Consequences of Data Error in Aggregate Indicators: Evidence from the Human Development Index
36 Pages Posted: 23 May 2008
Date Written: February 2008
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
JEL Classification: O10, C82
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
By A. Craig Burnside and David Dollar
-
Aid, Policies, and Growth: Revisiting the Evidence
By A. Craig Burnside and David Dollar
-
Who Gives Foreign Aid to Whom and Why?
By Alberto F. Alesina and David Dollar
-
Aid Allocation and Poverty Reduction
By David Dollar and Paul Collier
-
Aid and Growth: What Does the Cross-Country Evidence Really Show?
-
Aid and Growth: What Does the Cross-Country Evidence Really Show?
-
New Data, New Doubts: Revisiting 'Aid, Policies, and Growth'
By William Easterly, Ross Levine, ...
-
New Data, New Doubts: A Comment on Burnside and Dollar's "Aid, Policies, and Growth" (2000)
By William Easterly, Ross Levine, ...