Labor Supply Models and the External Validity of Randomized Welfare Experiments

本项目旨在通过以下方式增进对劳动力供给和项目参与决策的理解：1)利用随机福利实验的调查和行政数据开发和估计劳动力供给的行为模型；2)评估准确衡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了准确的劳动力供给和项目参与的定量模型。这些模型可用于评价改革对社会保险和税收制度的影响，并对拟议和现有政策的福利效果作出定量说明。我们的估计方法结合了激励和机会的观察和实验变化。福利规则的实验性变化使我们能够放松在估计对计划规则变化的行为反应时通常采用的许多关键假设。我们准确预测显著不同的随机实验结果的能力（或无能）为总体上评估我们的建模框架提供了一个与政策相关的度量，更具体地说，是我们将自我选择纳入计划和劳动力市场参与的能力。该项目将发表两篇研究论文。第一篇论文侧重于评估劳动力供给模型匹配实验影响的能力取决于测量问题和建模复杂性的程度。首先，我们将利用加州工作报酬示范项目（CWPDP）的数据，开发和估计福利参与和劳动力供给的模型。CWPDP是20世纪90年代初在加州实施的一项大规模随机福利改革实验。从这个实验中获得的纵向合并的行政和调查数据使我们能够比以前的研究更详细地衡量代理的预算（以及因此产生的激励）。此外，数据的各个组成部分包含对一些关键变量的独立重复测量，使我们能够以比以前在本文献中尝试的更令人满意的方式检测和建模记录和报告问题。然后，我们将检查当我们对代理可用的选择进行粗化，使用近似而不是精确的策略规则，或者忽略度量问题时，我们的估计是如何变化的。我们的重点将放在这些变化如何影响我们的能力，以匹配实验对直接政策利益数量的影响，如总福利支付和项目参与。分析还将提供与CWPDP待遇相关的激励效应的定量分离，使我们能够确定，例如，福利资格规则变化与无视收入变化的相对重要性。第二篇论文的动机是担心随机实验可能没有多少外部有效性。例如，CWPDP实验的样本是在加州就业市场持续繁荣期间居住在四个县的持续福利接受者。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、时间段和项目组合？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样本的估计来对另外两个状态的随机实验结果进行预测。这将需要开发方法，从每个州样本中可用的对照观察中重新估计不可观察的偏好和技能的分布。最后，我们将评估利用实验变量在生成可信的样本外政策预测方面的实际优势（如果有的话）。我们项目更广泛的影响将是开发方法，通过使用行为模型来增强从社会实验中学到的东西。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，并在未观察到的个体水平异质性存在的情况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领域的不断普及，这些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.