We present an overlapping generation growth model with an imperfect labor market where the links among crime, growth and unemployment are jointly considered, both in an endogenous and exogenous set-up. We test the major implications of our theory and verify the two model specifications through the Italian regional data, using the Pooled Mean Group estimator proposed by Pesaran, Shin and Smith (1999 ). The empirical results are in favor of the exogenous version of the model and suggest that crime and unemployment have long-run income level effects. Copyright � 2006 The Authors; Journal compilation � 2006 Blackwell Publishing Ltd.

This study re-examines the issue of causality between investment shares and economic growth. A methodology is applied based on Arellano and Bond (1991), and Holtz-Eakin, Newey and Rosen (1988) to quinquennial panel data on growth and investment shares for the post war period and shows that, contrary to previous results in the literature, causality between fixed investment and growth runs in both directions. Investment shares Granger-cause growth rates and growth rates Granger-cause investment shares. Granger causality from investment shares to growth rates is found to be negative. The result is in contrast with a capital fundamentalist view which sees fixed investment as the key to long run growth, but is fully consistent with the predictions of Solow-type growth models.

We analyze the consumption of non-life insurance across 103 Italian\ud provinces in 1998–2002 in order to assess its determinants, in the light of the\ud empirical literature. Using sub-regional data, we overcome an important limitation\ud of cross-country analyses, i.e. the systemic heterogeneity due to country-specific\ud characteristics. Individual heterogeneity is accounted for through panel data techniques.\ud However, considering spatial units within a single market raises issues of\ud cross-sectional or spatial dependence, either due to common nationwide and/or\ud regional factors or to spatial proximity. We carefully assess spatial dependence,\ud employing recent diagnostic tests, finding out that the regressors included in our\ud specification successfully account for spatial dependence. Insurance turns out to\ud depend on income, wealth and some demographics, as already established, but also\ud on trust, judicial efficiency and borrowing conditions. These findings help in\ud explaining the gap between Central-Northern Italy and the south of the country

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