Are Voters More Likely to Contribute to Other Public Goods? Evidence from a Large‐Scale Randomized Policy Experiment

Voting has been described as a contribution to a public good. Are people who vote frequently therefore more likely to contribute to other public goods? Does partisanship affect how likely a person is to engage in these cooperative behaviors? Although surveys suggest that the answer to these questions is “Yes,” few empirical studies examine these questions using observed behaviors. We examine them in the context of a large‐scale, randomized controlled trial to induce voluntary action in a common‐pool resource dilemma. During a drought in the southeastern United States, pro‐social messages that encouraged water conservation were randomly assigned to 35,000 out of 106,000 households. Frequent voters in primary and general elections (1990–2008) were substantially more responsive to the messages, but there was no detectable difference in the responses of Republican and Democrat households. Our results suggest that internalized pro‐social preferences promote action for the public good across behavioral contexts.     

Books in review

He is author of over sixty articles for various journals. Among his books are Conservative Millenariansand The Search for Historical Meaning.     

Predictive Crime Mapping: Arbitrary Grids or Street Networks?

Objectives

Decades of empirical research demonstrate that crime is concentrated at a range of spatial scales, including street segments. Further, the degree of clustering at particular geographic units remains noticeably stable and consistent; a finding that Weisburd (Criminology 53:133–157, 2015) has recently termed the ‘law of crime concentration at places’. Such findings suggest that the future locations of crime should—to some extent at least—be predictable. To date, methods of forecasting where crime is most likely to next occur have focused either on area-level or grid-based predictions. No studies of which we are aware have developed and tested the accuracy of methods for predicting the future risk of crime at the street segment level. This is surprising given that it is at this level of place that many crimes are committed and policing resources are deployed.

Methods

Using data for property crimes for a large UK metropolitan police force area, we introduce and calibrate a network-based version of prospective crime mapping [e.g. Bowers et al. (Br J Criminol 44:641–658, 2004)], and compare its performance against grid-based alternatives. We also examine how measures of predictive accuracy can be translated to the network context, and show how differences in performance between the two cases can be quantified and tested.

Results

Findings demonstrate that the calibrated network-based model substantially outperforms a grid-based alternative in terms of predictive accuracy, with, for example, approximately 20 % more crime identified at a coverage level of 5 %. The improvement in accuracy is highly statistically significant at all coverage levels tested (from 1 to 10 %).

Conclusions

This study suggests that, for property crime at least, network-based methods of crime forecasting are likely to outperform grid-based alternatives, and hence should be used in operational policing. More sophisticated variations of the model tested are possible and should be developed and tested in future research.