Research methods

This paper introduces a framework for selecting policies that maximize expected welfare under estimation uncertainty.

I prove that, in a simple but general framework, expected pass-through is unchanged by the presence of focal pricing constraints.

In this paper, we investigate the impact of new openings for civil service positions on sickness absences using data from Spain between 2009 and 2015.

This paper analyses trends in mortality inequality in 330 Chilean communes from 1990 to 2010 for different age groups and both genders.

We investigate the historical origins of female genital cutting (FGC) and how FGC is associated with the Red Sea route of the African slave trade.

Estimating intra-household sharing is crucial to understanding overall inequality. However, expenditure data is almost always at the household level.

We find that women have lower emissions propensities than men, and therefore greater female empowerment is associated with lower household emissions.

I show that the adoption and extent of focal pricing practices in an industry in general do not lower average pass-through of input cost changes.

The optimal design of incentive contracts critically depends on whether the tasks performed by agents are complementary or substitutable.

This paper provides estimation methods of such prediction sets given observed conditioning covariates when 𝑌 is censored or measured in intervals. 

We introduce a novel framework for individual-level welfare analysis.

Building on Bierens (1990), we propose penalized maximum statistics and combine bootstrap inference with model selection.

This paper develops a novel method for policy choice in a dynamic setting where the available data is a multi-variate time series.

We consider estimation and inference for a regression coefficient in panels with interactive fixed effects (i.e., with a factor structure).

The persuasion rate is a key parameter for measuring the causal effect of a directional message on influencing the recipient’s behavior.

Multivalued treatment is commonplace in applications. We explore the use of discrete-valued instruments to control for selection bias in this setting.

We study spillover effects within criminal networks by leveraging the deaths of co-offenders as a source of causal identification.

Measuring how assortative matching differs between two economies is difficult, we show how the use of different measures can create different outcomes