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Job Changing and the Decline in Long-Distance Migration in the United States

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Demography

Abstract

Interstate migration in the United States has decreased steadily since the 1980s, but little is known about the causes of this decline. We show that declining migration is related to a concurrent secular decline in job changing. Neither trend is primarily due to observable demographic or socioeconomic factors. Rather, we argue that the decline in job changing has caused the decline in migration. After establishing a role for the labor market in declining migration, we turn to the question of why job changing has become less frequent over the past several decades. We find little support for several explanations, including the rise of dual-career households, the decline in middle-skill jobs, occupational licensing, and the need for employees to retain health insurance. Thus, the reasons for these dual trends remain opaque and should be explored further.

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Notes

  1. Other research documenting the multiple-decade decline in interstate migration—the primary focus of this article—includes Cooke (2011 and 2013), Kaplan and Schulhofer-Wohl (forthcoming), and Molloy et al. (2011). Migration over shorter distances has also fallen over the second half of the twentieth century (Fischer 2002; Wolf and Longino 2005).

  2. As of 2005, long-distance migration within the United States was still higher than that in most other European countries (Molloy et al. 2011).

  3. Alternatively, the changing responsiveness of the population to local shocks may be a reflection of declining migration rather than a cause.

  4. By contrast, long-distance migration within the UK does not appear to exhibit a secular decline (Champion and Shuttlesworth 2016).

  5. The sample size for the ASEC ranges from about 20,000 in the 1940s, to 40,000 in the 1970s, and to nearly 100,000 in the 2000s.

  6. Because the interstate migration variable was biased by an imputation procedure from 1999 to 2005 (Kaplan and Schulhofer-Wohl 2012; Koerber 2007), we drop all observations where migration is imputed. In addition, from 1988 onward, we drop all observations that have any imputed responses as indicated by the suprec variable. Combined, these imputation flags cause us to drop approximately 10 % of the sample from 1988 onward. Our CPS data are provided by the Unicon Research Corporation because the Integrated Public Use Microdata Series, which is the more commonly used source of CPS microdata, does not include the suprec variable.

  7. The CPS did not ask the migration question for most of the 1970s.

  8. The CPS question instructs that if the respondent worked for more than one employer at the same time, only one employer should be counted. This question was asked from 1976 onward. A more common way to measure job-to-job transitions is to ask whether the respondent is working for the same employer as in the previous month. That measure is available only from 1994 onward; the aggregate fraction of job-to-job changes is very similar using either of these two measures (Molloy et al. 2016).

  9. Occupations and industries are defined using three-digit Standard Industrial Classification codes, identifying more than 200 separate industries and 500 occupations. Examples of three-digit industries include coal mining, retail bakeries, and grocery stores; examples of three-digit occupations include cashiers, civil engineers, and pharmacy aides. As with the migration responses, we drop all observations with imputed values as indicated by the suprec variable, as well as responses with imputed values for occupation, industry, occupation last year, industry last year, or number of employers in the previous year.

  10. One might be concerned that some of this relationship is mechanical because most geographic moves are accompanied by job changes. However, when we recalculate the fraction changing firms in each state conditional on not having moved in the past year, the positive relationship remains with virtually the same slope.

  11. Additional controls are the fraction of the state unemployed, the log of average annual income for the state, and the fraction of the state that is young (under 21) and of prime working age (21–64).

  12. One concern with these statistics is that the CPS does not record homeownership status in the previous year. However, using the PSID, Bachmann and Cooper (2012) documented declines in mobility among all four possible combinations of tenure: renter-renter, homeowner-homeowner, renter-homeowner, and homeowner-renter.

  13. The CPS did not include the migration question in 1985 or 1995. Prior to 1981, the CPS asked migration questions only in 1964–1971 and 1975. The pre-1980 data also contain far fewer relevant covariates, so we do not extend the analysis of this section back to periods before the 1980s.

  14. On the other hand, age and homeownership can explain roughly one-half of the decrease in short-distance moves (migration within counties) over this period; results are available upon request.

  15. Median house values are from the 2014 ACS, and regulation is measured as the state average of the Wharton Residential Land Use Regulatory Index (Gyourko et al. 2008).

  16. Specifically, we control for real income with indicators for quintiles of the distribution across all years of household income relative to the consumer price index. Thus, shifts in the distribution of real income over time are allowed to affect aggregate migration rates. The regressions do not include nativity because this information is not available in the CPS until 1994. In earlier work, we found the declines in migration since 1994 were similar for the native and foreign born populations (Molloy et al. 2011).

  17. These results are also available upon request.

  18. In other words, in the CPS, we incorrectly attributed individuals who were unemployed in the previous year and out of the labor force in the current year as “out of the labor force.” Imposing this (incorrect) assumption in the PSID does not alter the PSID results. Results are similar if we redefine labor force status to include the labor force status of anyone in the household.

  19. High-skill jobs include manager, professional, and technician occupations. Middle-skill occupations include sales jobs; office and administration jobs; production, craft, and repair jobs; and operator, fabricator, and laborer jobs. Low-skill occupations are service sector jobs and include protective services, food preparation, building and grounds cleaning, and personal services.

  20. Over this period, the percentage employed in middle-skill jobs fell 6 percentage points, and the percentage employed in manufacturing fell 7.5 percentage points. Applying the coefficients in Table 1, these changes could explain 0.3 percentage point of the 1.15 % decline in cross-state migration in the CPS. However, as noted earlier, these coefficients are not statistically significant, and we cannot reject that the true relationship is zero.

  21. For example, possibly many dual-earner households in the 1980s had one spouse who was not particularly attached to a career and who could therefore easily move to follow their spouse’s job (Benson 2014). But as more and more women have moved into occupations with longer career trajectories, changing locations may have become more difficult for more households.

  22. A rather extensive literature presents mixed findings on the extent to which health care–related “job lock” depresses job transition rates, although Gruber and Madrian (2002) argued that the most convincing evidence supports the job lock hypothesis. More recently, Garthwaite et al. (2014) found evidence of health insurance–related job lock among low-skill workers in the 2000s. At the same time, more consistent evidence suggests that the availability of employer-provided health insurance delays transitions to retirement and affects labor supply decisions of secondary earners (see also Madrian 2004).

  23. Results discussed in this paragraph available upon request.

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Acknowledgments

Wozniak would especially like to thank Frank Limehouse and the team at the Chicago Census Research Data Center for assistance with the restricted National Longitudinal Surveys. Ning Jia and Adam Scherling also provided invaluable research assistance. The authors would like to thank Moshe Buchinsky and James Spletzer as well as seminar participants at Temple University, University of Notre Dame, University of Wisconsin–Madison, University of Illinois–Chicago, University of California–Santa Barbara, the U.S. Departments of the Treasury and of Labor, and conference participants at the Federal Reserve System conference on Regional Analysis, the 2012 Census Research Data Center Researcher conference, and the Society of Labor Economists. Any opinions and conclusions expressed herein are those of the authors and do not indicate concurrence with other members of the research staff of the Federal Reserve, the Board of Governors, or the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. Any errors are our own.

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Authors and Affiliations

  1. Federal Reserve Board of Governors, Washington, DC, USA

    Raven Molloy & Christopher L. Smith

  2. Department of Economics, Flanner Hall, University of Notre Dame, Notre Dame, IN, 46556, USA

    Abigail Wozniak

  3. National Bureau of Economic Research, 1050 Massachusetts Avenue, Cambridge, MA, 02138, USA

    Abigail Wozniak

  4. IZA Institute of Labor Economics, Schaumburg-Lippe Strasse, Bonn, Germany

    Abigail Wozniak

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  1. Raven Molloy
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Correspondence to Christopher L. Smith.

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Molloy, R., Smith, C.L. & Wozniak, A. Job Changing and the Decline in Long-Distance Migration in the United States. Demography 54, 631–653 (2017). https://doi.org/10.1007/s13524-017-0551-9

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