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First published online June 2, 2009

Slaughterhouses and Increased Crime Rates: An Empirical Analysis of the Spillover From “The Jungle” Into the Surrounding Community

Amy J. Fitzgerald, Linda Kalof, and Thomas DietzView all authors and affiliations
Volume 22, Issue 2
https://doi.org/10.1177/1086026609338164

Abstract

More than 100 years after Upton Sinclair denounced the massive slaughterhouse complex in Chicago as a “jungle,” qualitative case study research has documented numerous negative effects of slaughterhouses on workers and communities. Of the social problems observed in these communities, the increases in crime have been particularly dramatic. These increases have been theorized as being linked to the demographic characteristics of the workers, social disorganization in the communities, and increased unemployment rates. But these explanations have not been empirically tested, and no research has addressed the possibility of a link between the increased crime rates and the violent work that takes place in the meatpacking industry. This study uses panel analysis of 1994-2002 data on nonmetropolitan counties in states with “rightto-work” laws (a total of 581 counties) to analyze the effect of slaughterhouses on the surrounding communities using both ordinary least squares and negative binomial regression. The findings indicate that slaughterhouse employment increases total arrest rates, arrests for violent crimes, arrests for rape, and arrests for other sex offenses in comparison with other industries. This suggests the existence of a “Sinclair effect” unique to the violent workplace of the slaughterhouse, a factor that has not previously been examined in the sociology of violence.

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1.
1. Similar contradictions have been noted in examinations of vivisection (see Adams, 2000; Balcombe, 2000; Dunayer, 2000; Fox, 2000). Animals are used in experiments precisely because they share many characteristics and qualities with humans, and all the while linguistic devices are employed to distance the experimenters from their subjects.
2.
2. Boomtown communities are characterized by the following features: They experience unprecedented population growth within a short amount of time; relatedly, they experience expanded employment opportunities; and they also experience heavy demands on social services (Camasso & Wilkinson 1990).
3.
3. The use of the term spillover here derives from the cultural spillover of violence theory developed by Larry Baron and Murray Straus (1987, 1988; Baron, Straus, & Jaffe 1988). The central tenet of this theory is that
4.
The more a society tends to endorse the use of physical force to attain socially approved ends—such as order in the schools, crime control, and military dominance--the greater the likelihood that this legitimation of force will be generalized to other spheres in life, such as the family and relations between the sexes, where force is less approved socially. (Baron et al., 1988, p. 80)
5.
Although the authors did not specifically discuss the slaughter of animals as part of this process, we argue here that it is a possibility.
6.
4. In these states, employees cannot be required to join or pay dues to a union and may resign from the union at any time, but still enjoy the benefits of the collective agreement. The following are the right-to-work states included in the analyses in this study: Alabama, Arizona, Arkansas, Georgia, Idaho, Iowa, Kansas, Louisiana, Mississippi, Nebraska, Nevada, North Carolina, North Dakota, Oklahoma, South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, and Wyoming.
7.
5. This time period is used because due to reporting changes in the Uniform Crime Report data, data prior to 1994 are not comparable with data from later years, and at the time of the study some of the demographic variables were not yet available at the county level for 2003 and later.
8.
6. In 1998, the classification of industries changed from the Standard Industrial Classification (SIC) system to the North American Industry Classification System (NAICS), and only some industries remain comparable across the time period.
9.
7. The arrest variables used include the following: Total arrests, Violent offenses, Murder, Rape, Offenses against the family, Sex offenses, Assault, Robbery, Burglary, Forgery, Possessing stolen property, Vandalism, Other assaults, and Disorderly conduct. The report variables used include: Index offenses, Murder, Rape, Assault, Robbery, Burglary, Motor vehicle theft, and Arson.
10.
8. The factor loadings are all above the commonly accepted minimum values of 0.3 to 0.4 and the Chronbach's alpha for the scale is .6728.
11.
9. Again, all of the loadings for these variables were above the acceptable range and Chronbach's alpha was .6062.
12.
10. As is commonly done in panel studies, in the analyses here the Slaughterhouse Employment variable and the comparison industry variables were lagged 1 year because their impact on crime would likely not be felt in the same year in these counties. More likely, the impact would be felt the following year (especially in cases where the industry opened or expanded late in the year).
13.
11. The variables analyzed include the following: Total number of arrests, Arrests for violent crimes, Arrests for murder, Arrests for rape, Arrests for offenses against the family, Arrests for sex offenses (excluding rape), Arrests for aggravated assault, Total reports for index offenses, Reports of murder, Reports of rape, and Reports of assault.
14.
12. Negative binomial regression requires that an exposure variable be identified to differentiate across cases differences in the possibility of being “exposed” to the effect. Long and Freese (2006) use the example of time as an exposure variable. In this study, however, it is not time that differentiates the likelihood of crime in the counties but the differences across counties in population (a larger number of people makes the possibility of offending or being victimized greater). Therefore, we set county population as the exposure variable. Including the exposure variable adds the natural log of the size of the population at risk to the model. Thus, in essence, the model analyzes per capita rates of crime instead of merely counts of crime even though the dependent variable is a count, not a rate. This is standard practice in the quantitative criminology literature (Osgood, 2000). Using the population as the exposure variable also permits an acknowledgement in the model that rates based on larger populations have greater precision, which addresses the issue of heterogeneity of variance, which is problematic in the use of OLS regression on count variables (Krivo & Peterson, 2004; Osgood, 2000)
15.
13. The IRR values can be interpreted as the multiplicative factor by which a one unit change in the independent variable affects the dependent variable, controlling for the other variables. Therefore, an IRR value below one indicates that the predictor variable (controlling for the other variables) decreases the incidence-rate, which demonstrates a negative effect. Accordingly, an IRR value above one indicates an increase in the incidence-rate, or a positive effect.
16.
14. The change in classification to include small custom slaughterhouses in the slaughterhouse category may affect these analyses in two ways. If the years after the reclassification are included, due to the way the County Business Patterns categorizes the employee data (e.g., 1-19, 20-99) instead of reporting the exact number of employees, the inclusion of small custom slaughter facilities could artificially increase the number of slaughterhouse workers in counties since the midpoint of the ranges are used in the analyses, therefore diluting the possible effect of slaughterhouse employment. In addition, work at a custom slaughterhouse may be episodic, involving the slaughter of a relatively small number of animals in any given time period rather than the routinized slaughter of the larger facilities. This means that workers may be less exposed to slaughter. If the years after the reclassification are excluded, then these problems are avoided but the sample size is reduced from 4,646 to 1,743, reducing the power of the analysis. Although this seems like a large sample, given the highly stochastic nature of crime in rural communities, substantial power is required to see significant effects. Unfortunately, there is no way to disaggregate the slaughterhouse data and exclude these facilities from the analysis.
17.
15. It is also possible that if violent offenses committed by family members were included in the offenses against the family category that the effect of slaughterhouse employment on offenses against the family would have been positive and significant (instead of positive but not significant for the period prior to the inclusion of custom slaughter facilities).
18.
16. This study should not, however, be considered the definitive testing of these theories, or predictive models of crime in general. Different operationalizations of the theories might have resulted in slightly different findings. Further, The R2 values of the models are low; however, the purpose of this research was to control for the variables implicated in the theorized causes in the literature to assess the effects of slaughterhouse employment. It is also worth noting that there is some degree of multicolinearity among the variables. Specifically, the total number of males, number of young males, and the number of people in poverty have variance inflation factor (VIF) values greater than 4 (the values are 19.25, 15.64, and 8.01, respectively). Because this colinearity is entirely among control variables, it has no important effect on the estimates of the effects of slaughterhouse employment (the VIF value of the lagged slaughterhouse employment variable is 1.47).

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Volume 22, Issue 2
Pages: 158 - 184
Article first published online: June 2, 2009
Issue published: June 2009

Keywords

  1. meatpacking industry
  2. slaughterhouses
  3. crime
  4. employment
  5. rural communities

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Amy J. Fitzgerald
University of Windsor
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Linda Kalof
Michigan State University
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Thomas Dietz
Michigan State University
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