主讲题目：Household Finance in China: Theory and Practice
主讲人：Guozhong Zhu, Associate Professor, Alberta School of Business, University of Alberta
Guozhong Zhu graduated from the University of Texas at Austin in 2009 with a Ph.D. in Economics. Before joining University of Alberta, he was an assistant professor in Guanghua School of Management of Peking University. His research focuses on household finance and real estate. Professor Zhu has also carried out some studies on human capital accumulation, labor supply and home production. The main approach is quantitative macro, emphasizing that a dynamic macro model should be able to deliver key statistics found in the data, especially in micro data from sources such as PSID, SCF and CEX.
We show household finance patterns in China, and compare them with patterns foundin the US data. Relative to the US, savings rate in China is high, stock market participation rate is low, stocks market investment as a fraction of total wealth is also low formarket participants, and housing is predominantly important in portfolio. These salientpatterns have three potential drivers. The first driver we consider is the institutionalfeatures, such as the weak social safety net or the high degree of income uncertainty.Secondly, the distinct patterns between China and the US can be driven by different preferences. For example, it is likely that households in China are more patient, or they caremore about inter-generational transfer. The third driver we consider is the degree of financial sophistication, as captured by stock market participation costs and adjustment costs.We show simulation results under the assumption of improved financial sophistication ofhouseholds in China.
主讲题目：A Bayesian State-Space Approach for Invasive Species Management: The Case of Spotted Wing Drosophila
主讲人：Xiaoli Fan, Assistant Professor, Department of Resource Economics and Environmental Sociology (REES)
University of Alberta
Xiaoli Fan received her Ph.D. degree in Applied Economics and Management from Cornell University. She joined the Department of Resource Economics and Environmental Sociology of Alberta in 2017. Her research interests are broadly in the food and agribusiness management, decision making for specialty crop management, bioeconomic models of invasive species. While the topics are interdisciplinary, she mainly uses mathematical programming in combinationwith economic modeling to conduct research.
Spotted wing drosophila (SWD) is an invasive pest with devastating effects on soft-skinned fruit crops. Due to zero tolerance of SWD infested fruit, current SWD management strategies usually focus on preventive broad-spectrum insecticide sprays. The industry is calling for management strategies that incorporate monitoring, an important SWD integrated pest management method, to reduce unnecessary applications of insecticide. However, most growers do not monitor because it is costly and traps do not provide perfect observation of the population size. To help inform optimal SWD monitoring and controlling decisions when only partial observation of the population size is possible, we first develop a Bayesian State-Space model to represent the population dynamics of SWD. Based on the estimated parameters, we then introduce control variables to the population model and run simulations to evaluate the performance of alternative SWD management strategies. By doing so, our paper extends the use of Bayesian State-Space modeling to inform decision making in invasive species management when state uncertainty and structural uncertainty present simultaneously. We show that the economic impact of alternative SWD control strategies depends on the efficiency of monitoring traps, the action threshold selected (i.e. the number of SWD in traps that triggers insecticide applications), and the efficacy of the insecticide. Overall, we find that as the efficiency of monitoring improves, management strategies based on monitoring are superior to spray-only strategies. Our results also suggest that higher action thresholds should be used when monitoring traps are more efficient.