Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Secure the Future of the Federal Statistical System? Weinberg, Daniel; Abowd, John M.; Belli, Robert F.; Cressie, Noel; Folch, David C.; Holan, Scott H.; Levenstein, Margaret C.; Olson, Kristen M.; Reiter, Jerome P.; Shapiro, Matthew D.; Smyth, Jolene; Soh, Leen-Kiat; Spencer, Bruce; Spielman, Seth E.; Vilhuber, Lars; Wikle, Christopher The National Science Foundation-Census Bureau Research Network (NCRN) was established in 2011 to create interdisciplinary research nodes on methodological questions of interest and significance to the broader research community and to the Federal Statistical System (FSS), particularly the Census Bureau. The activities to date have covered both fundamental and applied statistical research and have focused at least in part on the training of current and future generations of researchers in skills of relevance to surveys and alternative measurement of economic units, households, and persons. This paper discusses some of the key research findings of the eight nodes, organized into six topics: (1) Improving census and survey data collection methods; (2) Using alternative sources of data; (3) Protecting privacy and confidentiality by improving disclosure avoidance; (4) Using spatial and spatio-temporal statistical modeling to improve estimates; (5) Assessing data cost and quality tradeoffs; and (6) Combining information from multiple sources. It also reports on collaborations across nodes and with federal agencies, new software developed, and educational activities and outcomes. The paper concludes with an evaluation of the ability of the FSS to apply the NCRN’s research outcomes and suggests some next steps, as well as the implications of this research-network model for future federal government renewal initiatives. This paper began as a May 8, 2015 presentation to the National Academies of Science’s Committee on National Statistics by two of the principal investigators of the National Science Foundation-Census Bureau Research Network (NCRN) – John Abowd and the late Steve Fienberg (Carnegie Mellon University). The authors acknowledge the contributions of the other principal investigators of the NCRN who are not co-authors of the paper (William Block, William Eddy, Alan Karr, Charles Manski, Nicholas Nagle, and Rebecca Nugent), the co- principal investigators, and the comments of Patrick Cantwell, Constance Citro, Adam Eck, Brian Harris-Kojetin, and Eloise Parker. We note with sorrow the deaths of Stephen Fienberg and Allan McCutcheon, two of the original NCRN principal investigators. The principal investigators also wish to acknowledge Cheryl Eavey’s sterling grant administration on behalf of the NSF. The conclusions reached in this paper are not the responsibility of the National Science Foundation (NSF), the Census Bureau, or any of the institutions to which the authors belong
PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/52650 ER - TY - RPRT T1 - How Will Statistical Agencies Operate When All Data Are Private? Y1 - 2016 A1 - Abowd, John M. AB - How Will Statistical Agencies Operate When All Data Are Private? Abowd, John M. The dual problems of respecting citizen privacy and protecting the confidentiality of their data have become hopelessly conflated in the “Big Data” era. There are orders of magnitude more data outside an agency’s firewall than inside it—compromising the integrity of traditional statistical disclosure limitation methods. And increasingly the information processed by the agency was “asked” in a context wholly outside the agency’s operations—blurring the distinction between what was asked and what is published. Already, private businesses like Microsoft, Google and Apple recognize that cybersecurity (safeguarding the integrity and access controls for internal data) and privacy protection (ensuring that what is published does not reveal too much about any person or business) are two sides of the same coin. This is a paradigm-shifting moment for statistical agencies. PB - Cornell University UR - http://hdl.handle.net/1813/44663 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 - JOUR T1 - Noise infusion as a confidentiality protection measure for graph-based statistics JF - Statistical Journal of the International Association for Official Statistics Y1 - 2016 A1 - Abowd, John M. A1 - McKinney, Kevin L. AB - We use the bipartite graph representation of longitudinally linked employer-employee data, and the associated projections onto the employer and employee nodes, respectively, to characterize the set of potential statistical summaries that the trusted custodian might produce. We consider noise infusion as the primary confidentiality protection method. We show that a relatively straightforward extension of the dynamic noise-infusion method used in the U.S. Census Bureau's Quarterly Workforce Indicators can be adapted to provide the same confidentiality guarantees for the graph-based statistics: all inputs have been modified by a minimum percentage deviation (i.e., no actual respondent data are used) and, as the number of entities contributing to a particular statistic increases, the accuracy of that statistic approaches the unprotected value. Our method also ensures that the protected statistics will be identical in all releases based on the same inputs. VL - 32 UR - http://content.iospress.com/articles/statistical-journal-of-the-iaos/sji958 IS - 1 ER - TY - JOUR T1 - Synthetic establishment microdata around the world JF - Statistical Journal of the International Association for Official Statistics Y1 - 2016 A1 - Vilhuber, Lars A1 - Abowd, John M. A1 - Reiter, Jerome P. KW - Business data KW - confidentiality KW - differential privacy KW - international comparison KW - Multiple imputation KW - synthetic AB - In contrast to the many public-use microdata samples available for individual and household data from many statistical agencies around the world, there are virtually no establishment or firm microdata available. In large part, this difficulty in providing access to business microdata is due to the skewed and sparse distributions that characterize business data. Synthetic data are simulated data generated from statistical models. We organized sessions at the 2015 World Statistical Congress and the 2015 Joint Statistical Meetings, highlighting work on synthetic \emph{establishment} microdata. This overview situates those papers, published in this issue, within the broader literature. VL - 32 UR - http://content.iospress.com/download/statistical-journal-of-the-iaos/sji964 IS - 1 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 - NCRN Meeting Spring 2015: Can Government-Academic Partnerships Help Secure the Future of the Federal Statistical System? Examples from the NSF-Census Research Network Y1 - 2015 A1 - Abowd, John M. A1 - Fienberg, Stephen E. AB - NCRN Meeting Spring 2015: Can Government-Academic Partnerships Help Secure the Future of the Federal Statistical System? Examples from the NSF-Census Research Network Abowd, John M.; Fienberg, Stephen E. May 8, 2015 CNSTAT Public Seminar PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/40186 ER - TY - RPRT T1 - NCRN Meeting Spring 2015: Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods Y1 - 2015 A1 - Abowd, John M. A1 - Schmutte, Ian AB - NCRN Meeting Spring 2015: Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods Abowd, John M.; Schmutte, Ian Presentation at the NCRN Meeting Spring 2015 PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/40184 ER - TY - RPRT T1 - A New Method for Protecting Interrelated Time Series with Bayesian Prior Distributions and Synthetic Data Y1 - 2014 A1 - Schneider, Matthew J. A1 - Abowd, John M. AB - A New Method for Protecting Interrelated Time Series with Bayesian Prior Distributions and Synthetic Data Schneider, Matthew J.; Abowd, John M. Organizations disseminate statistical summaries of administrative data via the Web for unrestricted public use. They balance the trade-off between confidentiality protection and inference quality. Recent developments in disclosure avoidance techniques include the incorporation of synthetic data, which capture the essential features of underlying data by releasing altered data generated from a posterior predictive distribution. The United States Census Bureau collects millions of interrelated time series micro-data that are hierarchical and contain many zeros and suppressions. Rule-based disclosure avoidance techniques often require the suppression of count data for small magnitudes and the modification of data based on a small number of entities. Motivated by this problem, we use zero-inflated extensions of Bayesian Generalized Linear Mixed Models (BGLMM) with privacy-preserving prior distributions to develop methods for protecting and releasing synthetic data from time series about thousands of small groups of entities without suppression based on the of magnitudes or number of entities. We find that as the prior distributions of the variance components in the BGLMM become more precise toward zero, confidentiality protection increases and inference quality deteriorates. We evaluate our methodology using a strict privacy measure, empirical differential privacy, and a newly defined risk measure, Probability of Range Identification (PoRI), which directly measures attribute disclosure risk. We illustrate our results with the U.S. Census Bureau’s Quarterly Workforce Indicators. PB - Cornell University UR - http://hdl.handle.net/1813/40828 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 - TY - RPRT T1 - Improving User Access to Metadata for Public and Restricted Use US Federal Statistical Files Y1 - 2013 A1 - Block, William C. A1 - Williams, Jeremy A1 - Vilhuber, Lars A1 - Lagoze, Carl A1 - Brown, Warren A1 - Abowd, John M. AB - Improving User Access to Metadata for Public and Restricted Use US Federal Statistical Files Block, William C.; Williams, Jeremy; Vilhuber, Lars; Lagoze, Carl; Brown, Warren; Abowd, John M. Presentation at NADDI 2013 This record has also been archived at http://kuscholarworks.ku.edu/dspace/handle/1808/11093 . PB - Cornell University UR - http://hdl.handle.net/1813/33362 ER - TY - RPRT T1 - Data Management of Confidential Data Y1 - 2012 A1 - Lagoze, Carl A1 - Block, William C. A1 - Williams, Jeremy A1 - Abowd, John M. A1 - Vilhuber, Lars AB - Data Management of Confidential Data Lagoze, Carl; Block, William C.; Williams, Jeremy; Abowd, John M.; Vilhuber, Lars Social science researchers increasingly make use of data that is confidential because it contains linkages to the identities of people, corporations, etc. The value of this data lies in the ability to join the identifiable entities with external data such as genome data, geospatial information, and the like. However, the confidentiality of this data is a barrier to its utility and curation, making it difficult to fulfill US federal data management mandates and interfering with basic scholarly practices such as validation and reuse of existing results. We describe the complexity of the relationships among data that span a public and private divide. We then describe our work on the CED2AR prototype, a first step in providing researchers with a tool that spans this divide and makes it possible for them to search, access, and cite that data. PB - Cornell University UR - http://hdl.handle.net/1813/30924 ER - TY - RPRT T1 - An Early Prototype of the Comprehensive Extensible Data Documentation and Access Repository (CED2AR) Y1 - 2012 A1 - Block, William C. A1 - Williams, Jeremy A1 - Abowd, John M. A1 - Vilhuber, Lars A1 - Lagoze, Carl AB - An Early Prototype of the Comprehensive Extensible Data Documentation and Access Repository (CED2AR) Block, William C.; Williams, Jeremy; Abowd, John M.; Vilhuber, Lars; Lagoze, Carl This presentation will demonstrate the latest DDI-related technological developments of Cornell University’s $3 million NSF-Census Research Network (NCRN) award, dedicated to improving the documentation, discoverability, and accessibility of public and restricted data from the federal statistical system in the United States. The current internal name for our DDI-based system is the Comprehensive Extensible Data Documentation and Access Repository (CED²AR). CED²AR ingests metadata from heterogeneous sources and supports filtered synchronization between restricted and public metadata holdings. Currently-supported CED²AR “connector workflows” include mechanisms to ingest IPUMS, zero-observation files from the American Community Survey (DDI 2.1), and SIPP Synthetic Beta (DDI 1.2). These disparate metadata sources are all transformed into a DDI 2.5 compliant form and stored in a single repository. In addition, we will demonstrate an extension to DDI 2.5 that allows for the labeling of elements within the schema to indicate confidentiality. This metadata can then be filtered, allowing the creation of derived public use metadata from an original confidential source. This repository is currently searchable online through a prototype application demonstrating the ability to search across previously heterogeneous metadata sources. Presentation at the 4th Annual European DDI User Conference (EDDI12), Norwegian Social Science Data Services, Bergen, Norway, 3 December, 2012 PB - Cornell University UR - http://hdl.handle.net/1813/30922 ER - TY - CHAP T1 - A Proposed Solution to the Archiving and Curation of Confidential Scientific Inputs T2 - Privacy in Statistical Databases Y1 - 2012 A1 - Abowd, John M. A1 - Vilhuber, Lars A1 - Block, William ED - Domingo-Ferrer, Josep ED - Tinnirello, Ilenia KW - Data Archive KW - Data Curation KW - Privacy-preserving Datamining KW - Statistical Disclosure Limitation JF - Privacy in Statistical Databases T3 - Lecture Notes in Computer Science PB - Springer Berlin Heidelberg VL - 7556 SN - 978-3-642-33626-3 UR - http://dx.doi.org/10.1007/978-3-642-33627-0_17 ER - TY - RPRT T1 - A Proposed Solution to the Archiving and Curation of Confidential Scientific Inputs Y1 - 2011 A1 - Abowd, John M. A1 - Vilhuber, Lars A1 - Block, William AB - A Proposed Solution to the Archiving and Curation of Confidential Scientific Inputs Abowd, John M.; Vilhuber, Lars; Block, William We develop the core of a method for solving the data archive and curation problem that confronts the custodians of restricted-access research data and the scientific users of such data. Our solution recognizes the dual protections afforded by physical security and access limitation protocols. It is based on extensible tools and can be easily incorporated into existing instructional materials. PB - Cornell University UR - http://hdl.handle.net/1813/30923 ER -