Thursday, March 4, 2010

How not to do science

Check out this (warning: .pdf) study. It claims that African inequality is dropping rapidly. You don't have to go too far for big red WARNING signs to pop up:
The World Bank concurs: “In 1990, 28.3 percent of the people in low and middle‐income countries lived on less than $1 a day. By 1999 the share had fallen to 21.6 percent, driven mainly by strong growth in China and India (…) In Sub‐Saharan, where the GDP per capita fell by 5 percent, the extreme poverty rate rose from 47.4 percent in 1990 to 49 percent in 1999. The numbers are believed to be still rising” (World Bank (2004).) The U.N. Millenium Campaign Deputy Director for Africa says: “Poverty continues to intensify due to the exclusion of groups of people on the basis of class, caste, gender, disability, age, race, religion and other status,” (UN Millenium Campaign (2009).) This conventional wisdom is further documented and critically reviewed in Easterly (2009).
In this paper, we use the methodology of Pinkovskiy and Sala‐i‐Martin (2009) to estimate income distributions for African countries, and compute their poverty rates, and inequality and welfare indices for the period 1970‐2006. Our results show that the conventional wisdom that Africa is not reducing poverty is wrong. In fact, since 1995, African poverty has been falling steadily.

This should be setting off low-level bullshit detectors. But it gets better. The method the authors used is very simple: curve-fitting a lognormal distribution to PPP GDP and gini coefficients. The lognormal is entirely characterized by two numbers, the mean and the variance. Any curve-fitting to data is going to result in errors in mean and variance -- errors which, in the name of honesty, should be quoted. They don't show up anywhere.

So what are the authors doing in this paper? They are applying a model of income distributions to one set of data, refusing to specify uncertainties in the model's predictions, and using the results of the model to contradict existing data. To say the least, this is completely backwards: disagreement between the model's predictions and World Bank data should be evidence against the use of lognormal distributions and the fitting methods used in the paper, not evidence against the World Bank data.

This, kids, is how not to do science.

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