<p>In this dissertation, I discuss two important factors in individuals' decision-making processes: subjective expectation bias and time-inconsistent preferences. In Chapter I, I look at how individuals' own subjective expectations about certain future events are different from what actually happens in the future, even after controlling for individuals' private information. This difference, which is defined as the expectation bias in this paper, is found to have important influence on individuals' choices. Specifically, I look into the relationship between US elderly's subjective longevity expectation biases and their smoking choices. I find that US elderly tend to over-emphasize the importance of their genetic makeup but underestimate the influence of their health-related choices, such as smoking, on their longevity. This finding can partially explain why even though US elderly are found to be more concerned with their health and more forward-looking than we would have concluded using a model which does not allow for subjective expectation bias, we still observe many smokers. The policy simulation further confirms that if certain public policies can be designed to correct individuals' expectation biases about the effects of their genes and health-related choices on their longevity, then the average smoking rate for the age group analyzed in this paper will go down by about 4%.</p><p>In Chapter II, my co-author, Hanming Fang, and I look at one possible explanation to the under-utilization of preventive health care in the United States: procrastination. Procrastination, the phenomenon that individuals postpone certain decisions which incur instantaneous costs but bring long-term benefits, is captured in economics by hyperbolic discount factors and the corresponding time-inconsistent preferences. This chapter extends the semi-parametric identification and estimation method for dynamic discrete choice models using Hotz and Miller's (1993) conditional choice probability approach to the setting where individuals may have hyperbolic discounting time preferences and may be naive about their time inconsistency. We implement the proposed estimation method to US adult women's decisions of undertaking mammography tests to evaluate the importance of present bias and naivety in the under-utilization of mammography, controlling for other potentially important explanatory factors such as age, race, household income, and marital status. Preliminary results show evidence for both present bias and naivety in adult women's decisions of undertaking mammography tests. Using the parameters estimated, we further conduct some policy simulations to quantify the effects of the present bias and naivety on the utilization of preventive health care in the US.</p> / Dissertation

[pt] Os mercados de carbono oferecem uma promissora abordagem para enfrentar as mudanças climáticas. No entanto, seu avanço encontra desafios, especialmente na medição precisa da redução de emissões provenientes de atividades relacionadas à floresta. Este artigo apresenta um modelo dinâmico de escolha discreta adaptado para avaliar tais emissões, utilizando uma nova base de dados de dados em painel sobre o uso da terra em propriedades privadas, contendo suas características e participação em projetos de carbono. Nossa análise revela que aproximadamente 23 por cento dos estoques de carbono dentro de projetos de carbono florestal em propriedades privadas na Amazônia brasileira não têm exposição a riscos de desmatamento e, portanto, não devem ser negociados como créditos de carbono. Através de cenários simulados, demonstramos que maiores preços de carbono ou menores custos de participação nesses projetos poderiam aumentar substancialmente a oferta de emissões de carbono evitadas. Intervenções como redução de custos, subsídios de preço ou melhoras regulatorias poderiam recrudescer a oferta e contribuir para os esforços de mitigação das mudanças climáticas. Por fim, identificamos propriedades adequadas para participação futura em projetos, com o objetivo de mitigar os riscos de investimento e otimizar os retornos esperados. / [en] Carbon markets offer a promising avenue for tackling climate change, yet their advancement encounters challenges, notably in accurately measuring emissions avoidance from forest-related activities. This paper introduces a dynamic discrete choice model tailored for assessing such emissions, using a novel database of panel data on private property land use, characteristics, and carbon project participation. Our analysis reveals that approximately 23 percent of carbon stocks within forestry carbon projects on private properties in the Brazilian Amazon lack exposure to deforestation risks and should therefore not be tradable as carbon credits. Through simulated scenarios, we demonstrate that elevated carbon prices or reduced participation costs in these projects could substantially augment the supply of avoided carbon emissions. Interventions such as cost reductions, price subsidies or regulatory improvements could bolster supply and contribute to climate change mitigation efforts. Lastly, we identify suitable properties for future project participation, aiming to mitigate investment risks and optimize expected returns.