Spatially Varying Unemployment and Crime Effects in the Long Run and Short Run

Abstract

The Cantor and Land model of unemployment and crime separates the effects of long- and short-run unemployment. In the long run, increases in unemployment are expected to increase crime, whereas the same increases are expected to decrease crime in the short run. This model has been tested for decades, generally supporting these predictions. In this article, we investigate spatial variations in these relationships using geographically weighted regression. Using crime data from Vancouver, Canada (commercial burglary, residential burglary, mischief, theft, theft from vehicle, theft of vehicle, and aggregate property), we find global models do not exhibit statistically significant unemployment–crime relationships, but they do emerge in local (geographically weighted) regression. These results have important implications for theoretical development, policy formation, and policy evaluation.

失业和犯罪的Cantor-Land模型, 将失业的长期和短期效应分开。失业增多, 预计会在长期尺度上增加犯罪、而在短期内将会减少犯罪。Cantor-Land模型经历了数十年的检验, 总体上能够支持这些预计。通过地理加权回归分析, 本文探索了这些关系的空间差异。采用加拿大温哥华的犯罪数据, 包括：商业入室盗窃、居民入室盗窃、破坏、偷窃、机动车内物品偷窃、机动车偷窃和其它犯罪, 本文发现, 全局模型没有确定失业和犯罪的统计相关性, 但在局域（地理加权）回归上失业和犯罪确实有统计上的相关性。这些结果对于理论研究、政策制定和政策评估上具有重要的意义。

El modelo de Cantor y Land sobre desempleo y crimen separa los efectos de los tipos de desempleo de larga y corta duración. A largo plazo, se espera que los incrementos en desempleo incrementen el crimen, en tanto que a corto plazo incrementos similares se espera disminuyan la criminalidad. Este modelo ha sido puesto a prueba durante décadas, en general secundando estas predicciones. En este artículo investigamos las variaciones espaciales de estas relaciones usando regresión geográficamente ponderada. Usando datos de crimen de Vancouver, Canadá (hurto comercial, hurto residencial, delitos menores, robo, robo desde vehículo, robo de vehículos y otros delitos contra la propiedad), hallamos que los modelos globales no detectan relaciones desempleo–crimen estadísticamente significativas, aunque estas sí emergen en la regresión local (geográficamente ponderada). Estos resultados tienen implicaciones importantes para el desarrollo teórico, la formación de políticas y la evaluación de políticas.

Key Words:

• crime
• geographically weighted regression
• long run
• short run
• unemployment

关键词：犯罪:

• 地理加权回归
• 长期
• 短期
• 失业。

Palabras clave::

• a corto plazo
• a largo plazo
• crimen
• desempleo
• regresión geográficamente ponderada

Additional information

Notes on contributors

Martin A. Andresen

MARTIN A. ANDRESEN is an Associate Professor in the School of Criminology and Criminal Justice, Griffith University (Gold Coast Campus), Southport, QLD 4215, Australia. E-mail: [email protected]. His research interests include spatial–temporal crime analysis, crime and place, geography of crime, applied spatial statistics, and geographical information analysis.

Olivia K. Ha

OLIVIA K. HA is a Postdoctoral Researcher in the School of Criminology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada. E-mail: [email protected]. Her research interests include spatial–temporal crime analysis, unemployment and crime, and immigration and crime.

Garth Davies

GARTH DAVIES is an Associate Professor in the School of Criminology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada. E-mail: [email protected]. His research interests include terrorism, communities and crime, housing and crime, policing, criminological theory, statistical analyses, and research methods.