Estimating Income Poverty in the Presence of Missing Data and Measurement Error
提出利用工具变量或单调工具变量假设，推导贫困率的上界和下界，以解决调查数据中缺失数据和测量误差导致的贫困率估计偏差，并基于欧洲社区家庭面板数据对10个国家的贫困率进行比较。
Reliable measures of poverty are an essential statistical tool for public policies aimed at reducing poverty. In this article we consider the reliability of income poverty measures based on survey data which are typically plagued by missing data and measurement error. Neglecting these problems can bias the estimated poverty rates. We show how to derive upper and lower bounds for the population poverty rate using the sample evidence, an upper bound on the probability of misclassifying people into poor and nonpoor, and instrumental or monotone instrumental variable assumptions. By using the European Community Household Panel, we compute bounds for the poverty rate in 10 European countries and study the sensitivity of poverty comparisons across countries to missing data and measurement error problems. Supplemental materials for this article may be downloaded from the JBES website.