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The Effects of Immigrant Concentration on Changes in Neighborhood Crime Rates

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Abstract

Objectives

This study investigated the extent to which immigrant concentration is associated with reductions in neighborhood crime rates in the City of Los Angeles.

Methods

A potential outcomes model using two-stage least squares regression was estimated, where immigrant concentration levels in 1990 were used as an instrumental variable to predict immigrant concentration levels in 2000. The instrumental variables design was used to reduce selection bias in estimating the effect of immigrant concentration on changes in official crime rates between 2000 and 2005 for census tracts in the City of Los Angeles, holding constant other demographic variables and area-level fixed effects. Non-parametric smoothers were also employed in a two-stage least squares regression model to control for the potential influence of heterogeneity in immigrant concentration on changes in crime rates.

Results

The results indicate that greater predicted concentrations of immigrants in neighborhoods are linked to significant reductions in crime. The results are robust to a number of different model specifications.

Conclusions

The findings challenge traditional ecological perspectives that link immigrant settlement to higher rates of crime. Immigration settlement patterns appear to be associated with reducing the social burden of crime. Study conclusions are limited by the potential for omitted variables that may bias the observed relationship between immigrant concentration and neighborhood crime rates, and the use of only official crime data which may under report crimes committed against immigrants. Understanding whether immigrant concentration is an important dynamic of changing neighborhood patterns of crime outside Los Angeles will require replication with data from other U.S. cities.

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Notes

  1. More recent work from the Moving to Opportunity (MTO) experiments suggests that the long term effects of neighborhood characteristics on criminal propensity are modest, and appear to occur only for black females (see Kling et al. 2005). However, Clampet-Lundquist and Massey (2008) argue that the MTO study failed to deliver enough of a dosage in race-class differences in neighborhoods to adequately assess neighborhood effects. Ludwig et al. (2008) in the same issue provide a rebuttal.

  2. The use of historical immigration patterns as an instrument for current patterns has been done in economics to remove selection bias in estimating the effect of immigration on labor market outcomes for different skill groups (Altonji and Card 1991; Card 2001).

  3. FBI Uniform Crime Index offenses reflect the following criminal offenses: homicide, rape, robbery, aggravated assault, burglary, larceny/theft, and motor vehicle theft.

  4. http://factfinder.census.gov/.

  5. There are on average about 1.2 RDs per census tract. Because census tracts and RDs have different geographic shapes, spatial interpolation is necessary to re-configure all data sources into the same unit of analysis. The overall degree of overlap was approximately 85–90 % between RDs and census tracts. Location data (X–Y coordinates) for all index offenses were not routinely collected by the LAPD until 2006. The RD is the lowest level of reliable geography for LA.

  6. This method assumes that the source data or analysis units are uniformly distributed over the source zones. Since there is no clear process to inform how the crime units are spatially distributed, the areal weighting method is an acceptable approach (Goodchild and Lam 1980).

  7. When we calculate the predicted change in the percentage of residents that are of Hispanic/Latino ethnicity between 1990 and 2000 and the predicted change in the percentage of residents that are foreign born—the two are so highly correlated (r = 0.75) that they are statistically indistinguishable from each other.

  8. Approximately 85 % of the joint variance in foreign born and Latino is accounted for by the first principal component; 72 % of the variance in four measures of poverty is accounted for by the first principal component; 91 % of the joint variance in percentage owner occupied and units 5 years or more is accounted for by the first principal component.

  9. We are interested in neighborhoods with similar expected probabilities of receiving immigrant settlement, and comparing the difference in crime changes between those that actually receive this treatment versus those that don’t. We explicitly do not examine changes in neighborhood immigrant concentration between 1990 and 2000 as predictors of changes in crime from 2000 to 2005 because this assumes a uniform effect of across neighborhoods. The relative change in levels of immigrant concentration is biased toward neighborhoods with low immigrant populations in 1990. Neighborhoods with relatively high concentrations of immigrants in 1990 will have little change over time, as we note in our selection model.

  10. The vector of parameters from these variables should not be interpreted as causal estimates since they have not been identified in our selection model (Angrist and Krueger 2001).

  11. For geographic areas we rely on LAPD Divisions: 19 unique geographic boundaries that represent semi-autonomous areas for which police allocation and planning decisions are made.

  12. An alternative specification to control for larger spatial effects is to include a spatial lag to account for the fact that crime in neighboring areas may be correlated with changes in each neighborhood (i). A spatial lag comes from a spatial weight matrix that would typically be constructed by a weighted average of crime rates for each neighborhood (i) as a function of all other neighborhoods of LA either bordering each other or at different distances (j), with weights equal to the inverse of the distance (1/distance) i to j. This approach was developed for handling the problem of spatial autocorrelation in cross sectional data. There are, however, well known shortcomings to the spatial econometric approach as it applies to longitudinal data. If we included a spatial lag parameter in our model we would have to assume that the spatial elements are exogenous to the model and not correlated with the residuals. Instead, we control for spatial dependence by including police division parameters, which adjusts for the average level of crime in different geographic regions and controls for this form of spatial dependence non-parametrically. We did estimate a separate spatial errors model according to the following form:

    $$ \Updelta \hat{Y}_{i} = \mu + \theta i \, \hat{T} + X_{i}^{\prime } \beta + e_{d(i)} $$

    where \( e_{d(i)} \) represents a weight matrix of the residuals clustered within the 19 police divisions. The results from this spatial errors specification are substantively the same and presented in “Appendix 1”.

  13. We use natural cubic spline parameters with 3 knots computed from a B-spline basis.

  14. We do not calculate crimes per population for each census tract because the census tract population does not present a stable denominator of the population at risk. A number of high frequency census tracts are located in central-city business districts and industrial areas with relatively low populations. Therefore, calculating the crime rate per census tract population could lead to distorted estimates. Since our focus is on within-neighborhood change in crime, the lack of a specific denominator for population at risk does not present a problem. In fact all areas of LA were increasing in population suggesting that our use of the change in crime counts is a conservative estimate of the change in crime rates.

  15. The reduced form estimates are obtained by substituting Eq. 4 into Eq. 5.

  16. The Wu-Hausman test of the null hypothesis of endogeneity between immigrant concentration in 2000 and change in violent crime (F = 112.83) and total crime (F = 21.65) was rejected at the p < 0.001 level, suggesting that levels of immigration were endogenous to changes in crime and that OLS estimates would be inconsistent. The OLS estimates of including percent immigrants in 2000 as a covariate without taken into account selection find a small effect on violent crime (b = −0.608; t = −3.22; p = 0.001) and a non-significant effect for total crime (b = −1.20; t = −1.80; p = 0.072).

  17. The Pearson correlation between predicted immigrant concentration and residuals was 0.034 (p = 0.324) for violent crime and 0.017 (p = 0.621) for total crime.

  18. Freedman (2006) notes that the use of robust standard errors corrections on observational data requires the analyst to make the assumption that the model is correct, but that the standard errors estimated are wrong. If such is true then it might be useful to get the correct variance, but if interest is in interpreting the parameters then correcting the variance is not useful.

  19. Eighty-three percent of the covariance between these two variables was explained by the first component.

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Acknowledgments

Aaron Kofner at RAND provided GIS and other programming assistance. The authors thank Phil Cook, Jeffrey Morenoff, Jeffrey Fagan, and Anne Morrison Piehl for their helpful suggestions on an earlier draft of the manuscript. All errors and omissions are those of the authors. Support for this project was made possible in part by funding from Centers for Disease Control and Prevention: 1U49CE000773 to the RAND Corporation. The points of view are those of the authors only.

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Authors and Affiliations

  1. Department of Criminology, University of Pennsylvania, McNeil Building, Suite 483, 3718 Locust Walk, Philadelphia, PA, 19104-6286, USA

    John M. MacDonald

  2. University of California, Irvine, CA, USA

    John R. Hipp

  3. George Mason University, Fairfax, VA, USA

    Charlotte Gill

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  1. John M. MacDonald
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Correspondence to John M. MacDonald.

Additional information

Earlier drafts of this paper were presented at the 2008 Annual Workshop on Crime and Population Dynamics, MD and the 2008 American Society of Criminology meetings, St. Louis, MO.

Appendices

Appendix 1

See Table 6.

Table 6 Spatial error estimation of immigrant concentration on changes in neighborhood crime rates
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Appendix 2

See Table 7.

Table 7 Effect of foreign born latino concentration on changes in neighborhood crime rates
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MacDonald, J.M., Hipp, J.R. & Gill, C. The Effects of Immigrant Concentration on Changes in Neighborhood Crime Rates. J Quant Criminol 29, 191–215 (2013). https://doi.org/10.1007/s10940-012-9176-8

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