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Acquisitive Crime and Inflation in the United States: 1960–2012

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Abstract

Objectives

Inflation is conspicuous by its absence from recent research on crime and the economy. We argue that price inflation increases the rate of crimes committed for monetary gain by fueling demand for cheap stolen goods.

Methods

The study includes inflation along with indicators of unemployment, GDP, income, consumer sentiment, and controls in error correction models of acquisitive crime covering the period from 1960 to 2012. Both short- and long-run effects of the predictors are estimated.

Results

Among the economic indicators, only inflation has consistent and robust short- and long-run effects on year-over-year change in the offense types under consideration. Low inflation helps to explain why acquisitive crime did not increase during the 2008–2009 recession. Imprisonment rates also have robust long-run effects on change in acquisitive crime rates.

Conclusions

Incorporating inflation into studies of crime and the economy can help to reduce the theoretical and empirical uncertainty that has long characterized this important research area in criminology.

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Notes

  1. This quotation has been attributed, variously, to Milton Friedman, Jimmy Carter, and Paul Volker, former chair of the Board of Governors of the Federal Reserve System, all without much confirmation.

  2. The NCVS data are available at http://www.bjs.gov/index.cfm?ty=tp&tid=9#data_collections.

  3. Unemployment data are from the Bureau of Labor Statistics (http://www.bls.gov/cps/lfcharacteristics.htm#unemp); GDP and wage data are from the Bureau of Economic Analysis (http://www.bea.gov/national/index.htm#gdp). Consumer sentiment data are available from the Reuters/University of Michigan Surveys of Consumers (http://www.sca.isr.umich.edu/main.php).

  4. The price data are from the Bureau of Labor Statistics (ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.txt). The dates of US recessions are from the National Bureau of Economic Research (http://www.nber.org/cycles/cyclesmain.html).

  5. Two studies also report significant inflation effects on crime rates in European nations (Lauridsen et al. 2013; Rosenfeld 2014).

  6. Sutton et al. (2008: 5).

  7. Nunley et al. (2011: 5) offer a somewhat similar, if abbreviated, interpretation of the effect of inflation on acquisitive crime: “One can also think of inflation as a tax that generates a dominant income effect for ‘labor supply’ in the underground sector of the economy.”

  8. An alternative to the CPI is the Personal Consumption Expenditures (PCE) price index (see http://www.dallasfed.org/research/pce/index.cfm). We obtained the PCE data for the period since 1978, the earliest year the data were available in the public use file. The correlation (r) between the CPI and PCE series over that period is .944, which suggests that the results would not have changed appreciably had we substituted the PCE for the CPI inflation measure in our analyses.

  9. An alternative is the Engle–Granger two-step estimator (Engle and Granger 1987). See De Boef (2001) for a detailed and thoughtful comparison.

  10. We used the second order AIC (AICc), recommended for small samples, as the model selection criterion (Burnham and Anderson 2002). All regression models are estimated in Stata 13.1.

  11. All results not shown are available from the authors on request.

  12. Between 1985 and 1995, the percentage of the population between the ages of 15 and 24 fell from 16.7 to 13.7 %.

  13. The expected change in acquisitive crime is estimated using the margins command in Stata 13.1 (margins, at(l.inflation = (3.8) d.inflation = -4.2)). All other variables are at their mean values.

  14. Multiple lags of the outcome were added to the right-hand side of the of the burglary, larceny, and motor vehicle theft models to address autocorrelation in the error term. Results not shown.

  15. We also performed a Goldfeld–Quandt (GQ) test of heteroskedasticity with a break point at the midpoint of the observation period to determine whether variance in the error term of the acquisitive crime index model differed between the early and later years of the observation period. An F test revealed no significant difference in the error variance between the two sub-periods (F = 1.55).

  16. The regression equations were estimated initially with two lags of each variable. The results indicated a marginally significant effect of burglary on inflation (p = .052). When estimated with three lags, however, the burglary effect is non-significant (p = .193).

  17. See http://www.sca.isr.umich.edu/tables.html.

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Authors and Affiliations

  1. University of Missouri, St. Louis, MO, USA

    Richard Rosenfeld & Aaron Levin

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  1. Richard Rosenfeld
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  2. Aaron Levin
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Correspondence to Richard Rosenfeld.

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Rosenfeld, R., Levin, A. Acquisitive Crime and Inflation in the United States: 1960–2012. J Quant Criminol 32, 427–447 (2016). https://doi.org/10.1007/s10940-016-9279-8

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