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

Hendrik Wolff (), Howard Chong () and Maximilian Auffhammer

Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley

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.

Keywords: Measurement Error; International Comparative Statistics (search for similar items in EconPapers)
Date: 2008-01-01
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Citations: View citations in EconPapers (2)

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Working Paper: Consequences of Data Error in Aggregate Indicators: Evidence from the Human Development Index (2008)
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