A substantial and rapidly growing literature has developed around estimating earnings gains from two-year college degrees using administrative data. These papers almost universally employ a person-level fixed effects strategy to estimate earnings premia net of fixed attributes. We note that the...

This paper discusses pairing double/debiased machine learning (DDML) with stacking, a model averaging method for combining multiple candidate learners, to estimate structural parameters. We introduce two new stacking approaches for DDML: short-stacking exploits the cross-fitting step of DDML to...

This review paper articulates the relationship between prediction market data and event studies, with a special focus on applications in political economy. Event studies have been used to address a variety of political economy questions - from the economic effects of party control of government...

This review paper articulates the relationship between prediction market data and event studies, with a special focus on applications in political economy. Event studies have been used to address a variety of political economy questions – from the economic effects of party control of...

Discrete-choice models provide a simple way of representing utility-maximizing labor supply decisions in the presence of highly nonlinear and possibly non-convex budget constraints. Thus, it is not surprising that they are so extensively used for ex-ante evaluation of taxbenefit reforms. The...

This paper develops and tests a new model of asymmetric information in the labour market involving employer learning. In the model, I provide theoretical conditions for the identification – based on the experience and tenure profiles of estimated returns to ability and education – of...

We assess the labour supply effects of two 'making work pay' reforms in Germany. We provide evidence in favour of policies that distinguish between low effort and low productivity by targeting individuals with low wages rather than individuals with low earnings. In assessing the policies we...