TY - RPRT T1 - Proceedings from the 2017 Cornell-Census- NSF- Sloan Workshop on Practical Privacy Y1 - 2017 A1 - Vilhuber, Lars A1 - Schmutte, Ian M. AB - Proceedings from the 2017 Cornell-Census- NSF- Sloan Workshop on Practical Privacy Vilhuber, Lars; Schmutte, Ian M. ese proceedings report on a workshop hosted at the U.S. Census Bureau on May 8, 2017. Our purpose was to gather experts from various backgrounds together to continue discussing the development of formal privacy systems for Census Bureau data products. is workshop was a successor to a previous workshop held in October 2016 (Vilhuber & Schmu e 2017). At our prior workshop, we hosted computer scientists, survey statisticians, and economists, all of whom were experts in data privacy. At that time we discussed the practical implementation of cu ing-edge methods for publishing data with formal, provable privacy guarantees, with a focus on applications to Census Bureau data products. e teams developing those applications were just starting out when our rst workshop took place, and we spent our time brainstorming solutions to the various problems researchers were encountering, or anticipated encountering. For these cu ing-edge formal privacy models, there had been very li le e ort in the academic literature to apply those methods in real-world se ings with large, messy data. We therefore brought together an expanded group of specialists from academia and government who could shed light on technical challenges, subject ma er challenges and address how data users might react to changes in data availability and publishing standards. In May 2017, we organized a follow-up workshop, which these proceedings report on. We reviewed progress made in four di erent areas. e four topics discussed as part of the workshop were 1. the 2020 Decennial Census; 2. the American Community Survey (ACS); 3. the 2017 Economic Census; 4. measuring the demand for privacy and for data quality. As in our earlier workshop, our goals were to 1. Discuss the speci c challenges that have arisen in ongoing e orts to apply formal privacy models to Census data products by drawing together expertise of academic and governmental researchers; 2. Produce short wri en memos that summarize concrete suggestions for practical applications to speci c Census Bureau priority areas. Comments can be provided at h ps://goo.gl/ZAh3YE PB - Cornell University UR - http://hdl.handle.net/1813/52473 ER - TY - RPRT T1 - Proceedings from the Synthetic LBD International Seminar Y1 - 2017 A1 - Vilhuber, Lars A1 - Kinney, Saki A1 - Schmutte, Ian M. AB - Proceedings from the Synthetic LBD International Seminar Vilhuber, Lars; Kinney, Saki; Schmutte, Ian M. On May 9, 2017, we hosted a seminar to discuss the conditions necessary to implement the SynLBD approach with interested parties, with the goal of providing a straightforward toolkit to implement the same procedure on other data. The proceedings summarize the discussions during the workshop. PB - Cornell University UR - http://hdl.handle.net/1813/52472 ER - TY - RPRT T1 - Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods Y1 - 2017 A1 - Abowd, John A1 - Schmutte, Ian M. AB - Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods Abowd, John; Schmutte, Ian M. We consider the problem of the public release of statistical information about a population–explicitly accounting for the public-good properties of both data accuracy and privacy loss. We first consider the implications of adding the public-good component to recently published models of private data publication under differential privacy guarantees using a Vickery-Clark-Groves mechanism and a Lindahl mechanism. We show that data quality will be inefficiently under-supplied. Next, we develop a standard social planner’s problem using the technology set implied by (ε, δ)-differential privacy with (α, β)-accuracy for the Private Multiplicative Weights query release mechanism to study the properties of optimal provision of data accuracy and privacy loss when both are public goods. Using the production possibilities frontier implied by this technology, explicitly parameterized interdependent preferences, and the social welfare function, we display properties of the solution to the social planner’s problem. Our results directly quantify the optimal choice of data accuracy and privacy loss as functions of the technology and preference parameters. Some of these properties can be quantified using population statistics on marginal preferences and correlations between income, data accuracy preferences, and privacy loss preferences that are available from survey data. Our results show that government data custodians should publish more accurate statistics with weaker privacy guarantees than would occur with purely private data publishing. Our statistical results using the General Social Survey and the Cornell National Social Survey indicate that the welfare losses from under-providing data accuracy while over-providing privacy protection can be substantial. A complete archive of the data and programs used in this paper is available via http://doi.org/10.5281/zenodo.345385. PB - Cornell University UR - http://hdl.handle.net/1813/39081 ER - TY - RPRT T1 - Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods Y1 - 2017 A1 - Abowd, John A1 - Schmutte, Ian M. AB - Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods Abowd, John; Schmutte, Ian M. We consider the problem of determining the optimal accuracy of public statistics when increased accuracy requires a loss of privacy. To formalize this allocation problem, we use tools from statistics and computer science to model the publication technology used by a public statistical agency. We derive the demand for accurate statistics from first principles to generate interdependent preferences that account for the public-good nature of both data accuracy and privacy loss. We first show data accuracy is inefficiently under-supplied by a private provider. Solving the appropriate social planner’s problem produces an implementable publication strategy. We implement the socially optimal publication plan for statistics on income and health status using data from the American Community Survey, National Health Interview Survey, Federal Statistical System Public Opinion Survey and Cornell National Social Survey. Our analysis indicates that welfare losses from providing too much privacy protection and, therefore, too little accuracy can be substantial. PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/52612 ER - TY - JOUR T1 - Differentially private publication of data on wages and job mobility JF - Statistical Journal of the International Association for Official Statistics Y1 - 2016 A1 - Schmutte, Ian M. KW - Demand for public statistics KW - differential privacy KW - job mobility KW - matched employer-employee data KW - optimal confidentiality protection KW - optimal data accuracy KW - technology for statistical agencies AB - Brazil, like many countries, is reluctant to publish business-level data, because of legitimate concerns about the establishments' confidentiality. A trusted data curator can increase the utility of data, while managing the risk to establishments, either by releasing synthetic data, or by infusing noise into published statistics. This paper evaluates the application of a differentially private mechanism to publish statistics on wages and job mobility computed from Brazilian employer-employee matched data. The publication mechanism can result in both the publication of specific statistics as well as the generation of synthetic data. I find that the tradeoff between the privacy guaranteed to individuals in the data, and the accuracy of published statistics, is potentially much better that the worst-case theoretical accuracy guarantee. However, the synthetic data fare quite poorly in analyses that are outside the set of queries to which it was trained. Note that this article only explores and characterizes the feasibility of these publication strategies, and will not directly result in the publication of any data. VL - 32 UR - http://content.iospress.com/articles/statistical-journal-of-the-iaos/sji962 IS - 1 ER - TY - RPRT T1 - Modeling Endogenous Mobility in Earnings Determination Y1 - 2016 A1 - Abowd, John M. A1 - McKinney, Kevin L. A1 - Schmutte, Ian M. AB - Modeling Endogenous Mobility in Earnings Determination Abowd, John M.; McKinney, Kevin L.; Schmutte, Ian M. We evaluate the bias from endogenous job mobility in fixed-effects estimates of worker- and firm-specific earnings heterogeneity using longitudinally linked employer-employee data from the LEHD infrastructure file system of the U.S. Census Bureau. First, we propose two new residual diagnostic tests of the assumption that mobility is exogenous to unmodeled determinants of earnings. Both tests reject exogenous mobility. We relax the exogenous mobility assumptions by modeling the evolution of the matched data as an evolving bipartite graph using a Bayesian latent class framework. Our results suggest that endogenous mobility biases estimated firm effects toward zero. To assess validity, we match our estimates of the wage components to out-of-sample estimates of revenue per worker. The corrected estimates attribute much more of the variation in revenue per worker to variation in match quality and worker quality than the uncorrected estimates. Replication code can be found at DOI: http://doi.org/10.5281/zenodo.zenodo.376600 and our Github repository endogenous-mobility-replication . PB - Cornell University UR - http://hdl.handle.net/1813/40306 ER - TY - RPRT T1 - Economic Analysis and Statistical Disclosure Limitation Y1 - 2015 A1 - Abowd, John M. A1 - Schmutte, Ian M. AB -

Economic Analysis and Statistical Disclosure Limitation Abowd, John M.; Schmutte, Ian M. This paper explores the consequences for economic research of methods used by data publishers to protect the privacy of their respondents. We review the concept of statistical disclosure limitation for an audience of economists who may be unfamiliar with these methods. We characterize what it means for statistical disclosure limitation to be ignorable. When it is not ignorable, we consider the effects of statistical disclosure limitation for a variety of research designs common in applied economic research. Because statistical agencies do not always report the methods they use to protect confidentiality, we also characterize settings in which statistical disclosure limitation methods are discoverable; that is, they can be learned from the released data. We conclude with advice for researchers, journal editors, and statistical agencies.

PB - Cornell University UR - http://hdl.handle.net/1813/40581 ER - TY - JOUR T1 - Economic Analysis and Statistical Disclosure Limitation JF - Brookings Papers on Economic Activity Y1 - 2015 A1 - Abowd, John M. A1 - Schmutte, Ian M. AB - Economic Analysis and Statistical Disclosure Limitation Abowd, John M.; Schmutte, Ian M. This paper explores the consequences for economic research of methods used by data publishers to protect the privacy of their respondents. We review the concept of statistical disclosure limitation for an audience of economists who may be unfamiliar with these methods. We characterize what it means for statistical disclosure limitation to be ignorable. When it is not ignorable, we consider the effects of statistical disclosure limitation for a variety of research designs common in applied economic research. Because statistical agencies do not always report the methods they use to protect confidentiality, we also characterize settings in which statistical disclosure limitation methods are discoverable; that is, they can be learned from the released data. We conclude with advice for researchers, journal editors, and statistical agencies. VL - Spring 2015 UR - http://www.brookings.edu/about/projects/bpea/papers/2015/economic-analysis-statistical-disclosure-limitation ER - TY - RPRT T1 - Modeling Endogenous Mobility in Wage Determination Y1 - 2015 A1 - Abowd, John M. A1 - McKinney, Kevin L. A1 - Schmutte, Ian M. AB - Modeling Endogenous Mobility in Wage Determination Abowd, John M.; McKinney, Kevin L.; Schmutte, Ian M. We evaluate the bias from endogenous job mobility in fixed-effects estimates of worker- and firm-specific earnings heterogeneity using longitudinally linked employer-employee data from the LEHD infrastructure file system of the U.S. Census Bureau. First, we propose two new residual diagnostic tests of the assumption that mobility is exogenous to unmodeled determinants of earnings. Both tests reject exogenous mobility. We relax the exogenous mobility assumptions by modeling the evolution of the matched data as an evolving bipartite graph using a Bayesian latent class framework. Our results suggest that endogenous mobility biases estimated firm effects toward zero. To assess validity, we match our estimates of the wage components to out-of-sample estimates of revenue per worker. The corrected estimates attribute much more of the variation in revenue per worker to variation in match quality and worker quality than the uncorrected estimates. PB - Cornell University UR - http://hdl.handle.net/1813/40306 ER - TY - RPRT T1 - Modeling Endogenous Mobility in Wage Determination Y1 - 2015 A1 - Abowd, John M. A1 - McKinney, Kevin L. A1 - Schmutte, Ian M. AB - Modeling Endogenous Mobility in Wage Determination Abowd, John M.; McKinney, Kevin L.; Schmutte, Ian M. We evaluate the bias from endogenous job mobility in fixed-effects estimates of worker- and firm-specific earnings heterogeneity using longitudinally linked employer-employee data from the LEHD infrastructure file system of the U.S. Census Bureau. First, we propose two new residual diagnostic tests of the assumption that mobility is exogenous to unmodeled determinants of earnings. Both tests reject exogenous mobility. We relax exogenous mobility by modeling the matched data as an evolving bipartite graph using a Bayesian latent-type framework. Our results suggest that allowing endogenous mobility increases the variation in earnings explained by individual heterogeneity and reduces the proportion due to employer and match effects. To assess external validity, we match our estimates of the wage components to out-ofsample estimates of revenue per worker. The mobility-bias corrected estimates attribute much more of the variation in revenue per worker to variation in match quality and worker quality than the uncorrected estimates. PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/52608 ER - TY - RPRT T1 - Sorting Between and Within Industries: A Testable Model of Assortative Matching Y1 - 2014 A1 - Abowd, John M. A1 - Kramarz, Francis A1 - Perez-Duarte, Sebastien A1 - Schmutte, Ian M. AB - Sorting Between and Within Industries: A Testable Model of Assortative Matching Abowd, John M.; Kramarz, Francis; Perez-Duarte, Sebastien; Schmutte, Ian M. We test Shimer's (2005) theory of the sorting of workers between and within industrial sectors based on directed search with coordination frictions, deliberately maintaining its static general equilibrium framework. We fit the model to sector-specific wage, vacancy and output data, including publicly-available statistics that characterize the distribution of worker and employer wage heterogeneity across sectors. Our empirical method is general and can be applied to a broad class of assignment models. The results indicate that industries are the loci of sorting–more productive workers are employed in more productive industries. The evidence confirms that strong assortative matching can be present even when worker and employer components of wage heterogeneity are weakly correlated. PB - Cornell University UR - http://hdl.handle.net/1813/52607 ER -