IDEAS home Printed from https://ideas.repec.org/p/cen/tnotes/18-03.html
   My bibliography  Save this paper

The Creation and Use of the SIPP Synthetic Beta v7.0

Author

Listed:
  • Gary Benedetto
  • Jordan C. Stanley
  • Evan Totty

Abstract

This paper reports on the creation of a fully synthetic Census Bureau data product called the SIPP Synthetic Beta (SSB). It serves as an update to a previous paper, Benedetto, Stinson, and Abowd (2013), which described version 5.0 of the SSB. Our purpose is to inform users of the SSB about how the file was created and to provide an example of the application of data synthesis methods to those doing research in this area. We also hope to provide some guidance for other organizations which might be interested in creating their own synthetic data products.

Suggested Citation

  • Gary Benedetto & Jordan C. Stanley & Evan Totty, 2018. "The Creation and Use of the SIPP Synthetic Beta v7.0," CES Technical Notes Series 18-03, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:tnotes:18-03
    as

    Download full text from publisher

    File URL: https://www2.census.gov/ces/tn/CES-TN-2018-03.pdf
    File Function: Abstract
    Download Restriction: CES Technical Notes may contain confidential data and, thereby, disclosure is prohibited. Researchers on approved projects (to apply for access, please see https://www.census.gov/ces/rdcresearch/howtoapply.html) with the correct permissions can request full text notes from CES.Technical.Notes.List@census.gov.
    File URL: https://www.census.gov/about/adrm/ced/apply-for-access.html?CES-TN-2018-03
    File Function: First version, 2018
    Download Restriction: CES Technical Notes may contain confidential data and, thereby, disclosure is prohibited. Researchers on approved projects (to apply for access, please see https://www.census.gov/ces/rdcresearch/howtoapply.html) with the correct permissions can request full text notes from CES.Technical.Notes.List@census.gov.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Satkartar K. Kinney & Jerome P. Reiter & Arnold P. Reznek & Javier Miranda & Ron S. Jarmin & John M. Abowd, 2011. "Towards Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database," International Statistical Review, International Statistical Institute, vol. 79(3), pages 362-384, December.
    2. Reiter, Jerome P. & Raghunathan, Trivellore E., 2007. "The Multiple Adaptations of Multiple Imputation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1462-1471, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohitosh Kejriwal & Xiaoxiao Li & Evan Totty, 2020. "Multidimensional skills and the returns to schooling: Evidence from an interactive fixed‐effects approach and a linked survey‐administrative data set," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 548-566, August.
    2. Mohitosh Kejriwal & Xiaoxiao Li & Evan Totty, 2019. "Multidemsional Skills and Returns to Schooling: Evidence from an Interactive Fixed Effects Aproach and a Linked Survey-Administrative Dataset," Purdue University Economics Working Papers 1316, Purdue University, Department of Economics.
    3. Hampton, Matt & Totty, Evan, 2023. "Minimum wages, retirement timing, and labor supply," Journal of Public Economics, Elsevier, vol. 224(C).
    4. John M. Abowd & Ian M. Schmutte & William N. Sexton & Lars Vilhuber, 2019. "Why the Economics Profession Must Actively Participate in the Privacy Protection Debate," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 397-402, May.
    5. Melissa C. Chow & Teresa C. Fort & Christopher Goetz & Nathan Goldschlag & James Lawrence & Elisabeth Ruth Perlman & Martha Stinson & T. Kirk White, 2021. "Redesigning the Longitudinal Business Database," NBER Working Papers 28839, National Bureau of Economic Research, Inc.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Satkartar K. Kinney & Jerome P. Reiter & Javier Miranda, 2014. "Improving The Synthetic Longitudinal Business Database," Working Papers 14-12, Center for Economic Studies, U.S. Census Bureau.
    2. Joseph W. Sakshaug & Trivellore E. Raghunathan, 2014. "Generating synthetic microdata to estimate small area statistics in the American Community Survey," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 15(3), pages 341-368, June.
    3. Nowok, Beata & Raab, Gillian M. & Dibben, Chris, 2016. "synthpop: Bespoke Creation of Synthetic Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i11).
    4. Hang J. Kim & Jerome P. Reiter & Alan F. Karr, 2018. "Simultaneous edit-imputation and disclosure limitation for business establishment data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 63-82, January.
    5. Drechsler, Jörg & Reiter, Jerome P., 2011. "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3232-3243, December.
    6. Little Roderick J., 2013. "Discussion," Journal of Official Statistics, Sciendo, vol. 29(3), pages 363-366, June.
    7. Klein Martin & Sinha Bimal, 2013. "Statistical Analysis of Noise-Multiplied Data Using Multiple Imputation," Journal of Official Statistics, Sciendo, vol. 29(3), pages 425-465, June.
    8. Ron S. Jarmin & John M. Abowd & Robert Ashmead & Ryan Cumings-Menon & Nathan Goldschlag & Michael B. Hawes & Sallie Ann Keller & Daniel Kifer & Philip Leclerc & Jerome P. Reiter & Rolando A. Rodrígue, 2023. "An in-depth examination of requirements for disclosure risk assessment," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(43), pages 2220558120-, October.
    9. repec:cup:judgdm:v:15:y:2020:i:5:p:798-806 is not listed on IDEAS
    10. Yajuan Si & Jerome P. Reiter, 2013. "Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-Scale Assessment Surveys," Journal of Educational and Behavioral Statistics, , vol. 38(5), pages 499-521, October.
    11. Woodcock, Simon D. & Benedetto, Gary, 2009. "Distribution-preserving statistical disclosure limitation," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4228-4242, October.
    12. David R. Munro, 2021. "Consumer Behavior and Firm Volatility," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(4), pages 845-873, June.
    13. Tatiana Komarova & Denis Nekipelov & Evgeny Yakovlev, 2018. "Identification, data combination, and the risk of disclosure," Quantitative Economics, Econometric Society, vol. 9(1), pages 395-440, March.
    14. Jared S. Murray & Jerome P. Reiter, 2016. "Multiple Imputation of Missing Categorical and Continuous Values via Bayesian Mixture Models With Local Dependence," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1466-1479, October.
    15. Yulia V. Marchenko & Jerome P. Reiter, 2009. "Improved degrees of freedom for multivariate significance tests obtained from multiply imputed, small-sample data," Stata Journal, StataCorp LP, vol. 9(3), pages 388-397, September.
    16. Martin Klein & Ricardo Moura & Bimal Sinha, 2021. "Multivariate Normal Inference based on Singly Imputed Synthetic Data under Plug-in Sampling," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 273-287, May.
    17. Rashid, S. & Mitra, R. & Steele, R.J., 2015. "Using mixtures of t densities to make inferences in the presence of missing data with a small number of multiply imputed data sets," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 84-96.
    18. Ori Heffetz & Katrina Ligett, 2014. "Privacy and Data-Based Research," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 75-98, Spring.
    19. Daniel H. Weinberg & John M. Abowd & Robert F. Belli & Noel Cressie & David C. Folch & Scott H. Holan & Margaret C. Levenstein & Kristen M. Olson & Jerome P. Reiter & Matthew D. Shapiro & Jolene Smyth, 2017. "Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System?," Working Papers 17-59r, Center for Economic Studies, U.S. Census Bureau.
    20. Humera Razzak & Christian Heumann, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    21. Razzak Humera & Heumann Christian, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.

    More about this item

    Keywords

    SIPP Synthetic Beta;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cen:tnotes:18-03. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Danielle H. Sandler (email available below). General contact details of provider: https://edirc.repec.org/data/cesgvus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.