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The B.E. Journal of Economic Analysis & Policy

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Volume 14, Issue 3 (Jul 2014)


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The Minimum Wage and Crime

Andrew Beauchamp
  • Corresponding author
  • Department of Economics, Boston College, 140 Commonwealth Ave, Chestnut Hill, MA 02467, USA
  • Email:
/ Stacey Chan
  • Analysis Group, Denver, CO, USA
  • Email:
Published Online: 2014-02-04 | DOI: https://doi.org/10.1515/bejeap-2013-0130


Does crime respond to changes in the minimum wage? A growing body of empirical evidence indicates that increases in the minimum wage have a displacement effect on low-skilled workers. Economic reasoning provides the possibility that disemployment may cause youth to substitute from legal work to crime. However, there is also the countervailing effect of a higher wage raising the opportunity cost of crime for those who remain employed. We use the National Longitudinal Survey of Youth 1997 cohort to measure the effect of increases in the minimum wage on self-reported criminal activity and examine employment–crime substitution. Exploiting changes in state and federal minimum wage laws from 1997 to 2010, we find that workers who are affected by a change in the minimum wage are more likely to commit crime, become idle, and lose employment. Individuals experiencing a binding minimum wage change were more likely to commit crime and work only part time. Analyzing heterogeneity shows those with past criminal connections are especially likely to see decreased employment and increased crime following a policy change, suggesting that reduced employment effects dominate any wage effects. The findings have implications for policy regarding both the low-wage labor market and efforts to deter criminal activity.

Keywords: minimum wage; crime; unemployment


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About the article

Published Online: 2014-02-04

Published in Print: 2014-07-01

Neumark and Wascher (2006) conduct a review of studies that examine the employment effect of changes in the minimum wage. See also Currie and Fallick (1996), Ahn, Arcidiacono, and Wessels (2011), Burkhauser, Couch, and Wittenburg (2000) and Sabia, Burkhauser, and Hansen (2012).

Neumark and Wascher (1995) use matched CPS data to study the effect of minimum wages on employment and enrollment decisions of youth. They find that increases in the minimum wage raise the likelihood that lower-skilled teenagers will become unemployed, replaced by higher-skilled teenagers who leave school. They also find an increase in the probability that displaced workers will be not only unemployed but also not enrolled in school.

Jacob and Lefgren (2003) and Luallen (2006) estimate that daily juvenile property crime decreases by 14% and 28.8% on days that students must be in school, respectively. Raphael and Winter-Ember (2001) and Gould, Weinberg, and Mustard (2002) find that declining labor opportunities cause an increase in crime. In particular, Raphael and Winter-Ember (2001) find that an increase in unemployment causes a rise in property crimes, which are crimes often associated with illicit income.

Harris and Todaro (1970) show a minimum wage in a two-sector model can induce queuing and increase employment in the uncovered sector; Lochner (2004) discusses current and future opportunity costs of crime.

A similar panel analysis could be performed on state- or county-level crime data, but such an analysis would be unable to capture individual levels of heterogeneity and the ability to identify minimum wage workers.

Raphael and Winter-Ember (2001), Gould, Weinberg, and Mustard (2002), and Machin and Meghir (2004) demonstrate that criminal activity responds to both employment and wages.

Respondents to the NLSY97 report up to 11 wages in a given survey year. A respondent is considered to be bound by a minimum wage change if at least one of the reported jobs fits the aforementioned criteria. Jobs with reported wages of zero dollars are considered invalid and excluded from the data.

We are unable to make use of national incident-based data, such as National Incident-Based Reporting System data, due to changes in jurisdictional reporting during the time period that we study.

This approach replaces 31% of missing answers regarding activity in overall crime affecting approximately 74% of respondents who ever report criminal activity. We do not replace missing values for respondents who never report criminal activity (individuals who have only missing values and/or report zero crime). Alternative treatments of missing values did not impact our analysis.

The NLSY questionnaire suggests “other property” crimes as “fencing, receiving, possessing or selling stolen property, or cheat[ing] someone by selling them something that was worthless or worth much less than what [the respondent] said it was.”

It is difficult to make a direct comparison between these numbers and national crime statistics. Here we present crime participation rates, whereas national crime statistics are measures of crime incidence.

Age groups are admittedly ad hoc, though the division does capture major features of the crime-age profile. Results combining all teenagers were largely similar.

Lemos (2005) shows that minimum wage changes do not appear to be endogenous to employment conditions in Brazil.

Missing data for each variable are replaced as zeros for respondents who reported this information in any other year of the survey. Pooling the age groups improves precision in the employment regression; point estimates are similar with and without pooling.

Marginal effects from the logit specification are similar.

Marginal effects from the logit and OLS specifications are comparable.

Standard errors are not clustered at the state level because we include state fixed effects.

Thompson (2009) finds that workers of a similar age range, 19- to 22-years-old, do not experience disemployment effects due to the minimum wage.

Chaplin, Turner, and Pape (2003) also find that dropout-eligible teens leave school when the minimum wage rises.

Although the disemployment effects for 14- to 16-year-olds lack precision, teens who commit crime also experience the strongest disemployment effects. OLS estimates on employment become more negative and precise when limited to individuals who ever commit crime.

Full time is defined as greater than 48 weeks employed, being unemployed is having zero weeks.

Information on search is not available for much of our sample (76% are missing between-job search information). Nonetheless the conditional means of search were informative: individuals bound by a minimum wage who committed a crime searched at 43.4% annual rate, while those who did not commit a crime, but still experienced a binding minimum wage, search at 32.3% rate. Thus it appears a major reason for part-time work among those committing a crime is search frictions in the labor market.

Gang members were 25% more likely to be high school drop outs, and 38% more likely to not be employed over the current year.

A number of interesting questions regarding transitions between the licit and illicit labor market can in principle be addressed with our data, but in practice are difficult to document. Examining transitions of those committing a crime in t1 into the labor market at t showed the opposite of a queuing effect: the probability of non-employment increased among this group following a minimum wage increase. Similarly, looking at the increased probability of working (either full or part time) following participation in a crime at t1 we saw no significant impact of the changing minimum wage on work for this group.

Citation Information: The B.E. Journal of Economic Analysis & Policy, ISSN (Online) 1935-1682, ISSN (Print) 2194-6108, DOI: https://doi.org/10.1515/bejeap-2013-0130. Export Citation

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