We investigate the effects of civic norms and associational networks on crime rates. Civic norms may attach guilt and shame to criminal behavior, thus increasing its opportunity cost. Associational networks may increase returns to noncriminal activities and raise detection probabilities, but they may also work as communication channels for criminals and may offer official cover to criminal activities. The empirical assessment of these effects poses serious problems of endogeneity, omitted variables, measurement error, and spatial correlation. Italy's great variance in social and economic characteristics, its homogeneity in policies and institutions, and the availability of historical data on social capital in its regions allow us to minimize the first two problems. To tackle the last two problems, we use report-rate-adjusted crime rates and estimate a spatial lag model. We find that both civic norms and associational networks have a negative and significant effect on property crimes across Italian provinces. (c) 2009 by The University of Chicago. All rights reserved..

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The growth in the number of tourist arrivals in Spain in recent years has had significant economic repercussions; yet, little has been reported about its negative impact. This study goes some way to rectifying this by estimating the impact of tourist activity on crime rates in the Spanish provinces during the period 2000-2008. We use both 2-SLS and GMM techniques in a panel data framework to overcome the various challenges posed by estimating this relationship, namely, controlling for the unobserved characteristics of the provinces, and accounting for both the possible endogeneity of the tourist variable and the inertia of criminal activities. The results show that tourist arrivals have a positive and significant impact on crimes against both property and the person.JEL Codes: C23, H50, I2, J24, K24

Abstract. Bayesian model averaging (BMA) has been successfully applied in the empirical growth literature as a way to overcome the sensitivity of results to different model specifications. In this paper, we develop a BMA technique to analyze models that differ in the set of instruments, exogeneity restrictions, or the set of controlling regressors. Our framework allows for both cross-section regressions with instrumental variables and for the commonly used panel data model with fixed effects and endogenous or predetermined regressors. The large model space that typically arises can be effectively analyzed using a Markov Chain Monte Carlo algorithm. We apply our technique to the dataset used by Burnside and Dollar (2000) who investigated the effect of international aid on GDP growth. We show that BMA is an effective tool for the analysis of panel data growth regressions in cases where the number of models is large and results are sensitive to model assumptions.

This paper examines how a nationwide infrastructure investment policy, implemented at the local level, impacted local crime rates. The policy, developed in the wake of the global recession of 2008-09, was designed to boost local economies through job creation. Using monthly figures from the more than 900 municipalities making up the Spanish region of Catalonia, the paper exploits spatial and temporal variations in the Spanish Ministry of Public Administration's random approval of local investment policies, to estimate their impact on both unemployment and crime. The combination of difference-indifferences and IV estimates makes it possible to assess both the size and timing of the policy's impact on the local labour market and on municipal-level crime rates. While the policy did little to palliate the effects of the economic recession in the long run, local public finances did experience a short-term boost, resulting in a temporary reduction in local unemployment rates (as required by the policy), as well as a significant drop in crime rates.

We analyse the impact of regulation, industrial policy and jurisdictional allocation on broadband deployment using a theoretical model and an empirical estimation. Although central powers may be more focused and internalize interjurisdictional externalities, decentralized powers may internalize local horizontal policy spillovers and use a diversity of objectives as a commitment device in the presence of sunk investments. The latter may, for instance, alleviate the collective action problem of the joint use of rights of way and other physical infrastructures. In the empirical exercise, using data for OECD and EU countries for the period 1999-2006, we examine whether centralization promotes new telecommunications markets, in particular the broadband access market. The existing literature, in the main, claims it does, but we find no support for this claim in our data. Our results show that indicators of national industrial policy are a weakly positive determinant of broadband deployment and that different measures of centralization are either irrelevant or have a negative impact on broadband penetration. JEL Codes: L50, L96, K23, H77