We investigate how new mortality models perform with Taiwanese mortality data. Although much progress has been made in the mortality model literature, practitioners still adopt the Lee-Carter model (1992) for the mortality forecast of the Taiwanese population. We compare the recently developed Mitchell et al. (2013) models with one- and two-factor models, namely Mc-1 and Mc-2, that incorporate heavy-tailed distributions such as variance gamma, normal inverse Gaussian, and skewed t distributions into period effects with two increasingly popular parametric models with cohort effects, namely the M9 and M10 models. Using age-specific mortality rates from Human Mortality Database (HMD), we show that the female mortality trend in Taiwan is non-Gaussian, as the model selection criteria such as AICc strongly support this. Our results suggest the twofactor Mitchell et al. (2013) model captures the male mortality rates better, while the M10 model performs better for female mortality rates in terms of root sum of squared errors (RSSE) and mean absolute percentage error (MAPE). Keywords: mortality models, non-gaussian distributions, period effects

Housing choices are related to multiple aspects of adult life. Whereas young adults’ transition to full adulthood has gradually deviated from the traditional track along with social changes, the dynamics of housing transition have rarely been investigated in Taiwan. Although some studies examined the changes of living arrangements and housing tenure of the population aged under 26 or above 35, not many studies looked into the housing changes of those aged around 30. This study used data from the Panel Study of Family Dynamics and adopted the main respondents (ages 27-39) interviewed since 2003 as the sample. By dividing the sample into three cohorts (1964-1967, 1968-1972, 1973-1976), the analysis of housing transitions between 2003 and 2016 was conducted with sequence analysis. Research results showed that there were distinct differences in housing trajectories among different cohorts. Trajectories of staying at or returning to the parental home after leaving were found among the 1973-1976 cohort. Moreover, based on analysis of the long-term trajectories, changes of household composition and housing tenure tended to surge around age 30. This finding implies that age 30 is still the critical age of housing transitions, even though “independence at the age of thirty” may be hard to achieve. Keywords: housing transition, sequence analysis, homeownership, household composition

Population ageing is speeding up in Taiwan due to the lower fertility rates and declining mortality rates. Thus, the life arrangements after retirement have been receiving a lot of attention in recent years, and more public resources have been allocated to elderly-related policies. The elderly’s needs can be classified into three categories: finances, medical utilization, and living care, and many countries have devoted efforts in seeking the solutions to fulfill these needs. However, it is a difficult task, since we don’t have enough information about the elderly. Take the study of mortality improvement and life expectancy as an example. The trend toward longer lives surprises many people, and the annual increment in life expectancy is still over 0.2 years in many countries. Underestimation of life expectancy causes financial insolvency in public insurance systems and the private sector. Our study goal is to explore the medical needs of Taiwan’s elderly via estimating their medical utilization, such as inpatient and outpatient visits.The study material is a sample data set of one million elderly people from Taiwan’s National Health Insurance Database. The sample coverage is about 45.7%, i.e., almost one in two of Taiwan’s elderly are drawn, and this big data sample can greatly reduce the sampling bias and increase the accuracy of estimation. For example, we used this sample data to estimate the elderly mortality rates and found that they are very close to the official statistics. Also, the medical utilization of Taiwan’s elderly has a high degree of inertia. Keywords: population ageing, medical utilization, longevity risk, National Health Insurance, big data