Classification, Detection and Consequences of Data Error: Evidence from the Human Development Index
研究了人类发展指数中健康、教育和收入统计数据的三类错误来源，提出统计框架量化数据不确定性，发现高达34%的国家被错误分类，且关键参数估计因数据错误变化高达100%。
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.