Age, Education and Consumption of Medical Care: Evidence From Malaysia

Abstract

The present study examines age and education determinants of consumption of medical care using Malaysian data. The present study offers a better understanding of the effects of age and education on medical care and assists policy makers in developing more effective intervention measures to improve population health. A nationally representative data with a large sample size (n = 14838) was used for analyses. Several important findings are noteworthy. First, age and education are positively associated with consumption of medical care. Second, the impact of education on medical care varies across age. Third, there is no diminishing marginal effect of education on medical care. Findings of the present study suggest that age and education play an important role in determining consumption of medical care. When designing policies to improve population health, consideration should be given to the effects of age and education factors on consumption of medical care. It is important for policy makers to understand which age group and education level of people are more or less likely to use medical care.

Empirical Analysis of the Impact of Heterogeneous Aging Families on Consumption based on CFPS Data

“…This paper uses the balanced panel data of CFPS 2014, 2016, and 2018, based on the social background of aging, and focuses on the impact of heterogeneous aging households on consumption. The conclusions are as follows: (1) In terms of the impact on consumption level, the degree of aging families has a significantly negative impact on consumption levels and the negative influence of aging families with higher education is even smaller. (2) In terms of the impact on the consumption structure, the degree of aging families has a significantly negative impact on subsistence, enjoyment, and developmental consumption, but aging families without higher education have a greater inhibitory effect on various types of consumption.…”

“…In the regression model of column (1) in Table 3, the regression coefficient of the degree of aging households is significantly negative at the 1% significance level, and the elasticity coefficient is -0.359. In the regression model of column (5) in Table 3 , the regression coefficient of the degree of aging households and the regression coefficient is significantly negative at the 1% significance level, and the elasticity coefficient is -0.934, indicating that the higher the degree of aging families, the lower the consumption level, and in aging families without higher education, the inhibitory effect on consumption is greater.…”

“…In the regression model of column (5) in Table 3 , the regression coefficient of the degree of aging households and the regression coefficient is significantly negative at the 1% significance level, and the elasticity coefficient is -0.934, indicating that the higher the degree of aging families, the lower the consumption level, and in aging families without higher education, the inhibitory effect on consumption is greater. In the regression model of column (1) in Table 4 , the regression coefficient of the degree of aging families is significantly negative at the 1% significance level, and the elasticity coefficient is -0.618. In the regression model of column (5) in Table 4 , the regression coefficient of the degree of aging families is significantly negative at the significance level of 1%, and the elasticity coefficient is -1.033, indicating that the higher the degree of aging families, the lower the consumption level, and in aging families without higher education, the inhibitory effect on consumption is greater.…”

Sociodemographic Factors Associated With Consumption and Expenditure on Vitamin Supplements Among Youth in Malaysia

“…The dependent variable of the present study, i.e., monthly expenditure on vitamin supplements, was formatted as a continuous variable (in RM). Considering the lack of studies related to the demand for health supplements in Malaysia, the independent variables used in the present study were selected based on economic theories, previous studies related to health supplements conducted elsewhere, and studies focusing on other health-related goods (Grossman, 1972;Kim et al, 2010;Bailey et al, 2013;Cheah, 2013;Pouchieu et al, 2013;Cheah, 2014;Gong et al, 2018;Mohd Zaki et al, 2018;Cheah et al, 2020;Mohd Ashri et al, 2021). Note: For the dependent variable, the values refer to mean and standard deviation.…”