Inequality and Fiscal Policy
Chapter

Chapter 8. Fiscal Consolidation and Inequality in Advanced Economies: How Robust Is the Link?

Author(s):
Benedict Clements, Ruud Mooij, Sanjeev Gupta, and Michael Keen
Published Date:
September 2015
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Author(s)
Davide Furceri, João Tovar Jalles and Prakash Loungani 

[We need a] fiscal policy that focuses not only on efficiency, but also on equity, particularly on fairness in sharing the burden of adjustment, and on protecting the weak and vulnerable.

Christine Lagarde (2012)

Introduction

Fiscal policy played a key role in the response to the global financial crisis. At its onset, many Group of 20 countries implemented comprehensive support packages, mainly based on expenditure hikes, to try to stave off the crisis. Combined with the decline in tax revenues (as incomes fell), the increase in social spending (particularly unemployment benefits), and the costs of financial bailouts of banks and companies, the net result was a sharp rise in government debt. Public debt rose, on average, from 70 percent of GDP in 2007 to slightly more than 100 percent of GDP in 2014—its highest level in 50 years (IMF 2014b).

Concerned about the long-term sustainability of public finances, many governments across the world have begun implementing budgetary consolidation measures. The effects of such fiscal consolidations on output remain a matter of some debate, revolving in part around the measurement of fiscal consolidation. Using the cyclically adjusted primary balance (CAPB), some work suggests that fiscal consolidation could be expansionary (for example, Alesina and Perotti 1995; Alesina and Ardagna 2010, 2012).1 In contrast, using a narrative approach to measuring consolidation, Guajardo, Leigh, and Pescatori (2014) argue that consolidations are contractionary.

In addition to the aggregate effects of fiscal consolidations, the distributional impacts are also starting to receive attention. Many studies suggest that fiscal consolidation episodes are usually associated with increases in income inequality (Roe and Siegel 2011; Ball, Leigh, and Loungani 2013; Ball and others 2013; Bova and others 2013; Agnello and Sousa 2014; Agnello and others 2014).

This chapter examines the robustness of the link between fiscal consolidation and inequality. This relationship is important for several reasons. First, as noted above, the aggregate effects of fiscal consolidation appear to depend on how consolidation is measured. Are the distributional effects also sensitive to the measurement of consolidation? Second, the measurement of inequality is also the subject of some controversy. Many studies use the Standardized World Income Inequality Database (SWIID). But there are concerns about this data set because of the extensive use of interpolation and other assumptions to fill in missing data (Jenkins 2014). Thus, this chapter examines how robust the consolidation-inequality link is to the use of alternate measures of inequality. A third contribution of the chapter is to revisit the issues of whether spending-based and tax-based consolidations have different effects on inequality and whether the consolidation-inequality link is symmetric (that is, do fiscal expansions lower inequality?). Fourth, a number of technical robustness checks are carried out that confirm the validity of this chapter’s findings. Finally, in general, the distributional effects of consolidation must be balanced against the potential longer-term benefits that consolidation can confer as interest rates decline and the lighter burden of interest payments permits cuts in distortionary taxes. It should be recognized that there is scope for improving the targeting and efficiency of public programs and that, in this case, fiscal adjustments would not unavoidably run into such a trade-off between efficiency and equity.

The remainder of this chapter is organized as follows: First, the definitions and sources of the data are detailed, then the econometric methodology is presented. The main empirical findings are analyzed, followed by a conclusion and a discussion of some policy considerations.

Data

Inequality and Income Shares

Many studies use the SWIID because it provides long time series of Gini coefficients for a large group of countries. However, problems with comparability of data across years and countries, and with the imputation methodology used, have long been noted (Atkinson and Brandolini 2001) and have recently been confirmed in a comprehensive assessment by Jenkins (2014).

In light of such concerns, this analysis tests the robustness of the consolidation-inequality link using several measures of distributional outcomes. They comprise (1) the Gini coefficient for disposable income (both gross and net concepts), taken from SWIID; (2) the shares of wages and profits in GDP, obtained from the Organisation for Economic Co-operation and Development (OECD) Analytical Database; (3) the Gini coefficient for disposable income retrieved from the OECD Stats; and (4) the combined “all the Ginis” index compiled by Milanovic (2014) by merging several sources.2

Fiscal Consolidation Episodes

The literature addressing the identification of fiscal episodes is vast and has, for a long time, relied on changes in the CAPB. Some caveats surrounding this approach have been highlighted recently. In particular, the CAPB approach could bias empirical estimates toward finding evidence of non-Keynesian effects (Afonso and Jalles 2014). Many nonpolicy factors, such as price fluctuations, influence the CAPB and can lead to erroneous conclusions about the presence of fiscal policy changes.3 In addition, even when the CAPB accurately measures fiscal actions, these actions include discretionary responses to economic developments, such as fiscal tightening to restrain rapid domestic demand growth.

With these considerations in mind, an alternative “narrative approach” is considered, which relies on the identification of fiscal episodes based on concrete policy decisions. The episodes are identified by looking at IMF and OECD historical reports and by checking what countries intended to do when the reports were published.4 This policy-action-based approach makes use of descriptive historical facts that usually depict what happened to the deficit in a particular period but do not go into the details of policymakers’ intentions and discussions or congressional records. Proponents of this approach argue that the estimated size of the fiscal measures during the identified episodes have the advantage of not being affected by the cycle (since their construction is bottom up), can minimize identification problems,5 and are unlikely to embody risks of reverse causation (Guajardo, Leigh, and Pescatori 2014). However, the narrative approach could also have some drawbacks: it largely relies on judgment calls, and it may not entirely eliminate endogeneity problems (that is, fiscal policy reacting to output performance and not the other way around).

The analysis that follows thus relies on both the narrative and CAPB-based approaches. For the narrative approach, the analysis uses the publicly available data set compiled by Devries and others (2011), which uses the policy-action-based method for 17 advanced economies between 1978 and 2009.6 For the CAPB-based approach, the analysis relies on the following:

  • Alesina and Ardagna (1998), who adopt a fiscal episode definition that allows some stabilization periods to last only one year. More specifically, they define a fiscal episode to be a change in the CAPB that is at least 2 percentage points of GDP in one year or at least 1.5 percentage points, on average, in the past two years.

  • Giavazzi and Pagano (1996), who decrease the probability of fiscal adjustment periods that last only one year by using a limit of 3 percentage points of GDP for a single year consolidation. They propose using the cumulative changes in the CAPB that are at least 5, 4, or 3 percentage points of GDP in, respectively, four, three, or two years, or 3 percentage points in one year.

  • Afonso (2010), who defines the occurrence of a fiscal episode as being when either the change in the CAPB is at least one-and-a-half times the standard deviation (from the panel sample of 17 countries) in one year, or when the change in the CAPB is at least one standard deviation, on average, in the past two years.

Table 8.1 reports the fiscal episodes identified according to the four alternative methods. The number of fiscal contractions ranges from 29 in Afonso’s (2010) approach, to 43 in Alesina and Ardagna’s (1998) approach. In Devries and others’ (2011) narrative approach, the magnitude of the fiscal consolidation episode ranges between 0.1 percent and about 5 percent of GDP, with an average of about 1 percent of GDP. Moreover, it reports many more years in which fiscal contractions take place (171 years against an average of 70 for the CAPB approaches). For fiscal consolidations, the average duration of the reported episodes is, on average, 1.7 years for the CAPB approaches and about 3.8 years for the narrative approach. Finally, the three CAPB-based methods agree with the total number of years from the narrative approach about 50 percent of the time.

Table 8.1Fiscal Episodes Based on the Change in the CAPB and on the Narrative Approach
Narrative

Approach
CAPB Approaches
CountryDevries and others (2011)Alesina and Ardagna (1998)Giavazzi and Pagano (1996)Afonso (2010)
ContractionsExpansionsContractionsExpansionsContractionsExpansionsContractions
Australia1985–88, 1994–991975, 20091987–8820091987–8820091987–88
Austria1980–81, 1984, 1996–97, 2001–021976, 20041984, 1997, 2001, 20051976, 2004199720041984, 1997, 2001, 2005
Belgium1982–87, 1990–971981, 2005, 20091982–85, 1993, 20061981, 2005, 20091982–871981, 2005, 20091982–85
Canada1984–971977, 2001–02, 20091981, 1986–87, 1996–971975, 1977–78, 2002, 20091987, 1996–981975, 20091987, 1996–97
Denmark1983–86, 19951975–76, 1982, 1990–91, 1994, 2009–101983–861975–76, 1982, 1991, 20101983–871975–76, 1982, 1991, 20101983–86
Finland1992–971978–79, 1987, 1991–92, 2009–101976–77, 1981, 1984, 1988, 1996–97, 2000–011979–80, 1991–93, 20101976–77, 1997–98, 2000–011978–79, 1987, 1991–92, 20101976–77, 1996–97, 2000–01
France1987–92, 1995–20002009–102009–102009–10
Germany1982–84, 1991–2000, 2003–071975, 1990–91, 2001–021975, 1991, 2001–031975, 1990–91, 2001–02
Ireland1982–88, 20091974–75, 1978–79, 1995, 2001–02, 2007–091976–77, 1983–84, 1988, 20101975, 1979, 2001–03, 2007–101976–77, 1983–86, 1988–89, 20101974–75, 1978–79, 2001–02, 2007–091976–77, 1983–84, 1988, 2010
Italy1991–98, 2004–071981, 20011977, 1982–83, 1992–9320011977, 1982–83, 1992–941981, 20011977, 1982–83, 1992–93
Japan1980–83, 1997–98, 2003–071975, 1994–95, 1998, 2009–101998–99, 2005–061993–95, 1998, 2009–101998–2000, 2005–071993–94, 1998, 2009–101999–2000, 2006–07
Netherlands1981–88, 1991–93, 2004–052001–02, 2009–101991, 19932002, 20101991, 19932002, 2009–101991
Portugal1983, 2000–071978–79, 1985, 1990, 1993, 2005, 2009–101977, 1983–84, 1986, 1988, 1992, 1995, 20061978–80, 2005, 2009–101977, 1983–84, 19861978–79, 1993, 2005, 2009–101977, 1983–84, 1986, 1988, 1992
Spain1983–84, 1989–972008–091986, 1987, 20102008–1019872008–091987
Sweden1984, 1993–981974, 1979, 1991–93, 2002–03, 20101976, 1983–84, 1987, 1996–971974, 1979–80, 1991–94, 2002–031984, 1987, 1996–991974, 1979, 1991–93, 20021984, 1987, 1996–97
United Kingdom1980–82, 1994–991972–73, 1990, 1992–93, 2001–02, 2009–101981, 1997–98, 20001972–75, 1992–94, 2001–04, 2009–101981–82, 1997–20001972–73, 1992–93, 2001–03, 2009–101981, 1997–98
United States1980–81, 1985–982001–02, 2007–082001–02, 2007–101974, 2001–02, 2007–08
Years with Episodes171957995737859
Average Duration (years)3.81.61.52.02.11.61.6
Source: All measures computed by the authors except for the Devries and others (2011) column.Note: See chapter text for definitions. CAPB = cyclically adjusted primary balance.

Methodology

To estimate the distributional impact of fiscal consolidation episodes in the short and medium terms, the exercise follows the method proposed by Jorda (2005), which consists of estimating impulse response functions directly from local projections. For each period k, equation (8.1) is estimated using annual data:

in which k = 1, …, 8 and G represents one of the measures of distributional outcomes; is a dummy variable equal to 1 for the starting date of a consolidation episode in country i at time t and is 0 otherwise; αik are country fixed effects; Timetk is a time trend; and βk measures the distributional impact of fiscal consolidation episodes for each future period k. Since fixed effects are included in the regression, the dynamic impact of consolidation episodes should be interpreted as being compared with a baseline country-specific trend. In the main results, the lag length (l) is set at 2, even if the results are extremely robust to different numbers of lags included in the specification (see robustness checks and sensitivity presented in the next section). Equation (8.1) is estimated using the panel-corrected standard error estimator (Beck and Katz 1995).

Impulse response functions are obtained by plotting the estimated βk for k = 1, …, 8, with confidence bands computed using the standard deviations of the estimated coefficients βk. Although the presence of a lagged dependent variable and country fixed effects may, in principle, bias the estimation of γjk and in small samples (Nickell 1981), the length of the time dimension mitigates this concern.7 Reverse causality is addressed by estimating the distributional effect in the years that follow a fiscal consolidation episode. In addition, robustness checks for endogeneity confirm the validity of the results.8

An alternative way of estimating the dynamic impact of fiscal consolidation episodes is to estimate an autoregressive distributed lag (ARDL) equation of changes in inequality and consolidation episodes and to compute the impulse response functions from the estimated coefficients (Romer and Romer 1989; Cerra and Saxena 2008). However, the impulse response functions derived using this approach tend to be sensitive to the choice of the number of lags, thus making the impulse response functions potentially unstable. In addition, the significance of long-lasting effects with ARDL models can be driven simply by the use of one-type-of-shock models (Cai and Den Haan 2009). This is particularly true when the dependent variable is highly persistent, as in this analysis. In contrast, the approach used here does not suffer from these problems because the coefficients associated with the lags of the change in the dependent variable enter only as control variables and are not used to derive the impulse response functions, and because the structure of the equation does not impose permanent effects. Finally, confidence bands associated with the estimated impulse response functions are easily computed using the standard deviations of the estimated coefficients, and Monte Carlo simulations are not required.

Empirical Results

Gini Coefficient for Disposable Income

The impacts of fiscal consolidation (using Devries and others’ [2011] narrative approach to identifying episodes) on the four alternative definitions of the Gini index are shown in Figure 8.1. Each panel shows the estimated impulse response function and the associated one-standard-error bands (dotted lines). The horizontal axes measure years after the start of the fiscal consolidation episode.9

Figure 8.1Impact of Fiscal Consolidation on Inequality: A Comparison of Different Gini Indices

Source: Authors’ calculations.

Note: Dashed lines show one-standard-error confidence bands. SWIID = Standardized World Income Inequality Database.

In general, fiscal consolidation is followed by a persistent rise in income inequality.10 The Gini index increases by an average (across different proxies) of about 0.2 units (corresponding to a Gini index point) in the short term (one year after the occurrence of the consolidation episode) and by nearly 0.9 units in the medium term (eight years after the occurrence of the consolidation episode).11 The short-term response is consistent with Agnello and Sousa (2014), who find that fiscal consolidations lead to an increase in the Gini of about 0.3 units. A possible explanation for why persistent impulse response functions could be observed relates to the composition of the fiscal adjustment, discussed below. For example, permanent cuts to social assistance benefits can structurally reduce the distributional impact of government spending. Another explanation could be labor market changes triggered by macroeconomic developments or public policies (for example, a rise in unemployment during times of fiscal retrenchment would affect the market-income channel). However, further work is needed on the channels that lead to these outcomes, in particular the permanent versus temporary effects of fiscal adjustments.

The results of several additional robustness checks are shown in Figure 8.2. These results are shown for one particular measure of inequality, the SWIID net Gini index, but similar findings hold for the other measures as well. First, equation (8.1) is reestimated by including time fixed effects to control for specific time shocks, such as those affecting world interest rates. The results for this specification remain statistically significant and broadly unchanged (Figure 8.2, panel 2).

As shown by Teulings and Zubanov (2013), a possible bias introduced by estimating equation (8.1) using country fixed effects is that the error term of the equation may have a non-zero expected value, due to the interaction of fixed effects and country-specific arrival rates of consolidation episodes. This would lead to a bias in the estimates that is a function of k. To address this issue and check the robustness of the findings, equation (8.1) is reestimated by excluding country fixed effects from the analysis. The results reported in Figure 8.2, panel 3 suggest that this bias is negligible (the difference in the point estimate is small and not statistically significant).

Figure 8.2Sensitivity and Robustness: Different Sets of Regressors Using SWIID Net Gini Index (Interpolated)

Source: Authors’ calculations.

Note: Dashed lines show one-standard-error confidence bands.

Estimates of the impact of consolidation on inequality could be biased because of endogeneity, given that unobserved factors influencing the dynamics of the Gini coefficient may also affect the probability of the occurrence of a consolidation episode. In particular, a significant deterioration in economic activity, which would affect unemployment and inequality, may lead to an increase in the public debt ratio via automatic stabilizers, and therefore increase the probability of consolidation. To address this issue, equation (8.1) is augmented to control for (1) contemporaneous and past crisis episodes (banking, debt, and currency crises); (2) change in economic activity (proxied by real GDP growth); and (3) change in the total unemployment rate. The results of this exercise are reported in Figure 8.2, panel 4 and confirm the robustness of the previous findings.12

As an additional sensitivity check, equation (8.1) is reestimated for different lags (l) of changes in the Gini coefficient. The results confirm that previous findings are not sensitive to the choice of the number of lags (results are available upon request).

Finally, as noted earlier, another concern is that the different Gini alternatives use interpolations to fill in gaps in the inequality data.13 Although interpolations increase the number of observations, they also add some concerns about data quality. This analysis, therefore, uses raw data and panel regressions are estimated with the fifth year forward difference of the relevant Gini index as the dependent variable. The results are very robust (Table 8.2). Moreover, these results are also robust to a number of more technical checks as shown Table 8.2, including the inclusion of time fixed effects, the exclusion of country fixed effects, and the inclusion of a different set of control variables in the estimated regressions.

Table 8.2Panel Estimations of Different Gini Indices
SpecificationSWIID Gini Index, GrossSWIID Gini Index, NetOECD Gini IndexMilanovic’s All Ginis Index
Baseline1.332**

(0.646)
0.585**

(0.297)
0.595***

(0.185)
1.491***

(0.418)
Robustness
Time Fixed Effects0.672

(0.631)
0.241

(0.293)
0.598***

(0.195)
1.822***

(0.544)
Without Country Fixed Effects1.392**

(0.640)
0.564*

(0.301)
0.453*

(0.263)
1.478***

(0.459)
Additional Controls0.915

(0.699)
0.487

(0.313)
0.685***

(0.219)
1.729***

(0.476)
Source: Authors’ calculations.Note: The dependent variable is the fifth year forward difference of the corresponding inequality proxy as identified in the first row. The coefficients presented in the table denote the estimates of the consolidation episode (narrative approach). Each entry corresponds to an independent regression in which nonrelevant regressors (including a constant term) are omitted for reasons of parsimony. Robust standard errors are in parentheses.* p < 0.1; ** p < 0.05; *** p < 0.01.

The Role of the Composition of Consolidation Packages: Spending Based versus Tax Based

Does the composition of fiscal consolidation (spending based versus tax based) matter for inequality? The literature demonstrates a broad consensus that tax-based consolidations are typically more contractionary than spending-based ones, particularly in the medium term (Alesina and Ardagna 2010; IMF 2010a). In normal times, spending cuts tend to be more successful than tax increases at enhancing economic growth (Alesina and Perotti 1995; Alesina and Ardagna 2012) because spending cuts are generally perceived to be more credible by economic agents (Hernandez de Cos and Moral-Benito 2012).14 At the same time, however, most of the direct redistributive impact of fiscal policy in advanced economies has been achieved through the expenditure side of the budget—especially non-means-tested transfers (Bastagli, Coady, and Gupta 2012). Therefore, whether tax-based or spending-based consolidations are more harmful for income inequality is not clear a priori.

To evaluate whether the composition of the consolidation package matters, equation (8.1) is separately estimated for tax-based and spending-based adjustments, by constructing starting dummies of tax- and spending-consolidation episodes (in the Devries and others 2011 data set, the average magnitude of both spending- and tax-based consolidations is about 1 percent of GDP). The results presented in Figure 8.3 for a selected measure of income inequality, the SWIID net Gini index (though results are consistent across alternative proxies), show that spending- and tax-based programs have similar effects in the short and medium terms. This result, however, has to be treated with caution given that most past fiscal adjustments have involved both spending cuts and tax increases. To address this issue, following Guajardo, Leigh, and Pescatori 2014, equation (8.1) is separately estimated for (1) episodes in which tax-based adjustments have been larger than spending-based adjustments and (2) episodes in which spending-based adjustments have been larger than tax-based adjustments. These correspond to an alternative definition of tax- and spending-based consolidations. The results obtained with this exercise (available upon request) suggest that spending-based consolidations tend to have larger effects. In particular, the short-term effect of fiscal consolidations on income inequality is about 0.24 percent after one year for spending-based consolidations and 0.09 percent for tax-based ones.15 The medium-term effect after eight years is about 1.05 percent and about 0.13 percent, respectively, for spending-based and tax-based consolidations. At this point it is important to go back to the issue of persistence and mention that labor market indicators such as the unemployment rate do display some hysteresis given that they do not return to their initial levels after a fiscal adjustment that is mainly tax driven (see IMF 2014a, p. 24, for details).

Figure 8.3Composition of Fiscal Adjustments Using SWIID Net Gini Index (Interpo-lated): Tax versus Spending Based

Source: Authors’ calculations.

Note: Dashed lines show one-standard-error confidence bands.

Wages versus Profit and Rent Income

Another way to assess the distributional effects of fiscal consolidation measures is to examine their effects on different types of income. The traditional categories that make up total income are wages, profits, and rents, harkening back to times when the roles of workers, capitalists, and landlords were fairly distinct. Although these distinctions have eroded somewhat, the split between wages and other forms of income is a starting point for describing how income is divided between Main Street and Wall Street. To assess the effects of fiscal consolidations on the distribution of income between wage earners and others, equation (8.1) is estimated for the share of wage income in GDP and the share of profits in GDP.

The results of this empirical exercise are reported in Figures 8.4 and 8.5, respectively, for wages and profits. The results suggest that fiscal consolidation measures typically reduce the slice of the pie going to wage earners and increase the slice of the pie going to profit recipients. These findings are consistent with the results presented in panels 4, 5, and 6 of these two figures, which suggest that fiscal consolidations have a larger negative effect on the level of inflation-adjusted wage income than on the level of inflation-adjusted profit and rent incomes. Moreover, as before, spending-based adjustments seem to be the most detrimental, at least as far as wage incomes are concerned. In the case of profits, the distinction does not matter much as evidenced by confidence bands above and below the horizontal axis. Future work could consider adjusting the wage share to take into account the incomes of the self-employed (see Gollin 2002) and examining how fiscal consolidations affect the private wage share, since many of these episodes are likely to include measures that directly contain the wage bill for the public sector.

Figure 8.4The Impact of Fiscal Consolidations on Wage Income

Source: Authors’ calculations.

Note: Dashed lines show one-standard-error confidence bands.

Figure 8.5The Impact of Fiscal Consolidations on Profit Income

Source: Authors’ calculations.

Note: Dashed lines show one-standard-error confidence bands.

Narrative Approach versus CAPB-Based Methods for Identifying Fiscal Episodes

So far the results have been based on use of Devries and others’ (2011) narrative approach data set. What if the “traditional” method of identifying fiscal episodes using changes in the CAPB were to be used? Taking the three alternative approaches detailed above and estimating equation (8.1) for the SWIID net Gini index (though results are consistent across alternative proxies) gives the impulse response functions displayed in Figure 8.6. In general, fiscal consolidations are still found to lead to an increase in income inequality irrespective of the approach under scrutiny.

Figure 8.6CAPB-Based Identification of Fiscal Adjustments: A Comparison of Three Methods

Source: Authors’ calculations.

Note: Dashed lines show one-standard-error confidence bands.

Picking one approach, say Afonso’s (2010), the previous results are invariant to the choice of the dependent variable, that is, the source of the Gini index employed (results available upon request).16

What about Fiscal Expansions?

A final aspect is the following: to what extent is there symmetry in the results when considering a fiscal expansion instead of a fiscal consolidation? In this case, only the CAPB-based methods can provide a tentative answer. Reestimating equation (8.1) and constructing a figure analogous to Figure 8.6, where now Dit denotes the starting year of a fiscal expansion episode, yields the impulse response functions displayed in Figure 8.7. The results seem to suggest that fiscal expansions lower inequality, but the impact is generally short lived, dissipating after two to three years. This finding holds when using the SWIID net Gini index as well as Milanovic’s All Ginis index (see Figure 8.8).

Figure 8.7CAPB-Based Identification of Fiscal Expansions: A Comparison of Three Methods

Source: Authors’ calculations.

Note: Dashed lines show one-standard-error confidence bands.

Figure 8.8Fiscal Expansions: Afonso (2010) Method on Different Inequality Proxies

Source: Authors’ calculations.

Note: Dashed lines show one-standard-error confidence bands.

Concluding Remarks and Policy Considerations

This analysis finds, for a sample of 17 OECD countries for the period 1978–2009, that fiscal consolidations tend to lead to an increase in income inequality in the short and medium terms. Typical fiscal consolidations lead to an increase in income inequality on the order of 0.2–1.0 units (corresponding to a Gini index point) in the short and medium terms. This finding is robust to the use of alternate measures of consolidation (in particular, the traditional methods of identifying fiscal episodes based on changes in the CAPB) and to the use of alternate measures and sources of inequality data. The main finding is also robust to a vast array of technical checks such as inclusion of time fixed effects, the exclusion of country fixed effects, and the inclusion of different sets of control variables. The analysis also finds that more work is needed to sort out, among other things, the differences between tax-based and spending-based fiscal adjustments and whether the consolidation-inequality link is symmetric. This is particularly relevant when focusing on specific case studies in recent experience. Although the time span covered in this study ends in 2009, in Portugal, for example, the large tax increase in 2012 did not result in significant deterioration of the Gini coefficient in subsequent years. In Iceland, the massive austerity plan introduced in the aftermath of the global financial crisis, which successfully culminated in today’s balanced budget, led to a visible improvement in the degree of progressivity of the tax system.

Ultimately, the findings of this chapter do not suggest that countries should not undertake fiscal consolidation. The results do suggest, however, that the benefits of fiscal adjustments should be weighed against their likely distributional impact. Many governments assign some weight to distributional outcomes and, as discussed in other chapters of the book, may have the flexibility to design the consolidation in a way that mitigates at least some of the distributional impacts. History shows that fiscal plans succeed when they permit “some flexibility while credibly preserving the medium term consolidation objectives” (Mauro 2011, 256).17

As noted in IMF 2013, the results about the impact of consolidation on equity “strengthens the case for better targeting of both spending and revenue measures.” Specifically, the paper notes that “equity considerations suggest that a larger share of the adjustment burden could be borne by the rich, which could be achieved through revenue measures targeted at the higher income segments of the population. Revenue increases can therefore be an important component of consolidation packages, even in countries where the adjustment should focus on the expenditure side, as in a number of European countries. However, better targeted spending can also help achieve equity objectives, though there may be a trade-off between growth and equity concerns when choosing consolidation measures” (IMF 2013, 35).

All in all, this chapter’s results bolster the IMF’s general fiscal policy advice to advanced economies. At the onset of the Great Recession, the IMF played a key role in making the case for—and helping coordinate through the auspices of the Group of 20—a coordinated global fiscal stimulus (Spilimbergo and others 2008). Since many governments entered the crisis with high debt-to-GDP ratios, attention turned to consolidation once financial conditions started to stabilize. But cognizant of the adverse impact of fiscal consolidation on growth (IMF 2010c), the policy stance has been to support “a case-by-case assessment of what is an appropriate pace of consolidation” and to emphasize the need “to make fiscal policy more growth-friendly” (Lipton 2013). The results here strengthen that policy stance by suggesting that not only does consolidation lower aggregate incomes in the economy, but it adds to the pain of those most likely to benefit from income redistribution.

References

    Afonso, A.2010. “Expansionary Fiscal Consolidations in Europe: New Evidence.Applied Economics Letters17 (2): 1059.

    Afonso, A., and J. T.Jalles.2014. “Assessing Fiscal Episodes.Economic Modeling37 (February): 25570.

    Agnello, L., V.Castro, J. T.Jalles, and R. M.Sousa.2014. “The Impact of Income Inequality and Fiscal Stimuli on Political (In)stability.Unpublished, Organisation for Economic Co-operation and Development, Paris.

    Agnello, L., and R. M.Sousa.2014. “How Does Fiscal Consolidation Impact on Income Inequality?Review of Income and Wealth60 (4): 70226.

    Akitoby, B., and T.Stratmann.2006. “Fiscal Policy and Financial Markets.IMF Working Paper No. 06/16, International Monetary Fund, Washington.

    Alesina, A., and S.Ardagna.1998. “Tales of Fiscal Adjustments.Economic Policy13 (27): 489545.

    Alesina, A., and S.Ardagna.2010. “Large Changes in Fiscal Policy: Taxes versus Spending.” In Tax Policy and the Economy, Volume 24, edited by J. R.Brown.Cambridge, Massachusetts: National Bureau of Economic Research.

    Alesina, A., and S.Ardagna.2012. “What Makes Fiscal Adjustments Successful?Unpublished.

    Alesina, A., and R.Perotti.1995. “Fiscal Expansions and Adjustments in OECD Economies.Economic Policy10 (21): 20747.

    Atkinson, A. B., and A.Brandolini.2001. “Promise and Pitfalls of the Use of ‘Secondary’ Datasets: Income Inequality in OECD Countries as a Case Study.Journal of Economic Literature34 (3): 77199.

    Ball, L., D.Furceri, D.Leigh, and P.Loungani.2013. “The Distributional Effects of Fiscal Consolidation.IMF Working Paper No. 13/151, International Monetary Fund, Washington.

    Ball, L., D.Leigh, and P.Loungani.2013. “Okun’s Law: Fit at Fifty?NBER Working Paper No. 18668, National Bureau of Economic Research, Cambridge, Massachusetts.

    Bastagli, F., D.Coady, and S.Gupta.2012. “Income Inequality and Fiscal Policy.IMF Staff Discussion Note No. 12/08, International Monetary Fund, Washington.

    Beck, N. L., and J. N.Katz.1995. “What to Do (and Not to Do) with Time-Series Cross-Section Data.American Political Science Review89 (3): 63447.

    Bova, E., J.Woo, T.Kinda, and S.Zhang.2013. “Distributional Consequences of Fiscal Consolidation and the Role of Fiscal Policy: What Do the Data Say?IMF Working Paper No. 13/195, International Monetary Fund, Washington.

    Cai, X., and W J.Den Haan.2009. “Predicting Recoveries and the Importance of Using Enough Information.CEPR Discussion Paper No. 7508, Center for Economic and Policy Research, Washington.

    Cerra, V., and S.Saxena.2008. “Growth Dynamics: The Myth of Economic Recovery.American Economic Review98 (1): 43957.

    Devries, P., J.Guajardo, D.Leigh, and A.Pescatori.2011. “A New Action-Based Dataset of Fiscal Consolidation.IMF Working Paper No. 11/128, International Monetary Fund, Washington.

    Giavazzi, F., T.Jappelli, and M.Pagano.2000. “Searching for Non-Linear Effects of Fiscal Policy: Evidence from Industrial and Developing Countries.European Economic Review44 (7): 125989.

    Giavazzi, F., and M.Pagano.1996. “Non-Keynesian Effects of Fiscal Policy Changes: International Evidence and the Swedish Experience.Swedish Economic Policy Review3 (1): 67103.

    Gollin, D.2002. “Getting Income Shares Right.Journal of Political Economy110 (2): 45874.

    Guajardo, J., D.Leigh, and A.Pescatori.2014. “Expansionary Austerity: New International Evidence.Journal of the European Economic Association12 (4): 94968.

    Hernandez de Cos, P., and E.Moral-Benito.2011. “Endogenous Fiscal Consolidations.Banco de Espana Working Paper No. 1102, Banco de España, Madrid.

    International Monetary Fund (IMF). 2010a. “From Stimulus to Consolidation: Revenue and Expenditure Policies in Advanced and Emerging Economies.IMF Departmental Paper No. 10/3, International Monetary Fund, Washington.

    International Monetary Fund (IMF). 2010b. “Large Changes in Fiscal Policy: Taxes versus Spending.” In Tax Policy and the Economy, Volume 24, ed. by J. R.Brown.Cambridge: National Bureau of Economic Research.

    International Monetary Fund (IMF). 2010c. “Will It Hurt? The Macroeconomic Effects of Fiscal Consolidation.Chapter 3 in World Economic Outlook, Washington, October.

    International Monetary Fund (IMF). 2012. “What Makes Fiscal Adjustments Successful?Unpublished, Washington.

    International Monetary Fund (IMF). 2013. “Reassessing the Role and Modalities of Fiscal Policies in Advanced Economies.IMF Policy Paper, Washington.

    International Monetary Fund (IMF). 2014a. Fiscal Monitor: Back to Work—How Fiscal Policy Can Help.Washington, October.

    International Monetary Fund (IMF). 2014b. Fiscal Monitor: Public Expenditure Reform—Making Difficult Choices.Washington, April.

    Jenkins, S.2014. “World Income Inequality Databases: An Assessment of WIID and SWIID.IZA Discussion Paper No. 8501, Institute for the Study of Labor, Bonn.

    Jorda, O.2005. “Estimation and Inference of Impulse Responses by Local Projections.American Economic Review95 (1): 16182.

    Jorda, O., and A. M.Taylor.2013. “The Time for Austerity: Estimating the Average Treatment Effect of Fiscal Policy.Federal Reserve Bank of San Francisco Working Paper 2013/25.

    Lagarde, C.2012. China Daily, December27.

    Lipton, D.2013. “Bellwether Europe 2013.Speech, April25.

    Mauro, P.2011. Chipping Away at the Public Debt: Sources of Failure and Keys to Adjustment in Public Debt.Hoboken, New Jersey: Wiley.

    McDermott, C., and R.Wescott.1996. “An Empirical Analysis of Fiscal Adjustments.IMF Staff Papers43 (4): 72553.

    Milanovic, B.2014. “The Return of ‘Patrimonial Capitalism’: A Review of Thomas Piketty’s Capital in the Twenty-First Century.Journal of Economic Literature52 (2): 51934.

    Morris, R., and L.Schuknecht.2007. “Structural Balances and Revenue Windfalls: The Role of Asset Prices Revisited.ECB Working Paper No. 737, European Central Bank, Frankfurt.

    Nickell, S.1981. “Biases in Dynamic Models with Fixed Effects.Econometrica49 (6): 141726.

    Roe, M. J., and J. I.Siegel.2011. “Political Instability: Effects on Financial Development, Roots in the Severity of Economic Inequality.Journal of Comparative Economics39 (3): 279309.

    Romer, C., and D.Romer.1989. “Does Monetary Policy Matter? A New Test in the Spirit of Friedman and Schwartz.NBER Macroeconomics Annual4: 12170.

    Romer, C., and D.Romer.2010. “The Macroeconomic Effects of Tax Changes: Estimates Based on a New Measure of Fiscal Shocks.American Economic Review100 (3): 763801.

    Spilimbergo, A., S.Symansky, O.Blanchard, and C.Cottarelli.2008. “Fiscal Policy for the Crisis.IMF Staff Position Note No. 08/01, International Monetary Fund, Washington.

    Teulings, C. N., and N.Zubanov.2013. “Is Economic Recovery a Myth? Robust Estimation of Impulse Responses.Journal of Applied Econometrics29 (3): 497514.

The authors are grateful to Branko Milanovic for comments on related work and for providing his data on Gini coefficients. Thanks also go to Vitor Gaspar, Sanjeev Gupta, Ben Clements, Maura Francese, Patrick Petit, Estelle Liu, Jan Babecký, Andre Brandolini, Teresa Ter-Minassian, Pietro Tomasino, Petya Koeva Brooks, Giovani Callegari, and Thomas Warmedinger for their useful comments, suggestions, and advice. The authors are equally thankful to participants present in the FAD seminar series and in the Banca d’Italia Perugia Conference. The opinions expressed herein are those of the authors and do not necessarily reflect those of the IMF, its member states, or its policy.

In neoclassical models, fiscal policy affects economic activity by means of wealth effects, intertemporal substitution, and distortions. If consolidation measures remove uncertainty with respect to fiscal sustainability (signaling tax cuts in the future and raising discounted disposable income), hence boosting confidence, then the negative impact on output may be limited or even give rise to an “expansionary fiscal contraction.”

For example, a stock price boom raises the CAPB by increasing capital gains tax revenue, and also tends to coincide with an expansion in private domestic demand (Morris and Schuknecht 2007).

Note, however, that this approach differs from the one used by Romer and Romer (2010), who identify exogenous tax policy changes by carefully analyzing U.S. congressional documents.

However, as Jorda and Taylor (2013) argue, fiscal shocks may not be exogenous and can be predicted.

The countries are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, the Netherlands, Portugal, Spain, Sweden, the United Kingdom, and the United States.

The finite sample bias is on the order of 1/T, where T in the sample is 32.

For time-series properties of the underlying data, stationarity tests (not shown) confirm the need to use first differences in equation (8.1) to avoid unit root issues. Moreover, cross-section dependence tests (not shown) reject the null of cross-sectional independence for the homogeneous panel of countries under scrutiny in this study.

In what follows, the chapter uses a sample of 17 advanced economies, which gives a maximum of 608 observations between 1978 and 2009.

Bear in mind that the analysis does not distinguish between structural and temporary fiscal shocks and that there is no proxy that captures the characteristics of the shock (that is, whether the shock is realized through changes in progressive or regressive budget items; that is, two fiscal adjustments with opposite distributional characteristics—such as one raising progressive taxation and the other increasing more regressive taxation—are treated identically).

The scale of the Gini index (and therefore of the impulse response function’s vertical axis) goes from 0 to 100 as is standard practice in the literature.

It is important to note that the sample contains both large and small open economies. Large economies are relatively rarely hit by fiscal shocks. Small economies, however, are almost continuously hit by shocks and some of these (spending cuts, for instance) are not always exogenous but are the result of other external shocks. Although we use the narrative approach, we are aware that the current identification strategy should be subjected to improvements in future research.

The use of interpolated data (as opposed to “raw” data) is necessary because Jorda’s (2005) local projection estimator is sensitive to data gaps.

The majority of the empirical literature also supports the view that expenditure-driven consolidations increase the likelihood of success of the adjustment episode (see, for example, Giavazzi and Pagano 1996; McDermott and Wescott 1996; Alesina and Ardagna 1998; Giavazzi, Jappelli, and Pagano 2000). There is also evidence that consolidations, particularly reductions in public expenditure, can contribute to reducing sovereign debt spreads, and therefore the cost of servicing sovereign debt (Akitoby and Stratmann 2006).

This approach is also imperfect. Indeed, to properly differentiate between spending- and tax-based consolidations, one should consider episodes characterized by only spending- or tax-based adjustments. Doing so, however, would dramatically reduce the number of “pure” spending- and tax-based consolidations in the sample.

Using either Giavazzi and Pagano 1996 or Alesina and Ardagna 1998 instead does not qualitatively change the results.

For instance, plans could specify that unemployment benefits would be shielded from cuts in the event of slower growth than assumed in the plan.

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