Job Placement Agencies in an Agent-based Model of the Local Labor Market with the Long-term Unemployed and on-the-job Flows

In this paper, an agent-based search model of the labor market with heterogeneous agents and an on-the-job search is developed, i.e. the long-term unemployed and other job seekers compete for vacancies which differ in skills demands and in the sector of the economy. Job placement agencies help both types of unemployed persons find the proper vacant job by improving their search effectiveness and by sharing leveraged job advertisements. The agents’ interactions take place in an artificial world drawn from labor market search theory. Six global model parameters were calibrated with the Latin hypercube sampling technique for one of the largest urban areas in Poland. To investigate the impact of parameters on model output, two global sensitivity analysis methods were used, i.e. Morris screening and Sobol indices. The results show that both programs considerably influence unemployment and long-term unemployment ratios as well as the level of wages, duration of unemployment, skills demand and worker turnover. Moreover, strong cross-effects were detected: programs aimed at one group of job seekers affect other job seekers and the whole economy. This impact is sometimes positive and sometimes it is negative.

本文构建了一款面向劳动力市场的基于智能体的搜索模型，该模型纳入异质性智能体与在职求职行为：长期失业者与其他求职者在存在技能要求差异、所属经济部门差异的职位空缺间展开竞争。职业介绍机构通过提升两类失业者的求职效率、共享优质招聘广告，帮助其匹配到合适的空缺岗位。智能体间的交互行为发生在基于劳动力市场搜索理论构建的人工仿真环境中。针对波兰最大都市区之一，本文采用拉丁超立方抽样（Latin Hypercube Sampling）技术对6个全局模型参数进行了校准。为探究参数对模型输出的影响，本文采用两种全局敏感性分析方法：莫里斯筛选法（Morris Screening）与索博尔指标（Sobol Indices）。研究结果表明，此类帮扶项目对失业率、长期失业率、工资水平、失业持续时间、技能需求与员工流动率均存在显著影响。此外，本研究还检测到显著的交叉效应：针对某一类求职者的帮扶项目会对其他求职者乃至整体经济产生影响，此类影响有时为正向，有时为负向。