Erin Leahey

Gender Differences in Productivity: Research Specialization as a Missing Link

Since 1984, when Cole and Zuckerman referred to gender differences in productivity among academic scientists as a puzzle, sociologists have sought to explain these differences by incorporating primarily institutional-level factors. In addition to these factors, the author contends that an undertheorized and heretofore unmeasured concept—the extent of research specialization—can also help explain the process by which gender affects research productivity. Although some researchers have examined areas of specialization, the extent of research specialization has been completely neglected in studies of academic careers. Using a probability sample of academics in two disciplines (sociology and linguistics), primary data collection, and simultaneous equation modeling, the author finds that the extent of research specialization is a critical intervening variable: Women specialize less than men and thereby lose out on an important means of increasing their productivity.

Not by Productivity Alone: How Visibility and Specialization Contribute to Academic Earnings

The popular adage “publish or perish” has long defined individual career strategies as well as scholarly investigations of earnings inequality in academe, as researchers have relied heavily on research productivity to explain earnings inequality among faculty members. Academia, however, has changed dramatically in the last few decades: it has become larger and more demographically diverse, and fears of overspecialization prompt calls for interdisciplinary approaches. In this new environment, other factors, in addition to productivity, are likely relevant to our understanding of earnings differentials. In this article, I assess whether two additional factors—visibility and the extent of research specialization—contribute to mens earning advantage. Using probability samples of tenure-track academics in two disciplines, a variety of data sources, and innovative measures, I find that both factors are highly relevant to the process by which earnings are determined. Women earn less than men largely because they specialize less. Lower levels of specialization hinder productivity, productivity enhances visibility, and visibility has a direct, positive, and significant effect on salary. I discuss the practical implications of these findings and lay the foundation for a broader theory of the role of research specialization in work processes.

Parenting and research productivity: New evidence and methods

To date, studies on how having children affects the research productivity of academics, and whether the effects differ by gender, have had inconsistent findings. Using nuanced measures of parental obligations and linear growth modeling, we analyzed the effects of children on the entire careers of academics in two disciplines — linguistics and sociology — and tested for differential effects by gender. In addition, we modeled not only productivity, but also visibility, another component of scholarly success. Our findings suggest that after the birth a child, productivity growth declines, but more so for women. Thus, children account for part of the gender gap in rates of productivity over time. Children also have an impact on the research visibility of academics, but cannot account for gender differences in visibility.

Intellectuals and Democratization, 1905-1912 and 1989-1996

This article bridges the gap in studies of the social bases of democratization between qualitative studies focused on social groups and quantitative studies focused on national characteristics. Qualitative historical evidence suggests the importance of classes—in particular, the emerging class of intellectuals—in the wave of democratizations in the decade before World War I. Quantitative cross‐national data on a more recent wave of democratizations, from 1989 to 1996, confirm these findings. Models using direct maximum‐likelihood estimation find that the ratio of adults with higher education has a significant positive effect on change in democracy levels, as measured by two longitudinal scales (Polity IV and Polyarchy). Proxies for the working class and the middle class—candidates proposed in previous studies as the social basis of democratization—also have significant effects.

Opinion: Gender diversity leads to better science

Pick up any recent policy paper on women’s participation in science and you will find assurances that gender diversity enhances knowledge outcomes. Universities and science-policy stakeholders, including the European Commission and the US National Institutes of Health, readily subscribe to this argument (1⇓–3). But is there, in fact, a gender-diversity dividend in science? The data suggest that there is. Under the right conditions, teams may benefit from various types of diversity, including scientific discipline, work experience, gender, ethnicity, and nationality. In this paper, we highlight gender diversity (Fig. 1). Guided by key research findings, we propose the following “mechanisms for innovation” specifying why gender diversity matters for scientific discovery and what managers should do to maximize its benefits (Fig. 2). Encouraging greater diversity is not only the right thing to do: it allows scientific organizations to derive an “innovation dividend” that leads to smarter, more creative teams, hence opening the door to new discoveries. Fig. 1. When it comes to science collaborations, there’s ample data to suggest that gender diversity pays a substantial research and productivity dividend. Image courtesy of Dave Cutler (artist). Well-run, well-performing research teams have become increasingly crucial to the success of modern scientific investigations. Already, experimental research points to positive links between gender diversity and collective problem solving. In a study of group performance, Anita Woolley et al. (4) randomly assigned 699 participants to teams of varying sizes and asked them to solve a set of both simple and complicated tasks (e.g., visual puzzles, brainstorming, making collective moral judgments, and negotiating over limited resources). Through these experiments, the authors found evidence of a collective intelligence factor that predicts group performance better than the IQ of individual group members. Key components of this factor include the group members’ social perceptiveness and parity in conversational turn-taking. Furthermore, … [↵][1]1To whom correspondence should be addressed. Email: mwn{at}stanford.edu. [1]: #xref-corresp-1-1

Prominent but Less Productive The Impact of Interdisciplinarity on Scientists’ Research

Federal agencies and universities in the U.S. promote interdisciplinary research because it presumably spurs transformative, innovative science. Using data on almost 900 research-center–based scientists and their 32,000 published articles, along with a set of unpublished papers, we assess whether such research is indeed beneficial and whether costs accompany the potential benefits. Existing research highlights this tension: whereas the innovation literature suggests that spanning disciplines is beneficial because it allows scientists to see connections across fields, the categories literature suggests that spanning disciplines is penalized because the resulting research may be lower quality or confusing to place. To investigate this, we empirically distinguish production and reception effects and highlight a new production penalty: lower productivity, which may be attributable to cognitive and collaborative challenges associated with interdisciplinary research and/or hurdles in the review process. Using an innovative measure of interdisciplinary research that considers the similarity of the disciplines spanned, we document both penalties (fewer papers published) and benefits (increased citations) associated with it and show that it is a high-risk, high-reward endeavor, one that partly depends on field-level interdisciplinarity.

Sociological Innovation through Subfield Integration

Is domain-spanning beneficial? Can it promote innovation? Classic research on recombinant innovation suggests that domain-spanning fosters the accumulation of diverse information and can thus be a springboard for fresh ideas—most of which emanate from the merger of extant ideas from distinct realms. But domain-spanning is also challenging to produce and to evaluate. Here, the domains of interest are subfields. We focus on subfield spanning in sociology, a topically diverse field whose distinct subfields are still reasonably permeable. To do so, we introduce two measures of subfield integration, one of which uniquely accounts for the novelty of subfield combinations. We find (within the limits of observable data) the costs to be minimal but the rewards substantial: Once published, sociology articles that integrate subfields (especially rarely spanned subfields) garner more citations. We discuss how these results illuminate trends in the discipline of sociology and inform theories of recombinant innovation.

Diversity in Everyday Research Practice The Case of Data Editing

How should social science researchers deal with data inaccuracies? This article uses Web-based survey data collected from faculty members in three social science disciplines to document variation in views about data editing. Through an analysis of qualitative responses to a hypothetical vignette, the authors demonstrate that a wide range of opinion surrounds the “proper” use of data. Reactions are to some extent contingent on discipline and experience with different types of data and data collection methods. They also depend on characteristics of the data-editing situation—for example, whether the problem is with an independent or dependent variable. Even taking these social and situational factors into account, however, there is still substantial diversity in vignette responses. Normative standards that pervade other aspects of the research process have not yet emerged for data editing.

Straight from the source: Accounting for scientific success

How do highly cited scientists account for their success? A number of approaches have been used to explain scientific success, but none incorporates scientists’ own understandings, which are critical to a complete, process-oriented explanation. We remedy this oversight by incorporating scientists’ own descriptions of the value of their work, as reflected in essays written by authors of highly cited articles (‘Citation Classics’). As cultural objects, these essays reveal not only factors perceived to be associated with success but also reflect narrative conventions, and thereby elucidate the culture surrounding success. We enlist Charles Ragin’s Qualitative Comparative Analysis to analyze how factors mentioned in these accounts work in conjunction. Our results show that three ingredients – relationships, usefulness to others, and overcoming challenges – are found in a large majority of scientific success stories.

Overseeing Research Practice The Case of Data Editing

This article examines whether and how a particular research practice is overseen and supervised, and by whom. This investigation fills notable gaps in the literature on science, including a lack of emphasis on larger sociopolitical structures, a neglect of regulation, and indifference toward ethics. The author focuses on the oversight of a particular research practice; data editing; which embodies qualities that are intriguing to sociologists of science: invisibility, uncertainty, heterogeneity, and reliance on tacit knowledge. These characteristics pose unique challenges to oversight efforts. An analysis of in-depth interviews with gatekeepers reveals that although the methodological and ethical implications of data editing strategies can be substantial, oversight of such practices falls outside the stages and domains of current gatekeeping activity. These findings serve as the basis for recommendations to ensure data integrity while maintaining the professional autonomy of researchers.