TY - JOUR
T1 - Economists in the 2008 financial crisis
T2 - Slow to see, fast to act
AU - Levy, Daniel
AU - Mayer, Tamir
AU - Raviv, Alon
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/6
Y1 - 2022/6
N2 - 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.
AB - 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.
KW - 2008 financial crisis
KW - Dynamic topic modeling
KW - LDA textual analysis
KW - Machine learning
KW - great recessionNBER working papers
UR - http://www.scopus.com/inward/record.url?scp=85126040660&partnerID=8YFLogxK
U2 - 10.1016/j.jfs.2022.100986
DO - 10.1016/j.jfs.2022.100986
M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???
AN - SCOPUS:85126040660
SN - 1572-3089
VL - 60
JO - Journal of Financial Stability
JF - Journal of Financial Stability
M1 - 100986
ER -