Controlling for mental health in earnings equations: what do we gain and what do we lose?
Controlling for mental health in earnings equations: what do we gain and what do we lose?
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在收入方程中控制心理健康：我们得到什么，失去什么？
DOI：
10.1002/hec.4730040506
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发表时间：
1995
期刊：
影响因子：
2.1
通讯作者：
Savoca,E
中科院分区：
文献类型：
--
作者：
Savoca,E
本文探讨了在估计工资方程的偏差，可能会出现各种心理健康指标的测量误差-两个主观代理和一个临床评估。结果表明，基于个人是否报告因心理健康相关原因而缺课或工作的自我报告测量导致收入方程中解释变量系数的最小测量误差偏差。两个具体的结果导致这个结论。首先，其测量误差的随机分量的方差是三个指标中最小的，导致心理健康对工资影响的估计偏差最小。其次，在工资方程中随非健康回归变量而变化的系统性报告偏差似乎在这一措施中不存在。因此，这一指标遵循经典的测量误差模型。这意味着当心理健康替代因素被纳入回归时，非健康变量的影响系数估计值总是得到改善，即偏差较小。根据优秀、良好、一般或差的量表进行自我评价时，测量误差的方差最大，因此导致心理健康对收入的影响被大大低估。该测量还包含显著的报告偏差，这些偏差因性别、种族和教育而异--工资方程中的许多非健康回归因子。本文分析的模拟诊断测量中的测量误差方差也很大。因此，这一代理将产生心理健康对收入影响的不良估计。
This paper examines the biases in estimating wage equations that may arise from measurement errors in various mental health indicators—two subjective proxies and one clinical assessment. The results suggest that a self‐reported measure based on whether the individual reported missing school or work for mental‐health‐related reasons leads to the smallest measurement error bias in the coefficients of the explanatory variables in an earnings equation. Two specific results lead to this conclusion. For one, the variance of the random component of its measurement error is the smallest among the three indicators leading to the least biased estimates of the impact of mental health on wages. Second, systematic reporting biases which vary with the non‐health regressors in a wage equation do not appear to exit in this measure. Consequently, this indicator follows the classical measurement error model. This implies that the coefficient estimates of the impact of non‐health variables are always improved, in the sense of having a smaller bias, when this mental health proxy is included in the regression.The measurement error in a self‐evaluation according to the scale of excellent, good, fair, or poor has the largest variance thus leading to a substantial understatement of the impact of mental health on earnings. This measure also contains significant reporting biases that vary with gender, race, and education—many of the non‐health regressors in a wage equation.The measurement error variance in the simulated diagnostic measure analyzed in this paper is also large. Thus this proxy will yield poor estimates of the impact of mental health on earnings.
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