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ECIN Replication Package for "Specialization Trends in Economics Research: A Large-Scale Study Using Natural Language Processing and Citation Analysis"

Principal Investigator(s):  View help for Principal Investigator(s) Sebastian Galiani, University of Maryland; Ramiro H. Gálvez, Universidad Torcuato Di Tella; Ian Nachman, Brown University

Version:  View help for Version V1

Name File Type Size Last Modified
  specialization 03/13/2024 03:37:PM

Project Citation: 

Galiani, Sebastian, Gálvez, Ramiro H., and Nachman, Ian. ECIN Replication Package for “Specialization Trends in Economics Research: A Large-Scale Study Using Natural Language Processing and Citation Analysis.” Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-04-04. https://doi.org/10.3886/E198921V1

Project Description

Summary:  View help for Summary
We conduct a comprehensive analysis of specialization trends within and across fields of economics research. We collect data on 24,273 articles published between 1970 and 2016 in general research economics outlets and employ machine learning techniques to enrich the collected data. Results indicate that theory and econometric methods papers are becoming increasingly specialized, with a narrowing scope and steady or declining citations from outside economics and from other fields of economics research. Conversely, applied papers are covering a broader range of topics, receiving more extramural citations from fields like medicine, and psychology. Trends in applied theory articles are unclear.

Scope of Project

Subject Terms:  View help for Subject Terms Citation Analysis; Fields of economics research; Natural language processing; Machine learning
JEL Classification:  View help for JEL Classification
      A11 Role of Economics; Role of Economists; Market for Economists
      A14 Sociology of Economics
Manuscript Number:  View help for Manuscript Number ECIN-Sep-2023-0387
Time Period(s):  View help for Time Period(s) 1/1/1970 – 12/31/2016
Collection Date(s):  View help for Collection Date(s) 1/1/2023 – 1/31/2023
Data Type(s):  View help for Data Type(s) aggregate data; other; text
Collection Notes:  View help for Collection Notes
Data is downloaded from Constellate and the Semantic Scholar API. We cannot
share the raw downloaded data, but we are providing scripts to download all the
data from scratch. Note that downloaded data may differ, as APIs get updated.
If unable to replicate the full paper, the authors can share the data downloaded
by them privately.



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Published Versions

V3 [2024-08-02]

V2 [2024-06-05]

V1 [2024-04-04]

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