On the relationship between healthcare expenditure and longevity: evidence from the continuous wavelet analyses

On the Network Transmission Mechanisms of Disease-Specific Healthcare Expenditure Spillovers: Evidence from the Connectedness Network Analyses

“…This result reflects the fact that the rapidly increasing rate of population ageing in Taiwan expands demand for healthcare services. Our results are consistent with those from previous studies on the determinants of healthcare expenditure growth [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Although demographic transition is an important determinant of total healthcare expenditure spillover, the effect of demographic transition on individual disease-specific healthcare expenditure spillovers is variant, based on the results from Table 1 .…”

“…The persistent increase in healthcare expenditure is a potential risk jeopardizing the sustainability of the healthcare system and its capacity to deliver affordable healthcare services to the public. The major determinants of healthcare expenditure growth identified by previous studies include population ageing [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ], business cycles and income [ 2 , 3 , 6 , 8 , 9 , 10 , 11 , 12 ], technological innovation [ 1 , 2 , 11 , 13 , 14 , 15 ], and Baumol’s cost disease, a term used to describe the increase of healthcare price due to unbalanced growth of productivities between the healthcare sector and the whole economy [ 3 , 16 , 17 , 18 ]. It is crucial to point out that the network transmission mechanisms underlying these determinants effecting healthcare expenditure change come from the expansion or compression of morbidities resulting from demographic, socioeconomic, and environmental changes in the process of human development [ 19 , 20 ].…”

“…This research contributes to the existing literature on the study of the determinants of healthcare expenditure growth in three ways: First, in contrast to previous research on the determinants of healthcare expenditure growth, this study elucidates the network transmission mechanisms underlying the determinants of healthcare expenditure growth rather than simply investigating factors influencing healthcare expenditure [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Second, we applied the TVP-VAR-based connectedness network analyses developed by Antonakakis and his colleagues [ 21 ] to construct dynamic connectedness indices for disease-specific and total healthcare expenditure spillovers.…”

Co-movements of returns in the health care sectors from the US, UK, and Germany stock markets: Evidence from the continuous wavelet analyses

“…Previous studies such as Chen (2016), Chen, Wen, Lin, and Liang (2016), and Chen and Lin (2016) select the maximum decomposition level J to be three levels (2 j , j=1, 2, 3), indicating three different time scales corresponding to short-, middle-, and long-run time scales, respectively. We follow these previous studies' set-up of the maximum decomposition level J to be three levels (2 j , j=1, 2, 3) and categorize time scales into three frequency bands: 1-2, 2-4, and 4-8 months, corresponding to short, medium, and 5 Because the control variable, industrial production index, is only available in monthly data, this study employs monthly data.…”

The Evolution of Out‐of‐Hospital Medical Costs to and through Retirement

“…Much of the literature on health expenditure growth analyses the relationship between health expenditure and national accounts, population growth and changes in demographic structure (see, for example, Hansen & King, ; Di Matteo, ; Hosoya, ; Chen et al ., ; Treasury, ). Meanwhile, the bulk of micro papers come from the United States and developing countries where most health care is paid privately (Xu et al ., ; Ekman, ; Martín et al ., ).…”