Match bias from earnings imputation in the current population survey: The case of imperfect matching
Match bias from earnings imputation in the current population survey: The case of imperfect matching
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DOI：
10.1086/504276
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发表时间：
2006-07-01
影响因子：
3.8
通讯作者：
Hirsch, Barry T.
中科院分区：
文献类型：
--
作者：
Bollinger, Christopher R.;Hirsch, Barry T.
本文探讨了由盈余估算引起的匹配偏差。工资方程参数估计从混合样本的工人报告和不报告收入，后者分配的捐助者的收入。包括不用作插补匹配标准的属性的回归（例如，例如，在一个实施例中，#21453;的严重偏见。匹配偏差也会出现属性作为匹配标准，但匹配不完美。教育（年龄）的不完美匹配会影响教育（年龄）组内的收入状况，并造成组间的跳跃。假设条件随机缺失，一般解析表达式校正匹配偏差的推导和比较的替代品。重新加权仅受访者样本证明是一种有吸引力的方法。
This article examines match bias arising from earnings imputation. Wage equation parameters are estimated from mixed samples of workers reporting and not reporting earnings, the latter assigned earnings of donors. Regressions including attributes not used as imputation match criteria ( e. g., union) are severely biased. Match bias also arises with attributes used as match criteria but matched imperfectly. Imperfect matching on schooling ( age) flattens earnings profiles within education ( age) groups and creates jumps across groups. Assuming conditional missing at random, a general analytic expression correcting match bias is derived and compared to alternatives. Reweighting a respondent-only sample proves an attractive approach.