Queen’s University, Canada, NYU Shanghai, China, and NBER, USA
IZA World of Labor role
Associate Professor, School of Policy Studies and Department of Economics, Queen’s University, Canada; Associate Professor of Economics, FAS, NYU Shanghai, China; Global Network Professor, Department of Economics, New York University, USA
Economics of education, health economics, causal inference, experimental economics, applied econometrics for data science
Olin Post Doctoral Fellow in Medical Economics, Wharton School, University of Pennsylvania, USA; Visiting Scholar, Center for Labor Economics, University of California—Berkeley; Assistant Professor, School of Policy Studies and Department of Economics, Queen’s University, Canada
PhD Economics, University of Pittsburgh, 2001
"Targeted or universal coverage? Assessing heterogeneity in the effects of universal childcare." Journal of Labor Economics 35:3 (2017): 609–653 (with M. Kottelenberg).
"Bargaining and reputation: Experimental evidence on bargaining in the presence of irrational types." Review of Economic Studies 82:2 (2015): 608–631 (with M. Embrey and G. Frechette).
"Cohort of birth modifies the association between FTO genotype and BMI." Proceedings of the National Academy of Sciences 112:2 (2015): 354–359 (with N. Rosenquist, J. O’Malley, A. Zavalsky, J. Smoller, and N. Christakis).
"Genetic lotteries within families." Journal of Health Economics 30:4 (2011): 647–659 (with J. Fletcher).
"Estimating treatment effects from contaminated multi-period education experiments: The dynamic impacts of class size reductions." Review of Economics and Statistics 92:1 (2010): 31–42 (with W. Ding).
What is the role for molecular genetic data in public policy?
There is potential value from incorporating genetic data in the design of effective public policy, but also some risksWeili DingSteven F. Lehrer, October 2017Both the availability and sheer volume of data sets containing individual molecular genetic information are growing at a rapid pace. Many argue that these data can facilitate the identification of genes underlying important socio-economic outcomes, such as educational attainment and fertility. Opponents often counter that the benefits are as yet unclear, and that the threat to individual privacy is a serious one. The initial exploration presented herein suggests that significant benefits to the understanding of socio-economic outcomes and the design of both social and education policy may be gained by effectively and safely utilizing genetic data.MoreLess