Right to Buy" (RTB), a large-scale natural experiment by which incumbent tenants in public housing could buy properties at heavily-subsidised prices, increased the homeownership rate in Britain by over 10 percentage points between 1980 and the late 1990s. This paper studies its impact on crime, showing that RTB generated significant reductions in property and violent crime that persist up to today. The gentrification of incumbent tenants and their behavioural changes were the main drivers of the crime reduction. This is evidence of a novel means by which gentrification, and housing provision, may have contributed to the sizable crime drops observed in several Western economies in the 1990s and early 2000s.

SUMMARYRecursive-weight forecast combination is often found to an ineffective method of improving point forecast accuracy in the presence of uncertain instabilities. We examine the effectiveness of this strategy for forecast densities using (many) vector autoregressive (VAR) and autoregressive (AR) models of output growth, inflation and interest rates. Our proposed recursive-weight density combination strategy, based on the recursive logarithmic score of the forecast densities, produces well-calibrated predictive densities for US real-time data by giving substantial weight to models that allow for structural breaks. In contrast, equalweight combinations produce poorly calibrated forecast densities for Great Moderation data.

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A popular account for the demise of the UK's monetary targeting regime in the 1980s blames the fluctuating predictive relationships between broad money and inflation and real output growth. Yet ex post policy analysis based on heavily-revised data suggests no fluctuations in the predictive content of money. In this paper, we investigate the predictive relationships for inflation and output growth using both real-time and heavily-revised data. We consider a large set of recursively estimated Vector Autoregressive (VAR) and Vector Error Correction models (VECM). These models differ in terms of lag length and the number of cointegrating relationships. We use Bayesian model averaging (BMA) to demonstrate that real-time monetary policymakers faced considerable model uncertainty. The in-sample predictive content of money fluctuated during the 1980s as a result of data revisions in the presence of model uncertainty. This feature is only apparent with real-time data as heavily-revised data obscure these fluctuations. Out of sample predictive evaluations rarely suggest that money matters for either inflation or real output. We conclude that both data revisions and model uncertainty contributed to the demise of the UK's monetary targeting regime.JEL Classification: C11, C32, C53, E51, E52.

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