Job Search Behavior of the Unemployed

失业者的求职行为

• 批准号: 84087813
• 负责人: Professor Dr. Marco Caliendo
• 金额: --
• 依托单位: Centre de Recherche en Économie et Statistique (CREST)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2008
• 资助国家: 德国
• 起止时间: 2007-12-31 至 2009-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/84087813?language=en
• 关键词: Job; Search; Behavior; Unemployed

In this research project we analyze the transition process from unemployment to employment. Thereby we focus on the determinants of reservation wages and the type and intensity of job search as well as their impact on unemployment duration. Both components depend on factors like education, age, unemployment benefits and household composition. Next to these "classical" variables the process may depend on factors like personality traits or social networks, which are usually unobservable for the econometrician.In addition to administrative data we base our analysis on unique survey data containing a large inflow sample of unemployed. Individuals are interviewed shortly after entering unemployment and are followed over time for up to three years. This data set allows us to observe individual job search intensity, reservation wages and job offers at different points in time as well as realized transitions into employment. Risk aversion, locus of control and social networks are part of the rich information and will help us to understand the determinants of the transition process from unemployment to employment more deeply. Identifying the underlying key factors will give us valuable information for improving the employment probability of individuals with a high risk of long-term unemployment like low skilled or elderly workers.

在这个研究项目中，我们分析了从失业到就业的过渡过程。因此，我们专注于保留工资的决定因素和类型和强度的求职，以及它们对失业持续时间的影响。这两个组成部分取决于教育、年龄、失业救济和家庭组成等因素。除了这些“经典”变量之外，这一过程还可能取决于人格特质或社交网络等因素，这些因素通常是计量经济学家无法观察到的。除了行政数据，我们的分析还基于包含大量失业流入样本的独特调查数据。个人在进入失业状态后不久接受采访，并在长达三年的时间内接受跟踪。这个数据集使我们能够观察不同时间点的个人求职强度，保留工资和工作机会，以及实现的就业过渡。风险厌恶、心理控制源和社交网络是这些丰富信息的一部分，有助于我们更深入地理解失业向就业过渡过程中的决定因素。识别潜在的关键因素将为我们提供有价值的信息，以提高低技术或老年工人等长期失业风险高的个人的就业概率。