Unveiling Specialization Trends in Economics Research : A Large-Scale Study Using Natural Language Processing and Citation Analysis

This article presents a comprehensive analysis of trends in the publication and citation of economics scholarly research, with a focus on specialization within fields of economics research (i.e., applied, applied theory, econometrics methods, and theory). We collected detailed data on 24,273 articles published from 1970 to 2016 in highly regarded general research economics journals. We then used state-of-the-art machine learning and natural language processing techniques to further enrich the collected data. Our findings reveal significant disparities in article content and citations across fields of economics research. The analysis indicates growing specialization trends in theory and econometric methods. These articles have shown a narrowing breadth of topics covered, and their share of extramural citations and citations from other fields of economics research has either remained stable or decreased. In contrast, applied papers are covering a wider range of topics and receiving an increasing proportion of extramural citations over time, particularly from disciplines such as medicine, psychology, law, and education. In addition, citations to applied papers coming from other fields of economics research have increased during the period analyzed. By 2016, applied ranked among the most or second most cited field by any other field of economics research. These patterns are consistent with applied papers becoming more multidisciplinary. Applied theory articles have also demonstrated a growing breadth of topics covered (similar to applied articles); however, this has not been accompanied by an increase in extramural citations or in the share of citations received from other fields of economics research (as observed with theory articles). This makes it challenging to determine their specialization status