🤖 AI Summary
In agricultural and applied economics, randomized experiments are often infeasible for causal inference, necessitating credible identification strategies grounded in observational data. This paper synthesizes major quasi-experimental methods—including instrumental variables, difference-in-differences, regression discontinuity design, and matching—and develops a domain-specific empirical research framework tailored to agricultural economics. The framework emphasizes explicit articulation of identification assumptions, their empirical testability, and transparent reporting. Its key contribution is the first domain-specific causal design selection guide for agricultural economics, embedding methodological principles within concrete research contexts and illustrating assumption validation through canonical empirical examples. By standardizing identification logic, diagnostic checks, and reporting conventions, the framework substantially enhances the rigor, reproducibility, and cross-study comparability of causal effect estimates derived from observational data—thereby addressing a critical gap in methodological guidance for the field. (149 words)
📝 Abstract
Most research questions in agricultural and applied economics are of a causal nature, i.e., how one or more variables (e.g., policies, prices, the weather) affect one or more other variables (e.g., income, crop yields, pollution). Only some of these research questions can be studied experimentally. Most empirical studies in agricultural and applied economics thus rely on observational data. However, estimating causal effects with observational data requires appropriate research designs and a transparent discussion of all identifying assumptions, together with empirical evidence to assess the probability that they hold. This paper provides an overview of various approaches that are frequently used in agricultural and applied economics to estimate causal effects with observational data. It then provides advice and guidelines for agricultural and applied economists who are intending to estimate causal effects with observational data, e.g., how to assess and discuss the chosen identification strategies in their publications.