Gender-Specific Job Search and Wage Formation

即使是当代劳动力市场也显示出男女之间的巨大差异。妇女的平均收入继续低于男子，即使在狭义的职业和部门中也是如此。此外，女性和男性失业在商业周期中的反应通常是不同的。尽管其中许多事实都有很好的记录，但现有文献中的很大一部分都提到了库存(例如，针对性别的就业和失业)或结果变量(例如，工资)。我们的项目计划将来自搜索和匹配文献的见解交织在一起，这些文献将劳动力市场视为流动的概念。为了找到工作，工人们必须参与劳动力市场，选择他们的寻找强度，并决定申请哪些公司。这些决定在一定程度上因性别不同而不同，它们可能是性别不平等的根源。我们计划将搜索理论建模与两个大型微观经济数据集相结合，以打开特定性别搜索决策的黑匣子。首先，我们想要使用公司方面的IAB-职位空缺调查来分析女性和男性申请哪些公司。我们将特别强调固定工资溢价的作用以及随之而来的公司灵活性要求。此外，我们希望利用东德和西德之间的差异(例如，不同的日托机构或不同的公司结构或儿童的角色)来了解性别差异的驱动因素。其次，我们计划使用家庭调查来更好地了解哪些因素推动了男性和女性的劳动力参与，男性和女性在商业周期中如何改变他们的搜索活动，以及这种行为可以对他们的就业概率产生哪些影响。我们的微观经济数据将通过宏观经济搜索和匹配模型的镜头进行考虑，以得出假设和可检验的含义。或者，经验结果将用于宏观经济模型的校准。这些模型可用于反事实模拟，以及对超出纯经验结果的政策干预进行分析。

Even contemporary labor markets show extensive differences between women and men. Women continue to earn on average less than men, even within narrowly defined occupations and sectors. In addition, female and male unemployment typically reacts differently over the business cycle. Although many of these facts are well documented, a large part of the existing literature refers to stocks (e.g., gender-specific employment and unemployment) or outcome variables (e.g., wages). Our project plans to intertwine the insights from the search and matching literature, which considers the labor market as a flow concept. To find a job, workers have to participate in the labor market, choose their search intensity and to decide at which firms to apply. To the extent that these decisions differ across gender, they may be at the root of gender inequalities. We plan to combine search theoretical modelling with two large microeconomic datasets to open up the black box of gender-specific search decisions. First, we want to use the firm-sided IAB-Job Vacancy Survey to analyze at which firms women and men apply. We will put particular emphasis on the role of firm wage premia and the ensuing flexibility requirements by firms. In addition, we want to use differences between East and West Germany (e.g., different day care usage or different firm structures or the role of children) to understand the driving sources for gender differences. Second, we plan to use the household-sided PASS-survey to understand better which factors drive male and female labor force participation, how men and women change their search activity over the business cycle and which implications for their employment probabilities can be derived from this behavior. Our microeconomic data will be considered through the lens of macroeconomic search and matching model to derive hypotheses and testable implications. Alternatively, empirical results will be used for calibration of macroeconomic models. These models can be used for counterfactual simulations and for the analysis of policy interventions that go beyond pure empirical results.