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Classification, Detection and Consequences of Data Error: Evidence from the Human Development Index

  • Hendrik Wolff

    (University of Washington)

  • Howard Chong

    (University of California, Berkeley)

  • Maximilian Auffhammer

    (University of California, Berkeley)

We measure and examine data error in health, education and income statistics used to construct the Human Development Index. We identify three sources of data error which are due to (i) data updating, (ii) formula revisions and (iii) thresholds to classify a country’s development status. We propose a simple statistical framework to calculate country specific measures of data uncertainty and investigate how data error biases rank assignments. We find that up to 34% of countries are misclassified and, by replicating prior studies, we show that key estimated parameters vary by up to 100% due to data error.

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File URL: http://faculty.washington.edu/hgwolff/EJOnlineWebVersionofHDI_Wolffetal2010.pdf
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Paper provided by University of Washington, Department of Economics in its series Working Papers with number UWEC-2008-10-P.

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Date of creation: Jan 2011
Date of revision:
Publication status: Published in Economic Journal, Volume
Handle: RePEc:udb:wpaper:uwec-2008-10-p
Contact details of provider: Postal: Box 353330, Seattle, WA 98193-3330
Web page: http://www.econ.washington.edu/
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  1. Juan D Moreno-Ternero & John E Roemer, 2006. "Impartiality, Priority, and Solidarity in the Theory of Justice," Econometrica, Econometric Society, vol. 74(5), pages 1419-1427, 09.
  2. ?gel de la Fuente & Rafael Dom?ech, . "Human Capital In Growth Regressions: How Much Difference Does Data Quality Make?," UFAE and IAE Working Papers 446.00, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
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