Abstract
Researchers have examined the correlates of inter-group relationships, relying heavily on Blau’s (The American Journal of Sociology, 83, 26–54, 1977) macrostructural opportunity theory. The results of these studies have given mixed support for the relationship between social structure and inter-racial violence. This study builds on existing research on inter-group violence by examining what social structural correlates may influence intra- and inter-group violence using the macrostructural opportunity theory as a guiding framework. Data from the National Incident-Based Reporting System as well as the American Community Survey are utilized to construct a large sample of counties across the United States. The findings provide mixed support for Blau’s hypotheses, with heterogeneity and segregation showing some effects on inter-group violence, while racial inequality remains largely a non-significant predictor.
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Notes
NIBRS allows for the consideration of multiple victims and multiple suspects within an incident. Because the interest here is on inter-racial incidents, incidents involving multiple victims or suspects that were different races were included in the other inter-racial category. Thus, incidents that are coded as Black-on-White, for instance, are incidents in which all of the victims were White and all of the Suspects were Black, if there were multiple victims/suspects.
While Blau’s (1977) original theory, and his subsequent tests of the theory (Blau & Blau, 1982), utilized the SMSA as the unit of analysis, given that many of the social structural arrangements he is discussing likely operate at lower levels of analysis, the county was chosen here in an effort to better examine variation in social structure in these smaller units, while maintaining statistical power in the sample size.
While Blau’s (1977) hypotheses may refer to all types of inter-racial crime, violence was chosen as the focus for the current analysis due to the large amount of missing data on the race of the suspect for property crimes in the NIBRS dataset.
Hispanic victims and suspects are not included in the analysis. NIBRS does include a measure of victim ethnicity, however, it does not include a measure of suspect ethnicity. Additionally, the victim ethnicity measure has been shown to include a large amount of missing data that is not missing at random in prior studies (Stacey et al., 2017). As a result, the current analysis will focus on the racial composition of crimes.
The other intra-racial measures include those cases involving an Asian suspect and victim and a Native American suspect and victim. The other inter-racial measures include all cases in which the victim and suspect were not of the same race and are not included in one of the other measures. The other inter-racial measures also include cases in which there were multiple suspects and/or victims of different races.
While NIBRS does provide information about whether a case is cleared, and for arrests the arrestee demographic characteristics, Blau’s (1977) theory does not pertain to arrest or cleared cases only, but rather applies to the overall crime rate. Thus, the racial characteristic of the offender in each case was drawn from the suspected offender rather than the arrestee data. While it is possible that this information could be inaccurate for some cases, because the focus here is on violent crime requiring direct interaction between the victim and suspect, as well as concern over the ability to match a particular arrestee’s characteristics or victim characteristics in multiple arrestee/victim incidents, and prior research utilizing the suspect race as opposed to the arrestee race (Messner, McHugh, & Felson, 2004), this measure is preferred here.
The heterogeneity index is calculated using the following groups as defined by the ACS: African-American, Asian, Caucasian, American Indian/Alaskan Native, Native Hawaii/Pacific Islander, Some Other Race Alone, and Two or More Races.
To compute this measure at the county level, race specific census tract data was used to calculate the proportions in each group and then aggregated to the county level.
The inclusion of two measures of inequality presents questions of multicollinearity. VIFs were estimated to determine whether multicollinearity is a problem within the analysis. The average VIF was a 1.24 and the highest was 1.43. This suggests that multicollinearity is not a problem within the analysis.
Attempts were made to create a factor measure using principle components analysis, consistent with prior research on black deprivation (Messner & Golden, 1992), however, the Cronbach’s alpha values were low, indicating that the factors were not reliable.
The P* measure is not used b/c it is influenced by group size and would likely be collinear with other measures.
The dissimilarity index is calculated using the following formula: 1-\( \sum \left(\frac{b_i}{B_i}-\frac{b_i}{B_i}\right) \) where bi is equal to the size of the Black population in an individual census tract, Bi is the size of the Black population in the county, wi is the size of the White population in an individual census tract, and Wi is the size of the White population in the county. Data from the 5-year estimates of the ACS at the census tract level was used to calculate this index prior to aggregation to the county level.
The Breusch-Pagan/Cook-Weisburg test for heteroskedasticity was examined in STATA indicating non-constant variance in the error term.
Results available from the author upon request.
These models were also estimated with the Black/White income ratio in the model. The results were unchanged.
This analysis was also conducted for the intra-racial violence models (results available upon request). For the intra-racial analysis the White/White exposure rate and the Black/Black exposure index were used. The results of this analysis are consistent with those of the inter-racial analysis discussed, with the exposure term significant and positive in all models.
References
Baumer, E. P., & Lauritsen, J. L. (2010). Reporting crime to the police, 1973-2005: A multivariate analysis of long-term trends in the National Crime Survey (NCS) and National Crime Victimization Survey (NCVS). Criminology, 48, 131–185.
Blau, J. R., & Blau, P. M. (1982). The cost of inequality: Metropolitan structure and violent crime. American Sociological Review, 47, 114–129.
Blau, P. M. (1977). Macrosociological theory of social structure. The American Journal of Sociology, 83, 26–54.
Cahill, M., & Mulligan, G. (2007). Using geographically weighted regression to explore local crime patterns. Social Science Computer Review, 25(2), 174–193.
Cohn, E. G., & Rotton, J. (2003). Even criminals take a holiday: Instrumental and expressive crimes on major and minor holidays. Journal of Criminal Justice, 31, 351–360.
Cooper, A. & Smith, E. L. (2011). Homicide trends in the United States. (NCJ236018). Bureau of Justice Statistics. U.S. Department of Justice: Washington, D.C.
D’Alessio, S. J., Stolzenberg, L., & Eitle, D. (2002). The effect of racial threat on interracial and intraracial crimes. Social Science Research, 31, 392–408.
Federal Bureau of Investigation (FBI). (2004). Uniform Crime Reporting Handbook. Washington, D.C.: U. S. Department of Justice.
Federal Bureau of Investigation (FBI). (2014). Crime in the United States, 2013. Washington, D.C.: U.S. Department of Justice.
Galster, G. C. (1982). Black and white preferences for neighborhood racial composition. Real Estate Economics, 10, 39–66.
Gini, C. (1912). Variabilita e mutabilita. Reprinted in Memorie di Metodologica statistica (Ed: Pizetti E, Salvemini, T). Rome: libreria Eredi Virgilio Veschi.
Heathcote, J., Perri, F., & Violante, G. L. (2010). Unequal we stand: An empirical analysis of economic inequality in the United States, 1967-2006. Review of Economic Dynamics, 13, 15–51.
King, K, Starsinic, M, Viver, A. H., & Beaghen, M. (2015). The Reliability of ACS 5-Year Estimates of Race Groups and American Indian and Alaskan Native Populations. 2015 American Community Survey Research Memorandum Series ACS15-R-01. Washington, D.C.: United States Department of Commerce. Retrieved from https://www.census.gov/content/dam/Census/library/working-papers/2015/acs/2015_King_01.pdf.
Kopczuk, W., Saez, E., & Song, J. (2010). Earnings inequality and mobility in the United States: Evidence from social security data since 1937. The Quarterly Journal of Economics, 125, 91–128.
Koss, M. P., Dinero, T. E., Seibel, C. A., & Cox, S. L. (1988). Stranger and acquaintance rape: Are there differences in the victim’s experience? Psychology of Women Quarterly, 12, 1–24.
Kubrin, C. E. (2000). Racial heterogeneity and crime: Measuring static and dynamic effects. Research in Community Sociology, 10, 189–218.
LaFree, G. D. (1980). The effect of sexual stratification by race on official reactions to rape. American Sociological Review, 45, 842–854.
LaFree, G. D. (1982). Male power and female victimization: Toward a theory of interracial rape. American Journal of Sociology, 88, 311–328.
Lambert, P. J., & Aronson, J. R. (1993). Inequality decomposition analysis and the gini coefficient revisited. The Economic Journal, 103, 1221–1227.
Massey, D. S., & Denton, N. A. (1988). The dimensions of residential segregation. Social Forces, 67, 281–315.
Messner, S. F., & Golden, R. M. (1992). Racial inequality and racially disaggregated homicide rates: An assessment of alternative theoretical explanations. Criminology, 30, 421–445.
Messner, S. F., McHugh, S., & Felson, R. B. (2004). Distinctive characteristics of assaults motivated by bias. Criminology, 42, 585–618.
Messner, S. F., & South, S. J. (1986). Economic deprivation, opportunity structure, and robbery victimization: Intra-and interracial patterns. Social Forces, 64, 975–991.
Messner, S. F., & South, S. J. (1992). Interracial homicide: A macrostructural-opportunity perspective. Sociological Forum, 7, 517–536.
Morgan, R. E. (2017). Race and Hispanic origin of victims and offenders, 2012–15. Bureau of Justice Statistics, Special Report. (NCJ 250747). Washington, D.C.: U.S. Department of Justice.
Pais, J. F., South, S., & Crowder, K. (2009). White flight revisited: A multiethnic perspective on neighborhood out-migration. Population Research and Policy Review, 28, 321–346.
Parisi, D., Lichter, D. T., & Taquino, M. C. (2011). Multi-scale residential segregation: Racial exceptionalism and America’s changing color line. Social Forces, 89, 829–852.
Reardon, S. F., & Firebaugh, G. (2002). Measures of multigroup segregation. Sociological Methodology, 32, 33–67.
Rosenfeld, R., & Fornango, R. (2007). The impact of economic conditions on robbery and property crime: The role of consumer sentiment. Criminology, 45, 735–769.
Rugh, J. S., & Massey, D. S. (2014). Segregation in post-civil rights American: Stalled integration or end of the segregated century? Du Bois Review, 11, 205–232.
South, S. J., & Felson, R. B. (1990). The racial patterning of rape. Social Forces, 69, 71–93.
South, S. J., & Messner, S. F. (1986). Structural determinants of intergroup association: Interracial marriage and crime. American Journal of Sociology, 91, 1409–1430.
Stacey, M., Martin, K. H., & Brick, B. (2017). Victim and suspect race and the police clearance of sexual assault. Race and Justice, 7, 226–255.
Stolzenberg, L., Eitle, D., & D’Alessio, S. J. (2006). Race, economic inequality, and violent crime. Journal of Criminal Justice, 34, 303–316.
Wadsworth, T., & Kubrin, C. E. (2004). Structural factors and black interracial homicide: A new examination of the causal process. Criminology, 42, 647–672.
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Stacey, M. Macrostructural Opportunity and Violent Crime: The Impact of Social Structure on Inter- and Intra-Racial Violence. Am J Crim Just 44, 125–145 (2019). https://doi.org/10.1007/s12103-018-9446-6
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DOI: https://doi.org/10.1007/s12103-018-9446-6