Saturday, October 21, 2017

Can mass mobilization wars increase income inequality?

It has  become somewhat of a truism to hold that big mass mobilization cause income inequality to decrease. I think we may date it to Max Beloff “Welfare and warfare” or perhaps to the Beveridge commission, or perhaps even earlier. The logic is not hard to understand. Big wars, like the First and Second World Wars, were fought by millions of people who had to be clothed, shed, fed, armed and thus kept at least above subsistence while not contributing any marketable goods and services. Workers in factories too would not be let to starve, as Avner Offer reminds us in his “The First World War: An agrarian interpretation”. For that was a constant fear of the ruling classes: if domestic labor rebels, the war is lost. Somebody obviously had to pay for all that: and who else but the rich. Hence inequality had to go down. On top of that, countries on whose territory wars were fought were devastated, and losses were more than proportionately borne by the rich factory owners, land proprietors and the like.

In this explanation, we have to distinguish two parts. The part which is connected directly with redistribution (higher taxes to pay for soldiers) and the part which is connected with destruction and reduction of overall income. If overall income declines substantially, it is hard to maintain real incomes of the rich without courting the danger of regime overthrow or defeat in war (or both). So, in part the decrease in inequality comes from impoverishment.

(It is here that it is useful to add to the usual Gini or top share statistics, the Inequality Extraction Ratio which measures how close to the maximum possible inequality a country is. It could easily happen that the measured inequality goes  down during a war while the Inequality Extraction Ratio goes up—I other words, the elite has, in relative terms, become even more rapacious.  But  we shall leave this part for the specialists.)

War as a great leveler hypothesis has recently received further support from the discussion in Thomas Piketty’s “Capitalin the Twenty First Century”, and even more explicitly in Walter Scheidel’s “The Great Leveler”. There mass conscription wars are one of the four horsemen of the apocalypse who alone are able to bring inequality down (“the cure is worse than the disease” as Scheidel’s  book epigraph states it). I have argued along the similar lines in my “Global inequality”.

But is it always so? It has been hinted  in some books about the World War I (notably in Niall Ferguson’s “Pity of War”) that inequality in Germany might have increased during the 1914-18 period (i.e. prior to the  November collapse) because the tax system continued being unfair or regressive.  Junkers and big industrial capitalists, Ferguson argued, were unwilling to pay for the war—even if they wanted it won. Similar observations are present, in a dispersed form, in the already mentioned Offer’s book and in Adam Tooze’s recent “Deluge”.  

Now come two economic historians, Maria Gomez-Leon and Herman de Jong who using detailed data on social structure of England and Germany, and on the evolution of occupational wages and income from property for dozens of categories, calculate the so-called “dynamic social tables” for the two countries for the period 1900-1950. And what they find is that German inequality indeed increased during the Great War while English went down (see the graph).

This could provide in part the explanation for who lost and who won the war, and thus might have political significance. But for people who deal with inequality it sends a message about contingencies and human agency: even things that appear to be very logical (that the war needs to be financed by the rich) and find strong empirical support in many cases, need not hold in all cases. That is, even a modern (20th century) mass mobilization wars may be accompanied by rising inequality—during the war years themselves.

(Incidentally, Gomez-Leon and de Jong show similar evolution of German inequality during the Second World War but the mechanism there was different. It consisted of  forced foreign labor, pillage of conquered territories and their populations all in order to desperately try to maintain food consumption and real incomes  of the German population from collapsing during the war—an objective that the Nazi authorities achieved until approximately 1944. But, this is a somewhat different story.)

Monday, October 16, 2017

Figuring out various income inequalities: what can they tell us?

The recently released World Bank data on national accounts (GDP per capita, national consumption, CPIs, exports and imports etc.) give us an opportunity to update calculations of international inequality. Things are not always very obvious and I will have to explain some methodological choices too. Using the classification that I introduced in my “Worlds Apart” (2005), I will review three types of international and global inequalities.

Let’s start with the simplest one. Take all countries’ GDPs per capita (all expressed in mutually and over-time comparable 2005 international dollars –based on 2011 International Comparison Project) and calculate Gini across them. (Ignore whether they are small or populous countries.) The number of countries varies as there are more countries in the world today than in 1952 when our series begins, so best would be to look at the red line only after 1980. After that date the number of countries is about constant, and today’s independent countries (say, Ukraine, Macedonia, Slovakia, Eritrea) are considered as separate countries even then, when they were parts of larger wholes. (USSR, Yugoslavia etc. produced the data for their constituent units like the US produces GDP data for its states).

What do we see in the red line? Increasing Gini between mid-1980s and 2000, implying a divergence of country mean incomes, and then, after the turn of the century, a convergence. The main reasons for the convergence are faster growth rates in Africa, Asia and Latin America than in rich countries (especially after 2007). (People who studied growth economics can easily recognize here the story of unconditional convergence or divergence calculated using Gini rather than a regression.)

So that would be the end of the story if each country had the same population. But obviously they do not. For world inequality, convergence of China and Chad, India and Israel, are not equivalent. If Chinese GDP per capita converges to that of the rich countries, this is obviously going to matter more. To see how much look at the blue line where we use countries’ GDPs per capita as before but weigh them now by populations. The striking thing is that after 1978, precisely when Chinese growth picks up, the blue line starts to go down. At first slowly and then more and more precipitously.  After about 2000, Gini seems to be in a free fall. When we tease out the data a little bit more, we find that up to 2000, the entire “job” of inequality reduction was done by China. Without China, the blue line would have gone up. But after 2000, even if we drop China out, the population-weighted inequality goes down: it is driven down by the fast growth of India (and also Indonesia, Vietnam etc.). This is why we now have two big engines of international income reduction: China and India. However, as China becomes richer it may not play that role for very long. Today, China’s GDP per capita is almost exactly equal to the world’s average, but India’s is at less than ½ of world average. So in the near future, India’s growth rate compared to the world would be of paramount importance.

This would be the end of the story if within each country all citizens had the same income (i.e., there were no within-national inequalities). Note that the blue line implicitly assumes this: that all Chinese have the mean income of China, all Americans the mean income of the United States etc. Obviously, this is not true. Moreover we know that income inequality in most countries has gone up. When we try to look at inequality across all citizens of the world (global inequality shown by the green dots) we leave the world of national accounts because that world cannot give us data on individual incomes and move to the world of household surveys. (From US national accounts, I can learn that the average value of output produced in the US annually is $53,000. But I have no idea what is the income of the top 1% or of the bottom decile.  For that I need to move to the world of household surveys.)

It should be obvious that the green dots must lie above the blue line. It should also be obvious that the more important the within-national inequality, the greater would be the gap between the green dots and the blue line. This is indeed what we see: global inequality, calculated using the same international dollars, starts by being around Gini of 0.7, and then, pulled down by the blue line, begin its downward slide ending at around Gini of 0.63.

Movements in global inequality today reflect two forces: a big one of Asia’s convergence that brings mean incomes of China, India, Vietnam, Indonesia etc.  closer to the rich world, and a smaller, but important, force of within-national widening of inequalities. If in the future Asian convergence ceases, and within–national inequalities go up, the green dots will move back towards Gini of 0.7. But if alternatively convergence continues, expands to Africa, and within-national inequalities cease to grow, the green dots will keep on going down.

This would be the of the story were it not for another problem. Our household surveys tend to underestimate top incomes (the proverbial top 1%). And the question can then be asked: perhaps we underestimate the height of the green dots by not accounting fully for the super-rich. Christoph Lakner andI have tried, the best we could, to adjust for that, assuming country-specific underestimates, and raising, in a Pareto fashion, incomes of the top 10%. That’s how we got the high orange dots.  

Now the situation is not as splendid as before: global inequality is down compared to the 1980s, but the decrease is more modest: around 3.5 Gini points rather than almost 7. Could the adjustment overturn the entire decrease and produce an increase in global inequality? It is possible but unlikely—because the strength of Asian convergence is so big, and affects so many people (almost 3 billion) that even very strong increase in within-national inequalities cannot fully offset this gain. But we need more and better data to say that with certainty, and indeed one of the topics that several people are working on right now is how to combine household survey data (that are very good for 90-95% of national distributions)  with fiscal data that are better for top 1%, and possibly even top 5%.

So, stay tuned!