Human Capital Development and Public Health Expenditure: Assessing the Long-Term Sustainability of Economic Development Models

Abstract

This study investigates the role of public health expenditure on human capital development in South Africa to promote economic development. Despite extensive public health investments and economic reforms, persistent socioeconomic challenges such as poverty, unemployment, and inequality impede sustainable economic growth. This study uses an autoregressive distributed lag model, a vector error correction model (VECM), quantile regression, and Granger causality analysis to assess the relationship between fiscal health policies and human development. The findings confirm that government health spending significantly enhances human development in the short and long run, while unemployment and population growth exert adverse effects. VECM variance decomposition results indicate that the influence of public health expenditure remains persistent, though diminishing over time, with growing contributions from unemployment. Quantile regression shows the heterogeneous impact of health spending across different levels of economic development, emphasising its greater effectiveness at higher development stages. Causality analysis reveals a unidirectional relationship from public health expenditure to human development; this shows the need for sustained healthcare investment. The study calls for policies combining health spending with economic strategies to boost productivity, reduce inequality, and promote inclusive growth. Strengthening institutional efficiency and ensuring macroeconomic stability are crucial for maximising long-term human capital to promote sustainable development.

1. Introduction

Economic development in developing countries remains unstable due to structural challenges like low income, inequality, unemployment, climate change, limited education, and poor infrastructure (Asaleye and Ncanywa 2025; Pereira et al. 2025). Sustainable growth depends on improving living standards (Jie et al. 2023; Yin et al. 2021; Androniceanu et al. 2021), as seen in Luxembourg’s high GDP driven by consistent investment (Danescu 2021). South Africa’s economy shifted from subsistence agriculture (Green 2024) to industrialisation in the 19th century, supported by foreign investment and trade (Goga and Mondliwa 2021). Since 2000, its HDI has ranged from medium to high. Prior to COVID-19, South Africa’s economy was already underperforming, with a 1% average growth (2012–2021) and a 5.6% drop in per capita income during the pandemic (UNECA 2020). Power shortages remained a constraint (Makwembere et al. 2024). Reforms in 2021 aimed to ease structural barriers, including increased private participation in electricity generation. However, reliance on mineral exports and oil imports leaves the economy exposed to global price shifts (Hanson 2024). Sustained investment, job creation, and industrial diversification are vital (El Anshasy and Khalid 2023), along with public spending on education, training, and healthcare to build a skilled workforce (Silalahi and Walsh 2023).

This study is grounded in four key justifications: promoting human development and economic sustainability, informing public policy, addressing social inequality, and filling a gap in the empirical literature. Human development through investments in health, education, and quality of life is both a driver and goal of sustainable economic growth (Xholo et al. 2025). Countries that prioritise human capital via public expenditure achieve more inclusive and resilient economies (United Nations 2021). Public health spending enhances productivity by improving life expectancy, reducing disease burdens, and supporting a healthier labour force (Kouadio and Njong Mom 2024). According to the WHO (2024), each dollar invested in health can yield up to four dollars in returns. Economic sustainability further depends on intergenerational equity and balanced development (Summers and Smith 2014), with health investments helping to prevent crises and stabilise labour markets (Morgan and James 2022).

Effective policy frameworks are supported by empirical research, enabling efficient allocation of public resources to meet societal needs (Parker et al. 2022; Van Tulder et al. 2021; Asaleye et al. 2021). Well-informed policies drive economic development by guiding investments toward high social returns (Widjaja 2023). In South Africa, where inequality is pronounced, policy decisions must promote equity and address developmental challenges. Aligning health spending with population needs improves both health outcomes and economic productivity (Mbau et al. 2023; Omri et al. 2025). However, persistent issues such as unequal healthcare access and inefficient spending highlight the need for data-driven reforms (Maphumulo and Bhengu 2019; Jacobs et al. 2025; Ngene et al. 2023). Empirical analysis of public health expenditure can guide resource prioritisation and enhance policy effectiveness. Performance-based budgeting and outcome-driven policies yield better results under fiscal constraints (Bonomi Savignon et al. 2019), while evidence-based tools like cost-effectiveness analysis help optimise limited health budgets (Fuller and Dwivedi 2019; Broccia et al. 2022; Al-Worafi 2023). This study contributes by offering empirical insights to inform public policy and improve health-related human development in South Africa.

South Africa ranks among the most unequal countries globally, with the top 10% of the population holding over 80% of financial assets and a net wealth Gini coefficient of 76, indicating the extreme inequality in the country (Sulla et al. 2022). Public health expenditure may play a role in equitable access to quality healthcare, which can narrow health differences, improve human development, and promote social cohesion (Jensen et al. 2022). Investments in public health are particularly effective in reducing inequalities because health-related issues are both a cause and a consequence of social disadvantage. Studies documented that marginalised populations often experience worse health-related issues due to limited access to healthcare, inadequate living conditions, and socioeconomic exclusion (Rami et al. 2023; Baker et al. 2022). In South Africa, health performance remains deeply influenced by the legacy of apartheid, which contributed to cross-provincial socioeconomic disparities, fragmented health system development, and unequal resource distribution between public and private sectors (Achoki et al. 2022). Moreover, tackling social inequality through public health expenditure has regional relevance, particularly in sub-Saharan Africa, where many countries face similar development challenges. A report from the African Development Bank indicates that countries investing in equitable healthcare systems experience faster poverty reduction and improved economic performance (African Development Bank 2024).

Consequently, reducing social inequality through public health spending also contributes to achieving global development goals. The United Nations Sustainable Development Goal (SDG) 3, which aims to ensure healthy lives and promote well-being, is linked to SDG 10, which calls for reducing inequalities within and among countries (United Nations 2015). South Africa’s experience and attempt to align public health investments with human development objectives can serve as a model for national policies with global sustainability targets. Additionally, cross-regional knowledge sharing and collaboration can enhance the effectiveness of interventions to reduce health inequalities and create a collective impact (UNDP 2022); this study contributes to ways to aid South Africa’s efforts to align health investments with global development goals and contribute insights to the collective effort to address inequality and promote human well-being welfare across sub-Saharan Africa.

Despite a substantial body of literature examining the relationship between healthcare expenditure and human development, much of it remains limited to cross-country or regional analyses (Miranda-Lescano et al. 2023; Pervaiz et al. 2021), with insufficient attention to country-specific structural and institutional contexts. This study addresses this gap by employing a novel combination of the Autoregressive Distributed Lag (ARDL) model, Vector Error Correction Model (VECM), and quantile regression to capture both the temporal and distributional dynamics of the healthcare–human development nexus. The ARDL model enables estimation of short- and long-run relationships under variable stationarity conditions, while VECM offers insights into long-term equilibrium and necessary policy adjustments. Quantile regression further uncovers distributional heterogeneity, revealing how the effects of healthcare expenditure vary across different levels of human development, thereby contributing a more comprehensive and context-sensitive perspective to the literature.

Based on this, the study aims to examine the impact of human development and public health expenditure on the long-term sustainability of South Africa’s economic development model. The specific objectives are as follows:

i.

To examine public health expenditure’s short- and long-term effects on human development.

ii.

To analyse the response of human development to shocks in public health expenditure.

iii.

To assess the distributional heterogeneity in the impact of public health expenditure on human development across different levels of development.

iv.

To test for the causal relationship between public health expenditure and selected indicators of economic development.

2. Literature Review

2.1. Theoretical Perspective and Empirical Review

Theoretically, the relationship between public health expenditure and human development is well-established in economic and public policy theory. Human capital theory (Schultz 1961; Becker 1964) posits that investments in health and education enhance individual productivity, thereby fostering economic growth. Health, as a core component of human capital, boosts labour productivity and economic participation (Nademi and Kalmarzi 2025; Leoni 2025). Public health spending reduces morbidity and mortality, strengthening the labour force (Self and Grabowski 2003). Endogenous growth models (Romer 1990; Lucas 2009) further highlight human capital’s role in innovation and long-term development, where a healthier population accelerates knowledge acquisition and technological progress (Tübbicke and Schiele 2024). Sen’s capability approach reframes health as both a means and an end of development, underscoring its role in expanding individual freedoms and social equity (Dang 2014). Complementary to this, Welfare State theory (Andersen 2012) emphasises public health investment as a driver of social cohesion and reduced inequality (Camminatiello et al. 2024). Finally, the Brundtland Commission’s sustainable development framework (United Nations 1987) and models like universal health coverage affirm the centrality of equitable healthcare access to economic sustainability and societal well-being (Yilmaz 2024).

Empirically, Miranda-Lescano et al. (2023) conducted a panel data analysis across 57 developed and developing countries (2000–2018), showing that public health expenditure from both central and subnational governments significantly improves the Human Development Index (HDI) and its sub-components, whereas education spending primarily enhances the education dimension. Using a system dynamics model, Azeem Qureshi (2009) demonstrated that in Pakistan, underinvestment in human capital critically impairs human development and economic outcomes, while increased public investment yields dual dividends. Railaite and Ciutiene (2020), analysing 28 EU countries, found that health spending positively influences life expectancy, with additional effects from education, GDP growth, and old-age dependency, while alcohol consumption reduces life expectancy. Banik et al. (2023), employing a two-step system-GMM on 161 countries (2005–2019), revealed that governance quality, particularly political stability, enhances the effectiveness of health expenditure in improving human development. Their interaction analysis showed regional asymmetries: the effect was negative in sub-Saharan Africa, insignificant in low-income and South Asian countries, underscoring the contextual role of institutional quality.

Yang (2020), using a panel threshold model, finds a nonlinear relationship between health expenditure and economic growth: negative at low human capital levels, insignificant at moderate levels, and significantly positive at high levels. The effect is worsened by ageing populations and low fertility in low-human-capital contexts. Edeme et al. (2017), applying a distributional impact assessment across 20 Nigerian states, show that investments in education, health, agriculture, and water resources significantly enhance human development, whereas spending on energy, housing, and environmental protection yields diminishing returns. Pervaiz et al. (2021), using DOLS and FMOLS on BRICS data (2000–2014), demonstrate that health expenditure and electricity use raise CO₂ emissions, while HDI and renewable energy reduce them. They also identify an N-shaped environmental Kuznets curve and a bidirectional causal link between HDI and financial development, underscoring the need for integrated health and sustainability policies. Similarly, Nuhu et al. (2018) find that healthcare expenditure significantly mediates the relationship between HDI and neonatal and maternal mortality, confirming its role in improving health performance.

Evidence from theoretical and empirical reviews has stressed the role of public health expenditure in advancing human development. The findings consistently indicate the positive influence of healthcare investment on indicators such as life expectancy, human capital, and economic growth. Additionally, studies emphasise the importance of governance quality, human capital levels, and sectoral allocations in mediating these performances. Despite these insights, studies assessing the long-term sustainability of public health expenditure’s impact on human development remain limited and continue to grow, showing one of the key areas for further investigation.

2.2. Gap in Empirical Literature and Development of Hypotheses

While much of the literature has examined the impact of human capital on economic growth, employment, and environmental issues (Molina-García et al. 2024; Railaite and Ciutiene 2020; Yang 2020; Nie et al. 2021), recent studies have shifted focus toward the relationship between healthcare expenditure and human development. For example, Banik et al. (2023) explore the moderating role of governance quality, Edeme et al. (2017) assess the distributional effects of public spending, and Pervaiz et al. (2021) link health expenditure with environmental sustainability. Nuhu et al. (2018) further identify healthcare spending as a mediator between HDI and mortality. Despite these contributions, empirical research on the long-term impact of healthcare expenditure on human development, especially within the context of sustainable economic development, remains limited. While Miranda-Lescano et al. (2023) adopt a multi-country panel approach, this study contributes a single-country perspective using the ARDL, VECM, and quantile regression frameworks, with robustness checks via FMOLS and DOLS to ensure result reliability.

Public health expenditure is an important determinant of human development because it influences indicators such as life expectancy, literacy rates, and overall well-being (Uddin et al. 2024; Țarcă et al. 2024). However, the short- and long-run impacts, shock responses, distributional heterogeneity, and causal relationships between public health expenditure and economic development require further empirical investigation; this study formulates the following hypotheses based on theoretical and empirical insights:

Hypothesis 1. 

Public health expenditure has short- and long-term effects on human development.

Economic theory suggests that investment in public health leads to improved human capital formation, enhancing economic productivity and development in the short and long run (Madsen 2016; James and Forrester-Jones 2022). Empirical studies demonstrate that increased public health expenditure significantly improves human development indicators (Onofrei et al. 2021; Akbar et al. 2021). However, the degree of persistence and the time frame within which these effects materialise remain empirical questions, based on the preliminary analysis of the series used in this study, and the issue of degree of persistence and the time frame warrants this study to use an ARDL approach to distinguish short-run fluctuations from long-term equilibrium effects.

Hypothesis 2. 

Human development responds significantly to shocks in public health expenditure.

Macroeconomic shocks, such as financial crises, pandemics, or policy shifts, can cause abrupt changes in public health expenditure, affecting human development (Loayza and Pennings 2020; Gaies 2022). Theoretically, studies have shown that government expenditure shocks can have asymmetric effects depending on institutional efficiency and economic structure (Ma and Qamruzzaman 2022). Empirical evidence indicates that healthcare spending shocks can have immediate and lagged effects on health and economic performance (Abdulqadir et al. 2024). Therefore, this study employs a Vector Error Correction Model (VECM) with variance decomposition and impulse response analysis to help assess the transmission mechanism and adjustment dynamics following a shock in public health expenditure.

Hypothesis 3. 

The impact of public health expenditure on human development varies across different levels of economic development.

The relationship between public health expenditure and human development is not uniform across the economy (Balani et al. 2023). Studies suggest that the effectiveness of healthcare spending is contingent on institutional quality, income levels, and governance structures (Martín-Fernández et al. 2021; Hort et al. 2017). Quantile regression analysis is instrumental in capturing heterogeneous effects (Cheng et al. 2021; Harding et al. 2020); while traditional regression techniques may obscure variations in impact across different economic strata (Varian 2014). Therefore, this study posits that public health expenditure has a more pronounced impact on human development at lower levels of economic development, attributable to diminishing marginal returns in higher-income settings.

Hypothesis 4. 

A causal relationship exists between public health expenditure and selected indicators of economic development.

The causal linkage between public health expenditure and economic development has been widely debated in the literature. The human capital theory posits that investments in health enhance labour productivity, leading to sustained economic growth (Schultz 1961; Becker 1964). Empirical studies likewise show that higher health expenditures improve economic performance (Raghupathi and Raghupathi 2020; Sethi et al. 2024), although the direction of causality remains contested. Given this, employing Granger causality tests will allow this study to assess the presence and direction of causality between public health expenditure and key economic indicators.

3. Material and Methods

3.1. Theoretical Framework and Model Specification

The relationship between health expenditure and human capital for this study is built on two theories, which serve as the theoretical framework: the human capital theory and the health production function. The human capital theory by Becker (1964) is specified as follows:

$$HC=f(HH).$$

(1)

Equation (1) expresses human capital (HC) as a function of human health (HH). Human health refers to the health dimension of human capital, while human capital also includes education; Equation (1) isolates the health component to assess its standalone contribution in order to achieve the objective of the study. However, the analysis accounts for education indirectly through the Human Development Index (HDI), which incorporates both health and education indicators, ensuring these influences are not excluded from the overall model. Human health depends on government health expenditure and economic conditions. Therefore, human health can be specified as follows:

$$HH=f(GE,IF,PP,UE).$$

(2)

In Equation (2), GE is government expenditure on health, IF is the inflation rate, PP is the population growth rate, and UE is the unemployment rate. The human development index (HDI) is the function of human health. Although human capital and human development are conceptually distinct, this study adopts the Human Development Index (HDI) as a proxy for human capital due to its multidimensional coverage of education, health, and income, core elements traditionally associated with human capital formation. While HDI also reflects well-being, its composite nature allows for an empirically validated representation of human capital in macroeconomic analysis, consistent with prior studies (Pervaiz et al. 2021; Banik et al. 2023). Therefore, HH substituting gives

$$HDI=f(GE,IF,PP,UE).$$

(3)

The health production function by Grossman (2017) further states that human health is produced using healthcare inputs and macroeconomic variables. Therefore, we have

$$HH=A\hspace{0.17em}\hspace{0.17em}G{E}^{{\alpha }_1}\hspace{0.17em}I{F}^{{\alpha }_2}\hspace{0.17em}P{P}^{{\alpha }_3}\hspace{0.17em}U{E}^{{\alpha }_4}.$$

(4)

In Equation (4), A represents a constant term capturing baseline human health not directly explained by the model variables. Since HDI is a function of human health, Equation (4) can be modified as follows:

$$HDI=A\hspace{0.17em}\hspace{0.17em}G{E}^{{\alpha }_1}\hspace{0.17em}I{F}^{{\alpha }_2}\hspace{0.17em}P{P}^{{\alpha }_3}\hspace{0.17em}U{E}^{{\alpha }_4}.$$

(5)

Taking the natural logarithm of Equation (5) gives

$$InHD{I}_t={\alpha }_0+{\alpha }_{1}In\hspace{0.17em}G{E}_t+{\alpha }_{2}In\hspace{0.17em}I{F}_t+{\alpha }_{3}In\hspace{0.17em}P{P}_t+{\alpha }_{4}In\hspace{0.17em}U{E}_t+e_t.$$

(6)

In Equation (6), ${\alpha }_0=InA$, which is the constant productivity parameter, and t is the observation period. The parameters ${\alpha }_1,{\alpha }_2,{\alpha }_{3,}\mathrm{and} {\alpha }_4$ are the elasticities of the respective variables. The error term is denoted by $e$. The study expected a positive relationship between government expenditure and HDI (that is ${\alpha }_1>0$) (Manullang et al. 2024; Lantion et al. 2023), a negative relationship is expected between inflation and HDI (that is ${\alpha }_2<0$) (Yolanda 2017; Islam 2022), the relationship between population and HDI is ambiguous (Tripathi 2021) and negative relationship between unemployment and HDI (that is ${\alpha }_4<0$) (Runtunuwu 2020).

3.2. Techniques of Estimation and Information About the Series

3.2.1. Techniques of Estimation

The following hypotheses stated in null forms are tested in this study:

H₀₁. 

Public health expenditure has no short- or long-term effects on human development.

H₀₂. 

Human development does not respond significantly to shocks in public health expenditure.

H₀₃. 

The impact of public health expenditure on human development does not vary across different levels of economic development.

H₀₄. 

There is no causal relationship between public health expenditure and selected indicators of economic development.

Based on the preliminary analysis and empirical justifications, the evaluation of the proposed hypotheses will be achieved as follows: the first hypothesis, which examines the short- and long-term effects of public health expenditure on human development, will be tested using the Autoregressive Distributed Lag (ARDL) model, as it effectively captures both short-run dynamics and long-run equilibrium relationships (Asaleye et al. 2023). Moreover, to assess the second hypothesis, which examines the responsiveness of human development to shocks in public health expenditure, the Vector Error Correction Model (VECM) will be applied, and variance decomposition will be used for the interpretation. The third hypothesis, concerning the heterogeneous impact of public health expenditure across different levels of economic development, will be evaluated using quantile regression analysis, which shows the estimated effects across the distribution of economic development. Lastly, the fourth hypothesis, which investigates the causal link between public health expenditure and selected economic development indicators, will be tested using the Granger causality framework.

Equation (6) serves as the long-run equation for the ARDL, while the short-run general ARDL $(p,q_1,q_2,q_3,q_4)$ is given as follows:

$$\mathrm{\Delta }HD{I}_t={\beta }_0+{\displaystyle \sum _{i=1}^p{\beta }_1\mathrm{\Delta }}In\hspace{0.17em}HD{I}_{t-i}+{\displaystyle \sum _{j=0}^{q_1}{\beta }_2\mathrm{\Delta }}In\hspace{0.17em}G{E}_{t-j}+{\displaystyle \sum _{k=0}^{q_2}{\beta }_3\mathrm{\Delta }}In\hspace{0.17em}I{F}_{t-k}+{\displaystyle \sum _{m=0}^{q_3}{\beta }_4\mathrm{\Delta }}In\hspace{0.17em}P{P}_{t-m}++{\displaystyle \sum _{m=0}^{q_3}{\beta }_5\mathrm{\Delta }}In\hspace{0.17em}U{E}_{t-n}+\mathsf{\Phi }EC{M}_{t-1}+v_t.$$

(7)

In Equation (7), $\mathrm{\Delta }$ is the first difference operator and ${\beta }_0$ is the intercept. The dependent and independent variables kept their initial definitions. ${\beta }_1,{\beta }_2,\dots ,{\beta }_5$ are the respective short-run coefficients. $\mathsf{\Phi }EC{M}_{t-1}$ is the error correction term, which is expected to be negative and statistically significant, measuring the speed of adjustment back to the long-run equilibrium. $v_t$ is the error term.

The series are ordered in the VECM framework as follows: GE, UE, IF, PP, and HDI. Government expenditure on health (GE) is treated as the most exogenous variable since it is determined by fiscal policy (Luo et al. 2025). The unemployment rate (UE) is the second variable because it affects inflation and population growth (Moridian et al. 2024). Inflation rate (IF) is the third variable affecting population growth and development (Zaman et al. 2024). Population growth (PP) is the fourth because it affects development performance (Maja and Ayano 2021), and HDI is the most endogenous variable (and target variable) responding to all the other factors. The VECM is given as

$$\mathrm{\Delta }{Y}_t=\prod Y_{t-1}+{\displaystyle \sum _{i=1}^{k-1}{\mathsf{\Gamma }}_i\mathrm{\Delta }{Y}_{t-1}}+c+{\epsilon }_t.$$

(8)

In Equation (8), $Y_t=\left[InG{E}_t,InU{E}_t,InI{F}_t,InP{P}_t,InHD{I}_t\right]$, $\mathrm{\Delta }{Y}_t$ is the first difference of $Y_t$ and $\prod$ are the long-run equilibrium matrix containing information about the cointegration and ${\mathsf{\Gamma }}_i$ is the short-run adjustment for the lagged first difference. c and ${\epsilon }_t$ are the vectors of constant and error terms, respectively. The variance decomposition table is used in the interpretation, and the impulse response function is presented in the Appendix A.

The quantile regression model at a given quantile τ is given as follows:

$$Q_{\tau }(InHD{I}_t/X_t)={\lambda }_{0\tau }+{\lambda }_{1\tau }InG{E}_t+{\lambda }_{2\tau }InI{F}_t+{\lambda }_{3\tau }InP{P}_t+{\lambda }_{4\tau }InU{E}_t+{\mu }_{\tau t}$$

(9)

In Equation (9), $Q_{\tau }(InHD{I}_t/X_t)$ represents the conditional τ-th quantile (e.g., lower τ = 0.2, medium 0.4 and 0.6, upper 0.8). ${\lambda }_{0\tau }$ is the intercept at quantile τ. ${\lambda }_{i\tau }$ are the quantile-specific coefficients, showing how the independent variables affect HDI at different quantiles. ${\mu }_{\tau t}$ is the error term.

In this study, we purposely consider four quantiles as follows: τ = 0.2 (lower), τ = 0.4 and τ = 0.6 (middle), and τ = 0.8 (upper). The τ = 0.2 quantile focuses on low HDI, where structural weaknesses, inadequate public health financing, and labour market inefficiencies may hinder the effectiveness of government health spending. The middle quantiles (τ = 0.4 and 0.6) represent moderately developed situations where improvements in public health expenditure may have varying effects depending on existing institutional capacity and economic stability. Finally, the τ = 0.8 quantile captures the experience of high HDI, where advanced healthcare systems, stronger economic fundamentals, and social policies may lead to different elasticities in response to public health expenditure and macroeconomic variables.

The pairwise causality for government expenditure on health and HDI is given as follows:

$$HDI={\alpha }_0+{\displaystyle \sum _{i=1}^p{\beta }_{i}HD{I}_{t-i}}+{\displaystyle \sum _{i=1}^p{\gamma }_{i}G{E}_{t-i}}+{\epsilon }_{1t},$$

(10)

$$G{E}_t={\delta }_0+{\displaystyle \sum _{i=1}^p{\varphi }_{i}G{E}_{t-i}}+{\displaystyle \sum _{i=1}^p{\lambda }_{i}HD{I}_{t-i}}+{\epsilon }_{2t}.$$

(11)

In Equations (10) and (11), ${\epsilon }_{1t}$ and ${\epsilon }_{2t}$ are the error terms. If the coefficients ${\gamma }_i$ are statistically significant, GE Granger causes HDI. Similarly, if ${\lambda }_i$ are significant, HDI Granger causes GE. If both hold, a bidirectional causality exists. A similar approach is used to investigate causality between other variables used in this study.

3.2.2. Information About the Series

In this study, the Human Development Index (HDI) is the primary dependent variable, showing the overall progress in human well-being, including health, education, and economic standards. Given its multidimensional nature, HDI provides a comprehensive measure of socioeconomic development, making it an ideal indicator for assessing the long-term effects of public health expenditure and macroeconomic factors. Likewise, to ensure empirical robustness, the study utilises secondary time-series data from 1994 to 2023, capturing three decades of South Africa’s socioeconomic. Data on HDI and key explanatory variables—including government health expenditure (GE), inflation rate (IF), population growth rate (PP), and unemployment rate (UE)—are sourced from Quantec and the South African Reserve Bank (SARB). While the HDI includes a GDP index adjusted for purchasing power parity (PPP), the government health expenditure (GE) data used in this study are reported in constant South African Rand (ZAR) and sourced from Quantec and the South African Reserve Bank (SARB). To ensure consistency in interpretation and reduce exchange rate distortions, the analysis focuses on percentage-based relationships and time series trends, rather than absolute cross-country PPP comparisons.

The chosen time frame is relevant and covers the structural shifts in South Africa’s economy and policy, including post-apartheid reforms, health sector transformations, and economic fluctuations.

A series of preliminary analyses are conducted to ensure the reliability and suitability of the data; these include descriptive statistics, correlation analysis, lag selection criteria, unit root tests (such as the Augmented Dickey–Fuller (ADF), and Phillips–Perron (PP) tests) and cointegration tests (such as the Johansen cointegration test and bound test), among others. All econometric analyses were conducted using EViews 13 including the Autoregressive Distributed Lag (ARDL) model, Vector Error Correction Model (VECM), and robustness checks employing Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS). The software facilitated unit root testing, cointegration analysis, lag selection, model estimation, and post-estimation diagnostics to ensure statistical validity and robustness of the results.

4. Presentation of Result and Discussion

4.1. Presentation of Result

4.1.1. Presentation of Preliminary Analysis Results

Table A2. Correlation and descriptive statistics result.

Descriptive Statistics
	HDI	GE	IF	PP	UE
Mean	0.6132	0.6821	0.7211	0.0459	1.2539
Median	0.6200	0.6843	0.7577	0.0752	1.2365
Maximum	0.7231	0.7371	1.0032	0.3168	1.5352
Minimum	0.5200	0.6191	−0.1598	−0.4119	1.1766
Std. Dev.	0.0648	0.0378	0.2266	0.1434	0.0774
Skewness	0.0287	−0.1490	−2.1611	−0.9888	3.2430
Kurtosis	1.6308	1.6391	9.1504	5.0492	12.0086
Sum	17.7854	19.7817	20.9124	1.3315	36.3638
Sum Sq. Dev.	0.1176	0.0401	1.4386	0.5765	0.1681
Observations	30	30	30	30	30
Correlation Analysis
	HDI	GE	IF	PP	UE
HDI	1
GE	0.7392	1
IF	0.0367	−0.3368	1
PP	0.4142	0.0253	0.2489	1
UE	0.1727	0.6419	−0.4567	−0.4492	1

Source: Authors’ computation.

presents descriptive statistics and correlation analyses in the Appendix A. The Human Development Index (HDI), Government Expenditure on Health (GE), Inflation Rate (IF), Population Growth Rate (PP), and Unemployment Rate (UE) exhibit mean values of 0.6132, 0.6821, 0.7211, 0.0459, and 1.2539, respectively, with corresponding standard deviations of 0.0648, 0.0378, 0.2266, 0.1434, and 0.0774. HDI shows strong positive correlations with GE (0.7392) and IF (0.0367) and moderate correlations with PP (0.4142) and UE (0.1727). GE correlates negatively with IF (−0.3368) and positively with PP (0.0253) and UE (0.6419). IF correlates positively with PP (0.2489) and negatively with UE (−0.4567), while PP and UE exhibit a strong negative correlation (−0.4492).

Table A3. Stationarity Test Result.

Philips Perron Unit Root Test
Variables	Model specification	T-Statistics	p-value	Order of Integration
HDI	Trend and intercept	−1.8016	0.6747	I (1)
D (HDI)		−20,361 **	0.0443
GE	Trend and intercept	−1.1312	0.9038	I (1)
D (GE)		−3.8819 **	0.0285
IF	Trend and intercept	−3.1638 *	0.0717	I (1)
D (IF)		−6.6179 ***	0.0001
PP	Trend and intercept	−3.2440 *	0.0973	I (1)
D (PP)		−11.0534 ***	0.0000
UE	Trend and intercept	−2.6105	0.2788	I (1)
D (UE)		−4.4305 ***	0.0088
Augmented Dickey–Fuller Unit Root Test
Variables	Model specification	T-Statistics	p-value	Order of Integration
HDI	Trend and intercept	−2.8027	0.2092	I (1)
D (HDI)		−1.9225 **	0.0130
GE	Trend and intercept	−1.9225	0.613	I (1)
D (GE)		−3.0645 **	0.0368
IF	Trend and intercept	−4.1741 **	0.0416	I (0)
D (IF)		-	-
PP	Trend and intercept	−3.7072	0.0399	I (1)
D (PP)		0.2835 **	0.0471
UE	Trend and intercept	−2.7919	0.2127	I (1)
D (UE)		−4.5436 ***	0.0069

Note: *, **, and *** indicate that each variable in this study is significant at 10%, 5%, and 1% significance level. Source: Authors’ computation.

presents the stationarity test results using the Phillips–Perron and Augmented Dickey–Fuller approaches, conducted under the trend and intercept specification, which accounts for deterministic components in macroeconomic time series (Perron 1989; Dickey and Fuller 1981). The results indicate that all variables are integrated of order one, except IF and PP, which are stationary at level at the 10% significance level under the Phillips–Perron test, while IF is stationary at level at the 5% significance level under the Augmented Dickey–Fuller.

Table A4. Test for Cointegration.

Statistics	Values
F-Statistics	9.102
Significance	Lower Bounds	Upper Bounds
10%	3.47	4.45
5%	4.01	5.07
2.50%	4.52	5.62
1%	5.17	6.36

Source: Authors’ computation.

presents the cointegration test results using the ARDL bounds test approach. The F-statistic (9.102) exceeds the upper bound critical values at all significance levels (10%: 4.45, 5%: 5.07, 2.5%: 5.62, 1%: 6.36), confirming cointegration. The optimal lag length was selected using the Akaike Information Criterion (AIC), which is suitable for small sample sizes (n = 30) and balances model fit and complexity (Pesaran et al. 2001). Additionally, the Johansen cointegration test (Appendix A,

Table A1. Johansen cointegration.

Number of Cointegrating Relations by Model
Data Trend:	None	None	Linear	Linear	Quadratic
Test Type	No Intercept	Intercept	Intercept	Intercept	Intercept
	No Trend	No Trend	No Trend	Trend	Trend
Trace	5	2	0	1	1
Max-Eig	0	0	0	0	0
Critical values based on MacKinnon, Haug, and Michelis (1999)
Selected (0.05 level)

Source: Authors’ computation.

) corroborates a long-run relationship, further validating the chosen models used in this study for analysis.

4.1.2. Empirical Results from ARDL, VECM, Causality, and Quantile Regression Analysis

ARDL Results

Table 1. ARDL result.

Long-run ARDL ECM results
Dependent variable: HDI
Variable	Coefficients	Standard Error	t-statistics	Prob.
GE	0.2897 ***	0.0384	7.5374	0.0000
IF	0.0170 ***	0.0057	2.9952	0.0091
UE	−0.0038 **	0.0229	−0.1681	0.0187
PP	−0.0102 **	0.0043	−2.3697	0.0316
Constant	−0.4500 ***	0.0637	−7.0605	0.0000
Short-run elasticities and ECM
Dependent variable: HDI
Variables	Coefficient	Standard error	t-Statistics	Prob.
GE	0.0170 ***	0.0020	4.2250	0.000
IF	0.0100 **	0.0040	2.3210	0.031
PP	0.0100	0.0070	1.4190	0.171
UE	0.2276 ***	0.0645	3.7940	0.004
Coint. Equation (−1)	−0.1650 ***	0.0190	−8.2983	0.000
Diagnostic Checks
Serial Correlation
F-Statistics	1.569	Prob.	0.226
Obs R Squared	2.085	Prob.	0.148
Heteroscedasticity test: Breusch pagan test
F-statistics	0.989	Prob.	0.46
Obs R-Squared	6.188	Prob.	0.402
Scales explained SS	1.88	Prob.	0.929
Normality Test
Jargue–Bera	1.2419	Prob.	0.5374
Stability Test
Cusum Test			Stable
CusumQ Test			Stable

Note: **, and *** indicate that each variable in this study is significant at 5%, and 1% significance level; Source: Authors’ computation.

presents the results of the autoregressive distributed lag (ARDL) model, with the human development index (HDI) as the dependent variable. In the long run, HDI exhibits a statistically significant relationship with government expenditure on health (0.2897), inflation (0.0170), unemployment (−0.0038), and population growth (−0.0102), suggesting that higher government health spending and inflation positively contribute to human development, while rising unemployment and population growth exert a negative influence. In the short run, HDI remains significantly influenced by government health expenditure (0.0170), inflation (0.0100), and unemployment (0.2276), while population growth, though positive, is not statistically significant. The error correction term (−0.1650) is negative and statistically significant, confirming the presence of a stable long-run equilibrium relationship, with adjustments occurring at a moderate speed.

In our model, the goodness of fit is reflected in the R-squared, indicating that the independent variables collectively explain a substantial portion of the variation in HDI. The F-statistic is statistically significant at the 5% level, confirming the overall model’s reliability. The Durbin–Watson statistic is close to 2, suggesting no severe autocorrelation concerns. Diagnostic tests further validate the robustness of the model. The Breusch–Godfrey serial correlation test yields an F-statistic of 1.569 (p = 0.226) and an observed R-squared value of 2.085 (p = 0.148), indicating no significant serial correlation. The Breusch–Pagan heteroscedasticity test produces an F-statistic of 0.989 (p = 0.46) and an observed R-squared value of 6.188 (p = 0.402), confirming homoscedasticity. Additionally, the Jarque–Bera statistic’s normality test (1.2419, p = 0.5374) suggests that the residuals are normally distributed, as shown in Figure A1 in Appendix A. Stability diagnostics, including the cumulative sum (CUSUM) and cumulative sum of squares (CUSUMQ) tests, confirm that the model parameters remain stable over time, as shown in Figure A2 and Figure A3 in Appendix A.

Variance Decomposition Results

Table 2. Variance decomposition of government expenditure on health.

Variance Decomposition of GE
Period	GE	UE	IF	PP	HDI
1	100	0.0000	0.0000	0.0000	0.0000
2	94.9652	1.1011	2.0441	0.0100	1.8792
3	84.9152	10.2512	2.8804	0.4194	1.5335
4	85.2194	9.5591	3.1651	0.7517	1.3045
5	86.5861	8.4227	3.0780	0.6601	1.2528
6	87.4363	7.4694	3.1370	0.5837	1.3733
7	81.3784	13.4090	2.8936	1.0529	1.2659
8	80.5236	14.4543	2.7775	1.1115	1.1329
9	81.6557	13.4143	2.8190	1.0173	1.0934
10	82.2931	12.7143	2.8643	0.9493	1.1787

Source: Authors’ computation.

presents the variance decomposition results from periods 1 to 10, illustrating the percentage of forecast error variance in government health expenditure explained by shocks in itself and other variables. In the initial period, government expenditure accounts for 100% of its own variation. Over time, its contribution declines, reaching 82.2931% by the tenth period, indicating that other variables increasingly influence its fluctuations. Unemployment has a growing impact on government expenditure, rising from 0.0000% in period 1 to 12.7143% in period 10, suggesting that government health spending significantly influences labour market conditions in the long run. Inflation remains relatively stable, contributing between 2.0441% and 2.8643% to forecast error variance, implying a modest yet consistent effect. Population growth has a minimal influence, starting at 0.0000% and peaking at 1.1115% in period 8 before slightly declining. Similarly, the human development index exerts a limited effect, fluctuating between 1.8792% and 1.1787%, indicating that while human development is linked to government expenditure, its short-term influence remains modest.

The selection of the optimal lag length was guided by information criteria, including the Akaike Information Criterion, Schwarz Criterion, and Hannan–Quinn Criterion. We ensured that the most appropriate lag structure was chosen to capture the dynamic relationships among variables. The impulse response function in Figure A4 in Appendix A further illustrates the impact of shocks over time, while the stability of the vector error correction model (VECM) lag structure is confirmed through the AR Root Graph in Figure A5.

Quantile Regression Results

Table 3. Quantile regression results.

Dependent Variable: HDI
tau = 0.2
Variable	Coefficient	Std. Error	t-Statistic	Prob.
GE	−0.0638	0.5289	−0.1206	0.9050
IF	0.0088	0.0445	0.1980	0.8447
UE	0.8160 ***	0.2640	3.0902	0.0050
PP	0.4156 ***	0.1479	2.8094	0.0097
C	−0.4359 *	0.2211	−1.9711	0.0603
tau = 0.4
Variable	Coefficient	Std. Error	t-Statistic	Prob.
GE	0.4393	0.4215	1.0422	0.3077
IF	0.0260	0.0516	0.5039	0.6189
UE	0.5708 **	0.2448	2.3318	0.0284
PP	0.3646 **	0.1661	2.1953	0.0381
C	−0.4510 **	0.2150	−2.0973	0.0467
tau = 0.6
Variable	Coefficient	Std. Error	t-Statistic	Prob.
GE	0.7485 *	0.3712	2.0163	0.0551
IF	0.0413	0.0613	0.6734	0.5071
UE	0.5274 *	0.2719	1.9395	0.0643
PP	0.3347	0.2137	1.5664	0.1303
C	−0.6006 **	0.2575	−2.3321	0.0284
tau = 0.8
Variable	Coefficient	Std. Error	t-Statistic	Prob.
GE	0.9445 **	0.3564	2.6495	0.0140
IF	0.1246 **	0.05923	2.104295	0.0460
UE	0.0330	0.162797	0.203025	0.8408
PP	0.0283	0.097551	0.290615	0.7738
C	−0.11497	0.293531	−0.391685	0.6987

Note: *, **, and *** indicate that each variable in this study is significant at 10%, 5%, and 1% significance level; Source: Authors’ computation.

presents the quantile regression results for τ = 0.2, 0.4, 0.6, and 0.8, with the human development index as the dependent variable. The results reveal varying effects of government expenditure on health, inflation, unemployment, and population growth across different quantiles, showing the heterogeneous relationships across the distribution. At the lower quantile (τ = 0.2), unemployment and population growth are statistically significant, with coefficients of 0.8160 and 0.4156, respectively, indicating their more substantial influence on human development at the lower end of the distribution. However, government expenditure and inflation do not exhibit statistical significance. In the τ = 0.4 quantile, unemployment and population growth remain significant, though their effects slightly decrease to 0.5708 and 0.3646, respectively, while government expenditure and inflation remain statistically insignificant.

As the quantile increases to τ = 0.6, government expenditure becomes significant with a coefficient of 0.7485, alongside unemployment at 0.5274, suggesting that at the median level of human development, fiscal policies and labour market conditions play an essential role. Inflation and population growth, however, remain insignificant. At the upper quantile (τ = 0.8), government expenditure and inflation become significant, with values of 0.9445 and 0.1246, respectively, while unemployment and population growth lose their statistical significance; this indicates that at higher levels of human development, public expenditure and price stability play a more prominent role compared to labour market and demographic factors.

Pairwise Causality Test Result

Table 4. Causality test.

Null Hypothesis:	F-Statistic	Prob.
$HDI\to GE$	2.2896	0.1249
$GE\to HDI$	4.9120 **	0.0172
$IF\to GE$	0.1947	0.8244
$GE\to IF$	0.0567	0.9450
$PP\to GE$	0.0128	0.9872
$GE\to PP$	0.1424	0.8680
$UE\to GE$	0.0929	0.9116
$GE\to UE$	0.9994	0.3842
$IF\to HDI$	0.5710	0.5731
$HDI\to IF$	0.1880	0.8299
$PP\to HDI$	0.3792	0.6887
$HDI\to PP$	0.1758	0.8399
$UE\to HDI$	0.2004	0.8198
$HDI\to UE$	1.2257	0.3128
$PP\to IF$	0.9681	0.3954
$IF\to PP$	0.8762	0.4304
$UE\to IF$	0.02283	0.9775
$IF\to UE$	0.53323	0.5941
$UE\to PP$	0.58017	0.5681
$PP\to UE$	0.26144	0.7723

Note: ** indicates significance at a 5% level. Source: Authors’ computation.

presents the pairwise causality test among government expenditure on health, human development index, inflation, unemployment, and population growth. The results indicate that, for most variables, there is no significant causal relationship, suggesting independence among them. However, a unidirectional causality is observed from government expenditure on health to the human development index, implying that changes in public health spending significantly influence human development, while the reverse does not hold; this finding stressed the role of government investment in healthcare as a driver of human development in South Africa.

4.1.3. Robustness Checks

Robustness checks were performed using FMOLS and CCR to confirm the consistency of the estimated relationships. The results in

Table 5. Fully modified least squares and canonical cointegrating regression result.

Dependent Variable: HDI
Fully Modified Least Squares (FMOLS)
Variable	Coefficient	Std. Error	t-Statistic	Prob.
GE	1.1206 ***	0.3341	3.3536	0.0028
IF	0.0899 *	0.0508	1.7696	0.0901
UE	0.2703	0.1676	1.6118	0.1206
PP	−0.1989 **	0.0876	−2.2696	0.0329
C	−0.5597 **	0.2516	−2.2240	0.0362
R-squared	0.6295	Adjusted R-squared	0.5477
Canonical Cointegrating Regression (CCR)
Variable	Coefficient	Std. Error	t-Statistic	Prob.
GE	1.1257 ***	0.3099	3.6319	0.0010
IF	0.0820	0.0560	1.4625	0.1571
UE	0.2755	0.2140	1.2871	0.2109
PP	−0.1988 *	0.0989	−2.0098	0.0563
C	−0.5648 *	0.2907	−1.9427	0.0644
R-squared	0.6396	Adjusted R-squared	0.559634

Note: *, **, and *** indicate that each variable in this study is significant at 10%, 5%, and 1% significance level; Source: Authors’ computation.

confirm that all variables positively influence the human development index (HDI), except for population growth, which exhibits a negative effect. In the FMOLS estimation, government expenditure on health, inflation, and population growth are statistically significant, with coefficients of 1.1206, 0.0899, and 0.1989, respectively. Unemployment is positive but not statistically significant. The model demonstrates a strong explanatory power, with an R-squared of 0.6295 and an adjusted R-squared of 0.5477.

Similarly, the CCR estimation aligns with these findings, showing that all variables, except for population growth, positively influence HDI. However, only government expenditures on health and population growth are statistically significant, with coefficients of 1.1257 and 0.1988, respectively. Unemployment remains positive but not statistically significant. The R-squared and adjusted R-squared for this model are 0.6396 and 0.5596, respectively. A comparison with the autoregressive distributed lag (ARDL) model results supports the robustness of these findings. Table 4 presents the long-run estimates from ARDL, where HDI maintains a statistically significant relationship with government expenditure on health (0.2897), inflation (0.0170), unemployment (−0.0038), and population growth (−0.0102). However, slight differences regarding the signs of unemployment in both FMOLS and CCR are not statistically significant. Likewise, the inflation rate in CCR is not significant. The consistency of the direction and significance of these relationships across different estimation techniques provides strong evidence of the reliability and stability of the results.

4.2. Discussion of Results

Hypothesis One: The hypothesis that public health expenditure has short- and long-term effects on human development is accepted, as the empirical results support its validity. The positive and statistically significant relationship between government health expenditure and the human development index (HDI) in both the short run (0.0170) and long run (0.2897) confirms that increased public health spending contributes meaningfully to human development; this finding aligns with the economic theory that posits that investment in public health enhances human capital formation, thereby improving productivity and long-term economic growth (Madsen 2016). The results are further supported by previous empirical studies demonstrating that higher public health expenditure leads to improvements in key human development indicators (Onofrei et al. 2021; Akbar et al. 2021).

The implications of these findings are significant for South Africa’s economic development strategy. The long-run effect suggests that sustained government investment in health is crucial for promoting human development and supporting economic growth. The observed short-run significance indicates that even immediate increases in health spending can yield measurable improvements in human development, justifying policy efforts to prioritise public health funding. However, the extent to which these gains can be maintained depends on the macroeconomic environment and the efficiency of public expenditure allocation. The unexpected positive relationship between inflation and HDI (0.0170 in the long run and 0.0100 in the short run) presents an interesting deviation from theoretical expectations, which typically suggest an inverse relationship between inflation and development (Yolanda 2017; Islam 2022); this could be explained by the possibility that moderate inflation facilitates higher government revenues and increased social spending, particularly in health and education. If inflation remains controlled within a manageable range, it may enhance real income in essential sectors, improving health and well-being. However, if inflation rises uncontrollably, it could erode purchasing power and reduce access to critical services, potentially reversing human development gains.

The negative impact of unemployment on HDI (−0.0038 in the long run and −0.2276 in the short run) is consistent with expectations and the existing literature (Runtunuwu 2020), aligning with the argument that labour market stability is fundamental to human development. High unemployment levels limit income and access to healthcare and contribute to social inequalities that undermine economic progress. The short-run effect indicates that fluctuations in employment conditions have an immediate and significant impact on human development, showing the need for policies prioritising job creation, skills development, and inclusive economic growth. The ambiguous role of population growth in influencing HDI aligns with prior findings that its impact is context-dependent (Tripathi 2021). The long-run negative relationship (−0.0102) suggests that rapid population growth, if not matched by proportional increases in public health expenditure, could strain resources and weaken economic development. In contrast, the short-run effect, though positive, lacks statistical significance, indicating that immediate changes in population may not have an evident impact on HDI; this states the need for long-term planning to balance population growth with investments in healthcare, education, and infrastructure to maximise human capital development.

Hypothesis 2: The hypothesis that human development responds significantly to shocks in public health expenditure is rejected, as the results indicate only a modest and limited influence of human development on government health expenditure fluctuations. The variance decomposition results reveal that while government expenditure initially accounts for 100% of its own variation, its influence diminishes to 82.2931% by the tenth period as other macroeconomic variables increasingly drive its changes. Notably, unemployment emerges as the most influential factor over time, growing from 0.0000% in the first period to 12.7143% in the tenth, suggesting that government health spending plays a critical role in influencing the labour market. Inflation maintains a relatively stable but minor impact, while population growth exerts an even smaller effect. The most striking result is the limited role of the human development index (HDI), which fluctuates between 1.8792% and 1.1787% in explaining public health expenditure shocks; this suggests that while human development and government health spending are interrelated, the responsiveness of HDI to expenditure shocks is not statistically significant in the short run.

The rejection of the hypothesis carries important implications for South Africa’s economic and social development policy. The finding contrasts with theoretical expectations that macroeconomic disruptions such as financial crises, pandemics, or policy shifts can trigger abrupt changes in public spending, subsequently affecting human development (Loayza and Pennings 2020; Gaies 2022); this could be attributed to the structural rigidity of South Africa’s public health financing, where policy inactivity and institutional constraints limit the immediate pass-through effects of spending shocks. Moreover, existing studies stated that government expenditure shocks often have asymmetric effects depending on institutional efficiency and economic structure (Ma and Qamruzzaman 2022). The results suggest that South Africa’s institutional framework may dampen the responsiveness of human development to sudden changes in public health spending, potentially due to bureaucratic inefficiencies, delayed implementation of health programmes, or rigid budgetary constraints.

Empirical studies have documented that healthcare spending shocks can have immediate and lagged effects on health and economic performance (Abdulqadir et al. 2024). However, the findings of this study indicate that such effects may not be as pronounced in South Africa, at least within the observed period; this suggests that any positive impact of increased health expenditure on human development may take longer to materialise, requiring sustained and investment rather than sporadic fiscal responses. The implication is that short-term increases in health spending, particularly those triggered by economic shocks, may not yield immediate improvements in human development unless they are complemented by structural reforms that enhance the efficiency and reach of public health programmes.

Furthermore, the results show the dominant role of unemployment in explaining variations in public health expenditure over time, supporting the argument that labour market conditions and social welfare policies are connected. If high unemployment persists, government health spending may increasingly be directed towards social safety nets rather than proactive human capital development; this suggests that policymakers must adopt a perspective integrating labour market interventions with health policies to ensure that public expenditure translates into meaningful improvements in human development.

Hypothesis three: The hypothesis that the impact of public health expenditure on human development varies across different levels of economic development is accepted, as the quantile regression results for South Africa demonstrate heterogeneous effects across the distribution of the human development index (HDI). The statistical significance and magnitude of government health expenditure, inflation, unemployment, and population growth differ across quantiles, confirming that the influence of public health spending is contingent on the level of human development.

At the lower quantile (τ = 0.2), where South Africa’s human development is relatively low, unemployment (0.8160) and population growth (0.4156) exert a significant influence, while government health expenditure and inflation remain statistically insignificant; this suggests that in the periods of lower development, labour market conditions and demographic factors are more critical determinants of human well-being than direct public health investment; these findings align with studies indicating that the effectiveness of healthcare spending is contingent on institutional efficiency, governance quality, and economic conditions (Martín-Fernández et al. 2021; Hort et al. 2017). In South Africa, where structural unemployment and demographic challenges persist, public health expenditure alone may be insufficient to drive human development without complementary labour market policies and social interventions.

As development levels rise to the median quantile (τ = 0.6), government health expenditure becomes statistically significant (0.7485), alongside unemployment (0.5274), while population growth and inflation remain insignificant; this suggests that at moderate levels of human development, fiscal policy and labour market conditions become the primary drivers of social well-being. The increasing significance of public health spending at this stage reflects the growing efficiency of government interventions, particularly when economic structures and governance frameworks improve (Balani et al. 2023); this supports the need for South Africa to enhance institutional efficiency in healthcare delivery to maximise the returns on public expenditure.

At the upper quantile (τ = 0.8), representing higher levels of human development, government health expenditure (0.9445) and inflation (0.1246) become significant, while unemployment and population growth lose their statistical relevance; this shift indicates that in more developed segments of the economy, macroeconomic stability and sustained fiscal investment in health play a more prominent role in influencing human development. The positive relationship between inflation and HDI at this stage, contrary to the expected inverse relationship (Yolanda 2017; Islam 2022), suggests that controlled inflation may be linked to higher economic activity and government revenues, enabling greater social spending; this shows the need for maintaining price stability and also ensuring consistent public investment in health services.

The confirmation of varying effects across different levels of human development shows the need for a differentiated policy approach. In lower-income areas, government health expenditure should be complemented by targeted labour market reforms and population management initiatives to enhance its impact. In contrast, at higher levels of development, policies should prioritise fiscal efficiency, institutional strengthening, and macroeconomic stability to sustain human development gains. Also, public health expenditure strategy must be adaptive rather than static. The evolving role of government health spending across quantiles indicates that a uniform fiscal approach may be ineffective in addressing the country’s diverse socioeconomic challenges. Strengthening governance structures and improving budgetary efficiency ensure that health investments yield meaningful long-term benefits.

Hypothesis 4: The hypothesis that a causal relationship exists between public health expenditure and selected indicators of economic development is partially accepted, as the results indicate a unidirectional causality from government expenditure on health to the human development index (HDI) but no significant causal relationships with other variables; this finding shows the role of government investment in healthcare as a direct driver of human development in South Africa while suggesting that other economic factors, such as inflation, unemployment, and population growth, do not exhibit direct causality with public health expenditure.

The unidirectional causality from government health spending to HDI aligns with the human capital theory, which posits that investments in health enhance labour productivity and overall economic development (Schultz 1961; Becker 1964); this result supports existing empirical evidence that higher public health expenditure contributes to improved economic performance (Raghupathi and Raghupathi 2020; Sethi et al. 2024), although it does not establish a ‘feedback causality’ where economic growth further drives healthcare investment. The lack of reverse causality suggests that improvements in human development, while influenced by health spending, do not necessarily lead to higher public health investments in the short run, potentially due to budgetary constraints or competing fiscal priorities within South Africa’s economic framework.

The absence of causality between public health expenditure and other macroeconomic indicators suggests that while inflation, unemployment, and population growth are widely recognised as influential economic variables, their lack of direct causality with government health spending operates independently of these factors within the South Africa this finding diverges from theoretical expectations that inflationary pressures and labour market conditions might influence fiscal allocations to health services. It may indicate that South Africa’s public health spending follows a structural budgetary framework rather than responding dynamically to short-term macroeconomic fluctuations.

The implications for South Africa’s economic development strategy are significant. First, the confirmation that government health expenditure directly influences human development strengthens the need for sustained investments in healthcare to drive long-term improvements in living standards. Given South Africa’s persistent socioeconomic challenges, including high inequality and unemployment, strengthening public health infrastructure can be a foundational pillar for human capital development, supporting economic growth. Second, the absence of causality with other economic indicators suggests that health expenditure planning should be integrated with economic policies to ensure alignment with the labour market and macroeconomic stability. Lastly, the lack of reverse causality implies that increases in human development do not automatically translate into higher government health spending; this finding suggests that public health financing in South Africa is driven more by policy decisions than by endogenous economic improvements. Thus, ensuring sustained investment in healthcare requires deliberate policy commitment rather than reliance on economic gains to generate additional fiscal motivation.

5. Conclusions and Policy Recommendations

Despite public health investments and economic reforms, the persistent challenges of human development and structural challenges to reduce poverty, unemployment, and inequalities persist in South Africa, showing the need to assess the long-term sustainability of the country’s economic development model. As human capital is a fundamental driver of economic progress, the role of public health expenditure in influencing economic development is essential. The study was motivated by the need to empirically evaluate how much public health spending influences human development and whether its effects vary across different economic conditions. Given this, this study employed autoregressive distributed lag (ARDL), vector error correction model (VECM), quantile regression and Granger causality to investigate the relationship between fiscal health policies and human development in South Africa.

The ARDL model confirms that government health spending significantly enhances human capital development in the short and long run, while unemployment and population growth exert negative pressures. Evidence from the VECM using variance decomposition results reveals that shocks in public health expenditure have a diminishing yet persistent influence over time, with growing contributions from unemployment. Quantile regression analysis further establishes that the impact of public health spending varies across different levels of economic development, with government expenditure becoming more influential at higher quantiles. At the same time, unemployment and demographic factors dominate lower development levels. Lastly, the causality analysis shows a unidirectional relationship between public health expenditure and human development, strengthening the argument that sustained investment in healthcare is a fundamental driver of social progress. However, as shown in the results of the investigation period, it remains largely independent of inflation, unemployment, and population growth.

Based on these findings, the study suggests improving the public health expenditure strategy to enhance human development and sustain South Africa’s economic development. Given the significant long-run impact of government health spending, policymakers should prioritise sustained and efficient investment in healthcare infrastructure, ensuring resource allocation aligns with developmental priorities. The heterogeneous effects observed across different levels of economic development stress the need for a differentiated approach at lower development levels; complementary policies addressing unemployment and demographic challenges should be integrated with health spending, while at higher development levels, macroeconomic stability and institutional efficiency must be strengthened to maximise the returns on investment. The observed unidirectional causality from health expenditure to human development further shows the need for an imperative for proactive policy commitments, as improvements in human development do not automatically translate into increased health funding. Policymakers must, therefore, institutionalise mechanisms to safeguard healthcare financing against economic volatility, ensuring consistent investments that drive long-term human capital formation. Lastly, given the relationship between public health expenditure and labour market conditions, health policies should be integrated into economic strategies, enhancing interactions that encourage productivity, reduce inequality, and promote inclusive growth.

The study discovered the following limitations. First, its macroeconomic focus may overlook micro-level determinants such as healthcare accessibility and regional inequalities. Second, it does not account for governance factors like corruption and institutional efficiency, which influence the effectiveness of public health spending. Therefore, future research should incorporate micro and regional analyses integrating governance indicators to help maximise the benefit of the relationship between health expenditure and human capital development in the economy.