Julia Lane is a Professor at the NYU Wagner Graduate School of Public Service, at the NYU Center for Urban Science and Progress, and a NYU Provostial Fellow for Innovation Analytics.
Previous to this, Julia was a Senior Managing Economist and Institute Fellow at American Institutes for Research. In this role Julia established the Center for Science of Science and Innovation Policy Program, and co-founded the Institute for Research on Innovation and Science (IRIS) at the University of Michigan. Julia has held positions at the National Science Foundation, The Urban Institute, The World Bank, American University and NORC at the University at Chicago.
In these positions, Julia has led many initiatives, including co-founding the UMETRICS and STAR METRICS programs at the National Science Foundation. She conceptualized and established a data enclave at NORC/University of Chicago. This provides a confidential, protected environment within which authorized researchers canaccess sensitive microdata remotely and provides data producers with a secure disseminationplatform. She also initiated and led the creation and permanent establishment of the Longitudinal Employer-Household Dynamics Program at the U.S. Census Bureau. This program began as a small two year ASA Census Bureau fellowship and evolved into the first large-scale linked employer-employee dataset in the United States. It is now a permanent Census Bureau program with appropriated funds of $11 million per year.
Julia has published over 70 articles in leading economics journals, and authored or edited ten books. She is an elected fellow of the American Association for the Advancement of Science and a fellow of the American Statistical Assocation. She has been the recipient of over $50 million in grants; from foundations such as the National Science Foundation, the Alfred P. Sloan Foundation, the Ewing Marion Kauffman Foundation, the MacArthur Foundation, the Russell Sage Foundation, the Spencer Foundation, the National Institutes of Health; from government agencies such as the Departments of Commerce, Labor, and Health and Human Services in the U.S., the ESRC in the U.K., and the Department of Labour and Statistics New Zealand in New Zealand, as well as from international organizations such as the World Bank. Julia is the recipient of the 2014 Julius Shiskin award and the 2014 Roger Herriot award.
Julia received her PhD in Economics and Master's in Statistics from the University of Missouri.
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.
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.