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Forecasting Crime in Germany in Times of Demographic Change

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Abstract

Studies have shown the impact of a population’s age structure on the crime rate. Germany is — like many other industrialized countries — facing an ageing of its population. This trend will continue in the future: Until the year 2020 the share of younger people aged 14 to 24 years will decrease from 12.3 % to 10.7 % and the share of elderly persons aged 60 years and older will increase from 25.9 % to 30.1 %. Crime is, however, not only influenced by age, other factors also play an important role. Research has shown that the level of social disorganization is especially related to the crime rate. The aim of the present contribution is to explain the crime trends between 1995 and 2010 using multivariate panel estimators that take into account the demographic changes and social disorganization. These models are in a second step used to forecast the crime trends until the year 2020. The data base consists of pooled time-series at the county level from four German states (Bavaria, Brandenburg, Lower Saxony and Saxony-Anhalt). The results show that the age structure plays only a limited role in explaining the past crime trends. The most important factor is residential instability. The forecasts expect a decline of the number of registered offences till 2020. However, the decline will be faster in the eastern states than in the western states and some offences are expected to increase in the future.

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Notes

  1. The figures are based on data from the German Federal Statistical Agency. The population forecasts are derived from the 12th Coordinated Population Forecast from the German Federal Statistical Agency (www.destatis.de).

  2. A more detailed representation of the methodology and the results can be found in Hanslmaier et al. (2014).

  3. For an overview on other approaches, see Berk (2008).

  4. We renounce to present tables of descriptive and bivariate statistics of the crime rates and the independent variables, because simple descriptive statistics and correlation tables cannot take into account the two-dimensional structure of the data. A disaggregated representation by state and dimension, however, would require too much space. These tables are available upon request from the corresponding author.

  5. The formulas are based on common text books (Baum 2006; Kittel 1999).

  6. This strategy is, for example, also used by Metz and Sohn (2008).

  7. For a detailed description see Hanslmaier et al. (2014, pp. 205-218).

  8. The 2009 data were latest available data for inmates.

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Acknowledgments

We want to thank the two anonymous reviewers for their very helpful comments on this paper.

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Authors and Affiliations

  1. Criminological Research Institute of Lower Saxony, Lützerodestraße 9, 30167, Hannover, Germany

    Michael Hanslmaier & Dirk Baier

  2. Faculty of Law, University of Hamburg, Rothenbaumchaussee 33, 20148, Hamburg, Germany

    Stefanie Kemme

  3. Criminological Service for the Berlin Prison Authority and the Justice Social Services, Friedrich-Olbricht-Damm 16, 13627, Berlin, Germany

    Katharina Stoll

Authors
  1. Michael Hanslmaier
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  2. Stefanie Kemme
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  3. Katharina Stoll
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  4. Dirk Baier
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Correspondence to Dirk Baier.

Additional information

Dr.Kemme is a professor of University of Hamburg.

Baier and Hanslmaier hold PhDs, Criminological Research Institute of Lower Saxony.

Appendix

Appendix

Table 3 Number of offences 2010 (real) to 2020 (forecast)
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Hanslmaier, M., Kemme, S., Stoll, K. et al. Forecasting Crime in Germany in Times of Demographic Change. Eur J Crim Policy Res 21, 591–610 (2015). https://doi.org/10.1007/s10610-015-9270-1

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