Bayesian analysis of individual decisions in health and labour economics

该项目旨在利用新兴的贝叶斯马尔可夫链蒙特卡罗方法来开发新的方法来模拟经济决策。 这些方法将产生对当前两个重要政策辩论的见解。这包括（一）大麻、酒精和烟草的使用以及大麻使用的合法化;（二）育儿假政策、产假决定和母亲的劳动力市场动态。虽然政策在观察到的健康和劳动力市场行为中发挥重要作用，但由于决策过程的复杂性，往往难以量化政策对个人决策和结果的确切影响。该项目的成果将包括不同法律的设想方案下物质使用变化的新证据，并提供各种好处，如提供关于劳动力市场和健康行为影响的重要证据，以支持决策者和加强澳大利亚在贝叶斯分析方面的研究能力。

This project aims to exploit emerging Bayesian Markov chain Monte Carlo methods to develop new approaches to modelling economic decision making. These methods will generate insights into two current and important policy debates. This includes (i) marijuana, alcohol and tobacco use and legalisation of marijuana use; and (ii) parental leave policies, maternity leave decisions and mothers' labour market dynamics. Although policies play an important role in observed health and labour market behaviours, their exact effects on individuals' decisions and outcomes are often difficult to quantify due to the complex nature of the decision process. Outcomes from the project will include new evidence of changes in substance uses under different legal scenarios and provide benefits such as yielding vital evidence on labour market and health behaviour impacts to support policy makers and strengthen Australia's research capacity in Bayesian analysis.