How to measure behavioural spillovers: a methodological review and checklist

A growing stream of literature at the interface between economics and psychology is currently investigating ‘behavioural spillovers’ in (and across) different domains, including health, environmental, and pro-social behaviours. A variety of empirical methods have been used to measure behavioural spillovers to date, from qualitative self-reports to statistical/econometric analyses, from online and lab experiments to field experiments. The aim of this paper is to critically review the main experimental and non-experimental methods to measure behavioural spillovers to date, and to discuss their methodological strengths and weaknesses. A consensus mixed-method approach is then discussed which uses between-subjects randomisation and behavioural observations together with qualitative self-reports in a longitudinal design in order to follow up subjects over time. In particular, participants to an experiment are randomly assigned to a treatment group where a behavioural intervention takes place to target behaviour 1, or to a control group where behaviour 1 takes place absent any behavioural intervention. A behavioural spillover is empirically identified as the effect of the behavioural intervention in the treatment group on a subsequent, not targeted, behaviour 2, compared to the corresponding change in behaviour 2 in the control group. Unexpected spillovers and additional insights (e.g., drivers, barriers, mechanisms) are elicited through analysis of qualitative data. In the spirit of the pre-analysis plan, a systematic checklist is finally proposed to guide researchers and policy- makers through the main stages and features of the study design in order to rigorously test and identify behavioural spillovers, and to favour transparency, replicability, and meta-analysis of studies.