Skip to main content

Advertisement

Log in

Redistribution, inequality, and growth: new evidence

  • Published:
Journal of Economic Growth Aims and scope Submit manuscript

Abstract

We investigate the relationship between inequality, redistribution, and growth using a recently-compiled dataset that distinguishes clearly between market (pre-tax and transfer) and net (post tax and transfer) inequality, and allows us to calculate redistributive transfers for a large number of advanced and developing countries. Across a variety of estimation methods, data samples, and robustness checks, we find: (1) lower net inequality is robustly correlated with faster and more durable growth, controlling for the level of redistribution; (2) redistribution appears benign in terms of its impact on growth, except when it is extensive; and (3) inequality seems to affect growth through human capital accumulation and fertility channels.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Source: SWIID 3.1 and authors’ calculations

Fig. 3

Source: Penn World Tables version 7.1, SWIID 3.1, and authors’ calculations

Fig. 4

Source: Penn World Tables version 7.1, SWIID 3.1, and authors’ calculations

Fig. 5

Source: Penn World Tables version 7.1, SWIID 3.1, and authors’ calculations

Similar content being viewed by others

Notes

  1. See for example Perotti (1996), Forbes (2000), Barro (2000), Panizza (2002), Banerjee and Duflo (2003) and Voitchovsky (2005).

  2. See also Perotti (1996) and Aghion et al. (1999).

  3. The relationship between inequality and growth may be nonlinear, as in the theoretical model of Benhabib (2003), in which increases in inequality from low levels provide growth-enhancing incentives, while increases past some point encourage rent-seeking and lower growth.

  4. In particular, as emphasized in Halter et al. (2014), estimates based on time-series variation only (e.g. estimations relying on fixed-effects or first-differences estimators such as those in Forbes 2000) typically pick up only the (positive) short-run effects.

  5. Milanovic (2000) uses data on market and net inequality for a small set of mostly OECD countries that allow direct measurement of redistribution market inequality. He shows that with these data, the evidence is supportive of the Meltzer–Richard hypothesis: more unequal societies do engage in more redistribution.

  6. Benabou (1996) finds that growth is positively related to some categories of social spending.

  7. Easterly (2007) examines the effects of inequality instrumented with colonial landholding patterns and argues that contemporary inequality is the legacy of plantation-based agricultural systems dictated by geography.

  8. Bourguignon (2004) examines the interactions between inequality and growth and how they relate to absolute poverty. In his Poverty–Growth–Inequality “Triangle Model”, Bourguignon suggests that changes in poverty depend on growth, income distribution and changes in income distribution, so reducing poverty requires a combination of country-wide policies focused on growth and reducing inequality.

  9. For surveys, see Atkinson and Brandolini (2001, 2009).

  10. Solt labels his database “Standardized World Income Inequality Database” (SWIID). Other sources exist albeit for smaller samples, such as the advanced countries, as discussed below in the robustness section.

  11. Solt follows the definitions of net and market inequality in the Luxembourg Income Study (LIS), a harmonized set of income inequality data for a number of upper and middle-income countries. These definitions do not capture the provision of most in-kind health and education services by the government or of indirect taxes (as discussed below). Other transfers are in principle captured in the difference between market and net income.

  12. Atkinson and Brandolini (2001) discuss inequality data quality and consistency. They point out that differences between the various welfare definitions and equivalence scales vary across countries and over time, and that “definitional differences interact.” The SWIID incorporates Atkinson and Brandolini’s recommendations to provide the most comparable data available (Solt 2015). For example, the SWIID’s adjustments are calculated for each distinct combination of definition and scale.

  13. Jenkins (2014) compares WIID with Solt’s SWIID and challenges various aspects of the SWIID database. However, Solt (2015) argues that the SWIID database remains useful for cross-country work. He also shows that Jenkins (2014) even more clearly exposes the great difficulties involved in doing cross-country work with other databases such as the WIID that are not standardized.

  14. That the term “market inequality” is potentially misleading is emphasized, for example, in Stiglitz (2012).

  15. Summary statistics are provided in “Appendix A”. “Appendix B” presents variables and summary statistics of the market and net inequality data, redistribution, and the rest of the variables used in the analysis.

  16. As discussed in “Appendix C”, we find that Granger causality tests reject causality from redistribution in our sample, but not the reverse. We investigate the market inequality–redistribution relationship empirically in the next section.

  17. For the estimation of the direct effects of inequality and redistribution on growth, but not for the overall effects, we can be agnostic about whether there is significant two-way causality between redistribution and market inequality, because our multivariate techniques isolate the effects of each variable holding the other constant.

  18. Our timing convention, which is standard in the literature, implies that for inequality and redistribution, \(Z_{i,t-\tau }\) is measured as the average over the period from \(t-1\) to \(t-\tau \). As a robustness check we experimented with different lag structures and found that lagging inequality generally resulted in similar but attenuated effects.

  19. See Stock et al. (2002). Kraay (2015) analyzes the implications of weak instruments for Ostry et al. (2014), which is a precursor to this paper with a more limited robustness analysis.

  20. While our preferred specification is one that includes both inequality and redistribution, for completeness, the Online Appendix Table 1.1 presents the results for alternative specifications. When first net and then market inequality enters separately in a specification with initial income as the only control, we find strong negative effects for both; when redistribution enters alone it is still insignificant; and when both net and market inequality are included as regressors, only net inequality is statistically significant (with a negative effect). We thank one of the referees for the suggestion.

  21. We find no evidence for a break in the inequality–growth or the redistribution–growth relationship from the 50th through the 90th percentile of redistribution.

  22. As discussed earlier, a typical concern with sGMM estimation relates to the strength and validity of instruments as well as robustness to variations in the instrumentation strategy. Our empirical approach was motivated by an effort to maintain consistency in the instruments used across specifications while abiding by the Roodman (2006) “minimally arbitrary rule of thumb” to keep the number of instruments around the same number as the cross sections. While some specifications in Table 2—particularly those with additional controls which inevitably increase the number of instruments—may result in higher p values of for the test of over-identifying restrictions, a battery of robustness tests presented in Sect. 7 (including variations in the instrumentation strategy) support the overall conclusions of our paper.

  23. This presentation sheds light on the issues related to the potential measurement error of redistribution. The lack of significance of the estimated coefficient of redistribution in Table 2 could be interpreted as implying that the redistribution data are just too noisy to generate statistically significant results. Table 1.2 in the Online Appendix shows, however, that there is a statistically significant positive relationship between redistribution and growth, controlling for market inequality. We discuss below additional ways to address the potential measurement error in our robustness tests.

  24. A negative association between inequality and growth is found using simple cross-country OLS estimates (Column [1] of Table 9) in agreement with the findings presented in earlier studies (e.g. Alesina and Rodrik 1994; Persson and Tabellini 1994; Deininger and Squire 1998).

  25. The p value for the test that the two coefficients on redistribution in Table 2 [3] are equal is 0.095. As the specification in column 3 shows, the negative effect of redistribution in the linear case is driven by the high-redistribution cases. As a robustness check, we have examined different definitions of “high redistribution” from above the 50th to above the 90th percentile; the likelihood is maximized around the 75th percentile.

  26. Our chosen specification for the hazard function in (5) allows us to express the effect of a variable either in terms of its effect on the hazard that a spell will end in the next year or, with a transformation of the hazard function, as the expected duration of the spell.

  27. Pellegrini and Gerlagh (2004) follow a similar approach in investigating the effect of corruption on growth.

  28. Presumably because some of the estimated channels have little time-series variation, some of the AR1 tests in Table 4 are larger than desirable. Alternatively, since the channels regressions in (8) do not include a lagged dependent variable, they can be consistently estimated with instrumental variables (IV). When we do so, we find very similar results to those reported in Table 4 (results available on request).

  29. The contribution of the direct effect in the total effect of inequality on growth is \(\alpha /(\alpha +\sum _k {\gamma ^{k}} \lambda _N^k )\), while the contribution of the indirect effect (through all channels) in the total is \(\sum _k {\gamma ^{k}} \lambda _N^k /(\alpha +\sum _k {\gamma ^{k}} \lambda _N^k ).\) The contribution of each channel k in the indirect effect is \(\gamma ^{k}\lambda _N^k{/} \sum _k {\gamma ^{k}} \lambda _N^k \).

  30. The microeconomic literature on the effect of income (and hence from a macroeconomic perspective inequality) on fertility can help address the question of timing. In particular, recent papers such as Black et al. (2013) and Cohen et al. (2013) find some suggestive evidence of a nearly contemporaneous (albeit weak) relationship between income and fertility.

  31. Galor and Zeira (1993) emphasize the role of both high fertility and inequality in lowering growth, through effects on human capital accumulation. Galor and Zang (1997) show that fertility per se is more important than labor force growth in driving growth. De Gregorio and Lee (2004) and Barro (2008) present evidence that inequality affects growth in part through its effect on total fertility. See also Li and Zhang (2007) and Acemoglu and Johnson (2007), who use various external instruments to identify independent negative effects of fertility and population growth on GDP per capita.

  32. Kremer and Chen (2002) argue that the effect of inequality on differential fertility is stronger among developing than advanced countries. Our results on channels, and in particular fertility, are similar when we exclude advanced economies.

  33. Inference about spells is generally not possible in samples more restricted than the baseline, because in these smaller samples we fail to observe the start of enough spells.

  34. We have also investigated a spell-specific restricted sample that requires that there be one net and one market measurement per spell, instead of per 5-year period. This makes no important difference to the results. “Appendix A” provides the sample definitions and stylized facts.

  35. We define OECD membership as being the members as of 1975.

  36. The LIS has earned a reputation as the best data available for making cross-national comparisons of income inequality. It is based on highly-comparable, high-quality harmonized micro-data from national household income surveys, and allows the calculation of net and market income, and hence redistribution. However, LIS data are available essentially only for OECD countries.

  37. Cingano (2014) confirms our results that inequality is bad and redistribution is insignificant for growth using an OECD sample and OECD data, which is similar to but distinct from the LIS data (the underlying micro-data are generally the same but the processing and various assumptions made may be different).

  38. Our results for the OECD sample are in contrast with the results of Thewissen (2014), who looks at similar issues for a smaller set of OECD countries. However, he uses a fixed-effects methodology, which does not account for the cross-sectional variation. In addition, using fixed effects to estimate a dynamic panel model with small and fixed time dimension generates biased estimates of the coefficients.

  39. We have also examined our results using a subsequently-available vintage of the Solt database (Solt 2016). Reassuringly, the main conclusions remain the same with this updated data (results available on request).

  40. Specifically, we use the Stata multiple imputation or “mi” command, as suggested in Solt (2009).

  41. In particular, we examine difference-in-Hansen tests for the validity of instrument subsets, particularly those for the full set of instruments for the levels equation and those based on initial income. This provides some comfort that the stationarity restrictions on the initial conditions process required for the validity of sGMM are valid.

  42. The number of instruments grows quadratically with T. Bun and Windmeijer (2010) and Windmeijer (2005) discuss that the finite sample properties of GMM estimators are sensitive to the number of moment conditions used. To address the instrument proliferation problem, Roodman (2009) proposes collapsing the instrument matrix and limiting the number of lags used.

  43. The Online Appendix Table 3.1 presents similar variations to the number of instruments for the remaining specifications in Table 2.

  44. In order to apply the NW CUE to our dynamic panel data setting, we use wxyz_xtabond2 to generate transformed variables, ivreg2 to obtain CUE estimates, and Farbmacher (2012) to obtain appropriate SEs (using the Stata program nwind). Exogenous regressors are “partialled out” before the CUE estimates are calculated.

  45. Stock and Wright (2000) and Bun and Windmeijer (2010) discuss why weak-instrument diagnostics for linear instrumental variables regression do not carry over to the more general setting of GMM, while Stock et al. (2002) point out that weak identification is a more difficult issue in GMM than in IV regression.

  46. We agree with Kraay (2015) that a full consideration of weak instrumentation issues, such as we have undertaken here, makes inference challenging (if the instruments are indeed weak). However, in our view, and as discussed earlier in this section, the literature on weak instruments in sGMM is rapidly developing and the properties of these intervals, notably implications for power, are not well-established. It would be interesting, however, to apply the weak instrument-robust methods used here to variables other than inequality. Our experience is that inequality seems generally speaking at least as robust as other variables typically considered to belong in the “pantheon” of growth determinants, as discussed for example in Berg et al. (2012).

  47. Following the suggestion of the referee, as a robustness test we re-estimate our baseline specification where we include direct measures of redistribution such as taxes, government spending, transfers, as instruments for our measure of redistribution (results available on request). This instrumentation strategy may help address potential measurement error of the redistribution variable because the measurement errors in the two measures of redistribution are not likely to be correlated. Our results are preserved.

  48. In Berg and Ostry (2011) we also found a role for inequality in a narrative (historical) analysis of the ends of growth spells, in the context of growth spell duration analysis.

  49. To test the relationship more formally, we supplement the analysis with two sets of Granger causality tests. Estimated panel VARs fail to reject the hypothesis that redistribution does not Granger cause market inequality, while we reject the hypothesis that market inequality does not Granger cause redistribution, at conventional levels of significance. Also, unit root and Granger causality tests for each country at a time suggest that, on aggregate, in the overwhelming majority of countries there is no Granger causality from redistribution to market inequality, while in these countries market inequality also Granger causes redistribution.

  50. The Hausman test confirms empirically the use of fixed effects.

References

  • Acemoglu, D., & Johnson, S. (2007). Disease and development: The effect of life expectancy on economic growth. Journal of Political Economy, 115(6), 925–985.

    Article  Google Scholar 

  • Acemoglu, D., & Robinson, J. A. (2000). Why did the West extend the franchise? Democracy, inequality, and growth in historical perspective. The Quarterly Journal of Economics, 115(4), 1167–1199.

    Article  Google Scholar 

  • Adams, C., Kothari, S., & Lizarazo Ruis, S. (2018). Ghana: Macro shocks and income distribution. mimeo.

  • Aghion, P., Caroli, E., & Garcia-Penalosa, C. (1999). Inequality and economic growth: The perspective of the new growth theories. Journal of Economic Literature, 37(4), 1615–1660.

    Article  Google Scholar 

  • Alesina, A., & Perotti, R. (1996). Income distribution, political instability and investment. European Economic Review, 40(6), 1203–28.

    Article  Google Scholar 

  • Alesina, A., & Rodrik, D. (1994). Distributive politics and economic growth. Quarterly Journal of Economics, 109(2), 465–490.

    Article  Google Scholar 

  • Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58, 277–298.

    Article  Google Scholar 

  • Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68, 29–51.

    Article  Google Scholar 

  • Ashraf, Q. H., Weil, D. N., & Wilde, J. (2013). The effect of fertility reduction on economic growth. Population and Development Review, 39, 97–130.

    Article  Google Scholar 

  • Atkinson, A. B., & Brandolini, A. (2001). Promise and pitfalls in the use of ‘secondary’ data-sets: Income inequality in OECD countries as a case study. Journal of Economic Literature, 39(3), 771–799.

    Article  Google Scholar 

  • Atkinson, A. B., & Brandolini, A. (2009). The evolution of income inequality across time and across countries. Cambridge Journal of Economics, 33(3), 381–404.

    Article  Google Scholar 

  • Banerjee, A. V., & Duflo, E. (2003). Inequality and growth: What can the data say? Journal of Economic Growth, 8(3), 267–299.

    Article  Google Scholar 

  • Barro, R. J. (1990). Government spending in a simple model of endogenous growth. Journal of Political Economy, 98(5), 103–125.

    Article  Google Scholar 

  • Barro, R. J. (2000). Inequality and growth in a panel of countries. Journal of Economic Growth, 5(1), 5–32.

    Article  Google Scholar 

  • Barro, R. J. (2008). Inequality and growth revisited. Working Paper Series on Regional Economic Integration No. 11, Asian Development Bank.

  • Barro, R. J., & Lee, J.-W. (2013). A new data set of educational attainment in the world, 1950–2010. Journal of Development Economics, 104, 184–198.

    Article  Google Scholar 

  • Bassett, W. F., Burkett, J. P., & Putterman, L. (1999). Income distribution, government transfers, and the problem of unequal influence. European Journal of Political Economy, 15(2), 207–28.

    Article  Google Scholar 

  • Bazzi, S., & Clemens, M. A. (2013). Blunt instruments: Avoiding common pitfalls in identifying the causes of economic growth. American Economic Journal: Macroeconomics, 5(2), 152–86.

    Google Scholar 

  • Benabou, R. (1996). Inequality and growth. NBER Macro Annual, 11, 11–92.

    Article  Google Scholar 

  • Benabou, R. (2000). Unequal societies: Income distribution and the social contract. American Economic Review, 90(1), 96–129.

    Article  Google Scholar 

  • Benhabib, J. (2003). The tradeoff between inequality and growth. Annals of Economics and Finance, 4(2), 491–507.

    Google Scholar 

  • Berg A., & Ostry, J. (2011). Inequality and unsustainable growth: Two sides of the same coin? IMF Staff Discussion Note 11/08, International Monetary Fund.

  • Berg, A., Ostry, J. D., & Zettelmeyer, J. (2012). What makes growth sustained? Journal of Development Economics, 98(2), 149–66.

    Article  Google Scholar 

  • Black, D., Kolesnikova, N., Sanders, S., & Taylor, L. (2013). Are children ‘normal’? Review of Economics and Statistics, 95, 21–33.

    Article  Google Scholar 

  • Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.

    Article  Google Scholar 

  • Bourguignon, F. (2004). The poverty-growth-inequality triangle, mimeo. Presented at the Indian Council for Research on International Economic Relations, New Delhi.

  • Bun, M. J. G., & Windmeijer, F. (2010). The weak instrument problem of the system GMM estimator in dynamic panel data models. Econometrics Journal, 13(1), 95–126.

    Article  Google Scholar 

  • Caminada, K., Goudswaard, K., & Koster, F. (2012). Social income transfers and poverty: A cross-country analysis for OECD countries. International Journal of Social Welfare, 21(2), 115–26.

    Article  Google Scholar 

  • Caselli, F., Esquivel, G., & Lefort, F. (1996). Reopening the convergence debate: A new look at cross-country growth empirics. Journal of Economic Growth, 1(3), 363–389.

    Article  Google Scholar 

  • Cingano, F. (2014). Trends in income inequality and its impact on economic growth. OECD Working Paper No. 163. OECD Publishing.

  • Cohen, A., Dehejia, R., & Romanov, D. (2013). Financial incentives and fertility. The Review of Economics and Statistics, 95, 1–20.

    Article  Google Scholar 

  • De Gregorio, J., & Lee, J.-W. (2004). Growth and adjustment in East Asia and Latin America. Economia, 5(1), 69–134.

    Google Scholar 

  • de la Croix, D., & Doepke, M. (2003). Inequality and growth: Why differential fertility matters. American Economic Review, 93(4), 1091–1113.

    Article  Google Scholar 

  • Deininger, K., & Okidi, J. (2003). Growth and poverty reduction in Uganda, 1999–2000: Panel data evidence. Development Policy Review, 21, 481–509.

    Article  Google Scholar 

  • Deininger, K., & Squire, L. (1998). New ways of looking at old issues: Inequality and growth. Journal of Development Economics, 57(2), 259–87.

    Article  Google Scholar 

  • Easterly, W. (2007). Inequality does cause underdevelopment: Insights from a new instrument. Journal of Development Economics, 84(2), 755–76.

    Article  Google Scholar 

  • Farbmacher, H. (2012). GMM with many weak moment conditions: Replication and application of Newey and Windmeijer (2009). Journal of Applied Econometrics, 27, 343–346.

    Article  Google Scholar 

  • Forbes, K. J. (2000). A reassessment of the relationship between inequality and growth. American Economic Review, 90(4), 869–87.

    Article  Google Scholar 

  • Galor, O. (2009). Inequality and development: The modern perspective. Cheltenham: Edward Elgar Publishing.

    Google Scholar 

  • Galor, O., & Moav, O. (2004). From physical to human capital accumulation: Inequality and the process of development. Review of Economic Studies, 71(4), 1001–1026.

    Article  Google Scholar 

  • Galor, O., Moav, O., & Vollrath, D. (2009). Inequality in landownership, human capital promoting institutions and the great divergence. Review of Economic Studies, 76(1), 143–79.

    Article  Google Scholar 

  • Galor, O., & Zang, H. (1997). Fertility, income distribution, and economic growth: Theory and cross-country evidence. Japan and the World Economy, 9(2), 197–229.

    Article  Google Scholar 

  • Galor, O., & Zeira, J. (1993). Income distribution and macroeconomics. Review of Economic Studies, 60, 35–52.

    Article  Google Scholar 

  • Halter, D., Oechslin, M., & Zweimüller, J. (2014). Inequality and growth: The neglected time dimension. Journal of Economic Growth, 19(1), 81–104.

    Article  Google Scholar 

  • IMF. (2014). Fiscal policy and income inequality, IMF Policy Paper.

  • IMF. (2015). Malawi: Selected issues, IMF Country Report No 15/346.

  • Jaimovich, N., & Rebelo, S. (2012). Non-linear effects of taxation on growth, NBER Working Paper No. 18473. Cambridge, MA: National Bureau of Economic Research.

  • Jenkins, S. (2014). World income inequality database: An assessment of WIID and SWIID, ISIR Working Paper Series 2014-31. Essex: Institute for Social and Economic Research.

  • Kaldor, N. (1957). A model of economic growth. The Economic Journal, 67(268), 591–624.

    Article  Google Scholar 

  • Keefer, P., & Knack, S. (2002). Polarization, politics and property rights: Links between inequality and growth. Public Choice, 111(1–2), 127–54.

    Article  Google Scholar 

  • Kraay, A. C. (2015). Weak instruments in growth regressions: Implications for recent cross-country evidence on inequality and growth, Policy Research Working Paper WPS 7494. World Bank Group.

  • Kremer, M., & Chen, D. L. (2002). Income distribution dynamics with endogenous fertility. Journal of Economic Growth, 7, 227–258.

    Article  Google Scholar 

  • Lane, P. R., & Milesi-Ferretti, G. (2007). The external wealth of nations mark II: Revised and extended estimates of foreign assets and liabilities, 1970–2004. Journal of International Economics, 73(2), 223–250.

    Article  Google Scholar 

  • Lazear, E. P., & Rosen, S. (1981). Rank-order tournaments as optimum labor contracts. Journal of Political Economy, 89(5), 841–64.

    Article  Google Scholar 

  • Li, H., & Zhang, J. (2007). Do high birth rates hamper economic growth? Review of Economics and Statistics, 89(1), 110–117.

    Article  Google Scholar 

  • Li, H., & Zou, H. (1998). Income inequality Is not harmful for growth: Theory and evidence. Review of Development Economics, 2, 318–334.

    Article  Google Scholar 

  • Lindert, P. H. (2004). Growing public: Social spending and economic growth since the eighteenth century. Cambridge: University Press.

    Book  Google Scholar 

  • Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. Quarterly Journal of Economics, 107(2), 407–37.

    Article  Google Scholar 

  • Meltzer, A., & Richard, S. (1981). A rational theory of the size of government. Journal of Political Economy, 89(5), 914–27.

    Article  Google Scholar 

  • Milanovic, B. (2000). The median voter hypothesis, income inequality, and income redistribution: An empirical test with the required data. European Journal of Political Economy, 16(3), 367–410.

    Article  Google Scholar 

  • Milanovic, B. (2010). Four critiques of the redistribution hypothesis: An assessment. European Journal of Political Economy, 26(1), 147–54.

    Article  Google Scholar 

  • Newey, W. K., & Windmeijer, F. (2009). Generalized method of moments with many weak moment conditions. Econometrica, 77, 687–719.

    Article  Google Scholar 

  • Nickell, S. J. (1981). Biases in dynamic models with fixed effects. Econometrica, 49(6), 1417–26.

    Article  Google Scholar 

  • OECD. (2011). Divided we stand: Why inequality keeps rising. OECD Publishing. http://www.rrojasdatabank.info/dividedwestand2011.pdf.

  • Okun, A. M. (1975). Equality and efficiency: The big trade-off. Washington: Brookings Institution Press.

    Google Scholar 

  • Ostry J., Berg, A., & Tsangarides, C.G. (2014). Redistribution, inequality, and growth, IMF Staff Discussion Notes 14/02. International Monetary Fund.

  • Panizza, U. (2002). Income inequality and economic growth: Evidence from American data. Journal of Economic Growth, 7(1), 25–41.

    Article  Google Scholar 

  • Paulus, A., Čok, M., Figari, F., Hegedüs, P., Kump, N., Lelkes, O., et al. (2009). The effects of taxes and benefits on income distribution in the enlarged EU. In O. Lelkes & H. Sutherland (Eds.), An enlarged role for tax benefit models: Assessing policies in the enlarged European Union. Farnham: Ashgate.

    Google Scholar 

  • Pellegrini, L., & Gerlagh, R. (2004). Corruption’s effect on growth and its transmission channels. Kyklos, 57(3), 429–56.

    Article  Google Scholar 

  • Perotti, R. (1996). Growth, income distribution, and democracy: What the data say. Journal of Economic Growth, 1(2), 149–87.

    Article  Google Scholar 

  • Persson, T., & Tabellini, G. (1994). Is inequality harmful for growth? American Economic Review, 84(3), 600–21.

    Google Scholar 

  • Rodrik, D. (1999). Where did all the growth go? External shocks, social conflict, and growth collapses. Journal of Economic Growth, 4(4), 385–412.

    Article  Google Scholar 

  • Roodman, D. (2006). How to do xtabond2: An introduction to “difference” and “system” GMM in Stata. Working Paper No 103, Center for Global Development.

  • Roodman, D. (2009). A note on the theme of too many instruments. Oxford Bulletin of Economics and Statistics, 71(1), 135–58.

    Article  Google Scholar 

  • Saint-Paul, G., & Verdier, T. (1993). Education, democracy and growth. Journal of Development Economics, 42(2), 399–407.

    Article  Google Scholar 

  • Saint-Paul, G., & Verdier, T. (1997). Power, distributive conflicts, and multiple growth paths. Journal of Economic Growth, 2(2), 155–68.

    Article  Google Scholar 

  • Stiglitz, J. (2012). The price of inequality. New York: W.W. Norton and Co.

    Google Scholar 

  • Solt, F. (2009). Standardizing the world income inequality database. Social Science Quarterly, 90(2), 231–42.

    Article  Google Scholar 

  • Solt, F. (2015). On the assessment and use of cross-national income inequality datasets. The Journal of Economic Inequality, 13(4), 683–691.

    Article  Google Scholar 

  • Solt, F. (2016). The standardized world income inequality database. Social Science Quarterly, 97(5), 1267–1281.

    Article  Google Scholar 

  • Stock, J. H., & Wright, J. H. (2000). GMM with weak identification. Econometrica, 68(5), 1055–96.

    Article  Google Scholar 

  • Stock, J. H., Wright, J. H., & Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business and Economic Statistics, 20(4), 518–29.

    Article  Google Scholar 

  • Tanzi, V., & Zee, H. (1997). Fiscal policy and long-run growth. IMF Staff Papers, 44(2), 179–209.

    Article  Google Scholar 

  • Thewissen, S. H. (2014). Is it the income distribution or redistribution that affects Growth? Socio-Economic Review, 12(3), 545–71.

    Article  Google Scholar 

  • UNU-WIDER. (2014). World income inequality database (WIID3.0b). http://www.wider.unu.edu/research/Database/en_GB/wiid/.

  • Voitchovsky, S. (2005). Does the profile of income inequality matter for economic growth? Journal of Economic Growth, 10(3), 273–96.

    Article  Google Scholar 

  • Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics, 126(1), 25–51.

    Article  Google Scholar 

  • Wooldridge, J. (2002). Econometric analysis of cross section and panel data. Cambridge: MIT Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jonathan D. Ostry.

Additional information

We thank the Editor, the Associate Editor, and the anonymous referees for their constructive comments, which helped us to improve the manuscript. We are also grateful to Olivier Blanchard, Kaushik Basu, Sam Bazzi, François Bourguignon, Chih Ming Tan, Jamie Galbraith, Stephen Jenkins, Aart Kraay, Paul Krugman, Martin Ravallion, Mark Schaffer, Frederick Solt, Joe Stiglitz, Larry Summers, numerous IMF colleagues, and participants at the 2014 LACEA Meetings (São Paulo), the 2014 Inequality, Financial Stability, and Sustained Growth Conference (Stockholm), the University of Cyprus (Nicosia), the 2014 Inequality and the Future of Capitalism Conference (Berlin), the 2014 UNU-WIDER Inequality Conference (Helsinki, Finland), the IEA-World Bank 2014 Roundtable on Shared Prosperity and Growth (Dead Sea, Jordan), the Center for Global Development Seminar (Washington, DC), the 2015 Annual Conference of the Royal Economic Society (Manchester), the Higher School of Economics Conference (Moscow), the European Dialogue on Inequality (Brussels), and the University of North Dakota for helpful comments and suggestions. We are grateful for financial support from the UK’s Department for International Development (DFID). The views expressed in this paper are those of the authors and do not necessarily represent those of the IMF, its Executive Board, or its management, or of DFID.

Electronic supplementary material

Appendices

Appendix A: Various samples of redistribution

Name

Description

Observations

Mean

SD

Min

Max

Baseline

All available data in Solt (2009) as long as either (1) the country has at least one observation based on a net and one based on a market inequality measure; or (2) the ratio of the difference between market and net inequality to the associated standard error is greater than 1.96, but excluding transition countries and a set of specific observations handpicked for deletion by Solt (2009)

3667

7.5

7.2

\(-\) 14.2

30.3

Full

Full sample of all available data in Solt (2009)

4396

6.9

6.9

\(-\) 14.2

30.4

Restricted

All available data in Solt (2009) as long as the country has at least three observations based on net and three based on market inequality measures, but excluding transition countries, all developing countries prior to 1985, all developed countries prior to 1975, and a set of specific observations handpicked for deletion by Solt (2009)

2158

7.7

7.3

\(-\) 11.9

30.3

Very restricted

All available data in Solt (2009) where country-year observation from the actual survey was available

997

14.3

5.7

2.5

30.3

OECD (1975)

Subsample of the baseline for OECD countries who became members prior to 1975; total of 24 countries

806

9.1

7.3

\(-\) 11.9

30.3

Appendix B: Data and summary statistics

Description

Source

Unit of measurement

Obs

Mean

SD

Min

Max

Gini of market income

SWIID 3.1

Index of market income inequality (0–100)

828

46.0

8.9

24.3

73.4

Gini of net income

SWIID 3.1

Index of net income inequality (0–100)

828

38.3

10.2

19.8

66.1

Redistribution (full)

SWIID 3.1

Gini market—Gini net for full sample

828

7.6

7.1

\(-\) 10.6

27.9

Redistribution (baseline)

SWIID 3.1

Gini market—Gini net for baseline sample

828

7.7

7.1

\(-\) 10.6

27.9

Redistribution (restricted)

SWIID 3.1

Gini market—Gini net for Solt restricted sample

462

7.4

7.3

\(-\) 10.6

27.9

Redistribution (very restricted)

SWIID 3.1

Gini market—Gini net for very restricted sample

334

8.0

7.2

\(-\) 10.6

27.9

Log(initial income)

PWT 7.1

Real GDP per capita, PPP adjusted, chain series, in 2005 prices

828

8.6

1.3

5.7

11.2

Log(investment)

PWT 7.1

Real investment to GDP, in 2005 US dollars at PPP

828

3.1

0.4

1.3

4.3

Log(population growth \(+\) 5)

WEO

Population growth

828

1.9

0.2

\(-\) 0.5

2.7

Log(education)

Barro and Lee (2013)

Average years of primary and secondary schooling in the total population over 25

751

1.8

0.6

\(-\) 0.8

2.6

Terms of trade growth (dummy)

WEO

1 if in the bottom three deciles, and 0 otherwise

683

0.3

0.2

0.0

1.0

Polity 2

Polity IV

Scale from \(-\) 10 (autocratic) to 10 (democratic)

705

3.6

6.8

\(-\) 10.0

10.0

Description

Source

Unit of measurement

Obs

Mean

SD

Min

Max

Openness

PWT 7.1

Openness at current prices (%)

828

0.7

0.5

0.0

4.2

External debt liabilities

Lane and Milesi-Ferretti (2011)

External debt liabilities from WEO and Global Development Finance database

730

0.9

2.3

0.0

32.9

Summary statistics for the survival sample for h \(=\) 5, p \(=\) 10

   Gini of net income

SWIID 3.1

Index of net income inequality (1–100)

640

40.4

9.2

21.4

65.5

   Redistribution (full)

SWIID 3.1

Gini market—Gini net for full sample

640

4.8

6.0

\(-\) 6.6

26.8

   Redistribution (baseline)

SWIID 3.1

Gini market—Gini net for baseline sample

640

4.8

6.0

\(-\) 6.6

26.8

   Redistribution (restricted)

SWIID 3.1

Gini market—Gini net for Solt restricted sample

364

4.5

5.7

\(-\) 5.9

25.6

   Log(Investment)

PWT 7.1

Real investment to GDP, in 2005 US dollars at PPP

640

3.2

0.4

1.5

4.3

   Log(population growth \(+\) 5)

WEO

Population growth

640

1.9

0.1

1.6

2.5

   Log(education)

Barro and Lee (2013)

Average years of primary and secondary schooling in the total population over 25

633

1.7

0.6

\(-\) 0.4

2.5

Description

Source

Unit of measurement

Obs

Mean

SD

Min

Max

   US interest rate (dummy)

FED 3 month treasury bill

1 if upper three deciles, and 0 otherwise

640

0.4

0.5

0.0

1.0

   Terms of trade growth (dummy)

WEO

1 if in the bottom three deciles, and 0 otherwise

616

0.3

0.4

0.0

1.0

   Polity 2

Polity IV

Scale from − 10 (autocratic) to 10 (democratic)

614

2.1

6.7

\(-\) 9.0

10.0

   Openness

PWT 7.1

Openness at current prices (%)

640

86.6

74.7

8.5

433.0

   External debt liabilities

Lane and Milesi-Ferretti (2011)

External debt liabilities from WEO and Global Development Finance database

597

163.2

490.8

3.0

3468.3

Appendix C: Correlation between market inequality and redistribution

As discussed in Sect. 3 countries with more market inequality tend to redistribute more, with a stronger effect in the OECD sample. This is so in a model with country-specific fixed effects (which focuses on the variation across time within countries) while controlling for unobserved heterogeneity, as well as with IV-GMM estimation, where market inequality is instrumented with its lagged differences.Footnote 49 The effect is modest but nontrivial: an increase in market inequality from the 50th to the 75th percentile of the sample is associated with an increase in redistribution by 3–4 Gini units (Table 8).

Table 8 Correlation between market inequality and redistribution.

Appendix D: Reconciling with the literature: alternative panel estimation methods to investigate the growth-inequality-redistribution relationship

In an attempt to reconcile disparate results in the literature, this Appendix, presents results from additional panel estimators that have been used in the literature to investigate the growth–inequality–redistribution relationship. By using the same dataset, we are able to ensure and apples-to-apples comparison and isolate differences between our results and those in the literature using alternative estimation methods.

One of the advantages of panel data estimators is that it allows modeling (or controlling for) the time-invariant, unobserved individual effect ui in Eq. (3). The standard estimator pooled OLS (POLS) ignores the ui which is correlated with the lagged dependent variable in the dynamic panel specification, as it is part of the process that generates the lagged dependent variable yi, \(t-1\), and hence, it is biased (upwards). The fixed effects (FE) estimator (obtained by OLS estimation on the time-demeaned variables) allows the individual effect ui to be correlated with the regressors. In a finite dynamic panel model with fixed T, FE will be (downward) biased of order 1/T because the transformed time varying component of the error term vt and the transformed lagged dependent variable are correlated (Nickell 1981). (FE is also called the “within” estimator, because it uses the time variation within each cross-section.) The between estimator (BE) is obtained by OLS estimation on the cross-sectional equation on time averages of the variables, and effectively ignores variables’ changes over time. As in the case of the POLS, the between estimator is biased (upwards) since the ui is correlated with the lagged dependent variable. Finally, the random effects (RE) estimator assumes that the individual effect is uncorrelated with the regressors, which is violated in the case of the dynamic panel setting.

To illustrate the effect of these estimators, Table 9 uses a specification with inequality, redistribution and standard growth determinants as controls and applies econometric techniques used in the literature. Columns [1]–[6] present results from POLS [1]; FE-within (FEw, [2]) which is based on time variation; the between (BW, [3]) which looks at the between-country effects and uses the cross section variation in averages to identify the parameters; RE [4] which is effectively a weighted average for the within and between estimators;Footnote 50 over random effects the difference GMM estimator (dGMM, [5]) which explores time series variation; and our preferred systems GMM estimator which explores both the time-series and cross sectional variation-replicating the specification in Table 2 [3]. (Unlike the rest of the estimators the two GMM estimators allow consistent estimation in the presence of a dynamic panel and potentially endogenous variables.)

Overall, our results confirm findings in the literature and the findings of Halter et al. (2014). Estimates based only on time-series variation such as the dGMM used in Forbes (2000) generally find a positive impact of inequality on growth. On the other hand, estimation methods exploiting the cross-sectional variation in the data tend to find a negative relationship (e.g. Alesina and Rodrik 1994; Persson and Tabellini 1994; Deininger and Squire 1998; Barro 2000). Combining both cross-sectional and time series variation as in sGMM is indeed important.

As an additional illustration, we explore the growth–inequality relationship in the short-run and long-run context graphically. The left column in Fig. 6 plots per capita growth rates against the (lagged) change of inequality or redistribution, thus representing short-run changes. The right column plots the level of per capita GDP against the (lagged) level of inequality or redistribution. Two distinct patterns emerge: in the long-run there is a strong negative relationship between per capita income and inequality; however, the growth–inequality relationship is (slightly) positive in the short-run. The bottom row of the figure suggests that higher transfers may increase long-run growth, but there is no significant relationship in the short-run.

Fig. 6
figure 6

Market inequality, net inequality, redistribution, and growth: evidence from short-run and long-run effects

Table 9 Alternative panel estimation methods: the effect of inequality and redistributive transfers on growth.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Berg, A., Ostry, J.D., Tsangarides, C.G. et al. Redistribution, inequality, and growth: new evidence. J Econ Growth 23, 259–305 (2018). https://doi.org/10.1007/s10887-017-9150-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10887-017-9150-2

Keywords

JEL Classification

Navigation