The Role of Immigration: Race/Ethnicity and San Diego Homicides Since 1970

Abstract

Objectives

The temporal variation in homicide is examined by studying trends in race/ethnic specific killings (e.g. Blacks, Latinos and Whites). Two substantively important issues are also addressed—a closer examination of the role community heterogeneity plays in homicide levels and the treatment of immigration as an endogenous social process.

Methods

Data are reported homicides in the city of San Diego, California over the period 1960–2010. The address of each killing is geocoded into 341 census tracts.

Results

We find that neighborhoods experiencing increases in the foreign-born population tend to be less violent. White and Latino homicide victimization was reduced significantly as a product of increases in the neighborhood concentration of foreign-born individuals. Supplementary analyses did not find empirical evidence that the influx of foreign-born individuals could (or should) be considered a disruptive social process. Over the past five decennial census periods, the exponential increase in immigration in this border city is not associated with an increase in homicide victimization.

Conclusions

When examined through a wider temporal lens than is typically employed, and accounting for the endogeneity of immigrant residential settlement, we find no support for the claims that immigration is a crime generating social process.

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Notes

  1. 1.

    Between 1970 to 2010, the percent Latino in the average community increased by roughly 122 % (13–29 %) and the size of the Asian population increased by approximately the same amount, growing over 119 % (7–16 %), increases driven primarily by increases in the size of the foreign-born populations for both groups. The size of the foreign-born in the average neighborhood also experienced a significant increase over this period, increasing by 225 % (7.5–25.5 %).

  2. 2.

    Across the five time periods, we were able to match geographically approximately 94 % of the homicides, well above the recommended minimum of 85 % (Ratcliffe 2004). This high “hit rate” and the fact unmatched cases were dispersed across all time periods, suggests that the missing cases will not influence our findings.

  3. 3.

    Aggregation over these periods was necessitated by the relatively low homicide rate (and low number of homicides) in the city, particularly in the first and last time points. An anonymous reviewer suggested that we run additional analyses in order to verify that the aggregation for the earliest period did not generate anomalous findings. Specifically, it was suggested that we (a) run the models excluding the 1970 time point; and (b) run the models with homicides aggregated over a shorter period (1965–1969). In a separate series of analyses, we ran all regression models (both primary and supplemental analyses) following both suggestions—in all cases the results were substantively similar, particularly for the effect of immigration on homicide. For this reason, all of the results include the earliest time period.

  4. 4.

    Of course we are not able to determine variations within immigration status or even types unique to the border. But use this global measure as a rough proxy for immigration.

  5. 5.

    The Neighborhood Diversity Index is computed using the following equation:

    $$ ND = \frac{1}{2}\left( {\left| {C_{w} - T_{w} } \right| + \left| {C_{B} - T_{B} } \right| + \left| {C_{H} - T_{H} } \right| + \left| {C_{A} - T_{A} } \right|} \right) $$

    where C is the city-wide percentage for a given racial/ethnic group (Non-Hispanic white; Non-Hispanic Black; Hispanic; or Asian) and T is the percentage for a racial group in a tract (see pp. 40–41).

  6. 6.

    Interpreting the Neighborhood Diversity Index, Higher values indicate more homogenous (i.e., segregated) neighborhoods. Note that because we were not able to obtain reliable measure for the Asian population in 1970, we impose a linear interpolation of the Neighborhood Diversity Index for the earliest time point. The results from the analyses do not differ substantively when similar analyses are run that do not include the 1970 period. See Appendix 1.

  7. 7.

    The fixed effects regression models drop observations for which there is no variation over time. The most common source is communities that experienced zero homicides in any of the time periods. We do not believe that omission of these neighborhoods is problematic because the focus of this analysis is on quantifying changes in homicide as a result of structural changes in communities (see Martinez et al. 2010, 815).

  8. 8.

    1970 values for Neighborhood Diversity Index have been interpolated linearly.

  9. 9.

    At present, there is no established protocol for estimating fixed effects negative binomial regression models including instrumental variables. Nor have the statistical properties for such analytical approaches been established. As such, the dependent variable in the following models is logged homicide rates. This is a similar approach to that used by MacDonald et al. (2013). Further, in the following models we focus on total homicide rates because endogeneity analyses for each group reveal a highly consistent set of results (see Appendix 1).

  10. 10.

    The method of MacDonald et al. (2013) is particularly informative for the current study, as it is the first study of immigration and crime conducted at the neighborhood level to include instrumental variables for immigration.

  11. 11.

    An anonymous reviewer suggested that we run the models using fully-standardized measures for our independent variables, as a means of capturing the effect of overall change on levels of lethal violence, rather than relative changes in structural characteristics between subsequent time points. The results from t these analyses are presented in Appendix 2. What we see I from these results is that overall increases in the size of the foreign-born population are associated with reductions in levels of homicide, net of other community characteristics. These findings underscore the robust effect of immigration, indicating that absolute changes in immigration, are linked to reductions in the number of killings. Note: the 2SLS models were run using the same analytical approach, which yielded similar effects as those reported in Table 5 (results available upon request).

  12. 12.

    We include percent the 1970 percent foreign-born following Ousey and Kubrin (2014). The effect of the instrumented immigration measure is substantively identical when the 1970 percent foreign-born is omitted.

  13. 13.

    See MacDonald et al. (2013, 201) equations 5 and 6.

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Correspondence to Ramiro Martinez Jr..

Appendices

Appendix 1

See Table 6.

Table 6 Fixed effects negative binomial regression of racial/ethnic homicide victims on neighborhood structural factors, San Diego, 1980–2010

Appendix 2

See Table 7.

Table 7 Standardized effect of immigrant concentration and social structural factors on changes in overall homicide rates, San Diego, 1980–2010

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Martinez, R., Stowell, J.I. & Iwama, J.A. The Role of Immigration: Race/Ethnicity and San Diego Homicides Since 1970. J Quant Criminol 32, 471–488 (2016). https://doi.org/10.1007/s10940-016-9294-9

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Keywords

  • Communities and crime
  • Crime trends
  • Immigration and crime
  • Longitudinal studies
  • Racial and ethnic disparities