INTRODUCTION
Background
Equity in health care finance is a critical global issue, particularly for low- and middle-income countries like Viet Nam, which face challenges in achieving universal health coverage due to reliance on out-of-pocket payments Countries are striving to create health financing systems that ensure affordable access to health interventions for all, promoting equity and financial risk protection Equitable financing is essential for effective health care systems, as highlighted in various policy documents and analyses Understanding the equity implications of different health care financing sources—such as taxation, social health insurance, and out-of-pocket payments—can guide policymakers in fostering a more equitable health financing landscape.
This research aims to evaluate the equity of health care financing in Vietnam, utilizing quantitative techniques in a new context The study employs the concentration index to measure inequality and the Katwani index to assess inequity within health care finance It analyzes four distinct financing sources—outpatient and inpatient expenditures, health insurance, and out-of-pocket payments—both independently and in combination to evaluate the overall financing system Additionally, the research examines inequality in health care expenditures as well as total household expenditures, which include food and non-food costs To further understand the impact on inequity, the author applies expenditure decomposition methods to identify which financing sources most significantly influence health care finance inequity.
Equity in health care financing refers to the fairness of financial contributions among households with differing abilities to pay (ATP) It is assessed by examining the degree of inequality in health care payments, as outlined by Doorslaer and Wagstaff (1993) ATP is a crucial factor in evaluating the inequity of a health care finance system and can be measured by the total household expenditures, which include costs for food, non-food items, and health care services.
Numerous studies have highlighted the significance of health care finance in relation to Average Treatment Payment (ATP), examining inequities in countries like Denmark, the UK, Ireland, Portugal, Spain, Italy, the Netherlands, and Switzerland The alignment of health care payments with ATP is considered a crucial goal in Belgium, France, Germany, and the Netherlands Policymakers across various nations are increasingly committed to financing health care in accordance with ATP principles.
Kakwani (1997), Doorslaer (1997,2000),Doorslaer andMasseria(2004), Wagstaffand Doorslaer (1993, 1997),Wagstaff(2002)have studied income- relatedinequalityinhealthcareutilization, equity in health care delivery, equity in health care finance, and inequalities in health by using ATP
The Ministry of Health (MOH) in Vietnam also agree to use the new national health financing scheme be related to ATP (PAHE, 2011)
With all reasons above, the author also uses ATP tomeasureandexplaininequality and inequity in health care finance in Vietnam
This study utilizes the concentration index and Kakwani index, as proposed by Wagstaff and Doorslaer (2000), to evaluate inequality and inequity in healthcare financing in Vietnam The research aims to identify the presence of inequities in the healthcare system and to determine the key factors influencing these disparities in health financing.
Research Objectives
This study conducts an inequity assessment of Vietnam's health financing system by integrating all sources of financial support to evaluate its overall structure The primary aim is to analyze the inequity in healthcare financing based on the ability-to-pay quintiles of Vietnamese households Specific objectives include identifying disparities in health financing across different income levels.
1 To calculate the inequality indices (CIs) and the inequity indices (Katwani indices)of healthcare finance variables of households such astotal expenditure, health payments, out-of-pocket for health, food or non-food payments
2 To decompose the inequality of households’ totalexpenditure and total health expenditure
3 To calculate the factors affect to total expenditure or ATP through both OLS and
Data source
This study uses the datasets of Vietnam Living Standards Survey 2012 and 2010
(VHLSS 2012, 2010) with households as observations.
Study Design
Chapter 1 focusesonthebackgroundand preciselystatestheproblemsthathavetobeaddressbythisresearch It also establishes the significance of thisresearch
Chapter 2presents general definition of inequality and inequity in health, health finance variables, and methods measure inequity indices
Chapter 3 briefly reviews the relevant literatures and outlines the detailed method used for this study
Chapter 4 calculates inequity indices and decomposes the health care finance variables
Finally, Chapter 5 briefly discusses the conclusions, policy implications and limitation of this study.
LITERATURE REVIEW
Definition
Today, there are many definitions about equity of different schools, here are some perspectives:
Libertarians emphasize a respect for natural rights, focusing in particular on two of the rights: rights to life and to possessions
Utilitarians aim at maximizing the sum of individual utilities or welfare, though some utilitarian writers have incorporated a concern for individual autonomy into this maximand
Rawlsians (1971) advocate two fundamental principles of social justice: first, individuals should enjoy the greatest possible liberty that aligns with equal liberty for all; second, any intentional inequalities are deemed unjust unless they benefit those who are least advantaged.
Marxists prioritize the concept of "needs," advocating for a principle of "distribution according to need," which can be understood as "from each according to his ability to pay." Health equity is a multifaceted issue, with various researchers and institutions worldwide offering diverse perspectives, as outlined in Table 1.
Horizontal equity requires equal treatment for equal need
Vertical equity: different treatment for different need
2 Aday 1984 Health care is equitable when resource allocation and access are determined by health needs
3 Whitehead 1990, 1992 Health inequities are differences in health that are avoidable, unjust and unfair
Equity in health care means equal utilization, distribution according to need, equal access and equal health outcomes
International Society for Equity in Health (ISEqH), 2005
Health equity is the absence of systematic and potentially remediable differences in one or more aspects of health across populations or population subgroups defined socially, economically, demographically or geographically
“Health Equity is the absence of potentially avoidable differences in health (or health risks that policy can influence) between groups of people who aremore and less advantaged socially”
Hurst (1985) emphasizes that the financing of health care should be based on individuals' ability to pay, highlighting the inherent inequities in the current system This perspective serves as a foundational principle for analyzing health care finance and addressing disparities.
Egalitarians advocate for a health care system that finances services based on individuals' ability to pay, ensuring equal access to care for everyone They emphasize the importance of organizing health care delivery to allocate resources according to need, ultimately promoting health equality across the population.
Health care finance is influenced by various determinants, including individual lifestyle choices, social and community networks, as well as broader socio-economic, cultural, and environmental factors The World Health Organization (WHO) illustrates these complex interactions in Figure 1.
Figure 1: Social determinants of health and health equity
Health inequalities refer to the disparities in health outcomes and their determinants among different segments of the population, influenced by social, demographic, environmental, and geographic factors These inequalities highlight the observed differences in health that exist across various groups.
Health inequity arises from ethical considerations regarding the fairness of health disparities It pertains to inequalities that are perceived as unjust, resulting from systemic factors, and reflects the "absence of potentially avoidable differences in health between socially advantaged and disadvantaged groups" (PAHE, 2013).
Health inequality refers to the natural differences in illness rates between older and younger individuals, which is largely due to biological factors and does not carry a moral judgment However, when the elderly poor experience a higher incidence of illness compared to their non-poor counterparts, this situation is categorized as health inequity, influenced by socio-economic factors The distinction between health inequality and health inequity is further illustrated in Figure 2.
Figure 2: Health inequality vs health inequity
To effectively assess health care inequity, various research fields must adhere to established techniques, as illustrated in Figure 3 (PAHE, 2013) This article focuses specifically on inequity in health care financing, with a greater emphasis on the disparities related to household payments for health care services.
Figure 3: Process to analyze inequity
The goal of reducing inequity in health care financing is to bridge the gap between public and personal health care expenditures Achieving this objective requires effective management and control of three key perspectives, as illustrated in Figure 4.
Figure 4: Three dimensions of health coverage
2.1.4 Vertical equity and Horizontal equity
Vertical equity: persons or families of unequal ability to pay making appropriately dissimilar payments for health care, and
Horizontal equity refers to the principle that individuals or families with the same financial capacity should contribute equally This concept emphasizes that those with equal ability to pay should make comparable payments, irrespective of factors such as gender, marital status, union affiliation, or geographic location.
In this study, the author analyzes inequality and inequity in health care finance mostly based on vertical equity perspective
In a developing-country context, like Viet Nam, given the lack of organized labor markets and the high variability of incomes over time, household consumption
Household expenditure is often viewed as a more accurate indicator of welfare and financial capacity than income This thesis aims to evaluate the proportional relationship between health payments and living standards, utilizing gross household expenditures as a key measure Consequently, the study defines the ability to pay as the total consumption of a household, encompassing all healthcare-related expenses.
Concentration index and Concentration curve
Wagstaff (1991) published a paper on the measurement of inequalities in health
This paper aims to critically review various measures of health inequality and identify the most effective ones for assessing disparities in health outcomes It highlights three key measures: (a) the range, (b) the Gini coefficient derived from the Lorenz curve, and (c) the concentration index based on the concentration curve.
The Concentration Index (CI) quantifies the degree of socioeconomic inequality by measuring the area between the concentration curve and the line of equality Ranging from -1 to +1, the CI equals zero when there is no inequality present A negative CI indicates that the concentration curve is above the line of equality, while a positive CI signifies that it lies below The absolute value of the CI reflects the magnitude of inequality, with larger values indicating greater disparity in socioeconomic status.
A concentration index below 0.2 signifies low inequality, while an index ranging from 0.2 to 0.39 indicates moderate inequality A concentration index between 0.4 and 0.6 represents high inequality, marking a critical threshold that requires urgent attention Lastly, an index of 0.6 or above denotes a very high level of inequality.
Table 2: The magnitude of inequality based on the value of CIs
Absolute value of concentration index Interpretation
The concentration curve plots the cumulative percentage of the health variable
The graph illustrates the relationship between health variables and living standards, with the y-axis representing health outcomes and the x-axis displaying the cumulative percentage of the population, ranked from the poorest to the richest This visual representation effectively shows how health shares correlate with different quantiles of living standards, as depicted in Figure 5.
Katwani indices and Concentration curves
Kakwani (1997) elucidated the connection between two prominent health inequality indices: the relative index of inequality (RII) and the concentration index (CI) He argued that these indices are superior to others commonly found in the literature, highlighting their effectiveness in measuring health disparities.
CIissensitivetosocioeconomicdimensionofinequalitiesinhealthbecauseitsvaluelies between-1to1.ApositiveCIrepresentsthepro-richandanegativeCIrepresentspro-poor inequality in health
Figure 5: Lorenz curve for prepayment income and concentration curve for health care payment
Source: Handbook of Health Economics
L pre (p) is the Lorenz curve for pre-payment income
L pay (p) represents the payment concentration curve, illustrating the relationship between the cumulative proportion of the population—ranked by pre-payment income, similar to L pre (p)—and the cumulative proportion of health care payments.
The level of progressivity can be evaluated by examining the area between the pre-payment income curve (L pre (p)) and the payment curve (L pay (p)) This analysis involves the Gini coefficient for pre-payment income (G pre) and the concentration index for payments (C pay).
Kakwani's index of progressivity, K or πK, is defined as : πK = C pay – G pre
Inequity or Progressivity of health care finance
Many questions must be answered when analyzing the inequity health care finance, every problem contributes its role Example,
Who pays for health care?
To what extent are payments toward health care related to ability to pay?
Or is it progressive - do health care payments account for an increasing proportion of ability to pay (ATP) as the latter rises?
Or, is there a regressive relationship, in the sense that payments comprise a decreasing share of ATP?
Which standards used to calculate and analyze for answering these questions, here are some suggestions
The Kakwani index, introduced by Kakwani in 1977, serves as a key measure of progressivity in tax and health finance literature, as noted by O'Donnell and Wagstaff in 1992 and Wagstaff in 1999 This index is calculated as twice the area between the payment concentration curve and the Lorenz curve, expressed mathematically as πK = C pay – G pre, where C pay represents the concentration index for health payments and G pre denotes the Gini coefficient of the ATP variable The Kakwani index values can range from -2 to 1, providing insights into the equity of health financing.
A negative number indicates regressivity; L pay (p)lies inside L pre (p)
A positive number indicates progressivity; L pay (p) lies outside L pre (p)
In proportionality, the concentration is positioned above the Lorenz curve, resulting in an index value of zero However, it's important to note that the index can also equal zero if the curves intersect, leading to the cancellation of both positive and negative differences between them.
Given this, it is important to use the Kakwani index, or any summary measure of progressivity, as a supplement to, and not a replacement of, the more general graphical analysis.
Decomposition
The concentration index quantifies the contributions of various determinants to the observed inequality in health outcomes, highlighting the factors that most significantly impact this disparity When prior analyses reveal economic inequality in healthcare payments, regression modeling is employed to derive parameters that decompose the contributions of different determinants, thereby elucidating the socio-economic inequities present in the variable of interest.
The rule of thumb was to consider only the concentration index for economic inequality of equal or greater than 0.2 (Moderate, severe or extreme inequality)for decomposition analysis
Wagstaff, Doorslaer, and Watanabe (2003) illustrate that the health concentration index can be broken down into the individual contributions of various factors affecting income-related health inequality Each contribution is determined by the sensitivity of health to that factor and the extent of income-related inequality associated with it This analysis applies to any linear additive regression model of health (y).
𝑦𝑦 𝑘𝑘 = 𝛼𝛼+∑ 𝛽𝛽𝑘𝑘𝑥𝑥 𝑘𝑘 +𝜀𝜀 𝑘𝑘 (a) the concentration index for y, C, can be written as follows:
The equation \( C = \sum \beta_k \mu \bar{x}_k C_k + GC\epsilon \) illustrates that the concentration index \( C \) is determined by a weighted sum of the concentration indices of the regressors \( x_k \) In this context, \( \bar{x}_k \) represents the mean of \( x_k \), \( C_k \) denotes the concentration index for \( x_k \), and \( GC\epsilon \) is the generalized concentration index for the error term \( \epsilon \) The weights applied to each \( x_k \) are derived from the elasticity of \( y \) concerning \( x_k \), specifically represented by \( \epsilon_k \beta_k \bar{x}_k \) This formulation emphasizes the relationship between the concentration indices and the respective contributions of each regressor to the overall concentration index \( C \).
The residual component, represented by the final term, indicates the income-related health inequality that remains unexplained by systematic variations in the income regressors For a well-specified model, this residual should ideally approach zero.
This method aims to identify the root causes of health sector inequalities and their evolution over time These inequalities stem from disparities in the determinants of the variable in question By applying the decomposition outlined in Equation (b), it is possible to evaluate the relative significance of these various inequalities in contributing to the overall disparities in the variable of interest.
Review emperical studies about health equity finance
In their 2005 handbook, "Analyzing Health Equity Using Household Survey Data," O’Donnell, Doorslaer, Wagstaff, and Lindelow provide researchers with a practical guide to measuring various aspects of health equity, particularly in developing countries This resource aims to enhance the analysis of health equity, leading to improved monitoring of health trends, a deeper understanding of the causes of inequalities, and a more thorough evaluation of the impact of development programs on health equity The authors emphasize the importance of effective policies and programs to reduce health disparities They employ multiple methods for assessing inequity, notably utilizing the Concentration index and Kakwani index for evaluation.
To understanding the definition of equity, Culyer and Wagstaff (1993) have published thearticle researched the equity in USA Oneof objectivesistoclarifythemeaningofthetwodefinitionsofequitywhichseemleastclear:
“distributionaccordingto need” and “equalityof access” Authors also concludethatthe principlesof“distributionaccordingtoneed”and“equalityofaccess”have been,andcontinuetobe,interpretedinanumberofdifferentways,andthatthevariousinterp retationsaremutuallyincompatible
Wagstaff and Doorslaer (1994) compared health inequities between a developed country, Canada, and a developing country, Vietnam, using datasets VHLSS 1998 and NPHS 1994 Their research presents a framework for empirically assessing overall health inequality and socioeconomic health inequality, applicable to both individual-level and grouped data The study highlights malnutrition among Vietnamese children and health utility among Canadian adults, revealing that socioeconomic factors contribute to approximately 25% of the overall health inequality in both contexts.
In their 1997 study, Kakwani, Wagstaff, and Doorslaer analyzed health inequality using the Dutch HIS 1980/81 dataset, highlighting the relationship between two prominent indices of health inequality and demonstrating their superiority over other indices in existing literature The study also introduces asymptotic estimators for variances and emphasizes the importance of demographic standardization in understanding socioeconomic disparities in health.
In their 1997 study, Doorslaer, Wagstaff, and colleagues analyzed income-related health inequalities across nine industrialized nations, utilizing datasets from Sweden, Switzerland, the UK, the US, and Germany from the 1980s to 1990s By employing health interview survey data, they constructed concentration curves for self-assessed health, treated as a latent variable The findings revealed that health inequalities consistently favored higher income groups and were statistically significant in all examined countries.
Inequalities were particularly high intheUnited StatesandtheUnitedKingdom Amongst otherEuropeans,Sweden,FinlandandtheformerEastGermanyhadthelowest inequality.Across countries, a strongassociation was found betweeninequalitiesinhealth andinequalitiesinincome
A study by O'Donnell, Doorslaer, and partners (2005) examined health care financing inequalities across 13 territories representing 55% of Asia's population The research revealed that high-income households contribute more to health care financing than low-income households, particularly in low and lower-middle-income regions where wealthier individuals pay a higher proportion relative to their ability The impact of out-of-pocket (OOP) payments varies by economic development; in high-income countries with extensive insurance, low-income households face a heavier OOP burden, while in poorer nations, wealthier individuals tend to spend more OOP This finding challenges existing literature, indicating that the impoverished cannot afford health care in low-income economies Notably, Hong Kong exemplifies progressive financing through taxation, effectively protecting low-income individuals from OOP costs, a model also seen in Thailand.
Numerous researchers have employed decomposition methods to identify the key factors contributing to health care inequity It is important to note that only those factors with concentration indices (CIs) equal to or greater than 0.2 indicate moderate to severe inequality, making them suitable for decomposition analysis The following studies from around the world illustrate these findings.
Wagstaff, Doorslaer, and Watanabe (2003) conducted a study on health sector inequalities, focusing on malnutrition disparities in Vietnam using data from the Vietnam Household Living Standards Survey (VHLSS) of 1993 and 1998 Their research highlights that inequalities in a variable, such as health outcomes, can be decomposed into various causes, including changes in average consumption and the inequalities in its determinants The findings reveal that the disparities in height-for-age among Vietnamese children during these years were primarily driven by consumption inequalities and unobserved commune-level factors Additionally, the increase in overall inequalities is attributed to rising average consumption and its protective effects, along with broader improvements at the commune level.
To compare inequality decomposition from Vietnam and other countries, Wagstaff (2005) also researchedinequality decomposition and geographic targeting with applications to China and Vietnam.Inthis research they used dataset VHLSS
A 1998 study investigates the extent to which income-related health inequalities arise from disparities between affluent and less affluent regions, rather than differences among individuals within those areas The research presents a methodology to address this question, supported by two empirical examples Findings reveal that health subsidies in Vietnam disproportionately favor wealthier provinces, which receive larger per capita allocations Similarly, in rural China, pro-rich disparities in health insurance coverage are attributed to better-off villages successfully maintaining their insurance schemes, unlike their poorer counterparts.
A study conducted by Chai Ping Yu, Whynes, and Sach (2008) examined healthcare financing in Malaysia using the HE 92/93 dataset The research aimed to assess the equity of healthcare financing in Malaysia, employing quantitative techniques in a new context The study analyzed five distinct financing sources: direct taxes, indirect taxes, contributions to the Employee Provident Fund and Social Security Organization, private insurance, and out-of-pocket payments By evaluating these sources individually and in combination, the study assessed the overall financing system The findings indicated that Malaysia's predominantly tax-financed system exhibited slight progressivity, with a Kakwani progressivity index of 0.186.
In their 2011 report, “A Health Financing Review of Viet Nam with a Focus on Social Health Insurance,” Tran Van Tien, Hoang Thi Phuong, Inke Mathauer, and Nguyen Thi Kim Phuong provide a comprehensive overview of equity in Vietnam's health financing system The assessment evaluates the current institutional design and organizational practices related to key health financing functions, including resource collection, pooling, and purchasing By analyzing these elements, the report identifies necessary changes to enhance the system's performance and advance towards achieving universal health coverage in Vietnam.
Researching equity in health care finance is crucial, particularly in developing countries like Vietnam This article aims to address whether health care finance inequity in Vietnam warrants examination and how it compares to similar issues in other nations The findings will provide policymakers with insights to tackle the existing challenges effectively.
METHODOLOGY
Analytical framework
The assessment of equity in health care financing relies on established public finance techniques, emphasizing that financing aligned with ability to pay (ATP) is deemed equitable To evaluate whether health payments support or hinder equity, it is essential to analyze the connection between health payments and ATP in practice Progressivity indicates the extent of deviation from proportionality in this relationship, highlighting inequalities in health care payment among households with varying ATP A health payment system is considered progressive if it requires higher ATP households to contribute a larger proportion of their income, while a regressive system results in lower contributions from wealthier households Conversely, a proportional system ensures that all households, regardless of ATP, spend the same percentage on health care financing.
Figure 6: Framework of analysing inequity
Inequality (CI index)& Inequity (Katwani index) in Vietnam?
Health expenses Food Expense Non-food Expense
Model
In this research, five basis approaches were used, namely
5) Decomposition of the concentration index for economic inequality
Table 3: Summary formulae analyzing inequity
Step 1: OLS and Quantile regression to check which factors affect to y (ATP)
Examine the regression function y i =x i β+ εi with y i : dependent variable, x i : vector of independent variales; εi : error terms; the estimation of regression function 𝑦𝑦� 𝑖𝑖 =𝑥𝑥 𝑖𝑖 𝛽𝛽̂+𝑒𝑒 𝑖𝑖
Follow OLS method (OLS – Ordinary Least Square), sample regression function was estimated so that total of square of error is minimize, means that
Koenker and Basset (1978) highlighted the limitations of the Ordinary Least Squares (OLS) method, noting its sensitivity to assumptions and asymmetric observations, which can obscure the overall understanding of research quantities To address these shortcomings, they proposed the quantile regression methodology, which analyzes every quantile of the dependent variable rather than focusing solely on the conditional mean function as OLS does This approach provides a more comprehensive view of the research problem.
The conditional quantile regression of dependent variable Y follow X at quantile
𝜏𝜏 ∈(0,1) is the function 𝑄𝑄 𝜏𝜏 (𝑦𝑦 𝜏𝜏 ) =𝑥𝑥 𝑖𝑖 𝛽𝛽�, within 𝜏𝜏 𝛽𝛽� is choiced as if total of error 𝜏𝜏 different of quantile τ is minimize Means that:
Step 2: Calculate Concentration indices (CIs)
The concentration index is a convenient formula that quantifies the relationship between a health variable and the fractional rank in the distribution of living standards, as established by Jenkins (1988), Kakwani (1980), and Lerman and Yitzhaki (1989).
The concentration index (C) can be easily calculated from microdata using the "convenient covariance" formula In cases where the sample is not self-weighted, it is essential to apply weights during the computation of covariance, the mean of the health variable, and the fractional rank By leveraging the relationship between covariance and ordinary least squares (OLS) regression, an equivalent estimate of the concentration index can be derived from a "convenient regression" that analyzes a transformation of the health variable in relation to the fractional rank within the living standards distribution (Kakwani, Wagstaff, and Doorslaer, 1997).
The variance of the fractional rank is represented by 𝜎𝜎 𝑟𝑟 2 The Ordinary Least Squares (OLS) estimate of β serves as an equivalent measure of the concentration index, similar to the outcome derived from equation (3) The definition of the weighted fractional rank is provided as follows:
The formula for fractional rank is given by \( rr_i = \frac{1}{n} \sum_{j=1}^{i-1} w_j + \frac{1}{2} w_i \), where \( w_i \) represents the sample weight normalized to sum to one, and observations are arranged in ascending order of living standards, with \( w_0 = 0 \) The variance \( \sigma_{rr}^2 \) and mean \( \mu \) of the fractional rank are solely determined by the sample size, indicating no sampling variability Consequently, this leads to a revised model for analysis.
𝑦𝑦 𝑖𝑖 =𝛼𝛼+𝛽𝛽𝑟𝑟 𝑖𝑖 +𝜀𝜀 𝑖𝑖 (5) the estimate of the concentration index is given by
Step 3: Calculate Kakwani indices (Ks)
The Kakwani index is derived from the difference between the concentration index and the Gini index, both of which can be easily calculated using a regression method Consequently, the value of the Kakwani index can be directly obtained from a single, convenient regression analysis.
𝑦𝑦�= (𝛼𝛼 2 − 𝛼𝛼 1 ) + (𝛽𝛽 2 − 𝛽𝛽 1 ) ×𝑟𝑟 𝑖𝑖 +𝜀𝜀 𝑖𝑖 (10) The OLS estimate of β =(𝛽𝛽 2 − 𝛽𝛽 1 ) is an estimate of the Kakwani index
Lorenz dominance analysis is a comprehensive method for identifying deviations from proportionality and understanding the factors affecting the distribution of the ability to pay This study utilizes the Kakwani index, a prominent measure of progressivity commonly referenced in tax and health finance literature, as established by Kakwani in 1977 and further supported by researchers such as Wagstaff and others in 1992 and 1999, as well as O'Donnell and colleagues in 2005.
The Kakwani index measures the progressivity of health payments by calculating twice the area between the payment concentration curve and the Lorenz curve, expressed as πK = C pay – G pre Here, C pay represents the concentration index for health payments, while G pre is the Gini coefficient of the ATP variable The index value ranges from -2 to 1, where a negative value signifies regressivity, indicating that L pay (p) is inside L pre (p), and a positive value signifies progressivity.
L pay (p) lies outsideL pre (p) See more detailed in 2.4
Step 5: Decomposition inequality of ATP
Wagstaff, Doorslaer, and Watanabe (2003) illustrate that the health concentration index can be broken down to reveal how individual factors contribute to income-related health inequality Each factor's contribution is determined by the sensitivity of health to that factor and the level of income-related inequality associated with it This analysis applies to any linear additive regression model of health (y).
𝑦𝑦 𝑘𝑘 =𝛼𝛼+� 𝛽𝛽 𝑘𝑘 𝑥𝑥 𝑘𝑘 +𝜀𝜀 𝑘𝑘 Then the concentration index for y, C, can be written as follows:
Data
This study utilizes data from the most recent Vietnam Household Living Standards Survey (VHLSS), conducted by the General Statistical Office of Vietnam (GSO) with technical assistance from the World Bank (WB) in 2012 and 2010 The 2012 survey included 9,320 households, while the 2010 survey encompassed 9,179 households These samples are representative at national, rural, urban, and regional levels.
The surveys utilized household and community-level questionnaires to gather comprehensive data on demographics, education, health, income, and expenditure Key metrics include per capita income and expenditure, alongside details about health insurance coverage for household members Additionally, the surveys track annual outpatient and inpatient hospital visits and associated out-of-pocket expenses However, the data lacks specificity regarding these out-of-pocket payments, which encompass not only treatment fees but also related costs such as doctor bonuses, service charges for additional medications, equipment, and transportation (Nguyen, 2012).
Variables
In numerous studies utilizing World Bank household survey data, researchers frequently employ socioeconomic variables to examine various issues Similarly, my thesis analyzes inequity in healthcare finance in Vietnam by incorporating these critical variables The explanatory household variables used for comparison include:
5)Expenditure and income of households
Table4 describes the basis values of data both year 2012 and 2010
Table4: Variables of socioeconomic factors and expenditures
Explanatory Variables Mean Min Max Mean Min Max
Household in urban areas, urban=1 0.289 0 1 0.283 0 1
Householdin Central Highland and North Moutain 0.174 0 1 0.173 0 1
Expenditure for both out-patient and in-patient 3,488 0 206,900 2,797 0 258,000
Total expenditure for health, not including subsidy 4,477 0 207,420 3,472 0 260,000
Out-of-pocket for health 4,208 0 207,420 3,340 0 260,000
Total Expenditure for food and non-food 69,359 1,539 755,853 34,614 1,926 363,197 Total Expenditure for food , non-food, and health care 74,944 2,413 698,185 38,086 2,186 368,941
RESULTS
Results
4.4.1 OLSand Quantile Regression of Household Total expenditure
Table 6 the results of regression household Total expenditure by OLS and Quantile regression
Table6: OLS and Quantile Regression of Household Total expenditure - ATP (‘000 VND)
OLS QR_25 QR_50 QR_75 OLS QR_25 QR_50 QR_75
Explanatory Variables b/se b/se b/se b/se b/se b/se b/se b/se
Household in urban areas, urban=1 20890*** 10166*** 12436*** 13120*** 13359*** 4996*** 5389*** 5652***
Not completed Primary school, yes=1 7740*** 570.00 2,588.00 3605* 5534*** 1,099 2369*** 4088***
Completed Lower-secondary school, yes=1 15116*** 4298*** 5359*** 6311*** 9602*** 3537*** 4295*** 5067***
Completed Upper-secondary school, yes=1 21918*** 5028*** 8273*** 10922*** 12654*** 4061*** 4648*** 6311***
Household in Red River Delta 879.00 2389** 2712** 2903* 1,598 (791) 329 689
Household in Central Highland and North Moutain -9086*** -2566**
Household in North Central Coast -4363* 1139 1560 5127*** -2451** 16 -79 631
Household in Mekong River Delta -1648 4338*** 3449*** 6580*** 1073 745 891 1818***
*significant at 10%, **significant at 5%, ***significant Standard errors in brackets Source: Estimation from panel data of VHLSSs 2012 and 2010
4.4.2 Average Per household Health Finance and Shares of Total Financing
Table 7 presents an analysis of household health financing and total expenditure across different quintiles, organized by ascending total expenditure The first column highlights the average total expenditure per household, including health care costs, while subsequent columns detail health financing sources and total health financing All financial figures are adjusted to reflect economies of scale, ensuring a fair comparison among households The final row summarizes the data for the entire population.
In 2012, data from Table 7 reveals that the poorest quintile has an average consumption of 26,321, while the richest quintile consumes 157,400 Overall, the average gross consumption for the entire population is 74,944, indicating that financing increases with each quintile across all sources.
The data reveals a stark disparity in expenditure across income quintiles, with the poorest quintile accounting for just 6.8 percent of total spending compared to 40.9 percent for the wealthiest Inpatient costs are predominantly shouldered by the richest, who contribute 49.1 percent, while the first three quintiles contribute significantly less at 5, 9.1, and 14.2 percent, respectively Although financing shares rise with each quintile, the differences in contributions from other financing sources are generally less pronounced Notably, the richest quintile contributes 32.5 percent to insurance, which is more than five times the 5.8 percent contributed by the poorest quintile.
In 2010, data from Table 7 reveals that the poorest quintile had an average consumption of 14,373, while the richest quintile consumed 80,396 Overall, the average gross consumption across the entire population was 38,094, indicating that financing increases with each quintile for all sources of financing.
The data indicates a significant disparity in expenditure across income quintiles, with the poorest quintile accounting for only 7.3% of total spending, compared to 40.6% for the wealthiest Inpatient costs are predominantly shouldered by the richest, who contribute 53.1%, while the first three quintiles collectively account for just 24.8% Although financing shares increase with each quintile for other sources, the differences are generally less pronounced than those seen in inpatient expenditures Notably, the richest quintile contributes 35.3% through insurance, which is approximately five times more than the 7.3% contributed by the poorest quintile.
The optional VHLSS weights were applied in this research The Table thus displays the entire sources of financing, irrespective of their final contribution to the health system
Table7: Average Per household Health Finance (‘000 VND) and Shares of Total Financing (%)
Total expenditure for daily activity
Total expenditure for daily activity
4.4.3 Distributional Incidence of Sources of Household Health Finance
Table 8 analyzes the progressivity of health financing by presenting average expenditure and financing shares across different quintiles, ranked by households' ability to pay (ATP) This information highlights income inequality, as a larger share of expenditure from the richest quintiles indicates greater inequality Additionally, the sources of financing reveal how the income distribution contributes to health financing; a higher share from the wealthiest quintiles signifies that financing is more concentrated among the rich, reflecting a pro-rich bias in health funding.
Table 8 presents key measures of financing concentration and progressivity, with the financing concentration index indicating the relationship between financing and ATP A positive index value suggests that wealthier individuals contribute more to financing, signifying pro-poor financing, while a negative value indicates the opposite An index close to zero implies no correlation between income and financing The Kakwani index, a crucial metric in Table 8, assesses financing progressivity by comparing the concentration index with the gross consumption Gini index A positive Kakwani index indicates that financing is more heavily weighted towards the rich, reflecting progressivity Essentially, progressivity can be understood as an increasing budget share of financing relative to ATP as income rises.
In 2012, all concentration indexes were positive, indicating that wealthier individuals contributed more to health care financing than poorer ones The concentration index for inpatients was the highest at 0.4122, reflecting a progressive contribution, while social insurance contributions had the lowest index at 0.2696, indicating a less progressive system.
The Kakwani index reveals a progressive trend for inpatients (0.0681) and out-of-pocket payments (0.0054), while it approaches zero for food payments, total daily activity expenditure, and total health expenditure Conversely, it shows moderate regressivity for outpatient services (-0.0205), social insurance contributions (-0.0745), and non-food payments (-0.0102).
In 2010, all concentration indexes were positive, indicating that wealthier individuals contributed significantly more to healthcare financing than their poorer counterparts Among these, the concentration index was highest for inpatients at 0.4421, reflecting a progressive contribution, while social insurance contributions had the lowest index at 0.3021, suggesting a less progressive funding model.
The Kakwani indexes reveal a positive progressivity for both inpatient care (0.1025) and out-of-pocket payments (0.0715) However, the index for food payments is nearly zero, reflecting a neutral stance Similar to the findings from 2012, the social insurance contributions (-0.0375) and food payments (-0.0182) indicate regressivity, while outpatient care shows a moderately positive index (0.0701), suggesting progressivity.
Table 8: Distributional Incidence of Sources of Household Health Finance in Vietnam, 2012and 2010
Total expenditure for daily activity
Total expenditure for daily activity
Dominance tests: – indicates the 45-degree line/Lorenz curve dominates the concentration curve
+ indicates concentration curve dominates 45-degree line/Lorenz curve blank: non- dominate a Gini index for equivalent household expenditure
4.4.4 Decomposition inequality of Household Total expenditure
This study utilizes equation (11) proposed by Wagstaff, van Doorslaer, and Watanabe (2003) to analyze the inequality of total expenditure (ATP) among households in Vietnam for the years 2010 and 2012 The findings are summarized in Table 9, which details the elasticity of ATP concerning various factors, the concentration index for each factor, and the overall contribution of each factor to the ATP concentration index.
Table 9 displays the results for the years 2012 and 2010, highlighting that the significant elasticities of ATP in relation to these factors greatly contribute to the ATP concentration index.
In 2012, food and non-food expenditures exhibited the highest elasticities of 0.772 and 0.162, respectively, making significant contributions to inequality in ATP at 0.266 and 0.054, which account for 77.25% and 15.72% of the total contributions Although outpatient and inpatient expenditures have higher concentration indices, their contributions are relatively low at 2.41% and 3.24% due to their very low elasticities.
In 2010, food and non-food expenditures exhibited the highest elasticities at 0.7049 and 0.2068, respectively, significantly contributing to inequality in the Atkinson index (ATP) with values of 0.2265 and 0.0771 These expenditures accounted for the largest shares of contributions, representing 66.72% and 22.70% Although outpatient and inpatient services had concentration indices of 4.22% and 4.63%, their contributions were minimal due to their very low elasticities.
4.4.5 Decomposition inequality of Health Care
Compare with international studies
This study evaluates the equity of health care financing in Vietnam using Kakwani's progressivity index, marking the first comprehensive analysis of the progressivity of various financing sources and the overall system Additionally, the findings are compared with similar studies from other countries to provide a broader perspective on health care financing equity globally.
Table 10: Compared results with international studies
Proportion of main finance source (%)
Belgium, 1997 (Wagstaff et al 1999) 42.1 -0.0001 Nearly proportional Hungary, 1999 (Wagstaff et al 1999) 44.1 -0.0181 Mildly regressive South Korea, 2000 (O'Donnell et al 2005) 33.9 -0.0239
West Germany, 1989 (O'Donnell et al
Nepal, 1996 (O'Donnell et al 2005) 23.5 0.0625 Mildly progressive
The Vietnam health financing system demonstrated progressive contributions in both 2010 and 2012, with concentration indices of 0.3396 and 0.344, respectively In 2012, households made progressive contributions towards inpatient and out-of-pocket payments, albeit at a minimal level Conversely, in 2010, households contributed progressively to outpatient, inpatient, and out-of-pocket payments, while contributions to social insurance were not progressive Additionally, the Katwani index was higher in 2010 compared to 2012, indicating a greater degree of financial equity in that year.
Discusion
This section concludes the findings of Chapters 3 and 4 Chapter 4 concentrates on the calculation and interpretation of consumption-related inequality in health care payment
Two important indicators when researching financial inequity is Concentration index CI and Katwani index K CI> 0 or CI0 means that there is inequitytowards progressivity, the rich pay much more than theirability to pay, and contrary
The results from the 2010 and 2012 studies indicate persistent inequalities in household health expenditure, with VHLSS and CI values of 0.3396 in 2010 and 0.344 in 2012 An analysis of the data reveals that the progressivity of out-of-pocket (OOP) expenses in 2012 is less clear than in 2010 Although the K value for 2012 is nearly zero, suggesting minimal inequity according to VHLSS data, the overall picture remains complex.
K 2010 equivalent to China, the mildly progressive
The decomposition inequality analysis indicates that both inpatient and outpatient spending significantly contribute to health care spending inequalities, as evidenced by Table 9 This is primarily due to the higher ratios of expenditure in these areas, which exhibit greater elasticity and a more substantial impact on overall health care inequality compared to other factors.
This study highlights key insights into Vietnam's healthcare spending as indicated by VHLSS data, providing valuable information for policymakers It aims to assist in quantifying recommendations for enhancing the healthcare system, ultimately striving to reduce inequities and disparities in healthcare payments.
OOP is an important incidence which contribute to inequality health care finance, we need to reduce OOP spending, and increased funding for health care
CONCLUSION AND POLICY IMPLICATION
Conclusion
This section concludes the findings of Chapters 3 and 4 Chapter 4 concentrates on the calculation and interpretation of consumption-related inequality in health care payment
Two important indicators when researching financial inequity is Concentration index CI and Katwani index K CI> 0 or CI0 means that there is inequitytowards progressivity, the rich pay much more than theirability to pay, and contrary
The study's results from 2010 and 2012 indicate persistent inequalities in household health expenditures, with VHLSS data showing a CI of 0.3396 in 2010 and 0.344 in 2012 Although the chart (Figure 10) reveals that the progressivity of out-of-pocket (OOP) spending in 2012 is less clear than in 2010, the K value for 2012 is nearly zero, suggesting minimal inequity based on VHLSS data.
K 2010 equivalent to China, the mildly progressive
The decomposition inequality analysis reveals that inpatient and outpatient spending significantly contribute to health care spending inequalities, as indicated by Table 9 This is primarily due to the higher ratios of spending in these areas, which exhibit greater elasticity and a more substantial impact on overall health care inequality compared to other factors.
Policy implication
This study highlights key insights into Vietnam's healthcare spending, based on VHLSS data, providing policymakers with a framework to quantify and enhance strategies for improving the healthcare system The findings aim to address and reduce inequities and inequalities in healthcare payment, ultimately contributing to a more equitable health service for all.
OOP is an important incidence which contribute to inequality health care finance, we need to reduce OOP spending, and increased funding for health care
Limitation 53 REFERENCE
This study is based on data from VHLSS 2012 and 2010, these factors should be considered in studies of health care spending can not find adequate in this data set
Inequity in healthcare finance is a complex research area that demands advanced econometric knowledge This specialized understanding often varies among researchers, presenting a significant limitation in the field.
Research on health care finance inequity focuses primarily on health care spending, while government funding for health care remains outside the scope of this study This limitation highlights the need for more comprehensive research to address both aspects effectively.
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