Is the U-curve of US income inequality that pronounced?

For some time now, I have been skeptical of the narrative that has emerged regarding income inequality in the West in general and in the US in particular. That narrative, which I label UCN for U-Curve Narrative, simply asserts that inequality fell from a high level in the 1910s down to a trough in the 1970s and then back up to levels comparable to those in the 1910s.

To be sure, I do believe that inequality fell and rose over the 20th century.  Very few people will disagree with this contention. Like many others I question how “big” is the increase since the 1970s (the low point of the U-Curve). However, unlike many others, I also question how big the fall actually was. Basically, I do think that there is a sound case for saying that inequality rose modestly since the 1970s for reasons that are a mixed bag of good and bad (see here and here), but I also think that the case that inequality did not fall as much as believed up to the 1970s is a strong one.

The reasons for this position of mine relates to my passion for cliometrics. The quantitative illustration of the past is a crucial task. However, data is only as good as the questions it seek to answer. If I wonder whether or not feudal institutions (like seigneurial tenure in Canada) hindered economic development and I only look at farm incomes, then I might be capturing a good part of the story but since farm income is not total income, I am missing a part of it. Had I asked whether or not feudal institutions hindered farm productivity, then the data would have been more relevant.

Same thing for income inequality I argue in this new working paper (with Phil Magness, John Moore and Phil Schlosser) which is a basically a list of criticisms of the the Piketty-Saez income inequality series.

For the United States, income inequality measures pre-1960s generally rely on tax-reporting data. From the get-go, one has to recognize that this sort of system (since it is taxes) does not promote “honest” reporting. What is less well known is that tax compliance enforcement was very lax pre-1943 and highly sensitive to the wide variations in tax rates and personal exemption during the period. Basically, the chances that you will report honestly your income at a top marginal rate of 79% is lower than had that rate been at 25%. Since the rates did vary from the high-70s at the end of the Great War to the mid-20s in the 1920s and back up during the Depression, that implies a lot of volatility in the quality of reporting. As such, the evolution measured by tax data will capture tax-rate-induced variations in reported income (especially in the pre-withholding era when there existed numerous large loopholes and tax-sheltered income vehicles).  The shift from high to low taxes in the 1910s and 1920s would have implied a larger than actual change in inequality while the the shift from low to high taxes in the 1930s would have implied the reverse. Correcting for the artificial changes caused by tax rate changes would, by definition, flatten the evolution of inequality – which is what we find in our paper.

However, we go farther than that. Using the state of Wisconsin which had a tax system with more stringent compliance rules for the state income tax while also having lower and much more stable tax rates, we find different levels and trends of income inequality than with the IRS data (a point which me and Phil Magness expanded on here). This alone should fuel skepticism.

Nonetheless, this is not the sum of our criticisms. We also find that the denominator frequently used to arrive at the share of income going to top earners is too low and that the justification used for that denominator is the result of a mathematical error (see pages 10-12 in our paper).

Finally, we point out that there is a large accounting problem. Before 1943, the IRS provided the Statistics of Income based on net income. After 1943, there shift between definitions of adjusted gross income. As such, the two series are not comparable and need to be adjusted to be linked. Piketty and Saez, when they calculated their own adjustment methods, made seemingly reasonable assumptions (mostly that the rich took the lion’s share of deductions). However, when we searched and found evidence of how deductions were distributed, they did not match the assumptions of Piketty and Saez. The actual evidence regarding deductions suggest that lower income brackets had large deductions and this diminishes the adjustment needed to harmonize the two series.

Taken together, our corrections yield systematically lower and flatter estimates of inequality which do not contradict the idea that inequality fell during the first half of the 20th century (see image below). However, our corrections suggest that the UCN is incorrect and that there might be more of small bowl (I call it the Paella-bowl curve of inequality, but my co-authors prefer the J-curve idea).

InequalityPikettySaez.png

Did Inequality Fall During the Great Depression ?

Inequality

The graph above is taken from Piketty and Saez in their seminal 2003 article in the Quarterly Journal of Economics. It shows that inequality fell during the Great Depression. This is a contention that I have always been very skeptical of for many reasons and which has been – since 2012 – the reason why I view the IRS-data derived measure of inequality through a very skeptical lens (disclaimer: I think that it gives us an idea of inequality but I am not sure how accurate it is).

Here is why.

During the Great Depression, unemployment was never below 15% (see Romer here for a comparison prior to 1930 and this image derived from Timothy Hatton’s work). In some years, it was close to 25%. When such a large share of the population is earning near zero in terms of income, it is hard to imagine that inequality did not increase. Secondly, real wages were up during the Depression. Workers who still had a job were not worse off, they were better off. This means that you had a large share of the population who saw income reductions close to 100% and the remaining share saw actual increases in real wages. This would push up inequality no questions asked. This could be offset by a fall in the incomes from profits of the top income shares, but you would need a pretty big drop (which is what Piketty and Saez argue for).

There is some research that have tried to focus only on the Great Depression. The first was one rarely cited NBER paper by Horst Mendershausen from 1946 who found modest increases in inequality from 1929 to 1933. The data was largely centered on urban data, but this flaw works in favor of my skepticism as farm incomes (i.e. rural incomes) fell more during the depression than average incomes. There is also evidence, more recent, regarding other countries during the Great Depression. For example, Hungary saw an increase in inequality during the era from 1928 to 1941 with most of the increase in the early 1930s. A similar development was observed in Canada as well (slight increase based on the Veall dataset).

Had Piketty and Saez showed an increase in inequality during the Depression, I would have been more willing to accept their series with fewer questions and doubts. However, they do not discuss these points in great details and as such, we should be skeptical.

Can we use tax units to measure living standards?

In the debate on inequality, I am a skeptic of how large a problem the issue is. Personally, I tend to believe that worries of inequality only increase when growth is stagnant. In fact, I also believe that there are numerous statistical biases causing us to misidentify stagnation as rising inequality. Most of the debate on inequality is plagued with statistical problems of daunting magnitudes (regional convergence in income, regional price levels, demographic changes, increasing heterogeneity of preferences, increasing heterogeneity of personal characteristics, income not being purely monetary, the role of taxes and transfers etc.)

One of them centers around the use of tax data. This has been the domain of Thomas Piketty and Emmanuel Saez. I can understand the appeal of using tax data since it is easily available and usable. Yet, is it perfect?

A year or two ago, I would have been inclined to simply say “yes” and not bother with the details. Theoretically, taxes should be an “okay” proxy for the income distribution and should follow average income even if at different levels. Yet, after reading the article of Phil Magness and Robert Murphy in the Journal of Private Enterprise I confess that I am no longer accepting anything as “granted” in the inequality debate. So, I simply decided to chart GDP per capita with the average taxable income per tax unit. Just to see what happens. Both are basically averages of the overall population, they should look pretty much the same (theoretically).  The data for the tax units is made available in the Mark W. Frank dataset based on the Piketty-Saez data (see here) and I deflated with both the CPI and the implicit price deflator available at FRED/St-Louis.

The result is the following and it shows two very different stories! Either the GDP statistics are wrong and we have average stagnation (which does not mean that there is no increase in inequality) or the taxable income data is wrong in estimating the trend of living standards and the GDP are closer to reality (which does not that there is no increase in inequality).  In the end, there is a problem to be assessed with the quality of the data used to measure inequality.

Tax Data