Machine learning approaches to testing institutional hypotheses: the case of Acemoglu, Johnson, and Robinson (2001)
Crossref DOI link: https://doi.org/10.1007/s00181-021-02110-7
Published Online: 2021-09-02
Published Print: 2022-05
Update policy: https://doi.org/10.1007/springer_crossmark_policy
Diallo, Boubacar
Text and Data Mining valid from 2021-09-02
Version of Record valid from 2021-09-02
Article History
Received: 24 September 2020
Accepted: 26 July 2021
First Online: 2 September 2021
Declarations
:
: There is no potential conflicts of interest and the research did not involve human participants and/or animals.