Recipient Organization
IOWA STATE UNIVERSITY
2229 Lincoln Way
AMES,IA 50011
Performing Department
ECONOMICS - CALS/AES
Non Technical Summary
This project will develop conceptual and empirical models of the US agrochemical market as an imperfectly competitive differentiated product industry. The analysis will rely on a novel empirical discrete-choice representation of farm-level demand for agrochemicals that incorporates both observed and unobserved heterogeneity. The estimation of the models will be based on a large proprietary dataset of observed pesticide choices by representative samples of about 10,000 corn and soybean operations per year, over the period 1998-2016. Farm-level demands will be aggregated to market demands and coupled with an explicit formulation of the supply side suitable for a structural representation of Nash equilibrium in this imperfectly competitive industry. Simulations of counterfactual equilibria will permit analyses of several policy-relevant questions, including a characterization of the extent of market power in the industry and its evolution over time, the pricing and welfare consequences of mergers and industry consolidation, the competitive effects of patent expiration, and the impacts of possible bans (or taxes, or restrictions) on individual pesticide products.
Animal Health Component
80%
Research Effort Categories
Basic
20%
Applied
80%
Developmental
(N/A)
Goals / Objectives
The goals of this project are: To provide new knowledge and insights into the economics of agrochemicals in US agriculture, with emphasis on the role of competitiveness and the exercise of market power; To develop novel empirical models suitable for the econometric analysis of farm-level demand for highly-differentiated agrochemical products; To understand the key features of imperfect competition as it applies to the agrochemical market, reflecting the integration of seed and agrochemical businesses, and the role of intellectual property; To study the welfare implications of market power in an equilibrium setting, including the impact on farmers, and to understand the scope of possible policies addressing competitiveness in the agrochemical and seed markets, as well as regulations on agrochemical use by farmers. The objectives we have identified for the pursuit of these goals are:To develop conceptual models of agrochemicals as a differentiated product industry, and specifically to develop discrete choice models of agrochemical product demand at the farm level.To devise suitable identification strategies and simulation algorithms for the estimation of these agrochemical demand models, while accounting for farmers' heterogeneity and firms' exercise of market power.To build the empirical analysis on a unique dataset, spanning the period 1998 to 2016, which contains plot-specific farm-level agrochemical use from representative samples of about 10,000 corn and soybean operations per year.To use the estimated empirical models, along with an explicit representation of Nash equilibrium for differentiated products, to conduct counterfactual simulations aimed at characterizing the impact of competition in agrochemical markets, and assess the impacts of market structure on competition, efficiency, and welfare outcomes for market participants.To use the estimated agrochemical demand models to investigate how the changing competition landscape due to patent expiration affected overall pesticide use, and to evaluate the impact of possible pesticide use restrictions on farmers' pesticide use patterns, pricing, and welfare.
Project Methods
We propose an econometric analysis of agrochemical markets in the vein of modern empirical industrial organization. Modeling the demand side is a critical aspect of this approach, and to this end we propose to leverage a unique, rich dataset of farm level agrochemical choices by a representative sample of US corn and soybean farmers. The demand model will be complemented with a differentiated product Bertrand pricing supply model. Combing the demand model estimates with the equilibrium pricing conditions from the supply model permits the recovery of marginal costs and in turn the degree of market power. Counterfactual simulations of interest can then be conducted to ascertain the impacts of various structural or policy changes.