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Title: Empirical analyses on the economics of criminal justice
Author: Lai, Kit To Keith
ISNI:       0000 0004 7231 1377
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
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This thesis covers three empirical analyses on the economics of criminal justice, completed using a new micro-dataset that links up the administrative criminal, employment and benefits records of offenders in England and Wales. The first analysis considers the effectiveness of post-custody supervision in reducing recidivism and improving labour market outcomes. It employs a regression discontinuity design and to exploit an age cut-off point in the compulsory provision of post-custody supervision, and finds that there are no effects on recidivism, employment or benefits outcomes, contrary to the belief that lead to a recent policy change. The second analysis considers the labour market effect, or scarring, of criminal convictions. It employs a distributed lag model with fixed effects to estimate the potential damage to earnings and employment likelihood of a criminal conviction. It finds evidence that contrary to the popular belief (and simple OLS results), once individual fixed effects are controlled for, a criminal conviction even in the event where the punishment is imprisonment is only associated with moderate damages. The third analysis considers the effect of prison sentences on later outcomes. After the England riots in 2011, judges in riot areas were statistically handing out more prison sentences to o enders who had nothing to do with riots than judges in non-riot areas. This creates a valid instrument for testing the effect of imprisonment (at least on non-rioters). It shows that once self-selection is controlled for, prison sentences can in fact induce reduction in recidivism, likely through specific deterrence, but the effect dies out after 6 months and gives way to criminogenic factors. There are no statistically significant effects on employment, at least not within one year, though somewhat surprisingly the estimates tend to be positive rather than negative.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available