Clustered into control: Heterogeneous causal impacts of water infrastructure failure.
We estimate economic impacts from decaying water infrastructure in the United States. Using water main breaks in Washington, DC, and a yearlong panel of hourly traffic speeds, we estimate causal effects of water main failures on traffic congestion. We use k‐means clustering to create clusters of str...
| Publicado en: | Economic Inquiry Vol. 59; no. 3; pp. 1417 - 1440 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Wiley-Blackwell
Jul2021
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| 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=150539874&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 150539874 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00952583 EIQ jtl: Economic Inquiry issn: 00952583 maglogo: Y pubinfo: dt: Jul2021 vid: 59 iid: 3 pid: 480 pub: Wiley-Blackwell artinfo: ui: 150539874 10.1111/ecin.12975 ppf: 1417 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P size: 1.7MB tig: atl: Clustered into control: Heterogeneous causal impacts of water infrastructure failure. aug: au: Cunningham, Brandon LaRiviere, Jacob Wichman, Casey J. affil: Institute for Health Metrics and Evaluation, University of Washington, Seattle Washington,, USA Office of the Chief Economist, Microsoft, Redmond Washington,, USA University of Tennessee, Knoxville Tennessee,, USA School of Economics, Georgia Institute of Technology, Atlanta Georgia,, USA Resources for the Future, Washington District of Columbia,, USA su: Washington (D.C.) Water-pipes Traffic congestion Traffic patterns K-means clustering Traffic speed sug: subj: Washington (D.C.) Water and Sewer Line and Related Structures Construction Water-pipes Traffic congestion Traffic patterns K-means clustering Traffic speed keyword: k‐means clustering program evaluation traffic congestion water infrastructure water main breaks k‐means clustering program evaluation traffic congestion water infrastructure water main breaks ab: We estimate economic impacts from decaying water infrastructure in the United States. Using water main breaks in Washington, DC, and a yearlong panel of hourly traffic speeds, we estimate causal effects of water main failures on traffic congestion. We use k‐means clustering to create clusters of streets that are similar to each other: treated observations are compared to other units in the cluster. We identify heterogeneous treatment effects algorithmically while retaining straightforward standard error calculations. We find strong evidence of heterogeneous treatment effects across clusters but small welfare impacts of water main breaks on traffic patterns overall. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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