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).


Can we trust US interwar inequality figures?

This question is the one that me and Phil Magness have been asking for some time and we have now assembled our thoughts and measures in the first of a series of papers. In this paper, we take issue with the quality of the measurements that will be extracted from tax records during the interwar years (1918 to 1941).

More precisely, we point out that tax rates at the federal level fluctuated wildly and were at relatively high levels. Since most of our inequality measures are drawn from the federal tax data contained in the Statistics of Income, this is problematic. Indeed, high tax rates might deter honest reporting while rapidly changing rates will affect reporting behavior (causing artificial variations in the measure of market income). As such, both the level and the trend of inequality might be off.  That is our concern in very simple words.

To assess whether or not we are worrying for nothing, we went around to find different sources to assess the robustness of the inequality estimates based on the federal tax data. We found what we were looking for in Wisconsin whose tax rates were much lower (never above 7%) and less variable than those at the federal levels. As such, we found the perfect dataset to see if there are measurement problems in the data itself (through a varying selection bias).

From the Wisconsin data, we find that there are good reasons to be skeptical of the existing inequality measured based on federal tax data. The comparison of the IRS data for Wisconsin with the data from the state income tax shows a different pattern of evolution and a different level (especially when deductions are accounted for). First of all, the level is always inferior with the WTC data (Wisconsin Tax Commission). Secondly, the trend differs for the 1930s.

Table1 for Blog

I am not sure what it means in terms of the true level of inequality for the period. However, it suggests that we ought to be careful towards the estimations advanced if two data sources of a similar nature (tax data) with arguably minor conceptual differences (low and stable tax rates) tell dramatically different stories.  Maybe its time to try to further improve the pre-1945 series on inequality.

From the Comments: Weber, Geloso on inequality

How did I not see these before? Rick chimed in on Zak’s post about inequality and libertarianism awhile back. As usual, he tries to give the opposition the benefit of the doubt:

Taking public choice logic seriously means considering the political distortions/impediments to proposed policy. Taking inequality seriously is the flip side of that. Perceptions of (and attitudes towards) inequality matter and libertarians (and conservatives) would do well to acknowledge it.

I suspect that the problem is that 1) (like any ideology) we’ve got a blind spot, and inequality is in that spot. 2) Our liberal friends can see into that blind spot. 3) They’ve got a blind spot that leads them to make silly policy prescriptions (e.g. ignoring public choice roots of inequality and instead calling for policies that would reduce growth). And as a result, 4) we’re turned off by discussion of inequality before considering it.

Vincent, in the usual French manner, has a different take:

Okay massive disagreement here:

A: Inequality is not something “measurable” in the sense of utility. I chose to be an economist. My income is X% below that of my wife who went to school fewer years than I did and her income grows faster than mine and she will live longer than me (in probabilistic terms given life expectancy differences M/F). According to that definition, my couple is an unequal one and growing more unequal. Yet, I would not trade her job for mine even if her job was twice as remunerative (she is an attorney). I chose a path of lesser income because it made me happy. Income maximization was, in that case, not synonymous with utility maximization. By definition, rich societies will have more cases like that since gains in marginal utility may not be associated with marginal gains in monetary income. See the issue of the backward-bending labor supply curve.

B: The literature on linking growth to inequality is VERY weak. Look at the empirical papers, the results often depend on the choice of variables and the time window. It NEVER accounts for what I mentioned in point A. More importantly, there is NO THEORETICAL LINK with neoclassical theory on this (with the notable exception of Herb Gintis and Sam Bowles and I am working on a paper tackling their logic) that is axiomatically consistent. An empirical observation without a theory that is logically sound (the most repeated is the general Keynesian argument about consumption, but that is very weak and that rebuttal is powerful in the theoretical papers) is basically rubbish.

C: The Great Gatsby Curve is also rubbish since most of the past observations are based on the weird assumptions that mobility based on father-sons is a proper estimate to compare with modern estimates. You can consult the very convincing rebuttals made by Scott Winship. Moreover, the Great Gatsby curve is again a case of empirical observations without theory. I don’t need any of this story to see that mobility is down (modestly) at the same time that labor market restrictions are up.

There is more discussion, too.

Did Inequality Fall During the Great Depression ?


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.

A hidden cost of the war on drugs

AI just completed another paper (this time with my longtime partner in crime Vadim Kufenko) where we question an hypothesis advanced by Samuel Bowles regarding the cost of inequality. In the process, we proposed an alternative explanation which has implications for the evaluation of the war on drugs.

In recent years, Samuel Bowles (2012) has advanced a theory (well-embedded within neoclassical theoretical elements while remaining elegantly simple) whereby inequality increases distrust which in turn magnifies agency problems. This forces firms to expend more resources on supervision and protection which means an expansion of the “guard labor force” (or supervisory labor force). Basically, he argues there is an over-provision of security and supervision. That is the cost of inequality which Bowles presents as a coordination failure. We propose an alternative explanation for the size of the guard and supervisory labor forces.

Our alternative is that there can be over-provision of security and supervision, but this could also be the result of a government failure. We argue that the war on drugs leads to institutional decay and lower levels of trust which, in turn, force private actors to deploy resources to supervise workers and protect themselves. Basically, efforts at prohibiting illicit substances require that limited policing resources be spread more thinly which may force private actors to expend more resources on security for themselves (thus creating an overprovision of security). This represents a form of state failure, especially if the attempts at policing these illicit substances increase the level of crime to which populations are vulnerable. To counteract this, private actors invest more in protection and supervision.

Using some of the work of Jeffrey Miron and Katherine Waldock, we show that increases in the intensity of prohibition enforcement efforts (measured in dollars per capita) have significant effects on the demand for guard labor. Given that guards represent roughly 1 million individuals in the US labor market, that is not a negligible outcome. We find that a one standard deviation increase in the level of drug enforcement efforts increases the ratio of guards to the population by somewhere between 12.92% and 13.91% (which is the equivalent of roughly 100,000 workers).

While our paper concentrated on proposing an alternative to the argument advanced by Bowles regarding the cost of inequality, we (more or less accidentally) measured a hidden cost from the war on drugs. The insecurity (increased crime rates and spillovers from illegal markets into formal markets) brought forth by drug prohibition  forces an over-provision of security and supervision (our supervision measure which includes workers that supervise other workers were smaller than with the security guard measure).

Basically, a hidden (private cost) of the war on drugs is that we must reallocate resources that we could have used otherwise. Its a little like when I say that it is meaningless to compare healthcare expenditures to GDP in Canada and the United States because Canadians assume costs in a hidden manner through rationing. Waiting lists in Canada are longer than in the US. The cost is lost wages and enduring pain and that cost will not appear in measures of expenditures to GDP. The war on drugs works the same way. There is a fiscal cost (expenditures dedicated to it and the taxes that we must impose), there is a crime cost (destruction of lives and property) and there is a reallocation cost of privately providing security which is hard to measure.

*The paper is available here. 

Did 89% of American Millionaires Disappear During the Great Depression?

Over the years, I became increasingly skeptical of using tax data to measure inequality. I do not believe that there is no value in computing inequality with those sources (especially after the 1960s, the quality is much better in the case of the US). I simply believe that there is a great need for prudence in not overstretching the results. This is not the first time I make this point (see my paper with Phil Schlosser and John Moore here) and I think it is especially crucial for anything prior to 1943 (the introduction of tax withholding).

One of my main point is that the work of Gene Smiley which ended up published in the Journal of Economic History has generally been ignored. Smiley had highlighted many failings in the way the tax data was computed for measuring inequality. His most important point was that tax avoidance foiled the measurements of top incomes and how well they could transposed on the overall national accounts.

More precisely, Smiley argued that the tax shelters of the 1920s and 1930s would have affected reporting behavior. As long as corporations could issue stock dividends rather than cash dividends, delaying the payment of dividends until shareholders were in lower tax brackets, there would be avoidance. Furthermore, state and municipal securities were exempted from taxation which meant that taxpayers could shelter income and end up in lower brackets. All this combined to wide fluctuations in marginal tax rates conspires to reduce the quality of the tax data in computing inequality. Rather than substantial increases in inequality, Smiley found that his corrected estimates (which kept tax rates constant) suggested no increase in inequality during the 1920s and a minimal decrease when you exclude capital gains.

Alongside John Moore, Phil Schlosser and Phil Magness, I am in the process of attempting to extend the Smiley corrections to include everything up to 1941 (Smiley had ended in 1929). As a result, I had to assemble the tax data and the tax rates and I was surprised to see that, even without regressions, we can see the problem of relying on the tax data for the interwar period.

The number of millionaires in the tax reports is displayed below. As one can see, it is very low from 1917 to 1924 – a period of high tax rates. However, as tax rates fell in the 1920s, the number of millionaires quintupled. And then, when the Depression started in synchronicity with the increases in top marginal tax rates, it went back down. It went down by 89% from 1929 to 1941. Now, I am quite willing to entertain that many millionaires were wiped out during the Great Depression. I am not willing to entertain the idea that 9 out of every 10 millionaires disappeared. What I am willing to entertain is that the tax data is clearly and heavily problematic for the pre-withholding era.* This is evidence in favor of caution and prudence in interpreting inequality measures derived from tax data.



I am of those who believe that inequality was lower than reported elsewhere in the 1920s, higher than reported in the 1930s and 1940s. Combined together, these would mean that inequality would tend to follow a L-curve or a J-curve from the 1920s up to the present rather than the U-curve often reported.  I will post more on this as my paper with Moore, Schlosser and Magness progresses. 

On Gentrification, Inequality and Zoning

On the CityLab blog, Richard Florida posted a piece pointing out that gentrification has virtually no effects on homeowners. I can buy that result, especially since I wrote a policy piece for a think tank back in the summer of 2016 on the issue. The important point that Florida underlines (by citing a paper by Martin and Beck in Urban Affairs Review) is that homeowners are not being displaced, but renters are more likely to be. This will probably fuel some people who are concerned about inequality. I disagree.

I want to point out that my interest in the issue is entirely related to the issue of inequality which some individuals have tried to tie to gentrification (sometimes without understanding that causality can run both ways). If you want to tie the two issues together, then you must realize that there are four “types” of gentrification. First of all, gentrification always appear in an area that is poor and it is always a result of a shift in demand for land in that area. However, that area can be largely unoccupied or heavily inhabited. It can also be in a district where zoning is lax or burdensome. In each of these situations, you will different effects with different interpretations for inequality.

  • Scenario 1 (largely vacant, lax zoning laws): in this situation, demand shifts right but there is slack in the local housing market and in any case, supply can adjust easily. In that case, the effects on rents will be minimal and will probably be smaller than the economic gains in terms of local economic activity. In this situation, there is little displacement and there is in fact a reduction in inequality.
  • Scenario 2 (largely vacant, heavy zoning laws): same happens, except that the restrictions on construction and building conversions put a ceiling on the capacity of a local area to adapt. The effect on rents is ambiguous and depends largely on the relative quantity changes (how many people relative to empty units). There are probably small to moderate gains in the area. There are ambiguous effects on inequality.
  • Scenario 3 (heavily occupied, lax zoning laws): in this situation, the influx of individuals creates a temporary surge in rents. This is because, in the short-term, housing supply is inelastic. In the long-run, the supply is more elastic and new units can be added to counterbalance the price effects. So, there is a long-term benefit that comes after a small bump. More individuals will be displaced than in scenario 1. Overall, a reduction in inequality might occur.
  • Scenario 4 (heavily occupied, heavy zoning laws): in this situation, the influx happens in a market where the supply is highly inelastic (short and long-run). In that case, the shift in demand creates a substantial increase in rents. This is where gentrification can hurt and be tied to inequality.

These four scenarios are important because they show something important that some people have to understand. Gentrification can increase inequality. However, that depends on the context and the institutions (zoning) surrounding the area in which it happens. In all cases, gentrification is a normal process that can’t really be stopped but turns sour because of zoning laws. Thus, if you really want to tie gentrification to inequality, it should twice removed since the first parents are zoning laws and construction limits.