A decrease in mortality among the elderly is particularly evident since the end of the 20th century. As a result, the living arrangements after retirement, including as financial planning and health needs, become more important. In recent years, Taiwan’s government has issued varies social policies and a social insurance system to face the rapid population ageing. The National Health Insurance and National Pension Insurance systems are two famous examples. However, the financial solvency of these social insurance systems depends on reliable planning and prediction of future mortality rates, in particular, those of the elderly. Hence, many studies focus on building mortality models for the elderly. The goal of this study is to evaluate popular mortality models for the elderly via empirical data. There are two types of mortality models: relational and stochastic. The former includes the Gompertz, Coale-Kisker, and Discount Sequence models (Wang and Yue 2015), and the latter includes the Lee-Carter (1992), Renshaw and Haberman (2006), and CBD models (2006). For this empirical study, we consider short-term and longterm forecasts, and the evaluation is via cross validation for 5 age groups and single age data from the Human Mortality Database. We found that the Discount Sequence model has about the same prediction accuracy as stochastic models.

Fertility rates have declined in most countries during these years, and Taiwan has confronted a crisis of lowest-low fertility. As a result, government authorities have proposed some policies to respond to the trend, such as maternity payment, child-care subsidy and social-insurance maternity benefit. Some counties and cities have implemented a cash payment for newborn babies (baby bonus) to encourage fertility intentions based on their financial situation and policy agenda, with implementing time and payment varying between counties. Therefore, the purpose of this study is to assess whether the implementation of baby bonus payments in counties and cities can effectively improve couples' fertility intentions. The study applied data from the Panel Study of Family Dynamics (PSFD) in 2005- 2011, using a quasi-experimental, pre-post with non-randomized control design, and the household as the unit of analysis. We excluded singles and persons aged 43 or older. The intervention plan is the baby bonus policies implemented by counties and cities during 2006-2010. The difference-indifferences (DID) method was used to compare the differences in fertility intentions between counties implementing the baby bonus or not. The Generalized Estimation Equation (GEE) model was used to control for autocorrelation among repeated measures as well as other covariates. The results indicated that after controlling for wife's age, household income, parity, husband's age, husband's education, wife's education, wife's employment status, fertility pressure and degree of urban development, the baby bonus policies had no statistically significant effects on fertility intentions. The cash benefits may not change the fertility intentions. Therefore, it is suggested that the local government should consider whether the policy is suitable or not due to the high costs of these policies.

The purpose of this study is to assess the effects of individual socioeconomic status and county-level variables in a sample of diabetes patients aged 20-64 in Taiwan. Methods: Data on individual-level characteristics, health behavior variables, socioeconomic status and health related quality of life (SF- 36) were obtained from National Health Interview Survey in 2005 (2005 NHIS). The health data was confirmed by National Health Insurance Research Databases. Sex, age, and marital status; health behavior variables including smoking, drinking, betel nut, sports and obesity; and individual socioeconomic status measured by employment, education attainment, and household income per month were included. Health related quality of life was calculated Physical Component Summary (PCS, SF-36) and Mental Component Summary (MCS, SF-36). Two county-level variables were used: (1) area disadvantage index; (2) index of concentration at the extremes (ICE) of income. We excluded missing data on individual information and the remaining study sample included 756 DM patients and 14,360 non- DM patients nested within 23 counties. Individual socioeconomic status such as unemployment and low household income were related to PCS and MCS. County-level variables including area disadvantages and ICE were negatively associated with PCS but not with MCS. However, near-zero ICE is positively associated with better MCS. Disadvantaged diabetes patients lived in area of concentrated disadvantages had worse health-related quality of life. The link between area-based disadvantages and individual-level poverty had created a double jeopardy effect in diabetes patients.

No abstract available.