Ethnic Diversity Increases Scientific Impact

Inspired by the numerous social and economic benefits of diversity [1, 2, 3, 4, 5, 6], we analyze over 9 million papers and 6 million scientists spanning 24 fields of study, to understand the relationship between research impact and five types of diversity, reflecting (i) ethnicity, (ii) discipline, (iii) gender, (iv) affiliation and (v) academic age. For each type, we study group diversity (i.e., the heterogeneity of a paper’s set of authors) and individual diversity (i.e., the heterogeneity of a scientist’s entire set of collaborators). Remarkably, of all the types considered, we find that ethnic diversity is the strongest predictor of a field’s scientific impact (r is 0.77 and 0.55 for group and individual ethnic diversity, respectively). Moreover, to isolate the effect of ethnic diversity from other confounding factors, we analyze a baseline model in which author ethnicities are randomized while preserving all other characteristics. We find that the relation between ethnic diversity and impact is stronger in the real data compared to the randomized baseline model, regardless of publication year, number of authors per paper, and number of collaborators per scientist. Finally, we use coarsened exact matching to infer causality, whereby the scientific impact of diverse papers and scientists are compared against closely matched control groups [7]. In keeping with the other results, we find that ethnic diversity consistently leads to higher scientific impact. Diversity is highly valued in modern societies [8, 9, 10]. Social cohesion, tolerance and integration are linked to tangible benefits including economic vibrancy [4, 11] and innovativeness [9, 12, 13, 14]. Far from being an abstract ideal, this conviction has guided many government and hiring policies and can have broad and long-lasting effects on society [15, 16]. However, diversity is a complicated issue that can be difficult to fully understand. For one, there are numerous types of diversity, e.g., groups can be diverse in terms of their ethnic, educational or experiential composition. There are also cases where homogeneity—or the lack of diversity—can have positive effects. For example, there is some evidence that having greater ∗Department of Computer Science, Masdar Institute (Khalifa University). Masdar City, Abu Dhabi, UAE. P.O.Box 54224. concentrations of specific ethnic groups in a given region—a phenomenon known as ethnic density—can have positive effects on health [17, 18] and economic growth [19]. Furthermore, diversity can be a divisive topic that is clouded by emotion, partisan loyalties and political correctness, all of which can hinder impartial discussions [20]. The factors above strongly motivate an objective study on the value of diversity, and on whether more diverse groups achieve greater success. One domain in which this question can be effectively addressed is academia [3, 6]. The structure of academic collaboration is observable via co-authorships, which frequently involve scientists from different locations, disciplines and backgrounds [21, 22]. Furthermore, academia has an objective, widely-accepted measure of success—citation count [23, 24]. This amenability to analysis has already attracted attempts at identifying the factors which underlie success in academia, an enterprise known as the “science of science” [25]. Although many such factors have been studied, including gender [26], academic age [27], team size [28], interdisciplinarity [29], ethnicity [30], and affiliation [31, 32], several questions remain unanswered, some of which are addressed in our study. In particular, we are the first to (i) compare different types of diversity, (ii) examine the relationship between the diversity and research impact at the level of scientific fields, (iii) study diversity from the perspective of groups and individuals, (iv) study the evolution and effect of diversity across time, team size and number of collaborators, and (v) estimate the causal effect of diversity on scientific impact. The results of these multiple angles of analysis are combined to form a far richer picture of diversity than has been possible in the past. We use the Microsoft Academic Graph dataset, and analyze 1,045,401 multi-authored papers, authored by 1,529,279 scientists, spanning 8 main fields and 24 subfields of science. Moreover, for each such scientist with at least 10 collaborators, we analyze his/her entire set of collaborators, amounting to a total of 5,103,877 collaborators over 9,472,439 papers (Sections S1.1 through S1.6). Our analysis involves five types of diversity, reflecting: (i) ethnicity, denoted by “eth”; (ii) discipline, “dsp”; (iii) gender, “gen”; (iv) affiliation, “aff ”; and (v) academic age, “age” (Section S2.1). These types reflect many https://www.microsoft.com/en-us/research/project/microsoftacademic-graph/