Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/308008 
Year of Publication: 
2024
Series/Report no.: 
IFRO Working Paper No. 2024/03
Publisher: 
University of Copenhagen, Department of Food and Resource Economics (IFRO), Copenhagen
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.
Subjects: 
causal inference
observational data
instrumental variables
difference indifferences
regression discontinuity
JEL: 
C21
C23
C24
C26
C51
C52
Document Type: 
Working Paper

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