Fiscal Politics
Chapter

Chapter 5. Now or Later? The Political Economy of Public Investment in Democracies

Author(s):
Vitor Gaspar, Sanjeev Gupta, and Carlos Mulas-Granados
Published Date:
April 2017
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Author(s)
Sanjeev Gupta, Estelle X. Liu and Carlos Mulas-Granados 

Introduction

Public investment is often touted as a way to raise output, both in the short term by promoting aggregate demand and in the long term through enhanced supply potential of the economy (IMF, 2014a; Felice, 2016). However, even when shovel-ready projects are available and budget processes are sufficiently strong for implementing investment programs, public investment may not occur. This may be because public investment is less noticeable (Rogoff, 1990) or may have a lower short-term economic multiplier than certain other types of public spending, including public consumption. When elections approach, policymakers may seek to increase public consumption at the expense of public investment to accelerate the economy faster and increase their probabilities of reelection.

In this chapter, we explore the potential relationship between public investment and electoral cycles. In particular, we seek to answer the following question: does the proximity of elections affect the growth rate of public investment? Our empirical approach relies on a unique database covering 67 advanced, emerging and low-income economies with presidential and parliamentary democracies. Using this data, we try to answer to what extent the growth rate of public investment can be explained by the proximity of elections, after controlling for relevant considerations.

In contrast to previous studies that relied on a linear relationship or binary association, we show that the relationship between the growth rate of public investment and the electoral cycle follows an inverted U-shape. Our results show that the growth rate of public investment is larger at the beginning of the mandate, peaks about 28 months before elections, and then declines fast as elections approach.1 One month closer to the next election the growth rate of public investment declines about 0.7 percentage points. Other political and institutional variables are less important. Our results are robust to a number of tests, in particular to the potential endogeneity of election dates.

The chapter is organized as follows. The second section reviews the relevant literature. The third section presents the data and some stylized facts. The fourth section reports the results of the regression analysis on the baseline model. The fifth section performs various robustness tests. And the sixth section summarizes the main findings and concludes.

Literature Review

Since the concept of political business cycles (PBC) was first proposed by Nordhaus (1975), the literature on the political economy of fiscal policy has mainly focused on the political and institutional factors behind budget deficits. Only a few papers have dealt with the political economy of public investment and capital accumulation.2 The literature can be grouped into three approaches (Eslava, 2006). The first would be the opportunistic approach, according to which electoral incentives influence government’s budget balance. The second could be labeled as ideological, and would include all the papers that see fiscal deficits as arising from conflicts of interest among different political parties with heterogeneous preferences. The third approach—which focuses on rules and institutions—highlights their importance behind fragmentation in the decision-making process, thereby affecting budget composition and damaging public investment. The literature on the political economy of public investment can be grouped along the same lines.

The opportunistic or electoral approach is summarized by Rogoff (1990) who provided a firm theoretical foundation for electoral shifts leading to changes in the composition of public spending. He showed that electoral incentives may induce the incumbent to shift public spending towards more “visible” government consumption and away from public investment. Government consumption expenditures are more “visible” before elections, while capital expenditures (e.g., infrastructure) are mostly long-term projects that increase voters’ utility upon completion. Drazen and Eslava (2010) developed this idea further and predicted that changes in composition of public spending during election periods were the result of incumbents attempting to signal that their preferences were closer to those of voters. Empirical evidence in this regard is mixed.3 Most multicountry studies at the general government level show that elections tend to shift public spending in favor of current spending and away from public investment (Schuknecht, 2000; Block, 2002; Vergne 2009; Katsimi and Sarantides, 2012).4 However, the evidence from single country studies (e.g., Canada, Colombia, Portugal, and Norway) suggests that at the local government level opposite forces are at play. Local elections are correlated with a shift toward “visible” investment (which at the subnational level takes the form of local infrastructure) together with targeted public transfer programs (Blais and Nadeau, 1992; Kneebone and McKenzie, 2001; Veiga and Veiga, 2007; Alesina and Paradisi, 2014).

The ideological or partisan approach tries to link the size and the composition of budget with the sign of partisan preferences. In partisan models, parties of the left are expected to favor a larger government and have less aversion to public deficits than parties of the right (Tufte, 1978; Alesina and Tabellini, 1990; Alt and Lassen, 2006). The greater preference for redistribution of left-wing parties would imply more spending, on social transfers. In addition, their preference for a more activist role of the state in the provision of public physical and human capital would imply higher public investment on infrastructure, health and education. Empirical findings support the effect of partisanship on the composition of public spending (Boix, 1997; Francese, 2002; Brauninger, 2005; Potrafke, 2010; Angelopoulus et al., 2012) and during fiscal adjustments, with left-wing parties opting for revenue-based adjustments and right-wing parties opting for expenditure-based ones (Perotti, 1998; Mulas-Granados, 2003, 2006; Mierau et al., 2007).5 But there can be exceptions to partisanship. Given that politicians need to be opportunistic to win elections, rational politicians may go against their ideological policy preferences (Persson and Svensson, 1989; Pettersson-Lidbom, 2001).

The rules or institutional approach encompasses a great variety of issues, such as the role that rules and institutions play in constraining or facilitating public investment decisions. In certain cases, the focus is on the way they shape the political and economic context in which governments operate. These contributions can be grouped in three broad areas:

First, the role of electoral rules and political traditions in generating fragmented party systems and weak governments. Minority governments, divided legislatures, coalitions and multiparty cabinets, with a large number of ministers, and with a weak coordinating role for the Ministry of Finance, are all associated with fiscal profligacy and low productive investment (Hallerberg and Von Hagen, 1997; Von Hagen, Hallett and Strauch, 2001; Perotti and Kontopoulos, 2002; Hallerberg, Strauch, and Von Hagen, 2007). Institutional frameworks that reinforce and centralize budget commitments help eliminate electoral manipulation of budget cycles (Saporiti and Streb, 2008), and frequent changes in government are associated with lower average public investment (De Haan and Sturm, 1997).

Second, the impact of good governance on the level and composition of public finances. Better governance, more transparency, less corruption, and a smaller amount of veto players are all correlated with better quality of public finances. This is true not only at the national but also at the sub-national level, where transparency helps restrict electoral manipulation of spending (Schneider, 2010; Bove and Efthyvoulou, 2013). In this respect, higher levels of public investment could just be the result of corrupt processes and inefficient public management systems.

Finally, the impact of budget rules and institutions on the sustainability of public finances (IMF, 2014b). While the presence of golden rules has not had a differential impact in sustaining higher levels of public investment, there is some evidence that strong budget institutions have been successful in preserving investment from budget cuts during the crisis (IMF, 2014c).

Data and Stylized Facts

This study uses data from 67 democracies during 1975 and 2012, covering countries from all regions and income levels.6 We focus on elections for a national executive figure or a national legislative body, and restrict the sample to countries and periods where competitive elections have taken place. The sample excludes countries where data on public fixed capital formation are not available. Additional details on sample size and selection criteria can be found in Annex Tables 5.1.1 and 5.1.2).

Data on public gross fixed capital formation comes from three sources: World Economic Outlook (WEO), World Development Indicators (WDI) and Haver Analytics.7 Data on fiscal variables are drawn from the WEO, including total government expenditure, interest payments and current spending, the primary balance and the debt-to-GDP ratio. All these variables are estimated at the general government level.8 Data on macroeconomic control variables, including real GDP growth and the rate of inflation were collected from WEO.

These variables help control for the state of the economy that might affect both the political cycle and investment decisions.

We define our dependent variable as the growth rate of nominal public investment.9 We choose to focus on nominal public investment because in every country budgetary allocation takes place in nominal terms and this is the variable of decision for policymakers. By looking at the change of public investment from one year to the next, scaled by the initial level of investment, our approach allows us to capture the dynamic behavior of the variable and pool together multiple countries and periods with different starting levels of investment.10 Because this chapter aims at analyzing the impact of months-to-election, which is a time-variant variable, we think that the growth rate of nominal public investment is the best option to capture these dynamics.

On average, the growth rate of public investment in the whole sample is 12.8 percent, but there are important differences by year (Figure 5.1). For example, in 1976, 1994 and 2007 the growth rate of nominal public investment was equal or above 20 percent, while in 1983, 2000 and 2009 it was around 5 percent or below. In such a heterogeneous sample of 67 countries, these yearly average growth rates combine different cross-country economic and political conditions affecting public investment outcomes.

Figure 5.1.Growth Rate of Public Investment

(Sample average by year, 1976–2012)

Sources: Haver Analytics; IMF, World Economic Outlook database; World Bank, World Development Indicators; and authors’ calculations.

Note: Figure 5.1 illustrates that the average change in public investment varies over time.

To analyze the impact of politics on public investment dynamics, our main focus is on election cycles. While previous studies used election year as a dummy variable (e.g., Katsimi and Sarantides, 2012), we created a variable which measures the months remaining to the next election to better capture the impact of election cycles. For example, if an election was held in November 2012, the variable “months to the next election” would take the value 11 in 2011, and value 23 in 2010. Data on election dates by month and year are from the Database of Political Institutions (DPI).11 For countries with parliamentary systems, we use legislative elections, while for countries with presidential systems, we use executive elections.12 A majority of the countries in the sample held elections every four years (48-months). As elections approach we observe a reduction in growth rates of public investment, coupled with a slight increase in growth rates of public consumption (Figure 5.2).13 The observed pattern is consistent with previous findings that electoral incentives may induce the incumbent to shift public spending toward more “visible” government consumption and away from public investment goods.

Figure 5.2.Growth Rates of Public Consumption and Public Investment

(Over 48 months before elections)

Sources: IMF, World Economic Outlook database; and authors’ calculations.

Note: Figure 5.2 shows that as elections approach, the growth rate of public consumption increases and the growth rate of public investment declines.

Short-Term Changes in Public Investment: The Role of Elections

Following previous studies, we analyze the impact of politics on public investment in a dynamic fixed effects model specification.14

where GPIi,t=(PIi,tPIi,t1PIi,t1)*100 is the growth rate of nominal public investment in country i in year t. Note that we include the lagged dependent variable, since public investment dynamics might display a great deal of persistence. Among the set of independent variables, we include Eleci,t as the vector of electoral variables (including the number of months to the next elections, both in linear and quadratic terms), Econi,t as the vector of economic controls (including the inflation rate, the real GDP growth, the lagged debt-to-GDP ratio and the change in the primary balance), and Condi,t as the vector of additional social and political conditions (including the initial level of development aid to GDP, the share of people above 65 years of age, the number of government parties, a dummy variable for the ideology of the government, and another dummy to account for the existence of fiscal rules). Finally, equation (5.1) includes μi representing country-specific fixed effects and εi,t as the error term.15

We expect the distance from elections to be positively associated with the growth rate of public investment (the further that distance, or the earlier in the electoral cycle, the higher the growth rate of investment). We also expect that the decline of the growth rate of public investment becomes stronger as we move closer to election date. This is why our baseline model takes into account the nonlinear dynamics of elections by including the months-to-next election variable both in standard and squared terms.

Among economic controls, we expect the lagged dependent variable to have a negative impact on the growth of public investment, since once sufficient resources are allocated for public capital stock accumulation there is typically a lower need for further investment growth. We also expect real GDP and the inflation rate to have a positive impact on nominal public investment, as its values move with prices and growing economic activity generates more resources for capital investment. In addition, we expect initial indebtedness to be a positive sign that financing is available for subsequent investment activity, while positive changes in the primary balance will probably reduce the fiscal space for new investment projects.

Finally, among the set of accompanying conditions, we expect that initial high levels of development aid facilitate investment growth, while aging societies are probably less likely to keep accumulating new infrastructures (Jäger and Schmidt, 2016). In line with the literature presented above, a larger number of government parties and more leftist cabinets should be associated with higher growth rates of investment.16,17 Controls for the existence of budget rules are included to account for legal limits that may be binding total spending or affecting its composition.18,19

We use fixed-effect panel regressions in our benchmark model and complement it with ordinary least squares (OLS) and generalized method of moments (GMM) estimations. 20 The inclusion of a lagged dependent variable introduces a potential bias by not satisfying the exogeneity assumption of the error term, and this is why the model is also estimated by using GMM, following Arellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond (1998).21

Additional biases might exist in the benchmark model. First is reverse causality, as fiscal variables may be closely linked to macroeconomic variables, such as real GDP growth. To address this potential issue, we run our regressions using contemporaneous and lagged macroeconomic variables and results were very similar. We also used system GMM with instruments (i.e., sample average debt-to-GDP ratio as instrument for the debt-to-GDP ratio, and sample average real GDP growth as instrument for the real GDP growth) to double check the robustness of the model.22 Second, with large N and small T panel data, there is potential cross-section dependence. We tested for cross-sectional dependence in the fixed-effects models. The test results strongly reject the null hypothesis of no cross-sectional dependence. Therefore, we use an adjustment proposed by Driscoll and Kraay (1998) to ensure that standard errors are robust to heteroscedasticity.

Table 5.1 summarizes the main model specifications estimated for the sample period.23 Columns (1)–(6) use country fixed effects with Driscoll-Kraay robust standard errors, while column (7) uses country and time fixed effects.

Table 5.1.Benchmark Results: Impact of Election Cycles
Growth Rate of Nominal Public Investment
(1)(2)(3)(4)(5)(6)(7)
Dependent VariablePanel Regression with Fixed EffectsCountry and Time Fixed Effects
Number of Months before Election0.638***0.773***0.588**0.637**0.641**0.638**0.671***
[0.281][0.480][0.286][0.279][0.284][0.280][0.306]
Number of Months before Election−0.011**−0.014**−0.010*−0.011**−0.012**−0.011**−0.012***
[0.006][0.009][0.006][0.006][0.007][0.006][0.009]
Lagged Dependent Variable−0.138*−0.156*−0.137*−0.139*−0.141*−0.138*−0.147***
[0.074][0.076][0.074][0.073][0.072][0.074][0.032]
Inflation Rate1.149**1.049*1.152**1.143**1.126**1.163**1.190***
[0.484][0.530][0.525][0.487][0.485][0.496][0.260]
Real GDP Growth2.379***2.718***2.343***2.380***2.355***2.379***2.614***
[0.425][0.534][0.453][0.427][0.413][0.424][0.411]
Lagged Debt/GDP0.190*0.379**0.212*0.190*0.190*0.189*0.219***
[0.099][0.164][0.104][0.098][0.099][0.099][0.060]
Δ Primary Balance−1.631***−1.367***−1.661***−1.624***−1.618***−1.630***−1.519***
[0.357][0.405][0.379][0.358][0.353][0.356][0.359]
Lagged Official Development Aid/GDP0.378
[0.621]
Old Population Share−0.921
[0.681]
Number of Government Parties0.956***
[0.305]
Ideology of Government3.493***
[Dummy: 1 = left-wing government)[0.907]
Fiscal Rule1.184
[Dummy: 1 = existence of any fiscal rule)[2.840]
R20.11150.13170.11160.11260.11340.11160.1401
Countries67486667676767
Observations949512937949949949949
Peak Investment Growth (number of months before election)29282929292928
Source: Authors’ calculations.Note: Columns (1)–(6) present regressions with Driscoll-Kraay standard errors. The number of months at which public investment growth peaks is calculated using the coefficients of the standard and square terms of the months-to-election variable. Standard errors in brackets.*p < 0.10, **p < 0.05, ***p < 0.01
Source: Authors’ calculations.Note: Columns (1)–(6) present regressions with Driscoll-Kraay standard errors. The number of months at which public investment growth peaks is calculated using the coefficients of the standard and square terms of the months-to-election variable. Standard errors in brackets.*p < 0.10, **p < 0.05, ***p < 0.01

All models point to a significant impact of elections on the pace of public investment, confirming our hypothesis that distance from elections is associated with higher growth of public investment. The lagged dependent variable is weakly significant, while the rest of the economic controls are strongly significant and with the expected signs. Among the additional social and political variables, only the number of government parties and the ideology of the government are significant.

Overall, the main result of these regression analyses is that when the next election is one month closer, the growth rate of public investment decreases around 0.7 percentage point. The negative coefficients on the squared term of months-to-next election suggest a nonlinear dynamic behavior of public investment before elections. Results are consistent with Rogoff’s (1990) hypothesis that rents from staying in office and information asymmetry induce incumbents to manipulate fiscal policy towards “visible” public goods.24

Figure 5.3 plots the estimated behavior of the pace of public investment over 48 months before elections, using information for 67 countries included in Table 5.1, column (1). Using both coefficients for the standard and squared terms of the electoral variable, we see that the growth rate of public investment to GDP peaks around 28 months before the elections, and then declines sharply as elections approach.

Figure 5.3.Public Investment prior to Elections: Inverted U-Shape

Sources: IMF, World Economic Outlook database; and authors’ calculations.

Note: Figure 5.3 shows that as elections approach, the growth rate of public investment ratio declines. This figure is derived from results in Table 5.1, column (7), and includes all the control variables in that estimation.

In our view, the nonlinear pattern suggests that governments tend to frontload investment in capital projects at the beginning of their terms, shifting spending towards other items as the next election approaches. This said, we recognize that the observed pattern may be attributable to overlapping of capital project cycles with electoral cycles. The initial phase of the project cycle tends to be time consuming (in terms of project appraisal and selection) and the newly elected governments may require time to start implementing capital projects. However, no project level data are available to test the validity of this hypothesis in a diverse set of countries, such as those included in the study. The only evidence on 258 rail, bridge, tunnel, and road projects in 20 countries suggests that the average project cycle is different from the standard electoral cycle (Flyvberg, 2009).

Robustness of Our Results

In this section we test the robustness of our results to country characteristics and the potential endogeneity of election dates.

Country Characteristics

An important concern when working with a heterogeneous sample has to do with the potential presence of group characteristics that cannot be captured using country fixed effects. We therefore perform an additional round of robustness checks to control for the level of development (advanced vs. emerging economies), the age of each democratic country (old vs. new democracies) and corruption.25 We also look at the relative efficiency of public investment (high vs. low efficiency).26 Our results are robust to these additional tests, but interesting nuances emerge (Table 5.2).

Table 5.2.Robustness Check: Country Characteristics and Fixed Elections
Growth Rate of Nominal Public Investment
(1)(2)(3)(4)(5)(6)(7)(8)(9)(10a)(10b)
Dependent VariableBaselineAdvanced EconomiesEmerging MarketsOld DemocraciesNew DemocraciesHigher Public Investment EfficiencyLower Public Investment EfficiencyIf Last Term in OfficeNonendogenous ElectionsIf Fixed Term Election System (Milner 2006)If Fixed Term Election System (Drazen and Eslava 2010)
Number of Months0.671***0.302**1.087***0.283**0.805***0.513**0.745**0.934***0.727**0.433*0.775***
before Election[0.306][0.203][0.394][0.298][0.431][0.230][0.765][0.265][0.285][0.290][0.287]
Number of Months−0.012***−0.005**−0.020**−0.004**−0.015***−0.008*−0.015**−0.016***−0.013**−0.010*−0.014**
before Election[0.009][0.004][0.008][0.004][0.009][0.005][0.015][0.005][0.006][0.006][0.006]
Lagged Dependent−0.147***−0.088**−0.109**−0.115**−0.144***−0.111***−0.159***−0.094***−0.096***−0.128**−0.133***
Variable[0.032][0.042][0.048][0.046][0.039][0.037][0.056][0.025][0.036][0.064][0.045]
Inflation Rate1.190***0.654**0.527*0.575*1.043***0.639**1.149**0.724***0.596***0.789**0.580**
[0.260][0.268][0.284][0.319][0.296][0.249][0.446][0.218][0.214][0.308][0.262]
Real GDP Growth2.614***1.636***2.820***1.064***2.647***2.072***3.051***2.030***2.220***1.678***2.838***
[0.411][0.287][0.425][0.382][0.456][0.291][0.845][0.315][0.323][0.375][0.391]
Lagged Debt/GDP0.219***0.0590.226**0.0550.314***0.0270.394***0.100**0.107*0.0180.074
[0.060][0.040][0.107][0.037][0.083][0.045][0.125][0.051][0.059][0.112][0.071]
Δ Primary Balance−1.519***−2.528***−1.860***−2.229***−1.475***−2.776***−1.438**−1.342***−1.550***−2.385***−0.653**
[0.359][0.364][0.495][0.406][0.430][0.367][0.664][0.289][0.295][0.472][0.329]
R20.140.150.170.140.150.180.140.140.120.220.20
Countries6722341552292967501638
Observations949441390324625500313660655207541
Peak Investment
Growth (number of
months before election)2830273527322529282228
Source: Authors’ calculations.Note: All models include country and year fixed effects. Standard errors in brackets.*p < 0.10, **p < 0.05, ***p < 0.01
Source: Authors’ calculations.Note: All models include country and year fixed effects. Standard errors in brackets.*p < 0.10, **p < 0.05, ***p < 0.01

Overall, in advanced economies, old democracies, and countries with more efficient management systems, public investment growth tends to peak much later during the electoral cycle. Also, the deceleration of public investment is lower in magnitude, implying a milder investment fluctuation due to electoral cycles. The observed pattern could be explained from three complementary perspectives. First, public investment processes are more robust in advanced economies that are mostly old democracies, and the scope for manipulating public investment to enhance reelection possibilities is thus lower than in countries with weaker institutions. Second, in mature democracies there are less information asymmetries and the electorate punishes electoral manipulation of spending by the government. And third, in mature democracies incumbent governments have other means to show their “competencies” to the electorate and do not need to signal them through spending manipulation as predicted by Rogoff (1990).27

Potential Endogeneity of Election Dates

Since elections may not be exogenous and can be called earlier in many parliamentary and presidential democracies, we perform tests to ensure the robustness of our results in a subsample of countries with different legal provisions about the timing of their elections. Note, however, that this is not a straightforward exercise because the distinction between electoral systems where the election date is exogenously fixed and systems where early elections may be called is not as clear-cut as it may at first appear. In many countries fixed election periods are set by law and early elections may only be called under “exceptional circumstances,” but in fact early elections are the rule rather than the exception. That is, what determines “exceptional circumstances” may in practice be quite different than what appears to be the case from a simple reading of the election laws (Brender and Drazen, 2005).

We conduct robustness tests using various sources of data, mainly to ensure that our conclusions do not have any selection bias. First, using data on elections from the National Elections Across Democracy and Autocracy (NELDA) database we run our baseline model on a sample of countries where governments faced their last term in office (Table 5.2, column (8)) or where elections are not endogenous (Table 5.2, column (9)).28 In both cases, our baseline results hold. Second, we identify countries where elections are fixed and run the model on these subsamples. For this purpose, we use two main sources of data available in the literature: Milner (2006) identifies 16 countries with “fixed-term elections,”29 while Brender and Drazen (2005) identify 38 countries with “pre-determined elections” according to both legal and empirical criteria.30,31 We prefer the second sample, because it has more countries and their selection is more robust. Note that having a legal obligation to hold fixed elections is not a sufficient criterion to be sure that this actually occurs. Many countries have some provision for elections at a date earlier than the end of the legally mandated term of office for the executive or the legislature, and whether electoral calendars are actually fixed and elections occur at the legally determined date is an empirical question.32 Results in Table 5.2, columns (10a) (using Milner data) and column (10b) (using Brender and Drazen data) show that our results hold when using the wider sample of countries with fixed elections.33

Conclusion and Policy Implications

This chapter explores the impact of political and institutional variables on the growth rate in public investment. Using a sample of 67 presidential and parliamentary democracies between 1975 and 2012, the chapter finds that the growth rate of public investment-to-GDP is higher at the beginning of electoral cycles and decelerates as the next election approaches. We estimate that during standard 4-year electoral cycles, the peak in public investment growth occurs around 28 months before elections. Thereafter, changes in public investment decline as elections approach. This holds even when controlling for economic factors and other political variables (i.e., cabinet ideology and government fragmentation). Preliminary evidence on budget rules and institutions suggests that stronger institutions help attenuate the impact of elections on investment, but available information is insufficient to reach definitive conclusions.

Two important policy implications can be drawn from this chapter. First, even when macroeconomic conditions in terms of fiscal space and monetary policy are appropriate and effective “shovel ready” investment projects are available, it may not be possible to expand public investment closer to elections. The incentive for incumbent governments is to increase “visible”, more tangible, current spending on tax cuts or transfer programs to shore up political support. Going forward, such spending may be difficult to unwind, thereby creating a deficit bias. It may also impact on the long-term potential of the economy. Second, fiscal consolidation programs would need to explicitly recognize the bias against public investment about two years prior to elections. A strengthening of fiscal frameworks during this period could help in restraining a permanent ratcheting of certain spending items.

Annex 5.1. Sample and Selection Criteria

We select countries and time periods based on the following criteria. First, voters must directly elect the person or persons appearing on the ballot to the national post in question. Second, mass voting must take place. Third, over the sample period, countries should have a multi-party system. Presidential elections which involve an Electoral College such as the United States are included because the Electoral College mechanically implements the outcome of a popular vote. Fourth, each election in the sample should be generally regarded as being sufficiently competitive, meaning that there is a real possibility of change in government. Two major criteria apply in this aspect: a) there were no significant concerns before elections that elections will not be free and fair; b) there were no allegations by Western monitors, if any, of significant voter fraud. For example, although elections in Mexico never resulted in a change of government before 2000, since there were competitive running parties, we included these elections in our sample. Finally, we excluded pre-election years of the first democratic election in each country’s history. Using the above criteria, 80 countries were selected. Only for 67 of them, there was consistent data on all variables used in the regression analysis. In this final sample, 44 of these countries were parliamentary democracies, and 23 of them were presidential democracies.

Primary sources of electoral data include the National Elections across Democracy and Autocracy (NELDA) by Hyde and Marinov (2012), the World Economic Yearbook, the Economist Intelligence Unit, the CIA World Fact Book and Freedom House.

This methodology differs from most previous studies. Previous studies often focus on old democracies to ensure competitiveness of elections, e.g., Katsimi and Sarantides (2012). A few other studies covered a wide range of countries, but didn’t make enough effort to identify competitive elections. For example, Brender and Drazen (2005) studied 102 countries, including 68 democracies with competitive elections using level of democracy from POLITY IV project as the only criteria. Ebeke and Olcer (2013) used a sample of 68 low-income economies, but didn’t differentiate competitive elections from the rest.

Annex Table 5.1.1.Parliamentary System: 44 Countries
CountryStarting Year
Advanced Economies
Australia1975
Austria1975
Belgium1975
Canada1975
Czech Republic1993
Estonia1992
Finland1975
France1975
Germany1975
Greece1987
Israel1975
Italy1975
Japan1975
Luxembourg1975
Netherlands1975
New Zealand1975
Norway1975
Portugal1983
Slovak Republic1993
Switzerland1975
United Kingdom1975
Low-Income Economies
Bangladesh1976
Lesotho1975
Moldova1992
Emerging Market Economies
Albania1992
Bahamas, The1975
Barbados1975
Belize1982
Bosnia and Herzegovina2003
Botswana1975
Bulgaria2002
Cabo Verde1976
Croatia2001
Fiji1975
Guyana1992
India1975
Macedonia, FYR1992
Mauritius1975
Panama1979
Poland1991
Romania1990
St. Lucia1980
Trinidad and Tobago1975
Turkey1981
Source: Authors’ calculations.
Source: Authors’ calculations.
Annex Table 5.1.2.Presidential System: 23 Countries
CountryStarting Year
Advanced Economies
United States1975
Emerging Economies
Argentina1975
Brazil1986
Chile1989
Colombia1991
Costa Rica1975
Dominican Republic1978
Ecuador1975
Guatemala1996
Mexico1975
Namibia1995
Paraguay1993
Peru21975
Philippines1987
Uruguay1975
Low-Income Economies
Bolivia1975
Ghana1992
Honduras11982
Malawi1994
Mali1992
Mozambique1990
Nicaragua1975
Senegal1990
Source: Authors’ calculations.

From 2009 onwards Honduras is counted due to the coup in 2009.

Elections in 1995 and 2000–01 in Peru are not counted.

Source: Authors’ calculations.

From 2009 onwards Honduras is counted due to the coup in 2009.

Elections in 1995 and 2000–01 in Peru are not counted.

Annex 5.2. Descriptive Statistics
Annex Table 5.2.1.Descriptive Statistics of Variables Used in Table 5.1
ObservationsMeanStandard DeviationMinimumMaximum
Growth Rate of Public Investment1,46212.824.2−88.2101.0
Months to Election1,46223.413.61.048.0
Months to Election1,462733.5687.21.02,304.0
Inflation Rate1,4609.318.9−7.8333.5
Real GDP Growth1,4623.43.9−30.923.4
Lagged Public Debt/GDP93155.132.93.7210.2
Δ Primary Balance1,157−0.12.9−34.634.3
Old Population Share1,4438.95.12.423.0
Number of Government Parties1,4622.11.80.016.0
Ideology of Government (1 = left)1,4620.40.50.01.0
Fiscal Rule (1 = existence of any fiscal rule)1,4620.40.50.01.0
Lagged Official Development Aid/GDP9044.87.6−2.574.1
Source: Authors’ calculations.
Source: Authors’ calculations.
Annex 5.3. Panel Unit Root Test

If our dependent variable is not stationary, we are faced with a spurious relationship when that variable is entered on the right-hand side of the equation. Only a few tests for unit roots are directly applicable to unbalanced data (see Breitung and Pesaran, 2008). Here we rely on the Fisher test to check for the presence of a unit root. We conduct unit-root tests for each panel individually, and then combine the p-values from these tests to produce an overall test. The test assumes that all series in the panel are stationary under the null hypothesis against the alternative that at least one series in the panel is stationary.

Annex Table 5.3.1.Fisher-Type Panel Unit Root Test
VariableTest StatisticsProbability Value
Growth Rate of Public Investment569.10.00
Δ Primary Balance408.20.00
Inflation Rate734.60.00
Real GDP Growth589.80.00
Lagged Public Debt/GDP701.10.00
Source: Authors’ calculations.Note: The null hypothesis that all panels contain unit roots can be rejected at the levels of the variables.
Source: Authors’ calculations.Note: The null hypothesis that all panels contain unit roots can be rejected at the levels of the variables.
Annex 5.4. The Short-Term Trade-Off Between Current and Capital Spending

We test here if there is a short-term trade-off between current and capital spending by directly including changes in primary current spending as an additional explanatory variable. Annex Table 5.4.1 presents these results. All models use country and time fixed effects, and control for the change in the primary balance to ensure that the observed change in the composition of public spending are independent of fiscal consolidations and expansions. Results show evidence that there is a significant negative relationship between public investment and current expenditure.34

Annex Table 5.4.1.The Tradeoff between Public Investment and Current Expenditure
Growth Rate of Public Investment
(1)(2)
Dependent VariableCountry and Time Fixed Effects
Number of Months before Election0.534***0.621***
[0.269][0.218]
Number of Months before Election−0.009**−0.011***
[0.006][0.004]
Lagged Dependent Variable−0.024−0.056
[0.075][0.036]
Growth Rate of Public Consumption−0.021**−0.021***
[0.008][0.007]
Inflation Rate0.453**0.514**
[0.203][0.205]
Real GDP Growth1.702***2.153***
[0.351][0.296]
Lagged Debt/GDP0.058*0.100*
[0.067][0.053]
R20.100.14
Countries6364
Observations777785
Peak Investment Growth (number of months before election)2828
Source: Authors’ calculations.Note: Standard errors in brackets.*p < 0.10, **p < 0.05, ***p < 0.01
Source: Authors’ calculations.Note: Standard errors in brackets.*p < 0.10, **p < 0.05, ***p < 0.01
Annex 5.5. Robustness of Results to Alternative Definitions of the Dependent Variable and Panel Truncation

Finally, following a suggestion by the editor, we tested our results to different definitions of the dependent variable. Annex Table 5.5.1 compares our baseline results using the growth rate of nominal public investment (model 2), with similar regressions using annual change in nominal public investment as a share of GDP in the previous year (model 1), and using the growth rate of real public investment (where we had considerably less observations available due to missing data in the WEO series). In general, the influence of the electoral cycle on public investment is confirmed under the three definitions.

Annex Table 5.5.1.Robustness Using Alternative Definitions of Dependent Variable
(1)(2)(3)
Dependent VariableΔ Public Investment/ GDP (t – 1)Growth Rate of Public Investment (Nominal)Growth Rate of Public Investment (Real)
Number of Months before Election0.077***0.671***0.066**
[0.025][0.306][0.204]
Number of Months before Election−0.008**−0.012***−0.009**
[0.001][0.009][0.000]
Lagged Dependent Variable−0.0487*−0.147***−0.093**
[0.039][0.032][0.045]
Inflation Rate0.028**1.190***0.044*
[0.015][0.260][0.084]
Real GDP Growth0.245***2.614***0.102**
[0.554][0.411][0.181]
Lagged Debt/GDP0.011*0.219***0.055**
[0.204][0.060][0.212]
Δ Primary Balance−0.167**−1.519***−0.055**
[0.223][0.359][0.203]
R20.0960.14010.088
Countries666744
Observations902949477
Peak Investment Growth (number of months before election)272826
Source: Authors’ calculations.Note: Standard errors in brackets.*p < 0.10, **p < 0.05, ***p < 0.01
Source: Authors’ calculations.Note: Standard errors in brackets.*p < 0.10, **p < 0.05, ***p < 0.01

We also tried using slightly different sample selection criteria. In our benchmark selection we truncated our panel and ran our model only on those observations where the electoral cycle was 4-years (48 months) or shorter. The purpose of this choice was twofold: on the one hand, this would help avoid some outliers in which the variable month-to-election could reach up to 120 months, either due to exceptional political circumstances or as a result of missing values in the earlier years of the sample period. On the other hand, these selection criteria would clean the possible noise generated by overlapping cycles, where the last year of one term becomes the first of the next one. By imposing a 48-month limit we were able to extract the full information from the data (as 90 percent of cases have 4-year electoral cycles), while avoiding the noise created by missing values or overlapping dates. Nonetheless, our results are robust to using the whole unrestricted or alternative truncation criteria. Annex Table 5.5.2 reports the results of running our baseline model on the complete (un-truncated) sample (model 1), on the 48-month sample (model 2), and on a shorter 36-month sample (model 3). Because the overwhelming majority of cases have 4-year election cycles, results hold well using other sample selection criteria.

Annex Table 5.5.2.Robustness Using Alternative Definitions of Dependent Variable
Growth Rate of Public Investment
(1)(2)(3)
Dependent VariableWhole Sample48-Month Sample36-Month Sample
Number of Months before Election0.623**0.671***1.201**
[0.245][0.306][0.485]
Number of Months before Election2−0.011**−0.012***−0.027**
[0.004][0.009][0.012]
Lagged Dependent Variable−0.168***−0.147***−0.177***
[0.032][0.032][0.035]
Inflation Rate1.577***1.190***2.072***
[0.239][0.260][0.295]
Real GDP Growth0.888**2.614***0.898*
[0.408][0.411][0.482]
Lagged Debt/GDP0.222***0.219***0.231***
[0.065][0.060][0.074]
Δ Primary Balance−3.395**−1.519***−3.569**
[1.444][0.359][1.608]
R20.1250.14010.151
Countries666766
Observations1013949768
Peak Investment Growth (number of months before election)292822
Source: Authors’ calculations.Note: Regression are estimated with country and time fixed effects. Standard errors in brackets.*p < 0.10, **p < 0.05, ***p < 0.01
Source: Authors’ calculations.Note: Regression are estimated with country and time fixed effects. Standard errors in brackets.*p < 0.10, **p < 0.05, ***p < 0.01
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This chapter is reprinted from the European Journal of Political Economy, Vol. 45, Sanjeev Gupta, Estelle X. Liu, and Carlos Mulas-Granados, “Now or Later? The Political Economy of Public Investment in Democracies,” ©2016, with permission from Elsevier.

Note also that public investment deceleration is accompanied by an acceleration of current spending. Our results confirm at an aggregate multi-country level what other studies suggested at the single-country level. For example, Klein (2004) examined the political cycles in Israel, and found that in the 1980s and 1990s (a period that includes six general elections) in the period before an election public investment declined and civilian consumption rose significantly. Fiva and Navik (2013) also explored these issues at the municipal level in Norway.

It should be stressed that PBC models are all based on the assumption of competitive elections, which is more applicable to developed established democracies, rather than to emerging and low-income countries, many of which are “new” democracies.

Drazen and Eslava (2010) and Aidt, Veiga and Veiga (2011) show instead that public investment grows during electoral years.

For further evidence on the existence of partisan effects in public spending and tax policies and on the impact of partisanship on specific categories of public spending, such as social and welfare policies, see Cusack (1997). Regarding the impact of ideology on the composition of fiscal revenues, see Hallerberg and Basinger (1998) and Belke et al. (2007).

As fiscal data prior to 1990 are generally regarded with poor quality, we replicate our analysis using data after 1990, and find similar conclusions.

Public investment is equivalent to public gross fixed capital formation in this study. Fiscal data on public investment and current spending are taken from two different sources to maximize data availability. This could however create some inconsistencies between the two components. To minimize them, we recalculated non-interest current expenditure as total government spending minus interest payments and minus public investment to ensure mathematical identity. Our series are robust to alternative matching options.

Previous studies in this area are mainly based on central government data. This is the case of papers that study aggregate fiscal variables (see Brender and Drazen, 2005; Shi and Svensson, 2006) as well as papers that look at the composition of public spending (see, e.g., Schuknecht, 2000; Block, 2002; Brauninger, 2005; Vergne, 2009; Katsimi and Sarantides, 2012). However, the reason why we use general government data on public gross fixed capital formation is because we have a wider sample of countries and this variable is available for a larger number of countries at that level.

Our results are robust to alternative definitions of the dependent variable. Following the recommendation of the editor, we also worked with the first differences of nominal public investment divided by GDP in t-1, and with the growth rate of real public investment. See Annex Table 5.5.1 for results using these alternative definitions of the dependent variable. In earlier versions of this chapter, we also used the annual percentage change of the public investment-to-GDP ratio, finding consistent results (these tables are available from the authors upon request.)

Other papers in this field, like Katsimi and Sarantides (2012) and Potrafke (2010) use the first differences of levels of public investment. Our results are also robust to the use of their version of the dependent variable.

DPI is compiled by the Development Research Group of the World Bank.

Note that some election dates might be endogenous. For example, elections might be called earlier than their predetermined date due to adverse economic conditions arising from a slump in investment. We explore this issue further in the fifth section.

The increase in public consumption in percent of GDP does not match exactly the decrease in public investment as a percent of GDP. This provides evidence in favor of the political business cycle hypothesis, according to which governments affect both the size and the composition of the budget to ameliorate GDP and increase their probability of reelection. The simultaneous increase in GDP, which typically follows short-term increases in public spending, affects both the numerator and denominator of both public consumption and public investment ratios. Because the spending multipliers are different for public consumption and investment, the changes in their respective GDP ratios do not exactly match each other.

Summary statistics of the main variables used in table 5.1 for our benchmark results are reported in the Annex Table 5.2.1.

The average number of parties in our sample was 2.4. We acknowledge that the number of government parties might not be a precise indicator of political fragmentation. For example, the degree of fragmentation is likely to be lower in a parliament where one party takes the majority, regardless of the number of parties in government. We also used a dummy for coalition governments and results were very similar. In addition, government fragmentation might have different implications in presidential and parliamentary systems. We conducted additional tests, with the existence of a majority government as a control and divided the sample into parliamentary and presidential systems. The results remained robust.

To build this variable (1=left; 0=right) using information from DPI database. Ideology of the government is used for parliamentary system, while ideology of the executive is used for presidential system. However, as there is a high correlation between the government and executive ideology, our results do not change significantly by using either of the two ideology variables as an alternative. When there is a multiparty government, the government’s ideology corresponds to the party with the highest number of posts in the cabinet. In our sample left-wing governments held office 56 percent of the time.

Before estimating the model, we test for unit roots in our data. Test results (Annex Table 5.3.1) show that we can reject the null hypothesis of non-stationarity at the 1 percent significance level.

Aside from the above political variables, we also examined the impact of electoral rules, various institutional variables and the existence of fiscal rules. These data come from World Development Indicators (WDI) and the Database of Political Institutions (DPI) of the World Bank and from the fiscal rules database of the IMF. Results were inconclusive and thus not reported.

A fiscal rule imposes a long-lasting constraint on fiscal policy through numerical limits on budgetary aggregates. We include a dummy that takes value 1 if the country has any fiscal rule (expenditure, debt, balanced budget or golden rules), and takes value 0 in the absence of any rule. Recent evidence has shown that the golden rule helped preserve public investment following periods of fiscal contraction (IMF 2014c).

Note that applying Arellano and Bond (1991) or Arellano and Bover (1995)/Blundell and Bond (1998) GMM estimators does not alter our results. Note also that the estimated bias of this formulation is of order 1/T, where T is the time length of the panel, even as the number of countries becomes large (see among others Nickell, 1981; Kiviet, 1995). The average time series length of our panel depends on the fiscal indicator, but in general is around 10 years and the bias is probably not large, but we still use the system GMM as a sensitivity test.

These additional tables are available from authors upon request.

We run our benchmark models on a sample of 4-year (48-month) election cycles in order to better capture the impact of a new government following elections and to avoid outliers and overlapping executive terms. Note that 90 percent of cases have 4-year cycles. Nonetheless, we show in the Annex Table 5.5.2 that our results are robust to using the whole sample or a sample of shorter cycles (36-month).

Annex Table 5.5.1 provides a simple test for the trade-off between public investment and current expenditure.

When corruption indicators from the Database of Political Institutions (DPI) are included in the baseline regressions, they have a positive sign but are statistically insignificant. Corruption indicators are also very correlated with the variables that measure the level of development and the age of democracies. Since the latter generate more robust results, we have reported them in Table 5.2. For interested readers, regression results using corruption indicators are available upon request from the authors.

Data for public investment efficiency is taken from IMF (2015).

Note that in Rogoff’s model, the government distorts allocations because it is striving to signal its “competence” in the eyes of voters under asymmetric information. Since voters do not observe public investment and economic growth immediately, the only way for a “competent” incumbent to signal its “competence” is to increase readily identifiable transfers ahead of elections, inducing voters to believe that the government will bring economic growth and revenues to finance those transfers.

Non-endogenous elections are those which take place at their expected date (not called earlier or later). Background information to build this variable by the authors is taken from question 6 in NELDA’s database, which is formulated as follows: “If regular, were these elections early or late relative to the date they were supposed to be held per established procedure?” In addition, in order to gather information on whether governments faced their last term in office, the authors use question 8 in NELDA’s database, which is formulated as follows: “Did the incumbent reach their term limit?”

These countries are Chile, Costa Rica, Cyprus, Estonia, Finland, Latvia, Luxembourg, Mexico, Netherlands, Norway, Poland, Portugal, Slovak Republic, Sweden, Switzerland and United States.

These countries are Argentina, Austria, Belgium, Bolivia, Brazil, Bulgaria, Chile, Colombia, Costa Rica, Czech Republic, Dominican Republic, Ecuador, Estonia, Fiji, Finland, France, Germany, Greece, Guatemala, Honduras, Italy, Israel, Luxembourg, Mali, Mauritius, Netherlands, Nicaragua, Norway, Paraguay, Philippines, Portugal, Poland, Romania, Slovak Republic, Sri Lanka, Switzerland, Trinidad and Tobago, United States, and Uruguay.

For more information about budgetary consequences of fixed term elections, see Dahlberg and Mørk (2011).

Using a sample of 68 countries Brender and Drazen (2005) identify 29 countries with pre-determined elections around the world (according to legal criteria). But this number becomes 38 (when a second criterion based on actual frequency of elections is applied).

In the sample using Milner’s data, the squared term of the “months-to-next-election” variable is not significant by a small margin. Still the negative relationship between proximity to elections and changes in public investment is significant. Because this is a regression with only 16 countries, it is difficult to make definitive conclusions. By contrast, we view the results using Brender and Drazen’s data as more robust.

Katsimi and Sarandides (2012) use the first differences of capital expenditure and current expenditure to total expenditure ratio as a dependent variable to analyze the impact of election year (dummy variable) on expenditure composition in old democracies, and find that capital expenditure is likely to decelerate during elections while current expenditure accelerates. We used a similar methodology in our complete sample of 67 countries and got similar results.

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