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How to make a pie: Reproductible research for empirical economics and econometrics

Valérie Orozco (), Christophe Bontemps (), Élise Maigné (), Virginie Piguet (), Annie Hofstetter (), Anne Lacroix, Fabrice Levert () and Jean‐marc Rousselle
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Élise Maigné: US ODR - Observatoire des Programmes Communautaires de Développement Rural - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
Virginie Piguet: CESAER - Centre d'Economie et de Sociologie Rurales Appliquées à l'Agriculture et aux Espaces Ruraux - AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
Annie Hofstetter: CEE-M - Centre d'Economie de l'Environnement - Montpellier - UM - Université de Montpellier - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro - Montpellier SupAgro - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement
Fabrice Levert: SMART-LERECO - Structures et Marché Agricoles, Ressources et Territoires - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - INSTITUT AGRO Agrocampus Ouest - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement
Jean‐marc Rousselle: CEE-M - Centre d'Economie de l'Environnement - Montpellier - UM - Université de Montpellier - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro - Montpellier SupAgro - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement

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Abstract: Empirical economics and econometrics (EEE) research now relies primarily on the application of code to data sets. Handling the workflow that links data sets, programs, results and finally manuscript(s) is essential if one wishes to reproduce results. Herein, we highlight the importance of "reproducible research" in EEE and propose three simple principles to follow: organize your work, code for others and automate as much as you can. The first principle, "organize your work", deals with the overall organization of files and the documentation of a research workflow. "Code for others" emphasizes that we should take care in how we write code that has to be read by others or later by our future self. Finally, "automate as much as you can" is a proposal to avoid any manual treatment and to automate most, if not all, of the steps used in a research process to reduce errors and increase reproducibility. As software is not always the problem and will never be the solution, we illustrate these principles with good habits and tools, with a particular focus on their implementation in most popular software and languages in applied economics.

Keywords: Empirical Economics; Literate Programming; Replication; Reproducibility; Soft- ware; Workflow * (search for similar items in EconPapers)
Date: 2020-12
Note: View the original document on HAL open archive server: https://hal.inrae.fr/hal-03014999
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Published in Journal of Economic Surveys, Wiley, 2020, 34 (5), pp.1134-1169. ⟨10.1111/joes.12389⟩

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Journal Article: HOW TO MAKE A PIE: REPRODUCIBLE RESEARCH FOR EMPIRICAL ECONOMICS AND ECONOMETRICS (2020) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03014999

DOI: 10.1111/joes.12389

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