个体水平变量诱导的工资差距的两个组成部分:企业内还是企业间?

IF 1.7 Q2 SOCIOLOGY Japanese Journal of Sociology Pub Date : 2016-01-22 DOI:10.1111/ijjs.12040
Koji Takahashi
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引用次数: 3

摘要

众所周知,性别、教育程度、职业和就业类型等个人层面的变量是导致劳动力市场工资差距的因素。本文旨在将工资差距分为两个部分——每个企业内部产生的工资差距(企业内部工资差距)和不同企业之间产生的工资差距(企业间工资差距)——并通过个人水平变量衡量它们各自的比例,因为研究表明,基于个人水平变量的每一种工资差距都是企业内部和企业间工资差距的独特组合。这一测量有助于丰富对工资差距产生过程的研究,并有助于制定有效的劳动政策来缩小工资差距。因此,我们使用日本政府收集的全国范围内的雇主-雇员匹配数据,比较了混合普通最小二乘模型和固定效应模型估计的工资函数的自变量系数。我们发现,性别和就业类型的工资差距主要由企业内部的工资差距组成,而教育和职业的工资差距在企业间的工资差距中所占的份额更大。研究结果表明,需要采取不同的研究策略来研究每一种工资差距的产生过程,对组织过程进行监管和干预对于减少基于性别和就业类型的工资差距至关重要。
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Two Components of Wage Gaps Induced by Individual-level Variables: Intra-firm or Inter-firm?

Individual-level variables such as gender, education, occupation, and employment type are well-known factors that induce wage gaps in the labor market. This article aims to divide wage gaps into two components—those arising within each firm (the intra-firm wage gap) and those arising between different firms (the inter-firm wage gap)—and measure their respective proportions by individual-level variables, as studies suggest that each wage gap based on an individual-level variable has a unique mix of an intra-firm and inter-firm wage gap. This measurement can help enrich investigation into the generative process of wage gaps and formulate effective labor policies to reduce them. Accordingly, we compared the coefficients of the independent variables of the wage function estimated by a pooled ordinary least squares model and a fixed-effect model, using nationwide employer–employee matching data collected by the Japanese government. We found that wage gaps by gender and employment type mostly consist of intra-firm wage gaps, and those by education and occupation have a larger share of inter-firm wage gaps. The findings suggest that different research strategies are required to investigate the generative process of each wage gap, and that regulations on and interventions in organizational processes are important to reduce wage gaps based on gender and employment type.

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来源期刊
CiteScore
1.90
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0.00%
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