Diversity and Inclusion in Regional Science Participation: Through the Lens of Data Mining

Despite increasing awareness of the need to promote diversity, equity and inclusion in regional science, it remains unclear how and to what extent underrepresentation exists in the field. In addition, although inequalities in science along these lines have been studied empirically across a wide range of fields and disciplines, they have yet to be examined specifically in the field of regional science per se. This study aims to fill this research gap by investigating the extent to which there are disparities in publication and citation rates based on race, gender and region, and how topic selection affects these differentials. To achieve this goal, we analyzed 4,930 bibliometric records for nine journals with an explicit regional science brand over the period from 1961 to 2021, we apply Latent Dirichlet Allocation (LDA) technique regression modeling to assess how citation impact is affected by the gender, race, nationality of authors, topic selections, and other relevant factors. The results of this study provide evidence on patterns of inequality in regional science and help to understand the distribution of scientific productivity and impact by gender, race and origins. While the field is becoming more diverse and inclusive in terms publications and citations, significant gaps and disparities remains, especially regarding the Global South and minorities. We conclude with policy and programmatic recommendations for promoting broader participation in regional science in the future.