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Berkeley Unified Numident Mortality Database: Public administrative records for individual-level mortality research

Author

Listed:
  • Casey Breen

    (University of California, Berkeley)

  • Joshua R. Goldstein

    (University of California, Berkeley)

Abstract

Background: While much progress has been made in understanding the demographic determinants of mortality in the United States using individual survey data and aggregate tabulations, the lack of population-level register data is a barrier to further advances in mortality research. With the release of Social Security application (SS-5), claim, and death records, the National Archives and Records Administration (NARA) has created a new administrative data resource for researchers studying mortality. We introduce the Berkeley Unified Numident Mortality Database (BUNMD), a cleaned and harmonized version of these records. This publicly available dataset provides researchers access to over 49 million individual-level mortality records with demographic covariates and fine geographic detail, allowing for high-resolution mortality research. Objective: The purpose of this paper is to describe the BUNMD, discuss statistical methods for estimating mortality differentials based on this deaths-only dataset, and provide case studies illustrating the high-resolution mortality research possible with the BUNMD. Methods: We provide detailed information on our procedure for constructing the BUNMD dataset from the most informative parts of the publicly available Social Security Numident application, claim, and death records. Contribution: The BUNMD is now publicly available, and we anticipate these data will facilitate new avenues of research into the determinants of mortality disparities in the United States.

Suggested Citation

  • Casey Breen & Joshua R. Goldstein, 2022. "Berkeley Unified Numident Mortality Database: Public administrative records for individual-level mortality research," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(5), pages 111-142.
    • Handle: RePEc:dem:demres:v:47:y:2022:i:5
    DOI: 10.4054/DemRes.2022.47.5
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    References listed on IDEAS

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    1. Lior Rennert & Sharon X. Xie, 2018. "Cox regression model with doubly truncated data," Biometrics, The International Biometric Society, vol. 74(2), pages 725-733, June.
    2. Steven Ruggles, 2014. "Big Microdata for Population Research," Demography, Springer;Population Association of America (PAA), vol. 51(1), pages 287-297, February.
    3. Alexander, Monica, 2018. "Deaths without denominators: using a matched dataset to study mortality patterns in the United States," SocArXiv q79ye, Center for Open Science.
    4. William Greene, 2005. "Censored Data and Truncated Distributions," Working Papers 05-08, New York University, Leonard N. Stern School of Business, Department of Economics.
    5. David Card & Carlos Dobkin & Nicole Maestas, 2008. "The Impact of Nearly Universal Insurance Coverage on Health Care Utilization: Evidence from Medicare," American Economic Review, American Economic Association, vol. 98(5), pages 2242-2258, December.
    6. Trifon Missov & Vladimir Canudas-Romo & James W. Vaupel & Adam Lenart & Laszlo Nemeth, 2015. "The Gompertz force of mortality in terms of the modal age at death," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 32(36), pages 1031-1048.
    7. Jennifer Karas Montez & Anna Zajacova & Mark D. Hayward & Steven H. Woolf & Derek Chapman & Jason Beckfield, 2019. "Educational Disparities in Adult Mortality Across U.S. States: How Do They Differ, and Have They Changed Since the Mid-1980s?," Demography, Springer;Population Association of America (PAA), vol. 56(2), pages 621-644, April.
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    Cited by:

    1. Risto Conte Keivabu & Ugofilippo Basellini & Emilio Zagheni, 2022. "Racial disparities in deaths related to extreme temperatures in the United States between 1993 and 2005," MPIDR Working Papers WP-2022-028, Max Planck Institute for Demographic Research, Rostock, Germany.
    2. Joshua R. Goldstein & Maria Osborne & Serge Atherwood & Casey F. Breen, 2023. "Mortality Modeling of Partially Observed Cohorts Using Administrative Death Records," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(3), pages 1-20, June.
    3. Casey F. Breen, 2023. "Late-Life Changes in Ethnoracial Self-identification: Evidence from Social Security Administrative Data," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(1), pages 1-18, February.

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    More about this item

    Keywords

    administrative data; mortality; United States of America; statistical methodology;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

    Statistics

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