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Alesina, Alberto, and Dani Rodrik, 1994, “Distributive Politics and Economic Growth,” Quarterly Journal of Economics, Vol. 109 (May), pp. 465-490.
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Atkinson, A.B., 1996, “The Distribution of Income: Evidence, Theories and Policy,” De Economist, Vol. 144, No. 1 (April), pp. 1-21.
Blejer, Mario I., and Isabel Guerrero, 1988, “The Impact of Macroeconomic Policies on Income Distribution: An Empirical Study,” IMF Working Paper 88/57 (Washington: International Monetary Fund, July).
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Bulir, Ales, and Anne-Marie Gulde, 1995, “Inflation and Income Distribution: Further Evidence on Empirical Links”, IMF Working Paper 95/86 (Washington: International Monetary Fund, August).
Clements, Benedict, 1996, “Income Distribution and Social Expenditure in Brazil”, FAD Working Paper 96/1 (Washington: International Monetary Fund, September).
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Deininger, Klaus, and Lyn Squire, 1997, “Economic Growth and Income Inequality: Reexamining the Links”, Finance and Development, Vol. 34, No. 1 (March), pp. 38-41.
Deininger, Klaus, and Lyn Squire, 1996, “A New Data Set Measuring Income Inequality,” The World Bank Economic Review, Vol. 10, No. 3 (Washington: The International Bank for Reconstruction and Development), pp. 565-91.
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The author thanks David Bigman, David Robinson, Amrit Singh, and Christian Thimann, for helpful discussions and comments on an earlier draft.
For example, Galor and Zeira (1993) show that in the presence of credit markets’ imperfections and indivisibilities in investment in human capital, the initial distribution of wealth affects aggregate output and investment both in the short run and in the long run. Alesina and Rodrik (1994) present a theoretical model which implies that income inequality affects growth negatively, through the following channel: (i) income (and wealth) inequality causes the tax rate to go above its optimal level (because the median voter gains from the redistributive effects of a higher tax rate if the degree of inequality is large); and (ii) a higher-than-optimal tax rate reduces investment and growth. Alesina and Perotti (1996) find empirical evidence that income inequality affects growth negatively, through the following channel: (i) income inequality, by fueling social discontent, increases socio-political instability; and (ii) socio-political instability, by creating uncertainty in the politico-economic environment, reduces investment and growth. Bruno, Ravallion, and Squire (1996) find that initial distribution of assets and income affects subsequent growth; high inequality countries have lower growth and remain inegalitarian, whereas low inequality countries remain egalitarian and achieve rapid poverty reduction from the process of growth. Deininger and Squire (1997) review empirical research done by them using their new data set (described in Deininger and Squire (1996)) and conclude that there is evidence of a negative link between initial income inequality and subsequent growth. Schmidt-Hebbel and Serven (1996), on the other hand, find that cross-country data do not reveal any strong association between income distribution and saving ratios (after controlling for other saving determinants).
Renowned examples of this line of work include Barro (1991) and Fischer (1993); now, this literature is very voluminous.
Fischer (1993), for example, shows that growth is negatively associated with inflation, large budget deficits, and distorted foreign exchange markets. He also finds evidence that the causation runs from macroeconomic policy to growth. For example, inflation reduces growth by reducing investment and productivity growth.
Some notable examples are Bruno, Ravallion, and Squire (1996), and Deininger and Squire (1997). Fishlow (1995), however, argues that a complete dismissal of the original Kuznets parabolic relationship between inequality and income may be in error. Milanovik (1994) finds that inequality is lower in richer countries not only because of structural factors (such as employment and rural-urban compositions), but also because the importance of social-choice factors (such as income redistribution and employment policies) increases as the level of income rises.
Typical examples are Bulir and Guide (1995) and You and Dutt (1996). Bulir and Guide (1995), using both pooled cross country and single country time series models, find that the level of inflation, inflation variability, and the variability of the nominal exchange rate have a negative impact on overall income equality. You and Dutt (1996) examine the effect of government debt on income distribution in a post-Keynesian framework, and find that it depends on the circumstances under which government debt rises.
Examples of this kind of studies include Blejer and Guerrero (1988), Cole and Towe (1996), and Razin and Sadka (1996). Blejer and Guerrero (1988) looked at the experience of the Philippines in the 1980s and found that underemployment, inflation, and government expenditure are strongly regressive, while a depreciation of the exchange rate tends to reduce inequality. Cole and Towe (1996) examine the factors underlying the rise in U.S. income inequality since the mid-1970s. Their results suggest that the increase in income inequality has not been related to macroeconomic developments, but mainly to developments in labor markets, technology, and demographics. Razin and Sadka (1996) show, in the context of the Israeli experience, that inflation results in a high tax burden on workers and a low tax burden on the business sector (capital owners and self-employed).
The effects of some of these variables are examined by Clemens (1996), in the context of a case study of Brazil.
We are grateful to Klaus Deininger for kindly supplying us the full database.
The Gini coefficient, although not a perfect tool, is a relatively good summary indicator of income inequality (for a discussion on the merits and drawbacks of using the Gini indicator, see Deininger and Squire (1996, p. 567)). However, in addition to the issues of choosing the right inequality indicator, there are more fundamental questions regarding the concept of inequality in general. For example, Kusnic and Davanzo (1986) show that measured inequality is overstated, because it includes only market activities. Including nonmarket activities, such as leisure, lowers inequality. Fukushige (1996) discusses the difference between cross-section income inequality and life-time income inequality. These fascinating issues are beyond the scope of the present study.
Comparing changes in Gini coefficients measured by one type of survey for each country is clearly better than the alternatives. However, it still implies some strong assumptions. For example, this comparison assumes that a change of one percentage point in the Gini generated by a “personal/expenditure” type of survey is comparable to an identical change in the Gini generated by a “household/gross income” type of survey.
The particular minimal number of years required can be anywhere between 5 and 9, without affecting the resulting sample.
In case two survey types have the same number of observations, we keep the type that spans a longer period.
In principle, it may be interesting to look also at the socialist countries. However, it is probably not a good idea to pool them together with the other countries. Both during the socialist period and the post-socialist reforms, these countries suffered from very special shocks. Including them in the sample is likely to distort the results.
Interpreting the adjusted Gini from Table 2 for a socialist country is a little intricate. The variable measures the amount of inequality that we would expect in such a country, if it was not a socialist country. The actual (unconditional) inequality can be derived by subtracting 17.8 points from the adjusted Gini.
For example, there is a perception that technological factors caused a general increase in inequality during the 1980s and 1990s. For some countries the samples cover a later period and they would capture this effect, while for other countries the samples cover an earlier period and they would fail to capture it. Including the average year of the sample controls for this effect, assuming it is linear in time.
We could, in principle, choose to use the Gini at the beginning of the period. However, given that (i) the cross-country differences in income inequality are in general much larger than the time-series differences, and (ii) the individual income inequality data points are not very precise, we feel that using the average for the sample period is more appropriate in this case.
Assuming the income effect is linear, we estimate the following functional form: y = a0 + a1x1 + a2x1ly + …, where ly is defined as deviation from mean log income. If the estimated a2 coefficient is significant, then the total effect of x1 on y will be a1 + a2 ly. If the estimated a2 is not different than zero, then the effect of x1 on y is simply a1, for every level of income.
This finding is difficult to interpret, but it probably should be viewed as a proxy for demographic dynamics. When these two variables were omitted, the other demographic variables (population growth rate and the percent of population below 15 years of age) became significant.
We also tried other specifications, such as a6 log y + a7 (log y)2, and the estimated coefficients a1-a5 were not significantly affected.