Shulin Xu, Zefeng Tong, Cheng Li and Shuoqi Chen
High-quality labor supply is inevitable to maintain sustainable and steady economic growth. This study mainly explores the impact of the social pension system on the health of…
Abstract
Purpose
High-quality labor supply is inevitable to maintain sustainable and steady economic growth. This study mainly explores the impact of the social pension system on the health of human capital, and further explores its impact mechanism.
Design/methodology/approach
On the basis of the data from China Family Panel Studies from 2012 to 2018, this article uses the fixed effect model and the mediation effect model to empirically study the influence of the social pension scheme on the health of human capital and further explore its influence mechanism.
Findings
This study shows that the social pension scheme can significantly improve the physical and mental health of laborers, especially for low-income and agricultural groups. The implementation of the social pension scheme contributes to increasing medical services and reducing the labor supply for the benefit of human health capital. Therefore, the government should continue to expand the coverage of the social pension scheme and comprehensively improve the importance of human health capital on economic growth.
Practical implications
Medical costs and labor supply play a mediating effect in the relationship between social pension and rural labors' health status, which indicates that medical costs and labor supply level are still important factors affecting the health status of rural labor. There are essential factors affecting the health status of the rural labor force, and their role should be given more consideration in the process of system design and improvement.
Originality/value
The existing studies have more frequently studied the effect of the implementation of social pension schemes from the perspective of economic performance, but this paper evaluates the policy effect of social pension schemes based on the perspective of health human capital, which enriches research on health performance in related fields.
Details
Keywords
Brent Lagesse, Shuoqi Wang, Timothy V. Larson and Amy Ahim Kim
The paper aims to develop a particle matter (PM2.5) prediction model for open-plan office space using a variety of data sources. Monitoring of PM2.5 levels is not widely applied…
Abstract
Purpose
The paper aims to develop a particle matter (PM2.5) prediction model for open-plan office space using a variety of data sources. Monitoring of PM2.5 levels is not widely applied in indoor settings. Many reliable methods of monitoring PM2.5 require either time-consuming or expensive equipment, thus making PM2.5 monitoring impractical for many settings. The goal of this paper is to identify possible low-cost, low-effort data sources that building managers can use in combination with machine learning (ML) models to approximate the performance of much more costly monitoring devices.
Design/methodology/approach
This study identified a variety of data sources, including freely available, public data, data from low-cost sensors and data from expensive, high-quality sensors. This study examined a variety of neural network architectures, including traditional artificial neural networks, generalized recurrent neural networks and long short-term memory neural networks as candidates for the prediction model. The authors trained the selected predictive model using this data and identified data sources that can be cheaply combined to approximate more expensive data sources.
Findings
The paper identified combinations of free data sources such as building damper percentages and weather data and low-cost sensors such as Wi-Fi-based occupancy estimator or a Plantower PMS7003 sensor that perform nearly as well as predictions made based on nephelometer data.
Originality/value
This work demonstrates that by combining low-cost sensors and ML, indoor PM2.5 monitoring can be performed at a drastically reduced cost with minimal error compared to more traditional approaches.
Details
Keywords
Sharifa Ezat Wan Puteh, Chamhuri Siwar, Rozita Hod, Azmawati Mohammed Nawi, Idayu Badilla Idris, Izzah Syazwani Ahmad, Nor Diana Mohd Idris, Nurul Ashikin Alias and Mohd Raihan Taha
River flood exposes the population to multiple attacks from the physical, mental, health risks and its related negative effects. This study focused on the Pahang River and the…
Abstract
River flood exposes the population to multiple attacks from the physical, mental, health risks and its related negative effects. This study focused on the Pahang River and the three worst-hit district population (Pekan, Kuantan and Temerloh). Tools on areas of self-perceived health symptoms, QOL, depression, PTSD and community empowerment were assessed. Semi-guided questionnaires were distributed to a total of 602 victims. Questions on health symptoms were asked to respondents (R) and household members (HM). PTSD screening, i.e., the Trauma Screening Questionnaire, was used. Depression was assessed through the Beck Depression Inventory (BDI). WHOQOL-BREF assessed four domains of QOL, i.e., physical activity, psychological, social relationships and environment. Community empowerment using the Individual Community Related Empowerment tool to assess five domains, i.e., self-efficacy, participation, motivation, intention and critical awareness. Prevalent disease showed that majority suffered from hypertension (11.0%) and diabetes (7.3%). Two main symptoms experienced were cough (R = 47.2%, HM = 43.7%) and flu (R = 42.7%, HM = 40.4). Monthly health expenditure was higher post flood. Purchase of prescription medications rose from MYR24.40 to 31.02. A total of 33 people were suspected to suffer from PTSD. Through BDI assessment, it was estimated that as many as 104 (17.3%) suffered overt (high) depression. The prevalence of QOL domains are as such: low physical activity was highest at 59%, low psychological activity at 53.3%, low social relationships at 43% and low environment at 45.2%. On community empowerment, low empowerment was seen on four domains: self-efficacy at 52%, participation at 55%, motivation at 54.2% and critical awareness at 74.4%. The domain with good intention and willing to participate was at 54%. Results indicate that the community was not adaptable to flood events. This is evident from high amount of experienced symptoms, low QOL (physical and psychological aspects) and empowerment (except intention). Proportion of PTSD and overt (high) depression was however quite low.