Constructing Confidence Intervals for BIFSG Disparity Estimates.
This article explores the use of Bayesian Improved First Name and Surname Geocoding (BIFSG) to estimate race and ethnicity in data that lack this information. The authors propose a new method to estimate the uncertainty associated with using BIFSG estimates in analyzing disparities. They apply this...
| Publicado en: | AEA Papers & Proceedings Vol. 114; pp. 638 - 644 |
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| Autores principales: | , , |
| Formato: | Artículo |
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American Economic Association
May2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=177441014&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 177441014 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 25740768 LNGQ jtl: AEA Papers & Proceedings issn: 25740768 maglogo: N pubinfo: dt: May2024 vid: 114 pid: 22 pub: American Economic Association artinfo: ui: 177441014 10.1257/pandp.20241035 ppf: 638 ppct: 6 formats: tig: atl: Constructing Confidence Intervals for BIFSG Disparity Estimates. aug: au: DERBY, ELENA DOWD, CONNOR MORTENSON, JACOB affil: Joint Committee on Taxation, US Congress su: Racism Demographic characteristics Confidence intervals Earned income tax credit sug: subj: Racism Demographic characteristics Confidence intervals Earned income tax credit ab: This article explores the use of Bayesian Improved First Name and Surname Geocoding (BIFSG) to estimate race and ethnicity in data that lack this information. The authors propose a new method to estimate the uncertainty associated with using BIFSG estimates in analyzing disparities. They apply this method to estimate differences in earned income tax credit (EITC) dollars claimed among various racial and ethnic groups in the United States. The article also discusses the challenges and sources of uncertainty in BIFSG estimates, including sampling error, measures of race and ethnicity, clustering, and covariance between errors in predicted race and ethnicity. The text suggests a bootstrap procedure to incorporate this uncertainty when estimating differences in outcomes across racial and ethnic groups, which produces more conservative standard errors and confidence intervals. However, it does not address potential statistical bias introduced by BIFSG. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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