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

尽管德国的失业率近年来一直在下降，但提高失业人员重返劳动力市场的机会，对政策制定者和劳动力市场管理部门来说仍然是一个重要的挑战。在我们提议的项目中，我们希望更仔细地了解社会工作者在安置开始和安置过程中使用的重要工具。分析将失业人员划分为少数几个类别。这些类别塑造了积极的劳动力市场政策待遇的组合，个人随后将面临这些待遇。我们建议使用相对较新的经济学方法，在与安置过程密切相关的实验中，更深入地分析失业人员的概况以及不同待遇的影响，从而分析概况问题。近年来，涵盖失业人员的行政数据集在大小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，经济学家才开始对使用大数据的数据挖掘和机器学习技术感兴趣，管理数据是大数据的一个变体。在其他潜力中，大数据(和相关的大数据技术)允许进行高度特定的细分，并通过自动化算法支持人类决策。因此，我们建议的一个关键目标是探索大数据技术在分析失业者和确定不同待遇效果方面的潜力：(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对失业持续时间的准确预测，并将结果与社会工作者进行的软分析结果进行比较。在这样做的同时，我们还将解决分析和评估活跃的劳动力市场计划之间的复杂相互作用。根据以前的分析结果，我们将致力于启动一个项目，在随机实地试验的框架内测试一种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现场试验的数据，该试验随机改变a)融合协议和b)额外新开发的安置工具的用法，以及c)接受联邦就业局密集的内部咨询服务的可能性。安置过程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，而且也将引起德国政策制定者和劳动力市场管理部门的高度兴趣。

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.