Classification, Detection and Consequences of Data Error: Evidence from the Human Development Index
AbstractWe 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 countryâs 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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Bibliographic InfoArticle provided by Royal Economic Society in its journal The Economic Journal.
Volume (Year): 121 (2011)
Issue (Month): 553 (06)
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Other versions of this item:
- Hendrik Wolff & Howard Chong & Maximilian Auffhammer, 2010. "Classification, Detection and Consequences of Data Error: Evidence from the Human Development Index," NBER Working Papers 16572, National Bureau of Economic Research, Inc.
- 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.
- C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
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