Identification Problems in the Social Sciences

社会科学中的识别问题

• 批准号: 9722846
• 负责人: Charles Manski
• 金额: $ 28.76万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-12-15 至 2002-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9722846&HistoricalAwards=false
• 关键词: Identification; Problems; Social; Sciences

Econometricians have long found it useful to separate inferential problems into statistical and identification components. Studies of identification seek to characterize the conclusions that could be drawn if the researcher had available a sample of unlimited size. Studies of statistical inference seek to characterize the generally weaker conclusions that can be drawn given a sample of positive but finite size. Statistical and identification problems limit in distinct ways the conclusions that may be drawn in empirical research. Statistical problems are most severe when the available sample is small. Identification problems are most severe when the researcher knows little about the population under study and the sampling process yields only weak data on the population. This research continues the investigator's program of work on identification problems in the social sciences. Four projects combining methodological research and empirical analysis are planned: Nonparametric Regression When Outcomes and/or Regressors are Censored Whereas there is a voluminous literature on regression analysis when outcomes are censored, surprisingly little attention has been given to the inferential problems that arise when regressors are censored. This project examines regressor censoring from the same nonparametric perspective used earlier by the investigator to analyze outcome censoring and other non identifying sampling processes. The findings are used to characterize the possible implications for survey research of item and interview nonresponse. Monotone Treatment Response The standard formalization of the analysis of treatment effects assumes that each member of a population of interest receives one of a set of mutually exclusive and exhaustive treatments, and that the outcome under the realized treatment is observable. Outcomes under the non realized treatments are necessarily unobservable; hence these outcomes are censored. In earlier work, the investigator has studied the nonparametric identification of treatment effects in the classical case where there are two treatments and the objective is to compare distributions of outcomes under these treatments. This project examines the identifiability of treatment effects when treatments are ordered and response functions are known to be monotone or concave monotone. The findings have application to the econometric analysis of market demand and of production. Empirical Analysis of Sentencing and Recidivism The question of how judges should sentence convicted juvenile offenders has long concerned policy makers and criminologists. The investigator is using nonparametric methods to compare the effects on recidivism of the two main sentencing options available to judges: confinement in residential treatment facilities and diversion to nonresidential treatment. This project seeks to illuminate the basic ideas of nonparametric analysis of treatment effects through a serious empirical case study, to add new methodological findings, and to contribute substantively to the field of criminology. Analyses of the Survey of Economic Expectations The nature of subjective expectations is a shared concern of researchers who study human behavior. The investigator has recently designed and implemented the Survey of Economic Expectations (SEE). SEE is an ongoing national survey initiated in an effort to learn how Americans perceive their near term economic prospects. Probabilistic questions are used to elicit expectations about income, employment, health insurance coverage, and crime victimization one year into the future. Recently completed analyses of SEE data examine respondents' income expectations and their perceptions of economic insecurity. This project would continue collection of SEE data at the present rate of about 1500 interviews per year and would perform a set of special studies that should enhance the value of the survey.

计量经济学家长期以来发现将推理问题分解为统计和识别部分是有用的。鉴定研究旨在描述如果研究人员拥有无限大小的样本就可以得出的结论。统计推断研究试图描述在给定正数但有限大小的样本的情况下可以得出的通常较弱的结论的特征。统计和识别问题以不同的方式限制了实证研究中可能得出的结论。当可用样本较小时，统计问题最为严重。当研究人员对所研究的群体知之甚少且抽样过程仅产生关于群体的微弱数据时，识别问题最为严重。这项研究延续了研究者关于社会科学识别问题的工作计划。计划将方法论研究和实证分析相结合的四个项目： 结果和/或回归量被审查时的非参数回归 虽然有大量关于结果被审查时回归分析的文献，但令人惊讶的是，人们很少关注回归量被审查时出现的推理问题。该项目从研究者之前用于分析结果审查和其他非识别抽样过程的相同非参数角度来检查回归量审查。研究结果用于描述项目和访谈不答复的调查研究可能产生的影响。 单调治疗反应治疗效果分析的标准形式化假设感兴趣群体的每个成员都接受一组相互排斥且详尽的治疗中的一种，并且所实现的治疗的结果是可观察的。未实现治疗的结果必然是无法观察到的；因此，这些结果受到审查。在早期的工作中，研究者研究了经典案例中治疗效果的非参数识别，其中有两种治疗，目的是比较这些治疗下的结果分布。该项目检查当治疗被安排并且响应函数已知是单调或凹单调时治疗效果的可识别性。研究结果适用于市场需求和生产的计量经济分析。 量刑与累犯的实证分析 法官如何对已定罪的少年犯进行量刑的问题长期以来一直是政策制定者和犯罪学家关注的问题。调查人员正在使用非参数方法来比较法官可以选择的两种主要量刑选择对累犯的影响：监禁在住院治疗设施中和转移到非住院治疗机构。该项目旨在通过严肃的实证案例研究阐明治疗效果非参数分析的基本思想，增加新的方法论发现，并为犯罪学领域做出实质性贡献。 经济预期调查分析 主观预期的本质是研究人类行为的研究者共同关心的问题。调查人员最近设计并实施了经济预期调查（SEE）。 SEE 是一项正在进行的全国调查，旨在了解美国人如何看待他们的近期经济前景。概率问题用于得出对未来一年的收入、就业、健康保险覆盖范围和犯罪受害情况的预期。最近完成的 SEE 数据分析考察了受访者的收入预期及其对经济不安全感的看法。该项目将继续以目前每年约 1500 次访谈的速度收集 SEE 数据，并将开展一系列特殊研究，以提高调查的价值。