Consequences of Data Error in Aggregate Indicators: Evidence from the Human 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 ‘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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Bibliographic InfoPaper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 3346.
Length: 36 pages
Date of creation: Feb 2008
Date of revision:
Publication status: published as 'Classification, Detection and Consequences of Data Error: Evidence from the Human Development Index' in: Economic Journal, 2011, 121 (553), 843 - 870
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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 humand development index," CUDARE Working Paper Series 1051, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy.
- Wolff, Hendrik & Chong, Howard & Auffhammer, Maximilian, 2008. "Consequences of Data Error in Aggregate Indicators: Evidence from the Human Development Index," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt18s0z7mj, Department of Agricultural & Resource Economics, UC Berkeley.
- 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; Data Access
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