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Crime and Inflation in U. S. Cities

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Abstract

Objectives

The current study replicates prior national-level research on the relationship between crimes committed for monetary gain and inflation in a sample of 17 U. S. cities between 1960 and 2013.

Methods

A random coefficients model is used to estimate the effects of inflation on the change in acquisitive crime over time, controlling for other influences.

Results

The estimates yield significant effects of inflation on acquisitive crime rates in the 17 cities. City-specific coefficients reveal nontrivial variation across the cities in the significance, size, and impact of inflation on acquisitive crime.

Conclusions

Continued low inflation rates should restrain future crime increases in many US cities. U. S. monetary policy should be evaluated with respect to its effect on crime.

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Notes

  1. An exception is a city-level study of crime and economic conditions by Baumer et al. (2013). In that study, however, inflation is measured at the level of US Census regions.

  2. The robbery data are from the 2012 Uniform Crime Reports (https://www.bjs.gov/ucrdata/).

  3. The price comparisons, which should be viewed as rough approximations, are from Bankrate (http://www.bankrate.com/calculators/savings/moving-cost-of-living-calculator.aspx).

  4. Cecchetti et al. (2002) report, for example, that although city inflation trends converge to a common mean, convergence may take several years to occur. To our knowledge, a similar assessment has not been conducted for city crime rate trends.

  5. We thank Roland Chilton for sharing crime data for this analysis.

  6. We measure income in nominal dollars because the inflation rate controls for price changes.

  7. The factor scores are based on an orthogonal rotated solution. A single factor combining the four measures (eigenvalue = 2.82) was retained that explains 99% of the variance. The rotated and unrotated solutions are very similar.

  8. With single year effects included, the model would not produce estimates of the effects of the explanatory variables based on the pooled data. Time intervals shorter than five years yielded missing estimates in the city-specific results. The likely reason is that these time intervals absorbed degrees of freedom required to estimate the pooled and city-specific coefficients. The Chi square test of parameter constancy, for example, is computed on 288 degrees of freedom (see Table 2).

  9. Lagged dependent variables are controversial. Many analysts discourage their use because they can induce downward bias in the coefficients on the explanatory variables (e.g., Allison 2015). In the present case, however, including the lagged acquisitive crime rate in the model slightly increases the coefficient on inflation. The coefficients on the other explanatory variables are non-significant regardless of whether the lagged outcome is included (see Table 2).

  10. With the lagged outcome omitted from the model, the unstandardized and standardized coefficients on inflation are 78.0 and .080, respectively (p < .05). The other explanatory variables remain non-significant (see fn9).

  11. All results not shown are available from the first author by request.

  12. In Model 1, the mean VIF = 1.43 and the max VIF = 1.74. The comparable values in Model 2 are increased by the interaction term, but remain within an acceptable range (mean VIF = 3.28, max VIF = 6.62).

  13. The variance in the estimated effects is not due to the lagged acquisitive crime rate. With only the lagged crime rate in the model, the parameter constancy χ2 = 30.7, p = .531.

  14. We thank Eric Baumer for this insight.

  15. See https://www.federalreserve.gov/publications/annual-report/2015-monetary-policy-and-economic-developments.htm#xsubsection-14-c4f66975.

  16. For example, some economists expect inflation rates to increase if “trade wars” break out under the Trump Presidency (Zumbrun 2016). See, also, Rosenfeld (2016; Rosenfeld et al. 2017) on unexpected homicide increases in American cities during 2015 and 2016.

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Author information

Authors and Affiliations

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

    Richard Rosenfeld, Matt Vogel & Timothy McCuddy

  2. OTB - Research for the Built Environment, TU Delft, Delft, The Netherlands

    Matt Vogel

Authors
  1. Richard Rosenfeld
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  2. Matt Vogel
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  3. Timothy McCuddy
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Corresponding author

Correspondence to Richard Rosenfeld.

Appendix

Appendix

Sample of 17 Metropolitan Areas and Central Cities

Atlanta, GA

 

Boston-Brockton-Nashua, MA-NH-ME-CT

 

Chicago-Gary-Kenosha, IL-IN-WI

 

Cincinnati-Hamilton, OH-KY-IN

 

Cleveland-Akron, OH

 

Detroit-Ann Arbor-Flint, MI

 

Houston–Galveston-Brazoria, TX

 

Kansas City, MO-KS

 

Los Angeles-Riverside-Orange County, CA

 

Milwaukee-Racine, WI

 

New York-Northern New Jersey-Long Island, NY-NJ-CT-PA

 

Philadelphia-Wilmington-Atlantic City, PA-NJ-DE-MD

 

Pittsburgh, PA

 

Portland-Salem, OR-WA

 

San Francisco-Oakland-San Jose, CA

 

Seattle-Tacoma-Bremerton, WA

 

St. Louis, MO-IL

 

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Rosenfeld, R., Vogel, M. & McCuddy, T. Crime and Inflation in U. S. Cities. J Quant Criminol 35, 195–210 (2019). https://doi.org/10.1007/s10940-018-9377-x

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