Openness, growth, and inequality: It’s (partly) how you look at it Mattias Lundberg 18 June 2002

Openness, growth, and inequality No robust universal trends No fixed relationships No Kuznet’s law Sometimes up, sometimes down Sometimes concave, sometimes convex What you see depends on WHERE and HOW you look No fixed relationships – No Kuznets law – what my mother-in-law would call a “pie-crust” law: easily made, easily broken. L&S tried to find Kuznets curves for 47 countries: 8 Kuznets, 11 inverse Kuznets, and 30 with no relationship.

Trends in inequality, 85 countries This shows the initial and final (5-year average) Gini coefficients for 85 countries. There is no obvious trend. The numbers towards the bottom are the mean overall trends among those countries for which we have data beginning in each 5-year period. For those countries for which we have data beginning in the period 1960-65, the Gini has decreased on average four one-hundredths of one point each year. For those countries that only join the sample in 1985-90, the Gini has increased on average 1.2 points per year.

Openness and growth (dependent variable – across-period growth in per capita GDP) This is a regression of income on openness, in differences. The openness measure is instrumented by the twice-lagged level. When the regression is population-weighted, as in Dollar and Kraay, greater openness leads to faster growth.

Openness and growth (dependent variable – across-period growth in per capita GDP) When the regression is re-run on unweighted data, the relationship disappears. Also when we choose (arbitrarily) to weight the data by Gini coefficient.

Openness and growth (dependent variable – across-period growth in per capita GDP) Most importantly, even the population-weighted regressions are heavily influenced by China. Without China, there is no relationship.

Openness and inequality (dependent variable – across-period change in Gini coefficient) Similar story with regard to inequality (again in first differences, with openness instrumented by the twice-lagged level). Population-weighted regressions find that openness is bad for distribution, but not the unweughted regressions.

Openness, growth, and inequality (dependent variable – across-period change in Gini coefficient) Here we try to find an aggregate Kuznets curve, again in differences. Surprisingly, what we get is an inverse Kuznets curve, but it’s not robust.

Openness, growth, and inequality (results from Lundberg and Squire, 2001) In Lundberg and Squire (2001), we try to find the policies that get influence BOTH outcomes. We find that the Sachs-Warner measure of trade openness leads to faster growth and greater inequality, but this result is not robust to other measures of openness.

Openness, growth, and inequality Growth and distribution both driven by policies (such as openness) and environment Empirical results are not often robust Inequality changes very little over time Hard to find significance in cross-country regressions The Kuznets curve is missing the point. The growth of income, and its distribution are at least in part the results of the same process.

Openness, growth, and inequality (results from Lundberg and Squire, 2001) The point the Lundberg and Squire paper was trying to make is that these two are simultaneously determined. We couldn’t find any variable that was uniquely significant only to one outcome.

Openness, growth, and inequality The search for causality between growth and inequality is futile: A mechanistic relationship that ignores the role of policy The two outcomes are clearly jointly determined Policies designed to advance one will affect the other

Openness, growth, and inequality More important to understand: The conditions under which openness leads to growth The conditions under which openness leads to inequality-reducing growth How more people can profit from the benefits of increased openness

Openness, growth, and inequality Case studies offer alternative (possibly superior?) research technique, eg: Take 10 countries that have opened to trade, What has happened to growth? What has happened to distribution? What intermediating policies/factors drive the difference? Finally, a suggestion (or plea):