Social policy research at heart is interested in identifying causal relationships. We are interested in the reasons for policy changes, we are interested in evaluating the consequences of policies for society, and we are interested in how policies affect individual behavior or attitudes. At base, the questions motivating our research are causal. Yet, social policy analysis has always struggled with the establishment of causal relationships because it has to deal with a special set of problems. We often encounter issues such as collinearity, multiple alternative explanations, and limited variation in our explanatory variables as a consequence of the country-comparative setup of our research.
However, in recent decades the social sciences have witnessed a surge in studies that closely follow the basic idea of counterfactual designs (or a so-called potential outcomes framework of causality). More and more social policy researchers have come to embrace counterfactual designs, as they offer a multitude of ways how to tackle these issues and how to identify causal relationships in our research field. In contrast to other methodological developments, counterfactual designs do not overtly emphasize advanced econometric models but put the focus on research design. Following the basic idea of randomized experiments, it brings along a distinct way of thinking about how to set up studies and how to identify causal relationships.
This stream will explore methodological innovations in comparative social policy analysis. We invite contributions that closely follow and apply a counterfactual design. In particular, we encourage papers relying on natural or quasi-natural experiments, survey and framing experiments, matching, instrumental variables, fixed effects panel designs, difference-in- ifferencesapproaches, and regression discontinuity designs. Paper proposals for the stream should thus not only include the research question, theoretical background, and results, they should provide specific detail on the analytical approach taken to establish causality.