reflecting weak domestic institutions or political ineffectiveness. The chapter then proposes an empirical strategy to test this supposition and other predictions of our model using a large cross-section of low- and middle-income countries. The chapter documents a negative statistical association between energy subsidies and public social spending before conducting a more systematic examination of a potential causal relationship between these two aggregates. Because energy subsidies and public social spending may be jointly determined in the budget process, OLS estimates would suffer from a simultaneity bias. To address this concern and other potential sources of endogeneity in the cross-section estimations, an identification strategy is adopted whereby subsidies in a given country are instrumented by the level of subsidies in neighboring countries and political constraints on the executive. The instrumental variable estimations indeed suggest a causal relationship between energy subsidies and public social spending. More specifically, the analysis finds that public spending on education and health were, on average, two-thirds percentage point of GDP lower in countries where energy subsidies were 1 percentage point of GDP higher. Moreover, the crowding out is stronger when governance is weak and in an environment of political ineffectiveness.

The findings have important policy implications. On the one hand, they suggest that non-resource-rich countries with narrow fiscal space would have to move expeditiously with subsidy reform to relax the constraints weighing on public social spending. On the other hand, resource-rich economies will find it challenging to keep energy subsidies, in view of mounting social spending pressures, including from the youth, given the volatile nature of commodity prices. The recent sharp drop in global oil prices seems to validate this point. In fact, in line with the conceptual framework and empirical findings, resource-rich countries were somewhat able to afford high energy subsidies with relatively limited crowding out of public social spending thanks to their large fiscal space at a time when oil prices were relatively high. Those subsidy regimes will clearly be harder to sustain at much-depressed oil prices, as existing fiscal buffers get eroded. On the positive side, reforming energy subsidies is likely to pose fewer political headaches at low international oil prices. The recent sharp drop in global oil prices, therefore, represents a golden opportunity for governments, resource-rich and non-resource-rich alike, to durably reform energy subsidies. In that vein, depo-liticizing domestic energy pricing by, for instance, adopting an automatic pricing mechanism (see Coady and others 2012), seems to be a good transition toward fully deregulated energy prices.

Annex 7.1. Supplementary Tables and Figures
Annex Table 7.1.1.List of Countries in the Sample
AlbaniaEcuadorMalawiSyria
AlgeriaEgyptMalaysiaTajikistan
AngolaEl SalvadorMaliTanzania
ArgentinaEquatorial GuineaMauritaniaThailand
ArmeniaEritreaMauritiusTogo
AzerbaijanFijiMexicoTrinidad and Tobago
BahrainGabonMoldovaTunisia
BelarusThe GambiaMoroccoTurkey
BeninGeorgiaMozambiqueUganda
BhutanGhanaNamibiaUkraine
BoliviaGuatemalaNepalUnited Arab Emirates
BotswanaGuineaNicaraguaUruguay
BrazilGuinea-BissauNigerVenezuela
BulgariaGuyanaOmanVietnam
Burkina FasoHungaryPakistanYemen
BurundiIndiaPanamaZambia
CambodiaIndonesiaParaguay
CameroonIranPeru
Cabo VerdeJordanPhilippines
Central African RepublicKazakhstanPoland
ChadKenyaQatar
ChileKuwaitRepublic of Congo
ChinaKyrgyz Rep.Romania
ColombiaLao P.D.R.Russia
ComorosLatviaRwanda
Costa RicaLebanonSaudi Arabia
CroatiaLesothoSenegal
Côte d’IvoireLiberiaSierra Leone
Democratic Republic of the CongoLibyaSolomon Islands
DjiboutiLithuaniaSouth Africa
Dominican RepublicMadagascarSwaziland

Annex Figure 7.1.1.Energy Subsidies Intensity around the Globe (2006 vs. 2011)

(Percent of GDP)

Annex Figure 7.1.2.Energy Subsidies Intensity: Zoom on the MENA Region (2006 vs. 2011)

(Percent of GDP)
References

    AbedG. T. and S.Gupta.2002. Governance Corruption and Economic Performance.Washington, DC: International Monetary Fund.

    AcemogluD.2014. Political Economy Lecture Notes. MITCambridge, MA.

    AcemogluD. and J. A.Robinson.2001. “A Theory of Political Transitions.American Economic Review91 (4): 93863.

    Arze del GranadoF. J.D.Coady and R.Gillingham.2012. “The Unequal Benefits of Fuel Subsidies: A Review of Evidence for Developing Countries.World Development40 (11): 223448.

    BaqirR.2002. “Social Sector Spending in a Panel of Countries.Working Paper 02/35International Monetary FundWashington, DC.

    ClementsB.D.CoadyS.FabrizioS.GuptaT.Alleyne and C.Sdralevich.2013. Energy Subsidy Reform: Lessons and Implications.Washington, DC: International Monetary Fund.

    ClementsB.S.Gupta and M.Nozaki.2013. “What Happens to Social Spending in IMF-Supported Programmes?Applied Economics45: 402233.

    CoadyD.J.Arze del GranadoL.EyraudH.JinV.ThakoorA.Tuladhar and L.Nemeth.2012. “Automatic Fuel Pricing Mechanisms with Price Smoothing: Design, Implementation, and Fiscal Implications.Technical Notes and Manuals 12/03International Monetary FundWashington, DC.

    CoateS. and S.Morris.1995. “On the Form of Transfers to Special Interests.Journal of Political Economy103 (6): 121035.

    EbekeC. and D.Öçer.2013. “Fiscal Policy over the Election Cycle in Low-Income Countries.Working Paper 13/53International Monetary FundWashington, DC.

    FatásA. and I.Mihov.2013. “Policy Volatility, Institutions, and Economic Growth.Review of Economics and Statistics95 (2): 36276.

    International Energy Agency (IEA). 2011. “Development in Energy Subsidies.” In World Energy Outlook.Paris: IEA.

    IEA Organization of the Petroleum Exporting Countries Organisation for Economic Co-operation and Development and World Bank. 2010. Analysis of the Scope of Energy Subsidies and Suggestions for the G-20 Initiative. Joint report submitted to the G-20 Summit Meeting Toronto Ontario Canada June2627.

    International Monetary Fund (IMF). 2003. “Fiscal Adjustment in IMF-Supported Programs.Independent Evaluation OfficeWashington, DC.

    International Monetary Fund (IMF). 2013a. “Energy Subsidy Reform: Lessons and Implications.Board Paper IMF Washington.

    International Monetary Fund (IMF). 2013b. Regional Economic Outlook Middle-East and Central Asia.World Economic and Financial Surveys. Washington, DC: International Monetary Fund.

    International Monetary Fund (IMF). 2014. “Fiscal Policy and Inequality.Policy PaperIMFWashington, DC.

    KeenM. and B.Lockwood.2010. “The Value Added Tax: Its Causes and Consequences.Journal of Development Economics92 (2): 13851.

    KoplowD.2009. “Measuring Energy Subsidies Using the Price-Gap Approach: What Does It Leave Out?International Institute for Sustainable Development Winnipeg.

    MauroP.1998. “Corruption and the Composition of Government Expenditure.Journal of Public Economics69 (2): 26379.

    ParryI.D.HeineE.Lis and S.Li.2014. Getting Energy Prices Right: From Principle to Practice.Washington, DC: International Monetary Fund.

    PlanteM.2014. “The Long-Run Macroeconomic Impacts of Fuel Subsidies.Journal of Development Economics107 (March): 12943.

    StrandJ.2013. “Political Economy Aspects of Fuel Subsidies: A Conceptual Framework.Policy Research Working Paper 6392World BankWashington, DC.

    World Bank. 2010. “Subsidies in the Energy Sector: An Overview.Background Paper for the World Bank Group Energy Sector Strategy Washington DC.

    World Bank. 2013. World Development Indicators Database. World BankWashington, DC.

See also IEA and others (2010), a joint report on the scope of energy subsidies (with suggestions for phasing them out) for the 2010 G20 summit meeting in Toronto.

Pretax energy subsidies (obtained as the difference between a benchmark price of energy and the price paid by consumers) are generally higher among net oil exporters. These subsidies absorbed about 22 percent of government revenues in the Middle East and North Africa region in 2011 (see IMF 2013b).

In a review of the evidence on the welfare impact of energy subsidy reform across 20 countries from Africa, Asia, the Middle East, and Latin America, Arze del Granado, Coady, and Gillingham (2012) find that the top income quintile captures six times more in subsidies than the bottom, in absolute terms. These distributional effects vary substantially across products, with the subsidies on gasoline being the most regressive. Also, IEA (2011) finds that the poorest 20 percent of households receive only about one-tenth of natural gas and electricity subsidies.

IMF (2014) provides empirical evidence that public support for redistributive policies has grown in recent decades, partly because of rising inequality. Energy subsidies, however, disproportionately benefit upper-income groups and would not reduce inequality, which adds to the puzzle.

The rich favor the high-energy-subsidies equilibrium because it provides large private savings on their energy bill (using public resources) and therefore more personal resources are left for non-energy consumption.

Baqir (2002) also finds that democratization is a significant predictor of government spending on education and health in a large panel of countries.

Throughout the chapter, the subscript r denotes rich agents (or members of the elite) and p denotes poor agents (part of the middle class).

It is important to stress that the results do not depend heavily on the specification of the utility function—the adopted functional forms aim at developing the main intuitions of the model through the lens of closed-form solutions. For instance, we briefly discuss below the implications of adopting a functional form that is nonseparable in c and k.

The model assumes that the share of the energy good (and of the non-energy good) is the same in the consumption basket of the rich and the poor. Solving the model with different consumption shares across the two groups of agents is straightforward.

This is equivalent to assuming that the quality of domestic institutions is exogenous. In reality, however, public social spending would improve the human capital of the poor and perhaps their participation in the electoral process in a democracy. This model is quasi-static in that sense.

The timing of the game may be thought of as reflecting that the middle class forms the majority of the population, prompting the elite to internalize their “move” (see backward induction below). This timing is common in the political economy literature (see, for example, the seminal paper by Acemoglu and Robinson [2001], and Acemoglu [2014, Chapter 13]).

The amount of energy subsidy is a combination of the subsidy rate and the amount of energy that agents consume in equilibrium.

This holds partly because the demand for energy of the rich is more elastic to the price of energy (and therefore to the subsidy) than that of the poor. In fact, the demand for the energy good, e, by agent i is written as ci,e=θ(1τ¯)yi/(1δ)pw(cr,e/δ)/(cp,e/δ)=yr/yp.

The model may also be set up in a way that z captures the probability that the politician will actually deliver on the public good’s promise.

Note that the non-energy good, n, does not enter the common pool budget constraint, given that it is entirely privately paid for by agents.

αp=ln((1τ¯)yppw)+θln(θ)+(1θ)ln(1θ).

Equation (7.7) suggests that this condition holds as long as the entire pool of common resources is not devoted to energy subsidies in equilibrium (which is highly likely).

The list of countries covered by the analysis is presented in Annex Table 7.1.1.

Given that pretax subsidies are positively defined, by definition, sie is censored and takes the value 0 whenever the price gap is negative.

Clements, Gupta, and Nozaki (2013) adopt a similar definition.

The countries covered in the sample are listed in Annex Table 7.1.1.

We consider averages over subperiods rather than yearly time series for robustness. In fact, changes in subsidies and social spending from one year to another could be arbitrary. The 2006–07 cutoff is chosen to reflect the dynamics in energy prices, but the point made here holds for alternative cutoff dates (and hence subperiods).

It is worth emphasizing the need to control for government size in the model. In fact, all categories of spending may also decline regardless of what happens to energy subsidies (for example, if tax revenues fall). We therefore control for government size (spending-to-GDP ratio, excluding subsidies) to make sure we isolate the marginal effect of subsidies on public social spending. We focus on social spending for political economy reasons, as highlighted in the introduction.

All the variables are averages over the sample period (2000–11).

The authors are grateful to David Coady for bringing this useful interpretation to our attention.

To anticipate the estimation results a bit, the extent of energy subsidies in neighboring countries alone accounts for one-quarter of the total variation in energy subsidies across countries and for up to half of the variation among net oil exporters.

Government size and tax revenues could not be included simultaneously in the regressions because of their high correlation.

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