Title: Dealing with Adversity: Religiosity or Science? Evidence from the Great Influenza Pandemic, co-authored with E.Berkes, D.Coluccia, and G Dossi.
Abstract: How do societies respond to adversity? After a negative shock, separate strands of research document either an increase in religiosity or a boost in innovation efforts. In this paper, we show that both reactions can occur at the same time, driven by different individuals within society. The setting of our study is the 1918–1919 influenza pandemic in the United States. To measure religiosity, we construct a novel indicator based on naming patterns of newborns. We measure innovation through the universe of granted patents. Exploiting plausibly exogenous county-level variation in exposure to the pandemic, we provide evidence that more-affected counties become both more religious and more innovative. Looking within counties, we uncover heterogeneous responses: individuals from more religious backgrounds further embrace religion, while those from less religious backgrounds become more likely to choose a scientific occupation. Facing adversity widens the distance in religiosity between science-oriented individuals and the rest of the population, and it increases the polarization of religious beliefs.

Title: Testing Inequalities Linear in Nuisance Parameters (with Gregory Cox and Yuya Shimizu) at the econometrics seminar.

 Abstract- This paper proposes a new test for inequalities that are linear in possibly partially

identified nuisance parameters, called the generalized conditional chi-squared (GCC)

test. It extends the subvector conditional chi-squared (sCC) test in Cox and Shi (2023,

CS23) to a setting where the nuisance parameter is pre-multiplied by an unknown

and estimable matrix of coefficients. Properly accounting for the estimation noise in

this matrix while maintaining the simplicity of the sCC test is the main innovation

of this paper. [How? New variance formula? Rank condition?] As such, the paper

provides a simple solution to a broad set of problems including subvector inference for

models represented by linear programs, nonparametric instrumental variable models

with discrete regressor and instruments, and linear unconditional moment inequality

models. We also derive a simplified formula for computing the critical value that makes

the computation of the GCC test elementary.