Abstract
This article estimates the effect of crime on migration rates for counties in U.S. metropolitan areas and makes three contributions to the literature. First, I use administrative data on migration flows between counties, which gives me more precise estimates of population changes than data used in previous studies. Second, I am able to decompose net population changes into gross migration flows in order to identify how individuals respond to crime rate changes. Finally, I include county-level trends so that my identification comes from shocks away from the trend. I find effects that are one-fiftieth the size of the most prominent estimate in the literature; and although the long-run effects are somewhat larger, they are still only approximately one-twentieth as large. I also find that responses to crime rates differ by subgroups, and that increases in crime cause white households to leave the county, with effects almost 10 times as large as for black households.
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Notes
The census defines central cities as “one or more of the largest population and employment centers of a metropolitan area.”
To calculate populations for areas at the substate level, the FBI uses decennial census data and the state population growth rate to determine the population in a city or county jurisdiction, which ignores any possible reallocation within the state. For more detail and explanation, see the Methodology section on the FBI UCR webpage (http://www.fbi.gov/about-us/cjis/ucr/frequently-asked-questions/ucr_faqs/%23methodology).
Importantly, any measurement error in the data is unlikely to be correlated with the measurement error in the FBI UCR population estimates and therefore will not introduce division bias.
This limits my sample from 205 to 105 MSAs. In results not shown, I find that my estimates do not change if I include all MSAs.
This means that the total outmigration rate in year t would be outmigrantst × 1,000 / (outmigrants t + nonmigrants t).
For example, Washtenaw County, Michigan (home of Ann Arbor, MI) has 14 agencies, which include the Washtenaw County Sheriff, Ann Arbor Police Department, Eastern Michigan University Police, and University of Michigan Police, among others.
The age distribution is measured in bins, ages 0–17, 18–24, 25–44, and 45–64. The omitted category is those 65 and older.
In the CL article, using contemporaneous changes in crime, if population fell for a reason not correlated with crime, that would have the effect of making net population change more negative while increasing crime rates mechanically (because the denominator is larger), making crime rates endogenous in the estimating equation.
My results are also robust to quadratic trends, but that is not my preferred specification.
In results not shown, I also calculated within-county migration using the CPS for the sample of counties that are identified. The coefficient on crime was insignificant and noisy.
Additionally, given the number of studies estimating how much central city depopulation is driven by various factors (Baum-Snow 2007; Boustan 2010; Margo 1992), it seems that we may be overexplaining central city depopulation. I am grateful to an anonymous referee for bringing this point to my attention.
In order to add them properly,
$$ {\upbeta}_{netmig}=\left({\upbeta}_{inmig- outMSA}+{\upbeta}_{inmig- inMSA}\right)-\left({\upbeta}_{outmig- outMSA}+{\upbeta}_{outmig- inMSA}\right). $$An alternative approach would be to include multiple lags of crime, which produces qualitatively similar results. The long-difference approach adopted here is more congruent with CL’s long-difference estimates.
The fractionalization measure is F = 1 − ∑ i s 2 i , where si is the share of the population for race/ethnicity i in an MSA; race/ethnicity is white, black, American Indian, Asian/Pacific Islander, or Hispanic. This measure is created in the spirit of a Herfindahl index.
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Acknowledgments
I thank Ann Stevens and Giovanni Peri for their helpful feedback on all the drafts of this article, as well as participants in the UC Davis Public/Labor Graduate Student Brown Bag series and the Western Economic Association International conference session. I also thank Ingrid Gould Ellen for her helpful correspondence on an earlier version of this article. Finally, I thank the anonymous referees for their helpful comments, which helped to sharpen and clarify this article.
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Appendices
Appendix A: Uniform Crime Reports Data Elimination Procedure
Because of the inconsistency of reporting for some reporting agencies (hereafter, ORIs), I had to eliminate some ORIs and sometimes eliminate an entire county from the sample. The process for eliminating these ORIs is described in this appendix section. First, I assign each ORI a start year, when it first entered the data set; and an end year, the last year it reported crime data. Between these years, some 2004 ORIs never reported crime statistics; I omit these ORIs. If between the start and end years, the ORI is missing more than three years in a row or is missing more than half of its data (if it has four years), then that ORI is marked as “missing” and is eventually dropped. Overall, 1,600 ORIs are marked as missing. If fewer than three years in a row are missing, I interpolate the crime counts for each individual crime.
If the largest or second largest agency in a county (defined by the maximum yearly crime count) is marked as missing, I drop the county entirely. This occurs for 78 and 42 ORIs respectively, leading to a total of 78 of 754 counties being dropped from my sample. If the agency that is missing in a county is not the largest or second largest, which is the most common case, then I drop the ORI for the whole sample but not the county, which accounts for the remaining dropped ORIs, totaling 1,412. The median population coverage of the ORIs dropped from my analysis is 3,676; the median population coverage of ORIs included in my analysis is 6,960. This procedure leaves me with a sample of 4,901 ORIs in 676 counties. Most of the ORIs dropped are small.
I test whether nonreporting is correlated with any of my explanatory variables at the agency or county level, finding only slight indications that the number of crimes is negatively correlated with being a nonreporting agency. Thus, smaller agencies are a large share of nonreporting agencies, indicating that nonreporting will not significantly affect my county-level crime rates.
Appendix B: Comparisons With Cullen and Levitt (CL)
My main results differ from those of CL, and one concern is that my sample is more broad than theirs. To explore how these effects differ by sample selection, I estimate Eq. (2) on a number of subsamples, which are displayed in Table 8.
CL’s main sample includes “127 U.S. [central] cities with populations greater than 100,000 in 1970” (Cullen and Levitt 1999:160) for the years 1975–1993. My sample includes all counties in all metropolitan areas with more than two counties from 1984–2010. To closely proxy their sample, I include in column 1 only counties that are part of the central city of an MSA that is in the top 25 % of the population at the beginning of my sample, for the years 1983–1995, and weights by lagged county population. Then, in column 2, I include all MSAs. In column 3, I include all the years of my sample, still restricting the sample to include only central-city counties. Column 4 includes my estimates for all counties. Finally, column 5 shows my preferred specification, which includes county-specific linear trends. Although the coefficient is somewhat larger in column 1, the estimates do not change much across columns, suggesting that sample selection is not driving the differences between my results and CL’s.
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Foote, A. Decomposing the Effect of Crime on Population Changes. Demography 52, 705–728 (2015). https://doi.org/10.1007/s13524-015-0375-4
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DOI: https://doi.org/10.1007/s13524-015-0375-4