IDEAS home Printed from https://ideas.repec.org/p/cen/tnotes/24-06.html
   My bibliography  Save this paper

Implementing Establishment Imputation Procedures for the Administrative Job Frame

Author

Listed:
  • Fil Babalievsky
  • Stephen Tibbets
  • Lawrence Warren
  • Moises Yi

Abstract

The imputation of workers to establishments within firms is a key component of the production of the Job Frame. This paper describes testing and implementation of adapting existing imputation methodologies from LEHD to the W2-based Job Frame. We start by describing the construction of alternative candidate establishment sets for each job within a multi-establishment firm. These alternative sets are based on the varying combination of two existing business frames, the LEHD Employer Characteristics File (ECF) and the Longitudinal Business Database (LBD). Furthermore, we produce additional candidate sets that restrict candidate lists to plausible sets based on establishment distance from the worker’s residence and state of employment information from LEHD. We then feed these alternative candidate sets to the existing LEHD U2W imputation model to assign workers to unique establishments. We test for the quality of the imputations by comparing the imputed industry and state of employment for each worker against information from the American Community Survey (ACS). We show that using candidate sets based primarily on LBD or on ECF or restricting candidate establishments by distance produces imputations with remarkably similar quality.

Suggested Citation

  • Fil Babalievsky & Stephen Tibbets & Lawrence Warren & Moises Yi, 2024. "Implementing Establishment Imputation Procedures for the Administrative Job Frame," CES Technical Notes Series 24-06, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:tnotes:24-06
    as

    Download full text from publisher

    File URL: https://www2.census.gov/ces/tn/CES-TN-2024-06.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-2024-06
    File Function: Confidential main document
    Download Restriction: Researchers need to have obtained appropriate permissions.
    ---><---

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

    More about this item

    Keywords

    LEHD;

    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:24-06. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.