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Accounting for Meso- or Micro-Level Effects When Estimating Models Using City-Level Crime Data: Introducing a Novel Imputation Technique

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Abstract

Objectives

Criminological scholars have long been interested in how macro-level characteristics of cities, counties, or metropolitan areas are related to levels of crime. The standard analytic approach in this literature aggregates constructs of interest, including crime rates, to the macro geographic units and estimates regression models, but this strategy ignores possible sub-city-level processes that occur simultaneously.

Methods

One solution uses multilevel data of crime in meso-level units within a large number of cities; however, such data is very difficult and time intensive to collect. We propose an alternative approach which utilizes insights from existing literature on meso-level processes along with meso-level socio-demographic measures in cities to impute crime data from the city to the smaller geographic units. This strategy allows researchers to estimate full multilevel models that estimate the effects of macro-level processes while controlling for sub-city-level factors.

Results

We demonstrate that the strategy works as expected on a sample of 91 cities with meso-level data, and also works well when estimating the multilevel model on a sample of cities different from the imputation model, or even in a different time period.

Conclusions

The results demonstrate that existing studies aggregated to macro units can yield considerably different (and therefore potentially problematic) results when failing to account for meso-level processes.

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Notes

  1. For an in-depth review of the history of geographical criminology, see Bruinsma (2017).

  2. The cities are: Akron Alexandria Asheville Atlanta Aurora Austin Bakersfield Baltimore Baton Rouge Bellevue Boston Boulder Buffalo Cambridge Carrollton Cary Chapel Hill Chattanooga Chicago Cincinnati Cleveland Corpus Christi Dallas Dayton Denton Denver Detroit Durham Fayetteville Flint Fort Collins Fort Worth Fresno Frisco Gilbert Glendale Grand Rapids Hartford Honolulu Houston Indianapolis Irving Kansas City Kent Lexington Los Angeles Louisville Milwaukee Minneapolis New Orleans New York Oakland Orlando Philadelphia Pittsburgh Portland Raleigh Reading Richmond Rochester Rockford Sacramento Salt Lake City San Antonio Sandy Springs San Francisco San Jose Savannah Scottsdale Seattle Sioux Falls Spokane St Louis Stockton St Paul Toledo Tucson Tulsa Urbana Washington.

  3. To account for the binning of the income data, we utilize the prln04.exe program provided by Francois Nielsen at the following website: http://www.unc.edu/~nielsen/data/data.htm. The program uses the Pareto-linear procedure (Aigner and Goldberger 1970; Kakwani and Podder 1976), which was adapted by Nielsen and Alderson (1997) from the U.S. Census Bureau strategy.

  4. We provide a Stata ado file (nestedimpute.ado) to create these estimates at the following web location: https://faculty.sites.uci.edu/johnhipp/research/software-tools/.

  5. The model includes population density at the tract level, which is conceptually distinct from city population size, or the change in population (Hipp and Roussell 2013).

  6. Note that a multilevel model cannot be estimated with this imputation strategy, given that the crime rate does not vary across tracts within a particular city. This is simply a necessary consequence of this imputation strategy, and we therefore estimate it as a linear regression model.

  7. We also estimated the standard city-level model. These results were quite similar to the naïve imputation strategy results.

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  1. Department of Criminology, Law and Society and Department of Sociology, University of California, 3311 Social Ecology II, Irvine, CA, 92697, USA

    John R. Hipp & Seth A. Williams

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  1. John R. Hipp
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Appendix

Appendix

See Table 6.

Table 6 Comparing imputation model results from 2000 and 2010 (fixed effects models)
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Hipp, J.R., Williams, S.A. Accounting for Meso- or Micro-Level Effects When Estimating Models Using City-Level Crime Data: Introducing a Novel Imputation Technique. J Quant Criminol 37, 915–951 (2021). https://doi.org/10.1007/s10940-020-09473-7

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