Women are credited less in science than men.

There is a well-documented gap between the observed number of works produced by women and by men in science, with clear consequences for the retention and promotion of women1. The gap might be a result of productivity differences2-5, or it might be owing to women's contributions not being acknowledged6,7. Here we find that at least part of this gap is the result of unacknowledged contributions: women in research teams are significantly less likely than men to be credited with authorship. The findings are consistent across three very different sources of data. Analysis of the first source-large-scale administrative data on research teams, team scientific output and attribution of credit-show that women are significantly less likely to be named on a given article or patent produced by their team relative to their male peers. The gender gap in attribution is present across most scientific fields and almost all career stages. The second source-an extensive survey of authors-similarly shows that women's scientific contributions are systematically less likely to be recognized. The third source-qualitative responses-suggests that the reason that women are less likely to be credited is because their work is often not known, is not appreciated or is ignored. At least some of the observed gender gap in scientific output may be owing not to differences in scientific contribution, but rather to differences in attribution.

Determinants of Disparities in Early COVID-19 Job Losses.

This study examines the sociodemographic divide in early labor market responses to the U.S. COVID-19 epidemic and associated policies, benchmarked against two previous recessions. Monthly Current Population Survey (CPS) data show greater declines in employment in April and May 2020 (relative to February) for Hispanic individuals, younger workers, and those with a high school diploma or some college. Between April and May, the demographic subgroups considered regained some employment. Reemployment in May was broadly proportional to the employment drop that occurred through April, except for Black individuals, who experienced a smaller rebound. Compared to the 2001 recession and the Great Recession, employment losses in the early COVID-19 recession were smaller for groups with low or high (vs. medium) education. We show that job loss was greater in occupations that require more interpersonal contact and that cannot be performed remotely, and that pre-COVID-19 sorting of workers into occupations and industries along demographic lines can explain a sizable portion of the demographic gaps in new unemployment. For example, while women suffered more job losses than men, their disproportionate pre-epidemic sorting into occupations compatible with remote work shielded them from even larger employment losses. However, substantial gaps in employment losses across groups cannot be explained by socioeconomic differences. We consider policy lessons and future research needs regarding the early labor market implications of the COVID-19 crisis.

Why the US science and engineering workforce is aging rapidly.

The science and engineering workforce has aged rapidly in recent years, both in absolute terms and relative to the workforce as a whole. This is a potential concern if the large number of older scientists crowds out younger scientists, making it difficult for them to establish independent careers. In addition, scientists are believed to be most creative earlier in their careers, so the aging of the workforce may slow the pace of scientific progress. We develop and simulate a demographic model, which shows that a substantial majority of recent aging is a result of the aging of the large baby boom cohort of scientists. However, changes in behavior have also played a significant role, in particular, a decline in the retirement rate of older scientists, induced in part by the elimination of mandatory retirement in universities in 1994. Furthermore, the age distribution of the scientific workforce is still adjusting. Current retirement rates and other determinants of employment in science imply a steady-state mean age 2.3 y higher than the 2008 level of 48.6.

Age dynamics in scientific creativity.

Data on Nobel Laureates show that the age-creativity relationship varies substantially more over time than across fields. The age dynamics within fields closely mirror field-specific shifts in (i) training patterns and (ii) the prevalence of theoretical contributions. These dynamics are especially pronounced in physics and coincide with the emergence of quantum mechanics. Taken together, these findings show fundamental shifts in the life cycle of research productivity, inform theories of the age-creativity relationship, and provide observable predictors for the age at which great achievements are made.

Effects of social distancing policy on labor market outcomes.

US workers receive unemployment benefits if they lose their job, but not for reduced working hours. In alignment with the benefits incentives, we find that the labor market responded to COVID-19 and related closure-policies mostly on the extensive (12 pp outright job loss) margin. Exploiting timing variation in state closure-policies, difference-in-differences (DiD) estimates show, between March 12 and April 12, 2020, employment rate fell by 1.7 pp for every 10 extra days of state stay-at-home orders (SAH), with little effect on hours worked/earnings among those employed. Forty percentage of the unemployment was due to a nationwide shock, rest due to social-distancing policies, particularly among "non-essential" workers.

Nevertheless She Persisted? Gender Peer Effects in Doctoral STEM Programs.

We study the effects of peer gender composition in STEM doctoral programs on persistence and degree completion. Leveraging unique new data and quasi-random variation in gender composition across cohorts within programs, we show that women entering cohorts with no female peers are 11.7pp less likely to graduate within 6 years than their male counterparts. A 1 sd increase in the percentage of female students differentially increases women's probability of on-time graduation by 4.4pp. These gender peer effects function primarily through changes in the probability of dropping out in the first year of a Ph.D. program.

The ripple effects of funding on researchers and output.

Using unique, new, matched UMETRICS data on people employed on research projects and Author-ity data on biomedical publications, this paper shows that National Institutes of Health funding stimulates research by supporting the teams that conduct it. While faculty-both principal investigators (PIs) and other faculty-and their productivity are heavily affected by funding, so are trainees and staff. The largest effects of funding on research output are ripple effects on publications that do not include PIs. While funders focus on research output from projects, they would be well advised to consider how funding ripples through the wide range of people, including trainees and staff, employed on projects.

Developing Science: Scientific Performance and Brain Drains in the Developing World.

Establishing a strong scientific community is important as countries develop, which requires both producing and retaining of important scientists. We show that developing countries produce a sizeable number of important scientists, but that they experience a tremendous brain drain. Education levels, population, and per capita GDP are positively related to the number of important scientists born in and staying in a country. Our analysis indicates that democracy and urbanization are associated with the production of more important scientists although democracy is associated with more out-migration.

Man versus machine? Self-reports versus algorithmic measurement of publications.

This paper uses newly available data from Web of Science on publications matched to researchers in Survey of Doctorate Recipients to compare the quality of scientific publication data collected by surveys versus algorithmic approaches. We illustrate the different types of measurement errors in self-reported and machine-generated data by estimating how publication measures from the two approaches are related to career outcomes (e.g., salaries and faculty rankings). We find that the potential biases in the self-reports are smaller relative to the algorithmic data. Moreover, the errors in the two approaches are quite intuitive: the measurement errors in algorithmic data are mainly due to the accuracy of matching, which primarily depends on the frequency of names and the data that was available to make matches, while the noise in self reports increases over the career as researchers' publication records become more complex, harder to recall, and less immediately relevant for career progress. At a methodological level, we show how the approaches can be evaluated using accepted statistical methods without gold standard data. We also provide guidance on how to use the new linked data.

High-impact and transformative science (HITS) metrics: Definition, exemplification, and comparison.

Countries, research institutions, and scholars are interested in identifying and promoting high-impact and transformative scientific research. This paper presents a novel set of text- and citation-based metrics that can be used to identify high-impact and transformative works. The 11 metrics can be grouped into seven types: Radical-Generative, Radical-Destructive, Risky, Multidisciplinary, Wide Impact, Growing Impact, and Impact (overall). The metrics are exemplified, validated, and compared using a set of 10,778,696 MEDLINE articles matched to the Science Citation Index ExpandedTM. Articles are grouped into six 5-year periods (spanning 1983-2012) using publication year and into 6,159 fields constructed using comparable MeSH terms, with which each article is tagged. The analysis is conducted at the level of a field-period pair, of which 15,051 have articles and are used in this study. A factor analysis shows that transformativeness and impact are positively related (ρ = .402), but represent distinct phenomena. Looking at the subcomponents of transformativeness, there is no evidence that transformative work is adopted slowly or that the generation of important new concepts coincides with the obsolescence of existing concepts. We also find that the generation of important new concepts and highly cited work is more risky. Finally, supporting the validity of our metrics, we show that work that draws on a wider range of research fields is used more widely.

Wrapping it up in a person: Examining employment and earnings outcomes for Ph.D. recipients.

In evaluating research investments, it is important to establish whether the expertise gained by researchers in conducting their projects propagates into the broader economy. For eight universities, it was possible to combine data from the UMETRICS project, which provided administrative records on graduate students supported by funded research, with data from the U.S. Census Bureau. The analysis covers 2010-2012 earnings and placement outcomes of people receiving doctorates in 2009-2011. Almost 40% of supported doctorate recipients, both federally and nonfederally funded, entered industry and, when they did, they disproportionately got jobs at large and high-wage establishments in high-tech and professional service industries. Although Ph.D. recipients spread nationally, there was also geographic clustering in employment near the universities that trained and employed the researchers. We also show large differences across fields in placement outcomes.