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

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Listed:
  • Hendrik Wolff

    (University of Washington)

  • Howard Chong

    (University of California, Berkeley)

  • Maximilian Auffhammer

    (University of California, Berkeley)

Abstract

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.

Suggested Citation

  • Hendrik Wolff & Howard Chong & Maximilian Auffhammer, 2011. "Classification, Detection and Consequences of Data Error: Evidence from the Human Development Index," Working Papers UWEC-2008-10-P, University of Washington, Department of Economics.
    • Handle: RePEc:udb:wpaper:uwec-2008-10-p
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    References listed on IDEAS

    as
    1. Angel de la Fuente & Rafael Doménech, 2006. "Human Capital in Growth Regressions: How Much Difference Does Data Quality Make?," Journal of the European Economic Association, MIT Press, vol. 4(1), pages 1-36, March.
    2. 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, September.
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    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation

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