Presentation on theme: "The Effects of Segregation on Crime Rates David Bjerk McMaster University and RAND Corp."— Presentation transcript:
The Effects of Segregation on Crime Rates David Bjerk McMaster University and RAND Corp.
I - Introduction Numerous authors in a variety of disciplines have documented the high rates of crime and victimization in poor minority neighborhoods (e.g. Kling, Ludwig, and Katz, 2005; Krivo and Peterson, 1996; Kotlowitz 1991; Patterson, 1991; Messner and Tardiff, 1996) While these studies highlight the correlation between segregated neighborhoods and crime, they do not necessarily identify whether greater segregation in a city (be it racial or economic) has a direct impact on the overall amount criminal activity or simply concentrates criminal activity to certain neighborhoods. Of primary concern is that segregation might not only affect crime, but criminal activity might also affect segregation. Therefore, to properly estimate the effect of segregation (racial or economic) on crime, it may be important to go beyond simple correlations and exploit plausibly exogenous sources of segregation.
I – Introduction (cont.) In this paper, I use differences across cities with respect to how housing assistance to the poor is allocated, as well as variables related to the local public finance of each city, to identify the direct effect of segregation on crime using instrumental variables methods. In doing so, I find that greater segregation appears to have little impact on basic property crimes such as burglary and larceny, leads to somewhat lower rates of motor vehicle theft, and most notably, leads to substantially higher rates of violent crimes such as robbery and aggravated assault.
II –The Relationship Between Segregation and Criminal Activity There are a variety of quite straightforward reasons for why poorer individuals may be more prone to criminal activity than richer individuals. For example, the value of illegally obtaining any given item or sum of money will generally be greater for a poor individual than a rich individual under the standard assumption that individuals have diminishing marginal utility in consumption. Hence, it is not necessarily surprising that crime is substantially higher in relatively poor neighborhoods. Moreover, given the strong correlation between race and poverty in the U.S., it is also not surprising that crime rates are generally higher in primarily black neighborhoods than in primarily white neighborhoods.
II(A) – How might Segregation affect Criminal Activity? However, greater segregation may also directly influence criminal activity. For example, if the poor are generally those most prone to criminality, and in segregated cities poor individuals generally only live near other poor individuals, then greater segregation may reduce the prevalence of property crimes by reducing the relative supply of things of value to steal for those most prone to theft. On the other hand, an individual who lives in a neighborhood made up primarily of poor people may expect a relatively high fraction of his neighbors to be prone toward crime. Therefore, an individual with primarily poor neighbors may have a strong incentive to act aggressively or criminally toward his neighbors as a means of deterrence (e.g. join a gang or become known as a neighborhood “tough” to prevent victimization). Hence, greater segregation may directly increase criminal activity, especially with respect to violent or interpersonal crimes.
II(A) – How might Segregation affect Criminal Activity? A variety of other potential mechanisms through which greater segregation can directly effect criminal activity have also been proposed, such as: Concentrating criminal know-how and information (Lochner and Heavner, 2002; Calvo-Armengol and Zenou, 2004). Generating a lack of positive role models and peer influences (Wilson, 1996; Glaeser, Sacerdote, and Sheinkman, 1996; Brock and Durlauf, 2001). Creating spatial mismatch that makes crime more lucrative relative to the legal labor market (Verdier and Zenou, 2004). Creating highly disadvantaged neighborhoods that lack strong social control and encourage criminogenic adaptation (Peterson, Krivo, Browning, 2005).
II(B) – How might Criminal Activity Effect Segregation? Alternatively, there are also ways in which crime might directly affect segregation. For illustrative purposes, assume for the moment that segregation has no direct effect on crime. (1) Consider a state of the world where cities start off relatively integrated, with well-off white families living in neighborhoods with poorer black families. Now consider the effect of rising crime rates in some cities. Such increasing crime rates may cause the richer white families to move to more racially and economically homogeneous neighborhoods in order to avoid the increasing crime (“white flight”), increasing the level of segregation in high crime cities. This will result in a positive correlation between segregation and crime rates, even if segregation has no direct effect on criminality (causing upward bias in OLS)
II(B) – How might Crime Effect Segregation? (cont.) (2) Alternatively, consider a state of the world in which cities start off relatively racially segregated, where the relatively small number of poorer white families live in neighborhoods with richer white families, but richer black families live in neighborhoods with the relatively large number of poor black families. Now, consider the effect of rising crime rates in some cities, where this crime occurs mainly in the poorer, higher minority neighborhoods. Such increasing crime rates may cause the richer blacks to move to the previously primarily white neighborhoods, decreasing the relative level of segregation in such high-crime cities. Hence, in this case, there will be a negative correlation between segregation and crime, even if once again, segregation has no direct affect on crime (causing downward bias in OLS).
III(A) – Empirical Analysis (Data) To look at the relationship between segregation and crime empirically, I use data from a variety of sources by MSA: 2000 FBI UCR Data - Crime rates by MSA for index crimes Basic Property crimes (no violence or threat of violence): Burglary, Larceny, Motor Vehicle Theft. Interpersonal Crimes (violence or threat of violence): Robbery, Aggravated Assault Census - Segregation Indices (Cutler, Glaeser, Vigdor, 1999) Dissimilarity index – answers the question “what share of black population would have to change census tracts for the black and non-black to be evenly distributed within the city.” Isolation Index – attempts to measure the extent to which individuals of one race will encounter members of another race within their own neighborhood/census tract.
III(A) – Empirical Analysis (Data) Other MSA level control variables: MSA Population Characteristics: Fraction Black, Fraction Hispanic, Population Size, Fraction Immigrant, Fraction of Adults with College Degree, a measure of relative economic prosperity (2000 Census). Previous year clearance rates for each type of crime in each MSA to attempt to control for effectiveness of local police force (1999 FBI UCR reports). Fraction of households in each MSA that receive housing subsidies (HUD’s “A Picture of Subsidized Households 1998”). Finally, to control for potential effects of weather (see Jacob, Lefgren, and Moretti, 2005), I construct measures of fraction of days above 90 degrees and fraction of days below freezing (data from the National Climatic Data Center).
III(B) – Empirical Analysis (OLS Results)
III(C) – Empirical Analysis (Instrumental Variables) As discussed previously, interpretation of these results is complicated by the potential simultaneity bias between segregation and crime, meaning these estimates may vastly overstate or vastly understate the true direct effect of segregation on citywide criminal activity. To obtain more plausible estimates of any causal impact of segregation on crime, we need to find and exploit differences across cities that may impact segregation, but are exogenous to current crime conditions. In particular, we want to find suitable instruments for segregation with respect to crime, then given such instruments, estimate the effects of segregation on crime using Two-stage Least Squares (2SLS).
III(C) – Empirical Analysis (Instrumental Variables) Three instruments will be used for this analysis: (1)The fraction of subsidized housing given in the form of housing in a publicly owned facility in 1998 (versus Section 8 housing vouchers). By construction, public housing facilities concentrate the poor into specific census tracts/neighborhoods, while vouchers allow the poor to spread more dispersedly throughout the city. Given the relationship between poverty and race, such concentration of the poor also likely means concentration of black families into specific tracts/neighborhoods. Hence, a higher fraction of housing aid given via apartments in housing projects will generally lead to greater segregation. However, given the public housing stock is largely a historical legacy generally dating back decades, the fraction of public housing given in-kind versus through via vouchers in 1998 is unlikely to be directly related to crime conditions in 2000.
III(C) – Empirical Analysis (Instrumental Variables) The other two instruments are taken from Culter and Glaeser (1997): (2) The number of municipalities within each MSA in A greater number of local municipalities allows local governments to cater public goods and taxes more narrowly, potentially causing more sorting into smaller communities with more homogeneous preferences, likely increasing segregation. (3) The fraction of MSA government revenue in 1962 coming from the Federal government. Similar intuition to above, the more revenue coming from the Federal government, the smaller gains to be made from sorting with respect to public goods and taxes, meaning more Federal money would lead to less segregation. There is no reason to think that either of these variables could be directly related to crime conditions around the year 2000.
III(C) – Empirical Analysis (First Stage OF 2SLS)
III(C) – Empirical Analysis (2SLS Results)
IV – Empirical Analysis (Economic Segregation) Recall that many of the explanations for why racial segregation may directly affect criminality actually hinged on the relationship between race and poverty. Therefore, racial segregation may simply be acting as an indicator for greater economic segregation, and it is this economic segregation that has the direct impact on violent crime and possibly motor vehicle thefts. We can look at this hypothesis to some degree by creating other indices of economic segregation besides the racial segregation indices. In particular, I construct analogous segregation indices using individual poverty status rather than race. Motivation for instruments is identical under for indices of segregation by poverty status as for racial segregation indices.
IV – Empirical Analysis (Economic Segregation) 2SLS results using the indices of poverty segregation are generally very similar to those using the indices of racial segregation. Namely, greater segregation by poverty status in a city has no real effect on burglary and larceny, a negative effect on motor vehicle thefts, and large significantly positive effects on robbery and aggravated assaults. The actual point estimates are actually almost twice as large for robbery and violent crime using the poverty as opposed to racial indices of segregation, But they are less precisely estimated, primarily due to weaker first stage relationships between the instruments and the poverty segregation indices than the racial segregation indices. This may be because cities have historically used allocation of apartments in public housing projects as a means of maintaining racial segregation patters (e.g. Chicago), and separate municipalities may have arisen as a way of not only segregating by tastes for public goods, but also as a way of increasing barriers to racial integration.
V - Conclusion In general, the results of this analysis suggest that greater racial and economic segregation have: Little direct effect on the rates of basic property crime such as burglary and larceny, but actually may slightly decrease rates of motor vehicle thefts, However, the strongest results suggest that greater segregation substantially increases rates of violent crime such as robbery and aggravated assault. Moreover, failing to account for the potential endogeneity of segregation with respect to crime will likely lead to underestimating the magnitudes of the direct effects of segregation on motor vehicle thefts and violent crime. In terms of policy implications, these results suggest that efforts to reduce racial and economic segregation in cities (e.g. through razing high-rise public housing) may lead to drastic improvements in quality of life for the poor, and arguably for city residents overall, through reducing the total amount of violence (but might come at the cost of more car thefts).
III(C) – Empirical Analysis (2SLS Results) Given we have more instruments than potentially endogenous variables, the model is overidentified and we directly test our exclusion restrictions regarding our instruments. –Specifically, we can take R-squared that results from regressing the residuals from the first stage regression on all of the exogenous control variables and including the excluded instruments, and multiply it by the number of observations. This statistic will have asymptotically have a chi-squared distribution with 2 degrees of freedom (instruments minus number of endogenous variables). –The exclusion restrictions with respect to these instruments can be argued to be invalid if this resulting statistic is significantly different than zero. –Not surprisingly, the resulting statistic is never close to significant for any of the crime categories used here.