Research Interests
Industrial Organization, Agricultural and Resource Economics, Applied Econometrics
Papers
- Cows and Trees (2026)
with Eduardo Souza-Rodrigues, Ted C. Rosenbaum, and Skand Goel
The Brazilian Amazon plays a crucial role in regulating global climate and preserving biodiversity, yet it faces mounting pressures from deforestation, driven primarily by cattle ranching. The expansion of pastureland is shaped by cattle’s dual role as both output and capital stock, leading to nontrivial dynamic patterns. We develop a structural empirical model of ranchers' cattle management and land use decisions that accounts for deforestation costs, herd dynamics, and forward-looking behavior. Model estimates reveal that deforestation is inelastic to temporary shocks to beef prices but highly elastic to persistent price changes, rationalizing existing estimates in the literature. We simulate various policies and discuss the implications of highly price-elastic deforestation. - Markup Estimation using Production and Demand Data: An Application to the US Brewing Industry (2026)
with Jan De Loecker
Accepted, Review of Economic Studies
Click for abstract
We compare and combine two distinct approaches to estimating market power in the US brewing industry—one based on production data, the other on demand. Both methods produce similar estimates of average markups and reveal a recent upward trend. We then combine the two approaches in two ways. First, we replace conventional instruments in demand estimation with a moment involving a production-based markup estimate, yielding similar results. We then evaluate the common (but controversial) as- sumption that retail markets operate competitively, finding that demand-based markups recovered with the assumption of competitive retail markets align with production-based estimates, as long as downstream costs are accounted for. - Market Structure, Investment, and Technical Efficiencies in Mobile Telecommunications (2025)
with Jonathan Elliott, Georges-Vivien Houngbonon, and Marc Ivaldi
Journal of Political Economy
Replication code | Online Appendix | Preprint
Click for abstract
We develop a model of competition in prices and infrastructure among mobile network operators. Although consolidation increases market power, it can lead to more efficient data transmission due to economies of scale, which we derive from physical principles. After estimating our model with French consumer and infrastructure data, equilibrium simulations reveal that while prices decrease with the number of firms, so do download speeds. Our framework also allows us to quantify the impact of spectrum allocation. The marginal social value of spectrum exceeds firms' willingness to pay in our model as well as observed prices in spectrum auctions. - Improving Estimates of Transitions from Satellite Data:
A Hidden Markov Model Approach (2025)
with Adrian Torchiana, Ted C. Rosenbaum, and Eduardo Souza-Rodrigues
Review of Economics and Statistics
Replication materials | Online Appendix | Preprint
Click for abstract
Satellite-based image classification facilitates low-cost measurement of the Earth's surface composition. However, misclassified imagery can lead to misleading conclusions about transition processes. We propose a correction for transition rate estimates based on the econometric measurement error literature to extract the signal (truth) from its noisy measurement (satellite-based classifications). No ground-truth data are required in the implementation. Our proposed correction produces consistent estimates of transition rates, confirmed by longitudinal validation data, while transition rates without correction are severely biased. Using our approach, we show how eliminating deforestation in Brazil's Atlantic forest region through 2040 could save $100 billion in CO2 emissions. -
Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models (2021)
with Myrto Kalouptsidi and Eduardo Souza-Rodrigues
Journal of Econometrics
Replication code | Online Appendix | Preprint
Click for abstract
In structural dynamic discrete choice models, unobserved or mis-measured state variables may lead to biased parameter estimates and misleading inference. In this paper, we show that instrumental variables can address such measurement problems when they relate to state variables that evolve exogenously from the perspective of individual agents (i.e., market-level states). We define a class of linear instrumental variables estimators that rely on Euler equations expressed in terms of conditional choice probabilities (ECCP estimators). These estimators do not require observing or modeling the agent's entire information set, nor solving or simulating a dynamic program. As such, they are simple to implement and computationally light. We provide constructive arguments for the identification of model primitives, and establish the estimator's consistency and asymptotic normality. Four applied examples serve to illustrate the ECCP approach's implementation, advantages, and limitations: dynamic demand for durable goods, agricultural land use change, technology adoption, and dynamic labor supply. We illustrate the estimator's good finite-sample performance in a Monte Carlo study, and we estimate a labor supply model empirically for taxi drivers in New York City. -
Identification of Counterfactuals in Dynamic Discrete Choice Models (2021)
with Myrto Kalouptsidi and Eduardo Souza-Rodrigues
Quantitative Economics
Replication materials | Online Appendix | Preprint
Click for abstract
Dynamic discrete choice (DDC) models are not identified nonparametrically, but the non-identification of models does not necessarily imply the nonidentification of counterfactuals. We derive novel results for the identification of counterfactuals in DDC models, such as non-additive changes in payoffs or changes to agents' choice sets. In doing so, we propose a general framework that allows the investigation of the identification of a broad class of counterfactuals (covering virtually any counterfactual encountered in applied work). To illustrate the results, we consider a firm entry/exit problem numerically, as well as an empirical model of agricultural land use. In each case, we provide examples of both identified and nonidentified counterfactuals of interest. -
On the Non-Identification of Counterfactuals in Dynamic Games (2017)
with Myrto Kalouptsidi and Eduardo Souza-Rodrigues
International Journal of Industrial Organization (Special issue EARIE)
Replication code | Preprint
Click for abstract
In single-agent dynamic discrete choice models, counterfactual behavior is identified for some (but not all) counterfactuals despite the fact that the models themselves are under-identified. We review recent results on the identification of counterfactuals in dynamic discrete choice settings. When it comes to dynamic discrete games, we argue that counterfactuals are not identified, even when analogous counterfactuals of single-agent models are identified. Using the example of a duopoly entry game, we explain why strategic considerations undermine the identification of counterfactual equilibria in dynamic games. - Indirect Estimation of Yield-Price Elasticities
(2013)
Click for abstract
While it is most common to estimate yield-price responses directly by regressing yields on prices, they may also be estimated indirectly by estimating fertilizer use elasticities and using basic optimization theory to derive yield-price elasticities. Indirect estimation has a practical advantage in terms of precision, for unpredictable weather variation makes yields a noisy measure of farmer's endogenous input use decisions. Indirect estimation suggests that yield-price elasticities are unlikely to be larger than .04 for US corn, .11 for soybeans, and .13 for wheat. Because indirect estimation delivers considerably smaller standard errors than direct estimation, these upper bounds are much tighter than existing estimates. - Dynamic Discrete Choice Estimation of Agricultural Land Use
(2013)
Click for abstract
I develop a new framework for analyzing land use change with dynamically optimizing landowners. My empirical approach allows for unobservable heterogeneity and avoids the burden of explicitly modeling the evolution of market-level state variables like input and output prices. Using a rich new data set on land use in the United States, I estimate a relatively large long-run cropland-price elasticity of 0.3. Compared to static estimates using the same data, my dynamic estimates suggest that biofuels production leads to dramatically more land use change and substantially smaller price increases in the long run.
Other Stuff
- Production Functions and Productivity Slides
- Single Agent Dynamics Slides
- Do motivated baseball players have higher batting averages? with Phil Birnbaum