Labor Supply Models and the External Validity of Randomized Welfare Experiments

该项目旨在通过以下方式提高对劳动力供应和计划参与决策的理解：1）使用随机福利实验的调查和管理数据开发和估计劳动力供给的行为模型，2）评估精确测量代理人的选择和激励对模型预测准确性的重要性，3）评估估计模型预测不同人群中其他实验结果的能力。本研究的智力价值在于生成准确的劳动力供应和计划参与的定量模型。这些模型可用于评估社会保险和税收制度改革的影响，并对拟议和现有政策的福利影响作出定量说明。我们的估计方法结合了激励和机会的观察和实验变化。福利规则的实验变化使我们能够放松许多关键假设，通常采用时，估计行为的变化程序规则的反应。我们的能力（或无法），以准确地预测结果的显着不同的随机实验提供了一个政策相关的指标，评估我们的建模框架一般，更具体地说，我们的能力，考虑到自我选择到程序和劳动力市场的参与。第一篇论文的重点是评估劳动力供给模型匹配实验影响的能力在多大程度上取决于测量问题和建模的复杂性。我们将开始通过开发和估计一个模型的福利参与和劳动力供应使用的数据从加州工作支付示范项目（CWPDP）-一个大规模的随机福利改革实验在加州在20世纪90年代初实施。纵向合并的行政和调查数据，从这个实验中，使我们能够衡量的预算（因此激励）的代理商在实质上比以前的研究更详细。此外，数据的各个组成部分包含独立的重复测量的一些关键变量，使我们能够检测和模型记录和报告的问题，在一个更令人满意的方式比以前已经尝试在此literation.We然后将研究我们的估计如何改变时，我们粗化的选择提供给代理，使用近似而不是精确的政策规则，或忽略测量问题。我们的重点将是这些变化如何影响我们的能力，以匹配实验的影响，对直接政策利益的数量，如总福利支付和计划参与。该分析还将提供一个定量的分拆与CWPDP治疗的激励效应，使我们能够确定，例如，福利资格规则的变化与变化的相对重要性收入disregers.The第二篇论文的动机是关注随机实验可能几乎没有外部效度。例如，CWPDP实验是在州就业市场持续繁荣期间，对居住在加州四个县的正在领取福利的人进行的。我们从加州实验中学到的东西在多大程度上可以推广到其他人群、时间段和计划组合？20世纪90年代进行的许多国家福利实验为我们提供了回答这个问题的机会。我们将使用我们对CWPDP样本的估计来预测另外两个州的随机实验结果。这将需要开发方法来重新估计分布的不可观察的偏好和技能，从控制观察每个国家的样本。最后，我们将评估获得实验变异在产生可信的样本外政策预测方面的实际优势（如果有的话），我们项目的更广泛影响将是开发通过使用行为模型来增强从社会实验中学到的东西的方法。我们试图说明如何建立和估计能够“汇集在一起”的经济模型，并在未观察到的个人水平的异质性的存在下解释多个实验的结果。我们相信，随着社会和实地实验在经济学中的不断扩散，这种方法将变得越来越重要。

This project aims to enhance understanding of labor supply and program participation decisions by: 1) developing and estimating a behavioral model of labor supply using survey and administrative data from a randomized welfare experiment, 2) assessing the importance of precise measurement of agents' choices and incentives on the accuracy of the model's predictions, and 3) evaluating the ability of the estimated model to predict the results of other experiments in different populations.The intellectual merit of this research is to generate accurate quantitative models of labor supply and program participation. Such models can be used to evaluate the impact of reforms to the social insurance and tax system and to make quantitative statements about the welfare effects of proposed and existing policies. Our estimation approach combines observational and experimental variation in incentives and opportunities. The experimental variation in welfare rules allows us to relax many of the key assumptions typically employed when estimating behavioral responses to changes in program rules. Our ability (or inability) to accurately predict the results of markedly different randomized experiments provides a policy-relevant metric for evaluating our modeling framework in general and, more specifically, our ability to account for self-selection into program and labor market participation.The project will deliver two research papers. The first paper focuses on assessing the extent to which the ability of labor supply models to match experimental impacts depends upon measurement issues and modeling complexity. We will begin by developing and estimating a model of welfare participation and labor supply using data from the California Work Pays Demonstration Project (CWPDP) -- a large scale randomized welfare reform experiment implemented in California in the early 1990s. The longitudinally merged administrative and survey data available from this experiment allow us to measure the budgets (and hence incentives) of agents in substantially more detail than previous studies. Moreover, the various components of the data contain independent repeated measurements on a number of key variables allowing us to detect and model recording and reporting problems in a more satisfactory manner than has previously been attempted in this literature.We will then examine how our estimates change when we coarsen the choices available to agents, use approximate rather than exact policy rules, or ignore measurement problems. Our focus will be on how these changes influence our ability to match experimental impacts on quantities of direct policy interest such as total welfare payments and program participation. The analysis will also provide a quantitative unbundling of the incentive effects associated with the CWPDP treatment, allowing us to ascertain, for example, the relative importance of changes in welfare eligibility rules vs. changes in earnings disregards.The second paper is motivated by the concern that randomized experiments may have little external validity. The CWPDP experiment, for instance, was conducted on a sample of on-going welfare recipients residing in four California counties during a sustained boom in the state job market. To what extent can what we learn from the California experiment be generalized to other populations, time periods, and program mixes? The many state welfare experiments conducted during the 1990s provide us with the opportunity to answer this question. We will use our estimates from the CWPDP sample to generate predictions about the results of randomized experiments in two other states. This will entail developing methods to re-estimate distributions of unobservable preferences and skills from the control observations available in each state's sample. We will conclude with an assessment of the practical advantages (if any) of access to experimental variation in generating credible out of sample policy predictions.The broader impact of our project will be to develop methods for enhancing what can be learned from social experiments through use of a behavioral model. We seek to illustrate how to build and estimate economic models capable of "pooling together" and interpreting the results of multiple experiments in the presence of unobserved individual-level heterogeneity. We believe such methods will become increasingly important as social and field experiments continue to proliferate in economics.