Abstract
This study investigates the relationship between migrant savings, remittances, and crime. Using a model in which the migrant maximizes utility over the choice of storing savings in the United States or in Mexico, the migrant’s decision is determined in part by the potential of losing part of his income to theft. Using probit and Tobit analysis I test this model, finding evidence that higher probabilities of victimization lead migrants to alter their remitting behavior. In particular, I find that increased robbery rates reduce the size and incidence of remittances, whereas increased burglary rates increase the size and incidence of remittances.
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Notes
For a detailed review of the literature on motivations to remit see Hagen-Zanker and Siegel [2007].
As Banarjee [1984] points out, the probit model is preferred over a linear probability model using OLS because the probit model bounds the predicted probabilities to between 0–1, and OLS yields inefficient estimates when applied to dichotomous dependent variables.
If the surveyed households have relatives who have permanently relocated to the United States, then an attempt is made to contact and survey them as well. This type of “snowball” sampling can lead to problems in the sample since those surveyed in the United States are, by definition, non-random. Therefore, the sample for this study has been restricted to include only households in which the primary residence is in Mexico.
Aggregate crime levels are used because of the fact that there is not a comprehensive database that reports crime based on victim ethnicity at the SMSA level. The FBI also reports crime rates for the crimes of murder, forcible rape, aggravated assault, larceny, motor vehicle theft, and arson. Robbery and burglary were chosen because they are the two most likely to have a direct impact on consumption/savings behavior.
Time and duration variables included in the regression analysis are not significantly different from zero, and the main results of interest are robust over all specifications. Migrants’ wage is also excluded from the empirical analysis because it is only available for a small subset of the observations and is insignificant when included.
The data set also included information on the migrants’ documentation status, that is, whether they were in the United States legally. Sixty-four percent of the migrants in the sample were in the United States illegally, and over 80 percent of the illegal immigrants used a coyote to cross the border However, because of the high collinearity between the migrants’ documentation status and the use of a coyote to cross the border I use the latter to proxy for both estimates using document status instead of the use of a coyote yield similar coefficients for the two variables, and coefficient estimates of the crime variables are robust to both specifications.
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Acknowledgements
The author would like to thank Keith Bender, Rebecca Neumann, John Heywood, participants in the UW-Milwaukee Labor Economics Workshop, and three anonymous referees for helpful comments and suggestions. A version of this paper appears as a chapter of my Ph.D. dissertation at UW-Milwaukee.
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Coon, M. “Walking ATMs”: Do Crime Rates Affect Remittances of Mexican Migrants in the United States?. Eastern Econ J 41, 6–23 (2015). https://doi.org/10.1057/eej.2014.12
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DOI: https://doi.org/10.1057/eej.2014.12