Income Distribution Heresies

In a recent paper published by the Cato Institute, I made the seemingly heretical observation that inequality in incomes, wages, consumption, and wealth among the U.S. population as a whole does not appear to have increased significantly since 1988. My Cato paper was condensed and revised from a paper I presented at the Western Economics Association (WEA) last July. That WEA paper, in turn, was condensed and revised from Chapter 5 in my book Income and Wealth, written early last year as part of a series of college textbooks for Greenwood Press.

Income and Wealth did not say that U.S. inequality probably stopped rising after 1988. That thesis arrived much later, and tentatively, in two pages of the Cato paper. My book’s tutorial is about avoiding common statistical pitfalls involved with measuring the growth of average wages and incomes over time, the division of income between labor and capital, the concepts of mobility and lifetime incomes, the concentration of wealth ownership, etc. Early chapters explain why inequality might be expected to increase over time, because I too had initially assumed the “professional consensus” asserting an ongoing increase in inequality was an adequate substitute for facts.

I have been bemused by the emotional, non-factual responses at various blogs to my inability to discover any evidence of a “significant and sustained” rise of inequality since 1988. By “significant” I mean to exclude the .003 increase in the Gini index for pretax money income between 2001 and 2005 that Brookings’ Gary Burtless mentioned. Oxford University economist Anthony Atkinson suggests the Gini coefficient must rise by 3 percentage points in order to be considered a significant new trend. By “sustained,” I mean to exclude the tax-induced spike in capital gains realizations in 1986, the 1993 break in Census Bureau data, and the Internet stock euphoria of 1997-2000.

Surveying the Evidence

My heretical claim did not originate with me. My Cato paper cited the following evidence:

Card and DiNardo found that wage inequality did not increase from 1988 to 2000. Johnson, Smeeding and Torrey found that a Gini coefficient for a broad measure of consumption, from the Survey of Consumer Finances, fell slightly from 0.283 in 1986 to 0.280 in 2002. Kopczuk and Saez found that the top shares of wealth were stable during the 1990s. The top 1 percent’s share of wealth was also stable from 1995 to 2004, according to more recent estimates from Arthur Kennickell.

Unless these scholars have erred, that takes care of wages, wealth, and consumption. And it does so without even mentioning the Consumer Expenditure Survey, which I am supposedly enamored of, according to Gary Burtless.

What about disposable (after-tax) income? Last November, a study by Burkhauser, Oshio and Rovba found that, “For the United States … real mean household size-adjusted after-tax income increased by 10.93 percent over the 1980s and by 7.27 percent over the 1990s while median after-tax income increased by 5.95 percent and 7.10 percent respectively over these periods… [But] income inequality rose substantially over the business cycle of the 1980s whether measured by the 90/10 ratio (23.67 percent) or by the Gini coefficient (14.17 percent). In contrast, income inequality fell over the 1990s business cycle [1989 to 2000] whether measured by the 90/10 ratio (-6.82 percent) or the Gini coefficient (-2.24 percent).” From 1989 to 2000, they added, “the confluence of significant economic growth and work-based welfare reforms dramatically improved the employment and economic well-being of single women with children relative to the rest of the population and more generally did so for lower-skilled workers.”[1]

The aforementioned 90/10 ratios, incidentally, compared incomes between the highest and lowest deciles of full-time workers. The ratio was 6.9% in 1987-90 and 6.7% in 2003-2004, which lends support to the Card-DiNardo finding that wage inequality did not increase even between polar extremes. I have discovered great resistance to this message, particularly from conservative economists. One reason appears to be a common misunderstanding about the assumed link between incomes and work.

CEA Chairman Edward P. Lazear says, “There is little doubt that there has been a 25-year trend of a growing gap, sometimes called income inequality, between the wages of the skilled and the unskilled.”

Despite the absence of doubt, Lazear’s assertion is clearly inconsistent with the research of Burkhauser, Oshio and Rovba, Card and DiNardo and others. But I am far more troubled by his conceptual muddling of skill gaps and inequality. An accompanying graph does not clearly show median hourly earnings for college grads growing faster than those with less schooling from 1989 to 1996 or from 2001 to 2004. But even if there was such a sustained trend, a growing gap between wages of college graduates and high-school dropouts would necessarily translate into increased income inequality only if: (1) everyone worked full-time all year, and (2) all income came from work, and (3) college graduates were not a rising share of the labor force and native high-school dropouts a declining share.

The number of people who worked full-time all year in 2005 amounted to only 3.2 million in the poorest fifth of households but 16.7 million in the top quintile. There are many more singles in the lower income groups (including students and widows), and many more mature two-earner couples at the top. The top quintile – every couple with a pretax income above $91,705 – accounted for 29.1 percent of all full-time, year-round workers, which is the largest single reason they received 40% of disposable income in 2004 [xls]. The top quintile also had more college grads, but that only affects market income if they work.[2]

Economists who try to explain most difference in household income by hourly wages are ignoring the huge differences in the number of hours worked per household. They are also ignoring transfer payments, most of which (except the EITC) are effectively limited to those who work little or not at all.

Other economists attempt to dismiss the Card-DiNardo conclusion by referring to the top 1 percent’s share of W2 income. First, let me make a general objection. We will never discover anything about the distribution of income among all or even most Americans by looking at only 1 percent of the tax-paying population. We gather no information about what is happening to living standards of the poor or the (typically undefined) “middle class” by examining only the upper tail of the income distribution. Pretending to describe the income distribution by using only the share of income going to the top 1% makes no more sense than doing the same with the incomes of the bottom 1%. Except under extreme zero-sum reasoning (arguing that an extra billion for Steve Jobs means a billion less for others), the top 1% fetish is essentially irrelevant.

Even if you insist on focusing on the top 1%, it is particularly misleading to compare – as Piketty and Saez do in their widely cited study – income reported on tax returns in two years (1980 and 2004) that fall before and after the 1986 Tax Reform Act. In the Piketty-Saez estimates of W2 labor income, the top centile’s share suddenly increased from 7.3% in 1986 to 9.4% in 1988 and then averaged 9.1% from 1988 through 1996. That is quite consistent with the reasonable estimates of elasticity of taxable income cited in my December 14th Wall Street Journal op-ed, and not with Piketty and Saez’s subsequent disavowal of Saez’s own estimates. Using the Social Security data that Burtless cites, Schwabish finds [pdf] “the share of earnings at the top of the distribution … has fallen precipitously” from 2000 to 2003. My Cato paper showed that is also true of CEO pay. Both rebounded in 2004 because more than 11% of SCF respondents reported receiving stock options in 2001 [pdf] — reported on W2’s when exercised, and first exercisable in 2004. That, too, is a predictable response to lower tax rates, and executives accounted for only a fourth of option grants by 1999 [pdf].

Statistical Mirages

My Cato paper compared the 20.7% real increase in top decile pay from 1979 to 2004 with the 21% increase in bottom quintile pay, using median income from the Survey of Consumer Finances. Not interesting? Critics prefer to focus on defects in Census Bureau data which, Burtless suggests, I am either unaware of or reluctant to reveal.

For example, my Cato paper compared the Census Bureau estimate of the income share of the top 5% with that of Piketty and Saez. The Census figure rose from 18% in 1986 to 20.9% in 2004, mostly because of a data break in 1993. The comparable Piketty-Saez figure jumped from 22.6% in 1986 to 27% in 1988, which I attribute to the 1986 Tax Reform and so did Piketty and Saez until recently. Their measure of the top 5 percent’s share of income (as they define it) hit 31.2% by 2004. Paul Krugman and others were a bit too quick to blame sample size and top coding of Census data. Even when we exclude all income above $5 million from the Piketty-Saez estimate of the top 5 percent’s share, that would narrow the gap by only 0.9% leaving nine percentage points unexplained. The fact that Census includes cash transfer payments in total income is also not nearly enough to begin to fill the gap.

My Cato paper also cited the Census Bureau’s Gini coefficient for their broad (14th) definition of disposable income, which subtracts income and payroll taxes, adds cash and in-kind transfer payments, and (unfortunately) also adds capital gains that show up on tax returns. This is the measure Burtless approves of yet does not disclose.

The Gini coefficient for disposable income can be rounded to 0.38 or 0.39 for all but one year between 1984 and 1992, meaning it was essentially unchanged. It briefly jumped to 0.41 in 1986 for just one year, but that was clearly due to a rush to sell assets before the capital gains tax went up. The index was identical in 1985, 1988 and 1992, at 0.385.

In 1993, when the Census survey methods were computerized and “top-coding” limits hugely increased, the Gini suddenly leaps to 0.40 in 1993 where it remained in 2004, following a brief rise to .41 when capital gains boomed in 1999-2000. In order to pretend to see any sustained upward trend in the Gini for disposable income from 1984 to 2004, one would have to argue that it happened in one year – 1993. That’s where the Census Bureau critics begin to stumble.

Burtless writes:

The Census Bureau questionnaire does not provide accurate or consistent assessments of the incomes of the top 2% or 2½ % of income recipients. One reason is that respondents’ answers are top-coded, or at least they were in the not-too-distant past. Another is that the sample of high-income recipients is too small to give an accurate or consistent estimate of the incomes of the very top income recipients, say, those with incomes above $750,000 a year. This means the Census Bureau probably gives us an underestimate of true income inequality every single year, no matter which concept of income we choose to measure.

Burtless claims the CBO misses a lot of income among those with more than $750,000 a year, which he seems to equate with the top 2% or 2 1/2%. But $750,000 is only slightly shy of the$1.1 million threshold defining the top one-tenth of one percent in Piketty and Saez. It is miles above the $266,800 threshold defining the top 1% in CBO’s definition of income which includes, absurdly 59.4% of corporate profits.

Sampling errors are random, so even if one believes 1080 is too small a sample for the top 2%, that cannot tell us whether such incomes will be overestimated or underestimated. It certainly does not imply “an underestimate of true income inequality every single year.” At worst, the estimate of top 2% incomes would be too large some of the time and too small at other times, which is presumably what Mr. Burtless means by “consistent.”

The core of the CBO and Piketty-Saez estimates relies on a sample of adjusted gross income from the Statistics of Income (SOI) division of the IRS. That sample is twice as large as the sample of 54,000 in the Current Population Survey (CPS) Annual Social and Economic Supplement. But the Census Bureau surveys everyone, while the SOI survey just covers those who file a tax return. The SOI sample oversamples incomes above $5 million, but excludes a few trillion dollars of lesser incomes mainly because of tax avoidance (the $1 trillion AGI gap) and legal nonfilers. The net result it to exaggerate the ratio of highest incomes to total income.

There is no top-coding of total income, contrary to what Piketty and Saez and Paul Krugman have implied, but there is top-coding of specific types of income in the public use data to protect respondents’ privacy. When the Census Bureau calculates the income share of the top 5%, they use internal data that is free of top-coding, but there remain what Census officials call “internal processing limits” on, for example, the maximum salary recorded for any one job.

Edward J. Welniak, Chief of the Income Statistics Branch of Census Bureau, explains: In 1979, the questionnaire allowed the recording of up to $99,999 for 23 income sources. In 1985, the limit for recording earnings from longest job increased to $299,999. The final recording limit increase occurred in 1993 when each of the four earned income sources allowed the recording of amounts to $9,999,999.[3] The post-1993 limits obviously pose no problem for total incomes above $750,000, much less for average incomes of the top 1% or top 5%. Welniak found that in 1999 there were no more than 26 cases excluded out of a sample of 54,000. He also found, as have Burkhauser and others, that the increases in internal processing limits have caused the increase in inequality over time to be overstated, not understated.

Welniak notes that public use data caused “overstatement of income inequality growth over the 1967-2001 period,” because the tighter restrictions in the past make it look as though inequality was lower than it really was, while the easing of top-coding limits in the 1990s looks like increased inequality (rather than just increased visibility of high-income data). In Welniak’s words, “The larger growth in income inequality using public-use data is the result of: 1) topcoded income in 1967 which reduced measured income inequality and 2) increased high income through the plugging of mean topcoded values beginning in 1996.”

The pregnant remark from Burtless about respondents’ answers being top-coded “in the not-too-distant past” is a major reason why I argue that Census Bureau Gini coefficients, or top income shares, do not show that inequality has increased since the late 1980s. Welniak calculates that the 1985 change in methodology caused “a slight increase” in measured inequality, when none really occurred. Since the Gini coefficients from 1979 to 1984 were understated, that means the apparent increase in Gini coefficients in the late eighties was overstated. In 1993, Welniak notes that “CPS ASEC introduced computer-assisted personal interviewing and increased the recording levels for earnings to $1 million as well as increasing the recoding levels for other income sources.”

When Burtless refers to 0.469 as “the highest Gini coefficient ever recorded,” he is technically correct that it is higher than 0.466 in 2001, but he is not correct to compare it with pre-1993 data. As the Economic Policy Institute has explained, “a change in survey methodology in 1993 led to a sharp rise in measured inequality.”[4] The dramatic 1993 increases in the amounts of income recorded, and the substitution of computers for pencils, caused a spurious jump in inequality measures.

Before 1993, in that not-so-distant past, some limits on the amount of certain types of income caused Gini coefficients to be understated, underestimating actual inequality and therefore creating a spurious increase when comparing years before and after 1993.

It is baffling why Paul Krugman, Piketty and Saez and Burtless all thought sample size and “top-coding” was some sort of refutation of my observation that income inequality has not increased since the late eighties. Sample size could go either way, and changes in top-coding points in the exact opposite direction of what they are trying to imply.

Conclusion

For reasons that were partly explained in my book and further explained in a forthcoming academic paper, not one of the four data sets that attempt to estimate income from a sample of tax returns are credible for comparing income share across periods of changing tax rates. CBO data is arguably the worst of the four, and Piketty and Saez is the best. Even W2 data can’t be compared across different tax regimes because (1) the timing of nonqualified stock options exercises is highly sensitive to stock prices and because (2) switching to restricted stock — which does not appear on the W2 — is highly sensitive to the tax rates on dividends and capital gains. Ask Bill Gates or Steve Jobs.

If there were any better data showing a significant and sustained increase in the inequality of disposable income, consumption, wages, or wealth since 1988, I suspect someone would have shared it with us by now.

Notes

[1] Richard V. Burkhauser, Takashi Oshio and Ludmila Rovba, “How the Distribution of After-Tax Income Changed Over the 1990s Business Cycle: A Comparison of the United States, Great Britain, Germany and Japan,” November 2006. http://www.mrrc.isr.umich.edu/publications/index_abstract.cfm?ptid=1&pid=463

[2] U.S. Census Bureau, Current Population Survey, 2004 and 2005 Annual Social and Economic Supplements.

[3] Edward J. Welniak, “Measuring Household Income Inequality Using the CPS,” James Dalton, J. & Kilss B., eds., Special Studies in Federal Tax Statistics: 2003, Select Papers Given at the Annual Meeting of the American Statistical Association. http://www.irs.gov/pub/irs-soi/03preprt.pdf

[4] Lawrence Mishel, et. al., The State of Working America 2004/2005 (Washington: Economic Policy Institute, January 2005), p. 67, http://www.epinet.org/content.cfm/books_swa2004.