Estimating the effects of universal transfers: new ML approach and application to labor supply reaction to child benefits

This paper evaluates effects of introduction of a universal child benefit program on female labor supply. Large scale government interventions affect economic outcomes through different channels of various magnitude and direction of the effects. In order to account for this feature, I develop a model in which a woman decides whether to participate in the labor market in a given period. I show how to use the resulting decision rules to explain flows in aggregate labor supply and simulate counterfactual paths of labor force. My framework combines flexibility of reduced form approaches with an appealing structure of dynamic discrete choice models. The model is estimated nonparametrically using recent advances in machine learning methods. The results indicate a 2-4 percentage points drop in labor force among the eligible females, mainly driven by changes in women's perceived trade-offs and beliefs that discouraged inflows.

In addition to this study, I also present a variety of sensitivity analyses. With the development of statistical theory behind the machine learning algorithms, they are becoming an important tool in the empirical economists' toolbox. By construction, they rely on a set of pre-specified hyper parameters governing the architecture of the algorithm chosen arbitrarily by a researcher. In this note, I show that the economic interpretation of the estimates (obtained via Generalized Random Forest by Susane Athey, Julie Tibshirani, and Stefan Wager ) is robust to different choices of the hyper parameters. This is an encouraging result suggesting that despite their complexity, the machine learning algorithms are likely to become a part of applied econometricians' toolbox.

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