Economic Mobility Is a Moving Target – Next City
The Equity Factor

Economic Mobility Is a Moving Target

(Photo by Wouter Engler)

Smartphones have changed the way many of us approach the world — and each other — but the massive advances in computing power since the 1960s are only just beginning to change the way we approach economic policy.

Economists Raj Chetty and Emmanuel Saez crunched millions upon millions of (anonymous) IRS tax return data to show how economic mobility is tied to ZIP code, that where people live is a huge determinant of how high they eventually rise on the economic ladder. Policymakers and community development leaders are only just beginning to grapple with that. Now, another research pair is exploring the question of how economic mobility itself is a moving target.

From 1968 on, economic mobility across the U.S. went down — until the mid-1980s. Since then, it has been on the rise, according to a recent study by Daniel R. Carroll and Anne Chen at the Federal Reserve Bank of Cleveland. But don’t get too excited.

“That’s within a certain span of numbers. I don’t want to exaggerate the degree to which it moved,” Carroll says.

According to Carroll and Chen, having a sense of the amount of income mobility in a society is critical because it affects how we interpret inequality.

“It matters whether we live in a world where people are stuck or we live in a world where people move quickly through different income levels,” Carroll says.

As a baseline, take the period from 2003-2013, the most recent 10 years available from the dataset that Carroll and Chen used. For those in the bottom 20 percent, Carroll and Chen found that after a decade, 64 percent of those households stayed in the bottom 20 percent. For the middle three quintiles, about half of households stayed where they were, and households were about equally as likely to move either up or down. For the top 20 percent, 72 percent of households stayed where they were. Higher mobility would mean fewer households staying in the same income quintile after 10 years across the board.

Carroll and Chen dug a little deeper, and found that income mobility is higher for younger workers. Splitting the sample they took into one cohort ages 18 to 30 and another ages 31 to 45, the younger cohort experienced higher mobility than the older, suggesting most economic shifting occurs in the early stages of careers.

(Credit: Carroll and Chen, “Income Inequality Matters, but Mobility Is Just as Important”)

On a hopeful note, they also disaggregated cohorts by quintile, and found that the poorer a household is, the more likely it will move up, suggesting some unexpected economic dynamism at the lower end of the spectrum. What might be causing that dynamism and what might be undermining it at higher levels wasn’t in the scope of their study.

Racial and gender differences also weren’t part of this early look at changes in mobility over time, but may be part of future, deeper analyses from Carroll and Chen. It is early days yet for this kind of economic research.

“These models take a lot of computer power to solve. Our ability to tackle complex problems with a lot of heterogeneity in them, a lot of differences among households, became possible only recently,” Carroll says.

The dataset they’re using, however, has been around all this time. Like over 3,000 peer-reviewed articles and thousands of others, Carroll and Chen used data from the Panel Study of Income Dynamics (PSID), based at the University of Michigan. The study began in 1968 as one of the federal War on Poverty programs. Funded by the National Science Foundation, National Institutes of Aging, and National Institute for Child and Human Development, it is the longest ongoing household survey, according to David S. Johnson, deputy director of PSID.

In March of every odd-numbered year, PSID researchers start calling and interviewing at least 25,000 people all over the U.S. The original 18,000 were randomly sampled nationwide, and they’ve since added children and even grandchildren of the original group. Each survey participant gets a $70 incentive to answer all the questions from the researchers, including questions about amount of income, sources of income, spending, saving, housing, health, education, race and ethnicity, and more. About 95 percent of the previous cycle’s interviewees typically respond each year.

“Interviewers spend a lot of time doing interviews, right up until the last moment,” says Johnson. It takes around a year or longer for the data to be processed, so the 2015 interview cycle data will be available next year.

As demographics have changed in the U.S., the study has tried to keep up. PSID added a major random sample of new immigrants in 1997, and will add another new immigrant sample in next year’s survey cycle. In some ways, Johnson says, demographics get updated automatically as households move from rural areas to more urban, or as children and grandchildren of original households move into cities and stay connected to the study. Johnson estimates they have complete data across three generations for around 1,200 families.

More than 150 new publications each year use PSID data, diving into topics like racial-ethnic differences, savings and wealth, social safety net usage, education, housing and more. Mobility is a topic that is heating up.

“As we move forward these mobility studies are critical. PSID is the only study where you can look at changes in mobility,” says Johnson, who himself co-authored a recent study comparing income, consumption and wealth mobility.

It’s too early in their research for Carroll and Chen to be comfortable recommending policies that increase mobility based on the data, but Carroll is quite clear about how understanding changes in income mobility, not just income inequality, has to change the framework in which policymakers make decisions.

“We have to be, I think, aware and sensitive to the different types of obstacles that people face at different income levels,” he says. “We’re getting away from what was the standard in the old days when we thought of a single representative person that makes all the decisions in the economy. We’re trying to take differences among people, heterogeneity, income inequality, wealth inequality, seriously.”

The Equity Factor is made possible with the support of the Surdna Foundation.

Oscar is a Next City 2015-2016 equitable cities fellow. A New York City-based journalist with a background in global development and social enterprise, he has written about impact investing, microfinance, fair trade, entrepreneurship and more for publications such as Fast Company and NextBillion.net. He has a B.A. in Economics from Villanova University.

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Tags: income inequalitypovertybig data