Partial Identification of State Dependence

国家依赖性的部分识别

• 批准号: 1756308
• 负责人: Alexander Torgovitsky
• 金额: $ 17.78万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1756308&HistoricalAwards=false
• 关键词: Partial; Identification; State; Dependence

Many empirical questions in economics require the answer to some form of the following question: To what extent do past outcomes determine current and future outcomes? For example, if employers make hiring decisions based in part on the past employment outcomes of an applicant, then previous unemployment may cause an applicant to remain unemployed, if employers view this as a negative signal of the applicant's quality as a worker. Using data on employment outcomes to measure the extent to which this phenomenon occurs is a notoriously difficult problem. This proposal develops new statistical models for addressing this problem, both as it applies to employment dynamics, and to other similar problems in economics. The project advances the field by developing new methods for data analysis. The application to employment outcomes also advances the national prosperity by giving us better information about how unemployment affects long run job prospects. The fundamental conceptual difficulty with measuring state dependence is that the effect of past outcomes will confound with temporally persistent unobservable heterogeneity across agents. For example, observing in a panel that previously unemployed agents are less likely to be employed could be due to a negative causal effect of past unemployment, but it could also result if unemployed agents are less likely to be employed due to other unobservable factors such as preferences or productivity. To date, the vast majority of statistical methods designed to measure state dependence use variants of highly-parameterized dynamic binary choice models. These models depend on many strong assumptions, including arbitrary functional form restrictions on the shape of heterogeneity. They are quite likely to be severely misspecified in many applications. The goal of this proposal is to develop and apply transparent nonparametric approaches for identifying state dependence from unobserved heterogeneity. Owing to the difficulty of point identifying state dependence, the proposed methods use partial identification techniques. In particular, the PI develops a new dynamic potential outcomes model, studies its properties, and applies it to questions of state dependence in employment outcomes.

经济学中的许多实证问题都需要回答以下问题：过去的结果在多大程度上决定了当前和未来的结果？例如，如果雇主部分根据申请人过去的就业结果作出雇用决定，那么以前的失业可能会导致申请人继续失业，如果雇主认为这是申请人作为工人的素质的负面信号。利用就业结果数据来衡量这一现象发生的程度是一个众所周知的难题。这一建议为解决这一问题开发了新的统计模型，既适用于就业动态，也适用于经济学中的其他类似问题。该项目通过开发新的数据分析方法来推进该领域。 就业结果的应用还通过为我们提供有关失业如何影响长期就业前景的更好信息来促进国家繁荣。测量状态依赖的基本概念困难在于，过去结果的影响将与代理之间暂时持续的不可观察的异质性混淆。例如，在一个小组中观察到以前失业的代理人不太可能被雇用，这可能是由于过去失业的负面因果效应，但如果失业的代理人不太可能被雇用，也可能是由于其他不可观察的因素，如偏好或生产力。迄今为止，绝大多数用于测量状态依赖的统计方法都使用高度参数化的动态二元选择模型的变体。这些模型依赖于许多强有力的假设，包括对异质性形状的任意函数形式限制。在许多应用中，它们很可能被严重错误地指定。这个建议的目标是开发和应用透明的非参数方法来识别未观察到的异质性的状态依赖。由于点识别状态依赖的困难，所提出的方法使用部分识别技术。特别是，PI开发了一个新的动态潜在结果模型，研究其属性，并将其应用于就业结果的国家依赖问题。