Collaborative Research: Labor Market and Public Policy Preferences

合作研究：劳动力市场和公共政策偏好

• 批准号: 2149371
• 负责人: Neil Thakral
• 金额: $ 26.43万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2149371&HistoricalAwards=false
• 关键词: Collaborative; Research; Labor; Market; Public

What people want (preferences) is central to economic analysis and policy formulation. Yet there is no easy or generally acceptable way of measuring preferences from data. The proposed research develops a portable, scalable, and easy to measure preference revelation (elicitation) method called the Bayesian Adaptive Choice Experiment (BACE). BACE improves upon existing preference elicitation approaches that are used in policy analysis, regulation, and litigation. The method will be applied to study important and policy-relevant questions: (i) preferences for job amenities such as workplace flexibility, and firm provision of such amenities---important considerations in the face of global technical and organizational changes. This is important as global technical and organizational changes have the potential to alter the structure of work as the costs of providing workplace amenities shift over time. (ii) in preferences for public policies, the research produces method of comparing the welfare benefits of various policies for different demographic groups. In addition to these applications, BACE shows the potential for a new generation of surveys and experimental methods to do much more in helping researchers and policymakers value non-market resources. The results of this research will contribute significantly to improve economic analyses as well as improve the quality of public policies and business decisions and thus increase economic growth. BACE provides an efficient dynamic elicitation procedure for conducting choice experiments. It does so by generating the information-maximizing sequence of choice scenarios based on a prior that gets updated with previous answers to obtain individual-level Bayesian posterior estimates. The procedure allows for a higher precision of the parameter estimates with fewer choice scenarios presented to each subject while also overcoming systematic biases in estimating average preference parameters from commonly used static approaches. Applying the method to preferences for job amenities, the resulting individual-level preference data makes it possible to estimate a new model of compensating differentials that extends the classic Rosen (1986) framework. Applying BACE to willingness to pay for public policies produces new evidence on the correlates of policy preferences and leads to novel estimates of the Marginal Value of Public Funds. The implementation of BACE in this research is a step forward in allowing such procedures to be widely adopted to measure important inputs for understanding a broad range of phenomena in social sciences. The results of this research will contribute significantly to improve economic analyses as well as improve the quality of public policies and business decisions and thus increase economic growth.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人们想要什么(偏好)是经济分析和政策制定的核心。然而，没有一种简单或普遍可以接受的方法来衡量数据的偏好。这项拟议的研究开发了一种可移植、可扩展且易于测量的偏好揭示(启发)方法，称为贝叶斯自适应选择实验(BACE)。BACE改进了政策分析、监管和诉讼中使用的现有偏好诱导方法。这一方法将用于研究重要的和与政策有关的问题：(1)对工作场所灵活性等工作便利设施的偏好，以及切实提供这种便利设施-面对全球技术和组织变化时的重要考虑因素。这一点很重要，因为全球技术和组织变化有可能改变工作结构，因为提供工作场所便利设施的费用随着时间的推移而变化。(2)在公共政策偏好方面，研究提出了比较不同人口群体不同政策的福利福利的方法。除了这些应用，BACE还展示了新一代调查和实验方法的潜力，以帮助研究人员和政策制定者评估非市场资源的价值。这项研究的结果将大大有助于改进经济分析以及提高公共政策和商业决策的质量，从而促进经济增长。BACE为进行选择性实验提供了一种有效的动态启发式方法。它通过根据先验生成信息最大化的选择场景序列来实现这一点，该先验根据先前的答案进行更新，以获得个体级别的贝叶斯后验估计。该程序允许参数估计的精度更高，向每个受试者呈现的选择情景更少，同时还克服了从常用的静态方法估计平均偏好参数时的系统性偏差。将该方法应用于工作便利偏好，得到的个人水平偏好数据使得估计一个新的补偿差异模型成为可能，该模型扩展了经典的Rosen(1986)框架。将BACE应用于公共政策的支付意愿，对政策偏好的相关性产生了新的证据，并导致了对公共资金边际价值的新估计。BACE在这项研究中的实施是向前迈出的一步，使这种程序能够被广泛采用，以衡量理解社会科学中广泛现象的重要投入。这项研究的结果将大大有助于改善经济分析以及提高公共政策和商业决策的质量，从而促进经济增长。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。