The Power of Data: how new stats are changing our understanding of inequality
Every Saturday my colleague Max Lawson, who’s Oxfam’s global inequality policy lead, sends round an email entitled ‘Some short reading for the weekend if you fancy it’. This week was particularly good, so I just lifted it:
This year has already been good for the improvement in data availability on inequality, with the launch of the Wealth and Incomes Database (WID) in Paris in January. This database allows you to easily search for the data on top incomes gathered by Piketty and colleagues. Although limited in its country coverage, for those countries it does manage to cover it is fascinating. Take these two charts looking at the incomes of the top 1% and top 10% in contrast to the bottom 50% in China for example.
The WID database is based on the painstaking collection of tax records which has revolutionised our understanding of the scale of inequality. It has revealed that inequality is significantly worse than we thought. This is because the usual way to calculate the Gini is the household survey, and this is notoriously poor at capturing the rich- either they do not make it into the sample, or they do not fully report the scale of their wealth. Sadly the top incomes method, whilst very revealing, can’t really be used in the majority of developing countries as tax records are very poor and also the numbers paying personal income tax at all are very few.
A really great paper from Laurence Chandy and Brina Seidel this week, explores another new way of working out what top incomes really are, and correspondingly the real level of inequality. This Brookings paper uses a new method, identified by Branko Milanovic and Christoph Lakner, which instead compares household surveys with the national accounts of a country. The results are really dramatic – increases across the board and many other changes in country position:
‘The ten countries in the world with the highest Ginis include five new entries: Nigeria, Mexico, Indonesia, Georgia, and Guatemala. Whereas the U.S. (0.41) and China (0.40) report very similar levels of inequality, when we adjust for missing top incomes the U.S. appears considerably more unequal than its rival (0.51 versus 0.44). Meanwhile, India has the dubious honor of leapfrogging both countries under our adjustment, as its Gini skyrockets from 0.36 to 0.56.’
This final finding echoes other studies on India, who use consumption to calculate their Gini which is another reason it is very low. Officially their Gini is the same as Ireland, whereas it is probably closer to Brazil.
The rapid development of research and interest in ways of calculating inequality better is very exciting. There is no doubt that in the next few years we need to see a redoubling of efforts to collect more and better data to show us a clear picture of the scale of inequality in every country across the world, and more accurately plot trends too.
Finally in the UK two influential think tanks, the Institute for Fiscal Studies and the Resolution Foundation have just made projections that inequality is set to rise over the next few years, having remained relatively static and even falling slightly in recent years by some measures. The incomes of the poorest in particular are set to fall considerably which is deeply concerning, especially as the UK already has a million people regularly using food banks to survive. The chart below illustrates this very well.
The ultimate aim of the WID database it to produce Distributional National Accounts (DINA) to provide annual estimates of the distribution of income and wealth using concepts of income and wealth that are consistent with the macroeconomic national accounts. This would be a fantastic development and would revolutionise the way we appraise government policy and our economies. Oxfam is planning to work with others to launch a major call for better data on inequality this year.