Abstract: General equilibrium models often rely on nested CES production functions to simulate energy and environmental policies. Yet these functions typically impose rigid substitution structures for tractability rather than empirical validity. This paper introduces a new framework for estimating flexible, multi-level CES functions using observable revenue and input data, without solving difficult equilibrium conditions. We also propose two tools for selecting the most empirically supported nesting structure: one based on significance tests of transformation terms, and another using testable restrictions within translog models. Using simulations and input-output data, we show that widely used CES structures misrepresent actual substitution patterns, especially in settings involving renewable and fossil energy.
A Green Trade-Off? Agricultural Technical Change and Deforestation (with Torfinn Harding)
Abstract: This paper examines the impact of labor scarcity on technology adoption. We exploit the legalization of the labor-saving technology of genetically engineered (GE) soybeans in Brazil as a natural experiment. GE-soy adoption is higher in municipalities with greater natural soy potential, and initial labor scarcity amplifies this relationship. The resulting reduction in labor demand in the soy sector creates a positive labor supply shock for other sectors. This facilitates the adoption of the land-saving technology of double-cropping (DC) maize, which introduces a second harvest season. We find that the two technologies are complementary in the longer run, enabling the parallel expansion of soy and maize production with limited cropland expansion and deforestation.
Abstract: There is a gap in the production function estimation literature, where factor-biased productivities and factor misallocations are typically examined separately. Factor-biased productivity studies often exclude misallocation by making specific market assumptions, while research on misallocation tends to ignore factor-biased productivity. We argue that this separation likely stems from an underidentification challenge that arises when both factor-biased technical change and misallocation are present simultaneously. Building on the framework of \cite{Doraszelski2018,Zhang2019}, we propose a robust production function estimation method to address this issue and predict the biases inherent in standard approaches that account for only a subset of productivities and misallocations. To confirm the validity of our method and the predicted biases, we conduct extensive Monte Carlo simulations.