In the lecture, we voice some reservations about the recent book by Thomas Piketty. His data was limited in scope to a handful of rich Western countries. What he suggested was that using the GINI index, labor income inequality in Europe was naught 0.26. And that's in the United States, so that naught 0.36. But this leaves the question of what the picture is elsewhere in the world. Now for several years the World Bank has been assembling information on the less developed countries. The results are based on income data from households, derived from household surveys and not from national text record. The compilers of the index had met, that is what I would call a dirty data set. But they argued that if they aimed at perfect compatibility, they'd have to eliminate too many countries. And if they tried to correct the data in some way, they might be inserting even more biases than the ones they were trying to eliminate. There are two main issues. The first is that there is no distinction between single earner households and multiple earner households. And then equality tends to be reduced in the multiple earner households for obvious reasons. The second issue is it takes no accounts of non-income benefits, such as free education or primary health care. The compilers of the index warn, we therefore caution researchers who use this data to interpret the results carefully. Now we’ve taken the data from only the years 2010 and 11, which gives us data for 60 countries. This map shows the countries for which data was missing. It includes all the richer Western countries which the World Bank didn't even attempt to include, but also large sways of the rest of the world. Now the index is expressed in a range of 0 to 100. With the lowest number expressing the greatest degree of homogeneity, and the highest showing the greatest diversity. We plotted the range for you to see. And we've also indicated the Piketty estimates for Europe and the United States. And it's clear that many less developed countries are in an even worse position than the United States, which was such a concern, or cause of concern to Thomas Piketty. Now, let's scroll through the map. Already in the first decile, we are leaving Europe behind, assuming of course that the non income transfers in Europe are more developed then those in Belarus or Kazakstan. In the fourth decile, we cross the GINI index for the United States. It's equivalent here is Cambodia, and once again, we can assume that American welfare spending is more than developed than in Cambodia. We'll pause here, and you can see Macedonia entering. In the next decile, you'll see Malaysia, but for the rest, the greatest inequality levels are concentrated in Central and South America and Sub-Saharan Africa. In 2011, the OECD published a separate report on income inequality in its member states and in several of the emerging economies. This report is based on income data derived from tax accounts from 2008, and it doesn't use the kind of household surveys employed by the World Bank. So remember, these two sets of data are not comparable. Since there are only 38 states with more than 1.5 million in the map, we've shown it together in just one map. But again, it does underlie the comparably high levels of inequality in the emerging economies. Well, we hope you've enjoyed this visualization. I remind you that both sets of data are in the database accompanying this course. But please, remember the warning. Exercise extreme caution in interpreting the results.
