Digital economy and urban insurance demand: empirical evidence from 289 cities in China

Dear Editor- in- Chief Voluntary health insurance schemes help those who are not included in social health insurance (1). These are divided into supplementary and self-employed insurance. Generally, the supplementary health insurance provides health facilities in nongovernmental sectors for the insured, fills the gap in services and commitments of basic health insurance, (2) makes the room for innovation, diversity and competition in the field of health insurance activities (2). In addition, self-employed health insurance is awarded to persons who work with an employer or alone (self-funding) based on work permits issued by the competent authorities or under the recognition of Social Security Organization (3). This study emphasizes on measuring main determinants of demand for supplementary health insurance. Data are extracted from website of Statistical Center of Iran in 2012. We select a sample of 4055 urban households and 1594 rural ones. Due to binary nature of dependent variable (y), we use a Logistic regression model to estimate supplementary health insurance demand (4). Pr⁡[y=1]=exp⁡(βx)1+exp⁡(βx) y indicates demand for supplementary health insurance and takes 1, if one buys supplementary health insurance policy, or 0 otherwise. Xs consist of both quantitative variables (head of household’s age, income and household size) and qualitative variables (head of household’s gender, and head of household’s education level). Occupation was classified into government employee, non-government employee and the retiree; head of household’s marital status was classified into single (base group), married and divorced or deceased. House ownership status was classified into the leased (base group), owner-occupied and mortgaged houses. The results of Logistic regression model estimation are reported in Table (1), in which likelihood ratio (LR) and Hosmer-Lemeshow test imply goodness of fit of the model. The odds ratios (OR) in Table 1 are of probabilistic interpretations. For example, for each year increase in age, the probability of demand for supplementary health insurance in both urban and rural households increases by 1.03 and 1.09 times, respectively. The similar interpretations are applicable for the remaining odds ratios. Table 1: The regression results of logistic model for supplementary health insurance demand The demand for supplementary health insurance is not related to gender of household’s head and household size in urban areas. However, in rural areas, as the size of household increases, the higher expenses for housing, clothing, food and transportation reduce demand for supplementary health insurance (5). The household head’s age is of positive impact on demand for supplementary health insurance; possible reasons are high motivation to improve faster and greater likelihood of developing a disease with aging. According to Table 1, the higher the income level, the more the ability of household to pay and the probability to demand for voluntary health insurance. A positive significant relationship between education level of household head and demand for supplementary health insurance shows that low-educated people are high risk-taker but high-educated people are risk-averse. Demand for supplementary health insurance in urban households is positively linked to occupation due to regulatory mechanisms, which provide more facilities to the retired and employed people. Lack of similar relationship in rural households originates mainly from self-employment of villagers in agriculture or animal husbandry activities. There is no significant relationship between demand for supplementary health insurance and marital status in both urban and rural households. In urban households demand for supplementary health insurance in house owner group is higher than tenant group, because rent costs impose additional burden on household income and as a result reduce household ability to pay [for insurance] (6). In rural households, demand for supplementary health insurance has no significant relationship with house ownership, since villagers as house owners or living together households have no extra rent expenses.

Health insurance provides financial support for health care expenditures. There are two types of health insurance: compulsory and voluntary. Voluntary health insurance can be divided into two categories: self-employed and supplementary. In this study, the main factors that affect the demand for self-employed health insurance in Iran were determined. In this cross-sectional study, data were derived from the 2013 Household Income and Expenditure Survey from the Statistical Center of Iran. Then, a logistic regression model was designed to determine the factors influencing health insurance demand. The age, income, and education level of the head of the household directly correlated with the demand for self-employed health insurance. There was no significant relationship between the demand for health insurance and the gender or marital status of the head of the household. In addition, there were no significant relationships between occupation or house ownership and the demand for health insurance in rural households. To promote voluntary health insurance, it is helpful to identify effective factors that stimulate the health insurance demand.

IntroductionThis study deals with examining factors that catalyze demand for community-based micro health insurance (MHI) schemes. We hypothesize that demand for health insurance is a collective decision in the context of informality and poverty. Our hypothesis challenges the classical theory of demand which posits individual expected diminishing utility. We examine factors beyond the traditional exogenous variables.MethodsThis study uses data collected through a household survey conducted among self-help groups in rural India in the states of Uttar Pradesh and Bihar before the implementation of three community-based MHI schemes. Additional information was extracted from the management information system maintained by the schemes. At the first step, we compared the estimated probability of a household joining the scheme (obtained by applying logistic regression) to the actual uptake. In the next step, we analyzed the role of consensus within groups on demand for health insurance (by applying ordinary least square regressions).ResultsThe results of the logistic regressions indicated that exogenous household characteristics could not explain the probability of joining health insurance. We observed that group consensus on several critical issues, such as the price of the insurance, perceptions about exposure to adverse health events, and perceptions of the quality of service of local health care providers, was the important determinant of demand for insurance.ConclusionBased on the analysis, we reject the null hypothesis that demand is an individual decision at the household level. The analysis upholds the assumption that demand is created through a process of consensus building on perceptions of risk exposure, welfare gains from the insurance, and quality of local health care provision. Success in catalyzing demand for health insurance in the informal sector depends on encouraging group dialog.

Introduction: This study deals with examining factors that catalyze demand for community-based micro health insurance (MHI) schemes. We hypothesize that demand for health insurance is a collective decision in the context of informality and poverty. Our hypothesis challenges the classical theory of demand which posits individual expected diminishing utility. We examine factors beyond the traditional exogenous variables. Methods: This study uses data collected through a household survey conducted among self-help groups in rural India in the states of Uttar Pradesh and Bihar before the implementation of three community-based MHI schemes. Additional information was extracted from the management information system maintained by the schemes. At the first step, we compared the estimated probability of a household joining the scheme (obtained by applying logistic regression) to the actual uptake. In the next step, we analyzed the role of consensus within groups on demand for health insurance (by applying ordinary least square regressions). Results: The results of the logistic regressions indicated that exogenous household characteristics could not explain the probability of joining health insurance. We observed that group consensus on several critical issues, such as the price of the insurance, perceptions about exposure to adverse health events, and perceptions of the quality of service of local health care providers, was the important determinant of demand for insurance. Conclusion: Based on the analysis, we reject the null hypothesis that demand is an individual decision at the household level. The analysis upholds the assumption that demand is created through a process of consensus building on perceptions of risk exposure, welfare gains from the insurance, and quality of local healthcare provision. Success in catalyzing demand for health insurance in the informal sector depends on encouraging group dialog.

Commercial health insurance is not only an important part of China's multi-tiered medical safeguards system, but it is inherently necessary if we are to achieve the strategy of a 'Healthy China.' Attempts to develop commercial health insurance in China fall within the general scope of 'supply-side structural reforms.' In order to understand and truly grasp the demand for commercial health insurance in China, in June and July 2017, the authors conducted a nationwide telephone survey on 'the demand for health insurance among urban and rural people' in the country. The questionnaire covered basic information on those being surveyed, including participation in social insurance and assessments of that program, purchase of commercial health insurance and evaluations of that insurance, future demand, and so on. About 70% of respondents believe that China's basic medical insurance programs can meet their needs, and more than 40% of respondents were willing to buy commercial health insurance in the following year.

The demand for health insurance has two tiers: first, whether the individual participates in health insurance programme or not; if yes, second, how much coverage they purchases. The demand for health insurance has been studied either by estimating the probability of participation in health insurance programme or by estimating the amount spent for the health insurance. Simultaneous estimation of the two tiers of the demand for health insurance is uncommon in literature. Against this end, the probability of enrolment and the premium paid, a proxy for coverage, for health insurance have been estimated simultaneously using National Sample Survey data (2017–2018) from India. Empirical findings indicate that both the decision to participate and the premium paid per year vary across per capita household income quintile. There exist significant differences in the demand for health insurance across the genders, working classes, regions and different social and religious communities. Age and education level significantly affect the decision to participate and spending amount for purchasing health insurance. The individuals having chronic illness have a higher rate of enrolment, but they spend less on insurance premiums. Health condition of the individual is more important compared to income to determine the demand for health insurance in India.

We identify the need for a theory of demand for health insurance suited to the informal sector in low- and middle income countries (LMIC) where some 3 billion people lack health cover. Excluded from formal governance structures, they rely on informal arrangements by which rules-in-use shape choices, behaviours and decisions. We explore the fundamental assumptions of standard economic theories of demand for health insurance in the light of arguments from the literature and field evidence. We show that the assumptions are largely inconsistent with the context of poverty and informality. And we propose a new theory based on assumptions better suited to the context of informality and poverty. Our major conclusion is that, in order to grow the demand for health insurance in the informal sector in LMIC, it is first necessary to strengthen ground-up governance consistent with group-based decision-making under local conditions.

We identify the need for a theory of demand for health insurance suited to the informal sector in low and middle income countries where some 3 billion people lack health cover. Excluded from formal governance structures, they rely on informal arrangements by which rules-in-use shape choices, behaviors and decisions. We explore the fundamental assumptions of standard economic theories of demand for health insurance in the light of arguments from the literature and field evidence. We show that the assumptions are largely inconsistent with the context of poverty and informality. And we propose a new theory based on assumptions better suited to the context of informality and poverty. Our major conclusion is that, in order to grow the demand for health insurance in the informal sector in low- and middle income countries, it is first necessary to strengthen ground-up governance consistent with group-based decision-making under local conditions.

According to a new theory advanced by Nyman (1999, 2003), an important access motivation underlies the demand for health insurance. However, little empirical research has attempted to quantify and explain changes in the access value of health insurance. By assuming the demand for health insurance is derived from the demand for good health, this article shows mathematically that the marginal access value of private health insurance can be reasonably indexed by dividing the price of health insurance by a composite measure of medical prices. For the period from 1960 through 2002, national data for the United States suggests that the marginal access value of private health insurance has tended to increase over time. Based upon multiple regression analysis, marginal access value is shown to have increased over time in response to rising income, more generous benefit coverage, new medical technologies, and, in recent years, the backlash against health maintenance organizations (HMOs). In addition, expansions in the Medicaid program are shown to have slowed the growth of the marginal access value of private health insurance.

This article analyzes the effects of uncertainty and increases in risk aversion on the demand for health insurance using a theoretical model that highlights the interdependence between insurance and health care demand decisions. Two types of uncertainty faced by the individuals are examined. The first one is the uncertainty in the consumer's pretreatment health and the second is the uncertainty surrounding the productivity of health care. Comparative statics results are reported indicating the impact on the demand for insurance of shifts in the distributions of pretreatment health and productivity of health care in the form of first‐order stochastic dominance, Rothschild–Stiglitz mean‐preserving spreads, and second‐order stochastic dominance. The demand for insurance increases in response to a Rothschild–Stiglitz increase in risk in the distribution of the pretreatment health provided that the health production function is in a special class and the price elasticity of health care is nondecreasing in the pretreatment health. Provided also that the demand for health care is own‐price inelastic, the same conclusion is obtained when the uncertainty is about the productivity of health care.

We study how demand for health insurance responds to family formation using a unique panel of young Australian women. Our data allow us to simultaneously control for the influence of state dependence and unobserved heterogeneity and detailed information on children and child aspirations. We find evidence that women purchase insurance in preparation for pregnancy but then transition out of insurance once they have finished family building. Children have a large, negative impact on demand for insurance, although this effect is smaller for those on higher incomes. We also find that state dependence has a large impact on insurance demand. Our results are robust to a variety of alternative modelling strategies.

Demand for voluntary health insurance in developing countries: The case of Vietnam’s school-age children and adolescent student health insurance program

What countermeasures should the public take as they become aware of the dangers of haze pollution? Insurance has the function of risk diversification, and little existing literature has focused on the relationship between haze pollution and commercial health insurance. This paper analyzes the impact of haze pollution on residents' demand for commercial health insurance and the heterogeneous impact of institutional environment using the 2017 China Household Finance Survey cross-sectional data (CHFS). The study finds that haze pollution raises residents' demand for commercial health insurance as their health risk perception level rises. The legal environment, market environment, and traditional culture affect the relationship between haze pollution and the demand for commercial health insurance. Further analysis reveals that the relationship between haze pollution and residents' demand for commercial health insurance can show significant regional heterogeneity, with a significant positive correlation in the eastern region and a significant negative correlation in the central and western regions. In addition, the preventive behaviors adopted by residents in the face of haze pollution can vary significantly depending on individual risk preferences. The findings of this paper are important for the public to take measures to cope with the haze pollution hazards. At the same time, insurance companies should improve their services to meet the needs of the public regarding haze pollution, which will contribute to the healthy development of the insurance industry.

Public health education is gaining significance globally, and it is important for managing health risks. This study empirically analyzed the effect of public health education on people's demand for commercial health insurance. And we used the fixed effects and the mediating effect models, and instrumental variables regression in our research based on panel data of 31 provinces (including municipalities and autonomous regions) in China from year 2009 to 2019. The findings show that public health education significantly increases people's demand for commercial health insurance, and this effect remains significant when considering endogeneity and robustness. We further analyzed and found that the increased demand for commercial health insurance is caused by health literacy, health risk perceptions and health risk attitudes. Through heterogeneity analysis, we found that there were significant differences in the effects of public health education in regions with different demographic and socioeconomic characteristics. We found that the effect of health education on promoting people's demand for commercial health insurance is more obvious in regions with high levels of urbanization, proportion of men, education, economic development, medical resources, and social medical insurance coverage. Governments are supposed to take further measures to enhance the effectiveness of public health education, develop high-quality commercial health insurance, and continuously improve health risk coverage.

Based on the panel data of 285 prefecture-level cities in China, this paper empirically tests the impact of digital economic development on carbon productivity by using a two-way fixed effect model, intermediary mechanism model and threshold mechanism model. The results show that: (1) the digital economy can significantly improve carbon productivity, and this conclusion is still valid after a series of robustness tests. (2) An intermediary mechanism test found that technological innovation, reducing energy consumption intensity and improving urban productivity are the three primary paths through which the digital economy significantly improves carbon productivity. (3) A threshold mechanism test found that the promotion effect of the digital economy on carbon productivity is also affected by the degree of marketization and the level of human capital, showing a single threshold effect and a U-shaped trend. (4) The impact of the digital economy on carbon productivity has regional heterogeneity, urban agglomeration heterogeneity, and resource-based city heterogeneity. This study provides substantial empirical evidence for the relevant authorities to formulate green development policies from the perspective of digital economy development.