Using machine learning to monitor the equity of large-scale policy interventions: The Dutch decentralisation of the Social Domain

Since individuals' social contexts vary strongly, large-scale policy interventions will likely have heterogeneous effects throughout a population. However, policy interventions are often assessed in narrow ways, either through aggregate effects or along a select number of a-priori hypothesised groups. Historically, such a narrow approach had been a necessity due to data and computational constraints. However, the availability of registry data and novel methods from the machine learning domain allow for a more rigorous, hypothesis-free approach to monitoring policy effects. I illustrate how these developments can revolutionise our measurement and understanding of policy interventions by studying the nationwide 2015 decentralisation of the social domain in The Netherlands. This policy intervention delegated responsibilities to administer social care from the national to the municipal level. The decentralisation was criticised beforehand for risk of producing inequitable effects across demographic groups or regions, but rigorous empirical follow-up remains lacking. Using machine learning methods on entire population data in The Netherlands, I find the policy induced strongly heterogeneous effects that include evidence of local capture and strong urban / rural divides. More generally, I provide a case study of how machine learning methods can be effectively used to monitor large-scale policy interventions.