My research focuses on the evaluation and optimization of public policies. The theoretical part of my research focuses on deriving optimal public policies as a function of observable indicators. This so-called sufficient-statistics approach has the advantage that optimal policies become easily implementable for policymakers. My research's empirical part focuses on recovering sufficient-statistics for optimal policy design by using novel quasi-experimental identification strategies. I am interested in applications in Public Economics, Environmental Economics, Health Economics, and Macroeconomics.
Job Market Paper:
This paper shows how to estimate agents' intensive and participation margin responses to nonlinear incentive schemes such as taxes, subsidies, and prices. The proposed semi-nonparametric estimator allows evaluating nonlinear incentive schedules when existing kink and discontinuity methods are inapplicable due to the presence of both margins. The paper's first contribution is to show that agents' reactions to kinks or discontinuities in an incentive scheme identify responses at both margins simultaneously. The only observable needed for estimation is the distribution of agents' choices, making the estimator widely applicable. The paper's second contribution is to evaluate the German subsidy for solar panels, which is a cornerstone in the country's energy-transition efforts. I find sizable elasticities along both margins and an optimal subsidy that is close to linear.
This paper finds that the combination of case detection and social distancing is crucial for the efficient eradication of a new infectious disease. Theoretically, I characterize the optimal suppression policy as a simple function of observables, which eases its implementation. Together with the number of infected, optimal social distancing decreases over time. The fundamental trade-off is between its intensity and its duration. Quantitatively, I calibrate the model to the COVID-19 pandemic in Italy. Given the observed prevalence and detection efficiency on May 10th, suppression costs 11 % of annual GDP. Efficient digital contact tracing reduces this cost to 0.4 %.
Work in progress:
Nonparametric Identification of Supply or Demand Using Nonlinear Budget Sets,
joint with Jean-Pierre Florens.
We find that kinks and discontinuities in an incentive scheme nonparametrically identify the utility or cost function underlying agents' responses to the incentive scheme. The paper relaxes the restrictive isoelasticity assumption so far standard in the literature. The utility or cost function is the solution to a "Schröder Equation," a functional equation not yet used in Econometrics. The result allows nonparametrically estimating the intensive margin response to taxes, subsidies, and other incentive schemes, which, in turn, allows to evaluate and optimize these policies.
Discontinuities and Optimal Dynamic Policies
On the example of the German subsidy for solar panels, I show that the subsidy's discontinuous changes over time identify the intertemporal response margin of adopters. Theoretically, it is well understood that economic agents react in the timing of their decisions when policies or prices change over time. Therefore, to design the optimal dynamic policy, it is essential to know the magnitude of dynamic reactions. The outlined methodology is applicable to corporate taxes and to sales of firms' products, shedding new light on intertemporal profit shifting and the optimal regulation of sales.