全球智库信息集成服务系统(G2TT): Sensitivity to Missing Data Assumptions: Theory and An Evaluation of the U.S. Wage Structure

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来源类型	Working Paper
规范类型	报告
DOI	10.3386/w15716
来源ID	Working Paper 15716
	Sensitivity to Missing Data Assumptions: Theory and An Evaluation of the U.S. Wage Structure
	Patrick Kline; Andres Santos
发表日期	2010-02-04
出版年	2010
语种	英语
摘要	This paper develops methods for assessing the sensitivity of empirical conclusions regarding conditional distributions to departures from the missing at random (MAR) assumption. We index the degree of non-ignorable selection governing the missingness process by the maximal Kolmogorov-Smirnov (KS) distance between the distributions of missing and observed outcomes across all values of the covariates. Sharp bounds on minimum mean square approximations to conditional quantiles are derived as a function of the nominal level of selection considered in the sensitivity analysis and a weighted bootstrap procedure is developed for conducting inference. Using these techniques, we conduct an empirical assessment of the sensitivity of observed earnings patterns in U.S. Census data to deviations from the MAR assumption. We find that the well-documented increase in the returns to schooling between 1980 and 1990 is relatively robust to deviations from the missing at random assumption except at the lowest quantiles of the distribution, but that conclusions regarding heterogeneity in returns and changes in the returns function between 1990 and 2000 are very sensitive to departures from ignorability.
主题	Econometrics ; Estimation Methods ; Labor Economics ; Labor Compensation
URL	https://www.nber.org/papers/w15716
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/573390
推荐引用方式 GB/T 7714	Patrick Kline,Andres Santos. Sensitivity to Missing Data Assumptions: Theory and An Evaluation of the U.S. Wage Structure. 2010.

条目包含的文件
文件名称/大小	资源类型	版本类型	开放类型	使用许可
w15716.pdf（980KB）	智库出版物		限制开放	CC BY-NC-SA	浏览