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Distributional Consequences of Fiscal Adjustments: What Do the Data Say?

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Abstract

The 2007–2009 Great Recession has led to an unprecedented increase in public debt in many countries, triggering substantial fiscal adjustments. What are the distributional consequences of fiscal austerity measures? This is an important policy question. This paper analyzes the effects of fiscal adjustments for a panel of 17 OECD countries over the last 30 years, complemented by a case study of selected fiscal adjustment episodes. The paper shows that fiscal adjustments are likely to raise inequality through various channels including their effects on unemployment. Spending-based adjustments tend to worsen inequality more significantly, relative to tax-based adjustments. The composition of austerity measures also matters: progressive taxation and targeted social benefits and subsidies introduced in the context of a broader decline in spending can help offset some of the adverse distributional impact of fiscal adjustments.

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Figure 1

Sources: Authors’ estimates; European Union, Statistics on Income and Living Conditions (EU-SILC).

Figure 2

Note: A higher number indicates greater inequality.

Figure 3

Sources: Authors’ estimates; IMF-Fiscal Affairs Department database; Standardized World Income Inequality Database (SWIID); and national sources.

Figure 4

Sources: Devries and others; European Commission; Organisation for Economic Co-operation and Development; and IMF staff estimates.

Figure 5

Sources: Authors’ estimates.

Figure 6

Sources: Authors’ estimates; IMF-Fiscal Affairs Department Database; Eurostat; Standardized World Income Inequality Database (SWIID); and national sources.

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Notes

  1. The distributional impact of failing to adjust is beyond the scope of the paper. However, the impact on income distribution of a delay in fiscal adjustment could be even worse, if it results in an eventual debt crisis that forces a sudden, even greater fiscal adjustment, accompanied by a severe recession.

  2. The paper focuses on the distributional effects of fiscal adjustments, but it is important to recognize the potential trade-off between equity and efficiency when designing redistributive policies. Redistributive tax and benefit systems can introduce economic inefficiencies with implications for long-term productivity and growth, as redistributive policies can influence the incentives for people to work, save, and invest. There is a large literature on the relationship between inequality and growth (besides the aforementioned papers, see also Alesina and Rodrik, 1994; Banerjee and Duflo, 2003; Bertola et al., 2005; Barro, 2008 and references therein).

  3. Notable exceptions are Agnello and Sousa (2014) for 18 OECD countries in 1978–2009 and Mulas-Granados (2005) for 15 EU nations in 1960–2000.

  4. In some emerging market economies, reforms of fuel and food subsidies are crucial to improving the equity impact of fiscal policy—evidence suggests that the rich often benefit the most from generalized subsidy programs. See Coady et al. (2010) for details.

  5. For example, Jenkins et al. (2012) find that in the first 2 years following the crisis, there was not much immediate change in disposable income distribution in many advanced economies as a result of government support via tax and benefits, with real income levels declining throughout the income distribution.

  6. For a review of income inequality trends and evolution of fiscal policies, see Bastagli et al. (2012), Chu et al. (2004) and references therein.

  7. These correlations (Figure 3) are for a sample of advanced, emerging, and low-income economies during the period of 1980–2009. Restricting to a sample of OECD economies yields similar results.

  8. Subsequent to our paper, Ball et al. (2013) also examine the inequality effects of fiscal adjustment for 17 OECD countries over 1978–2009. Overall, their results are consistent with those of this paper including a similar range of the impact magnitude and the same conclusion that expenditure-based adjustments tend to worsen the inequality more than tax-based ones.

  9. The analysis focuses on within-country income inequality and does not consider other dimensions of inequality in a broad term, such as inequality of opportunities and poverty or inequality among countries.

  10. Following Agnello and Sousa (2014), we impose cross-equation restrictions on the coefficients of fiscal adjustment measures in the market income inequality equation (i.e., these coefficients are assumed to be zero) under the common assumption that the fiscal austerity measures (discretionary changes in taxes and spending) only affect disposable income (i.e., income after taxes and transfers), while the indirect effects on both market and disposable income are controlled for by income per capita, unemployment, and other variables that are included in both equations. Note that if each equation contains exactly the same set of regressors, the SUR is equivalent to the OLS and hence there will be no gain in efficiency. For a discussion on the estimation of a SUR in the unbalanced panel data, see BiØrn (2004).

  11. The regressions results (e.g., the causal relationship between fiscal adjustment and inequality) may be subject to endogeneity and should be interpreted with caution. The causal relationship between fiscal adjustment and inequality is examined with a system generalized method of moments (SGMM) later (Appendix Table A1).

  12. The Kuznets curve relationship implies that inequality exhibits an inverted U-curve as the economy develops: economic development (including shifts from agriculture to industry and services and adoption of new technologies) initially benefits a small segment of the population, causing inequality to rise. Subsequently, inequality declines as the majority of people find employment in the high-income sector. However, the existing evidence for the Kuznets curve is mixed (see Barro, 2008; Kanbur, 2000 and references therein).

  13. For example, trade openness tends to exert downward pressure on the wage of low-skilled workers, increasing inequality. On the other hand, if openness has a positive effect on investment and growth so that the real incomes of the poorer groups in society also rise, this may enable these groups to invest in human capital and entrepreneurial activities, improving income distribution over the longer term.

  14. Foreign direct investment (FDI) is found to be associated with an increase in inequality (IMF, 2007). FDI inflows in emerging market and developing economies tend to increase the demand, and thus the wage premium, for skilled labor, whereas outward FDI in advanced economies tends to reduce the demand, and hence the wages, for lower-skilled labor. A related consideration is that trade openness may facilitate technology diffusion from advanced economies to emerging market and developing countries through FDI and imports of capital equipment (such as for information technology) as well as the international production network. In the receiving emerging market and developing countries, the new technologies tend to be more skill intensive than those in use before the liberalization of trade and FDI, which increases the demand for skilled labor and thus worsens income inequality. The fact that the earnings of highly skilled and highly educated workers have increased at the fastest rate in so many countries is also consistent with the view that higher international integration has introduced skill-biased technologies to the developing world.

  15. Alternative sources were also used, including data on consolidations from Alesina and Ardagna (2010) for 17 OECD countries, and structural balance data from the IMF. For interesting discussions on the issues of identification of fiscal consolidation episodes, measurement of the size of consolidation, and estimation of short-term growth effects of consolidations, see Perotti (2011) and Alesina and Ardagna (2012) as well as IMF (2010b).

  16. The CAPB is calculated by taking the actual primary balance—non-interest revenue minus non-interest spending—and subtracting the estimated effect of business cycle fluctuations on the fiscal accounts.

  17. The sample country and period is dictated by the availability of data from Devries et al. (2011). In addition, given the magnitude of the recent global financial crisis, focusing on the period prior to it allows us to disentangle the distributional impact of consolidation itself from that of a large financial crisis and ensuing recession.

  18. This is with respect to a baseline in which fiscal adjustment is not implemented and deficits continue to be financed without major disruptions. If the absence of fiscal adjustment leads to a fiscal crisis, with disruptive consequences for economic activity, income inequality could deteriorate even more. Our assumption that the coefficients of fiscal adjustment measures in the market income inequality equations are equal to zero are not rejected.

  19. To put this in perspective, note that the average Gini coefficient for disposable income in the 17 OECD countries increased by about 2 percent over 10 years (between 1995 and 2005).

  20. This seems to reflect the fact that large fiscal adjustments tend to be longer in duration and mostly spending based. Spending-based fiscal adjustment has been found to have more pronounced effects on inequality than tax-based adjustment. This is confirmed in the case study presented later in this paper.

  21. However, the FE coefficient estimates (columns 10–12) turn out to be smaller in size and insignificant.

  22. An international dollar is based on purchasing power parity exchange rates and has the same purchasing power as the U.S. dollar. Consumer price index inflation was also tried, but the resulting coefficients were not significant.

  23. The methodology closely follows Cerra and Saxena (2008) and IMF (2010b). The least squares approach to estimate dynamic panel regression in the presence of country-fixed effects causes a dynamic panel bias due to the inevitable correlation between country-fixed effects and the lagged dependent variable when the time dimension of the panel (T) is small. Nickell (1981) derives a formula for the bias, showing that the bias approaches zero as T approaches infinity. The order of bias is O(1/T), which is small in our data with T = 32 and N = 17 (Judson and Owen 1999). As a robustness check, a system generalized method of moments (SGMM) is tried and the results are very similar as shown in Appendix Table A1.

  24. Coefficients of the measure of fiscal adjustment or contraction and its two lagged terms are jointly significant at the conventional levels.

  25. Results are closely similar when Gini coefficient or its log is used as the dependent variable in the dynamic panel regression. The Gini coefficient is employed here to facilitate interpretation of the chart.

  26. The measure is a dummy variable taking a value of 1 in the year of a large fiscal consolidation or contraction and 0 otherwise, where a large fiscal consolidation is defined by Alesina and Ardagna (2010) to be larger than 1.5 percent of GDP. Thus, the result using this dummy variable is not directly comparable to that based on the consolidation measure (in percent of GDP) from Devries et al. (2011).

  27. This magnitude is similar to that in Ball et al. (2011).

  28. Many of these episodes took place as European Union member states attempted to meet the Maastricht criteria (i.e., the convergence criteria) for adoption of the euro as their currency.

  29. On average, the duration and size of the seven spending-based consolidations were about 4 years and 6.8 percent of GDP (as measured by change in structural balance), compared to about 2 years and 3.2 percent of GDP in the case of the seven tax-based consolidations.

  30. Asterisk indicates the countries included in the 17 OECD country sample, and † advanced economies.

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Correspondence to Tidiane Kinda.

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*Jaejoon Woo is Chief Korea Economist at the Bank of America Merrill Lynch and also an Associate Editor of International Economic Journal. He was a senior economist at the IMF when the paper was written. His work focuses on growth, fiscal policy, inequality, and macroeconomics. He received his Ph.D. in Economics from the Harvard University; his email address is jaejoon.woo@baml.com. Elva Bova is an Economist at the European Commission. Her work focuses on fiscal policy and macroeconomics. She received her Ph.D. in Economics from the University of London; her email address is elvabova@gmail.com. Tidiane Kinda is Special Assistant to the Director in the Asia and Pacific Department of the IMF. His work focuses on international economics, public finance, and income inequality. He received his Ph.D. in Economics from CERDI Université d’ Auvergne; his email address is tkinda@imf.org. Y. Sophia Zhang is an Economist in the IMF. Her work focuses on public finance. She received her Ph.D. in Economics from the University of California at Los Angeles; her email address is yzhang@imf.org. The authors would like to thank the editor, two anonymous referees, Martin Cerisola, David Coady, Carlo Cottarelli, Xavier Debrun, Markus Eller, Greetje Everaert, Lorenzo Forni, Davide Furceri, Phil Gerson, Martine Guerguil, Sanjeev Gupta, Mulas Granados, Frigyes Heinz, Andrea Lemgruber, Laura Jaramillo Mayor, Leandro Medina, Tigran Poghosyan, Marcos Poplawski Ribeiro, Serges Saksonovs, Andrea Schaechter, Bahrom Shukurov, Yan Sun, Anke Weber, and participants in IMF seminars and Villa Mondragone International Economic Seminar, Rome, Italy for helpful comments and discussions. Petra Dacheva and Carsten Jung provided excellent research assistance. The views expressed in this paper are those of the authors and do not represent those of the IMF or IMF policy.

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Appendix A: Description of Data and Sample Country List

Appendix A: Description of Data and Sample Country List

Measures of Income Inequality

  1. (1)

    Gini coefficients for disposable and market income, Solt (2009; 2012 update)

  2. (2)

    Gini coefficients for disposable income (alternative dataset), compiled by the authors using data from World Income Inequality Database (2008), World Bank’s PovcalNet (2012), Eurostat (2012), and national sources

  3. (3)

    Labor income share, EU KLEMS Database (2012)

  4. (4)

    Ratios of top to bottom income shares (by quintile or decile), data from World Income Inequality Database (2008), PovcalNet (2012), Eurostat (2012), and national sources

Other Variables

  1. (1)

    Real GDP per capita (in log), IMF’s World Economic Outlook (2012)

  2. (2)

    Average years of schooling of population of age over 15 (in log), Barro and Lee (2010).

  3. (3)

    Trade openness (percent of GDP), World Bank’s World Development Indicators (WDI) (2012).

  4. (4)

    CPI Inflation rate (log of (1 + π)), WDI (2012).

  5. (5)

    Unemployment rate, OECD (2012) and WDI (2012).

  6. (6)

    Information technology (IT) capital share of total capital stock, Jorgenson and Vu (2007).

  7. (7)

    Ratio of direct to indirect taxes, IMF/Fiscal Affairs Department Database (2012).

  8. (8)

    Cyclically adjusted individual and corporate income taxes and cyclically adjusted indirect tax, IMF/Fiscal Affairs Department Database (2012).

  9. (9)

    Government spending (wage bill, social benefits, subsidies, capital spending), IMF/Fiscal Affairs Department Database (2012).

  10. (10)

    Fiscal consolidation (spending and tax measures), percent of GDP, Devries et al. (2011).

  11. (11)

    Fiscal consolidation episodes, Alesina and Ardagna (2010).

  12. (12)

    Banking crisis incidence, Reinhart and Rogoff (2011).

Sample Country ListFootnote 30

48 Advanced and Emerging Economies: Argentina, Australia*, Austria*, Belgium*, Bulgaria, Brazil, Canada*, Chile, Colombia, Czech Republic, Denmark*, Finland*, France*, Germany*, Greece, Hong Kong, Hungary, Iceland, Indonesia, Ireland*, Israel, Italy*, Japan*, Korea, Lithuania, Luxembourg, Latvia, Malaysia, Netherlands*, Norway, New Zealand, Peru, Poland, Portugal*, Romania, Russia, Singapore, Slovak, Slovenia, South Africa, Spain*, Sweden*, Switzerland, Thailand, Turkey, Ukraine, United Kingdom*, and United States*

See Tables A1 and A2.

Table A1 Dynamic Effects of Fiscal Consolidation on Income Inequality
Table A2 Robustness Checks on Alternative Measures of Income Inequality, 1980–2010

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Woo, J., Bova, E., Kinda, T. et al. Distributional Consequences of Fiscal Adjustments: What Do the Data Say?. IMF Econ Rev 65, 273–307 (2017). https://doi.org/10.1057/s41308-016-0021-1

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  • DOI: https://doi.org/10.1057/s41308-016-0021-1

JEL

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