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

  • Hendrik Wolff
  • Howard Chong
  • Maximilian Auffhammer

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://www.nber.org/papers/w16572.pdf
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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 16572.

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Date of creation: Dec 2010
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Publication status: published as Hendrik Wolff & Howard Chong & Maximilian Auffhammer, 2011. "Classification, Detection and Consequences of Data Error: Evidence from the Human Development Index," Economic Journal, Royal Economic Society, vol. 121(553), pages 843-870, 06.
Handle: RePEc:nbr:nberwo:16572
Note: HE POL
Contact details of provider: Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.
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  1. Angel de la Fuente & Rafael Donénech, 2000. "Human Capital in Growth Regressions: How much Difference Does Data Quality Make?," OECD Economics Department Working Papers 262, OECD Publishing.
  2. MORENO-TERNERO, Juan D. & ROEMER, John E., . "Impartiality, priority, and solidarity in the theory of justice," CORE Discussion Papers RP -1896, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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