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Black-White Labour Market Conditions and Property Crime in the US: A Quantitative Analysis

Marco Cozzi

No 339, Computing in Economics and Finance 2005 from Society for Computational Economics

Abstract: This paper develops a dynamic general equilibrium model to identify the impact of worse labour market conditions on the property crimes involvement of black American males. The related empirical evidence unambiguously shows higher participation in crime for black than for white males. In 1996, for example, the property crimes arrest rate by race (per 1.000 people) was equal to 6,43 for Whites and 18,3 for Blacks. Another set of stylised facts show for the same racial group worse labour market performances, with the African Americans supplying less hours of labour, gaining lower wages, experiencing both higher unemployment duration and rates. The theoretical model exploits the latter source of information to quantitatively assess the differences in crime induced by the different labour market outcomes. An infinitely lived agents model is developed, allowing for agents to be heterogeneous along four dimensions: race (synthesised by the labour market opportunities), education, employment status and asset holdings. The model is calibrated relying on US data and solved numerically. Preliminary results show that the observed labour market outcomes fully account for the substantial differences in the crime behaviours of the two racial groups. The model is in turn used both to understand to what extent the patterns in the race wage differentials can explain the observed decrease in the black labour supply and to compare some policy experiments aimed at reducing the aggregate crime rate

Keywords: Property crimes; Race; Unemployment; Wealth Inequality. (search for similar items in EconPapers)
JEL-codes: D58 D99 J15 K42 (search for similar items in EconPapers)
Date: 2005-11-11
New Economics Papers: this item is included in nep-lab, nep-law and nep-ure
References: Add references at CitEc
Citations: View citations in EconPapers (6)

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