One doesn’t have to be a devotee of Kurosawa or even Jacques Derrida to understand that folks with access to the same facts can and will provide alternative interpretations of them. So what may seem like a simple question of fact is often far from it. Here I provide the perspective from which I consider whether income inequality has risen since 1989; my interpretation of what the March Current Population Survey (CPS) can tell us about it; and answer the deeper question—why does it matter?
As someone interested in measuring how economic well-being changes over time, I often focus on average United States household income and its distribution using public-use CPS data. Recently, after a lengthy process, I gained access to the restricted-use internal CPS data the Census Bureau used to estimate the household income Gini values Gary Burtless referenced in his comments. From my perspective Burtless’ simple comparison of the difference in these two values overstates the trend in income inequality after 1989 and fails to convince me that the income equality increase, once it is properly measured, matters.
Figure 1 using CPS data shows how median income changed in the United States between 1967 and 2005 in constant 2005 dollars. While median income rose substantially over this period, years of economic gain were followed by years of economic loss. Hence one could, with an appropriate choice of years, either demonstrate that median income increased, decreased, or stayed the same. To separate longer-run trends from differences in the business cycle, I believe it’s best to compare similar years—e.g. peak to peak or trough to trough. Doing so, one can characterize the income consequences of the 1980s business cycle comparing 1979-1989 and the 1990s business cycle comparing 1989-2000 or alternatively by comparing trough years 1983-1993 and 1993-2004. Either way, United States median household income improved significantly over both the 1980s and 1990s. This is good news that does matter—long-term economic growth has occurred over the past two business cycles improving the economic well-being of the average American.
Describing what happened to the income distribution of households is more complicated. In Burkhauser, Oshio, and Rovba (forthcoming) discussed by Reynolds, rather than using a single Gini coefficient to do so, we provide pictures of the entire distribution over the peaks years 1979-1989-2000 in the United States and compare them with peaks years in the 1990s business cycles in Great Britain, Germany, and Japan. Laying the 1989 distribution over the 1979 distribution (Figure 2), the middle of the distribution (the middle class) dramatically declined over the 1980s. This is consistent with the Gini increase discussed by Reynolds and Burtless. But the main point is that the vast majority of the “disappearing” middle moved to the right tail with a small minority moving to the left. That is, while inequality certainly increased in the United States in the 1980s, it did so primarily because the disappearing middle became disproportionately richer.
The news is much better for the 1990s. The 2000 distribution is to the right of the 1989 distribution. That is, in 2000 the person found at every point in the income distribution was better off than the person at that same point in the distribution in 1989—first order stochastic dominance. This is a major achievement for our society. But it is especially so when compared with Germany and Japan (Figure 3 and Figure 4) whose distributional changes over the 1990s look much like ours in the 1980s—a decline in the middle, with most people become richer but some becoming poorer. Great Britain matched our achievement in the 1990s. (Figure 5).
These pictures of reported household income minus estimated income and social security taxes do not differ from those unadjusted for taxes. (They would be closest in spirit to the CBO after-tax estimates Burtless reports.) But they provide a more nuanced view of what is behind the Gini changes discussed by Reynolds and Burtless.
We also estimate Gini coefficients and find a small decline in after-tax inequality between 1989 and 2000 and no change in before-tax income inequality over this period. So how does this square with the increasing Gini values Census produced using the internal CPS data reported by Burtless?
Like the vast majority of researchers, who would rather estimate their own Gini coefficients than depend on the Census, our results come from the public-use CPS. As can be seen in Figure 6 based on new work with Shuaizhang Feng and Stephen Jenkins, if we use public-use data unadjusted for top-coding, we also find a large rise in pre-tax income inequality 1989-2000. But this is in part because of substantial increases in the top codes and in the use of cell means since 1995 and in part because of other methodological changes in the CPS in 1993. Like Burtless, when he does his own work using the CPS, and others, we correct for this by consistently top-coding the public-use data—choosing the year between 1975 and 2004 in which an income source top code hits at the lowest point in the distribution and consistently top-coding all other years of that income source at this same low point in the distribution.
In Burkhauser, Butler, Feng, and Houtenville (2004), we show that while this method systematically understates levels of labor earnings inequality, it captures the trends in both the unadjusted Census internal and external Gini values controlling for the spikes in these data in 1992-1993 and 1994-1995, respectively. Our results are similar to those who simply “trim” the top 2 or 3 percent of the public-use data. Hence our results consistently report what has been happening to household income and its distribution for the bottom 97 or 98 percent of the distribution. I believe both Burtless and Reynolds would at least agree that the public-use CPS data when appropriately adjusted for these problems accurately captures trends for this part of the population.
What we have carefully documented since gaining access to the internal CPS data is that even these data have problems with inconsistent censoring at the top of the distribution. Burtless reports Census Gini values that are part of a series from the internal CPS data that is not corrected for censoring. As Reynolds discusses and as we independently document in Burkhauser, Feng, and Jenkins (2006), over the period 1975-2004, the internal CPS data, like the public-use data, do not systematically capture the upper end of the income distribution. Like the public-use CPS, internal censoring, including top-coding, occurs on each individual source of income rather than on overall income, and we find that the share of persons living in households with one or more income sources top-coded varies from 0.1 to 0.8 percent.
When we adjust the internal income data using our consistent top-coding procedure we find a more modest increase in our Gini values over the period after 1989 than is found using the uncorrected internal data. For 1989-2000 it is 4.67 rather than 6.82 percent. But even this is likely to be too high a rise in both because, even adjusting for top-coding and censoring, we still find a spike in the 1992-1993 internal CPS data that, while lower than the spike in the unadjusted internal data, is still implausibly high. In fact, if you only follow Gini trends from 1993-2004, all the years of internal data available to us since the 1992-1993 spike, Gini values only increase by 1.45 and 2.43 percent respectively. My bottom line is that the rise in inequality captured by internal Census data, once it is adjusted for censoring, tells much the same story for the bottom 99 percent of the income distribution as the adjusted public-use CPS data tells. Over the 1990s business cycle the entire distribution moved to the right with little or no change in income inequality. Since 1989 household income inequality has risen very little and much less than in the previous decade. This is very good news that matters.
I agree with Reynolds and Burtless that CPS data are less valuable for looking at the top 1 or 2 percent of the income distribution. Unfortunately, other data sets are also hard-pressed to do so and there is much greater uncertainty over how much the income of the top 1 percent changed since 1989 than there is for the rest of us.
But does this really matter? Our economy is not a zero sum game. My gain does not mean your loss or vice-versa. I know of no evidence that increases in the incomes of the top 1 percent of our population are the root cause of the challenges faced by those at the other end of the distribution. Over the last full business cycle, the incomes of rich and poor moved in the same direction. I suspect that for every Robert Nardelli or Hank McKinnell there are far more people like Tiger Woods, Steve Jobs, Oprah Winfrey, or Bill Gates, whose skills, vision, and effort allowed them to burst out of the pack of us ordinary 99 percent. But in doing so, the value of the goods and services they create for us greatly exceeds the earnings they receive. That is the consequence of our market economy and that does matter.
References
Burkhauser, Richard V., J. S. Butler, Shuaizhang Feng, and Andrew J. Houtenville. (2004) “Long Term Trends in Earnings Inequality: What the CPS Can Tell Us,” Economic Letters, 82 (2) (February): 295-299.
Burkhauser, Richard V., Shuaizhang Feng, and Stephen P. Jenkins (2006). “Using the P90/P10 Ratio to Measure Inequality Trends with the Current Population Survey: A View from Inside the Census Bureau Vaults, Cornell University Working Paper (November).
Burkhauser, Richard V., Takashi Oshio, and Ludmila Rovba (forthcoming). “Winners and Losers over the 1990s Business Cycles in Germany, Great Britain, Japan, and the United States,” Schmollers Jahrbuch: Journal of Applied Social Science, 127 (1).
Welniak, Edward J. 2003. “Measuring Household Income Inequality Using the CPS,” James Dalton and Beth Kilss (Eds.), Special Studies in Federal Tax Statistics 2003, Statistics of Income Directorate, Inland Revenue Service, Washington DC.