Labor market policy assignment at entry into unemployment: Analysis using machine learning techniques and randomized controlled trials

失业时的劳动力市场政策分配：使用机器学习技术和随机对照试验进行分析

• 批准号: 387482412
• 负责人: Professor Dr. Gerard J. van den Berg
• 金额: --
• 依托单位: Institut für Arbeitsmarkt- und Berufsforschung der Bundesagentur für Arbeit (BA)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/387482412?language=en
• 关键词: Labor; market; policy; assignment; entry

Even though unemployment has been decreasing in recent years in Germany, improving the chances reintegration of unemployed persons into the labor market remains an important challenge for policy makers as well as for the labor market administration. In our proposed project, we want to have a closer look at important tools used by caseworkers at the beginning and during the placement process. Profiling assigns unemployed individuals into a small number of categories. These categories shape the mix of active labor market policy treatments to which the individual is subsequently exposed. We propose to use methods relatively new to economics for a deeper analysis of profiling of the unemployed as well as of heterogeneous treatment effects in experiments closely related to the placement process and thus to profiling issues. During recent years, administrative data sets encompassing unemployed persons have improved in size and quality, and computers have become able to handle larger datasets. Only recently, economists have begun to take an interest in data mining and machine learning techniques to work with big data, of which administrative data are a variant. Among other potentials, big data (and related big data techniques) allow conducting highly specific segmentations and to support human decision making with automated algorithms. A key aim of our proposal is therefore to explore the potential of big data techniques for profiling the unemployed and for the identification of heterogeneous treatment effects: (1) In a first step, we will compare how accurately different econometric and machine-learning techniques predict the duration of unemployment and compare the results with those of a soft profiling conducted by caseworkers. In doing so, we will also address the complex interplay between profiling and the evaluation of active labor market programs. Dependent on the results of the previous analyses, we will aim to start a project to test a tool displaying the predicted duration of unemployed persons within the framework of a randomized field experiment. (2) In a second step, we will use data from a recently conducted unique field experiment that randomly varied the usage of a) integration agreements and b) an additional newly developed placement tool, as well as c) the possibility to receive intensive in-house counselling services of the Federal Employment Agency. These elements of the placement process are tightly related to the soft profiling conducted by caseworkers. An aim of this second step is to identify groups with heterogeneous treatment effects across groups using machine-learning techniques. Our work should not only provide an important scientific contribution to the literature, but will also be of high interest for policy makers and the labor market administration in Germany.

尽管近年来德国的失业率一直在下降，但提高失业人员重新融入劳动力市场的机会仍然是决策者和劳动力市场管理部门面临的一项重要挑战。在我们提出的项目中，我们想要仔细研究社会工作者在开始和安置过程中使用的重要工具。剖析法将失业人员划分为少数类别。这些类别决定了个人随后面临的积极劳动力市场政策待遇的组合。我们建议使用相对较新的经济学方法来深入分析失业者的特征，以及与安置过程密切相关的实验中的异质治疗效果，从而分析问题。近年来，包括失业人员在内的行政数据集在规模和质量上都有所改善，计算机已经能够处理更大的数据集。直到最近，经济学家才开始对处理大数据的数据挖掘和机器学习技术产生兴趣，而管理数据是大数据的一种变体。在其他潜力中，大数据（以及相关的大数据技术）允许进行高度具体的细分，并通过自动化算法支持人类决策。因此，我们的建议的一个关键目的是探索大数据技术在分析失业者和识别异质治疗效果方面的潜力：(1)在第一步，我们将比较不同的计量经济学和机器学习技术预测失业持续时间的准确性，并将结果与由社会工作者进行的软分析的结果进行比较。在此过程中，我们还将讨论分析和评估活跃劳动力市场计划之间复杂的相互作用。根据先前分析的结果，我们将开始一个项目，以测试一个在随机现场实验框架内显示失业人员预测持续时间的工具。(2)在第二步中，我们将使用最近进行的一项独特的实地实验的数据，该实验随机改变了a)整合协议和b)额外新开发的安置工具的使用情况，以及c)接受联邦就业局密集内部咨询服务的可能性。安置过程中的这些要素与个案工作者进行的软分析密切相关。第二步的目的是使用机器学习技术识别具有不同治疗效果的组。我们的工作不仅应该为文献提供重要的科学贡献，而且也将对德国的政策制定者和劳动力市场管理部门产生高度的兴趣。