About Lars Vilhuber
Dr. Lars Vilhuber has an undergraduate degree in Economics from Universität Bonn, Germany, and a Ph.D. in Economics from Université de Montréal, Montreal, Canada. He has worked in both research and government. He has consulted with government and statistical agencies in Canada and the United States.
His interest in statistical disclosure limitation issues is a consequence of his other research interest: working with highly detailed longitudinally linked data to analyze the effects and causes of mass layoffs, worker mobility, and the dynamics of the local labor market.
He is presently on the faculty of the Department of Economics at Cornell University, a Senior Research Associate at the ILR School at Cornell University, Ithaca, Executive Director of ILR’s Labor Dynamics Institute, and affiliated with the U.S. Census Bureau (Center for Economic Studies, CES).
Over the years, he has also gained extensive expertise on the data needs of economists and other social scientists, having been involved in the creation and maintenance of several data systems designed with analysis, publication, replicability, and maintenance of large-scale code bases in mind.
|2014 ‐ Present||Senior Research Associate, U.S. Census Bureau ‐ Center for Economic Studies, LEHD Program|
|2011 ‐ Present||Executive Director, Cornell University ILR School ‐ Labor Dynamics Institute|
|2007 ‐ Present||Economist, Senior Research Associate, Cornell University ILR School|
|2007 ‐ Present||Vice-President, ACES-Research, LLC|
Web page: http://www.cloutier-vilhuber.net/Lars/
Contributions to Books (2)
Contribution to Book
Adjusting imperfect data: Overview and case studies
“Wage Structure, Raises and Mobility: International Comparisons of the Structure of Wages Within and Across Firms (2005)
[Excerpt] In this chapter, instead of using the similarity in the cleaned datasets to investigate economic fundamentals, we focus on ...
Unpublished Papers (2)
Differential Privacy Applications to Bayesian and Linear Mixed Model Estimation
Labor Dynamics Institute (2012)
We consider a particular maximum likelihood estimator (MLE) and a computationally-intensive Bayesian method for differentially private estimation of the linear ...