%0 Journal Article %D forthcoming %T The Response of Consumer Spending to Changes in Gasoline Prices %A Gelman, Michael %A Gorodnichenko, Yuriy %A Kariv, Shachar %A Koustas, Dmitri %A Shapiro, Matthew D %A Silverman, Daniel %A Tadelis, Steven %X This paper estimates how overall consumer spending responds to changes in gasoline prices. It uses the differential impact across consumers of the sudden, large drop in gasoline prices in 2014 for identification. This estimation strategy is implemented using comprehensive, daily transaction-level data for a large panel of individuals. The estimated marginal propensity to consume (MPC) is approximately one, a higher estimate than estimates found in less comprehensive or well-measured data. This estimate takes into account the elasticity of demand for gasoline and potential slow adjustment to changes in prices. The high MPC implies that changes in gasoline prices have large aggregate effects. %G eng %0 Journal Article %D forthcoming %T Understanding Household Consumption and Saving Behavior using Account Data %A Gelman, Michael %G eng %0 Report %D 2017 %T Formal Privacy Models and Title 13 %A Nissim, Kobbi %A Gasser, Urs %A Smith, Adam %A Vadhan, Salil %A O'Brien, David %A Wood, Alexandra %X Formal Privacy Models and Title 13 Nissim, Kobbi; Gasser, Urs; Smith, Adam; Vadhan, Salil; O'Brien, David; Wood, Alexandra A new collaboration between academia and the Census Bureau to further the Bureau’s use of formal privacy models. %I NCRN Coordinating Office %G eng %U http://hdl.handle.net/1813/52164 %9 Preprint %0 Report %D 2017 %T NCRN Meeting Spring 2017: Formal Privacy Models and Title 13 %A Nissim, Kobbi %A Gasser, Urs %A Smith, Adam %A Vadhan, Salil %A O'Brien, David %A Wood, Alexandra %X NCRN Meeting Spring 2017: Formal Privacy Models and Title 13 Nissim, Kobbi; Gasser, Urs; Smith, Adam; Vadhan, Salil; O'Brien, David; Wood, Alexandra A new collaboration between academia and the Census Bureau to further the Bureau’s use of formal privacy models. %I NCRN Coordinating Office %G eng %U http://hdl.handle.net/1813/52164 %9 Preprint %0 Report %D 2017 %T Two Perspectives on Commuting: A Comparison of Home to Work Flows Across Job-Linked Survey and Administrative Files %A Green, Andrew %A Kutzbach, Mark J. %A Vilhuber, Lars %X Two Perspectives on Commuting: A Comparison of Home to Work Flows Across Job-Linked Survey and Administrative Files Green, Andrew; Kutzbach, Mark J.; Vilhuber, Lars Commuting 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. %I Cornell University %G eng %U http://hdl.handle.net/1813/52611 %9 Preprint %0 Journal Article %J Proceedings of the 2017 ACM International Conference on Management of Data %D 2017 %T Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics %A Samuel Haney %A Ashwin Machanavajjhala %A John M. Abowd %A Matthew Graham %A Mark Kutzbach %X 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 traditional SDL algorithms. Those queries are fodder for future research. %B Proceedings of the 2017 ACM International Conference on Management of Data %@ 978-1-4503-4197-4 %G eng %U http://dl.acm.org/citation.cfm?doid=3035918.3035940 %R 10.1145/3035918.3035940 %0 Report %D 2017 %T Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics %A Haney, Samuel %A Machanavajjhala, Ashwin %A Abowd, John M %A Graham, Matthew %A Kutzbach, Mark %A Vilhuber, Lars %X Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics Haney, Samuel; Machanavajjhala, Ashwin; Abowd, John M; Graham, Matthew; Kutzbach, Mark; Vilhuber, Lars National statistical agencies around the world publish tabular summaries based on combined employeremployee (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 traditional %I Cornell University %G eng %U http://hdl.handle.net/1813/49652 %9 Preprint %0 Report %D 2016 %T Hours Off the Clock %A Green, Andrew %X Hours Off the Clock Green, Andrew To what extent do workers work more hours than they are paid for? The relationship between hours worked and hours paid, and the conditions under which employers can demand more hours “off the clock,” is not well understood. The answer to this question impacts worker welfare, as well as wage and hour regulation. In addition, work off the clock has important implications for the measurement and cyclical movement of productivity and wages. In this paper, I construct a unique administrative dataset of hours paid by employers linked to a survey of workers on their reported hours worked to measure work off the clock. Using cross-sectional variation in local labor markets, I find only a small cyclical component to work off the clock. The results point to labor hoarding rather than efficiency wage theory, indicating work off the clock cannot explain the counter-cyclical movement of productivity. I find workers employed by small firms, and in industries with a high rate of wage and hour violations are associated with larger differences in hours worked than hours paid. These findings suggest the importance of tracking hours of work for enforcement of labor regulations. %I Cornell University %G eng %U http://hdl.handle.net/1813/52610 %9 Preprint %0 Book Section %B Handbook of Uncertainty Quantification %D 2015 %T Hierarchcial models for uncertainty quantification: An overview %A Wikle, C.K. %E Ghanem, R. %E Higdon, D. %E Owhadi, H. %B Handbook of Uncertainty Quantification %I Springer %G eng %0 Report %D 2015 %T How individuals smooth spending: Evidence from the 2013 government shutdown using account data %A Gelman, Michael %A Kariv, Shachar %A Shapiro, Matthew D %A Silverman, Dan %A Tadelis, Steven %X Using comprehensive account records, this paper examines how individuals adjusted spending and saving in response to a temporary drop in income due to the 2013 U.S. government shutdown. The shutdown cut paychecks by 40% for affected employees, which was recovered within 2 weeks. Though the shock was short-lived and completely reversed, spending dropped sharply implying a naïve estimate of the marginal propensity to spend of 0.58. This estimate overstates how consumption responded. While many individuals had low liquidity, they used multiple strategies to smooth consumption including delay of recurring payments such as mortgages and credit card balances. %I National Bureau of Economic Research %G eng %0 Journal Article %J Statistics in Medicine %D 2015 %T Multiple imputation for harmonizing longitudinal non-commensurate measures in individual participant data meta-analysis %A Siddique, J. %A Reiter, J. P. %A Brincks, A. %A Gibbons, R. %A Crespi, C. %A Brown, C. H. %B Statistics in Medicine %G eng %U http://onlinelibrary.wiley.com/doi/10.1002/sim.6562/abstract %R 10.1002/sim.6562 %0 Report %D 2015 %T NCRN Meeting Spring 2015: Comment on: Can Government-Academic Partnerships Help Secure the Future of the Federal Statistical System? Examples from the NSF-Census Research Network %A Groshen, Erica L. %X NCRN Meeting Spring 2015: Comment on: Can Government-Academic Partnerships Help Secure the Future of the Federal Statistical System? Examples from the NSF-Census Research Network Groshen, Erica L. Public Seminar Presentation by Erica L. Groshen at the Spring 2015 NCRN/CNSTAT Meetings %I NCRN Coordinating Office %G eng %U http://hdl.handle.net/1813/40187 %9 Preprint %0 Journal Article %J Journal of Disability Policy Studies %D 2014 %T Deprivation Among U.S. Children With Disabilities Who Receive Supplemental Security Income %A Ghosth, S. %A Parish, S. L. %B Journal of Disability Policy Studies %G eng %R 10.1177/1044207314539011 %0 Journal Article %J Science %D 2014 %T Harnessing Naturally Occurring Data to Measure the Response of Spending to Income %A Gelman, M. %A Kariv, S. %A Shapiro, M.D. %A Silverman, D. %A Tadelis, S. %X This paper presents a new data infrastructure for measuring economic activity. The infrastructure records transactions and account balances, yielding measurements with scope and accuracy that have little precedent in economics. The data are drawn from a diverse population that overrepresents males and younger adults but contains large numbers of underrepresented groups. The data infrastructure permits evaluation of a benchmark theory in economics that predicts that individuals should use a combination of cash management, saving, and borrowing to make the timing of income irrelevant for the timing of spending. As in previous studies and in contrast to the predictions of the theory, there is a response of spending to the arrival of anticipated income. The data also show, however, that this apparent excess sensitivity of spending results largely from the coincident timing of regular income and regular spending. The remaining excess sensitivity is concentrated among individuals with less liquidity. Link to data at Berkeley Econometrics Lab (EML): https://eml.berkeley.edu/cgi-bin/HarnessingDataScience2014.cgi %B Science %V 345 %G eng %U http://www.sciencemag.org/content/345/6193/212.full %N 11 %) www.sciencemag.org/cgi/rapidpdf/345/6193/212?ijkey=053Dv8UisTohY&keytype=ref&siteid=sci %& 212-215 %R 10.1126/science.1247727 %0 Journal Article %J Statistics in Medicine %D 2014 %T Imputation of confidential data sets with spatial locations using disease mapping models %A T. Paiva %A A. Chakraborty %A J.P. Reiter %A A.E. Gelfand %B Statistics in Medicine %V 33 %P 1928-1945 %G eng %0 Report %D 2014 %T NCRN Meeting Spring 2014: Adaptive Protocols and the DDI 4 Process Model %A Greenfield, Jay %A Kuan, Sophia %X NCRN Meeting Spring 2014: Adaptive Protocols and the DDI 4 Process Model Greenfield, Jay; Kuan, Sophia Presentation from NCRN Spring 2014 meeting %I NCRN Coordinating Office %G eng %U http://hdl.handle.net/1813/36393 %9 Preprint %0 Report %D 2014 %T NCRN Meeting Spring 2014: Summer Working Group for Employer List Linking (SWELL) %A Gathright, Graton %A Kutzbach, Mark %A Mccue, Kristin %A McEntarfer, Erika %A Monti, Holly %A Trageser, Kelly %A Vilhuber, Lars %A Wasi, Nada %A Wignall, Christopher %X NCRN Meeting Spring 2014: Summer Working Group for Employer List Linking (SWELL) Gathright, Graton; Kutzbach, Mark; Mccue, Kristin; McEntarfer, Erika; Monti, Holly; Trageser, Kelly; Vilhuber, Lars; Wasi, Nada; Wignall, Christopher Presentation for NCRN Spring 2014 meeting %I NCRN Coordinating Office %G eng %U http://hdl.handle.net/1813/36396 %9 Preprint %0 Generic %D 2014 %T NewsViews: An Automated Pipeline for Creating Custom Geovisualizations for News %A Gao, T. %A Hullman, J. %A Adar, E. %A Hect, B. %A Diakopoulos, N. %X Interactive visualizations add rich, data-based context to online news articles. Geographic maps are currently the most prevalent form of these visualizations. Unfortunately, designers capable of producing high-quality, customized geovisualizations are scarce. We present NewsViews, a novel automated news visualization system that generates interactive, annotated maps without requiring professional designers. NewsViews’ maps support trend identification and data comparisons relevant to a given news article. The NewsViews system leverages text mining to identify key concepts and locations discussed in articles (as well as po-tential annotations), an extensive repository of “found” databases, and techniques adapted from cartography to identify and create visually “interesting” thematic maps. In this work, we develop and evaluate key criteria in automatic, annotated, map generation and experimentally validate the key features for successful representations (e.g., relevance to context, variable selection, "interestingness" of representation and annotation quality). %G eng %U http://cond.org/newsviews.html %R 10.1145/2556288.2557228 %0 Conference Paper %B Proceedings of the Workshop on Usable Security (USEC) %D 2014 %T Spiny CACTOS: OSN Users Attitudes and Perceptions Towards Cryptographic Access Control Tools %A Balsa, E., %A Brandimarte, L., %A Acquisti, A., %A Diaz, C., %A Gürses, S. %B Proceedings of the Workshop on Usable Security (USEC) %G eng %U https://www.internetsociety.org/doc/spiny-cactos-osn-users-attitudes-and-perceptions-towards-cryptographic-access-control-tools %0 Conference Paper %B GIScience Workshop on Uncertainty Visualization %D 2014 %T Supporting Planners' Work with Uncertain Demographic Data %A Griffin, A. L. %A Spielman, S. E. %A Jurjevich, J. %A Merrick, M. %A Nagle, N. N. %A Folch, D. C. %B GIScience Workshop on Uncertainty Visualization %V 23 %G eng %U http://cognitivegiscience.psu.edu/uncertainty2014/papers/griffin_demographic.pdf. %0 Conference Paper %B Proceedings of IEEE VIS 2014 %D 2014 %T Supporting Planners' work with Uncertain Demographic Data %A Griffin, A. L. %A Spielman, S. E. %A Nagle, N. N. %A Jurjevich, J. %A Merrick, M. %A Folch, D. C. %B Proceedings of IEEE VIS 2014 %I Proceedings of IEEE VIS 2014 %P 9–14 %G eng %U http://cognitivegiscience.psu.edu/uncertainty2014/papers/griffin_demographic.pdf %0 Book Section %B Online Panel Surveys: An Interdisciplinary Approach %D 2014 %T The Untold Story of Multi-Mode (Online and Mail) Consumer Panels: From Optimal Recruitment to Retention and Attrition %A McCutcheon, Allan L. %A Rao, K., %A Kaminska, O. %E Callegaro, M. %E Baker, R. %E Bethlehem, J. %E Göritz, A. %E Krosnick, J. %E Lavrakas, P. %B Online Panel Surveys: An Interdisciplinary Approach %I Wiley %G eng %R 10.1002/9781118763520.ch5 %0 Conference Paper %B Metadata and Semantics Research %D 2013 %T Encoding Provenance Metadata for Social Science Datasets %A Lagoze, Carl %A Willliams, Jeremy %A Vilhuber, Lars %E Garoufallou, Emmanouel %E Greenberg, Jane %K DDI %K eSocial Science %K Metadata %K Provenance %B Metadata and Semantics Research %S Communications in Computer and Information Science %I Springer International Publishing %V 390 %P 123-134 %@ 978-3-319-03436-2 %G eng %U http://dx.doi.org/10.1007/978-3-319-03437-9_13 %R 10.1007/978-3-319-03437-9_13 %0 Journal Article %J Statistica Sinica %D 2013 %T On estimation of mean squared errors of benchmarked and empirical bayes estimators %A Rebecca C. Steorts %A Malay Ghosh %B Statistica Sinica %V 23 %P 749–767 %G eng %0 Journal Article %J TEST %D 2013 %T Two-stage Bayesian benchmarking as applied to small area estimation %A Rebecca C. Steorts %A Malay Ghosh %K small area estimation %B TEST %V 22 %8 2013 %G eng %N 4 %& 670 %0 Conference Paper %B 2012 Joint Statistical Meetings %D 2012 %T On Estimation of Mean Squared Errors of Benchmarked and Empirical Bayes Estimators %A Rebecca C. Steorts %A Malay Ghosh %B 2012 Joint Statistical Meetings %C San Diego, CA %8 August %G eng %0 Journal Article %J Annals of Applied Statistics %D 0 %T Biomass prediction using density dependent diameter distribution models %A Schliep, E.M. %A A.E. Gelfand %A J.S. Clark %A B.J. Tomasek %X Prediction of aboveground biomass, particularly at large spatial scales, is necessary for estimating global-scale carbon sequestration. Since biomass can be measured only by sacrificing trees, total biomass on plots is never observed. Rather, allometric equations are used to convert individual tree diameter to individual biomass, perhaps with noise. The values for all trees on a plot are then summed to obtain a derived total biomass for the plot. Then, with derived total biomasses for a collection of plots, regression models, using appropriate environmental covariates, are employed to attempt explanation and prediction. Not surprisingly, when out-of-sample validation is examined, such a model will predict total biomass well for holdout data because it is obtained using exactly the same derived approach. Apart from the somewhat circular nature of the regression approach, it also fails to employ the actual observed plot level response data. At each plot, we observe a random number of trees, each with an associated diameter, producing a sample of diameters. A model based on this random number of tree diameters provides understanding of how environmental regressors explain abundance of individuals, which in turn explains individual diameters. We incorporate density dependence because the distribution of tree diameters over a plot of fixed size depends upon the number of trees on the plot. After fitting this model, we can obtain predictive distributions for individual-level biomass and plot-level total biomass. We show that predictive distributions for plot-level biomass obtained from a density-dependent model for diameters will be much different from predictive distributions using the regression approach. Moreover, they can be more informative for capturing uncertainty than those obtained from modeling derived plot-level biomass directly. We develop a density-dependent diameter distribution model and illustrate with data from the national Forest Inventory and Analysis (FIA) database. We also describe how to scale predictions to larger spatial regions. Our predictions agree (in magnitude) with available wisdom on mean and variation in biomass at the hectare scale. %B Annals of Applied Statistics %V 11 %P 340-361 %G eng %U https://projecteuclid.org/euclid.aoas/1491616884 %N 1 %0 Generic %D 0 %T The Effects of Respondent and Question Characteristics on Respondent Behaviors %A Ganshert, Amanda %A Olson, Kristen %A Smyth, Jolene %G eng