POLICY EVALUATION BEYOND THE AVERAGE
As public expectations for government performance rise with rapid technological progress, governments face the challenge of enacting evidence-based policies that allocate resources efficiently and deliver customised services. Yet, current evaluation tools are inadequate because they often overlook that individuals differ in their responses even when treated equally. Instead, policy decisions routinely rely on average treatment effects, disregarding potential collateral damage and benefit.
My research aims to shape the future of policy evaluation and development by applying and extending cutting-edge methods to unveil the entire distribution of treatment effects. Exposing this distribution allows us to determine winners and losers of policy interventions while quantifying their impact on economic inequality. Establishing frontier methods will strengthen our capacity to evaluate economic policy interventions, leading to better targeted government policies that ultimately benefit public well-being and reduce unnecessary public spending.
My work builds on a newly developed method for identifying quantiles of the distribution of treatment effects under modest assumptions. This method can be used to answer a range of policy-relevant questions such as: Does a welfare program increase the average income despite hurting most people and benefitting only a few? Does an educational intervention that increases average test scores lead to higher inequality? What are the unintended consequences of an active labour market policy? How do alternative health insurance plans affect the way medical spending is distributed? What is the effect of a reminder from the tax office on the total amount of tax collected?
While my current research agenda focuses on applications in economics, it applies innovative methods that have immediate relevance for other research domains, such as political science, sociology, and psychology, as well as biomedical science and neuroscience. For instance, the methodological advancements are particularly suitable for biomedical data as they can reveal the proportions of patients who do and do not benefit from a specific health intervention.
