Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists

Arne Henningsen, Guy Low (), David Wuepper (), Tobias Dalhaus, Hugo Storm (), Dagim Belay and Stefan Hirsch ()
Additional contact information
Guy Low: Business Economics Group, Wageningen University & Research
David Wuepper: Institute for Food and Resource Economics, University of Bonn
Hugo Storm: Institute for Food and Resource Economics, University of Bonn
Stefan Hirsch: Department of Management in Agribusiness, University of Hohenheim

No 2024/03, IFRO Working Paper from University of Copenhagen, Department of Food and Resource Economics

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., the welfare of individuals or the society, the demanded or produced quantity, pollution). Only a small number of these research questions can be studied with economic experiments such as randomised controlled trials (RCTs), lab experiments or lab-in-the-field experiments. Hence, most empirical studies in agricultural and applied economics use observational data. However, estimating causal effects with observational data requires appropriate research designs and convincing identification strategies, which are usually very difficult or even impossible to devise. Likely as a consequence, in the applied economics literature, it can commonly be observed that results are interpreted as causal despite lacking a robust identification strategy, which has contributed to a credibility crisis in economics research. 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.

Keywords: causal inference; observational data; instrumental variables; difference in differences; regression discontinuity (search for similar items in EconPapers)
JEL-codes: C21 C23 C24 C26 C51 C52 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2024-12
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