An analysis of the factors determining crime in England and Wales: a quantile regression approach

Siddhartha Bandyopadhyay, Samrat Bhattacharya, Rudra Sensarma

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


We analyze the impact of policing and socio-economic variables on crime in England and Wales during 1992-2007 using the quantile regression model which enables us to analyze different points of the crime distribution. The quantile regression model allows us to analyze whether or not the factors that affect crime do so in the same way for high and low crime areas. By using data from 43 police force areas, we examine how the effect of real earnings, unemployment, crime detection rate, income inequality and proportion of young people varies across high and low crime areas. Six crime categories are examined - burglary, theft and handling, fraud and forgery, violence against the person, robbery, and sexual assault. We find statistically significant differences in the impact of explanatory variables on various types of crime for low and high crime areas. For example, higher detection rate reduces crime but the effect is stronger in low crime areas. Further, we find opposing effects of earnings and unemployment across high and low crime areas which may explain why recessions may have no impact on crime or even lower it.

Original languageEnglish
Pages (from-to)665-679
Number of pages15
JournalEconomics Bulletin
Issue number1
Publication statusPublished - 2015

Bibliographical note

Copyright 2015 Elsevier B.V., All rights reserved.

ASJC Scopus subject areas

  • Economics, Econometrics and Finance(all)


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