Consequences of data error in aggregate indicators : evidence from the humand development index
AbstractThis 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 âlowâ, âmediumâ, or âhighâ 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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Date of creation: 2008
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Other versions of this item:
- Wolff, Hendrik & Chong, Howard & Auffhammer, Maximilian, 2008. "Consequences of Data Error in Aggregate Indicators: Evidence from the Human Development Index," IZA Discussion Papers 3346, Institute for the Study of Labor (IZA).
- 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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