Julia Lane

Profound changes in technology and climate, com­bined with the COVID-19 pandemic shock, have fundamentally changed the nature of work for firms and employees. And, because most labor markets are local, that change requires rethinking how data and evidence can be generated in a timely and actionable way to inform local decisions.

The emergence of new types of local data, new cloud-based platforms allowing state and local agen­cies to securely share de-identified confidential data, and new training programs to build state workforce capacity means that there is new potential for pro­grams that are designed and shaped at the local level. A new workforce information system—a National Collaborative for Local Workforce Information (NCLWI)—can be designed that is driven by local needs and that is timely, actionable, and responsive.

This report describes why and how such a system should be constructed. The approach, inspired by the successful National Agricultural Extension Program, is fundamentally local in nature. It should be federally funded but driven by state and local needs, networks, and decision makers. It should build on the success of multistate data collaboratives in sharing data across agency and state lines and partnering with local uni­versities. The result will be bottom-up, locally gener­ated projects that can be tested, improved, and scaled to become products that can be put into practice across the country.

The 2018 Nobel Laureate in Economic Sciences, Paul Romer of New York University speaks with New York University Professor Julia Lane. His work on endogenous technological change transformed our understanding of economic growth by recognizing that ideas—particularly scientific ideas—are neither a private nor a public good. Although he originally argued that the search for ideas is best fostered through competition, his thinking about the best structure has changed over time.  In this interview he explains why.

This article describes how data science techniques—machine learning and natural language processing—can be used to open the black box of government data. It then describes how an incentive structure can be established—using human–computer interaction techniques —to create a new and sustainable data ecosystem. The particular focus is on the United States and on scientific researchers, who are major users of government data. However, the approach can be deployed to other use cases, such as data mentions in newspapers and government reports, and many other countries. 

Each issue of the Annals of the American Academy of Political and Social Science, guest edited by scholars and experts in the field, presents more than 200 pages of timely, in-depth research on a significant topic of interest to its readership which includes academics, researchers, policymakers, and professionals.

The research community should move to fill the regulatory gap for large-scale social data left by the recent revisions of the Common Rule, argues Julia Ingrid Lane.

Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation.

The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations.

The Jarmin and O'Hara piece describes a turning point in statistical data collection and dissemination. The federal agencies can no longer bear the full burden of producing and disseminating data for public policy research. Public policy researchers and schools should seize the new opportunity to complement the Census Bureau initiative—which also represents a turning point for both public policy education and research. We should adapt our educational programs so that we can produce the workforce capable of working with new data. We should adapt our research programs to move from largely artisanal individual efforts to large-scale “big science”; I have seen other scientific areas do so with great success.

We examine gender differences among the six PhD student cohorts 2004-2009 at the California Institute of Technology using a new dataset that includes information on trainees and their advisors and enables us to construct detailed measures of teams at the advisor level. We focus on the relationship between graduate student publications and: (1) their gender; (2) the gender of the advisor, (3) the gender pairing between the advisor and the student and (4) the gender composition of the team. We find that female graduate students co-author on average 8.5% fewer papers than men; that students writing with female advisors publish 7.7% more. Of particular note is that gender pairing matters: male students working with female advisors publish 10.0% more than male students working with male advisors; women students working with male advisors publish 8.5% less. There is no difference between the publishing patterns of male students working with male advisors and female students working with female advisors. The results persist and are magnified when we focus on the quality of the published articles, as measured by average Impact Factor, instead of number of articles. We find no evidence that the number of publications relates to the gender composition of the team. Although the gender effects are reasonably modest, past research on processes of positive feedback and cumulative advantage suggest that the difference will grow, not shrink, over the careers of these recent cohorts.

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.

We examine gender differences among the six PhD student cohorts 2004–2009 at the California Institute of Technology using a new dataset that includes information on trainees and their advisors and enables us to construct detailed measures of teams at the advisor level. We focus on the relationship between graduate student publications and: (1) their gender; (2) the gender of the advisor, (3) the gender pairing between the advisor and the student and (4) the gender composition of the team. We find that female graduate students co-author on average 8.5% fewer papers than men; that students writing with female advisors publish 7.7% more. Of particular note is that gender pairing matters: male students working with female advisors publish 10.0% more than male students working with male advisors; women students working with male advisors publish 8.5% less. There is no difference between the publishing patterns of male students working with male advisors and female students working with female advisors. The results persist and are magnified when we focus on the quality of the published articles, as measured by average Impact Factor, instead of number of articles. We find no evidence that the number of publications relates to the gender composition of the team. Although the gender effects are reasonably modest, past research on processes of positive feedback and cumulative advantage suggest that the difference will grow, not shrink, over the careers of these recent cohorts.

Recent years have seen an increase in the amount of statistics describing different phenomena based on “Big Data.” This term includes data characterized not only by their large volume, but also by their variety and velocity, the organic way in which they are created, and the new types of processes needed to analyze them and make inference from them. The change in the nature of the new types of data, their availability, and the way in which they are collected and disseminated is fundamental. This change constitutes a paradigm shift for survey research. There is great potential in Big Data, but there are some fundamental challenges that have to be resolved before its full potential can be realized. This report provides examples of different types of Big Data and their potential for survey research; it also describes the Big Data process, discusses its main challenges, and considers solutions and research needs.