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Exploring the crime drop in European Union homicide rates using econometric modelling

Author

Listed:
  • Ilka van de Werve

    (Vrije Universiteit Amsterdam and Tinbergen Institute)

  • Siem Jan Koopman

    (Vrije Universiteit Amsterdam and Tinbergen Institute)

  • Frank Weerman

    (Netherlands Institute for the Study of Crime and Law)

  • Arjan Blokland

    (Netherlands Institute for the Study of Crime and Law)

Abstract

In this study, we employ a newly developed time series econometric approach to investigate the development in crime rates in various members states of the European Union (EU) between 1968 and 2019. We propose a panel data model with stochastically time-varying factors that also includes country-specific effects. This model enables us to evaluate the existence of a common EU crime trend, including a crime drop, to describe how individual countries depart from this common trend, and to estimate its association with macroeconomic and demographic explanatory variables. To have an equivocal measure of crime over the countries for the period of interest, we use homicide rates based on the Mortality Database from the World Health Organization. Results confirm the presence of a crime drop in the EU, be it stronger in Western EU countries than in Eastern EU countries. We also find that economic conditions explain a small portion of the crime trends in the EU; with macroeconomic activity (economic growth) being more relevant for Eastern EU countries, and macroeconomic performance (welfare growth) for Western EU countries. The young adult ratio (share of 25 to 34 year-olds in the total population) substantially explains the crime trend and drop in Western EU countries only. Our findings illustrate how the new model can be used to analyze the trends in crime, the fit from explanatory variables, and the differences in countries.

Suggested Citation

  • Ilka van de Werve & Siem Jan Koopman & Frank Weerman & Arjan Blokland, 2025. "Exploring the crime drop in European Union homicide rates using econometric modelling," Tinbergen Institute Discussion Papers 25-053/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20250053
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    References listed on IDEAS

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    1. Robert C. Feenstra & Robert Inklaar & Marcel P. Timmer, 2015. "The Next Generation of the Penn World Table," American Economic Review, American Economic Association, vol. 105(10), pages 3150-3182, October.
    2. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
    3. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    4. Mateus Rennó Santos & Yunmei Lu & Rachel E Fairchild, 2021. "Age, Period and Cohort Differences Between the Homicide Trends of Canada and the United States [Generations, Cohorts, and Social Change’]," The British Journal of Criminology, Centre for Crime and Justice Studies, vol. 61(2), pages 389-413.
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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts

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