Econometric Study of Schooling, Information, and Smoking

描述（由申请人提供）：这是一份补充申请，以显着扩大我们目前资助的上学和吸烟研究的范围。在目前资助的家长补助金中，我们正在调查学校教育和吸烟之间的关系，作为一个案例研究，以阐明关于学校教育和健康之间联系的更普遍的问题。一个因果途径是通过健康信息，普通教育帮助消费者对有关吸烟等行为对健康后果的新信息作出反应。另外，学校教育和健康之间的联系可能不是因果关系，而是反映了不同学校教育水平的人之间难以观察到的差异。在父母资助的研究2中，我们使用工具变量（IV）的计量经济学方法来确定学校教育对吸烟的因果影响。本补充/修订申请的首要目标是加强母基金研究2中的计量经济分析。最近的模拟研究表明，在涉及内生性的回归环境中，未能考虑到固有的非线性，可能导致对因果效应的估计有很大的偏差。为实现这一目标，拟议的工作有三个具体目标。具体目标1是开发一个稳健的条件平均回归模型吸烟持续时间的前吸烟者，包括有序内生学校假人。该模型将以几种方式扩展目前资助的赠款中使用的计量经济学方法。我们将在Stata 10编程语言中实现这个估计器，并发布新的软件。具体目标2是进一步发展吸烟者吸烟持续时间模型。在从未吸烟者的数据中，有必要纠正由于正确审查观察持续时间而产生的偏差。大约有一半的吸烟者是以前的吸烟者，另一半是现在的吸烟者，根据定义，他们在采访结束后继续吸烟，所以他们的持续时间是正确的。我们将扩展为特定目标1开发的模型和方法，以解释由于分析样本（当前吸烟者）中存在右审查持续时间观察而产生的潜在偏差。在这里，与具体目标1一样，将制作相应的Stata 10软件并向公众提供。具体目标3是利用模拟和真实数据，评估将在具体目标1和2下开发的新估计器的性能。公共卫生相关性：拟议的研究侧重于学校教育与吸烟之间的联系。在美国，吸烟每年导致40多万人死亡，并被称为可预防的主要死亡原因。如果我们能找到更多证据证明学校教育和吸烟之间存在因果关系，我们就能进一步证明，对学校教育的投资也能改善公众健康。

DESCRIPTION (provided by applicant): This is a supplemental application to significantly expand the scope our currently funded study of schooling and smoking. In the currently funded parent grant, we are investigating the relationship between schooling and smoking as a case study to shed light on more general questions about the links between schooling and health. One causal pathway is through health information, where general schooling helps consumers respond to new information about the health consequences of behaviors such as smoking. Alternatively, the association between schooling and health may not be causal, but may instead reflect hard-to- observe differences between people with different levels of schooling. In the parent grant's Study 2 we are using the econometric method of instrumental variables (IV) to identify the causal impact of schooling on smoking. The overarching goal of this supplemental/revision application is to enhance the econometric analysis in the parent grant's Study 2. The goal is motivated by recent simulation studies that show that failing to account for inherent nonlinearity in regression contexts involving endogeneity can result in substantially biased estimates of causal effects. To meet its goal, the work proposed has three specific aims. Specific Aim 1 is to develop a robust conditional mean regression model of the duration of smoking among former smokers to include ordinal endogenous schooling dummies. This model will extend the econometric methods being used in the currently funded grant in several ways. We will implement this estimator in the Stata 10 programming language and disseminate the new software. Specific Aim 2 is to further develop the model of the duration of smoking among ever smokers. In data on ever smokers, it is necessary to correct for bias due to right censoring of observed durations. About half of ever smokers are former smokers, and the other half are current smokers who by definition continue to smoke beyond the time of the interview so their durations are right censored. We will extend the model and method developed for Specific Aim 1 to account for potential bias due to the presence of right-censored duration observations in the analysis sample (the current smokers). Here, as under Specific Aim 1, corresponding Stata 10 software will be produced and made publicly available. Specific Aim 3 is to evaluate the performance of the new estimators to be developed under Specific Aims 1 and 2, using simulated and real data. PUBLIC HEALTH RELEVANCE: The proposed study focuses on the link between schooling and smoking. Smoking contributes to more than 400,000 deaths annually in the US, and has been called the leading preventable cause of death. If we find more evidence of a causal link between schooling and smoking, we will strengthen the case that investments in schooling also improve public health.