1st Edition

False Feedback in Economics and the Case for Replication

  • Available for pre-order. Item will ship after July 22, 2021
ISBN 9781032033716
July 22, 2021 Forthcoming by Routledge
184 Pages

USD $160.00

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Book Description

This book provides a comprehensive account of the challenges encountered in empirical economics. It explains approaches used implicitly by researchers and shows where they are adequate and where they break down. It investigates why, in the field of economics, we make so little visible progress when compared to fields with a strong practical component like the computer sciences. The author asserts that the main impediment to progress in economics is “false feedback”, which is defined as a false result in an empirical study, such as empirical evidence produced by a statistical model that violates some of its assumptions. Because false feedback is hard to recognize, economists have difficulty knowing where they stand in their inquiries and regularly leads them to the wrong conclusions. The book searches for the reasons behind the emergence of such false feedback. It thereby contributes to a wider discussion in the field of metascience about the actual practices of researchers. The book thus offers a case study of metascience for the field of empirical economics. The main strengths of the book are the numerous smaller insights it provides throughout. It delves into deep discussions of various theoretical aspects, which it illustrates by many applied examples and a wide array of references, especially to philosophy. The book clarifies complicated and often abstract subjects, particularly when it comes to controversial topics such as data mining. Readers will gain an understanding of the main challenges present in empirical economic research, as well as, the possible solutions.

Table of Contents

1. Scientific Progress 2. Trial and Error 3. Conjectures and falsification 4. The garden of forking paths 5. The Duhem-Quine thesis 6. The detection of patterns 7. The illusion of true feedback 8. False feedback bubbles 9. The tree of knowledge 10. The locality of knowledge 11. Machine learning and sample splits 12. Practical experience 13. Robustness checks 14. Replication

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Andrin Spescha is a Postdoctoral Researcher at ETH Zurich, KOF Swiss Economic Institute, Zurich, Switzerland. He received his PhD from ETH Zurich (Dr. sc. ETH) in 2018. Prior to this, he completed a Bachelor of Arts in Political Sciences and a Master of Arts in Economics at University of Zurich.