Below is a table showing the rate of home ownership in the United States as of the first quarter of various years.1

YearHome Ownership Rate as of the
First Quarter

 1995 64.2 2000 67.1 2005 69.1 2010 67.1

Now, answer the following question: approximately what fraction of Americans aged 55 have owned a home?

1. a) 50 percent
2. b) 70 percent
3. c) 90 percent

As an economist familiar with the data in table above, I would have guessed that the answer to the question is 70 percent. However, according to sociologists Mark Robert Rank, Thomas A. Hirschl, and Kirk A. Foster, the correct answer is 90 percent. In their recent book, Chasing the American Dream,2 they write:

Fifty-six percent of Americans have purchased a home by age 30, 74 percent by age 35, 84 percent by age 45, and 89 percent by age 55. Consequently, the dream of home ownership is a reality for nine out of ten Americans.

“Like the boy in the fable, they have in an innocent, unintended fashion exposed statistical nakedness among many economists who are regarded as experts on the topic of inequality.”

Rank, Hirschl, and Foster are correct, and my intuition was incorrect. I was guilty of what might be called the time-series cross-section fallacy. In each year, the census bureau looks at the population, takes a survey, and calculates the proportion that owns a home. The annual statistics on homeownership give us a time-series cross-section, which is represented in the table.

However, the question that I asked concerns what demographers refer to as longitudinal information. If you follow given individuals over a long period, what sort of cumulative outcomes will you observe? In particular, over a lifetime, how many people will at some point own a home? To answer a longitudinal question, you need to use longitudinal data. To instead use time-series cross-section data risks making serious errors.

Most of the conventional wisdom about relative economic well-being, including the famous studies by Thomas Piketty and Emmanuel Saez, commits the time-series cross-section fallacy. Rank, Hirschl, and Foster did not set out to debunk this fallacy or to attack the many economists guilty of it. Instead, they took what seemed to them a natural approach for studying the evolution of wealth and poverty: longitudinal data. The result, in my reading, is that, like the boy in the fable, they have in an innocent, unintended fashion exposed statistical nakedness among many economists who are regarded as experts on the topic of inequality.

Once you think about it, the truth about homeownership rates makes sense. At some point in our lives, nearly all of us have been renters. In addition, most of us are likely to “downsize” as we grow older, and in the process many of us may choose to rent.

The conventional view is that those who own homes are “haves” and renters are “have-nots.” However, the longitudinal perspective leads you to realize that homeownership is in part a lifestyle choice. It makes most sense for a family rooted in its community by employment and school-aged children. It makes less sense for young singles or for retirees.

This is not to deny that there are “have-nots” among the population of renters. However, it is clearly not correct to infer that the proportion of housing “have-nots” in this country is over 30 percent based on the fact that fewer than 70 percent of the population owns their home.

The time-series, cross-section fallacy has a similar distorting effect on discussions of the distribution of income. That is, economists look at “inequality over time” by examining time-series, cross-sections of data, rather than by looking at longitudinal data. As a result, many of the statistical findings in Chasing the American Dream will prove surprising.

For example, the authors write,

… we first calculated the average percentage of Americans experiencing these different measures of economic insecurity in any given year. The results were that 14.8 percent were using a welfare program, 18.9 percent were in poverty or near poverty [within 150 percent of the poverty line], 12.4 percent had experienced [head of household’s] unemployment during the year, and 30.6 percent were experiencing one or more of these three measures…

By the age of 40, 37.9 percent of Americans have experienced at least one year [in which they used welfare], 46.3 percent have encountered poverty, 54.8 percent have experienced the head of household being unemployed, and 70.3 percent have experienced one or more of these three events. By age 60, the cumulative percentages are 44.8 percent, 54.1 percent, 66.8 percent, and 79.0 percent.

Taking the longitudinal perspective, over half of Americans experience near-poverty (income less than 150 percent of the poverty line) for at least one year between age 25 and age 60. By the same token, however, the percentage who will experience near-poverty for multiple years is lower. 38.9 percent are near-poor for a total of at least two years, 30.5 percent for a total of at least three years, 25.7 percent for a total of at least four years, 19.7 percent for a total of at least five years, and 10.3 percent for a total of at least ten years.

As a contrast with poverty, the authors define affluence as having a household income that exceeds nine times the official poverty line. They calculate the proportion of households that experience at least one year of affluence by age 60, and they break this down by race and level of education.

Whites are much more likely (56.4 percent) to encounter at least one year of affluence compared to nonwhites (23.6 percent). Likewise, 64.9 percent of those with more than 12 years of education versus 45.6 percent of those with 12 or fewer years of education will experience affluence.

I would note that for a family of four, the poverty line is currently more than \$23,000,3 so that 900 percent of the poverty means an income of over \$200,000. If 23.6 percent of non-whites will have such an income at least one year of their working lives, I find that fairly impressive, even though it compares unfavorably with the percentage of whites who will have such an experience.

This leads to another way that the authors cut the data. Some individuals will experience at least one year of poverty, but no years of affluence. The authors refer to this group as “the bottom.” At the top, some individuals will experience at least one year of affluence but no years of poverty. Some individuals will experience neither. And some individuals will experience both. The latter two groups represent the middle.

Using this breakdown, the authors find that 44.4 percent of whites are at the top, 40.1 percent are at the bottom, and 15.5 percent are in the middle. In contrast, only 16.0 percent of non-whites are at the top, 74.0 percent are at the bottom, and 10.0 percent are in the middle.

I was somewhat unsatisfied with this particular approach to analyzing cumulative data, for two reasons. First, as I hinted earlier, I think that the bar for affluence that the authors set—an income at least 900 percent of the poverty line—is too high. The only year in which my household was able to clear that benchmark was 1999, when my Internet business was sold near the peak of a bubble.

Second, I think that the question of whether a household experienced affluence or poverty in at least one year, while interesting and worth tabulating, is not a good way to sort households in terms of overall economic well-being. It puts all of the focus on the extremes, which in some ways are the least informative. The fact that my income was below the authors’ poverty threshold when I was still in graduate school at age twenty-five and twenty-six should not be used to suggest that I have experienced poverty.

I would be interested in what the data show if, rather than looking at the extremes, one does the opposite. That is, throw out each household’s lowest and highest three years of income. For the remaining years of income, take the average relative to the poverty line. If this average is below 150 percent of the poverty line, call it low. If it is above 500 percent of the poverty line (which works out to about 200 percent of the median), call it high. Then calculate the proportion of households that have high, medium, and low incomes by this longitudinal measure.

This would produce a very different breakdown. For instance, suppose that, rather than quitting my job to start an Internet business, I had kept working and that my salary had continued to increase gradually until I reached age 50. In that case, under the authors’ measure, our household would be in the bottom of the income distribution, because of the “poverty” of my graduate school years and my failure to achieve the income level that they require for “affluence.” However, using my approach, my household would have been somewhere in the vicinity of the boundary between high-income and middle-income. That seems much more reasonable to me.

Overall, as with homeownership data, the longitudinal view of income paints a picture in which life-cycle variation and idiosyncratic factors play a role. This role is overlooked in discussions of inequality that commit the time-series cross-section fallacy.

In particular, the “share of income going to the top x percent” in two different years is not a statistic that can be used to draw reliable inferences about inequality. Some of the people in the top x percent in any given year are, from a longitudinal perspective, in low-income households. Conversely, some people who are not in the top x percent in any given year are, from a longitudinal perspective, high-income households.

Apart from its use of longitudinal data, Chasing the American Dream makes no attempt to upend conventional ideas about income distribution. The authors, like many economists who study inequality, seem inclined to regard poor people primarily as victims of race and class handicaps. For example, when the authors report the correlation between education and their measures of household economic performance, they say that education is an indicator of social class, not of individual ability or effort. From a rhetorical perspective, they regard those who do not go to college as have-nots, rather than as choose-nots.

For more on the topics in this article, see “Poverty and Inequality”, by Pedro Schwarts, Library of Economics and Liberty, April 6, 2014 and the EconTalk podcast episode Bruce Meyer on the Middle Class, Poverty, and Inequality. See also Distribution of Income by Frank Levy and Housing by Benjamin Powell and Edward Stringham in the Concise Encyclopedia of Economics.

I focus on the longitudinal statistics reported in Chasing the American Dream because I think that they deserve more attention. I would like to see more economists work with this data. Perhaps doing so will bear out the conventional class-victim view of poverty. However, I suspect that it could lead to a more nuanced perspective.

In a review of Thomas Piketty’s Capital in the 21st Century,4 Bloomberg View columnist Clive Crook lamented that “the book has been greeted with such erotic intensity.”5Chasing the American Dream is not likely to be as widely praised and commented upon. But I think that longitudinal data has greater potential to satisfy those who value understanding more than ideological titillation.

Footnotes

The source is table 4 from the Census Bureau statistical release, “Residential Vacancies and Homeownership in the Fourth Quarter 2013,” January 31, 2014. PDF file.

Rank, Mark Robert, Hirschl, Thomas A., and Foster, Kirk A. Chasing the American Dream: Understanding What Shapes Our Fortunes. Oxford University Press, 2014.

2014 Poverty Guidelines. U.S. Department of Health and Human Services.

Thomas Piketty, Capital in the 21st Century. Belknap Press, 2014.

Clive Crook, “The Most Important Book Ever is All Wrong.” Bloomberg View, April 20, 2014.

*Arnold Kling has a Ph.D. in economics from the Massachusetts Institute of Technology. He is the author of five books, including Crisis of Abundance: Rethinking How We Pay for Health Care; Invisible Wealth: The Hidden Story of How Markets Work; and Unchecked and Unbalanced: How the Discrepancy Between Knowledge and Power Caused the Financial Crisis and Threatens Democracy. He contributed to EconLog from January 2003 through August 2012.

For more articles by Arnold Kling, see the Archive.