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Consequences of Data Error in Aggregate Indicators: Evidence from the Human Development Index

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Author Info
Wolff, Hendrik () (University of Washington)
Chong, Howard () (University of California, Berkeley)
Auffhammer, Maximilian () (University of California, Berkeley)

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Abstract

This paper examines the consequences of data error in data series used to construct aggregate indicators. Using the most popular indicator of country level economic development, the Human Development Index (HDI), we identify three separate sources of data error. We propose a simple statistical framework to investigate how data error may bias rank assignments and identify two striking consequences for the HDI. First, using the cutoff values used by the United Nations to assign a country as ‘low’, ‘medium’, or ‘high’ developed, we find that currently up to 45% of developing countries are misclassified. Moreover, by replicating prior development/macroeconomic studies, we find that key estimated parameters such as Gini coefficients and speed of convergence measures vary by up to 100% due to data error.

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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 3346.

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Length: 36 pages
Date of creation: Feb 2008
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Handle: RePEc:iza:izadps:dp3346

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Related research
Keywords: measurement error international comparative statistics

Find related papers by JEL classification:
O10 - Economic Development, Technological Change, and Growth - - Economic Development - - - General
C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data

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References listed on IDEAS
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  6. Alesina, Alberto & Dollar, David, 2000. " Who Gives Foreign Aid to Whom and Why?," Journal of Economic Growth, Springer, vol. 5(1), pages 33-63, March. [Downloadable!] (restricted)
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  7. Alberto Alesina & Beatrice Weder, 2002. "Do Corrupt Governments Receive Less Foreign Aid?," American Economic Review, American Economic Association, vol. 92(4), pages 1126-1137, September. [Downloadable!] (restricted)
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  8. Kenneth Y. Chay & Patrick J. McEwan & Miguel Urquiola, 2005. "The Central Role of Noise in Evaluating Interventions that Use Test Scores to Rank Schools," American Economic Review, American Economic Association, vol. 95(4), pages 1237-1258, September. [Downloadable!] (restricted)
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