CCP Estimation of Continuous-Time Job Search Models

这个研究项目将提供新的和创新的方法来分析决定接受或拒绝工作机会。 求职模型在劳动经济学中被广泛用于解决与不断变化的环境中的劳动力供应相关的基本问题。 这些模式的关键在于，个人找工作需要时间和精力。尽管这些模型很受欢迎，但通常很难知道模型的所有重要特征是否可以从劳动力市场转型和接受工资的数据中恢复。该项目将开发一个新的创新框架，使研究人员能够从劳动力市场数据中了解这些特征;模型也比以前的模型更简单。这种更简单的估计方法将使这些模型更广泛的经验研究人员。 然后，该项目将应用所提出的方法，从匈牙利劳动力市场的丰富的纵向数据集，以分析在失业拼写过程中的求职行为。 这一研究项目的结果将使人们更好地了解接受或不接受工作机会的决定，从而为改善劳动力市场功能的政策提供指导。因此，它有助于美国的经济增长。本项目适应的条件选择概率（CCP）估计方法，以连续时间的工作搜索环境。建议的框架将偏好冲击的搜索框架，导致价值函数和条件选择概率之间的紧密联系。这种方法使得有可能建立所有模型参数的建设性识别，这反过来又转化为一个简单和易于处理的估计过程。该模型适用于匈牙利劳动力市场的丰富的纵向数据集。 拟议的应用程序将提供新的见解，在何种程度上提供的工作机会到达率以及失业的效用在失业过程中的变化。它也将使人们有可能评估的作用，在这些变化的过程中，失业的搜索努力调整。该研究项目的成果将有助于更好地了解人们接受或不接受工作机会的决定，从而为改善劳动力市场的功能和促进经济增长的政策提供指导。该奖项反映了NSF的法定使命，通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

This research project will provide new and innovative ways to analyze decisions to accept or decline job offers. Job search models are widely used in labor economics to address fundamental questions related to labor supply in a changing environment. The key to these models is that it takes time and effort for individuals to find a job. Even though these models are popular, it is often difficult to know if all important characteristics of the model can be recovered from data on labor market transitions and accepted wages. This project will develop a new and innovative framework that allows the researcher to learn about these characteristics from labor market data; the models are also considerably much simpler for empirical analysis than previous models. This simpler estimation method will make these models accessible to a broader set of empirical researchers. The project will then apply the proposed method to a rich longitudinal data set from the Hungarian labor market to analyze job search behavior over the course of the unemployment spell. The results of this research project will provide better understanding of people’s decisions to accept or not accept job offers and therefore provide guidance into policies to improve the functional of labor markets. It therefore contributes to US economic growth.This project adapts the conditional choice probability (CCP) estimation method to a continuous-time job search environment. The proposed framework incorporates preference shocks into the search framework, resulting in a tight connection between value functions and conditional choice probabilities. This approach makes it possible to establish constructive identification of all model parameters, which in turn translates into a simple and tractable estimation procedure. This model is applied to a rich longitudinal data set from the Hungarian labor market. The proposed application will provide new insights on the extent to which the job offer arrival rates as well as the utility of unemployment vary over the course of unemployment. It will also make it possible to evaluate the role played by search effort adjustment in these variations over the course of unemployment. The results of this research project will provide better understanding of people’s decisions to accept or not accept job offers and therefore provide guidance into policies to improve the functional of labor markets and 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.