Applying the Mincer Wage Equation to Japanese Data (Japanese)
Applying the Mincer Wage Equation to Japanese Data (Japanese)
复制标题
将明瑟工资方程应用于日本数据（日语）
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Daiji Kawaguchi
中科院分区：
文献类型：
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作者：
Daiji Kawaguchi
本文指出了使用日本数据估算明瑟工资方程时的几个注意事项。使用 2005-2008 年工资结构基本调查的微观数据对明瑟工资方程进行的估计揭示了以下六点作为注意事项。由于强制退休，工资在 60 岁时就不连续了。工资率是方程的因变量，应进行对数转换。教育程度应作为离散虚拟变量，而不是作为受教育年限的单一指标。不同教育背景的工资潜力经验概况有所不同。对数工资潜力经验曲线可以很好地近似为二次函数。由教育背景和潜在经验定义的群体内的对数工资差异随着潜在经验年限的增加而增加。因此，Mincer 工资方程的误差项是异方差的。分析还发现，根据一月份的当前人口调查补充资料，日本的对数工资潜力经验概况比美国同行更陡峭。
This paper points to several caveats in an estimation of the Mincer wage equation using Japanese data. An estimation of the Mincer wage equation using microdata of the Basic Survey of Wage Structure 2005-2008 reveals following six points as caveats. Wage profile is discontinuous at age 60 because of mandatory retirement. Wage rate, which is the dependent variable of the equation, should be log transformed. Educational attainments should be included as discrete dummy variables rather than a single index as the years of education. Log wage-potential experience profiles differ across educational backgrounds. Log wage-potential experience profiles are well approximated as quadratic functions. Log wage variance within a group defined by educational background and potential experience increases as the years of potential experience increases. Thus, the error term of the Mincer wage equation is heteroskedastic. The analysis also finds that log wage-potential experience profiles of Japan are steeper than the US counterparts based on the Current Population Survey January Supplement .