Gender-Specific Job Search and Wage Formation

即使是当代的劳动力市场也显示出男女之间的巨大差异。妇女的平均收入仍然低于男子，即使是在狭义的职业和部门。此外，女性和男性失业在商业周期中的反应通常不同。虽然这些事实中有许多是有据可查的，但现有文献的很大一部分是指种群（例如，按性别划分的就业和失业）或结果变量（例如，工资）。我们的项目计划从搜索和匹配的文献，它认为劳动力市场作为一个流动的概念的见解。为了找到一份工作，工人必须参与劳动力市场，选择他们的搜索强度，并决定在哪些公司申请。由于这些决定因性别而异，它们可能是性别不平等的根源。我们计划将联合收割机搜索理论模型与两个大型微观经济数据集结合起来，以打开性别特定搜索决策的黑匣子。首先，我们想利用公司方面的IAB职位空缺调查来分析女性和男性申请的公司。我们将特别强调企业工资溢价的作用和企业随之而来的灵活性要求。此外，我们想利用东西德国之间的差异（例如，不同的日托使用或不同的公司结构或儿童的作用），以了解性别差异的驱动因素。其次，我们计划使用家庭方面的PASS调查，以更好地了解哪些因素驱动男性和女性的劳动力参与，男性和女性如何改变他们的搜索活动在商业周期，以及他们的就业概率的影响，可以从这种行为。我们的微观经济数据将通过宏观经济搜索和匹配模型的透镜来考虑，以得出假设和可检验的含义。或者，实证结果将用于校准宏观经济模型。这些模型可用于反事实模拟和分析超越纯经验结果的政策干预。

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.