Out-of-Pocket Health Expenditure and Associated Factors: Insights From National Health Accounts (NHA) Using Panel Data Analysis

Abstract

This study investigates the relationship between out-of-pocket (OOP) healthcare spending, economic growth, population growth, and government health expenditure as a proportion of general government expenditure using National Health Accounts (NHA) estimates. Out-of-Pocket (OOP) healthcare spending imposes a substantial financial burden on households, especially in developing economies such as India. Understanding the factors that influence OOP payments is crucial for policymakers seeking to enhance healthcare systems and achieve Universal Health Coverage (UHC). High OOP expenditures often lead to impoverishment and inequitable access to healthcare, making it a critical area for reform. Despite the well-known negative economic and social consequences of high OOP spending, there is limited research that thoroughly examines the interplay between key economic variables such as economic growth, population growth, and government healthcare expenditure (GHE) as a proportion of general government expenditure (GGE) in shaping OOP healthcare spending. Furthermore, although the National Health Accounts (NHA) offers comprehensive data across Indian states, few studies have leveraged this data to explore the dynamics of these factors. This study aims to fill this gap by providing empirical insights into how these economic and demographic elements influence OOP healthcare spending in India. The analysis employed fixed and random effects models on data from 19 Indian states spanning the years 2013-14 and 2019-20. Fixed effects models were selected based on the results of the Hausman test, which indicated that these models were more effective for controlling unobserved heterogeneity across states.The results indicate that a 1% increase in Gross State Domestic Product is associated with a 0.5% reduction in OOP payments. No significant relationship was identified between population growth or GHE/GGE ratio and OOP healthcare spending. These results imply that while economic growth can contribute to lowering healthcare costs, other factors, such as public health spending, may not be as effective unless they are more strategically targeted. The study underscores the vital role of economic growth in reducing OOP healthcare spending, especially in states facing significant financial burdens. Policymakers should consider aligning economic growth strategies with healthcare reforms to ensure that the benefits of development lead to reduced OOP expenditures. As the findings also suggest that GHE/GGE does not significantly affect OOP costs, policymakers should enhance the targeting and efficiency of public health expenditures while expanding health insurance coverage, and strengthening primary healthcare systems to mitigate OOP costs.

Contributions to the literature

• While previous studies have looked into how various macroeconomic factors like economic growth influences public health expenditure, little is know about the impact of these factors on out-of-pocket health expenditure.

• The study finds that economic growth significantly reduces out-of-pocket (OOP) healthcare expenditures, with a 1% increase in GSDP leading to a 0.5% decrease in OOP payments

• It highlights that neither population growth nor the GHE/GGE ratio significantly affects OOP spending, pointing to the need for further examination of how public health funds are allocated.

• The findings emphasize the need for integrating economic growth strategies with healthcare reforms to reduce OOP costs and address regional disparities.

Introduction

Health, the fundamental aspect of human life influences various social, psychological, and economic dimensions. As defined by the World Health Organization (WHO), health is “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.” Health directly impacts economic growth by enhancing worker productivity, increasing life expectancy, fostering human capital, and reducing the overall burden of sickness. ¹ The Commission on Macroeconomics and Health ² emphasized the importance of investing in healthcare infrastructure to promote economic development, aligning with the goal of Universal Health Coverage (UHC), which seeks to ensure affordable healthcare for all. Monitoring progress toward UHC is essential, particularly for achieving Sustainable Development Goal (SDG) 3: “Ensure Healthy Lives and Promote Well-Being for All at All Ages” by 2030. However, one of the major challenges in attaining UHC is the reliance on Out-of-Pocket (OOP) spending, which is regarded as an inefficient method of healthcare financing. OOP payments can have detrimental effects on households, pushing vulnerable populations into poverty. Despite global health goals, India continues to rely heavily on OOP payments, which adversely affects household living standards and lead to catastrophic health expenditures.

The most recent National Health Accounts (NHA) report for FY19 indicates that while public healthcare spending in India increased from 1.2% of GDP in FY14 to 1.3% in FY19, it remains significantly lower than global standards. According to data from the Global Health Expenditure Database (WHO), India’s Domestic General Government Health Expenditure (GGHE-D) was 1% of GDP in 2021, much lower than countries like the United States (10% of GDP), Brazil (5%), and China (3%). Even among its South Asian counterparts, such as Nepal and Sri Lanka, which allocate 2% of their GDP to public health, India’s spending falls short. This underinvestment in healthcare hinders progress toward achieving Sustainable Development Goal (SDG) third. In FY19, India’s Total Health Expenditure (THE) was estimated at ₹596 440 crore (3.2% of GDP), with capital expenditures accounting for 9.4% and current health expenditures (CHE) representing 90.6%. Both the Union and State Governments contribute to GHE, yet the reliance on OOP payments continues to be a significant concern. ³

Both the catastrophic spending and impoverishment resulting from OOP payments have worsened dramatically over time. ⁴ The economic reforms of the 1990s led to significant changes in the health sector, characterized by a gradual reduction in public health investment and an increased reliance on the private sector for healthcare services. As the Structural Adjustment Programme progressed, government healthcare spending was curtailed, creating a gap in public healthcare provision that was subsequently filled by the private sector.^{5 -7} This shift has led to rising healthcare costs, particularly for vulnerable populations.

The persistent reliance on OOP payments and the inequities in healthcare access, this study aims to examine the factors influencing OOP healthcare spending across Indian states, focusing on economic growth, population growth, and Government Health Expenditure (GHE) as a proportion of General Government Expenditure (GGE). Utilizing data from the National Health Accounts (NHA), this study seeks to provide empirical insights into the relationships between these factors and OOP healthcare spending. The findings could help inform policies aimed at alleviating the financial burden of healthcare on households and improving healthcare access across states.

The paper is organized as follows: Section 2 reviews the literature on the factors affecting OOP health expenditure. Section 3 describes the data, methodology, variables, and econometric methods used in the study. Section 4 presents the empirical findings and discusses the results. Section 5 provides the conclusions and policy implications.

Systematic Literature Review

While most empirical research has investigated how various factors affect total health expenditure (THE), there is a lack of literature focusing on socio-economic factors as determinants of out-of-pocket expenditure (OOP) in Indian states. A significant body of research indicates that India’s disparity between the supply and demand for public health services is driven primarily by reduced public investment. Household out-of-pocket (OOP) payments serve as the main source of healthcare funding in many developing nations, particularly India, where approximately 51% of health spending is borne by individuals (NHA Estimate, 2019). Excessive OOP health expenditures can push households into poverty or deteriorate living standards, which negatively impacts household welfare.^8,9

The debate regarding the factors influencing the rise in healthcare costs has evolved over time. Previously, healthcare was classified as a luxury good in assessing a nation’s level of wealth. However, this perspective has shifted toward a more nuanced viewpoint, recognizing several elements as; an aging population, the timing of healthcare expenses near the end of life, the adoption of new medical technologies, the degree of decentralization within healthcare systems, and payment methods for healthcare professionals as significant contributors. ¹⁰ Ultimately, the primary determinant of healthcare spending has been identified was found to be a nation’s level of wealth. More recently, Kaladharan and Manayath ¹¹ found that higher Domestic General Government Health Expenditure (GHE) and GDP growth increase OOP expenses in emerging economies, suggesting the complexity of healthcare financing. However, their study also points out that government schemes and compulsory health insurance reduce OOP spending, thus stressing the need for policies aimed at achieving universal health coverage.

Ladusingh and Pandey ¹² have highlighted the growing financial burden of healthcare expenses on households in developing countries. This burden is linked to factors such as limited government health spending, high poverty rates, low health insurance coverage, and consequently, a reduced capacity of households to afford healthcare. ¹³ Studies have further examined how fiscal constraints in emerging economies slow down public healthcare spending growth, highlighted the importance of creating fiscal space to allow for greater public health investments. ¹⁴ According to Duran et al, ¹⁵ increased government healthcare spending can alleviate the financial burden on households, especially the disadvantaged. Jakovljevic et al ¹⁶ compare healthcare spending patterns between the G7 and EM7 economies, noting distinct responses to the 2008 crisis. While EM7 countries showed resilience with steady GDP growth and high OOP health spending growth, G7 nations faced austerity cuts that reduced public health funding. GDP growth positively affected OOP spending in the G7, whereas it had a negative impact on health expenditure in the EM7, highlighting differing economic resilience and health spending responses.

In line with other low- and middle-income countries, the Indian health system experiences slower growth in government health spending, resulting in a greater reliance on out-of-pocket spending for medical care. ¹⁷ A cross country examination by Arslan Aras and Çil Koçyiğit ¹⁸ analyzed out-of-pocket health expenditures (OOPHE) across different income groups from 2010 to 2019 using World Bank data. The findings revealed that upper-middle-income countries saw the largest increase in OOPHE (63%), followed by high-income countries (36%), lower-middle-income countries (18%), and low-income countries (4%). While high-income countries had the highest OOPHE per capita (14%), they exhibited the lowest share of OOPHE in total health expenditure (THE). In contrast, low- and lower-middle-income countries experienced significant financial burdens, underscoring the need for targeted policies to alleviate disparities and ensure equitable healthcare access. One of the reasons for the slower increase in public healthcare spending in emerging nations like India is the limited fiscal space. ¹⁹ Creating fiscal space is crucial for the health sector as it allows for increased public health spending in a nation’s budget. In addition, increased investment in healthcare by the government has been found to reduce the reliance on out-of-pocket expenses for healthcare services, thereby alleviating the financial burden on disadvantaged households. In their study, “Fiscal Space for Domestic Funding of Health and Other Social Services,” Meheus and McIntyre ²⁰ underscores the significance of adopting proactive fiscal strategies and innovative financing approaches to enhance healthcare access and sustainability in low- and middle-income countries (LMICs). The research highlights the crucial need to strengthen domestic funding sources for healthcare.

Grigorakis et al ²¹ conducted a study using panel data on a dataset comprising 26 EU and OECD countries, spanning from 1995 to 2013. Their study focused on examining the macroeconomic and financing factors that influence out-of-pocket payments in OECD countries. It explores the intricate relationship between GDP growth and out-of-pocket spending, shedding light on their interplay. Challenging macroeconomic conditions, specific criteria for central fund allocation, limited capacity to absorb resources, and competing state priorities are all factors that impede the successful implementation of policies and the improvement of health outcomes. ²² The dynamic relationship between public health expenditure and macroeconomic factors in Indian states are being influenced by revenue generation, economic growth, and fiscal balance and these factors play an important role in strengthening health sector financing. ²³ Over time, the state’s gross domestic product has been found to positively influence the growth of government health spending. ²⁴ Public health spending is influenced by income and tends to exhibit lower elasticity over time. ²⁵ According to research conducted by Bhat and Jain, ²⁶ the elasticity of health expenditure is only 0.68 in response to changes in state gross domestic product (SGDP). For every 1% increase in state per capita income, there is a corresponding 0.68% increase in per capita public healthcare expenditure.

By examining the spending patterns of other emerging market economies on health, one can easily understand the limited policy focus of India on prioritizing healthcare in its budgetary allocations. In the fiscal year 2020, the Current Health Expenditure (CHE) in India amounted to approximately 3% of GDP. In comparison, budget estimates from the United States indicated an outlay of 19% of GDP, while Nepal allocated 5%, Sri Lanka 4%, Bhutan 4%, and Pakistan 4% (Global Health Expenditure Database of WHO). The Economic Survey, ²⁷ underscored the importance of increasing public health spending and analyzed market failures within the health sector. It demonstrated that raising public health expenditure (PHE) in India to 3% of GDP from current levels could potentially reduce out-of-pocket (OOP) expenses from the current 60.5% to 30%. The Economic Survey recommends increasing public spending on health from 1% to 3% of GDP, in accordance with the National Health Policy (NHP) 2017. This increase is considered essential to improve India’s performance on various health indicators, including reducing the share of out-of-pocket expenses (OOPE), ensuring equitable and high-quality access to healthcare, and enhancing the availability of healthcare infrastructure and human resources.

Even though India’s economy is the fifth largest in the world and has made significant improvements over the last 2 decades, it still lags behind Bangladesh and Sri Lanka on the Global Burden of Diseases Healthcare Access and Quality (HAQ) Index, ranking 145th out of 195 nations. Millions of Indians continue to face substantial barriers to accessing high-quality, affordable healthcare, particularly those from marginalized and underserved communities. Over time, India’s progress in addressing out-of-pocket (OOP) expenditure has been slower than that of other global economies. For example, in 1999 to 2000, the OOP expenditure ratios of China and India were quite similar. However, by 2020, China had reduced this ratio to nearly 35%, while India’s remained at 51% (WHO). In contrast, Bhutan and the Maldives have OOP expenditure rates of less than 17%, which is less than one-third of what is spent in India. As a developing country, a significant portion of India’s population lives in poverty, indicating that public policy must prioritize funding for critical sectors such as health. An analysis of state-level health spending revealed several issues: the average growth rate of the central government’s spending in the states with the poorest performance has been lower than in those that perform better overall. These factors have significant consequences for the Center’s initiatives aimed at promoting equity in the delivery of medical care. The slow rate of spending by the states is concerning, even though the Center’s per capita expenditure has increased in certain underperforming states, such as Uttar Pradesh and Bihar (Choudhury and Nath). ²⁸

While the proportion of out-of-pocket expenditure (OOPE) as a percentage of current health expenditure (CHE) in India is 51% and has decreased over the years, it remains significantly higher than in neighboring countries such as Sri Lanka and China, which have respective OOPE percentages of 47% and 35% as of 2020, according to the World Health Organization (WHO). Additionally, state-wise disparities persist: Assam (34.9%), Kerala (67.9%), Jharkhand (64.7%), Andhra Pradesh (63.6%), Bihar (54.3%), Madhya Pradesh (53.0%), Odisha (53.4%), Punjab (64.7%), and West Bengal (67.1%).

Given this backdrop, this study aims to investigate the impact of economic growth, population growth, and government health expenditure (GHE) and general government expenditure (GGE) on out-of-pocket (OOP) health expenditure across various Indian states. This research is significant because, to the best of our knowledge, no previous studies have assessed the effects of economic growth, population growth, and GHE/GGE on OOP health expenditure among different Indian states or utilized National Health Accounts (NHA) estimates data on health. This study will provide a crucial foundation for future empirical research and policy initiatives. Given this backdrop, this study aims to investigate the following key research objectives:

• (1) To study the effect of economic growth on out-of-Pocket expenditure on health. (2) To determine whether there is any countervailing effect of Government Health Expenditure as proportion of General Government Expenditure on out-of-Pocket expenditure on health. (3) To analyze the relationship between population growth and out-of-Pocket health spending.

This research is significant because, to the best of our knowledge, no previous studies have assessed the effects of economic growth, population growth, and GHE/GGE on OOP health expenditure among different Indian states or utilized National Health Accounts (NHA) estimates data on health. This study will provide a crucial foundation for future empirical research and policy initiatives.

Data and Methodology

The panel data of 19 States of India (Table 1) covering a period from 2013-14 to 2019-20 has been used in the present study. The main source of data is the National Health Accounts (NHA) Estimates of India. The selection of States is made as per the availability of data related to all variables identified. To confirm the reported results, Wald tests, panel unit root tests and Hausman tests, of all the series have been used in the study.

Table 1.

Selected Indian States.

1.	Assam
2.	Andhra Pradesh
3.	Bihar
4.	Chhattisgarh
5.	Gujarat
6.	Haryana
7.	J&K
8.	Jharkhand
9.	Karnataka
10.	Kerala
11.	Madhya Pradesh
12.	Maharashtra
13.	Odisha
14.	Punjab
15.	Rajasthan
16.	Tamilnadu
17.	Uttar Pradesh
18.	Uttarakhand
19.	Himachal Pradesh

Source. National Health Accounts (NHA) Estimates of India (2013-14 to 2019-20) (https://main.mohfw.gov.in).

Variables

The present study carefully investigates the variables of economic growth, population growth, and government health expenditure to conduct a thorough analysis of their effects on out-of-pocket (OOP) expenditure. All the variables were adjusted using logarithms to better capture their growth dynamics. The summary statistics of the variables is presented in Table 2 and the panel line plots in Figure 1. The variables chosen align with prior research conducted by Fan and Savedoff, as well as Musgrove et al.^29,30

Table 2.

Summary Statistics.

Variable	Observations	Mean	Std. Dev.	Min	Max
LogOOP	114	4.0240	0.2308	3.4595	4.4104
logGSDP	114	13.1849	0.8133	11.5394	14.8215
logPOP	114	1.3689	1.1178	−2.3026	3.1268
LogGHE/GGE	114	1.7230	0.1875	1.0296	2.0794

Source. Authors Estimation.

Figure 1.

Panel line plots of the variables. State-wise trends of Out-of-Pocket Expenditure (OOP) as a proportion of Total Health Expenditure, Gross State Domestic Product (GSDP), Government Health Expenditure as a ratio of General Government Expenditure, and Population Growth (2014-15 to 2019-20). The figure displays state-level trends across key variables affecting out-of-pocket health spending. log_oopthe represents the log of OOP expenditure as a proportion of total health expenditure; log_gsdp is the log of Gross State Domestic Product; log_ghgg refers to the log of Government Health Expenditure as a ratio of General Government Expenditure; and log_pop is the log of population growth. The graphs illustrate data trends for each state over time (years 2014-15 to 2019-20).

Source. National Health Accounts (NHA) Estimates of India from 2014-15 to 2019-20 (https://main.mohfw.gov.in).

Econometric Methods

The analysis utilizes panel data techniques to explore the relationship between out-of-pocket health expenditure (log-OOP/THE) and its determinants (log-GSDP, log-GHE/GGE, and log-POP) across 19 Indian states over the period 2013-14 to 2019-20. Given the panel nature of the data, the study begins with a pooled ordinary least squares (POLS) regression, which assumes homogeneity in intercepts and slopes across all entities. However, in the presence of unobserved heterogeneity, the Pooled OLS model may produce biased estimates, as it fails to account for factors unique to each tate that may influence the dependent variable. To address this, we tested for the presence of state-specific variance using the Breusch and Pagan Lagrangian Multiplier (LM) test for random effects. The LM test produced a statistic of 89.04 (P-value = .0000), suggesting that a random effects model, which allows for unobserved state-specific effects, is more appropriate than POLS. Random effects models assume that the unobserved state-specific effects are uncorrelated with the regressors. Random Effects is particularly suitable when we believe that state-specific factors, such as historical healthcare infrastructure, are random and uncorrelated with the independent variables in the model (GSDP, GHE/GGE, POP).

However, to test for the correlation between the state-specific effects and the explanatory variables, we performed the Hausman test, which rejected the null hypothesis of no correlation (P-value = .000). This finding favors the use of the fixed effects model, which accounts for time-invariant state-specific heterogeneity that may be correlated with the explanatory variables. Fixed Effects controls for all time-invariant differences between states (such as inherent healthcare system differences or regional disparities), which could otherwise bias the results if correlated with the independent variables. Panel data is especially useful in this context, as it allows us to control for both state-specific unobserved heterogeneity (eg, regional health policies) and time-specific effects (eg, economic shocks or health reforms), which is crucial when analyzing trends in out-of-pocket health expenditure across Indian states.

Thus, following is the desired panel data fixed effects equation for the study;

$$\begin{array}{l}\mathrm{Log}\mathrm{OOP}/\mathrm{THEit}=\mathrm{\beta }\mathrm{o}+\mathrm{\beta }1\mathrm{LogGSDPit}\\ +\mathrm{\beta }2\mathrm{LogGHE}/\mathrm{GGEit}+\mathrm{\beta }3\mathrm{LogPOPit}+\mathrm{\alpha }\mathrm{i}+\mathrm{\epsilon }\mathrm{it}.\end{array}$$

Where, i stands for a state and t for a year. β_o is the model’s constant, whereas β₁, β₂, and β₃ are the coefficients of the corresponding explanatory variables.

log OOP/THE_it is the natural logarithm of the outcome variable for state i in time period t,

logGSDP_it, logGHE/GGE_it, and log POP_it are the natural logarithms of the explanatory variables for state i in time period t,

Each state’s unobserved time-invariant heterogeneity is captured by the state-specific fixed effect, αi.

ϵit is the error term for i = 1,2,3. . ., M cross-sectional measurements monitored during years t = 1, 2, 3. . ., T. ³¹

Diagnostic Tests

To validate the model, several diagnostic tests were performed (Table 3). The Ramsey RESET test indicated no evidence of omitted variable bias (P = .1053), suggesting that the model is appropriately specified. The Breusch–Pagan/Cook–Weisberg test for heteroskedasticity also showed no evidence against the null hypothesis of constant variance (P = .0817), confirming that the residuals are homoskedastic. To examine for long-term relationships among the variables, the Westerlund cointegration test was conducted, revealing no significant evidence of cointegration (P = .0726), which implies that the variables do not exhibit a long-term equilibrium relationship. Additionally, the Levin–Lin–Chu unit-root test confirmed the absence of unit roots in the variables (P = .00), indicating that the data are stationary and suitable for analysis. Collectively, these results support the validity of the model and the appropriateness of the econometric methods employed.

Table 3.

Diagnostic Tests.

Test	Hypothesis	Test statistic	P-value	Conclusion
Ramsey RESET test for omitted variables	Model has no omitted variables	F(3, 107) = 2.09	.1053	Fail to reject H0
Breusch-Pagan/Cook-Weisberg test	Constant variance	Chi2(1) = 3.03	.0817	Fail to reject H0
Levin-Lin-Chu unit-root test for log-OOP/THE	Panels contain unit roots	Adjusted t* = −9.8993	.000	Reject H0
Levin-Lin-Chu unit-root test for log-GSDP	Panels contain unit roots	Adjusted t* = −34.0002	.000	Reject H0
Levin-Lin-Chu unit-root test for log-POP	Panels contain unit roots	Adjusted t* = −5.0e+02	.000	Reject H0
Levin-Lin-Chu unit-root test for log-GHE/GGE	Panels contain unit roots	Adjusted t* = −6.4435	.000	Reject H0
Westerlund test for cointegration	No co-integration	Variance ratio = 1.4564	.0726	Fail to reject H0
Hausman (1978) specification test	Difference in coefficients not systematic	Chi-square = 24.53	.000	Reject H0
Wald Test	(1) log-GSDP = 0	F(3, 92) = 36.73	.000	Reject H0
	(2) log-GHE/GGE = 0
	(3) log-POP = 0

Source. Authors estimation.

Results

The results from the pooled ordinary least squares (POLS) analysis revealed that log-GHE/GGE and log-POP were significant at the 1% level, while log-GSDP was significant at the 5% level (Table 4). In contrast, the Random Effects model indicated that log-GHE/GGE and log-POP remained significant, whereas log-GSDP was marginally insignificant (Table 4). These discrepancies arise from the distinct treatment of individual-specific effects and unobserved variability in 2 models; the Random Effects model accounts for individual-specific intercepts and unobserved heterogeneity, making it more robust against endogeneity compared to POLS, which assumes homogeneity in intercepts and slopes.

Table 4.

Regression Results (POLS, Random Effects & Fixed Effects).

Variable	POLS coefficient. (standard error)	Random effects coefficient (standard error)	Fixed effects coefficient (standard error)
Log-GSDP	−0.093 (0.037)**	−0.289 (0.052)***	−0.554 (0.067)***
Log-POP	0.106 (0.026)***	0.161 (0.038)***	0.038 (0.051)
LogGHE/GGE	−0.32 (0.11)***	−0.287 (0.103)***	−0.081 (0.105)
Constant	5.653 (0.431)***	8.111 (0.611)***	11.413 (0.809)***
Statistics
R-squared	.239	Overall: .149	.545
F-test/Chi-square	11.533 (Prob > F = .000)	Chi2 = 66.898 (Prob > Chi2 = 0.000)	36.729 (Prob > F = .000)
Number of obs	114	114	114
AIC	−34.996		−199.363
BIC	−24.051		−188.418

***p<.01, **p<.05, * p<.1.

Note: ***, **, * denotes the level of significance at 1, 5, and 10% respectively.

Log= natural logarithm.

Source. Author’s estimation.

The Fixed Effects model, which controls for time-invariant state-specific characteristics, provides a more reliable estimate of the relationship between GSDP and OOP spending. However, the insignificance of log-GHE/GGE and log-POP in the Fixed Effects model suggests that factors beyond Government Health Expenditure and population size may be influencing OOP spending, such as regional healthcare disparities or state-specific health policies. Although the Random and Fixed Effects models mitigate some concerns related to unobserved heterogeneity, they do not fully resolve endogeneity issues arising from omitted variables or reverse causality. Future studies could address these concerns using instrumental variable (IV) techniques or by introducing additional control variables, such as institutional quality or healthcare system efficiency, to better account for these endogeneity problems.

To ensure the robustness of our findings, we excluded 6 states with exceptionally high GSDP and population figures—Bihar, Gujarat, Maharashtra, Uttar Pradesh, Karnataka, and Tamil Nadu—in certain models. These states were excluded to test whether their substantial economic scale and population size could unduly influence the overall results. However, their exclusion did not alter the findings, which suggests that the observed relationships hold consistently across a range of state profiles, including those with both high and low GSDP. This consistency further validates the robustness of our results and underscores the broad applicability of our findings across states with varying economic conditions.

To clarify model selection further, each model (POLS, Random Effects, and Fixed Effects) addresses different econometric concerns within our dataset. For instance, while the Fixed Effects model captures time-invariant characteristics unique to each state, helping to control for underlying differences, the Random Effects model assumes no correlation between individual effects and predictors, providing robustness against endogeneity concerns but at the cost of some specificity. Acknowledging these choices enhances the transparency of our analysis and shows the trade-offs involved in selecting econometric models.

To ensure robustness, the regression models were also tested by excluding the variables log-GHE/GGE and log-POP, with the results remaining consistent. The Fixed Effects model confirms that Gross State Domestic Product (GSDP) growth significantly predicts out-of-pocket (OOP) spending as a proportion of total health expenditure (THE), with a 1% increase in GSDP leading to a 0.5% decrease in OOP spending.

These findings underscore that economic growth plays a critical role in reducing OOP spending. Given that results remained stable even after excluding high-GSDP states, policymakers can be more confident that promoting economic growth could lead to reductions in OOP spending across states with diverse economic profiles. Future studies could further strengthen these insights by introducing controls related to healthcare access or system efficiency, helping to refine policy recommendations for states across different economic tiers.

Conclusion and Policy Implications

This empirical study investigates the effects of economic growth, population growth, and government health expenditure (GHE) on out-of-pocket (OOP) health expenditures across various Indian states using data from the National Health Accounts (NHA). The study analyzes panel data from 19 states over a 6-year period (2014-15 to 2019-20) with Random and Fixed Effects models. The results reveal that economic growth, as measured by Gross State Domestic Product (GSDP), significantly influences OOP spending. This finding is consistent with previous research, such as Musgrave and Grigorakis et al. (2018), confirming that economic growth plays a key role in alleviating the financial burden of healthcare and reducing OOP expenditures.

In contrast, population growth and GHE/GGE do not significantly impact OOP spending within the scope of this study. These results suggest that, under the current conditions, these factors do not demonstrate a direct relationship with OOP expenditures, and further research is needed to explore additional variables or alternative methodologies that may explain these findings.

The policy implications of these findings are significant. The considerable impact of economic growth on reducing OOP spending underscores the necessity for policies that integrate economic development with healthcare reforms. Policymakers should consider aligning economic growth strategies with health sector initiatives to ensure that the benefits of economic progress translate into reduced financial burdens for households. Targeted economic support, such as state-specific stimulus packages, could help alleviate the need for high OOP spending on healthcare.

Additionally, the lack of significant findings concerning population growth and GHE/GGE emphasizes the need for a more efficient allocation of public health resources. Although, this study does not explore the specific inefficiencies in current public health spending, it highlights the importance of improving the effectiveness of health investments. Increasing public health funding, prioritizing preventive care, and improving the quality and accessibility of healthcare services could help reduce reliance on OOP expenditure. Expanding health insurance coverage and investing in primary healthcare infrastructure are crucial steps to ensure equitable access to healthcare and alleviate financial burdens on households.

A key finding of this study is the significant variation in OOP expenditures across states, which calls for tailored policy interventions. States with high OOP expenditures, such as Andhra Pradesh (63.6%), Bihar (54.3%), Jharkhand (64.7%), Kerala (67.9%), Punjab (64.7%), and Uttar Pradesh (71.8%), should be prioritized for targeted health reforms. In these states, addressing regional healthcare disparities, improving health insurance coverage, and increasing public health funding could play a crucial role in reducing the financial burden on households. Moreover, implementing state-specific subsidies and strengthening primary healthcare systems are necessary measures to alleviate OOP expenditures and improve healthcare accessibility.

On the other hand, states with lower OOP expenditures, such as Assam (34.9%), Chhattisgarh (36.7%), and Karnataka (31.8%), offer valuable insights into effective health policies. These states may serve as models for implementing cost-effective healthcare strategies. Key factors contributing to their lower OOP levels may include better health infrastructure, higher government health expenditure efficiency, or more robust health insurance schemes. Policymakers in high OOP states could learn from these successful initiatives and consider adopting similar strategies to mitigate financial barriers to healthcare.

Given the variation in OOP expenditures across states, tailored interventions are crucial. Identifying states with high OOP expenditures and prioritizing them for targeted health programs, subsidies, and community-based initiatives can improve healthcare accessibility and affordability. A multifaceted approach that combines economic growth strategies with focused health policy measures will be crucial in achieving equitable healthcare financing and alleviating the financial burden on individuals.

Limitations

This study did not investigate the underlying reasons for the significant impact of economic growth on out-of-pocket (OOP) spending, nor did it examine the reasons behind the insignificant impact of population growth and government health expenditure (GHE/GGE) on these expenses. Furthermore, the 6-year timeframe of the study restricts its generalizability. Future research could benefit from incorporating qualitative methods and extending the analysis over longer periods to provide a more comprehensive understanding of these dynamics.