The Long Term Impact of Income Transfers During Childhood

描述（由申请人提供）：最近的文献记录表明，不利的早期生活条件可能对教育，劳动力市场结果和死亡率产生负面的长期影响。然而，很少有证据表明，向贫困家庭提供收入转移等公共政策能否有效改善年轻人的基本状况。我们提出的第一个分析的长期影响的现金转移（福利）对受援儿童的影响，评估母亲的养老金（MP）计划的死亡率，残疾，收入和教育程度。研究结果将使决策者更好地了解收入转移的潜力，以减少童年环境差异对社会经济地位和健康的一生差异的影响。 MP计划（1911-1935）是美国第一个政府资助的有受抚养子女家庭的福利计划，并作为联邦计划的基础，目前被称为TANF。在各州的方案中，在资格（只针对丧偶妇女，而不是包括被遗弃/离婚妇女）、每月养恤金数额和期限以及领取条件（无条件、母亲工作或子女上学）方面存在相当大的差异。 [We收集了1911年至1935年期间14个州超过80，000名被接受和拒绝的国会议员申请人的信息。我们正在将这些个人数据与社会保障管理局的信息进行匹配，这些信息包括死亡日期，残疾申请/收据和收入，以及1940年人口普查和第二次世界大战（二战）入伍记录中的信息，包括完成学业的年份，职业，身高和智商，以及1940年的收入（目标1）。据我们所知，该数据集将构成第一个大规模的记录集合，其中儿童早期状况可以与中期和长期结果相关联，并且包含公共数据的去识别关联数据集将用于研究目的。我们将使用这些数据来评估儿童期现金转移对中期和长期结果的影响，使用依赖于构建适当控制组的识别技术。这些包括在同一个县和同一年被拒绝的申请人，不合格的母亲（例如，离婚或被遗弃的母亲）在县有严格的资格要求（例如，而母亲，则是一个不愿与人交往的人。 或很晚才建立母亲养恤金方案（目标2）。最后，我们正在开发新的经济计量技术，以使用不完全匹配的数据。目前的技术水平是将分析限制在仅完全或唯一匹配的数据，但这不必要地丢弃了大量信息（所有不匹配和多重匹配的数据）并忽略了测量误差（一些个体不正确地匹配）。我们正在开发计量经济学技术，通过使用合理的参数模型近似生成数据的过程并估计边界（目标3），利用数据中的所有信息（唯一，多重和无匹配）。

DESCRIPTION (provided by applicant): Recent literature documents that adverse early-life conditions can have negative long-term effects on education, labor market outcomes, and mortality. However, there is scant evidence as to whether public policies such as income transfers to poor families can effectively improve the underlying conditions of the young. We propose the first analysis of the long-term effects of cash transfers (welfare) on recipient children by evaluating the effects of Mothers' Pension (MP) programs on mortality, disability, earnings and educational attainment. The results will better inform policy-makers regarding the potential for income transfers to reduce the impact of disparities in childhood circumstances on lifetime differences in socioeconomic status and health. MP programs (1911-1935) were the first government sponsored welfare program for families with dependent children in the U.S. and served as the basis for the federal program that is currently known as TANF. Among the state programs, there was considerable heterogeneity in eligibility (widowed only vs. inclusion of abandoned/divorced women), amount of monthly pension and duration, and conditions of receipt (unconditional, maternal work, or child school attendance). [We have collected information on over 80,000 accepted and rejected MP applicants in 14 states who applied between1911 and1935. We are in the process of matching these individual data with information from the Social Security Administration that includes date of death, disability application/receipt, and earnings, as well as to information from the 1940 census and World War II (WWII) enlistment records on years of completed schooling, occupation, height and IQ, and 1940 income (Aim 1). To our knowledge, this dataset will constitute the first large-scale collection of records for which early conditions in childhood can be linked with medium and long-term outcomes and a de-identified linked dataset containing the public data will be made available for research purposes. We will use these data to evaluate the impact of cash transfers in childhood on medium and long-term outcomes using identification techniques that rely on the construction of appropriate control groups. These include rejected applicants in the same county and year, ineligible mothers (e.g., divorced or abandoned mothers) in counties with strict eligibility requirements (e.g., widows only), and widowed mothers in states that did not establish, or were very late to establish, a mothers pension program (Aim 2). Finally, we are developing new econometric techniques for using imperfectly matched data. The current state of the art is to limit analysis to only perfectly or uniquely matched data, but this unnecessarily discards a great deal of information (all unmatched and multiply matched data) and ignores measurement error (some individuals are incorrectly matched). We are developing econometric techniques that will make use all of the information in the data (unique, multiple and no matches) by approximating the process that generates the data using reasonable parametric models and also estimating bounds (Aim 3).]