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The fall in migration and the rise in geographic inequality in the 1980s marks an end to over a century of income convergence across U.S. regions (Nunn, Parsons, and Shambaugh, 2018). Similar patterns of higher regional inequality and lower (net) migration appear to also be happening within other advanced economies (Gbohoui, Lam, and Lledo, 2019).
More generally, Decker et al. (2014), analyzing measures such as the pace of job reallocation, the dispersion of growth rates across businesses, and within-business volatility, find falling economic dynamism in the United States since the mid-1980s. Our finding that lower long-distance migration is connected with increasing housing and income inequality links rising inequality to this wider trend toward less economic flexibility.
Using a model in which high productivity cities limit local employment opportunities using land restrictions, Hsieh and Moretti (2017) suggest that the resulting misallocation of labor could have halved US growth between 1964 and 2009. While this size of the effect may be implausible, the basic logic that growing divergences in house prices can support growing income inequality, labor misallocation, and reduced aggregate output, is intuitive.
Molloy, Smith, and Wozniak (2013) find that demographic shifts reduce within-state migration but has no statistical effect on interstate mobility (which is more likely to be linked to jobs), while Kaplan and Schulhofer-Wohl (2015) observe that migration rates have fallen across all age groups, suggesting a limited role for demographic factors.
Frey (2009) highlights the difficulty for households to obtain credit and the reduction in home values directly after the financial crisis as important factors in the most recent fall in mobility. Similarly, Donovan and Schnure (2011) describe the lock-in effect for households have negative housing equity (“underwater” mortgages) on their homes.
Partridge and Tsvetkova (2017), who compare trends in incomes across states and counties, conclude that analysis at the state-wide level masks rising within-state inequality, and that there has been a steady increase in income inequality over time.
See Moretti (2016) and Diamond (2017) on the rising concentration of skilled jobs in some metro areas, Autor (2014) and Gordon and Dew-Becker (2008) on the widening wage gap, and Alabdulkareem and others (2018) on differences in skills demanded and average wages across metro areas.
Ganong and Shoag (2015) document that rapidly rising housing prices in productive metro areas has deterred unskilled workers from moving there, while Feenstra, Ma, and Xu (2018) find that differences in house prices exacerbated employment losses in metro areas affected by the China shock. Bhutta, Laufer, and Ringo (2017) find that low-income families in high house price counties are being priced out of homeownership. More generally, Partridge and Tsvetkova (2017) find that income growth is higher and poverty levels are lower in counties with more favorable industrial structures.
In the typical lifecycle migration pattern, workers move to large metro areas to benefit from relatively fast wage growth, only to relocate to less-populated areas later in life. Wang (2013) argues that the increase in wage growth premiums in large metro areas has prompted workers to delay their relocation, dampening out migration. Similarly, Gyorko and others (2013) find that high house prices in superstar cities have resulted in relatively more high-income families and fewer middle-low income families across metro areas.
Current Population Survey Annual Social and Economic Supplement, 1997–2016
U.S. Census Bureau, Current Population Survey, March 1999; U.S. Census Bureau, Current Population Survey, November 2016
Current Population Survey Migration/Geographic Mobility Tables, 1991–2016
Zillow created its Home Value Index by estimating prices of both houses that were sold and ones that did not sell on a monthly basis, covering over 100 million homes nationwide. Zillow constructs its estimates based on an array of “automated valuation models”, which are retrained three times a week based on a latest data. The estimates are subject to minimal systematic error, meaning that estimation errors are as likely to overprice as underprice the value of a particular home. The Zillow series are highly correlated with other series that measure house prices at the city level, such as the Case-Shiller index (which covers only twenty CBSAs) and the FHFA series (which use a much more limited and less representative sample).
For example, the CBSA for Washington DC covers about 5,600 square miles (equivalent to a circle with an 80-mile diameter) that stretches from the Shenandoah Valley in the west to the Chesapeake Bay in the east, and from Frederick in the north to Fredericksburg in the south. We aggregated any data that was exclusively available on a county-basis to CBSAs using the Zillow Crosswalk Tool, following Howard (2016).
The geographical distribution that underlies the Zillow house price data looks as one would expect. Analyzing the ten metropolitan areas with fastest growing house prices, eight of them are in California, plus one in Florida (Key West) and one in Massachusetts (Vineyard Haven). Large metro areas like New York, Washington, DC, Boston, Denver, and Seattle are ranked in the top 50. Meanwhile, the ten metropolitan areas with the lowest house price growth are all in Indiana, Ohio, and Georgia.
Fischer, Johnson, Sneeding, and Thompson (2017) find that income, consumption, and wealth inequality have become more linked across individuals since 1989. Our results confirm the result in Diamond (2015) that this this is also happening across US metro areas.
The IRS Migration data covers county-to-county migration across the United States from 1990–2015 by tallying the number of tax returns and exemptions, proxies for households and individuals respectively, that have filed taxes for a given year from a different mailing address than the previous year, an indication the household in question has moved. Since the IRS database only covers households whose level of income requires them to file taxes it excludes some low-income families. However, this may be less of an issue given our focus on migration related to jobs, since most of the employed file taxes, especially as the earned income tax credit (a form of negative income tax) brings many of the working poor into the tax net.
In their “Reasons for Moving” issue, the Census reports that 31 percent of moves of up to 200 miles are motivated by job-related reasons, compared to 48 percent for migration of 200–499 miles; separate analysis finds that 47.5 percent of moves of over 500 miles are related to following or attempting to find a job (Ihrke, 2014).
We used three-year averages to reduce noise in the data. In 2013, the cut-off for the number of moves below which data was not reported was raised from 10 to 20. We adjusted the data for this change in methodology.
The Zillow house price database has data for 571 CBSAs, while the income database provides data for 381 CBSAs. The overlap comprises 323 CBSAs (35 percent of total number of CBSAs in the United States, but 82 percent by population). Finally, we calculated our bilateral distance variable using a trigonometric equation using county-level longitude and latitude coordinates taken from the US Census.
The gravity model is a flexible specification for examining geographic relationships which, in the context of trade, is compatible with a wide range of theoretical models (Costinot and Rodriguez-Clare, 2014).
The first stage regressions are well specified. The F-statistic for all first-stage regressions is well over the cutoff of 30. In addition, the Kleibergen-Paap rk LM and Wald F-statistics, which can be seen as generalizations of the Anderson LM and Cragg-Donald Wald statistics respectively to the case of non-i.i.d. errors, firmly reject the null hypotheses of under and weak identification.
This is economically significant; given that the median bilateral migration coefficient for a sample is .01206, the regression estimates suggest that a 1 percent increase in house price (income) dispersion leads to 0.33 percent decrease (0.92 percent increase) in bilateral migration.
A larger old-age population discourages mobility, particularly in the source CBSA since it is the young who tend to migrate; higher AGIs result in higher migration since it is less of a financial burden for the more prosperous to move; higher relative unemployment discourages mobility by lowering job opportunities; a larger divergence in population growth increases migration, as migrants are attracted to booming metro areas; and longer distances discourage mobility.
Results using income to calculate the uphill and downhill dummies are reported as a robustness check. That model finds similar results, but fits less well, suggesting that house prices are a more fundamental driver of our findings.
Possible reflecting the changing nature and location of low-skilled jobs or the greater importance of support from family and friends as the fraying of the social safety net has led to a transfer of economic risk from the government and firms to individuals (Hacker, 2012).
For house price inequality we have data on the difference between the 84th and 16th percentile of house prices. For income, we have Gini coefficients by CBSA. Unfortunately, both measures are only available for a subset of CBSAs, and the Gini coefficient series only start in 2006. Since both measures are positively correlated with population density, which is available for all CBSAs, we include this variable in the regression.