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Offending Frequency and Responses to Illegal Monetary Incentives

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Abstract

Objectives

We examine how responsive offenders are to illegal monetary incentives. We draw from rational choice theory, prospect theory, and models of labor supply to develop expectations regarding the relationship between criminal efficiency, which is the average earnings per offense, and frequency of offending.

Methods

We use OLS, fixed effects, and first-difference estimators to analyze data from 152 incarcerated male inmates from Quebec, Canada to study within individual monthly changes in criminal efficiency and offending frequency.

Results

There is an inverse relationship between criminal efficiency and frequency of offending, net of individual fixed effects, for market crimes, but not property crimes. We also find that the supply of crime is inelastic, meaning it is not highly sensitive to illegal wage changes.

Conclusions

In the months that offenders have an average bigger pay-off per crime, they offended less frequently. We conjecture that this negative relationship could be explained by two mechanisms: an income effect and/or through reference dependence. However, we are not able to disentangle between the two mechanisms. Moreover, we note that criminal efficiency is likely endogenous and should be treated as such in future scholarship.

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Notes

  1. Much of what is known about offending frequency is rooted in the criminal career literature. Interest in offending frequency surfaced in the 1980s and 1990s and has largely been associated with studies that compare predictors of participation and frequency to test the veracity of the criminal career framework (e.g., Blumstein 1986; Blumstein and Cohen 1987; Nagin and Smith 1990; Smith et al. 1991).

  2. This distinction is analogous to the difference economists refer to as the extensive versus intensive margin.

  3. To be clear, the terms ‘risk seeking’ and ‘risk averse’ are defined in terms of the amount of risk an individual is willing to take in relation to the expected payoff of a gamble.

  4. Some readers may at first glance object to frequency appearing on both the right-hand side and left-hand side of this equation. However, the choice problem this represents is in which a potential offender must choose how many offenses in which to participate based on their expectation of how lucrative each offense is represents a direct analog to a standard choice problem in labor supply in which a worker must choose how many hours to supply to the market based on the hourly wage ($/hour). In this case, similar to frequency in the current application, hours would technically appear on either side of the equation.

  5. About 57% of respondents reported being incarcerated in 2000 or 2001; about 21% in 1998 or 1999; 17% in 1996 or 1997; and 4% in 1994 or 1995. As sensitivity analysis, we conducted our analyses with respondents who were incarcerated in 2000 or 2001 (57% of the sample) and results were consistent with our main results.

  6. While robbery is often categorized as a violent crime, in the current context, robbery tends to be a low frequency non-transactional crime and therefore included as a property crime.

  7. This measure is similar to other studies that group together offense types as a measure of specialization (e.g., Thomas 2015; Piquero 2000).

  8. In a traditional wage regression in which earnings (rate) was the dependent variable, these values can be treated using a traditional selection correction model in which the model parameters are corrected to adjust for selection bias (see Loughran et al. 2013). However, given that in the present case the unobserved values are on the right-hand side as opposed to the left-hand side, such a correction is not applicable.

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Acknowledgements

We would like to thank Richard Felson, Sue-Ming Yang and the anonymous reveiwers for their thoughtful comments on prior versions of this manuscript. On a special note, we would like to dedicate this manuscript to Carlo Morselli, who was a pioneer in the study of criminal achievement and the social organization of illegal markets. Carlo's creativeness, tenacity and investment in mentoring has inspired countless indviduals.

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Authors and Affiliations

  1. Department of Sociology and Criminology, Pennsylvania State University, State College, USA

    Holly Nguyen & Thomas A. Loughran

  2. School of Criminology, International Centre for Comparative Criminology, Université de Montréal, Montreal, Canada

    Carlo Morselli & Frédéric Ouellet

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  1. Holly Nguyen
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  2. Thomas A. Loughran
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Correspondence to Holly Nguyen.

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Appendices

Appendix A

See Table 8.

Table 8 Panel models predicting illegal labor supply—lagged criminal efficiency (Robust Standard Errors)
Full size table

Appendix B

Pathways to Desistance and National Supported Work Measures

Pathway Measures

Frequency of Offending Subjects reported the frequency in which they engaged in income-generating crime during the recall period. Income-generating crimes include shoplifting, buying/receiving/selling stolen property, using checks/credit cards illegally, stealing a car or motorcycle, selling marijuana, selling other drugs, stealing something worth more than $100, entering a building to steal, taking something by force using a weapon, and taking something by force with no weapon.

Illegal Earnings Participants were asked, “Have you made money in other ways over the past N months, including from activities that are illegal?” If the respondent answered in the affirmative, the interviewer asked a follow-up question: “You mentioned that you had made money during the past N months from ways besides working. Did you make any money during this month from activities that are illegal?” For each month in which subjects engaged in any illegal work, they were asked to specify the amount of money per week they earned from all illegal activities during that month.

Criminal Efficiency was measured as the average payoff per crime, calculated by dividing total illegal earnings by the total number of crimes committed each month in which offenders were active.

Legal Job The legal employment variable was derived from a life-event calendar, which recorded detailed information regarding the total number of weeks each subject spent in legitimate employment and under-the-table legal work. This information was then aggregated across each observation period. If the respondent reported any time in legitimate employment in the recall period, s/he was coded as being involved in legal work in the recall period.

Street Time Is the proportion of time the respondent was not in a secure facility.

Specialization we consider the unique number of crime types reported by an individual in the observation period and generate an indicator equal to 1 if the individual reported engaging in one unique crime types during the observation period and 0 if otherwise.

Drug Use Because drug dependency is associated with increased illegal earnings and an “earnings imperative” (Uggen and Thompson 2003), we include a binary indicator if the respondent reported using.

National Supported Work (NSW)

Frequency of Offending Respondents were asked how many times they had engaged in income-generating crime during the 9-month recall period with a mean frequency of 29.6.

Illegal Earnings after answering questions about illegal weekly wages, respondents were asked, “About how much money altogether would you say you made illegally since the month of assignment?” Respondents reported a mean of $14,500 illegal earnings over a 9-month period.

Criminal Efficiency was measured as the average payoff per crime, calculated by dividing total illegal earnings by the total number of crimes committed each month in which offenders were active.

Legal Job This variable reflects the total number of hours the respondent worked across all legal jobs in the recall period. On average, respondents spent 57 h doing legal work over the 9-month period.

Street Time Is a variable that indicates the proportion of time the respondent was not incarcerated.

Specialization Is a measure that indicates that the respondent engaged in only one type of offense.

Drug Use Is a binary measure that = 1 if the respondent reported using illicit drugs and 0 = if the respondent abstained from using illicit drugs.

Appendix C

See Table 9.

Table 9 Description of pathways and national supported work samples
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Nguyen, H., Loughran, T.A., Morselli, C. et al. Offending Frequency and Responses to Illegal Monetary Incentives. J Quant Criminol 38, 239–265 (2022). https://doi.org/10.1007/s10940-021-09492-y

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