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)| false Paulus, A., M. Čok, F. Figari, P. Hegedüs, N. Kump, O. Lelkes, H. Levy, C. Lietz, S. Lüpsik, D. Mantovani, L. Morawski, H. Sutherland, P. Szivos, and A. Võrk, 2009, “ The Effects of Taxes and Benefits on Income Distribution in the Enlarged EU,” in An Enlarged Role for Tax Benefit Models: assessing policies in the enlarged European Union, ed. by ( O. Lelkesand H. Sutherland Farnham, U.K.: Ashgate).
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We thank Olivier Blanchard and numerous IMF colleagues for useful comments, Frederick Solt for help with the data, Yorbol Yakhshilikov for superb research support, and Anne Lalramnghakhleli Moses for assistance.
Others go further—beyond the scope of this paper—and focus on the social and political consequences of rising inequality trends in advanced countries (for example, Reich, 2011; Wilkinson and Pickett, 2009).
There have been many efforts in the literature to look at either market or net inequality, but Solt (2009) breaks new ground in his efforts to make the various underlying survey data comparable across time and countries for a large number of countries.
Easterly (2007) examines the effects of inequality associated with colonial landholding patterns. Some countries are more unequal now, he argues, as a legacy of plantation-based agricultural systems dictated by geography. Such variations in inequality seem to lower per capita income.
Solt divides the surveys into 21 types and uses the entire dataset to infer how to map each of these 21 survey types into standard measures of net and market inequality. He defines net inequality as that associated with income after direct taxes and subsidies, and market inequality as pre-tax and pre-subsidy income. Version 3.1 of the Solt (2009) data set, used in this paper, covers some 153 developing and advanced countries for as many years as possible from 1960 to 2010. Solt (2009) follows the broader literature in interpolating for some observations that fall between but close to actual survey years. Lustig, Pessino, and Scott (2013) present a more complete picture for a few countries in Latin America, but this does not constitute a broad enough sample for our purposes.
We follow Solt (2009) and restrict ourselves to a more reliable subsample in our baseline. We investigate and present below the sensitivity of our results to two different samples, a more expansive one that uses all the available data and a more restrictive one as in Solt (2009). All these samples are described in the notes to Table 4.
Much of this increase in inequality in the OECD has been in the income of the highest 1 percent or even more rarified groups. This paper uses the Gini because it is much more widely available, but the Gini is relatively insensitive to movements at the tail of the distribution, partly because the richest households tend to be underrepresented in the household surveys used to measure it (Alvaredo, 2011).
Additional estimations (not shown for brevity) control for other determinants of redistribution such as the degree of democracy, investment, population growth, as well as possible non-linearities. The estimations in Table 2 control for country-specific fixed effects and thus focus on the variation across time within countries. The results are similar when we use estimation methods that also explore between-country variations (random effects), and they are similar though somewhat attenuated when we use lagged inequality.
Looking over the past four decades, a country that spends all of its time in a growth spell (as defined below) enjoys average annual growth about 3½ percentage points higher than a country that spends only a fifth of the time in a growth spell.
If inequality or redistribution affects growth mainly by changing investment behavior, then adding investment as a control may result in only a weak effect of inequality or redistribution (since the specification gives the effect of inequality or redistribution, holding constant the level of physical or human capital investment).
A statistical test that takes into account the uncertainty about both estimates rejects the notion that this difference could occur by chance. Formally, the statistical test decisively rejects the hypothesis that the coefficients of equality and redistribution are equal.
In terms of Figure 1, the total effect is represented by lines C, D and E combined. For the estimation of the direct effects (lines D and E), we can be agnostic about whether there is significant two-way causality between redistribution and market inequality (whether line A should have arrows at both ends), because our multivariate techniques isolate the effects of each variable holding the other constant. However, our calculation of the overall effect of redistribution does assume that redistribution has no effect on market inequality, say by changing relative labor supplies at different skill levels and hence relative wages.
Results are reported using system-GMM where potentially endogenous right-hand side variables are instrumented using appropriate lagged values and first differences. The technique appropriately exploits both the cross-sectional and time-series variation in the data. For all results presented, standard tests for the validity of the instruments and first and second-order serial correlation are satisfied.
Separating the sample, we find that higher inequality is bad for growth for both OECD and non-OECD countries (with the effect higher in OECD than in non-OECD countries), while redistribution remains insignificant. This is in contrast with the results of Thewissen (2013), who looks at similar issues for a smaller set of OECD countries using the LIS inequality database and World Top Income data and finds no robust association between either inequality or redistribution and growth. However, unlike us he uses a fixed-effects methodology, which does not account for the cross-sectional variation of inequality and redistribution (and which is biased in the presence of lagged dependent variables, as in both his and our specifications).
The p-value for the test that the two coefficients on redistribution in column 1 are equal is 0.095. If nonetheless we include redistribution only linearly (i.e. without a distinction between highly redistributive cases and others), we find an overall negative effect of redistribution on spell duration. As the baseline specification in column 1 shows, however, this is driven by the high-redistribution cases.