Published and accepted papers
Published and accepted papers
"Fixed Effects and Beyond: Bias Reduction, Groups, Shrinkage, and Factors in Panel Data" (joint with Stéphane Bonhomme) [link ]
(Accepted at JPE Micro)
Many traditional panel data methods are designed to estimate homogeneous coefficients. While a recent literature acknowledges the presence of coefficient heterogeneity, its main focus so far has been on average effects. In this paper we review various approaches that allow researchers to estimate heterogeneous coefficients, hence shedding light on how effects vary across units and over time. We start with traditional heterogeneous-coefficients fixed-effects methods, and point out some of their limitations. We then describe bias-correction methods, as well as two approaches that impose additional assumptions on the heterogeneity: grouping methods, and random-effects methods. We also review factor and grouped-factor methods that allow coefficients to vary over time. We illustrate these methods using panel data on temperature and corn yields, and find substantial heterogeneity across counties and over time in temperature impacts.
"Estimating Heterogeneous Effects: Applications to Labor Economics" (joint with Stéphane Bonhomme)
Labour Economics 91, AASLE special issue, December 2024.
A growing number of applications involve settings where, in order to infer heterogeneous effects, a researcher compares various units. Examples of research designs include children moving between different neighborhoods, workers moving between firms, patients migrating from one city to another, and banks offering loans to different firms. We present a unified framework for these settings, based on a linear model with normal random coefficients and normal errors. Using the model, we discuss how to recover the mean and dispersion of effects, other features of their distribution, and to construct predictors of the effects. We provide moment conditions on the model’s parameters, and outline various estimation strategies. A main objective of the paper is to clarify some of the underlying assumptions by highlighting their economic content, and to discuss and inform some of the key practical choices.
“Estimating Individual Responses when Tomorrow Matters” (joint with Stéphane Bonhomme) [link] Updated October 15th, 2025
(Review and Resubmit at Journal of Econometrics)
We propose a regression-based approach to estimate how individuals’ expectations influence their responses to a counterfactual change. We provide conditions under which average partial effects based on regression estimates recover structural effects. We propose a practical three-step estimation method that relies on panel data on subjective expectations. We illustrate our approach in a model of consumption and saving, focusing on the impact of an income tax that not only changes current income but also affects beliefs about future income. Applying our approach to Italian survey data, we find that individuals’ beliefs matter for evaluating the impact of tax policies on consumption decisions.
"Heterogeneous and Uncertain Health Dynamics and Working Decisions of Older Adults” [link] Updated November 30th, 2025
(Reject and Resubmit at Journal of Human Resources)
I study heterogeneity in health dynamics of older adults, focusing on the rate at which health deteriorates with age, and its effects on working decisions. After showing evidence of this unobserved heterogeneity, I use subjective survival expectations to infer health beliefs in a Bayesian-learning framework, and I flexibly estimate how working decisions depend on those beliefs. The results show individuals incorrectly believe their health will deteriorate too fast, and a numerical exercise suggests that eliminating that bias would increase labor-force participation by more than 2 percentage points. Providing biomarker information has only small effects on beliefs and working decisions.