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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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Article provided by Royal Economic Society in its journal The Economic Journal.

Volume (Year): 121 (2011)
Issue (Month): 553 (06)
Pages: 843-870

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Handle: RePEc:ecj:econjl:v:121:y:2011:i:553:p:843-870
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  1. 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).
  2. 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.
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