Economists in the 2008 financial crisis: Slow to see, fast to act

Daniel Levy, Tamir Mayer, Alon Raviv

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

We study the economics- and finance-scholars’ reaction to the 2008 financial crisis using machine learning language analyses methods of Latent Dirichlet Allocation and dynamic topic modelling algorithms, to analyze the texts of 14,270 NBER working papers covering the 1999–2016 period. We find that academic scholars as a group were insufficiently engaged in crises’ studies before 2008. As the crisis unraveled, however, they switched their focus to studying the crisis, its causes, and consequences. Thus, the scholars were “slow-to-see,” but they were “fast-to-act.” Their initial response to the ongoing Covid-19 crisis is consistent with these conclusions.

Original languageEnglish
Article number100986
JournalJournal of Financial Stability
Volume60
DOIs
StatePublished - Jun 2022

Bibliographical note

Funding Information:
We thank three anonymous reviewers for constructive comments and suggestions and the editor Iftekhar Hasan for advice. In addition, we are grateful to Meni Abudy, George Akerlof, the late Alberto Alesina, Doron Avramov, Olivier Blanchard, Avinoam Blum, John Campbell, Wendy Carlin, Steve Cecchetti, John Cochrane, Antonio Fatas, Roy Gelbard, Michael Gofman, Yuval Heller, Arnold Kling, Narayana Kocherlakota, Bob Krainer, David Laidler, James Poterba, John Quiggin, Ricardo Reis, Dani Rodrik, Oliviero Roggi, Ken Rogoff, Alex Rosenberg, Amir Rubin, Jeffrey Sachs, and Vernon Smith for commenting (many of them in a great detail) on earlier versions of this manuscript. We thank also the participants of the 2020 Winter Meeting of the Econometric Society, the 2020 International Risk Management Conference, the 2019 Workshop on “Sentiments and Crises in Financial Texts” at the Data Science Institute at Bar-Ilan University, the 2019 Israeli Behavioral Finance Conference, and the seminars at the Research Department of the Bank of Israel, Ben-Gurion University, Università Cattolica del Sacro Cuore, and at the Department of Economics and at the Graduate School of Business at Bar-Ilan University, for useful comments, conversations and suggestions. We thank Sara Markowitz for answering our questions about the way NBER working papers are processed. This work was supported by the Data Science Institute at Bar-Ilan University, Israel [grant number 247009]. Harel Reinuss provided excellent research assistance. The usual disclaimer applies.

Funding Information:
We thank three anonymous reviewers for constructive comments and suggestions and the editor Iftekhar Hasan for advice. In addition, we are grateful to Meni Abudy, George Akerlof, the late Alberto Alesina, Doron Avramov, Olivier Blanchard, Avinoam Blum, John Campbell, Wendy Carlin, Steve Cecchetti, John Cochrane, Antonio Fatas, Roy Gelbard, Michael Gofman, Yuval Heller, Arnold Kling, Narayana Kocherlakota, Bob Krainer, David Laidler, James Poterba, John Quiggin, Ricardo Reis, Dani Rodrik, Oliviero Roggi, Ken Rogoff, Alex Rosenberg, Amir Rubin, Jeffrey Sachs, and Vernon Smith for commenting (many of them in a great detail) on earlier versions of this manuscript. We thank also the participants of the 2020 Winter Meeting of the Econometric Society, the 2020 International Risk Management Conference, the 2019 Workshop on ?Sentiments and Crises in Financial Texts? at the Data Science Institute at Bar-Ilan University, the 2019 Israeli Behavioral Finance Conference, and the seminars at the Research Department of the Bank of Israel, Ben-Gurion University, Universit? Cattolica del Sacro Cuore, and at the Department of Economics and at the Graduate School of Business at Bar-Ilan University, for useful comments, conversations and suggestions. We thank Sara Markowitz for answering our questions about the way NBER working papers are processed. This work was supported by the Data Science Institute at Bar-Ilan University, Israel [grant number 247009]. Harel Reinuss provided excellent research assistance. The usual disclaimer applies.

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • 2008 financial crisis
  • Dynamic topic modeling
  • LDA textual analysis
  • Machine learning
  • great recessionNBER working papers

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