Julia Lane

Professor

Julia Lane

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. She cofounded the Coleridge Initiative, whose goal is to use data to transform the way governments access and use data for the social good through training programs, research projects and a secure data facility.  The approach is attracting national attention, including the Commission on Evidence Based Policy and the Federal Data Strategy.

Previous to this, Julia was a Senior Managing Economist and Institute Fellow at American Institutes for Research. In this role Julia  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 Institute for Research and Innovation in Science (IRIS) at the University of Michigan and STAR METRICS programs at the National Science Foundation. 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 80 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, the International Statistical Institute and a fellow of the American Statistical Association.  She has been the recipient of over $70 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, the Overdeck Family Foundation, the Schmidt Futures Foundation and the Bill and Melinda Gates Foundation; 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. She is also the recipient of the 2017 Warren E. Miller Award.

Julia received her PhD in Economics and Master's in Statistics from the University of Missouri.

The goal of the Big Data Analytics for Public Policy is to develop the key data analytics skill sets necessary to harness the wealth of newly-available data. Its design offers hands-on training in the context of real microdata. The main learning objectives are to apply new techniques to analyze social problems using and combining large quantities of heterogeneous data from a variety of different sources. It is designed for graduate students who are seeking a stronger foundation in data analytics.

The course video provides more information.

 

Download Syllabus

The goal of the Big Data Analytics for Public Policy is to develop the key data analytics skill sets necessary to harness the wealth of newly-available data. Its design offers hands-on training in the context of real microdata. The main learning objectives are to apply new techniques to analyze social problems using and combining large quantities of heterogeneous data from a variety of different sources. It is designed for graduate students who are seeking a stronger foundation in data analytics.

The course video provides more information.

 

Download Syllabus

2019

Julia Ingrid Lane. Federal Funding of Doctoral Recipients: What can be learned from linked data.
Julia Lane. Understanding the Educational and Career Pathways of Engineers.
Julia Ingrid Lane, Nathan Goldschlag, Bruce A. Weinberg, Nikolas Zolas. Proximity and Economic Activity: An Analysis of Vendor-Business Transactions.

2018

Akina Ikudo, Julia Lane, Joseph Staudt, and Bruce A. Weinberg. Occupational Classifications: A Machine Learning Approach.
Britta Glennon, Julia Lane, and Ridhima Sodhi. Money for Something: The Links between Research Funding and Innovation.
Nathan Goldschlag, Julia Lane, Bruce A. Weinberg, and Nikolas Zolas. Proximity and Economic Activity: An Analysis of Vendor-University Transactions.
Julia Lane, Nathan Goldschlag, Ron Jarmin and Nikolas Zolas. The Link Between University R&D, Human Capital and Business Startups. The Measurement and Diffusion of Innovation, NBER CRIW (2018).
Forthcoming/Accepted
Julia Lane. Data Science for Undergraduates. National Academies of Sciences, Engineering, and Medicine. 2018. Data Science for Undergraduates: Opportunities and Options. Washington, DC: The National Academies Press. https://doi.org/10.17226/25104.
Julia Lane. Center for Economic Studies and Research Data Centers Research Report: 2017.
Julia Ingrid Lane. A Data Manifesto.
Michael Holland, Julia Ingrid Lane. Policy advisory committees: An operational view. Policy Analysis in the United States.
Julia Lane, Reza Sattari, and Chia-Hsuan Yang. The Structure of Research Funding. Measuring the Economic Value of Research: The Case of Food Safety, (2018).
Forthcoming/Accepted
Julia Lane, Matthew Ross and Akina Ikudo. The Food Safety Research Workforce and Economic Outcomes. Measuring the Economic Value of Research: The Case of Food Safety, (2018).
Forthcoming/Accepted
Julia Lane, Nathan Goldschlag, and Bruce Weinberg. The Conceptual and Empirical Framework. Measuring the Economic Value of Research: The Case of Food Safety, (2018).
Forthcoming/Accepted
Julia Lane and Evgeny Klochikhin. Identifying Food Safety related Research. Measuring the Economic Value of Research: The Case of Food Safety, (2018).
Forthcoming/Accepted
Abstract

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.

2017

Julia Ingrid Lane, Kaye Husbands-Fealing, John King, Stanley Johnson. Measuring the Economic Value of Research: The Case of Food Safety.
Edited by Andrew Reamer, Julia Lane, Ian Foster and David Ellwood. Developing the Basis for Secure and Accessible Data for High Impact Program Management, Policy Development, and Scholarship.
. Policy Analysis in Policy Advisory Committees.
Forthcoming/Accepted
Julia Lane. Science and Innovation Policy. Oxford Research Encyclopedia of Business and Management.
Abstract

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.

Abstract

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.

Abstract

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.

2015

Abstract

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.

Abstract

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.

Abstract

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.

1990

Julia Lane, Dennis Glennon and Russ Ray. Work Profiles of Research Statisticians. The American Statistician, Volume 44 (February 1990), pp. 9-13.

1989

Julia Lane, Dennis Glennon and James McCabe. Measures of local business climate: alternative approaches. Regional Science Perspectives, Volume 19 (Spring 1989), pp. 89-106.
Julia Lane and Tom Berry. A multi-state analysis of the targeted jobs tax credit programme. Applied Economics, Volume 21 (January 1989), pp. 85-95.

1988

Julia Lane and Robert Blewett. Development Rights and the Differential Assessment of Agricultural Land: Fractional Valuation of Farmland is Ineffective for Preserving Open Space and Subsidizes Speculation. American Journal of Economics & Sociology, Volume 47 (April 1988), pp. 195-206.
Julia Lane and Dennis Glennon. The Estimation of Earnings Profiles in Wrongful Death and Injury Cases: Authors Reply. Journal of Risk and Insurance, (March 1988), pp. 687-695.

1987

Julia Lane and Richard McHugh. The Age of Capital, the Age of Utilized Capital Tests of the Embodiment Hypothesis Revisited. Southern Economic Journal, Volume 53 (April 1987), pp. 915-925.
Julia Lane and Richard McHugh. The Decline of Labor Productivity in the 1970’s: The Surprising Role of Embodied Technological Change. Southern Economic Journal, Volume 53 (April 1987), pp. 915-925.
Julia Lane. Regional econometric models that reflect labor market relations.
Julia Lane, Dennis Glennon and Stan Johnson. Regional Econometric Forecasting Models that Reflect Aggregate Production Relations. International Journal of Forecasting, Volume 3, pp. 299-313.

1986

Julia Lane and Dennis Glennon. Imputing a Housewife’s Earnings in a Wrongful Death and Injury Case. Journal of Risk and Insurance, Volume 53 (December 1986), pp. 734-744.
Julia Lane and LaVonne Straub. Pay Equity in Occupations: The Nursing Profession. Population Research & Policy Review, (May 1986), pp. 31-45.
Julia Lane, Dennis Glennon, Stanley Johnson, and Edward Robb. Incorporating Labor Market Structure in Regional Econometric Models. Applied Economics, Volume 18 (May 1986), pp. 545 –556.

1985

Julia Lane and Dennis Glennon. The Estimation of Earnings Profiles in Wrongful Death and Injury Cases. Journal of Risk and Insurance, Volume 55 (December 1985), pp. 168-180.
Julia Lane and LaVonne Booton. Hospital Market Structure and the Return to Nursing Education. Journal of Human Resources, (Spring 1985), pp. 183-196.
Julia Lane. An Empirical Estimate of the Effects of Labor-Market Distortions on the Factor Content of U.S. Trade. Journal of International Economics, (February 1985), pp. 187-193.
Julia Lane. An empirical estimate of the effects of labormarket distortions on the factor content of U.S. trade.

1983

Julia Lane and Richard McHugh. The Embodiment Hypothesis: An Interregional Test. Review of Economics and Statistics, (May 1983), pp. 323-327.
Julia Lane and Richard McHugh. The Embodiment Hypothesis: An Interregional Test. Review of Economics and Statistics, (May 1983), pp. 323-327.