Revisiting the determinants of life expectancy in Asia—exploring the role of institutional quality, financial development, and environmental degradation

Uddin, Ijaz; Azam, Muhammad; Tariq, Muhammad; Khan, Farah; Malik, Zilakat Khan

doi:10.1007/s10668-023-03283-0

Cited by 16 publications

(7 citation statements)

References 63 publications

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“…It is worth noting that the GDP growth rate as of 2019 will exacerbate health poverty in the middle and high HPI countries in the East and Southeast Asia region while alleviating health poverty in the middle and high HPI countries in the Africa region. The East and Southeast Asia region has experienced rapid economic development since the 21st century [196][197][198], a period that has effectively improved the health capacity of these countries, with rising income levels, years of education, and life expectancy of nationals [199,200], as well as further improvements in the accessibility and quality of healthcare services[66], and all-around hints of the health capacity of nationals. At the same time, based on improved health capacity, these countries have further improved their social security systems, such as various types of social insurance, labor security and human resources for medical care, thus effectively guaranteeing the right to health of nationals.…”

Section: 3mentioning

confidence: 99%

Temporal and spatial deduction pattern of health poverty eradication in countries along the Belt and Road :A systematic analysis of 141 countries from 2008-2019

Lai,

Li,

et al. 2024

Preprint

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Background Among the numerous comprehensive development bottlenecks caused by multidimensional poverty, health poverty is the most fundamental and fatal one. Therefore, the eradication of health poverty is the basis for achieving the United Nations Sustainable Development Goals(SDGs) of eradicating poverty and the World Health Organization's proposal of universal health coverage. This study aims to analyze the spatial and temporal distribution patterns of health poverty and its influencing factors across countries in the Belt and Road(B&R), a region encompassing the poorest to the richest countries, and to provide a theoretical and practical basis for the subsequent development of differentiated health poverty eradication strategies. Methods Based on the theoretical framework of the three dimensions of health rights, health capabilities and health risks, this paper uses data from the World Bank's public databases and databases such as GBD2019 to select 10 corresponding indicators and uses the geometric mean method to calculate the health poverty index(HPI) of 141 countries along the B&R in the period of 2008–2019. We used 2.5% and 97.5% of each indicator as the maximum and minimum values for normalization to transform the values of on a scale of 0–1. For positive indicators(increase HPI), 0 is the best and 1 the worst, while the opposite is true for negative indicators(decrease HPI). This approach reduces sensitivity to extreme outliers in given location-years. Afterwards, this paper uses a Geographical and Temporal Weighted Regression (GTWR) model to analyze the impact of eight different factors on the HPI in each country to determine the differences in the influencing factors between countries with different HPI levels. Results From 2008 to 2019, the health poverty in B&R countries remains very high, with 29.1% of countries have an HPI greater than 0.6 in 2019. The HPI averages for high, upper-middle, lower-middle, and low-income countries in 2019 were 0.1747, 0.3676, 0.5298, and 0.6606, respectively. In terms of spatial distribution patterns, the HPI is lowest in Europe (0.0180–0.4027) and highest in Africa (0.3557–0.8820) in 2019, while intra-Asia heterogeneity is strongest (0.0778–0.7850). In terms of the trend in the temporal evolution of the HPI, most of the countries along the B&R have seen a decline in the HPI from 2008 to 2019, with only eight countries, including Greece, showing a slight increase in the HPI. But for 16 countries, including China and India, they have the largest decline in HPI (more than 0.1). The results of the GTWR model show that X2(Domestic general government health expenditure-% of GDP) is effective in mitigating the HPI in all countries and has the largest impact in low-income countries; for Asia and Africa, the coefficients of X6 (urban population-% of total population) show a shift from negative to positive from coastal areas or islands to the interior of the continent; X8 (GDP growth-annual %) is significantly correlated with the level of economic development of each country, with positive coefficients for economically developed Europe and the more rapidly developing East and Southeast Asian, and negative coefficients for the underdeveloped regions, especially in Central Africa and Central Asia. Overall, high HPI countries are more affected by different influencing factors and fluctuate more in time than low HPI countries. Conclusion Differences in HPI among the B&R countries are very large, reflecting the fact that health poverty has become a pressing global issue. The heterogeneity of health poverty between and within continents is caused by the unequal development of their social, cultural, political, and economic dimensions, and the accumulation of long-term inequalities has become an obstacle to the sustainable development of countries along the B&R. For low- and middle-income countries, the deprivation of the right to health through incapacitation remains the main cause of their high HPI. The B&R countries need to use the Health Silk Road as a link to establish vertical health assistance chains and horizontal regional mutual assistance and synergistic networks, to ultimately achieve the goal of eliminating health poverty.

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Section: 3mentioning

confidence: 99%

Temporal and spatial deduction pattern of health poverty eradication in countries along the Belt and Road :A systematic analysis of 141 countries from 2008-2019

Lai,

Li,

et al. 2024

Preprint

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“…To broaden its applicability to macro-level data, we reconfigured the components within vector X into distinct sub-sectoral vectors that encompass economic, social, and environmental dimensions. This modification keeps the core theoretical framework intact while facilitating a more expansive analysis of interconnected factors on a larger scale Fayissa & Gutema, 2005, p. 3;Azam et al, 2019;Uddin et al, 2023;Azam & Adeleye, 2023).…”

Section: Model Specificationmentioning

confidence: 99%

Preserving health, protecting economies: Mitigating the impact of forest fires on healthcare expenditure and environmental sustainability

Soontha,

Bhat

2023

Sustainable Development

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Forest fires release harmful pollutants, endangering public health and incurring substantial healthcare costs. This study empirically investigates the environment‐health‐economy relationship, utilizing a robust MM‐QR approach. Findings divulge that economic growth, urbanization, and tourism drive healthcare expenditure, while environmental factors like forest fires, resource depletion, species extinction, and environmental policy stringency also increase health spending. Conversely, renewable energy consumption lowers healthcare costs, while coal extraction raises them. Empirical estimates reveal that increase in forest fires raises health spending by , socioeconomic indicators by , and environmental factors by . Conversely, a rise in renewable energy usage cuts healthcare expenses by . This study underscores the urgency of addressing environmental degradation, especially forest fires, to mitigate their impacts on human well‐being.

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“…According to Dollar-Gatti (1999) in his study highlights significantly on the issue of macro-economic effects of women's education. The study finds out that, there is a strong correlation between secondary school enrollment of women and per capita income and found that gender inequality in education negatively affects economic growth but women who receive a low level of education, cultural elements, and civil rights are also affected by gender inequality Many previous studies highlighted the various factors explaining economic growth (Raza et al, 2020;Khan et al, 2019;Khan et al, 2020;Jehan et al, 2921;Khan et al, 2021;Khan et al, 2018;Uddin et al, 2023). In the light of Fennell ( 2009) in his literature review that gender inequality has reduced the economic growth of a country for minimizing the poverty and increasing development.…”

Section: Literature Reviewmentioning

confidence: 99%

An analysis of the relationship between gender inequality and economic growth of Pakistan

Ahmad,

Mukhtar

2023

GJHSSR

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This study makes an effort to finds out the relationship of gender inequality towards macroeconomic variables in Pakistan using annual time series data 2000-2020. The dependent variable is GDP, whereas independent variables are working Force engagement Ratio of Male, working Force engagement Ratio of Female, Gender similarly Index, and Openness of Trade using ordinary least square Stata, Augmented Dickey-Fuller (ADF) unit root test is used. Research findings show that GDP is a dependent variable, which has a positive relationship with independent variables that can be more effectively achieved by adopting the suggested and concluded policies of this research work. In the light of this research article, there are some following suggestions to solve the problem of gender inequality in the education sector and job opportunities.

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Revisiting the determinants of life expectancy in Asia—exploring the role of institutional quality, financial development, and environmental degradation

Cited by 16 publications

References 63 publications

Temporal and spatial deduction pattern of health poverty eradication in countries along the Belt and Road :A systematic analysis of 141 countries from 2008-2019

Temporal and spatial deduction pattern of health poverty eradication in countries along the Belt and Road :A systematic analysis of 141 countries from 2008-2019

Preserving health, protecting economies: Mitigating the impact of forest fires on healthcare expenditure and environmental sustainability

An analysis of the relationship between gender inequality and economic growth of Pakistan

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