Fiscal Politics
Chapter

Chapter 2. Governments and Promised Fiscal Consolidations: Do They Mean What They Say?

Author(s):
Vitor Gaspar, Sanjeev Gupta, and Carlos Mulas-Granados
Published Date:
April 2017
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Author(s)
Sanjeev Gupta, João Tovar Jalles, Carlos Mulas-Granados and Michela Schena 

Introduction

Several reasons could explain why fiscal outcomes may deviate from plans. First, the macroeconomic scenario may unfold differently from that foreseen in the plan (Frankel 2011). Second, the design of fiscal rules may be such that adherence to them requires fiscal discipline in plans but not in outcomes. Finally, policymakers may find it difficult to implement fiscal plans because of opposition from vested interest groups (Beetsma, Giuliodori, and Wierts 2009; von Hagen 2010). In fact, political factors can be particularly important in explaining fiscal outcomes when elections are approaching or if political fragmentation is large (Perotti 1998; von Hagen, Hallet, and Strauch 2001; Perotti and Kontopoulos 2002; Protrafke 2011).1

The difference between budget plans and budget implementation is labeled the promise gap. The size of promise gaps has a bearing on credibility and democratic accountability of elected governments. For example, large and systematic fiscal promise gaps may increase uncertainty for economic agents and lower the credibility of the government, thus increasing long-term interest rates on government bonds (Baldacci and Kumar 2010; Beetsma and others 2015). In addition, when governments do not deliver on their promises, the quality of democracy may suffer (Przeworski and others 1999). In a fully functioning democracy with rational, forward-looking voters and politicians, the electorate expects governments to be responsive to their economic and fiscal preferences.

This chapter studies two questions: First, what explains fiscal consolidation promise gaps? Second, what is the reaction of markets and voters to promise gaps incurred by incumbent parties? This chapter holds the view that these two issues are interrelated. Parties in government are motivated by specific policy agendas and by their willingness to remain in office (Müller and Strom 1999).2 When governments deliver on their promised policies they are likely to be reelected. If they fall short on these promises, they could be penalized by voters unless this deviation benefits the electorate in the short term. At the same time, financial markets monitor these actions, and if they see politically motivated deviations from promised fiscal discipline, they react negatively.

Because these questions are intertwined, the analysis tackles them empirically in two steps. For this purpose, a new database of fiscal consolidations was created that compares narrative budget plans with actual fiscal performance in 17 Organisation for Economic Co-operation and Development (OECD) countries during 1978–2015.3 We find that fiscal promise gaps were sizable (about 0.3 percent of GDP per year, or 1.1 percent of GDP over a typical three-year adjustment episode). Economic factors and forecast errors are important in explaining the differences between budget plans and fiscal outcomes, but political factors also play a role: greater electoral proximity, stronger political cohesion, and higher accountability were all associated with smaller promise gaps. Finally, governments that delivered on their promised fiscal consolidation plans were rewarded by financial markets and not penalized by voters.

This chapter makes three contributions to the existing literature: first, it updates the narrative database from Devries and others (2011) and Alesina and others (2015); second, it considers simultaneously the role of three political factors (electoral proximity, political strength, and institutional accountability) in explaining consolidation promise gaps; and third, it examines the consequences of promise gaps on market sentiment and government popularity among the electorate.

The remainder of the chapter is organized as follows; the second section briefly discusses the data and the definition of the dependent variable. The third section explores the economic and political causes of consolidation promise gaps. The fourth section looks into the reaction of markets and the electorate to these gaps. The fifth section inspects the consequences of consolidation promise gaps. The last section concludes and presents policy implications.

Defining Fiscal Promise Gaps

Identifying Fiscal Consolidation Episodes

The literature on fiscal adjustment episodes is vast and for a long time has relied on the positive approach, by which fiscal consolidations are associated with large changes in the cyclically adjusted primary balance (CAPB).4 More recently, scholars have identified consolidation episodes following a narrative approach, which relies on approved budget plans and historical accounts of past fiscal policy. Such an approach was first popularized by Romer and Romer (2010) and Devries and others (2011), who subsequently made publicly available a list of fiscal consolidation episodes for 17 advanced economies between 1978 and 2009.5 More recently, Alesina and others (2015) updated that database for a subset of European countries through 2012. This chapter updates the database for all 17 countries included in Devries and others (2011). It follows the same approach, and relies on historical description surrounding changes in budget deficits every year as recorded in national budget laws, the European Commission’s Stability and Convergence Programs, and the OECD’s country reviews.6

In this sample, 73 episodes of fiscal consolidations are identified. Table 2.1 summarizes these episodes by country. The number of fiscal contractions per country ranges from two in Canada and Finland, to seven in France and the United States. The size of fiscal consolidation episodes varies from 0.04 percent of GDP to 4.74 percent of GDP, with an average size of fiscal adjustment equal to 1.06 percent of GDP. The average duration of the reported fiscal episodes is 3.3 years, with the shortest duration corresponding to 1 year (21 episodes) and the longest duration corresponding to 14 years (Canada).

Table 2.1.Fiscal Consolidation Years, 17 Advanced Economies, 1978–2015
CountryFiscal Consolidation—Sample Years
Australia1985–88, 1994–99, 2010–12, 2014–15
Austria1980–81, 1984, 1996–97, 2001–02, 2011–12, 2015
Belgium1982–85, 1987, 1990, 1992–94, 1996–97, 2010–15
Canada1984–97, 2010–15
Denmark1983–86, 1995, 2012
Finland1992–97, 2011
France1979, 1987, 1989, 1991–92, 1995–97, 1999–2000, 2011–15
Germany1982–84, 1991–95, 1997–2000, 2003–04, 2006–07, 2011–12
Ireland1982–88, 2009–15
Italy1991–98, 2004–07, 2010–15
Japan1997–98, 2003–07
Netherlands1981–88, 1991–93, 2004–05, 2011–13, 2015
Portugal1983, 2000, 2002–03, 2005–07, 2010–15
Spain1983–84, 1989–90, 1992–97, 2009–15
Sweden1984, 1993–98, 2011, 2015
United Kingdom1979–82, 1994–99, 2010, 2012, 2014–15
United States1978, 1980–81, 1985–86, 1988, 1990–98, 2011, 2013–15
Source: Authors’ calculations.Note: See Annex 2.1 for details.
Source: Authors’ calculations.Note: See Annex 2.1 for details.

Figures 2.1 and 2.2 show the distribution of fiscal consolidation episodes over time as well as their distribution by size (in percentage of GDP). Most episodes took place in the mid-1980s; the late 1990s (led by European countries’ need to qualify for the single currency union); and between 2010 and 2015, in the aftermath of the recent financial crisis (following the cross-country coordinated fiscal stimuli that took place). In more than 35 percent of fiscal adjustment episodes, the size was between 0 and 0.5 percent of GDP. In only 5 percent of cases was the adjustment larger than 3 percent of GDP.

Figure 2.1.Absolute Frequency of Fiscal Consolidation Episodes over Time, 1978–2015

Source: Authors’ calculations.

Figure 2.2.Relative Frequency of Fiscal Consolidation Episodes, by Size of Consolidation

Source: Authors’ calculations.

Measuring Consolidation Promise Gaps

Consolidation promise gaps (CPGs) are defined as the difference between the size of planned fiscal adjustment (PFA), as measured by the narrative approach, and the size of the realized fiscal adjustment (RFA), as measured by changes in the budget balance (all expressed in percentage of GDP).7

The promise gap can have either sign. Governments can deliver less fiscal adjustment than initially planned (positive promise gap) or they can implement a larger adjustment than initially foreseen (negative promise gap). Using data at the general government level, Figure 2.3 shows the average size of promise gaps by country.8 Finland, Spain, Ireland, and Italy have the largest positive promise gaps, while Denmark, the United Kingdom, Germany, Canada, and Sweden managed to deliver, on average, larger fiscal consolidations than initially planned.

Figure 2.3.Size of Consolidation Promise Gaps (baseline), by Country

Source: IMF World Economic Outlook and authors’ calculations.

Panel 1 of Figure 2.4 shows the promise gap using the primary balance to correct for the effect of interest payments. Panel 2 of Figure 2.4 shows the promise gap using the structural balance, which allows the analysis to take into account the effects stemming from the economic cycle (for example, Finland) or one-off measures (for example, Ireland’s 2009 banking sector capitalization) that would otherwise distort the overall picture. Although the ordering of the countries varies, the average promise gap remains the same (0.3 percent of GDP) under all measures.

Figure 2.4.Size of Consolidation Promise Gaps (Alternatives), by Country

Sources: IMF Fiscal Monitor and authors’ calculations.

The Political and Economic Determinants of Consolidation Promise Gaps

Economic Factors: Stylized Facts

To explain the size of consolidation promise gaps, the role of initial fiscal conditions, such as the initial level of the debt-to-GDP ratio and the initial level of fiscal sustainability, is examined.9 The analysis also looks at the size of the output gap and at growth forecast errors.10,11Figure 2.5 plots average promise gaps for high and low levels of different variables of interest.

Figure 2.5.Size of Promise Gaps and Economic Conditions

Sources: IMF World Economic Outlook and authors’ calculations.

Note: “Initial” refers to the year before the start of a given consolidation episode. High and low levels are identified by cases above the 75th percentile of the distribution and below the 25th percentile, respectively.

The bar charts show that initially adverse fiscal conditions (high levels of debt and low fiscal sustainability) are subsequently associated with smaller consolidation promise gaps. This outcome is in line with results from Escolano and others (2014), who find that countries under fiscal stress are willing to undertake stronger fiscal adjustments and deliver on their commitments to undertake more sizable consolidations. In addition, Figure 2.6 shows that adverse economic conditions (as evidenced by large output gaps and high real GDP growth forecast errors) are also associated with smaller consolidation promise gaps.

Political Factors: Principal Components Analysis

Several political factors can affect the size of consolidation promise gaps. The existing literature mainly focuses on the possible role of elections and budget institutions (Beetsma, Giuliodori, and Wierts 2009; Beetsma and others 2013; Beetsma and others 2015) and finds that these variables are not very statistically significant. In our view, testing a few political variables can suffer from selection bias. This chapter proposes instead a more comprehensive analysis of political factors that can potentially affect promise gaps. It focuses on three political dimensions (each containing multiple variables) and builds political indicators through principal components analysis (PCA). These three political dimensions are detailed as follows:12

  • Electoral proximity: This dimension takes into account the time that policymakers have until upcoming elections. Politicians facing coming elections have stronger incentives to deliver on their budget promises and report lower consolidation promise gaps. Four variables are used to compute the proximity PCA. Higher electoral proximity is associated with more years in office, fewer years left in current term, a party of chief executive with a short tradition in office, and fewer months to the next election.13 Only the first principal component is retained.14

  • Political strength: This dimension takes into account the number of political actors participating in budgetary decisions who typically exhibit conflicting budgetary demands. These actors could be parties in government—or in opposition—interest groups, or, more generally, veto players. Strong governments are those that operate in less fragmented political environments and are likely to be subject to less stringent spending demands. Therefore, they are typically associated with tighter fiscal discipline and lower promise gaps. Four variables are used to compute the strength PCA. More political strength is associated with a high margin of parliamentary majority, low cabinet fragmentation, executive control of all houses, and a weak opposition. Only the first principal component is retained.

  • Political accountability: This dimension takes into account the institutional context within which fiscal policy decisions are made. When politicians operate under more transparency, better governance, and more mechanisms objectively monitoring their activities, they tend to be more responsive to citizens’ demands and more accountable to voters for the promises they make. Politicians operating in institutional contexts with more accountability would be associated with more fiscal discipline and lower promise gaps. Five variables are used to compute the accountability PCA. A higher accountability index is associated with more voice and accountability, with more regulatory quality, more government effectiveness, control of corruption, and with the rule of law.15 Only the first principal component is retained.

The proximity and strength variables are each represented by one factor composed of four underlying variables.16Accountability is represented by one factor composed of five underlying variables.17 The resulting principal components indices are described in Table 2.2; Table 2.3 lists the corresponding factor loadings.18 The principal components can be interpreted by focusing on the factor loadings and the uniqueness of each variable.19 With regard to political proximity, uniqueness is relatively low for all variables, which implies that the retained factor spans the original variables adequately. As to political strength, the factor appears to describe mostly the margin of majority and cabinet strength. In principle, both factors should enter with positive coefficients in the regressions. Finally, with respect to accountability, the factor is mainly driven by the role of government effectiveness.

Table 2.2.Summary of Political Composite Variables and Descriptive Statistics
ConceptVariables
ProximityYears in office
Years left in current term
Party of chief executive more time in office
Months to next election
StrengthMargin of majority
Cabinet strength
Executive control of all houses
Weak opposition
AccountabilityVoice and accountability
Regulatory quality
Government effectiveness
Control of corruption
Rule of law
Source: Authors’ calculations.Note: For each variable, the average is 0 and the standard deviation is 1.
Source: Authors’ calculations.Note: For each variable, the average is 0 and the standard deviation is 1.
Table 2.3.Factor Loadings and Uniqueness
Factors
VariablesProximityStrengthAccountabilityUniqueness
Years in office0.390.29
Years left in current term0.410.28
Party of chief executive more time in office0.370.29
Months to next election0.450.28
Margin of majority0.930.12
Cabinet strength0.900.17
Executive control of all houses0.760.42
Weak opposition0.720.47
Voice and accountability0.870.22
Regulatory quality0.890.10
Government effectiveness0.820.13
Control of corruption0.960.07
Rule of law0.940.11
Share Explained0.390.690.85
Source: Authors’ calculations.
Source: Authors’ calculations.

Figure 2.6 plots the average size of consolidation promise gaps for low and high levels of the three principal component indicators along the three dimensions associated with proximity, strength, and accountability. As expected, political strength and high institutional accountability are associated with lower consolidation promise gaps. Moreover, the higher the electoral proximity, the higher the inherent pressure for the incumbent government to deliver on its promises to maximize the possibilities of being reelected for a new term; that is, the higher the electoral proximity, the lower the consolidation promise gap. The difference between the average size of promise gaps under the high and low values of each principal component seems more important for political strength and accountability than for proximity.

Figure 2.6.Size of Promise Gaps and Political Conditions

Source: Authors’ calculations.

Note: For details on the three political variables (proximity, strength, and accountability), refer to the main text. High and low levels are identified by cases above the 75th percentile of the distribution and below the 25th percentile, respectively.

Economic and Political Factors: Panel Regressions

To test the role of the abovementioned economic and political factors simultaneously, panel regression analyses are used. The 229 years of fiscal consolidation in the panel database are used to estimate equation (2.1):

where CPGit is the consolidation promise gap in country i and year t, ICit is a vector of initial fiscal conditions (measured by the lagged value of the public-debt-to-GDP ratio),20‘ECit is a vector of economic conditions (measured by the output gap and the alternative measures of GDP forecast errors), and ‘ POLit is a vector of political variables (where each of the principal components—proximity, strength, and accountability—are first included, followed by a selection of political variables from each component). β, θ, and ρ are unknown coefficients to be estimated. εit is an independent and identically distributed disturbance term satisfying usual assumptions of zero mean and constant variance. Equation (2.1) is estimated by ordinary least squares with robust standard errors clustered at the country level.

Table 2.4 reports results for economic determinants and confirms that larger initial debt levels, annual improvements in the output gap, and larger forecast errors all lead to smaller consolidation promise gaps. Table 2.5 adds to the economic determinants the three political indicators computed by PCA and illustrates that the most important factors in explaining cross-country differences in consolidation promise gaps are political strength and accountability. Table 2.6 includes a subset of relevant political variables for each PCA one at a time and then jointly and shows that they are all associated with lower promise gaps.21

Table 2.4.Economic Determinants of Consolidation Promise Gaps
Specification
Regressors(1)(2)(3)(4)
Lagged Debt−0.026***−0.028***
(0.005)(0.007)
Change in Output Gap−0.540***−0.605***
(0.070)(0.092)
Lagged GDP Forecast Error−0.200−0.202*
(0.133)(0.107)
Constant1.690***0.087−0.3322.097***
(0.543)(0.388)(0.597)(0.725)
Observations228231162162
R20.1750.2870.1460.467
Source: Authors’ calculations.Note: Dependent variable = consolidation promise gaps as defined in the main text. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.*p < .1; ***p < .001.
Source: Authors’ calculations.Note: Dependent variable = consolidation promise gaps as defined in the main text. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.*p < .1; ***p < .001.
Table 2.5.Economic and Political Determinants of Consolidation Promise Gaps(Principal components analysis)
Specification
Regressors(1)(2)(3)(4)
Lagged Debt−0.020***−0.021***−0.063**−0.057*
(0.007)(0.007)(0.029)(0.032)
Change in Output Gap−0.557***−0.545***−0.497*−0.536*
(0.085)(0.084)(0.292)(0.312)
Proximity−0.0720.035
(0.138)(0.356)
Strength−0.531*−0.570
(0.284)(1.079)
Accountability−2.726*−3.202*
(1.561)(1.811)
Constant1.232**1.824***3.911*4.193*
(0.573)(0.651)(2.054)(2.345)
Observations1621624141
R20.4040.4170.8390.841
Source: Authors’ calculations.Note: Dependent variable = consolidation promise gaps as defined in the main text. The time span of this regression covers the period 1978–2009 to perfectly match the Devries and others (2011) data set. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.*p < .1; **p < .05; ***p < .01.
Source: Authors’ calculations.Note: Dependent variable = consolidation promise gaps as defined in the main text. The time span of this regression covers the period 1978–2009 to perfectly match the Devries and others (2011) data set. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.*p < .1; **p < .05; ***p < .01.
Table 2.6.Economic and Political Determinants of Consolidation Promise Gaps(Select individual variables)
Specification
Regressors(1)(2)(3)(4)
Lagged Debt−0.035***−0.026***−0.033***−0.014
(0.009)(0.007)(0.011)(0.014)
Change in Output Gap−0.304**−0.575***−0.493***−0.026
(0.123)(0.092)(0.139)(0.200)
Recent Elections−0.388***−0.302
(0.147)(0.307)
Margin of Majority−4.373**−7.480**
(2.007)(3.546)
Government Effectiveness−3.899***−4.104**
(1.310)(1.942)
Lagged GDP Forecast Error−0.173−0.181*−0.0620.026
(0.113)(0.107)(0.162)(0.190)
Constant4.905***4.082***8.413***12.755***
(1.248)(1.158)(2.305)(3.020)
Observations1191629465
R20.3750.4840.5340.501
Source: Authors’ calculations.Note: Dependent variable = consolidation promise gaps as defined in the main text. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.*p < .1; **p < .05; ***p < .01.
Source: Authors’ calculations.Note: Dependent variable = consolidation promise gaps as defined in the main text. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.*p < .1; **p < .05; ***p < .01.

To ensure that the results are not biased by the decision to combine three data sets that may have different approaches to narrating fiscal developments, the empirical analysis is also run on the original Devries and others (2011) database, which shows that even when using a single-source database the main results hold.22

The Economic and Political Consequences of Consolidation Promise Gaps

The preceding section shows that political factors such as proximity of elections, lack of political strength, or low systemic accountability can derail planned fiscal consolidations and increase the size of promise gaps. Policymakers in such circumstances may be tempted (or forced by surrounding political conditions) to deviate from initial consolidation plans, provided they can compensate for the negative backlash from markets. To gauge empirical support to this line of reasoning, the reactions of financial markets and the electorate to consolidation promise gaps are analyzed in this section. First, some stylized facts are presented, then the results of regression analysis and impulse-response functions are presented.

The Consequences of Consolidation Promise Gaps: Stylized Facts

If we assume that markets and voters expect governments to deliver on their fiscal promises, the analysis of their reaction to promise gaps can be better understood if the sample is split between episodes in which governments overperformed and managed to deliver more consolidation than initially planned, and episodes in which they underperformed and fell short of their promises. Note that in the sample, the average size of the promise gaps in underperforming episodes was equivalent to 2.8 percent of GDP, while the average size of the promise gap in overperforming episodes was 1.2 percent of GDP.

Figure 2.7 plots the evolution of government popularity23 and five-year bond spreads (against Germany) from four years before the consolidation episode started to four years after. On the one hand, government popularity seems to be generally unaffected by the sign of consolidation promise gaps. There is a slight decline in popularity in the aftermath of consolidation episodes in which governments fall short of their promises. Taking this evidence with a grain of salt (because it is not strongly significant), it shows that policymakers may be mistaken when they assume that they could obtain a boost in popularity by not delivering on their fiscal consolidation plans. Although it is possible that, in general, voters prefer more public goods and dislike fiscal adjustments, it seems that once a promise to consolidate the budget is made, they do not like it to be broken. On the other hand, markets seem to react more decisively: government bond spreads increase in both subsamples at the start of (and immediately after) the consolidation, but the spike is larger and more long lasting in the case of underperforming fiscal adjustments.

Figure 2.7.Voters’ and Markets’ Reactions to Consolidation Promise Gaps

The Consequences of Consolidation Promise Gaps: Impulse-Response Functions

To estimate the dynamic impact of promised fiscal consolidations over the short and medium term on both a financial market indicator and on government popularity, the analysis follows Jordà’s (2005) method to generate impulse-response functions (IRFs).24 This method consists of estimating IRFs directly from local projections. For each period k, the following regression is estimated:

with k=1,…,5 and where Y corresponds either to the five-year government bond spreads (relative to Germany) or government popularity; CPGi,t is the consolidation promise gap variable (in country i at time t); Xi,t is the same vector of control variables described in equation (2.2); δik are country fixed effects added to capture unobserved heterogeneity across countries and time-unvarying factors; αtk are time effects; γjk and ρk are coefficients to be estimated for the lagged dependent variable and set of controls, respectively; ɛi,tk is a disturbance term satisfying usual assumptions; and βk measures the distributional impact of fiscal consolidation episodes for each future period k. The lag length (l) is set at two as selected by the Akaike information criteria, but the findings are strongly robust to different lag structures.25Equation (2.2) is estimated using Beck and Katz’s (1995) panel-corrected standard error estimator. IRFs are obtained by collecting the estimated βk with confidence intervals computed using βk’s standard errors.26

Alternative ways of estimating dynamic impacts are available, but, as explained here, those are inferior options. The first possible alternative would be to estimate a panel vector autoregression. However, this type of estimation is generally considered a “black box” since all relevant regressors are considered endogenous. Moreover, one has to know the exact order in which they enter the system. Since economic theory rarely provides such an ordering, the Choleski decomposition is often used as a solution of limited value for providing structural information to a vector autoregression (VAR). Moreover, a major limitation of the VAR approach is that it has to be estimated to low-order systems. Since all effects of omitted variables are in the residuals, this may lead to large distortions in the IRFs, making them of little use for structural interpretations (see, for example, Hendry 1995). In addition, all measurement errors or misspecifications also induce unexplained information left in the error terms, making interpretation of the IRFs even more difficult (Ericsson, Hendry, and Prestwich 1998). One should bear in mind that because of its limited number of variables and the aggregate nature of the shocks, a VAR model should be viewed as an approximation of a larger structural system. In contrast, the approach used here does not suffer from these identification and size-limitation problems and, in fact, has been suggested by Auerbach and Gorodnichenko (2013), among others, as a sufficiently flexible alternative.

A second alternative for assessing the dynamic impact of fiscal consolidation episodes would be to estimate an autoregressive-distributed-lag model of changes in inequality and consolidation episodes and to compute the IRFs from the estimated coefficients (Romer and Romer 1989; Cerra and Saxena 2008). Note that the IRFs obtained using this method, however, tend to be lag sensitive, thus undermining the overall stability of the IRFs. Moreover, the statistical significance of long-lasting effects can result from one-type-of-shock models, particularly when the dependent variable is very persistent, as with the Gini coefficient (Cai and Den Haan 2009). In contrast, such issues are not experienced in the local projection method because lagged dependent variables enter as control variables and are not used to derive the IRFs. Finally, estimated IRFs’ confidence intervals are computed directly using the standard errors of the estimated coefficients without the need for Monte Carlo simulations.

To explore whether the impact of consolidation promise gaps on both markets and the electorate depends on the state of the business cycle, the following alternative regression is estimated:

with Y(zit)=exp(γzit)1+exp(γzit),γ>0,

where z is an indicator of the state of the economy (using the real GDP growth rate) normalized to have zero mean and unit variance. The remainder of the variables and parameters are defined as in equation (2.2). This method is equivalent to Granger and Teräsvirta’s (1993) smooth transition autoregressive model, whose advantage relative to estimating VARs for each regime is that it uses a larger number of observations to estimate the IRFs, thus increasing stability and precision.

Figure 2.8 confirms the previous results obtained by estimating equation (2.2). In the aftermath of a promise gap shock, five-year government spreads increase strongly and are statistically significant at usual levels. In contrast, the reaction of voters is weaker. Government popularity declines gradually, but the IRF is statistically weaker. Figure 2.9 shows the results of estimating equation (2.3). It seems that markets penalize incumbent governments more (by raising government bond spreads) and for a longer period for not delivering on their promises during bad times. As in Figure 2.8, the reaction of voters is not positive, and shows instead a marginally significant decrease in popularity during bad times one year after the promise gap shock.

Figure 2.8.Local Projection Method: Impulse Response Functions

Sources: Bloomberg; and authors’ calculations.

Figure 2.9.Local Projection Method: Impulse Response Functions Conditioned on the Phase of the Business Cycle

Sources: Bloomberg; and authors’ calculations.

Conclusions and Policy Implications

This chapter analyzes the causes and consequences of fiscal consolidation promise gaps, defined as the distance between planned fiscal adjustments and actual consolidations, and finds that these gaps are sizable; both economic and political factors affect them. In particular, smaller initial debt levels, annual improvements in the output gap, and smaller forecast errors result in narrower promise gaps. The role of political factors in explaining consolidation promise gaps is important: newly elected governments, with a large margin of majority and high government effectiveness are more likely to deliver on their planned fiscal adjustments. Finally, evidence is found that financial markets penalize (reward) underperformers (overperformers), but the electorate is less responsive.

These results have important policy implications. First, policymakers should be more cautious in formulating ambitious fiscal consolidations (especially when they are revenue driven). Second, governments can deliver on their promised spending cuts by improving their capacity to deliver public services efficiently. Also, stronger starting conditions (as reflected by lower fiscal imbalances) could lead to lower promise gaps later on. Finally, policymakers should not be tempted to deviate from initial adjustment plans hoping to compensate for a subsequent market backlash with a boost in popularity because voters do not react positively to broken promises.

Annex 2.1. Description of the Sample Selection

The data set on planned fiscal consolidations used in this chapter was constructed combining three sources of data. First we resorted to the Devries and others (2011) data, which cover fiscal consolidation plans in 17 advanced economies between 1978 and 2009. Despite some methodological differences in the selection of planned consolidation episodes, we lengthened the sample through 2013 using the enlarged narrative data set of Alesina and others (2015). These authors focus on 11 countries (Austria, Belgium, Denmark, France, Germany, Ireland, Italy, Portugal, Spain, United Kingdom, and United States) and expand the narrative data set through 2013. Finally, for the remaining countries and years, we used the European Commission’s Stability and Convergence Programs (first vintage), complemented with country budget sources to further expand coverage to 2015.27

Annex Table 2.1.1.Sample Selection
CountryYearsSource
Australia1978–2009Devries and others 2011
2010–2015Country sources
Austria1978–2009Devries and others 2011
2010–2013Alesina and others 2015
2014–2015Country sources
Canada1978–2009Devries and others 2011
2010–2015Country sources
Denmark1978–2009Devries and others 2011
2010–2013Alesina and others 2015
2014–2015SCP
Finland1978–2009Devries and others 2011
2010–2015SCP
Germany1978–2009Devries and others 2011
2010–2013Alesina and others 2015
2014–2015SCP
Ireland1978–2009Devries and others 2011
2010–2013Alesina and others 2015
2014–2015SCP
Italy1978–2009Devries and others 2011
2010–2013Alesina and others 2015
2014–2015SCP
Japan1978–2009Devries and others 2011
2010–2015Unavailable
Netherlands1978–2009Devries and others 2011
2010–2015SCP
Portugal1978–2009Devries and others 2011
2010–2013Alesina and others 2015
2014–2015SCP
Spain1978–2009Devries and others 2011
2010–2013Alesina and others 2015
2014–2015SCP
Sweden1978–2009Devries and others 2011
2010–2015SCP
United Kingdom1978–2009Devries and others 2011
2010–2013Alesina and others 2015
2014–2015Country sources
United States1978–2009Devries and others 2011
2010–2013Alesina and others 2015
2014–2015Country sources
Note: SCP = Stability and Convergence Program.
Note: SCP = Stability and Convergence Program.

The following is a description of the data used to expand the database until 2015 using the European Commission’s data and country sources. Where one-off items or rounding cause the consolidation size (reported as the primary budget) to differ from the sum of change in revenue and change in expenditure, the difference is divided evenly between the tax size and the expenditure size. These cases are noted with an asterisk (*).

Australia

Australia
YearConsolidation SizeTax SizeExpenditure SizeTax-Based ConsolidationExpenditure-Based Consolidation
20101.40.151.2501
20111.91.350.5510
20123.01.701.3010
20141.20.400.8001
20150.50.55−0.0510
Source: Commonwealth of Australia Budget.
Source: Commonwealth of Australia Budget.

Fiscal consolidation in 2010 amounted to 1.4 percent of GDP with expenditure cuts of 1.25 percent of GDP and tax hikes of 0.15 percent of GDP. Fiscal consolidation in 2010 was motivated by deficit reduction. They key objective of Australia’s deficit exit strategy was to limit expenditure growth by introducing a 2 percent cap on annual real public spending growth until the budget returned to surplus. On the revenue side, the government raised taxes on tobacco and continued implementation of its tax reform agenda.

Fiscal consolidation in 2011 amounted to 1.9 percent of GDP with expenditure cuts of 0.55 percent of GDP and tax hikes of 1.35 percent of GDP. As in 2010, fiscal consolidation in 2011 was motivated by deficit reduction.

Fiscal consolidation in 2012 amounted to 3.0 percent of GDP with expenditure cuts of 1.3 percent of GDP and tax hikes of 1.7 percent of GDP. As in 2011, fiscal consolidation in 2012 was motivated by deficit reduction.

Fiscal consolidation in 2014 amounted to 1.2 percent of GDP with expenditure cuts of 0.8 percent of GDP and tax hikes of 0.4 percent of GDP. As in 2012, fiscal consolidation in 2014 was motivated by deficit reduction, with the hope of achieving a budget surplus by 2023–24. A one-off government grant to the Reserve Bank of Australia in late 2013 contributed 0.6 percent of GDP to the consolidation in 2014.

Fiscal consolidation in 2015 amounted to 0.5 percent of GDP with an expenditure increase of 0.05 percent of GDP and tax hikes of 0.55 percent of GDP. Fiscal consolidation in 2015 was consistent with the government’s medium-term fiscal strategy of returning the budget to surplus, maintaining strong fiscal discipline, strengthening the balance sheet, and redirecting government spending to increase productivity and workforce participation.

Austria

Austria
YearConsolidation SizeTax SizeExpenditure SizeTax-Based ConsolidationExpenditure-Based Consolidation
20150.10.15−0.0510
Source: Stability and Convergence Program.
Source: Stability and Convergence Program.

*Fiscal consolidation in 2015 amounted to 0.1 percent of GDP with tax hikes of 0.15 percent of GDP and an expenditure increase of 0.05 percent of GDP. Structural fiscal consolidation was a key policy area for Austria in 2015. The country’s tax reform package was projected to tangibly reduce the tax burden, while cuts in expenditure on public administration and subsidies, tax fraud, and tax exemptions should generate adequate financing of the reform.

Belgium

Belgium
YearConsolidation SizeTax SizeExpenditure SizeTax-Based ConsolidationExpenditure-Based Consolidation
20140.4−0.901.3001
20150.4−0.651.0501
Source: Stability and Convergence Program.
Source: Stability and Convergence Program.

Fiscal consolidation in 2014 amounted to 0.4 percent of GDP with expenditure cuts of 1.3 percent of GDP and tax cuts of 0.9 percent of GDP. Consolidation, which slowed in 2014, was necessary to ensure debt sustainability in Belgium. In particular, the country’s high public expenditure offers scope for a larger role for spending cuts in fiscal consolidation, especially in social transfers and public consumption. Belgium faced further problems in 2014 because the tax structure was heavily tilted toward labor income and numerous tax expenditures distorted the system.

*Fiscal consolidation in 2015 amounted to 0.4 percent of GDP with expenditure cuts of 1.05 percent of GDP and tax cuts of 0.65 percent of GDP. As in 2014, consolidation in 2015 was motivated by deficit reduction and debt sustainability.

Canada

Canada
YearConsolidation SizeTax SizeExpenditure SizeTax-Based ConsolidationExpenditure-Based Consolidation
20100.40.40010
20110.30.050.2501
20120.2−0.100.3001
20130.40.200.2000
20140.80.250.5501
20150.20.45−0.2510
Source: Canada’s Economic Action Plan Budget.
Source: Canada’s Economic Action Plan Budget.

Fiscal consolidation in 2010 amounted to 0.4 percent of GDP with tax hikes of 0.4 percent of GDP. Fiscal consolidation in 2010 was motivated by deficit reduction and the objective of eliminating the deficit by 2015 through Canada’s Economic Action Plan. The government aimed to limit tax increases and to restrain growth in spending through targeted measures, including national defense spending, the international assistance envelope, and administrative costs.

*Fiscal consolidation in 2011 amounted to 0.3 percent of GDP with expenditure cuts of 0.25 percent of GDP and tax hikes of 0.05 percent of GDP. As in 2010, fiscal consolidation in 2011 was motivated by deficit reduction and the objective of eliminating the deficit by 2015.

Fiscal consolidation in 2012 amounted to 0.2 percent of GDP with expenditure cuts of 0.3 percent of GDP and tax cuts of 0.1 percent of GDP. As in previous years, fiscal consolidation in 2012 was motivated by deficit reduction and the objective of eliminating the deficit by 2015. The one-year Strategic and Operating Review was launched in 2011 with the aim of improving the efficiency and effectiveness of government operations and programs; it yielded savings of $5.2 billion on an ongoing basis.

Fiscal consolidation in 2013 amounted to 0.4 percent of GDP with expenditure cuts of 0.2 percent of GDP and tax hikes of 0.2 percent of GDP. As in previous years, fiscal consolidation in 2013 was motivated by deficit reduction and the objective of eliminating the deficit by 2015. The government further controlled direct program spending by expanding the use of tele-presence technologies to reduce travel expenses within the government, standardizing information technology, modernizing the production and distribution of government publications, and implementing targeted savings in the operations of the Canada Revenue Agency and Fisheries and Oceans Canada.

*Fiscal consolidation in 2014 amounted to 0.8 percent of GDP with expenditure cuts of 0.55 percent of GDP and tax hikes of 0.25 percent of GDP. As in previous years, fiscal consolidation in 2014 was motivated by deficit reduction and the objective of eliminating the deficit by 2015. The government also introduced measures to improve the integrity of the tax system, closing tax loopholes and strengthening tax compliance to ensure fairness.

*Fiscal consolidation in 2015 amounted to 0.2 percent of GDP with an expenditure increase of 0.25 percent of GDP and tax hikes of 0.45 percent of GDP. As in previous years, fiscal consolidation in 2015 was motivated by deficit reduction. The government fulfilled its promise to balance the budget in 2015.

Denmark

Denmark

Denmark did not show evidence of fiscal consolidation in 2014 or 2015.

Finland

Finland
YearConsolidation SizeTax SizeExpenditure SizeTax-Based ConsolidationExpenditure-Based Consolidation
20111.90.751.1501
Source: Stability and Convergence Program.
Source: Stability and Convergence Program.

*Fiscal consolidation in 2011 amounted to 1.9 percent of GDP with expenditure cuts of 1.15 percent of GDP and tax hikes of 0.75 percent of GDP. The government applied a system of spending limits, which proved effective during the recession, and also increased value-added taxes, energy taxes, excise duties on sweets and soft drinks, and the waste tax to tighten fiscal policy.

France

France
YearConsolidation SizeTax SizeExpenditure SizeTax-Based ConsolidationExpenditure-Based Consolidation
20140.50.050.4501
20150.1−0.200.3001
Source: Stability and Convergence Program.
Source: Stability and Convergence Program.

*Fiscal consolidation in 2014 amounted to 0.5 percent of GDP with expenditure cuts of 0.45 percent of GDP and tax hikes of 0.05 percent of GDP. The government’s plans for fiscal consolidation for 2014–16 have been cut back, and the deficit will not be reduced to the Maastricht ceiling (3 percent of GDP) until 2017. The weakness of consolidation efforts in 2014 was primarily due to low tax receipts, a result of weak economic growth and inflation.

*Fiscal consolidation in 2015 amounted to 0.1 percent of GDP with expenditure cuts of 0.3 percent of GDP and tax cuts of 0.2 percent of GDP. To meet the targets set in the Public Finance Planning Act, the government instituted €4 billion in savings measures. The country continued to reduce expenditure, as well as the rate of aggregate tax social security contributions, until 2017.

Germany

Germany

Germany did not show evidence of fiscal consolidation in 2014 or 2015.

Ireland

Ireland
YearConsolidation SizeTax SizeExpenditure SizeTax-Based ConsolidationExpenditure-Based Consolidation
20142.40.32.101
20151.2−0.92.101
Source: Stability and Convergence Program.
Source: Stability and Convergence Program.

Fiscal consolidation in 2014 amounted to 2.4 percent of GDP with expenditure cuts of 2.1 percent of GDP and tax hikes of 0.3 percent of GDP. As a result of the European Union–IMF (EU-IMF) financial assistance program and the National Recovery Plan (2011–14), Ireland emerged from the crisis with a declining fiscal deficit and a stronger fiscal framework. Fiscal consolidation was motivated by European Commission requirements to reduce the deficit in public finances to less than 3 percent of GDP by 2015.

*Fiscal consolidation in 2015 amounted to 1.2 percent of GDP with expenditure cuts of 2.1 percent of GDP and tax cuts of 0.9 percent of GDP. As in 2014, fiscal consolidation was motivated by European Commission requirements to reduce the deficit in public finances to less than 3 percent of GDP by 2015.

Italy

Italy
YearConsolidation SizeTax SizeExpenditure SizeTax-Based ConsolidationExpenditure-Based Consolidation
20140.4−0.20.601
20150.1−0.30.401
Source: Stability and Convergence Program.
Source: Stability and Convergence Program.

Fiscal consolidation in 2014 amounted to 0.4 percent of GDP with expenditure cuts of 0.6 percent of GDP and tax cuts of 0.2 percent of GDP. Since 2008, there has been a commitment to keep public finances in order by increasing revenues and decreasing expenditures. However, stimulus measures introduced in 2014 also included some reductions to the Social Contributions and the Regional Tax on Productive Activities.

*Fiscal consolidation in 2015 amounted to 0.1 percent of GDP with expenditure cuts of 0.4 percent of GDP and tax cuts of 0.3 percent of GDP. The government approved a legislative document for delivering more growth-friendly tax measures, including a reformed property tax, new environmental taxes, a reform of tax expenditures, and new actions against tax evasion.

Japan

Although the CAPB in Japan in 2014 and 2015 improved, this episode is not included in the sample because published data are unavailable.

Netherlands

Netherlands
YearConsolidation SizeTax SizeExpenditure SizeTax-Based ConsolidationExpenditure-Based Consolidation
20111.90.601.3001
20120.10.000.1001
20130.80.650.1510
20150.3−0.600.9001
Source: Stability and Convergence Program.
Source: Stability and Convergence Program.

*Fiscal consolidation in 2011 amounted to 1.9 percent of GDP with expenditure cuts of 1.3 percent of GDP and tax hikes of 0.6 percent of GDP. The primary goal of the Dutch consolidation strategy in 2011 was to restore financial sustain-ability. The government aimed to reach fiscal balance in 2015.

Fiscal consolidation in 2012 amounted to 0.1 percent of GDP with expenditure cuts of 0.1 percent of GDP. As in 2011, the goal of fiscal consolidation in 2012 was to restore financial sustainability. Expenditure cuts were focused on social benefits, the public wage bill, and subsidies.

*Fiscal consolidation in 2013 amounted to 0.8 percent of GDP with expenditure cuts of 0.15 percent of GDP and tax hikes of 0.65 percent of GDP. An additional consolidation package in spring 2012 and already planned measures increased consolidation measures in 2013. This package also contained a number of structural reforms, particularly in housing, pensions, and labor markets.

*Fiscal consolidation in 2015 amounted to 0.3 percent of GDP with expenditure cuts of 0.9 percent of GDP and tax cuts of 0.6 percent of GDP. Fiscal sustain-ability has achieved a positive outlook. The fiscal framework based on a spending ceiling but allowing automatic stabilizers to work on the revenue side has served the Netherlands well, as public debt shifted to less than 70 percent of GDP.

Portugal

Portugal
YearConsolidation SizeTax SizeExpenditure SizeTax-Based ConsolidationExpenditure-Based Consolidation
20140.9−0.41.301
20151.4−0.11.501
Source: Stability and Convergence Program.
Source: Stability and Convergence Program.

Fiscal consolidation in 2014 amounted to 0.9 percent of GDP with expenditure cuts of 1.3 percent of GDP and tax cuts of 0.4 percent of GDP. Fiscal consolidation in 2014 resulted in a general government deficit of 4.5 percent of GDP, lower than the 4.8 percent projected in the Draft Budgetary Plan for 2015. Excluding deficit-increasing one-off measures, the general government deficit fell to 3.3 percent of GDP, leading to an improvement of the baseline for 2015.

Fiscal consolidation in 2015 amounted to 1.4 percent of GDP with expenditure cuts of 1.5 percent of GDP and tax cuts of 0.1 percent of GDP. As in 2014, fiscal consolidation in 2015 was motivated by deficit reduction. The Stability Program maintained the headline target of the Draft Budgetary Plan for 2015 of a headline deficit of 2.7 percent of GDP.

Spain

Spain
YearConsolidation SizeTax SizeExpenditure SizeTax-Based ConsolidationExpenditure-Based Consolidation
20141.70.750.9501
20151.4−0.101.5001
Source: Stability and Convergence Program.
Source: Stability and Convergence Program.

Fiscal consolidation in 2014 amounted to 1.7 percent of GDP with expenditure cuts of 0.95 percent of GDP and tax hikes of 0.75 percent of GDP. Fiscal consolidation in 2014 was motivated by deficit reduction under the Stability Program, which aimed to bring the fiscal deficit to less than 3 percent of GDP in 2016 and to reach the medium-term objective of a balanced budgetary position in structural terms in 2017.

*Fiscal consolidation in 2015 amounted to 1.4 percent of GDP with expenditure cuts of 1.5 percent of GDP and tax cuts of 0.1 percent of GDP. As in 2014, fiscal consolidation in 2015 was motivated by deficit reduction under the Stability Program, which aimed to bring the fiscal deficit to less than 3 percent of GDP in 2016 and to reach the medium-term objective of a balanced budgetary position in structural terms in 2017.

Sweden

Sweden
YearConsolidation SizeTax SizeExpenditure SizeTax-Based ConsolidationExpenditure-Based Consolidation
20110.6−0.81.401
20150.50.10.401
Source: Stability and Convergence Program.
Source: Stability and Convergence Program.

Fiscal consolidation in 2011 amounted to 0.6 percent of GDP with expenditure cuts of 1.4 percent of GDP and tax cuts of 0.8 percent of GDP. The rollback of stimulus measures to local governments contributed the most to the consolidation plan in 2011; thus, effective consolidation is solely expenditure based.

Fiscal consolidation in 2015 amounted to 0.5 percent of GDP with expenditure cuts of 0.4 percent of GDP and tax hikes of 0.1 percent of GDP. Fiscal policy supported activity through the operation of automatic stabilizers. The fiscal response to the extended period of weak economic growth, in addition to some permanent personal and corporate income tax cuts and increased expenditures for sickness benefits and asylum seekers, decreased the fiscal balance.

United Kingdom

United Kingdom
YearConsolidation SizeTax SizeExpenditure SizeTax-Based ConsolidationExpenditure-Based Consolidation
20141.10.051.0501
20151.30.201.1001
Source: Budget Report of Her Majesty’s Treasury and Autumn Statement 2016.
Source: Budget Report of Her Majesty’s Treasury and Autumn Statement 2016.

*Fiscal consolidation in 2014 amounted to 1.1 percent of GDP, primarily with spending cuts of 1.05 percent of GDP and tax hikes of 0.05 percent of GDP. The government’s consolidation plans have been central to the reduction in the deficit. Reductions in expenditure were a result of the spending reduction announced in the Autumn Statement 2013 and the reduced costs of public service pensions.

Fiscal consolidation in 2015 amounted to 1.3 percent of GDP, primarily with spending cuts of 1.10 percent of GDP and tax hikes of 0.2 percent of GDP. Consolidation in 2015 was a continuation of the government’s long-term plan in 2010 to halve the deficit as a share of GDP. With an aim to achieve a surplus in 2019–20, the government aims to undertake about £37 billion of further consolidation measures.

United States

United States
YearConsolidation SizeTax SizeExpenditure SizeTax-Based ConsolidationExpenditure-Based Consolidation
20141.61.100.5010
20150.60.95−0.3510
Source: Budget of the U.S. government.
Source: Budget of the U.S. government.

Fiscal consolidation in 2014 amounted to 1.6 percent of GDP with tax hikes of 1.1 percent of GDP and spending cuts of 0.5 percent of GDP. Policy decisions such as the spending caps in the Budget Control Act of 2011, the increase in tax rates for top earners at the beginning of 2013, and the end of the temporary payroll tax holiday contributed to the government’s deficit reduction strategy.

*Fiscal consolidation in 2015 amounted to 0.6 percent of GDP, primarily with tax hikes of 0.95 percent of GDP and an expenditure increase of 0.35 percent of GDP. Arrangements such as the Bipartisan Budget Act of 2015 aided in avoiding a federal shutdown, partly relieved automatic federal spending cuts, and relaxed the federal debt limit. Government purchases, including consumption and gross investment, at the federal, state, and local levels, added to the consolidation.

Annex 2.2. Robustness of Results in a Homogeneous Sample

Combining three sources of data on narrative fiscal consolidations raises two potential problems. The first one is inherent to the narrative methodology, which is itself subjective. While potential (judgmental) measurement errors could have affected the three different sources of data, this analysis assumes that these would be evenly distributed across the three sources because the three data sources followed almost identical criteria for the selection of episodes. The second potential problem arises from possible structural breaks as a consequence of mixing similar but different data sources between 1978 and 2015. To make sure that the main results are robust, the analyses were replicated in the homogeneous sample of Devries and others (2011), which includes data from 17 advanced economies between 1978 and 2009 (see Annex Tables 2.2.1 and 2.2.2). The analysis was also replicated using a sample of promise gaps calculated in a sample built only with data from the Stability and Convergence Programs submitted by the 28 EU member states to the European Commission between 1998 and 2015 (see Annex Tables 2.2.3 and 2.2.4). As shown below, all the results from these tests on two alternative homogeneous samples confirm the robustness of the main findings.

Annex Table 2.2.1.Economic Determinants of Consolidation Promise Gaps, Devries’ Database, 1978–2009
Specification
Regressors(1)(2)(3)(4)
Lagged Debt−0.032***−0.051***
(0.007)(0.012)
Change in Output Gap−0.611***−0.619***
(0.082)(0.119)
Lagged GDP Forecast Error−0.404**−0.331***
(0.166)(0.123)
Constant1.892***0.181−0.1343.479***
(0.638)(0.450)(0.736)(0.908)
Observations1621659696
R20.2250.3680.4280.712
Source: Authors’ calculations.Note: Dependent variable = consolidation promise gaps as defined in the main text. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.**p < .05; ***p < .01.
Source: Authors’ calculations.Note: Dependent variable = consolidation promise gaps as defined in the main text. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.**p < .05; ***p < .01.
Annex Table 2.2.2.Economic and Political Determinants of Consolidation Promise Gaps, Devries’ Database, 1978–2015
Specification
Regressors(1)(2)(3)(4)
Lagged Debt−0.073***−0.051***−0.061**−0.017
(0.017)(0.013)(0.025)(0.053)
Change in Output Gap−0.418**−0.617***−0.440−0.346
(0.157)(0.121)(0.271)(0.536)
Recent Elections−0.377*−1.327
(0.393)(1.779)
Margin of Majority−0.420*−7.306
(2.847)(8.870)
Government Effectiveness−4.424**−2.952
(1.735)(5.379)
Lagged GDP Forecast Error−0.268**−0.329**−0.436−0.048
(0.126)(0.125)(0.323)(0.445)
Constant7.312***3.656**11.722***18.539**
(2.478)(1.504)(2.890)(7.286)
Observations73964129
R20.6360.7130.8680.798
Source: Authors’ calculations.Note: Dependent variable is consolidation promise gaps as defined in the main text. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.*p < .1; **p < .05; ***p < .01.
Source: Authors’ calculations.Note: Dependent variable is consolidation promise gaps as defined in the main text. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.*p < .1; **p < .05; ***p < .01.
Annex Table 2.2.3.Economic Determinants of Consolidation Promise Gaps, Stability and Convergence Program Database, 1998–2015
Specification
Regressors(1)(2)(3)(4)
Lagged Debt−0.026***−0.026***
(0.009)(0.008)
Change in Output Gap−0.242***−0.272***
(0.052)(0.053)
Lagged GDP Forecast Error−0.059−0.107**
(0.046)(0.043)
Constant1.850***0.5710.5091.906***
(0.708)(0.504)(0.503)(0.635)
Observations252253224224
R20.1480.1960.1220.263
Source: Authors’ calculations.Note: Dependent variable is consolidation promise gaps as defined in the main text. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.*p < .1; **p < .05; ***p < .01.
Source: Authors’ calculations.Note: Dependent variable is consolidation promise gaps as defined in the main text. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.*p < .1; **p < .05; ***p < .01.
Annex Table 2.2.4.Economic and Political Determinants of Consolidation Promise Gaps, Stability and Convergence Program Database, 1998–2015
Specification
Regressors(1)(2)(3)(4)
Lagged Debt−0.026***−0.026***−0.035***−0.033***
(0.008)(0.009)(0.009)(0.009)
Change in Output Gap−0.299***−0.271***−0.240***−0.266***
(0.054)(0.053)(0.055)(0.057)
Recent Elections−1.071*−1.289**
(0.586)(0.632)
Margin of Majority−0.110**−0.107**−0.091**−0.092**
(0.043)(0.043)(0.045)(0.045)
Government Effectiveness−1.330−3.654
(2.353)(2.775)
Lagged GDP Forecast Error−1.798**−1.795**
(0.898)(0.889)
Constant1.922***2.620*5.479***7.453***
(0.631)(1.414)(1.731)(2.257)
Observations224224188188
R20.2750.2640.3140.336
Source: Authors’ calculations.Note: Dependent variable = consolidation promise gaps as defined in the main text. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.*p < .1; **p < .05; ***p < .01.
Source: Authors’ calculations.Note: Dependent variable = consolidation promise gaps as defined in the main text. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.*p < .1; **p < .05; ***p < .01.
Annex 2.3. The Consequences of Consolidation Promise Gaps: Regression Analysis

To test the reaction of markets and voters to positive and negative promise gaps, the analysis follows a static approach and estimates equation (2.3.1):

where Yit denotes either five-year government bond spreads (relative to Germany) or government popularity in country i at time t; CPGit is the consolidation promise gap variable (in country i at time t); Xit is a vector of control variables (that varies with the dependent variable) and includes real GDP growth, public debt (as a percentage of GDP), changes in unemployment, and 21-month real GDP growth forecast errors to control for the macroeconomic environment and minimize endogeneity concerns due to omitted-variables bias. ϑ, π are unknown parameters to be estimated. εit is an independent and identically distributed disturbance term satisfying usual assumptions. Equation (2.3.1) is estimated by ordinary least squares with robust standard errors clustered at the country level.

Annex Table 2.3.1 reports results for the five-year bond spread reaction to fiscal promise gaps, and confirms that financial markets tend to punish fiscal underperformance. Once governments plan for a fiscal adjustment, markets will follow closely and react negatively when they are not able to meet the targets. Annex Table 2.3.2 reports the results of using government popularity as the dependent variable. In general, voters do not seem to react strongly to consolidation gaps, regardless of the sign (that is, whether one inspects under- or overperformers).

Annex Table 2.3.1.Market Reaction to Consolidation Promise Gaps
Dependent Variable = Five-Year Bond SpreadsPerformance
UnderperformersOverperformers
Consolidation Promise Gap0.283***0.247*0.260**0.0300.0770.027
(0.102)(0.140)(0.102)(0.329)(0.325)(0.303)
Real GDP Growth−0.058−0.370
(0.152)(0.262)
Public Debt0.0130.039**
(0.008)(0.016)
Constant1.101***1.192***−0.0021.627***2.138***−1.970
(0.243)(0.343)(0.750)(0.522)(0.628)(1.557)
Observations555555303030
R20.1280.1300.1660.0000.0690.180
Source: Authors’ calculations.Note: Dependent variable = five-year government bond spreads relative to Germany. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.*p < .1; **p < .05; ***p < .01.
Source: Authors’ calculations.Note: Dependent variable = five-year government bond spreads relative to Germany. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.*p < .1; **p < .05; ***p < .01.
Annex Table 2.3.2.Voters’ Reaction to Consolidation Promise Gaps
Performance
Dependent Variable = Government PopularityUnderperformersOverperformers
Consolidation Promise Gap−1.775**−0.8660.438−1.029
(0.829)(1.896)(1.465)(2.054)
Lagged GDP Forecast Error4.295***3.0230.815−4.351*
(1.235)(3.533)(1.483)(2.424)
Change in Unemployment Rate−2.4891.267
(1.904)(3.170)
Constant37.307***41.289***50.506***66.202***
(5.831)(10.582)(4.771)(6.985)
Observations66365735
R20.6030.6810.7480.792
Source: Authors’ calculations.Note: Dependent variable = government popularity as defined in the main text. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.*p < .1; **p < .05; ***p < .01.
Source: Authors’ calculations.Note: Dependent variable = government popularity as defined in the main text. Robust standard errors clustered at the country level are in parentheses. Time and country fixed effects are omitted for reasons of parsimony.*p < .1; **p < .05; ***p < .01.
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The authors are grateful to Vitor Gaspar, Ben Clements, and the participants in the IMF Fiscal Affairs Department seminar for useful comments and suggestions.

In addition to elections and political fragmentation, fiscal consolidations could be influenced by the government’s ideology. Although ideology is not relevant for explaining the size of consolidation promise gaps in the sample in this chapter, it has been shown to affect the composition of the budget. A number of scholars (Boix 1997; Franzese 2002; Mulas-Granados 2003, 2006; Tavares 2004; Konishi 2006; Mierau, Jong-A-Pin, and de Haan 2007; Angelopoulos, Malley, and Philippopoulos 2012) have shown that left-wing governments may prefer revenue-based adjustments to ensure financing for the welfare state, whereas right-wing governments may opt for expenditure-based consolidations.

According to Müller and Strom (1999) political parties can be guided by two objectives: the pursuit of policy and the pursuit of office. Pure policy seekers pursue the maximum leverage over public policy outcomes; pure office seekers strive to win and retain office as an end in itself or for the perks it affords. Normally, parties in government try to maximize both objectives at the same time, because staying in office guarantees further influence on the policy agenda, and delivering on the policies preferred by citizens typically increases the chances of remaining in office.

For a discussion of alternative ways to identify fiscal consolidations using the narrative and positive approaches, see Afonso and Jalles (2014) and Escolano and others (2014).

Some caveats surrounding the traditional CAPB approach have been highlighted recently (see Afonso and Jalles 2014). In particular, the CAPB approach could bias empirical estimates toward finding evidence of non-Keynesian effects. Many nonpolicy factors influence the CAPB and can lead to erroneous conclusions regarding fiscal policy changes. For example, a stock price boom raises the CAPB by increasing capital gains tax revenue and tends to coincide with an expansion of private demand (Morris and Schuknecht 2007). Even when the CAPB accurately measures fiscal actions, these actions could include discretionary responses to economic developments.

Note that the narrative approach used by Devries and others (2011) uses historical accounts from OECD and EU annual reports describing what happened to the budget deficit in a particular country and period, but they do not go into the details of policymakers’ intentions, discussions, and congressional records. This differs from the approach used in Romer and Romer (2010), who identify exogenous tax policy changes by carefully analyzing U.S. congressional documents.

A description of this update for each country and consolidation year is available in the Annex 2.1.

For robustness purposes, we also define the dependent variable using changes in the cyclically adjusted budget balance or changes in the primary balance and results are qualitatively similar. These results are available upon request from the authors.

The use of general government data allows us to compare countries with different degrees of fiscal decentralization. In some countries, like Canada or Spain, regional government finances are sizable and their fiscal accounts are not always correlated with those of the central government.

The sustainability factor is defined as the difference between the actual primary balance and the debt-stabilizing primary balance (see Escolano and others [2014] for further details). Higher values of the initial sustainability factor imply that countries have primary balances close to (or above) the debt-stabilizing optimum level, and therefore they are not under fiscal stress.

Output gap is calculated as actual minus potential GDP using data from the World Economic Outlook database.

We use the 3-, 9-, 15-, and 21-month GDP forecast errors from the World Economic Outlook database. Forecast errors are defined as the difference between actual real GDP growth and forecasted real GDP growth. A large positive forecast error implies that GDP grew more than predicted. All forecast error variables yield qualitatively similar results; hence, the chapter presents regression estimates using only one, the 9-months ahead variant.

We also explored the role of government ideology but results were inconclusive because more than two-thirds of consolidation episodes were planned and implemented by centrist governments. Results are available from the authors upon request.

This latter indicator refers to actual months left to next election, after the fact, while the variable “more years left in current term” is observed ex ante. Both are informative.

A likelihood ratio test was used to examine the “sphericity” case, allowing for sampling variability in the correlations. This test comfortably rejects sphericity at the 1 percent level. The first factor explains almost 40 percent of the variance in the standardized data (see Table 2.3).

Government effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. See Worldwide Governance Indicators at http://info.worldbank.org/governance/wgi/index.aspx#home.

The source for each component variable is the Database on Political Institutions 2015 (Cruz, Keefer, and Scartascini 2015).

The sources for each component variable is the World Bank’s World Governance Indicators.

PCA is based on the classical covariance matrix, which is sensitive to outliers. Here we conduct a robust estimation of the covariance matrix. A well-suited method is the minimum covariance determinant (MCD) that considers all subsets containing h percent of the observations and estimates the variance of the mean on the data of the subset associated with the smallest covariance matrix determinant. Specifically, we implement Rousseeuw and van Driessen’s (1999) algorithm. When we computed the same indices with the MCD version, we obtained similar results, suggesting that outliers are not driving the factor analysis.

Uniqueness of a variable is the share of its variance that is not accounted for by all the factors.

We do not include the sustainability factor because it is collinear with initial levels of debt.

From the Proximity PCA we take the months to next elections and build a variable that identifies whether an election has taken place recently (that is, in the past year); from the Strength PCA we take the margin of majority; and from the Accountability PCA we take the indicator of government effectiveness.

In Annex 2.2 we repeat the estimation but constrain the time span to 2009 to match exactly the Devries and others (2011) sample. Results are qualitatively similar and generally marked by more statistical significance throughout.

“Government popularity” refers to the average percentage of approval or support of the executive leader (president or prime minister) per year, or for the EU countries, the percentage of respondents who trust the national government (the EU conducts a survey of its member countries and this is the question that is asked).

Annex 2.3 presents the results of a static approach to testing the reactions of markets and voters to positive and negative promise gaps.

Results are not shown for reasons of parsimony but are available upon request.

The presence of a lagged dependent variable and country fixed effects could bias the estimation of γjk and βk in small samples (Nickell 1981). However, in this case, this is not a problem because the finite sample bias is about 0.03 (that is, 1/T, where T is 38).

By construction, even if a government has sustained a fiscal adjustment over various years (for example, the United Kingdom until 2015), the data would be compiled in tranches, corresponding to the three sources of data used.

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