A note on synthetic data for replication purposes in agricultural economics

Stefan Wimmer and Robert Finger

Journal of Agricultural Economics, 2023, vol. 74, issue 1, 316-323

Abstract: Empirical studies in agricultural economics usually involve policy implications. In many cases, such studies rely on proprietary or confidential data that cannot be published along with the article, challenging the replicability and credibility of the results. To overcome this problem, the use of synthetic data—that is, data that do not contain a single unit of the original data—has been proposed. In this note, we illustrate the utility of synthetic data generation methods for replication purposes using a range of methods from agricultural production analysis. More specifically, we compare input elasticities and technical efficiency scores based on different farm‐level production data between original data and synthetic data. We generate synthetic data using a non‐parametric method of classification and regression trees (CART) and parametric linear regressions. We find synthetic data result in elasticities and technical efficiency distributions that are very similar to the original data, especially when generated with CART, and conclude with implications for the research community.

Date: 2023
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