The Labor Dynamics Institute's mission is to create and make accessible novel data on the dynamics of the labor markets. We work with research networks and statistical agencies, developing appropriate statistics to inform policy makers, researchers, and simply people seeking knowledge. For more information, visit our website at www.ilr.cornell.edu/ldi.

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Documents from 2017

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Earnings Inequality and Mobility Trends in the United States: Nationally Representative Estimates from Longitudinally Linked Employer-Employee Data, John M. Abowd, Kevin L. McKinney, and Nellie Zhao

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Estimating Compensating Wage Differentials with Endogenous Job Mobility, Lavetti Kurt and Ian M. Schmutte

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Hours Off the Clock, Andrew Green

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Making Confidential Data Part of Reproducible Research, Lars Vilhuber and Carl Lagoze

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Modeling Endogenous Mobility in Earnings Determination, John M. Abowd, Kevin L. McKinney, and Ian M. Schmutte

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Proceedings from the 2016 NSF–Sloan Workshop on Practical Privacy, Lars Vilhuber and Ian M. Schmutte

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Proceedings from the 2017 Cornell-Census- NSF-Sloan Workshop on Practical Privacy, Lars Vilhuber and Ian M. Schmutte

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Proceedings from the Synthetic LBD International Seminar, Lars Vilhuber, Saki Kinney, and Ian M. Schmutte

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Recalculating - How Uncertainty in Local Labor Market Definitions Affects Empirical Findings, Andrew Foote, Mark Kutzbach, and Lars Vilhuber

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Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods, John Abowd and Ian M. Schmutte

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Sorting Between and Within Industries: A Testable Model of Assortative Matching, John M. Abowd, Francis Kramarz, Sebastien Perez-Duarte, and Ian M. Schmutte

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Two Perspectives on Commuting: A Comparison of Home to Work Flows Across Job-Linked Survey and Administrative Files, Andrew Green, Mark Kutzbach, and Lars Vilhuber

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Understanding the Effect of Procedural Justice on Psychological Distress, Julie Cloutier, Lars Vilhuber, Denis Harrison, and Vanessa Béland-Ouellette

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Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics, Samuel Haney, Ashwin Machanavajjhala, John M. Abowd, Matthew Graham, Mark Kutzbach, and Lars Vilhuber

Documents from 2016

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Estimating Compensating Wage Differentials with Endogenous Job Mobility, Kurt Lavetti and Ian M. Schmutte

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How Will Statistical Agencies Operate When All Data Are Private?, John M. Abowd

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Why Statistical Agencies Need to Take Privacy-loss Budgets Seriously, and What It Means When They Do, John M. Abowd

Documents from 2015

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Economic Analysis and Statistical Disclosure Limitation, John M. Abowd and Ian M. Schmutte

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Modeling Endogenous Mobility in Wage Determination, John M. Abowd, Kevin L. McKinney, and Ian M. Schmutte

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Noise Infusion as a Confidentiality Protection Measure for Graph-Based Statistics, John M. Abowd and Kevin L. McKinney

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Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods, John M. Abowd and Ian M. Schmutte

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Synthetic Establishment Microdata Around the World, Lars Vilhuber, John Abowd, and Jerome P. Reiter

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Using Partially Synthetic Microdata to Protect Sensitive Cells in Business Statistics, Javier Miranda and Lars Vilhuber

Documents from 2014

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Sorting between and within Industries: A Testable Model of Assortative Matching, John M. Abowd, Francis Kramarz, Sebastien Perez-Duarte, and Ian M. Schmutte

Documents from 2013

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A New Method for Protecting Interrelated Time Series with Bayesian Prior Distributions and Synthetic Data, Matthew J. Schneider and John M. Abowd