The Disturbing "Rise" of Global Income Inequality

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Xavier Sala-i-Martin (*)


First Draft: August 2001

March 12, 2002


We use aggregate GDP data and within-country income shares for the period 1970-1998 to assign a level of income to each person in the world. We then estimate the gaussian kernel density function for the worldwide distribution of income. We compute world poverty rates by integrating the density function below the poverty lines. The one-dollar-a-day poverty rate has fallen from 20% to 5% over the last twenty five years. The two-dollar-a-day rate has fallen from 44% to 18%. There are between 300 and 500 million less poor people in 1998 than there were in the 70s.

We estimate global income inequality using seven different popular indexes: the Gini coefficient, the variance of log-income, two of Atkinsonís indexes, the Mean Logarithmic Deviation, the Theil index and the coefficient of variation. All indexes show a reduction in global income inequality between 1980 and 1998. We also find that within-country inequalities are small compared with cross-country differences. Within-country disparities have increased slightly during the sample period, but not nearly enough to offset the substantial reduction in across-country disparities. The across-country reductions in inequality are driven mainly, but not fully, by the extraordinary growth rate of the incomes of the 1.2 billion Chinese citizens.

Unless Africa starts growing in the near future, we project that income inequalities will start rising again. If Africa does not start growing, then China, India, the OECD and the rest of middle-income and rich countries diverge away from it, and global inequality will rise. Thus, growth of the African continent should be the priority of anyone concerned with increasing global income inequality.

Keywords: Income inequality, poverty, convergence, growth.

JEL: D31, F0, I30, I32, O00.


(*) Columbia University, UPF and NBER. This paper was partly written when I was visiting Universitat Pompeu Fabra in Barcelona. I thank Sanket Mohapatra for extraordinary research assistance and for comments, suggestions and short speeches related to this paper. I also benefitted from the comments of Elsa V. Artadi, Tony Atkinson, FranÁois Bourguignon and Laila Haider .


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