Specialization trends in economics research: A large-scale study using natural language processing and citation analysis

被引：0
作者：
Galiani, Sebastian [1 ,2 ]
Galvez, Ramiro H. [3 ]
Nachman, Ian [4 ]
机构：
[1] Univ Maryland, College Pk, MD USA
[2] Natl Bur Econ Res NBER, Cambridge, MA USA
[3] Univ Torcuato Tella, Buenos Aires, Argentina
[4] Brown Univ, Providence, RI USA
来源：
ECONOMIC INQUIRY | 2025年 / 63卷 / 01期
关键词：
citation analysis; fields of economics research; machine learning; natural language processing; specialization trends; FOLK THEOREM; COMMUNICATION; FIELDS; CRIME; LABOR;
D O I：
10.1111/ecin.13261
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
This article conducts 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.
引用
收藏
页码：289 / 329
页数：41
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