Showing all posts tagged inequality:

Lower prices in many countries increases global purchasing power and decreases poverty and inequality

The Economist has a nice article on the fact that global GDP in PPP-dolars has been revised upwards because the International Comparison Programme has documented that many prices on many products in many countries are lower than last time they checked. Interestingly, the extra purchasing power is not evenly distributed, and the article mentions India, China, Russia and Ukraine.
The article is worth reading for many reasons, one of them being that it includes a simple explanation of PPP-dollars and why calculating them requires knowing prices.
The article does not, however, spell out what I assume is an immediate consequence of the revised numbers: That absolute poverty has fallen by more than previous data indicated, and that global inequality between countries have fallen (this should be the case because lower prices in China and India mean hifger purchasing power for many poor people).
After some searching I can confirm the consequences for global inequality. In a blog post by the World Bank we find the following remarkable statement:
The share of the global population living in economies where the mean GDP per capita is below the global average decreased from 75 percent in 2017 to 56 percent in 2021.
That is a substantial decrease in only 4 years! Also, the blog post says the following:
The intercountry Gini coefficient for PPP-based GDP per capita improved over the years, from 0.486 in 2011, to 0.466 in 2017, to 0.458 in 2021.
The blog post even has Lorenz-curves:
The article in The Economist also does not mention why some prices have fallen in a way that increases purchasing power of the poor. The fact is that the development is only to be expected as a result of market forces: Where more people are poor, it is more important to produce cheaply and to invent and produce cheaper substitute good. The article touches upon this when they write:
And often the same consumer need is met by different goods in different parts of the world. In rural Thailand, workers live on rice. In similar parts of Ethiopia, they live on teff. But "rice is hard to find in Ethiopia and teff is impossible to find in Thailand, so price comparisons are not possible," as Angus Deaton of Princeton University and Alan Heston of the University of Pennsylvania have pointed out.
Luckily, a paper in Southern Economic Journal by myself and Therese Nilsson shows that inequality of purchasing power is likely to be lower than inequality of income precicely because lower prices to some extent mitigate poverty. The revised data from the World Bank show that this is not merely a theoretical oddity, it an important mechanism behind falling poverty.
See further
Bergh, Andreas, & Therese Nilsson. "When More Poor Means Less Poverty: On Income Inequality and Purchasing Power". Southern Economic Journal 81: 232–46.

Should we use standardized inequality databases such as SWIID?

Here is my implicit point of view regarding the debate between Jenkins (2015) and Solt (2016):
Below is a table (Table 1) from Rudra (2004).
Do you notice anything strange about these Gini-coefficients? Hint: to verify inequality data, I always look at the country I know best, to see if data make sense...

[I will update this post with my thoughts eventually]

Clearly, something is wrong with the data regarding Sweden in the 1970s. The table suggests that inequality in Sweden was at its lowest level in 1975 (at 27.3) and at its highest level just a year later, in 1976 (33.1). In a country like Sweden, inequality never jumps that much from one year to another, and for sure not in 1976. Reexamining the Deininger and Squire database, it turns out that the 1975 value comes from the LIS database, whereas the 1976 value is taken from Statistics Sweden. Most likely, the latter includes capital income and the former does not. Checking other figures reveals that mosty data for Sweden are net household income, but for Brazil gross income is used, and for China the unit is the individual, not the household.

Rudra is not alone. In fact, she is better than many other papers because the inclusion of a table like Table 1 above means that the errors are possible to spot by reading the paper closely. Often, D&S data are just added to the analysis without even a simple visual inspection, which means that the analysis uses incomparable Ginis.

One of the biggest benefits of Solt's Swiid, is that all Ginis are converted to the same typ (LIS-standard), and mistakes like these are avoided.

Jenkins, Stephen P. 2015. "World Income Inequality Databases: An Assessment of Wiid and Swiid." Journal of Economic Inequality 13(4):629–71.
Rudra, N. 2004. "Openness, Welfare Spending, and Inequality in the Developing World." International Studies Quarterly 48(3):683-709. doi: 10.1111/j.0020-8833.2004.00320.x.
Solt, Frederick. 2016. "On the Assessment and Use of Cross-National Income Inequality Datasets." Journal of Economic Inequality (forthcoming).

On wealth inequality and growth

Is wealth inequality bad for growth? That depends on its origins, according to a new paper by Sutirtha Bagchia and Jan Svejnarb:
The abstract:
A fundamental question in social sciences relates to the effect of wealth inequality on economic growth. Yet, in tackling the question, researchers have had to use income as a proxy for wealth. We derive a global measure of wealth inequality from Forbes magazine's listing of billionaires and compare its effect on growth to the effects of income inequality and poverty. Our results suggest that wealth inequality has a negative relationship with economic growth, but when we control for the fact that some billionaires acquired wealth through political connections, the relationship between politically connected wealth inequality and economic growth is negative, while politically unconnected wealth inequality, income inequality, and initial poverty have no significant relationship.