Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics

National statistical agencies around the world publish tabular summaries based on combined employer-employee (ER-EE) data. The privacy of both individuals and business establishments that feature in these data are protected by law in most countries. These data are currently released using a variety of statistical disclosure limitation (SDL) techniques that do not reveal the exact characteristics of particular employers and employees, but lack provable privacy guarantees limiting inferential disclosures. In this work, we present novel algorithms for releasing tabular summaries of linked ER-EE data with formal, provable guarantees of privacy. We show that state-of-the-art differentially private algorithms add too much noise for the output to be useful. Instead, we identify the privacy requirements mandated by current interpretations of the relevant laws, and formalize them using the Pufferfish framework. We then develop new privacy definitions that are customized to ER-EE data and satisfy the statutory privacy requirements. We implement the experiments in this paper on production data gathered by the U.S. Census Bureau. An empirical evaluation of utility for these data shows that for reasonable values of the privacy-loss parameter ϵ≥1, the additive error introduced by our provably private algorithms is comparable, and in some cases better, than the error introduced by existing SDL techniques that have no provable privacy guarantees. For some complex queries currently published, however, our algorithms do not have utility comparable to the existing traditiona

Do labor market networks have an important spatial dimension?

We test for evidence of spatial, residence-based labor market networks. Turnover is lower for workers more connected to their neighbors generally and more connected to neighbors of the same race or ethnic group. Both results are consistent with networks producing better job matches, while the latter could also reflect preferences for working with neighbors of the same race or ethnicity. For earnings, we find a robust positive effect of the overall residence-based network measure, whereas we usually find a negative effect of the same-group measure, suggesting that the overall network measure reflects productivity-enhancing positive network effects, while the same-group measure may capture a non-wage amenity

Social Capital Determinants and Labor Market Networks

We explore the links between determinants of social capital and labor market networks at the neighborhood level. We harness rich data taken from multiple sources, including matched employer-employee data with which we measure the strength of labor market networks, data on neighborhood homogeneity that has previously been tied to social capital, and new data – not previously used in the study of social capital – on the number and location of non-profit sector establishments at the neighborhood level. We use a machine learning algorithm to identify the potential determinants of social capital that best predict neighborhood-level variation in labor market networks. We find evidence suggesting that smaller and less centralized schools, and schools with fewer poor students, foster social capital that builds local labor market networks, as does a larger Republican vote share. The presence of establishments in a number of non-profit-oriented industries are identified as predictive of strong labor market networks, likely because they either provide public goods or facilitate social contacts. These industries include, for example, churches and other religious institutions, fire and rescue services including volunteer fire departments, country clubs and golf courses, labor unions, chamber music groups, hobby clubs, and schools

Spatio-temporal evolution of the West African monsoon during the last deglaciation

On the basis of a multi-proxy data set from the Gulf of Guinea (eastern equatorial Atlantic) we reconstruct the spatio-temporal evolution of the West African monsoon (WAM) and present evidence for a decoupling between latitudinal shifts of the rain belt and WAM intensification. The onset of deglacial monsoon invigoration at ∼16,600 years before present lagged northward migration of a weak rainfall zone by ∼2800 years. Conversely, during the Younger Dryas (YD) time interval, WAM precipitation was severely reduced but we find no evidence for a large-scale retreat of the rainfall front. This observation is not in agreement with the hypothesis of a large-scale shift of the intertropical convergence zone south of the tropical WAM region during the YD. Our results can be better reconciled with the newly emerging concept of a strong influence of Tropical Easterly and African Easterly Jets on modern WAM

Two Perspectives on Commuting: A Comparison of Home to Work Flows Across Job-Linked Survey and Administrative Files

This paper is a newer version of https://hdl.handle.net/1813/50976Commuting flows and workplace employment data have a wide constituency of users including urban and regional planners, social science and transportation researchers, and businesses. The U.S. Census Bureau releases two, national data products that give the magnitude and characteristics of home to work flows. The American Community Survey (ACS) tabulates households’ responses on employment, workplace, and commuting behavior. The Longitudinal Employer-Household Dynamics (LEHD) program tabulates administrative records on jobs in the LEHD Origin-Destination Employment Statistics (LODES). Design differences across the datasets lead to divergence in a comparable statistic: county-to-county aggregate commute flows. To understand differences in the public use data, this study compares ACS and LEHD source files, using identifying information and probabilistic matching to join person and job records. In our assessment, we compare commuting statistics for job frames linked on person, employment status, employer, and workplace and we identify person and job characteristics as well as design features of the data frames that explain aggregate differences. We find a lower rate of within-county commuting and farther commutes in LODES. We attribute these greater distances to differences in workplace reporting and to uncertainty of establishment assignments in LEHD for workers at multi-unit employers. Minor contributing factors include differences in residence location and ACS workplace edits. The results of this analysis and the data infrastructure developed will support further work to understand and enhance commuting statistics in both datasets.This work received support from NSF grant SES-1131848 (NCRN Cornell).Downloads for this item at https://digitalcommons.ilr.cornell.edu/ldi/38/ as of 9/11/2020: 35

Recalculating - How Uncertainty in Local Labor Market Definitions Affects Empirical Findings

The analysis, conclusions, and opinions expressed herein are those of the author(s) alone and do not necessarily represent the views of the U.S. Census Bureau or the Federal Deposit Insurance Corporation. All results have been reviewed to ensure that no confidential information is disclosed, and no confidential data was used in this paper. This document is released to inform interested parties of ongoing research and to encourage discussion of work in progress. Much of the work developing this paper occurred while Mark Kutzbach was an employee of the U.S. Census Bureau.This paper evaluates the use of commuting zones as a local labor market definition. We revisit Tolbert and Sizer (1996) and demonstrate the sensitivity of definitions to two features of the methodology. We show how these features impact empirical estimates using a well-known application of commuting zones. We conclude with advice to researchers using commuting zones on how to demonstrate the robustness of empirical findings to uncertainty in definitions.Vilhuber acknowledges support under NSF Grant #1131848 (NCRN).Downloads for this item at https://digitalcommons.ilr.cornell.edu/ldi/45/ as of 9/11/2020: 20