Abstract
AbstractBystander programs contribute to crime prevention by motivating people to intervene in violent situations. Social media allow addressing very specific target groups, and provide valuable information for program evaluation. This paper provides a conceptual framework for conducting benefit–cost analysis of bystander programs and puts a particular focus on the use of social media for program dissemination and data collection. The benefit–cost model treats publicly funded programs as investment projects and calculates the benefit–cost ratio. Program benefit arises from the damages avoided by preventing violent crime. We provide systematic instructions for estimating this benefit. The explained estimation techniques draw on social media data, machine-learning technology, randomized controlled trials and discrete choice experiments. In addition, we introduce a complementary approach with benefits calculated from the public attention generated by the program. To estimate the value of public attention, the approach uses the bid landscaping method, which originates from display advertising. The presented approaches offer the tools to implement a benefit–costs analysis in practice. The growing importance of social media for the dissemination of policy programs requires new evaluation methods. By providing two such methods, this paper contributes to evidence-based decision-making in a growing policy area.
Highlights
Every economist would clearly agree that violence causes immense social damage, but agreement on how society can avoid the damage is less clear
For small-scale programs, we suggest a complementary approach, where benefit arises from the value of public attention generated by the program
We suggest the use of discrete choice experiments (DCEs)
Summary
Every economist would clearly agree that violence causes immense social damage, but agreement on how society can avoid the damage is less clear. We aim to fill this gap by providing a conceptual framework for conducting benefit–costs analysis of bystander programs, and put a special emphasis on the potential of social media in this context. The conceptual framework consists of an extended benefit–cost model, and a set of research approaches to estimating the program benefit Within this framework, benefit arises from the damages avoided by preventing violent crime. It explains how to use social media and web analytics tools to estimate the number of program participants It introduces a machine-learning approach for estimating the share of future bystanders among participants. It explains how to conduct a randomized experiment on Facebook to estimate the program effect on bystander behavior Fourth, it provides an estimator for the number of bystanders that are injured during their intervention.
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