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What Kind of Joblessness Affects Crime? A National Case–Control Study of Serious Property Crime

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Abstract

Objective

To assess whether joblessness affects the commission of serious property crime.

Methods

We studied serious property crime, applying a case–control design to nationally representative samples of (a) known serious property crime offenders and (b) nonoffenders. This was done by comparing a national sample of prison inmates convicted of robbery or burglary (the “cases”) with a general sample of the U.S. adult population (the “controls”). In contrast to prior individual-level research, the study sample included substantial numbers of serious offenders, and provided a formal basis for generalizing the findings to the U.S. adult population. We differentiated five labor force statuses: (1) unemployed (according to the official government definition), (2) underemployed, (3) out of the labor force for widely socially accepted reasons (OLFL), (4) out of the labor force for reasons not widely accepted (OLFN), and (5) fully employed.

Results

We found that when these distinctions are made, people are not more likely to engage in burglary or robbery when they are either completely unemployed or underemployed according to the official definitions. Instead, it is being out of the labor force for reasons not widely accepted as legitimate that is significantly and positively related to serious property offending.

Conclusions

The results suggest that offending among jobless persons may reflect preexisting differences in criminal propensity among those who stay out of the labor force, rather than effects of joblessness per se. Part-time work is associated with significantly less property crime, perhaps because the willingness to accept even part-time jobs serves as an indicator of commitment to pro-social attitudes.

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Notes

  1. An additional concern is that fixed effects models only use individuals that vary over time on the outcome of interest, which can lead to substantial attrition in general population samples.

  2. Using the highest evidence-based estimates (from the National Crime Victimization Survey), there were 688,310 personal robbery incidents in 2000 (U. S. Bureau of Justice Statistics 2014). This does not count commercial robberies, but we can roughly estimate total robberies using FBI data that indicate that the ratio of total robberies (including commercial robberies) over personal robberies was 1.3384 in 2000 (U.S. Federal Bureau of Investigation 2014). Thus, the estimated total robberies in 2000 was 1.3384 × 688,310 = 921,239. Arrest data indicate that 74.712 % of robberies in 2000 were committed by persons age 18 or over (U.S. Federal Bureau of Investigation 2014), so an estimated 0.74712 × 921,239 = 688,280 robberies (whether reported to the police or not) were committed by adults in the U.S. in 2000. Even if we implausibly assumed that there was no repeat robbery behavior, thereby spreading robbery behavior across the widest share of the population, no more than 688,280 U.S. adults could have committed a robbery in 2000—just 0.00329 of the adult population of 209,128,000. Even over a 10-year period, if this prevalence were to persist, and the probability of robbing was independent across years, the maximum fraction of the adult population that committed a robbery would be just 1 − (1 − 0.00329)10 = .032. Even if robbers were as likely as others to respond to surveys (another generous but unlikely assumption), this means that at most 3.2 % of GSS respondents could have committed a robbery in the preceding 10 years, and thus that our assumption that none were robbers would be correct 97 % of the time. Since repeat robbery is in fact quite common and the prevalence of robbery behavior is therefore likely to be considerably lower than 3.2 %, and since robbers are probably not as likely as other adults to cooperate with surveyors, we suspect that the accuracy of our assumption that none of the GSS Rs are robbers is likely to be closer to 99 %.

  3. For both crime types, the results were qualitatively identical when listwise deletion of cases with missing data was used, rather than using multiple imputation.

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Authors and Affiliations

  1. College of Criminology and Criminal Justice, Florida State University, Tallahassee, FL, 32306-1044, USA

    Gary Kleck & Dylan Jackson

Authors
  1. Gary Kleck
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  2. Dylan Jackson
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Corresponding author

Correspondence to Gary Kleck.

Appendix: Coding of Work Status in GSS and SISFCF Datasets (Using SPSS Commands)

Appendix: Coding of Work Status in GSS and SISFCF Datasets (Using SPSS Commands)

In both datasets, there are four work status categories into which each respondent is classified, and the fourth, NILF, is divided into two subtypes, OLFL and OLFN. A dummy variable (1/0) is created for each of these statuses, and 9 is the missing-data designator for all the variables.

  1. 1.

    Employed full-time, 35 or more hours—EMPLOYED (reference/omitted category)

  2. 2.

    Employed part-time (under 35 h, if number of hours known)—UNDEREMP

  3. 3.

    Unemployed (no job, part- or full-time, looking for job)—UNEMPLOY

  4. 4.

    Out of labor force (no job and not looking for job)—NILF

  5. 5.

    Out of labor force for a known socially accepted reason—OLFL

  6. 6.

    Out of labor force for no known socially accepted reason, or known reason that is not widely socially accepted—OLFN

Rs were considered to be out of the labor force for a socially accepted reason (OLFL) if they (1) had no job, full- or part-time, (2) were not looking for work, but (3) had a reason for not seeking work that would be widely regarded as socially acceptable—they were students, keeping house, or were retired. These Rs were be coded 1 on OLFL and 0 on OLFN. Rs who were NILF and who had no such reason for not seeking work were coded 0 on OLFL and 1 on OLFN.

General Social Survey (GSS) Controls

  • COMPUTE EMPLOYED=0.

  • IF (WRKSTAT EQ 1 & HRS1 GE 35 | (WRKSTAT EQ 2 & HRS1 GE 35) | (WRKSTAT EQ 3

  • & HRS2 GE 35)EMPLOYED=1.

  • IF (WRKSTAT EQ 9)EMPLOYED=9.

  • COMPUTE UNDEREMP=0.

  • IF ((WRKSTAT EQ 2 & HRS1 LT 35) | (WRKSTAT EQ 1 & HRS1 LT 35) | (WRKSTAT EQ

  • 3 & HRS2 LT 35))UNDEREMP=1.

  • IF (WRKSTAT EQ 9)UINDEREMP=9.

  • COMPUTE UNEMPLOY=0.

  • IF (WRKSTAT EQ 4)UNEMPLOY=1.

  • IF (WRKSTAT EQ 9)UNEMPLOY=9.

  • COMPUTE NILF=0.

  • IF (WRKSTAT GE 5 AND WRKSTAT LE 8)NILF=1.

  • IF (WRKSTAT EQ 9)NILF=9.

  • COMPUTE OLFL=0.

  • IF (NILF EQ 1 & (WRKSTAT EQ 5 | WRKSTAT EQ 6 | WRKSTAT EQ 7))OLFL=1.

  • IF (WRKSTAT EQ 9)OLFL=9.

  • COMUTE OLFN-0.

  • IF (NILF EQ 1 & OLFL EQ 0)OLFN=1.

  • IF (OLFL EQ 9)OLFN=9.

Survey of Inmates in State and Federal Correctional Facilities, 2002 (SISFCF) Cases

  • COMPUTE EMPLOYED=0.

  • IF (V1747 EQ 1 & V1748 EQ 1)EMPLOYED=1.

  • IF (V1747 EQ 7 | V1747 EQ 8 | V1748 EQ 7 | V1748 EQ 8)EMPLOYED=9.

  • COMPUTE UNDEREMP=0.

  • IF (V1747 EQ 1 & V1748 EQ 2)UNDEREMP=1.

  • IF (V1747 EQ 7 | V1747 EQ 8 | V1748 EQ 7 | V1748 EQ 8)UNDEREMP=9.

  • COMPUTE UNEMPLOY=0.

  • IF ( (V1747 EQ 2 & V1750 EQ 1) | (V1747 EQ 1 & V1748 EQ 3 & V1749 EQ 1) )

  • UNEMPLOY=1.

  • IF (V1747 EQ 7 | V1747 EQ 8 | V1748 EQ 7 | V1748 EQ 8)UNEMPLOY=9.

  • COMPUTE NILF=0.

  • IF ((V1750 EQ 2) | (V1749 EQ 3 & V 1749 EQ 2)NILF=1.

  • IF (V1747 EQ 7 | V1747 EQ 8 | V1750 EQ 7 | V1750 EQ 8)UNDEREMP=9.

  • COMPUTE OLFL=0.

  • IF (V1751 EQ 01 | V1758 EQ 08 | V1760 EQ 10 | V1765 EQ 15)OLFL=1.

  • IF (V1769 EQ 2 | V1769 EQ 8)OLFL=9.

  • COMPUTE OLFN=0.

  • IF (NILF EQ 1 & OLFL EQ 0)OLFN=1.

  • IF (V1769 EQ 2 | V1769 EQ 8)OLFN=9.

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Kleck, G., Jackson, D. What Kind of Joblessness Affects Crime? A National Case–Control Study of Serious Property Crime. J Quant Criminol 32, 489–513 (2016). https://doi.org/10.1007/s10940-016-9282-0

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