Introduction

Health is one of the key components of human capital which is crucial for economic development of any country. In developing countries like India, public health expenditure plays a critical role in improving population health and reducing mortality rates. Despite fast economic growth, India continues to face challenges such as high infant mortality and maternal mortality rates. Government spending on health is expected to improve health outcomes through enhanced infrastructure, availability of medical personnel, and improved access to healthcare services. While arguing in favour of the public health spending reported that there are various positive externalities associated with health (1). Apart from this, social factors such as female literacy also influence health outcome by influencing health awareness, maternal care practices and utilization of healthcare facilities. Literature studying the impact of public health expenditure on health outcome is limited. Therefore, it is very important to assess the relationship between public health expenditure and health outcome. Such assessment helps in implementing health policies in developing countries like India. In India, public health expenditure has historically lagged behind global norms, often hovering around 1-1.5% of GDP, compared to the WHO’s recommended 5% of GDP. Over the last three decades India has undertaken various reforms and increased government spending on health infrastructure, immunization programmes and maternal and child health services. Understanding how these expenditures translate into measurable outcomes is critical for policy makers.

Previous literature investigating the linkage between public health expenditure and health outcome reports inconclusive results. For example, studies investigated the impact of public health expenditure on infant mortality rate using an unbalanced panel of 31 states and UTs in India from 1984-2012 (2) reported that public health expenditure helps in reducing IMR. Another study reported that public health expenditure does not have significant effect on mortality rates in India (3). Despite the prior results one thing is clear that public expenditure may improve health outcome (4). Hence, an empirical assessment is needed to check the impact of public health expenditure on health outcome. The inconsistency in findings of earlier research led the researcher to empirically investigate the relationship between public health expenditure and health outcome using time series data from 1990-2024. The objective of the study is to investigate the impact of public health expenditure on health outcome. Three key indicators such as MMR, IMR and Life expectancy are used as dependent variable to measure health outcome. However, along with public health expenditure, other factors such as GDP per capita, and female literacy are also included in the study.

Data and Methodology

The study is based on secondary data sources. The required data for the period from 1990-2024 was extracted from World Development Indicators which is available online on the World Bank website. Health outcome was measured by using infant mortality, maternal mortality and life expectancy. Three separate models have been specified for each of the above-mentioned variables measuring health outcome as dependent variable. Independent variables are GDP per capita (US $), public health expenditure per capita (US $) and female literacy.

To meet the objective of the study, the following models has been specified.

Model-1

IMRt = f (GDPt, HEt, FLt) ……………………………………………..(1)

Model-2

MMRt = f (GDPt, HEt, FLt) ……………………………………………(2)

Model-3

LEt = f (GDPt, HEt, FLt) ……………………………………………….(3)

Where IMR is infant mortality rate, HE represents health expenditure and FL is the female literacy rate. All the variables were transformed into log form. Hence the log linear based models can be expressed as:

lnIMR_t= β₀ + β₁(lnGDP_t) + β₂(lnHE_t) + β₃(lnFL_t) + έ_t (Model-1)

lnMMR_t= β₀ + β₁(lnGDP_t) + β₂(lnHE_t) + β₃(lnFL_t) + έ_t (Model-2)

lnLE_t= β₀ + β₁(lnGDP_t) + β₂(lnHE_t) + β₃(lnFL_t) + έ_t (Model-3)

Where, β_s are coefficients of dependent variables, έ is the error term and t denote time series.

In order to examine the long run relationship among the study variables, the Autoregressive Distributed Lag (ARDL) Bound Test approach has been adopted. Before selection of the ARDL model, Augmented Dickey-Fuller (ADF) unit root test has been conducted to check the stationarity of the study variables. The results of the unit root test demonstrates that the variables under study have mixed I(0) and I(1) order of integration as shown in table 1. Given this the ARDL approach was chosen for the study.

Our models in ARDL form will be expressed as under

ΔlnIMR_t = α₀ + β₁lnIMR_t-1 + β₂lnGDP_t-1 + β₃lnHE_t-1 + β₄lnFL_t-1 + _j ΔlnIMR_t-j + _k ΔlnGDP_t-k + _l ΔlnHE_t-l + _m ΔlnFL_t-m + μ_t

ΔlnMMR_t = α₀ + β₁lnMMR_t-1 + β₂lnGDP_t-1 + β₃lnHE_t-1 + β₄lnFL_t-1 + _j ΔlnMMR_t-j + _k ΔlnGDP_t-k + _l ΔlnHE_t-l + _m ΔlnFL_t-m + μ_t

ΔlnLE_t = α₀ + β₁lnLE_t-1 + β₂lnGDP_t-1 + β₃lnHE_t-1 + β₄lnFL_t-1 + _j ΔlnLE_t-j + _k ΔlnGDP_t-k + _l ΔlnHE_t-l + _m ΔlnFL_t-m + μ_t

Where α₀ is a constant, μ_t is the error term.

Trends of Public Health Expenditure and Health Outcome during 1990-2024

Figure 1: Trends of Public Health Expenditure, IMR, MMR and Life Expectancy from 1990-2024 [Source: World Bank Data]

The trends of public health expenditure, infant mortality rate, maternal mortality rate, life expectancy in India are shown in Figure 1. It shows that public health expenditure was stagnant up to 2004 and then started an increasing trend. IMR has shown a downward trend from 84.3 to 23.6 per thousand live births during this time. MMR also shows a downward trend from 566 per lakh live births to 80 during 1990-2024. Life Expectancy shows a gradual increasing trend from 58.62 years to 70.62 years during the time period under study. A preliminary observation of the trendlines shows that there is a positive relationship between health expenditure with life expectancy and negative relationship with infant mortality and maternal mortality.

Results and Discussion

Table 1 shows the results of the ADF test for unit root and it was found that the variables infant mortality, maternal mortality and health expenditure are stationary at level and variables, Life Expectancy, GDP per capita and Female Literacy are stationary at levels.

ARDL Bound test F-statics was used at the first stage to investigate the long-run relationship among the variables. The results of ARDL Bound test (Table 2) for dependent variable lnIMR shows that the F-statistic value is greater than the upper bound I(1) value at 5% level of significance. This means that there is cointegration among the variables in the model. In Model-2 (lnMMR as dependent variable) and Model-3 (lnLE as dependent variable) the ARDL bound test shows F-statistic value for both the models is less than the upper bound value at 5% level of significance. This clearly indicates that there is no long-run relationship exists among the variables when lnMMR and lnLE are treated as dependent variable. The diagnostics test shows no issues with serial correlation, heteroscedasticity and normality of residuals. The Ramsey Reset test results for stability of the model is insignificant at 5% level.

Table 5 shows results of long-run relationship. Results reveal that although the ARDL bounds test confirms the existence of a long-run relationship among the variables, the estimated long-run coefficients are statistically insignificant at the 5 percent level. This suggests that while the variables move together in the long-run, none of the individual explanatory variables exerts a statistically significant independent influence on the dependent variable. Previous research reported that there is no significant effect of current public health expenditure on mortality rates in India during the period from 1980-1999(3). The finding of the study replicates the same during the period from 1990-2024 in the long run relationship between the variables.

The results of short-run relationship for model-1 is shown in Table 6. Results reveal that the lagged ECM is highly significant confirming long-run relation in the model. This is a clear indication that any short run deviations from equilibrium is adjusted to long run at 11 percent annually. Further it was seen that in the short run, one percent increase in GDP contributes 40 percent to health outcome as the coefficient is highly significant at 1% level. Likewise, one percent increase in health expenditure contributes 10% to health outcome (IMR). Also, one percent increase in female literacy contributes 47% to health outcome.

Table 7 demonstrates the short run relationship with dependent variable MMR. Results revealed that GDP in current period and lag (1) is significant at 5% level. Similarly, previous health expenditure lag (1) significantly affects MMR. Likewise, female literacy is also significantly contributed to reduction in MMR.

Table 8 portrays the results of model-3 where Life expectancy is treated as dependent variable in examining the relationship between public health expenditure and health outcome. The results reveal that GDP in current period and lag (1) is significant at 5% level. Similarly, current and previous health expenditure lag (1) significantly affect life expectancy. Likewise, female literacy is also significantly contributed to improvement in life expectancy.

Overall, the results reveal that public health expenditure has a statistically significant impact on health outcomes (in the short-run), highlighting the importance of sustained public investment in the healthcare sector. In contrast, female literacy exhibits a significant effect in the short-run, suggesting that improvements in women’s education rapidly influence health related behaviours and outcomes. The findings of the study are in line with findings of the earlier research (2,4,5).

Conclusion

The empirical results of the study show that public health expenditure has a positive and significant effect on life expectancy and negative and significant effect on IMR and MMR. The findings of the study have important policy implications. Public health expenditure plays a crucial role in improving overall health outcome of the country. Previous research opined that low level of public spending might be one of the possible reasons of low performance in health indicators in India(7). Human Development Report, 2024 reflects that life expectancy has increased from 58.6 in 1990 to 72 years in 2023. This progress is attributed to national health programmes like National Health Mission, Ayushman Bharat, Janani Suraksha Yojana and Poshan Abhiyaan. This implies that public healthcare expenditure plays a critical role in improving health outcomes in India. But India still falls in the medium human development category in HDI ranking with HDI value 0.685 but it is impressive that India is slowly approaching towards high human development category. To achieve this target the country, need to enhance investment in public health expenditure and improvement in health infrastructure.

References

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