Out-of-pocket payments have been used as the main variable in a number of studies by many researchers around the world. Based on such a huge amount of research, a variety of definitions and measurements for this variable is drawn. In the study with the sample of three provinces in Vietnam in 1999, Jowett et al. (2003) suggest that this variable can be measured by adding up both official and unofficial payments to achieve the total expenditure on healthcare. Sephri et al. (2006) did not mention official or unofficial payments but just consider out-of-pocket payments as the household expenditure on health during the past 12 months. Ekman (2007) has a quite different point of view that the difference between the income of a patient and the expense of the household on heath is the out-of-pocket payments.
Fan et al. (2012) divide healthcare services into two elements, inpatient and outpatient services. Each element is also the combination of different indicators at the household level.
Out-of-pocket payments are the sum of these two elements. This variable is adjusted to the monthly scale and is divided by the household size to have the spending per capita. Using the Indonesia’s Family Life Survey in four years, Aji et al. (2013) show that out-of-pocket payments include all medical costs such as hospitalization, clinic, physicians, traditional cures, and medicines except transportation costs due to the unavailability of the data. The effects of inflation on household expenditures on health are eliminated by instilling the Consumer Price Index (CPI) in 2007. Similar to Fan et al. (2012), the adjustment of the variable based on the household size is added to get payments per capita. Van Minh et al.
(2013) also hold that payments on health services should take into account consultation, medication, hospitalization, and medicine costs but not costs related to transportation and nutrition.
2.3.2.2 Estimation method
A wide range of methods has been used by different authors to test the relationship between out-of-pocket payments and a certain factor. To estimate the effects of health insurance on health expenditures, Jowett et al. (2003) use Ordinary Least Squares (OLS) for the regression models. However, this method is limited due to the assumption that there is no relationship between health expenditures and unobservable factors. Ekman (2007) also analyzes the impact of health insurance on health expenditures in Zambia, a low-income country, in 1998 only by multivariate regressions. Compared with these two cross-sectional studies, Sepehri et al. (2011) expand the study scope to the panel data of VHLSS 2004-2006 with the use of Fixed Effects (FE) and Random Effects (RE) for the insured and uninsured group. With a variety of methods for panel data regression like Pooled Ordinary Least Squares (POLS) and Fixed Effects (FE) for models without endogeneity as well as Two-stage Least Squares (2SLS) for models with instrumental variables (IV), Aji et al. (2013) discover a significantly negative relation between out-of-pocket payments and health insurance programs. According to the paper, household participation in the communal gatherings, women’s groups, and co-operation are considered as the three instrumental variables in the research.
Besides, some other studies provide researchers with different kinds of regression methods. Sepehri et al. (2006) apply Tobit and truncated regression models to interpret the relationship between health insurance and health expenditures. In these two models, Fixed Effects and Random Effects for the panel data of VLSS are included in the regression. Van Minh et al. (2013) approach the research with the logistic model to examine whether the catastrophe and poverty will decide the probability of having an out-of-pocket expenditure of a household.
Other methods commonly used by several researchers is Propensity Score Matching (PSM) and Difference-in-Difference (DID). While Wagstaff and Pradhan (2005) just apply double difference for their research, Wagstaff (2010) later extend this method, ranging from single difference, double difference, and triple difference, together with the matching method.
Nguyen (2012) combines OLS, IV, PSM, and DID in his research to measure the impact of voluntary insurance on health expenditures for VHLSS 2004 – 2006. Fan et al. (2012) also use DID analysis for their study in Southern India with the clear division of data into the treated and control group. Recently, the paper of Alkenbrack and Lindelow (2015) employs propensity score matching for 3000 households in Laos and then double treatment effects to examine the influence of out-of-pocket payments.
10 2.3.3.3 Control variables
A great number of control variables have been defined and listed in some studies related to out-of-pocket expenditures. In general, control variables are composed of two types, demographic and attitude variables.
Demographic variables include the characteristics of the household head (gender, age, education, marital status, occupation, ethnicity, health status), the characteristics of the household (size, income, expenditure, distance to healthcare providers, location, the elderly, children), and the feature of healthcare services (insurance types, health facilities, outpatient visits, outpatient contacts) (Jowett et al, 2003; Sepehri et al, 2006; Ekman, 2007; Sepehri et al, 2011; Fan et al, 2012; Nguyen, 2012; Aji et al, 2013; Van Minh et al, 2013).
Attitude variables are mention recently in the research of Alkenbrack and Lindelow (2015). In this paper, aside from the demographic variables presented in the previous papers, they take into account risk preferences of the household head and attitudes of households toward insurance.
2.3.4.4 Results
The results on the impacts of health insurance on out-of-pocket payments show a distinct polarization, making this relationship interesting for researchers across the globe to pay their attention to. Health insurance may have no, positive, or negative effects on out-of- pocket payments.
King et al. (2009) indicate the insignificant impact of health insurance on healthcare service expenditures for the case of households in Mexico. However, he says that this insignificance may be due to the fact that the health insurance program is only distributed to the poor and it lasts just for a short period of ten months. Nguyen (2012) points out the similar finding that the voluntary health insurance has no impact on out-of-pocket expenditures. His explanation is that health insurance only pays for the costs of healthcare and drugs, while he defines this variable as the total of treatment and other related treatment costs. Another problem may be due to measurement errors in measuring out-of-pocket expenditures for the research data.
Ekman (2007) researches households in Zambia and shows that health insurance has no role to play in the protection of household members from the catastrophe. He finds that this influence of health insurance is mainly guided by the quality and the provision of the insurance. The research suggests that the higher income a household has, the lower risks of disasters it incurs, and the further a house is, compared to the healthcare service providers, the
11 higher spending on catastrophe a household suffers. In addition, those who are employed or are farmers confront smaller risks of disasters than other groups.
On the contrary, Fan et al. (2012) found the negative relationship between health insurance and out-of-pocket payments. The research shows that during the first nine months after the introduction of health insurance, both inpatient and outpatient expenses reduce at the state of Andhra Pradesh in Southern India. This result is proved to be robust after the use of quantile regression and the matching method. Aji et al. (2013) give the same results when doing research in Indonesia. They suggest that the two largest health insurance programs are the main cause leading to the decrease in households’ out-of-pocket payments. Van Minh et al. (2013) identifying the determinants of health spending on catastrophe show that the enrollment of households in health insurance helps them lower the expenditures on catastrophe and impoverishment. Alkenbrack and Lindelow (2015) say that those who are insured have more opportunities to lower their out-of-pocket payments and disaster rates than those who are not. They show a surprising result that the health insurance program protects the rich better than the poor since the poor are unlikely to pay out-of-pocket costs.
The context of Vietnam rises some highlighted studies on the impacts of health insurance on out-of-pocket payments. Jowett et al. (2003) indicate that both voluntary health and student insurance make out-of-pocket payments decrease. Especially, health insurance helps reduce out-of-pocket payments for Vietnamese households up to 200%. This effect is clearly observed through the expenditures of the poor rather than the rich. Using the VLSS in 1993 and 1998, Wagstaff and Pradhan (2005) find that health insurance is positively linked to the adoption of healthcare services and is the reason for the increase in the households’ visits to hospitals. Finally, their study holds that health insurance is proved to cause out-of-pocket expenditures to fall. In the further study, Wagstaff (2010) discovers that although Vietnam’s Health Care Fund for the poor truly does not have any effect on the use of healthcare services, it has, in effect, reduced out-of-pocket expenditures. Sepehri et al. (2006), like Wagstaff and Pradhan (2005), use the panel data of VLSS in 1993 and 1998 to measure the effects of health insurance. They then come to the same conclusion that health insurance decreases out-of- pocket payments after controlling unobserved heterogeneity. They show that health insurance lowers out-of-pocket expenditures from 16% to 18% and that the decrease in expenditure is larger for the high than the low-income households. Later research of Sepehri et al. (2011) again supports the decrease in out-of-pocket expenditures by 24% if patients have either compulsory or voluntary health insurance. As for the poor, the use of health insurance helps them reduce their out-of-pocket payments by approximately 15%. The study proves that if
patients visit healthcare centers at the district or higher level instead of the communal level, they can lower their expenditures on health services. Both the compulsory and voluntary health insurance have no effects at the commune health facilitates. In addition, compared with those who are uninsured, those who are insured can decrease their out-of-pocket payments by 32% to 40%.
Health insurance participation and medical service usage Health insurance Inpatient Outpatient
Socio-Economic and Demographic Characteristics Age Gender Education Income Marital status Ethnics
Out-of-pocket expenses per visit
Living Environment characteristics Urban area Rural area
CHAPTER 3: RESEARCH METHODOLOGY AND DATA