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

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.

On the (big) conditions for a BIG

This week, EconTalk featured a podcast between Russ Roberts and Michael Munger (he of the famous Munger-proviso which I live by) discussed the Basic Income Guarantee (BIG). In the discussion, there is little I ended up disagreeing with (I would have probably said some things differently though). However, I was disappointed about a point (which I made here in the past) which economists often ignore when discussing a BIG: labor demand.

In all discussions of the BIG, the debates always revolve around the issue of labor supply assuming that it will induce some leftward shift of the supply curve. While this is true, it is irrelevant in my opinion because there is a more important effect: the rightward shift of the labor demand curve.

To make this argument, I must underline the conditions of a BIG for this to happen. The first thing to say is that a) the social welfare net must be inefficient relative to the alternative of simply giving money to people (shifting to a BIG must be Pareto-efficient); b) the shift mean that – for a fixed level of utility we wish to insure – the government needs to spend less and; c) the lower level of expenditures allows for a reduction in taxation.  With these three conditions, the labor demand curve could shift rightward. As I said when I initially made this point back in January 2016:

Yet, the case is relatively straightforward: current transfers are inefficient, basic income is more efficient at obtaining each unit of poverty reduction, basic income requires lower taxes, basic income means lower marginal tax rates, lower marginal tax rates mean more demand for investment and labor and thus more long-term growth and a counter-balance to any supply-side effect.

As I pointed out back then, the Canadian experiment (in Manitoba) with a minimum income led to substantial improvements in health outcomes which meant lower expenditures for healthcare. As a result, b) is satisfied and (by definition) so is a). If, during a shift to a BIG, condition c) is met, the entire discussion regarding the supply effects becomes a mere empirical issue.

I mean, equilibrium effects are best analyzed when we consider both demand and supply…

P.S. I am not necessarily a fan, in practice, of BIG. Theoretically, the case is sound. However, I can easily foresee policy drifts where politicians expand the BIG beyond a sound level for electoral reasons (or even tweak the details in order to add features that go against the spirit of the proposal). The debate between Kevin Vallier (arguing that this public choice reasoning is not relevant) and Phil Magness (who argues the reverse) on this issue is pretty favorable to Magness (in my opinion). UPDATE: Jason Clemens over at the Fraser Institute pointed to a study they made regarding the implementation of a BIG in Canada. The practical challenges the study points too build upon the Magness argument as applied in a Canadian perspective.