Fiscal Politics
Chapter

Chapter 13. Fiscal Rules to Tame the Political Budget Cycle: Evidence from Italian Municipalities

Author(s):
Vitor Gaspar, Sanjeev Gupta, and Carlos Mulas-Granados
Published Date:
April 2017
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Author(s)
Andrea Bonfatti and Lorenzo Forni 

This chapter presents evidence suggesting that fiscal rules can help moderate the political budget cycle. The term “political budget cycle” generally refers to increases in government spending or in the deficit, or decreases in taxes, in an election year or preelection years, which are perceived to have been motivated by the incumbent’s desire for reelection. Fiscal rules can limit the political budget cycle because they reduce the politician’s incentives to be profligate as a way to be reelected by increasing the cost of preelection profligacy if elected.

The focus of the chapter is on Italian municipalities during the early 2000s when they were subject to the Domestic Stability Pact (DSP), a subnational fiscal rule introduced in 1999. The effect of the rule on the political budget cycle is identified by using the fact that municipalities with fewer than 5,000 inhabitants are exempt from the rule. This analysis estimates that the political budget cycle increases real capital spending by about 35 percent, on average, in the three years before municipal elections and that the subnational fiscal rule reduces this figure by about two-thirds.

A number of recent papers have used Italian administrative municipal data to address an array of political economy issues. Cioffi, Messina, and Tommasino (2012) provide evidence of the political budget cycle in capital and overall spending, while Alesina and Paradisi (2014) on the revenue side exploit the introduction of a new real estate tax in 2011. Gagliarducci and Nannicini (2013) study the effect of wages on the performance of mayors. Alesina, Troiano, and Cassidy (2015) show that younger politicians behave more strategically than older ones. Particularly relevant for the purposes of this chapter is the paper by Grembi, Nannicini, and Troiano (2016), which shows that the relaxation of the DSP for smaller municipalities in 2001 triggered a significant deficit bias.1

This chapter is also related to three other branches of literature. By assessing how fiscal rules can limit the political budget cycle, this contribution naturally fits into the broad political budget cycles literature. See, among many, Rogoff and Sibert (1988); Rogoff (1990); Alesina, Cohen, and Roubini (1997); Persson and Tabellini (2000); Brender and Drazen (2005); Shi and Svensson (2006); and Brender and Drazen (2008). A number of contributions have empirically assessed the political budget cycle. For a recent one on the political cycle in capital expenditures, see Gupta, Mulas-Granados, and Liu (2015). Also related to the work in this chapter is the literature assessing the political budget cycle at the subnational level. For example, Coelho, Veiga, and Veiga (2006) and Veiga and Veiga (2007) provide evidence of the political cycle at the municipal level in Portugal; Foremny and Riedel (2014) in Germany; Drazen and Eslava (2010) in Colombia; and Brollo and Nannicini (2012) in Brazil. Finally, this chapter is also connected to the growing literature on national and subnational fiscal rules (for example, Beetsma and Debrun 2004, 2007; Debrun and others 2008). In this strand of literature, the recent contribution by Grembi, Nannicini, and Troiano (2016) is the first to propose a quasi-experimental design to control for omitted and unobservable factors that may affect previous results and to better establish the causal effect of the introduction of the rule.

This chapter contributes to these different strands of the literature in several ways. First, it provides further evidence on the existence of a political budget cycle at the local level in Italy and quantifies its effects. Second, it provides new evidence that the central government has enforced the DSP, which reduces concerns regarding the endogeneity of the rule, although it still leaves open the possibility that omitted and unobservable factors might affect how municipalities have reacted to the imposition of the rule. The regression discontinuity analysis addresses this issue, focusing on the behavior of municipalities around the 5,000-inhabitant threshold. Finally, and most important, it provides novel evidence that the imposition of the rule has reduced the political budget cycle. To the authors’ knowledge, this is the first work that provides robust evidence that fiscal rules can limit the political budget cycle. Even when the introduction of a fiscal rule proves effective, in the sense that it helps contain the deficit, it is very difficult to assess whether it is welfare improving. In contrast, a rule that mitigates the political budget cycle, at least in this respect, is welfare improving.

Institutional Setup and the Domestic Fiscal Rule

The DSP was introduced in 1999 to include subnational authorities (regions, provinces, and municipalities) in the effort to achieve the fiscal targets set at the European level. The operational target of the rule for municipalities (about 8,000 in Italy) has changed over the years and was defined as limits to the growth of spending in 2005 and 2006 and with reference to the overall balance before 2005 and from 2007 onward. The penalties established for not complying with the DSP included limits on hiring, on spending, and on borrowing for investments (Chiades and Mengotto 2015). Important to this analysis, since 2001, smaller municipalities (those with fewer than 5,000 residents) have been exempted from the DSP. The exemption aimed to provide some relief to small municipalities in the presence of economies of scale in managing the municipal government. In 2015 the DSP was discontinued and replaced with a budget balance rule for all local authorities.

With regard to governance and elections, the decision-making bodies at the municipal level are the mayor (sindaco); the executive committee (giunta comunale), which is appointed and headed by the mayor; and the municipal council (consiglio comunale), endowed with legislative powers. For municipalities with fewer than 15,000 inhabitants, a simple plurality electoral system applies whereby each candidate is supported by a single list.2 In municipalities with population greater than 15,000, mayoral candidates may be supported by more than one list, and a runoff election takes place if none of the candidates wins an absolute majority of votes in the first round. Since 1993, municipal elections have been held every four years. Since 2000, mayors have served for five years unless particular circumstances (such as the death of the mayor, the appointment to other public or private positions not compatible with the mayor’s service, or criminal charges) trigger earlier resignation. Elections usually occur during May and June.

The Data

This analysis focuses on the political cycle in capital spending. In 2007, the operational target for the fiscal rule was changed from a spending limit to a budget balance definition; therefore, this analysis focuses on the period before 2007. In addition, information from before 2004 was not available to run this analysis, so the data consist mainly of Italian municipalities’ budget information from 2004 to 2006. This information has been combined with data on elections at the municipal level, and with information on the mayor (age, education, gender, political party).3Annex Table 13.1.1 reports the variables and sources. A summary of the data set is reported in Table 13.1.

Table 13.1.Summary Statistics (2004–06)
VariableObservationsMeanStandard DeviationMedianMinimumMaximum
Municipalities
Capital Spending20,057564879336027,965
Current Spending20,0577914716882821,725
Total Spending20,0571,3561,2091,05017840,984
Total Transfers20,0577059154612033,049
Total Revenues20,0571,6021,7891,231398109,039
Long-Term Borrowing20,05712825662011,466
Total Outstanding Debt20,0411,1245,705821−1,317652,402
Taxable Income20,08411,8133,12812,0653,06631,525
Population (units)20,0847,40542,6552,458322,705,603
Population Ages 15–64
years (percent)20,08464.534.4565.2832.1781.58
Preelection Years (1/0)20,0840.510.5101
Mayors
Female (1/0)19,6740.100.29001
Age19,670519.64512286
Education (years)19,161143.0713520
Party Affiliated (1/0)19,6740.360.48001
Mandate (first = 1)20,0840.810.39101
Source: Authors’ calculations.Notes: Variables are in real per capita terms (2010 euro).
Source: Authors’ calculations.Notes: Variables are in real per capita terms (2010 euro).

Even within the window of 2004–06, the fiscal rule target changed. In 2004, the rule stated that the difference between current spending and current revenues could not be higher in real terms than it was in 2003. In 2005, current and capital spending was to have been lower than the average over 2001–03, increased by 10 percent. In 2006, current spending was to have been lower by 6.5 percent than it was in 2004 (by 8.1 percent for municipalities with per capita spending over the period 2002–04 greater than their population class average), while capital spending was not to have exceeded the 2004 value, increased by 8.1 percent. Even though the rule for 2004 did not include investment spending, it was expected that capital spending would be included in the rule starting in 2005.4 Therefore, given existing lags in investment implementation, municipalities anticipated that investment projects begun in 2004 could lead to payments in 2005.

Based on these data, total spending by municipalities in ordinary-statute regions5 represented almost 5 percent of GDP in 2004 (the starting year for this analysis). Capital spending represented about 38 percent of total spending. In real terms, municipalities spent about €600 per capita annually on investment. Regarding financing, transfers from the regions and the central government over the period represented about 40 percent of overall revenues; own revenues covered the rest. The main taxes financing municipalities were real estate taxes on home property (imposta comunale sugli immobili), which provided about 43 percent of municipal tax revenues, and a surcharge on the personal income tax (imposta sul reddito delle persone fisiche), which amounted to about 6 percent of municipal tax revenues.6

Identification Strategy

The models originally proposed to explain the political budget cycle help provide an understanding of the mechanism through which a fiscal rule can limit the cycle. The first models in this literature (Nordhaus 1975; Lindbeck 1976) were based on the premise that voters are myopic and that politicians are able to repeatedly fool them by tweaking policies before elections. Later models (for example, Rogoff and Sibert 1988; Rogoff 1990) assume that voters are rational but do not have full information about incumbents’ competence. Voters want to elect the most competent politicians and form rational expectations regarding the incumbent’s abilities based on observable current fiscal policy outcomes. A competent administrator is able to provide a given level of public goods at a lower level of taxes than an incompetent one can. The incumbent can signal his or her competence by increasing spending or showcasing new infrastructure projects without at the same time increasing taxes. Before the election, therefore, incumbents will attempt to signal their competence (and thereby increase their chances of reelection) by engaging in expansionary fiscal policy. This leads to a preelection increase in the government deficit even though competent politicians may be in office. However, even competent politicians that want to signal their higher competence might be reluctant to use all the available fiscal space because they are likely to remain in office and have to live with the consequences of this choice. Fiscal rules, such as the DSP, might increase the ex post cost of a preelection fiscal expansion.

To identify this effect in the context of this chapter, in the spirit of Grembi and others (2016), the analysis relies on the fact that the DSP does not apply to municipalities with fewer than 5,000 inhabitants. The identification scheme therefore compares municipalities with more than 5,000 inhabitants (subject to the DSP) with those with fewer than 5,000 inhabitants (not subject to the DSP) around election years. The analysis shows that indeed municipalities below the threshold, controlling for other characteristics, display a larger increase in capital spending in preelection years compared with those with more than 5,000 inhabitants, that is, they are subject to a stronger political budget cycle.

For a homogeneous sample, the baseline analysis focuses on municipalities with fewer than 15,000 inhabitants. The cutoff is established at 15,000 because a different electoral system applies to larger municipalities.7 An ample literature has shown how different electoral systems can affect fiscal outcomes (for example, Persson and Tabellini 2000; Milesi-Ferretti, Perotti, and Rostagno 2002; with specific reference to the Italian context, see Ferraresi, Rizzo, and Zanardi 2015); therefore, care must be taken in pooling municipalities with different electoral systems because it can lead to bias in the estimates. By limiting the analysis to municipalities with fewer than 15,000 inhabitants, the analysis loses about 600 municipalities from a sample of about 8,000.

Figure 13.1 plots the average level of per capita capital spending (in logarithms) around elections for smaller and larger municipalities. Smaller municipalities are those with fewer than 5,000 inhabitants. Larger ones are those with 5,000–15,000 inhabitants. On the horizontal axis, t represents the election year. The figure shows the average level of per capita capital spending in the two years before and after elections. It clearly shows the political budget cycle, with capital spending higher and increasing in election years and in the two years before compared with the two years after elections. The presence of a political budget cycle in capital spending is confirmed by a regression analysis (not reported) in which the log of per capita capital spending is regressed on a dummy equal to one in the election year and in the two preceding years (the political budget cycle variable), a measure of revenues (either total per capita real transfers or total per capita real revenues), a number of mayors’ characteristics (gender, age, education measured in years of schooling, affiliation with a national political party and its ideological stance), other time-varying municipality characteristics (proportion of people ages 15–64 years, taxable per capita income), and municipalities’ fixed effects and time effects to capture common shocks.8

Figure 13.1.Log of Capital Spending per Capita, by Electoral Year and Size of Municipality, 2004–06

Source: Authors’ calculations. Note: The variable t indicates year.

Next, the chapter shows evidence that the DSP has been enforced. In fact, for the DSP to have an effect on the political budget cycle, it is essential that a cost be associated with either overspending or breaching the fiscal rule. The literature to date shows no clear evidence of whether the DSP has generally been enforced. Grembi, Nannicini, Troiano (2016), for example, use budget data to estimate whether municipalities have respected the rule and then check whether penalties were subsequently enforced over the period 1999–2004. They find “suggestive evidence that the DSP penalties were enforced” (Grembi, Nannicini, Troiano 2016, 6), because there is a correlation between noncompliance (as estimated by the authors) and subsequent punishment.

For the years 2004–06 the Interior Ministry has provided the list of municipalities that did not comply with the DSP, allowing a direct test to be made of whether the DSP has been enforced. As discussed, municipalities breaching the DSP face limits on hiring, on spending, and on borrowing for investments in the following year. Figures 13.2 and 13.3 indeed show that hiring and long-term borrowing (accrual definition) are noticeably lower for the noncomplying municipalities in the year following a breach of the DSP as compared with complying municipalities. For current spending (Figure 13.4) the evidence is consistent, although less striking. The DSP required that purchases of goods and services be brought to a level not greater than in the last year in which the pact was respected. Overall, the evidence suggests that breaching the rule carried penalties as measured by fiscal aggregates.

Figure 13.2.Average Hiring of Municipalities, per 1,000 Inhabitants, by DSP Compliance in Previous Year, 2005–07

Source: Authors’ calculations. Note: DSP = Domestic Stability Pact.

Figure 13.3.Mean per Capita Long-Term Borrowing (Accrual Basis) of Municipalities, by DSP Compliance in Previous Year, 2005–07

Source: Authors’ calculations.

Note: DSP = Domestic Stability Pact.

Figure 13.4.Mean per Capita Purchase of Goods and Services (Cash Basis) of Municipalities, by DSP Compliance in Previous Year, 2005–07

Source: Authors’ calculations.

Note: DSP = Domestic Stability Pact.

Regression Discontinuity Analysis

To assess whether the political budget cycle is more muted in larger municipalities (subject to the rule) than smaller ones (not subject to the rule) a regression discontinuity (RD) analysis around the 5,000-inhabitant threshold is performed. Specifically, a difference-in-differences approach is combined with an RD design to get estimates of the difference in capital spending between pre- and postelection years just below and above the 5,000-inhabitant threshold. Around the 5,000-inhabitant threshold, the treatment of being subject to the fiscal constraints of the DSP should be as good as randomly assigned. The treatment changes deterministically at the threshold, while other characteristics should not, setting up a sharp identification scheme. To assess the validity of the exogeneity of the threshold, a McCrary (2008) density test is run around the 5,000-inhabitant threshold in 2006. Figure 13.5 shows no evidence of any statistically significant jump in the population distribution at the threshold, as would be the case if mayors managed to keep the population at less than 5,000 inhabitants to avoid the DSP rules, suggesting that the nonmanipulation assumption is not violated.

Figure 13.5.Checking Continuity of the Population Distribution around the 5,000-Inhabitant Threshold

Source: Authors’ calculations.

Note: Distribution of binned normalized population around the 5,000-inhabitant threshold in 2006 (population window 2,000–8,000). Bins are computed as normalized average frequencies over equally spaced population intervals. The blue line is a kernel estimate, and the dashed red lines are 95 percent confidence intervals (McCrary 2008). The discontinuity estimate (log difference in height) is −0.02 (standard error = 0.20).

Next, Figure 13.6 plots the log difference at the individual municipality level of pre- and postelection years per capita capital spending against municipality population size to see whether a discontinuity occurs around the 5,000-inhabitant threshold. The log difference of pre- and postelection years per capita capital spending uses the election year and the two previous years as “pre” election and the two years after elections as “post” election. The log difference is used as a measure of the intensity of the political budget cycle. In addition, two polynomials are fit in population size (of order four in the left panel and of order five in the right panel), one for the observations below and one for those above the 5,000-inhabitant threshold. The fitted lines suggest that indeed the political budget cycle is higher to the left of the 5,000-inhabitant threshold than to the right, where the DSP rule is active. However, the dispersion of the observations is high and, moreover, by taking the pre- and postelection difference in capital spending, many observations are lost.

Figure 13.6.Difference in Pre- and Postelection Log of Capital Spending per Capita

(Percentage change)

Source: Authors’ calculations.

Note: Domestic Stability Pact–complying municipalities only. Mimicking variance evenly spaced number of bins using polynomial regression (Calonico, Cattaneo, and Titiunik 2014). Variables are in real per capita terms (2010 euro).

To confirm this result more formally, an RD analysis is run.9 The baseline RD specification for per capita capital spending yit is the following:

which includes polynomials of order p in the normalized variable P*it = PitPc, where Pc is the 5,000-inhabitant threshold, its interactions with the treatment indicator Zit, equal to one for municipalities subject to the DSP and zero otherwise

and the electoral dummy Wit, equal to one in preelection years (t = −2, −1, 0), where t = 0 is the year of elections, and zero in postelection years (t = 1, 2). Additional covariates Xit include mayor’s characteristics (gender, age, education, party affiliation, ideological stance), total per capita transfers received by municipalities, the proportion of people ages 15–64 years, and taxable per capita income, while μi and λt are municipality fixed effects and year effects, respectively.10

Table 13.2 reports the estimates at the 5,000-inhabitant threshold of the political budget cycle effect (the α0 coefficient of the election dummy Wit) and the fiscal rule effect on the political budget cycle (the coefficient ϕ0 of the interaction between the election dummy and the treatment indicator Zit) from fifth-degree polynomial regressions over the 0–15,000-inhabitant and 4,000—6,000-inhabitant windows. The local estimates confirm the existence of budget cycle: capital spending is 36 percent higher in preelection years, while the fiscal rule proves effective in mitigating the cycle, reducing election expenditure by more than 60 percent (column (1)). If the sample is restricted to the 4,000–6,000-inhabitant window (column (3)), the reduction in capital spending in preelection years for larger municipalities more than offsets the average increase in expenditure in preelection periods. The inclusion of additional covariates (columns (2) and (4)), while confirming the baseline results, reduces the magnitude and significance of the estimated effects.

Table 13.2.Political Budget Cycle in Log of Capital Spending of Municipalities at the DSP Threshold; RD-FE Estimates (2004–06)
Population 0-15,000Population 4,000-6,000
(1)(2)(3)(4)
>5,000 Inhabitants0.0700.0510.383*0.272
(0.112)(0.105)(0.203)(0.205)
Preelection Years0.365***0.348***0.525***0.473***
(0.090)(0.085)(0.186)(0.182)
Preelection Years × >5,000
Inhabitants−0.238**−0.182*−0.721***−0.631**
(0.114)(0.109)(0.276)(0.289)
R20.0620.1760.1070.188
Municipalities5,1555,010620604
Observations14,84814,1491,7001,627
Source: Authors’ calculations.Note: Variables are in real per capita terms (2010 euro). All specifications include a fifth-degree population polynomial, its interactions with the electoral and population dummies, and time and municipality fixed effects. Other covariates include the mayor’s characteristics (gender, age, education, party affiliation, and political leaning) and municipality-level variables (per capita total transfers received, proportion of population ages 15–64 years, and per capita taxable income). Clustered standard errors at the municipality level are in parentheses. DSP = Domestic Stability Pact; RD-FE = regression discontinuity–fixed effects.*p < .1; **p < .05; ***p < .01.
Source: Authors’ calculations.Note: Variables are in real per capita terms (2010 euro). All specifications include a fifth-degree population polynomial, its interactions with the electoral and population dummies, and time and municipality fixed effects. Other covariates include the mayor’s characteristics (gender, age, education, party affiliation, and political leaning) and municipality-level variables (per capita total transfers received, proportion of population ages 15–64 years, and per capita taxable income). Clustered standard errors at the municipality level are in parentheses. DSP = Domestic Stability Pact; RD-FE = regression discontinuity–fixed effects.*p < .1; **p < .05; ***p < .01.

The analysis then checks whether the control variables, that is, the predetermined characteristics of mayors and of municipalities, are balanced on either side of the DSP threshold. Figure 13.7 shows scatter plots of mayors’ and municipalities’ characteristics averaged over evenly spaced population bins around the DSP cutoff. From visual inspection of the fifth-order fitted polynomials around the threshold, no evident discontinuities can be detected.

Figure 13.7.Checking Continuity of Covariates around the 5,000-Inhabitant Threshold for Population below 15,000 (2004–06)

Source: Authors’ calculations.

Note: Bins picked to match the variance of the variables (Calonico, Cattaneo, and Titiunik 2014). Fifth-degree population polynomials are fitted on both sides of the threshold. Variables are in real per capita terms (2010 euro).

Finally, the issue of the mayor’s wage must be addressed. By law, mayors earn more as the population size of the municipality grows (Table 13.3). Gagliarducci and Nannicini (2013) find that the change in wage for Italian municipalities above the 5,000-inhabitant threshold selects more educated and competent mayors into the job, although there is no evidence that they are less prone to the political budget cycle. The mayor’s wage, which sharply increases at the 5,000-inhabitant threshold, introduces incentives that can potentially confound the estimated effect of the fiscal rule. To investigate whether the higher wage induces mayors seeking reelection to be more fiscally disciplined, polynomial regressions are run, with a 1,000 bandwidth, at other population thresholds where the mayor’s wage increases, namely 1,000, 3,000, and 10,000 inhabitants. The rationale is that if the mayor’s wage really matters for the political budget cycle, some effect should also be found at these other thresholds. The results reported in Table 13.4 do not support this hypothesis: no significant effects are found. In particular, no effects are found at the 3,000-inhabitant threshold, which entails a 50 percent wage increase. This latter result is consistent with those of Gagliarducci and Nannicini (2013).

Table 13.3.Legislative Thresholds of Municipalities
PopulationWage of MayorWage of Executive Committee Members (%)Size of Executive CommitteeSize of City Council
≤1,0001,29115412
1,000–3,0001,44620412
3,000–5,0002,16920416
5,000–10,0002,78950416
10,000–15,0003,09955620
Source: Grembi, Nannicini, and Troiano (2016, 8).Note: “Wage of mayor” is the monthly gross amount in 2000 (current euro); “wage of executive committee members” is expressed as a percentage of the mayor’s wage; “size of executive committee” is the maximum allowed number of executives appointed by the mayor; and “size of city council” is the number of seats in the city council.
Source: Grembi, Nannicini, and Troiano (2016, 8).Note: “Wage of mayor” is the monthly gross amount in 2000 (current euro); “wage of executive committee members” is expressed as a percentage of the mayor’s wage; “size of executive committee” is the maximum allowed number of executives appointed by the mayor; and “size of city council” is the number of seats in the city council.
Table 13.4.Political Budget Cycle in Log Capital Spending of Municipalities at Population Thresholds Relevant for Mayor’s Wage; RD-FE Estimates (2004–06)
Population 1,000Population 3,000Population 5,000Population 10,000
(1)(2)(3)(4)(5)(6)(7)(8)
>Threshold0.106−0.1340.018−0.0410.383*0.272−0.158−0.152
(0.190)(0.175)(0.171)(0.182)(0.203)(0.205)(0.247)(0.225)
Preelection Years0.0950.0630.2080.1470.525***0.473***−0.219−0.061
(0.133)(0.117)(0.152)(0.159)(0.186)(0.182)(0.278)(0.251)
Preelection Years ×
>Threshold0.0540.071−0.1150.072−0.721***−0.631**0.2780.196
(0.207)(0.201)(0.200)(0.192)(0.276)(0.289)(0.409)(0.378)
Other CovariatesNoYesNoYesNoYesNoYes
R20.0470.1920.1100.1960.1070.1880.1290.176
Municipalities2,4852,4111,1831,146620604214211
Observations7,1306,7973,3173,1451,7001,627559539
Source: Authors’ calculations.Note: Variables are in real per capita terms (2010 euro). All specifications include a fifth-degree population polynomial, its interactions with the electoral and population dummies, and time and municipality fixed effects. Other covariates include the mayor’s characteristics (gender, age, education, party affiliation, and political leaning) and municipality-level variables (per capita total transfers received, proportion of population ages 15–64 years, and per capita taxable income). Clustered standard errors at municipality level are in parentheses. DSP = Domestic Stability Pact; RD-FE = regression discontinuity-fixed effects.*p < .1; **p < .05; ***p < .01.
Source: Authors’ calculations.Note: Variables are in real per capita terms (2010 euro). All specifications include a fifth-degree population polynomial, its interactions with the electoral and population dummies, and time and municipality fixed effects. Other covariates include the mayor’s characteristics (gender, age, education, party affiliation, and political leaning) and municipality-level variables (per capita total transfers received, proportion of population ages 15–64 years, and per capita taxable income). Clustered standard errors at municipality level are in parentheses. DSP = Domestic Stability Pact; RD-FE = regression discontinuity-fixed effects.*p < .1; **p < .05; ***p < .01.

Conclusions

This chapter uses data on Italian municipalities during the early 2000s to present evidence suggesting that fiscal rules can moderate the political budget cycle. The analysis uses the discontinuity in the application of the rule at 5,000 inhabitants to identify the effect of the rule on the political budget cycle. It finds that the political budget cycle increases real capital spending by about 35 percent, on average, in the years before municipal elections and that the subnational fiscal rule reduces these figures by about two-thirds as compared with municipalities not subject to the rule. It also provides evidence that the fiscal rule has been enforced by the central government, at least over the period 2004–06 for which data are available on the municipalities that have breached the DSP. To the authors’ knowledge, this is the first analysis to provide robust evidence that fiscal rules can limit the political budget cycle. To this extent, it adds to the small and growing literature attempting to establish the impact of fiscal rules on budget outcomes. In contrast to other papers showing that fiscal rules can have an effect on budget deficits, however, these results have more direct welfare implications. Results showing that fiscal rules can help contain the budget deficit suggest that those rules are enforced, but without implying that they are welfare improving. On the contrary, the political budget cycle is inherently inefficient because it distorts spending and revenues for electoral and political purposes. In this regard, the results in this chapter point to a possible partial welfare-improving role for fiscal rules. In practice, assessing the welfare implications of fiscal rules remains difficult. In the specific Italian case analyzed in this chapter, it is generally accepted that the rule has contributed to reducing local authorities’ deficits—but mainly by compressing capital spending. An assessment of the overall welfare effects of the rule, therefore, would have to include not only the benefits from reductions in deficits during the political budget cycle, but also the costs of the decline in capital spending.

Annex 13.1. Data Set Description
Annex Table 13.1.1..Data Set Description
VariableDescriptionSource
Capital Spending in Real per Capita Terms (cash definition)Sum of all cash capital expenditures by municipalities; the largest outlays refer to the construction of buildings, roads, local transport, and purchases of furniture, and other equipment. Nominal values are deflated by using the national consumption price index (all items, base 2010).Certificati di Conto Consuntivo, Ministero dell’Interno (http://finanzalocale.interno.it)
Current Spending in Real per Capita Terms (cash definition)Sum of all cash current expenditures by municipalities; the largest outlays refer to personnel and purchases of goods and services. Nominal values are deflated by using the national consumption price index (all items, base 2010).Certificati di Conto Consuntivo, Ministero dell’Interno (http://finanzalocale.interno.it)
Total Spending in Real per Capita Terms (cash definition)Sum of all cash current and capital expenditures by municipalities, as defined above. Nominal values are deflated by using the national consumption price index (all items, base 2010).Certificati di Conto Consuntivo, Ministero dell’Interno (http://finanzalocale.interno.it)
Long-term Borrowing in Real per Capita Terms (accrual definition)Sum of annual revenues from loans and bonds issued to fund investment projects. Nominal values are deflated by using the national consumption price index (all items, base 2010).Certificati di Conto Consuntivo, Ministero dell’Interno (http://finanzalocale.interno.it)
Preelection yearsDummy is equal to one in the three years before municipal elections, including the election year.Archivio Storico delle Elezioni, Ministero dell’Interno (http://elezionistorico.interno.it)
Taxable Income in Real per Capita TermsSum of total incomes at municipality level as available from personal income tax returns.Ministero dell’Economia e delle Finanze
Share of Population, Ages 15-64 YearsComputed as the ratio of population, ages 15–64 years, to total population.Demo, Istituto Nazionale di Statistica (http://demo.istat.it)
Age of MayorThe age dummy is equal to one if the mayor is older than the median.Anagrafe degli Amministratori Locali e Regionali, Ministero dell’Interno (http://amministratori.interno.it)
Education of MayorEducation is measured in years of schooling by converting International Standard Classification of Education (ISCED) levels. Dummies for three education categories are obtained by aggregating ISCED levels as follows: low (0–2), middle (3–4), and high (5–8).Anagrafe degli Amministratori Locali e Regionali, Ministero dell’Interno (http://amministratori.interno.it)
Party Affiliation of MayorDummy is equal to one if the list or coalition supporting the winning candidate for mayor at municipal elections is not a lista civica; that is, a list not affiliated with a national or regional party.Anagrafe degli Amministratori Locali e Regionali, Ministero dell’Interno (http://amministratori.interno.it)
References

    AcconciaA.G.Corsetti and S.Simonelli. 2014. “Mafia and Public Spending: Evidence on the Fiscal Multiplier from a Quasi-Experiment.American Economic Review104 (7): 2185209.

    AlesinaA.G. D.Cohen and N.Roubini. 1997. Political Cycles and the Macroeconomy.Cambridge, MA: MIT Press.

    AlesinaA. and M.Paradisi. 2014. “Political Budget Cycles: Evidence from Italian Cities.Working Paper 20570National Bureau of Economic ResearchCambridge, MA.

    AlesinaA.U.Troiano and T.Cassidy. 2015. “Old and Young Politicians.Working Paper 20977National Bureau of Economic ResearchCambridge, MA.

    BeetsmaR. M. W J. and X.Debrun. 2004. “Reconciling Stability and Growth: Smart Pacts and Structural Reforms.IMF Staff Papers51 (3): 43156.

    BeetsmaR. M. W J. and X.Debrun. 2007. “The New Stability and Growth Pact: A First Assessment.European Economic Review51 (2): 45377.

    BonfattiA. and L.Forni. 2016. “Do Fiscal Rules Reduce the Political Budget Cycle? Evidence from Italian Municipalities.Marco Fanno Working Paper 208 Department of Economics and ManagementUniversity of PaduaPadova, Italy.

    BrenderA. and A.Drazen. 2005. “Political Budget Cycles in New versus Established Democracies.Journal of Monetary Economics52 (7): 127195.

    BrenderA. and A.Drazen. 2008. “How Do Budget Deficits and Economic Growth Affect Reelection Prospects? Evidence from a Large Panel of Countries.American Economic Review98 (5): 220320.

    BrolloF. and T.Nannicini. 2012. “Tying Your Enemy’s Hands in Close Races: The Politics of Federal Transfers in Brazil.American Political Science Review106 (4): 74261.

    CalonicoS.M. D.Cattaneo and R.Titiunik. 2014. “Robust Data-Driven Inference in Regression-Discontinuity Design.Stata Journal14 (4): 90946.

    ChiadesP. and VMengotto. 2015. “Il calo degli investimenti nei Comuni tra Patto di stabilitä interno e carenza di risorse” (“The decline in municipal investments between Domestic Stability Pact and lack of financial resources”). Economia Pubblica42 (2): 544.

    CioffiM.G.Messina and P.Tommasino. 2012. “Parties, Institutions and Political Budget Cycles at the Municipal Level.Temi di discussione (Economic Working Papers) 885Bank of Italy.

    CoelhoC.F. J.Veiga and L. G.Veiga. 2006. “Political Business Cycles in Local Employment: Evidence from Portugal.Economics Letters93 (1): 8287.

    DebrunX.L.MoulinA.TurriniJ.Ayuso-i-Casals and M. S.Kumar. 2008. “Tied to the Mast? National Fiscal Rules in the European Union.Economic Policy23: 297362.

    DrazenA. and M.Eslava. 2010. “Electoral Manipulation via Voter Friendly Spending: Theory and Evidence.Journal of Development Economics92 (1): 3952.

    FerraresiM.L.Rizzo and A.Zanardi. 2015. “Policy Outcomes of Single and Double-Ballot Elections.International Tax and Public Finance22 (6): 97798.

    ForemnyD. and N.Riedel. 2014. “Business Taxes and the Electoral Cycle.Journal of Public Economics115: 4861.

    GagliarducciS. and T.Nannicini. 2013. “Do Better Paid Politicians Perform Better? Disentangling Incentives from Selection.Journal of the European Economic Association11 (2): 36998.

    GrembiV.T.Nannicini and U.Troiano. 2016. “Do Fiscal Rules Matter?American Economic Journal: Applied Economics8 (3): 130.

    GuptaS.C.Mulas-Granados and E.Liu. 2015. “Now or Later? The Political Economy of Public Investment in Democracies.Working Paper 155International Monetary FundWashington, DC.

    LindbeckA.1976. “Stabilization Policies in Open Economies with Endogenous Politicians.American Economic Review Papers and Proceedings66 (2): 119.

    McCraryJ.2008. “Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test.Journal of Econometrics142 (2): 698714.

    Milesi-FerrettiG. M.R.Perotti and M.Rostagno. 2002. “Electoral Systems and Public Spending.Quarterly Journal of Economics117 (2): 60957.

    NordhausW.1975. “The Political Business Cycle.Review of Economic Studies42 (2): 16990.

    PerssonT. and G.Tabellini. 2000. Political Economics: Explaining Economic Policy.Cambridge, MA: MIT Press.

    Petterson-LidbomP.2008. “Do Parties Matter for Economic Outcomes? A Regression-Discontinuity Approach.Journal of the European Economic Association6 (5): 103756.

    RogoffK.1990. “Equilibrium Political Budget Cycles.American Economic Review80 (1): 2136.

    RogoffK. and A.Sibert. 1988. “Elections and Macroeconomic Policy Cycles.Review of Economic Studies55 (1): 116.

    ShiM. and J.Svensson. 2006. “Political Budget Cycles: Do They Differ across Countries and Why?Journal of Public Economics90 (89): 136789.

    VeigaL. and F.Veiga. 2007. “Political Business Cycles at the Municipal Level.Public Choice131 (1): 4564.

The authors thank seminar participants at the IMF and the University of Padua.

Acconcia, Corsetti, and Simonelli (2014) use data on investment expenditure of Italian municipalities to estimate the fiscal multiplier at the local level.

An electoral list is a group of persons, either affiliated to a political party or formally independent (lista cívica), supporting a candidate mayor and eligible to become members of the municipal council.

When a special commissioner is appointed to run a municipality, information on the mayor’s characteristics is missing. In these cases, and when information on expenditures or revenues from financial reports is not available, we keep the municipality in the sample, using an unbalanced panel.

For example, the Budget Law of 2003 (Law 289, December 27, 2002, Art. 29 Comma 11) included a provision stating that the rule for 2005 would have included capital spending, and the Economic and Financial Planning Document for 2004–07, issued in July 2003, mentioned the same point.

We exclude regions with special autonomy (regioni a statuto speciale) because they were allowed to set their own fiscal rules for municipal governments.

Municipalities can borrow, but only for investment purposes.

A relevant issue in analyzing capital spending at the municipal level is that, in recent years, municipalities have outsourced some capital spending to private companies, usually partially or totally owned by the municipality itself. This practice has sometimes been instrumental in circumventing the fiscal rule. Unfortunately, information on these companies is extremely scant. One advantage in focusing on small municipalities (with fewer than 15,000 inhabitants) is that they have outsourced capital spending much less often than larger municipalities (Chiades and Mengotto 2015).

See Bonfatti and Forni (2016) for a more detailed discussion of this point.

Robustness checks were performed using polynomials of different orders and windows of varying widths around the 5,000-inhabitant threshold. Results are generally robust but lose significance when the order of the polynomial is less than three or the population window is greater than 3,000 inhabitants.

For other works using municipality fixed effects in RD analysis, see, for example, Petterson-Lidbom (2008) and Ferraresi, Rizzo, and Zanardi (2015).

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