Classification, Detection and Consequences of Data Error: Evidence from the Human Development Index

• 成果类型: Article
• 署名作者: Wolff, Hendrik; Chong, Howard; Auffhammer, Maximilian
• 署名单位: University of Washington; University of Washington Seattle; University of California System; University of California Berkeley
• 刊物名称: ECONOMIC JOURNAL
• ISSN/ISSBN: 0013-0133
• DOI: 10.1111/j.1468-0297.2010.02408.x
• 发表日期: 2011
• 页码: 843-870
• 关键词: international comparisons; GROWTH; countries; CONVERGENCE; INEQUALITY; happiness; poverty; aid

摘要

We measure and examine data error in health, education and income statistics used to construct the Human Development Index. We identify three sources of data error which are due to data updating; formula revisions; and thresholds to classify a country's development status. We propose a simple statistical framework to calculate country specific measures of data uncertainty and investigate how data error biases rank assignments. We find that up to 34% of countries are misclassified and, by replicating prior studies, we show that key estimated parameters vary by up to 100% due to data error.