Hey there! I’m an Assistant Professor in Econometrics at the Department of Economics at the University of Zurich. I obtained my PhD from the Department of Economics at the University of Bern. You can download my CV here.
Working Papers
Quantile on Quantiles
(Winner of International Association for Applied Econometrics 2024 conference best student paper prize)
I develop an econometric framework to analyze the impact of a treatment on inequality within and between groups. Outcomes are represented by a two-dimensional quantile surface mapping within-group and between-group ranks to outcome levels. This representation captures the distributional structure of the data without imposing normative assumptions, providing a foundation for assessing trade-offs between different dimensions of inequality. Within a broad class of linear welfare functions, the two-dimensional quantile surface is the empirical primitive for welfare evaluation. I propose a two-step quantile regression estimator and establish its weak convergence to a bivariate Gaussian process. An application to business training in Kenya shows that treatment effects are concentrated among high-performing firms in strong markets, highlighting complementarities between individual and group performance.
Minimum Distance Estimation of Quantile Panel Data Models
We propose a minimum distance estimation approach for quantile panel data models where unit effects may be correlated with covariates. This computationally efficient method involves two stages: first, computing quantile regression within each unit, then applying GMM to the first-stage fitted values. Our estimators are applicable to (i) classical panel data, tracking units over time, and (ii) grouped data, where individual-level data are available, but treatment varies at the group level. Depending on the exogeneity assumptions, this approach provides quantile analogs of classic panel data estimators, including fixed effects, random effects, between, and Hausman-Taylor estimators. In addition, our method offers improved precision for grouped (instrumental) quantile regression compared to existing estimators. We establish asymptotic properties as both the number of units and observations per unit jointly diverge to infinity. Additionally, we introduce an inference procedure that automatically adapts to potentially unknown convergence rates of the estimators. Monte Carlo simulations demonstrate that our estimator and inference procedure perform well in finite samples, even when the number of observations per unit is moderate. In an empirical application, we examine the impact of the food stamp program on birth weights. Our findings reveal that the program's introduction increased birth weights predominantly at the lower end of the distribution, demonstrating our method's ability to capture heterogeneous effects across the outcome distribution.
Publications and Accepted Papers
The Apple Does Not Fall Far From the Tree: Intergenerational Persistence of Dietary Habits
Accepted at The Review of Economics and Statistics
Inadequate diets harm individual health, generate substantial healthcare costs, and reduce labor market income. Yet, the determinants of unhealthy eating remain poorly understood. This paper provides novel evidence on the intergenerational transmission of dietary choices from parents to children by exploiting unique grocery transaction records matched with administrative data. We document a strong intergenerational persistence of diet that exceeds income transmission across all measures we consider. At the same time, substantial heterogeneities in the persistence of diet indicate that the socioeconomic background and location of children may be crucial to fostering beneficial eating habits and breaking unhealthy ones. We discuss potential mechanisms and show in a counterfactual analysis that only 10% of the intergenerational persistence in diet can be explained by the transmission of income and education. In line with these results, we introduce a habit formation model and argue that the formation of dietary habits during childhood and their slow alteration are key drivers of our findings.
The Impact of Air Pollution on Birthweight: Evidence from Grouped Quantile Regression
Published in Empirical Economics, 2022
Estimates of the average effect of pollution on birthweight might not provide a complete picture if more vulnerable infants are disproportionately more affected. To address this, I focus on the distributional effect of particulate matter pollution (PM2.5) on birthweight. To estimate the impact, this paper uses grouped quantile regression, a methodology developed by Chetverikov et al. (2016), which allows estimating the impact of a group-level treatment on an individual-level outcome when there are group-level unobservables. The analysis reveals nonhomogeneous effects indicating that pollution disproportionately affects infants in the lower tail of the conditional distribution, whereas average effects suggest only minimal and not economically significant impact of pollution on birthweight. The findings are also consistent across different specifications.
Footnote: The Figure shows the effect of the business training on the within market and the between market distributions of income from work estimated using the method suggested in the paper Quantile on Quantiles (see paper for details).
