Transparency, Reproducibility, and the Credibility of Economics Research

By G. Christensen, Edward Miguel. Journal of Economic Literature, 2018

389 citations recorded by the source corpus.

There is growing interest in enhancing research transparency and reproducibility in economics and other scientific fields. We survey existing work on these topics within economics and discuss the evidence suggesting that publication bias, inability to replicate, and specification searching remain widespread in the discipline. We next discuss recent progress in this area, including through improved research design, study registration and pre-analysis plans, disclosure standards, and open sharing of data and materials, drawing on experiences in both economics and other social sciences. We discuss areas where consensus is emerging on new practices, as well as approaches that remain controversial, and speculate about the most effective ways to make economics research more credible in the future. ( JEL A11, C18, I23)

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4d65bab6f9b1fd61885fe187e6014a89592df187

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Citations in the corpus

Model Averaging and Its Use in Economics (2017, background): “iscard estimates inconsistent with the competitionfragility hypothesis, one of the two main hypotheses in this area. For an in-depth discussion of publication bias and meta-analyses in economics, see Christensen and Miguel (2018). 71 nants of output changes after crises and conclude that for high-in”
REPORTING GUIDELINES FOR META‐ANALYSIS IN ECONOMICS (2020, background): “…assessed the incidence and impact of publication bias and p-hacking in economics (Brodeur et al., 2016; Bruns and Ioannidis, 2016; Ioannidis et al., 2017; Brodeur et al., 2018; Christensen and Miguel, 2018), and attention has turned to journal editorial policies (Blanco-Perez and Brodeur, 2020).”
The influence of hidden researcher decisions in applied microeconomics (2020, neutral): “…allow us to determine variation in results that arises from the use of different samples, when testing the same hypothesis in new data, or from major analysis choices, when testing the same hypothesis in the same data with a new method (Christensen & Miguel, 2018; Clemens, 2017; Hamermesh, 2007).”
RCTs to Scale: Comprehensive Evidence from Two Nudge Units (2020, neutral): “, 2016; Oostrom, 2021) and research transparency (Miguel et al., 2014; Christensen and Miguel, 2018).”
Enhancing Rigor in Quantitative Entrepreneurship Research (2019, background): “This “replication crisis” has now been broadly acknowledged in economics (Christensen & Miguel, 2018), sociology (Freese, 2007), political science (Laitin & Reich, 2017), and psychology (Lindsay, 2015).”
Factorial Designs, Model Selection, and (Incorrect) Inference in Randomized Experiments (2019, background): “Third, there is well-documented publication bias towards significant findings (e.g., Franco et al., 2014; Andrews & Kasy, 2018; Christensen & Miguel, 2018; Abadie, 2020).”
Benchmarking Embedding Models for ESG Data (2026, unknown): “[21] analyzed the heterogeneity of ESG reporting standards and highlighted the inconsistency in terminology and metrics across different frameworks such as GRI, SASB, and TCFD.”