Health Expenditures and Social Sustainability in Nigeria: A Disaggregated Grossman Analysis of Short-Run Gains and Long-Run Paradoxes

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Nigeria exemplifies such paradox: with increased health spending, life expectancy has declined, while infant mortality is elevated, jeopardising Sustainable Development Goal 3 (Good Health and Well-being) attainment. This research examines how disaggregated health financing segments: health expenditure per capita, recurrent health expenditure, capital health expenditure, and out-of-pocket health spending (OPHS) impact social sustainability indicators in the form of life expectancy, and infant mortality. Methods By utilising annual time series from 1990-2023 through the use of an Autoregressive Distributed Lag (ARDL) panel to address potential endogeneities, short- as well as long-run impacts are accounted for. Results Results indicate that per capita, recurrent and capital expenditures are significant in enhancing life expectancy in the long run, whereas all the financing segments are absent from having any statistically significant long-run impact on infant mortality. Paradoxical short-run mortality increases are observed in relation to increased recurrent as well as capital expenditures, which is indicative of inefficiencies as well as misappropriation. OPHS has mixed short-run impacts, as well as is insignificant in the long run, which accentuates its regressive burden. Conclusions The study concludes that financing volume alone is insufficient; expenditure composition, governance, and institutional reforms are critical to achieving socially sustainable health outcomes. Policy recommendations include reducing OPHS reliance, prioritising primary healthcare, and embedding sustainability principles in health financing so as to align Nigeria’s health system with SDG 3 targets by 2030. 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F1000Research 2025, 14 :1055 ( https://doi.org/10.12688/f1000research.171015.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Health Expenditures and Social Sustainability in Nigeria: A Disaggregated Grossman Analysis of Short-Run Gains and Long-Run Paradoxes [version 1; peer review: 1 approved with reservations] Pureheart Ogheneogaga Irikefe https://orcid.org/0000-0002-5486-1389 1 , Jonathan Ojarikre Oniore https://orcid.org/0000-0002-6526-722X 2 , Mohammad Falahat https://orcid.org/0000-0002-0423-984X 1 , Ihtisham Ullah https://orcid.org/0009-0009-6755-7173 1 , Nohman Khan https://orcid.org/0000-0001-9714-6273 1 , Bernard Ojonugwa Anthony https://orcid.org/0000-0002-4429-9599 3 Pureheart Ogheneogaga Irikefe https://orcid.org/0000-0002-5486-1389 1 , Jonathan Ojarikre Oniore https://orcid.org/0000-0002-6526-722X 2 , [...] Mohammad Falahat https://orcid.org/0000-0002-0423-984X 1 , Ihtisham Ullah https://orcid.org/0009-0009-6755-7173 1 , Nohman Khan https://orcid.org/0000-0001-9714-6273 1 , Bernard Ojonugwa Anthony https://orcid.org/0000-0002-4429-9599 3 PUBLISHED 09 Oct 2025 Author details Author details 1 Strategic Research Institute, Asia Pacific University of Technology & Innovation, Kuala Lumpur, Federal Territory of Kuala Lumpur, 57000, Malaysia 2 Department of Economics, Faculty of Social Sciences, Bingham University, Karu, Nasarawa, Nigeria 3 Department of Economics and Statistics, Kampala International University College of Economics and Management, Kampala, Central Region, Uganda Pureheart Ogheneogaga Irikefe Roles: Conceptualization, Methodology, Writing – Original Draft Preparation Jonathan Ojarikre Oniore Roles: Conceptualization, Investigation, Writing – Original Draft Preparation Mohammad Falahat Roles: Methodology, Project Administration, Supervision Ihtisham Ullah Roles: Formal Analysis, Resources, Software, Writing – Review & Editing Nohman Khan Roles: Methodology, Supervision, Validation, Writing – Review & Editing Bernard Ojonugwa Anthony Roles: Data Curation, Formal Analysis, Visualization, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Sociology of Health gateway. Abstract Background Global health expenditure has increased dramatically in the past decades, yet poor health outcomes in many emerging markets, including Nigeria, pose efficiency and sustainability questions in health financing. Nigeria exemplifies such paradox: with increased health spending, life expectancy has declined, while infant mortality is elevated, jeopardising Sustainable Development Goal 3 (Good Health and Well-being) attainment. This research examines how disaggregated health financing segments: health expenditure per capita, recurrent health expenditure, capital health expenditure, and out-of-pocket health spending (OPHS) impact social sustainability indicators in the form of life expectancy, and infant mortality. Methods By utilising annual time series from 1990-2023 through the use of an Autoregressive Distributed Lag (ARDL) panel to address potential endogeneities, short- as well as long-run impacts are accounted for. Results Results indicate that per capita, recurrent and capital expenditures are significant in enhancing life expectancy in the long run, whereas all the financing segments are absent from having any statistically significant long-run impact on infant mortality. Paradoxical short-run mortality increases are observed in relation to increased recurrent as well as capital expenditures, which is indicative of inefficiencies as well as misappropriation. OPHS has mixed short-run impacts, as well as is insignificant in the long run, which accentuates its regressive burden. Conclusions The study concludes that financing volume alone is insufficient; expenditure composition, governance, and institutional reforms are critical to achieving socially sustainable health outcomes. Policy recommendations include reducing OPHS reliance, prioritising primary healthcare, and embedding sustainability principles in health financing so as to align Nigeria’s health system with SDG 3 targets by 2030. READ ALL READ LESS Keywords Health Expenditures, Social Sustainability, Infant Mortality, Life Expectancy, SDG 3, Out-of-Pocket Health Spending, Grossman Model, ARDL Corresponding Author(s) Bernard Ojonugwa Anthony ( [email protected] ) Close Corresponding author: Bernard Ojonugwa Anthony Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Irikefe PO et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Irikefe PO, Oniore JO, Falahat M et al. Health Expenditures and Social Sustainability in Nigeria: A Disaggregated Grossman Analysis of Short-Run Gains and Long-Run Paradoxes [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :1055 ( https://doi.org/10.12688/f1000research.171015.1 ) First published: 09 Oct 2025, 14 :1055 ( https://doi.org/10.12688/f1000research.171015.1 ) Latest published: 12 May 2026, 14 :1055 ( https://doi.org/10.12688/f1000research.171015.2 )  There is a newer version of this article available. Suppress this message for one day. 1. Introduction The health sector within the global economy is characterized by a contradictory reality where spending on healthcare rose but does not directly result in a rise in frontier and developing economies’ outcomes. Total global health spending reached $7.9 trillion USD in 2017 and is projected to rise to $11.0 trillion USD by 2030, amounting to 8.6% of the global economy in 2016. 1 Irrespective of this expansion, persistent and glaring inequalities exist. A case in point is Sub-Saharan Africa (SSA), where they continue to face some of the worst health indicators globally, highlighting the disjuncture between expenditure and health outcomes. 2 African leaders acknowledged this issue, which they brought up in the 2001 Abuja Declaration when they pledged to allocate 15% of national budgets to health; however, in 2013 only 8 of 47 World Health Organisation’s (WHO) African Region nations had attained that target. 3 This continual deficit underlies chronic SSA-wide underinvestment as well as excessive dependence on out-of-pocket health spending (OPHS), which slows universal health coverage gains as well as entrenches poverty. 4 , 5 Nigeria, which is Africa’s largest economy as well as most populous nation, exemplifies this deficit. The Nigerian government in 2018 only dedicated 5% of its annual expenditure towards health; far less than Abuja Declaration promise, 6 whereas health expenditure led by OPHS continued to be intractable; as nearly 30% of West Africans are dragged into poverty each year by health expenditure in the form of OPHS. 6 The consequence of this remain severe stark as evidence in 2020 revealed that maternal mortality was 512 per 100,000 live births as well as life expectancy 54.8 years compared with 75 in Mauritius. 7 Despite gradual progress; with life expectancy increased to 58.6 years between 2000-2010, Nigeria is still a long way towards its 70-year health plan target in a show of health financing ineffectiveness in dire need for adjustment. 7 , 8 Significant research verifies that spending on public health strengthens major health outcomes, including life expectancy and infant mortality, in the longer term. 9 – 11 These results are consistent with Grossman’s 12 model of health capital, which understands health as a durable stock in physical capital available for use over longer periods which people can improve with medical care and healthy behaviors in order to lengthen life. 13 Critics suggest Grossman’s model simplifies health dynamics at a systematic level while understating policy determinants. 14 Indeed, not all evidence points to a straightforward link. De La Maisonneuve et al. 15 note that increased health spending may fail to yield outcomes in contexts characterized by weak financial systems or inefficiencies. This nuance is particularly relevant in SSA, where governance structures and health system constraints often dilute the benefits of spending. In SSA, studies show a non-linear and uneven relationship between health expenditure and outcomes. Adegoke et al. 2 find asymmetric quadratic effects, where health spending initially improves outcomes but eventually diminishing returns sets in. Beşer et al. 16 similarly report improvements in life expectancy with rising health investment, but these gains remain fragile. Persistent inefficiencies in the form of misalignment between spending priorities and health system needs, limit progress. 2 Hence, governance has emerged as a decisive factor. Rahman et al. 17 and Langnel and Buracom 18 show that government effectiveness amplifies the benefits of health expenditure in SSA and BRICS economies. Similarly, Ndzignat Mouteyica and Ngepah 19 underscore the uneven progress across Africa and stress the importance of governance and financing reforms for sustainable health improvements. Nigeria epitomises the paradox of rising health spending without proportional outcomes as depicted in Figure 1 . Figure 1. Health outcomes: Nigeria vs World (2000-2023). Line chart comparing Nigeria’s health expenditure per capita (US$), life expectancy at birth (years), and infant mortality rate (deaths per 1,000 live births) with global life expectancy and infant mortality over 2000-2023. Abbreviations: HEXPc = health expenditure per capita (US$); LEX = life expectancy at birth (years); IMR = infant mortality rate (deaths per 1,000 live births). Notes: All series are plotted on the same vertical axis; units differ by series as indicated (US$, years, and deaths per 1,000). Figure 1 , using World Development Indicator’s data, depicts that Nigeria’s health expenditure per capita (pc) is rising over time, life expectancy gradually improving, and infant mortality rates steadily declining; however, the outcomes pales in comparison to global trends. Adegoke et al. 7 explain that despite intensified expenditure since the Millenium Development Goals (MDGs), Nigeria remains not on target for Sustainable Development Goal (SDG) 3: “good health and well-being.” Nnamdi and Ngwu 8 provide further depth: while private expenditure raises outcomes, public expenditure generates less potent impacts, while OPHS is a negative force, reducing life expectancy as it raises death rates. Thus, health finance is not a question of quantum-of-resources but their composition, their efficiency, and their management. As Pitkänen et al. 20 emphasized, incorporating principles of sustainability in welfare and health finance is essential for social sustainability because effective and justified schemes provide a foundation for sustained welfare in society. Contemporary social-welfare frameworks call for a measurement in health policy for sustainability in line with social sustainability for balanced gains in a decline in mortality and greater longevity aside from successive gains only. 21 Consequently, in this study, health outcomes: life expectancy and infant mortality are framed as social sustainability indicators in alignment with SDG 3. 7 , 16 As Komp-Leukkunen and Sarasma 22 averred, social sustainability refers to society’s capacity for ensuring quality life and longevity for current and succeeding generations. Broadening Grossman’s microeconomic perspective, 23 this study positions health spending in a system-wide as well as a macroeconomic framework in order to determine how variable components in finance: public spending, private spending, out-of-pocket spending, and external spending, culminate in national health capital. Despite considerable literature, some fundamental gaps remain. Several works are based on cross-section or short-panel data, which restrict their capacity for modelling longer-run dynamics and lagged effects over time. 8 , 24 As for Nigeria in particular, there is a lack of investigation into how individual financing components; other than aggregate expenditure by governments, influence outcomes over time. Existing literature is prone to generalising for developing nations or treats public expenditure exclusively as a driver while ignoring opposing roles for private as well as OPHS. 8 Furthermore, only a handful of studies try to estimate the sustainable spending levels needed to meet SDG 3. 7 This study fills that gap by examining how different health financing components shape Nigeria’s health and social sustainability over both the short and long run; specifically, it aims to: (i) Determine the long- and short-run impact of health expenditure per capita, recurrent health expenditure, capital health expenditure, and out-of-pocket health spending on life expectancy; (ii) Long- and short-run impact of health expenditure per capita, recurrent health expenditure, capital health expenditure, and out-of-pocket health spending on infant mortality. Methodologically, this study uses an Autoregressive Distributed Lag (ARDL) method appropriate for time-series with mixed integration orders, 8 , 25 based on annual data ranging between 1990 and 2023. It permits robust analysis for both long-run equilibria as well as short-run responses. Its contribution is three-fold. At a theoretical level, it extends Grossman’s health capital model 12 by locating health expenditure in systemic multiple financing settings. At an empirical level, it provides one of the most detailed long-run health financing analyses for Nigeria covering several decades, filling in the gaps for those parts not covered by cross-sectional research. At a practical level, its results enable policy-relevant information for optimising expenditure composition; reducing OPHS; and aligning financing for attaining SDG 3 targets. The paper proceeds as follows: Section 2 reviews the literature; Section 3 describes data and methodology; Section 4 presents and discusses findings; Section 5 concludes with policy recommendations. 2. Literature review 2.1 Health expenditures and social sustainability Health expenditure refers to the financial resources allocated by governments, private actors, households, and international partners to maintain and improve population health. 24 It is an all-encompassing factor covering expenses for health issues, prevention, pharmaceutical costs, diagnostic costs, and health-related programmes, including training, research, awareness, and development. 7 From a broad perspective, health spending covers basic infrastructure, services, and supplies to deliver health care but not the expenditure on informal care and capital purchase of big items. 1 In a socially sustainable approach Sentürk et al., 26 averred that health spending is not only an input into the provision of services but a system that guarantees equity, resilience, and long-term intergenerational well-being. Social sustainability in health, therefore, is the ability of a system to achieve fair access, minimize exposure to poverty and catastrophe shocks, and support long-term duration in a fashion that maintains welfare in the long term in. 20 , 22 Framing health spending in the broader sustainability framework helps scholars in the subsequent empirical reviews to examine the question of whether increased investment translates into long-lasting welfare improvements or whether inefficiencies, inequities, and frail institutions sabotage results. Adegoke et al., 7 with the assistance of ARDL and causality tests of a panel dataset of Nigeria (1995-2020) uncover that while health outcomes in the first phase are beneficial from the expenditure, falling returns are quick to emerge and shed light on inefficiencies and inappropriateness between priorities and needs. The result highlights the significance of the health expenditure composition and the systems of governance through which it is channeled in the quest to achieve social sustainability. Disaggregated evidence for Nigeria strengthens this observation. Nnamdi and Ngwu 8 find that while private expenditure has a positive impact on life expectancy, public expenditure delivers detrimental weaker effects, reducing longevity while increasing mortality. These findings reveal that financing structure, rather than sheer volume, determines social sustainability. Voto et al. 6 also confirm that almost 30% of West Africans are pulled into poverty each year because of OPHS, widening inequities and undermining the sustainability of health gains. Such findings support Rahman et al., 17 utilizing panel cointegration and causality to document the mediating effect of governance in BRICS countries (1995-2021): functional government strengthens returns from spending, while faltering institutions weaken them. In Nigeria, where health spending is routinely fragmented and governance issues are rampant, such evidence explains why increased expenditures failed to shut sustainability gaps. The broader SSA and developing comparative evidence provides additional methodological and conceptual refinement. Owusu et al., 11 employing quantile regression of a panel (2000-2017), uncover heterogeneous effects across the health outcomes range and find that spending decreases infant and maternal deaths but with disparate advantages. Beşer et al. 16 also find using System-GMM of a panel (1990-2020) that health investment increments are associated with life expectancy increments in Africa but tenuous when fiscal and environmental sustainability are excluded from the picture. These confirm the necessity of policy arrangements that synthesize efficiency, governance, and sustainability principles in health systems financing. They also reinforce the extension of Grossman’s 12 model from an individual-level stock of health capital to a system-level dynamic shaped by policy and macroeconomic institutions. Outside Africa, evidence also highlights expenditure composition, efficiency, and institutional context. Onofrei et al., 10 analysing EU developing countries using panel regression (2000-2018), demonstrate that public spending is positively associated with improved outcomes, but fiscal discipline and institutional quality determine the scale of benefits. Polcyn et al. 27 show, using CS-ARDL methods for Asian economies panel data (1995-2019), that higher health spending increases life expectancy, but effects are asymmetric and contingent on demographic pressures. Also, Gillani et al. 24 establish lagged reactions in Asian panel datasets (1980–2018), and long-run equilibria are very different from short-run impacts—an empirical result very relevant to Nigeria’s policy environment. Magida et al. 25 in South Africa using quantile regressions on the data (1994-2020) report that the spending effect varies across the mortality level and therefore verify Nigeria’s heterogeneous health finance outcomes. In general, the studies verify that the relationship between health finance and outcomes is nonlinear and not universal but is shaped by context, system efficiency, and governance architecture. At the frontier of theory, Bala et al. 13 put Grossman’s demand-side health capital framework in perspective with a concurrent supply-side model and reveal that health outcomes result not just from private investment but also from health system financing and institutional mechanisms. Such theoretical refinement is consistent with empirical evidence from SSA and Nigeria where institutional frailties and dispersed financing systems vitiate health sustainability in the face of increased expenditure. Vărzaru 9 contributes a predictive angle where only nations applying sustainability values in health finance will realize stable welfare improvements. These results resonate with Pitkänen et al. 20 and Adler, 21 who argue that social sustainability requires financing schemes that reduce inequalities while ensuring longevity and intergenerational welfare. Confirmatory studies from other regions strengthen these conclusions. In India, Pradhan and Behera 28 document catastrophic OPHS between 2000 and 2017, which mirrors the Nigerian experience of expenditure-induced traps of poverty. Sayuti and Sukeri 29 similarly show, using time-series regression (2000-2018), how high OPHS undermines health outcomes of Malaysia, reinforcing that household-dominated financing models are unsustainable. In OECD countries, Silva et al. 30 emphasize with panel regression (1995-2020) that private health expenditures interact with fiscal capacity, determining long-run sustainability. In China, Liang et al. 31 demonstrate using panel regression (2000-2016) that resource allocation: specifically health worker density, has stronger effects on mortality than raw spending volumes, highlighting the role of efficiency. Studies by Xiu et al.’s 32 and Gani’s 33 continue the discussion and relate natural resource rents and wealth with health outcomes and reveal the constraint that fiscal space places on the success of spending. Despite the circumstances differing between the studies, they steadfastly verify the fundamental finding that without consistency of the structure of financing to institutional ability and sustainability principles, health spending is not achieving sustainable social outcomes. 2.2 Research gap Despite an expanding empirical base, critical gaps remain that this study addresses. Much of the SSA literature emphasises aggregate spending without disaggregating financing streams, limiting insight into how public, private, out-of-pocket and external components differently affect social sustainability. 2 , 6 , 16 Nigeria-specific analyses similarly prioritise aggregate trends, leaving the distinct roles of private versus public financing and the poverty-inducing effects of OPHS under-specified. 7 , 8 Further, cross-sections or short-panels cannot reveal long-run equilibria, structural breaks, and lagged adjustment important for sustainability assessment in the long term. 1 , 24 , 34 Empirically, threshold and interaction terms are under-estimated in the case of Nigeria although with implications of their relevance in regional and BRICS applications. 7 , 17 Therefore, the lacuna calls for robust time-series analysis that disaggregates spending and models nonlinearity and structural change to inform policy consistent with SDG3. This study uses ARDL and cointegration methods on long-run Nigerian series to explicitly estimate policy-relevant thresholds. 3. Materials and methods 3.1 Theoretical framework The current study follows an ex post facto research design grounded in Grossman’s health-capital model, extended to the macro level and embedded within a social-welfare (social-sustainability) interpretation. The presentation proceeds in four steps: (i) Grossman’s micro health production, (ii) the macro reformulation, (iii) the social-welfare (social-sustainability) function, and (iv) the empirical reduced form and dynamic specification used in this study. Grossman 12 models health as a stock that individuals produce using a vector of inputs. In reduced form: (1) H = f ( X ) where H is the individual health stock and X is a vector of inputs (medical care, nutrition, education, time invested in health, and environmental and innate endowments). 23 Health naturally depreciates at rate δ and evolves according to: (2) H t + 1 = ( 1 − δ ) H t + I t , where I t is gross investment in health (healthcare consumption, preventive measures, etc.). This formulation motivates viewing health as an accumulable capital stock that responds to flows of public and private inputs. 23 Empirical and policy analyses extend Grossman to the aggregate (country) level. 35 – 37 Fayissa and Gutema 38 offer a compact macro specification that organises determinants into economic, social, environmental and health-service blocks: (3) H = f ( Y , S , V , D ) where at the national level H denotes aggregate health outcomes (e.g., life expectancy, infant/under-5 mortality), Y is a vector of economic variables (GDP per capita, public health spending, employment), S denotes social variables (education, demographic structure), V stands for environmental conditions (sanitation, disease burden, water access), and D captures health-service and system variables (coverage, facility capacity, immunization). In scalar (expanded) form this is: (4) H = f ( y 1 , … , y n ; s 1 , … , s n ; v 1 , … , v n ; d 1 , … , d n ) Equation (3) organises potential inputs so that empirical work can test which blocks—and which specific inputs within blocks—drive population health. According to Sentürk et al.’s 26 findings, population health is both an outcome and a fundamental component of social sustainability: health improvements support equity, resilience and intergenerational welfare. 39 The current study, thus, formalise social sustainability (SS) as a social welfare function in line with Adler 21 that depends on health and complementary social goods: (5) SS = g ( H , E , I , P ) , where E is education/human capital, I is institutional inclusiveness and governance, and P denotes poverty reduction and equity. Under Equation (5) , improvements in H raise SS ceteris paribus ; conversely, persistently weak health undermines long-run social sustainability. 17 , 21 , 22 , 26 Substituting the macro health production Equation (3) into Equation (5) gives the structural relation that guides empirical identification: (6) SS = g ( f ( Y , S , V , D ) , E , I , P ) Equation (6) clarifies that health expenditures (part of Y and D ) influence social sustainability indirectly by changing H , consistent with the Grossman 12 health production framework. 14 , 23 The empirical strategy therefore examines whether disaggregated health financing (per-capita spending, recurrent, capital, out-of-pocket) yields durable improvements in the health stock that translate into higher social sustainability. 3.2 Empirical model specification For operational clarity, the current study treats two observed SS health outcomes models: life expectancy at birth (LEX) and infant mortality rate (IMR), as proximate measures of H (and, by extension, of social sustainability). The baseline reduced-form relations are: (7) LE X t = α 0 + α 1 HEXP c t + α 2 RHE X t + α 3 CHE X t + α 4 OPH S t + Γ Z t + ε t , and (8) IM R t = β 0 + β 1 HEXP c t + β 2 RHE X t + β 3 CHE X t + β 4 OPH S t + Λ Z t + μ t , where HEXPc = health expenditure per capita, RHEX = recurrent health expenditure, CHEX = capital health expenditure, OPHS = out-of-pocket spending; Z t is a vector of control variables drawn from the Y , S , V blocks (e.g. GDP per capita, female literacy, sanitation coverage); ε t , μ t are disturbances. The current study’s a priori expectation is thus: α 1 , 2 , 3 > 0 and β 1 , 2 , 3 < 0 ; signs on α 4 , β 4 are ambiguous because OPHS can reflect both access and financial burden. 4 , 6 Because health is a stock and adjustments take time, a dynamic specification is required. We adopt the ARDL-Error Correction (ECM) approach 40 to estimate short-run and long-run effects while allowing regressors of mixed integration orders. The ARDL form for LEX (Model 1) is: (9) Δ LE X t = ϕ 0 + ∑ i = 1 p ϕ 1 i Δ LE X t − i + ∑ j = 0 q ϕ 2 j Δ HEXP c t − j + ∑ k = 0 r ϕ 3 k Δ RHE X t − k + ∑ l = 0 s ϕ 4 l Δ CHE X t − l + ∑ m = 0 u ϕ 5 m Δ OPH S t − m + λ ( LE X t − 1 − θ ′ X t − 1 ) + η t and analogously for IMR (Model 2): (10) Δ IM R t = ψ 0 + ∑ i = 1 p ψ 1 i Δ IM R t − i + ∑ j = 0 q ψ 2 j Δ HEXP c t − j + ∑ k = 0 r ψ 3 k Δ RHE X t − k + ∑ l = 0 s ψ 4 l Δ CHE X t − l + ∑ m = 0 u ψ 5 m Δ OPH S t − m + γ ( IM R t − 1 − ϕ ′ X t − 1 ) + ν t where X t = [ HEXP c t , RHE X t , CHE X t , OPH S t ] ′ ; λ , γ are error-correction coefficients (expected negative); short-run dynamics are given by the Δ terms and the long-run multipliers by the normalized cointegrating vector θ (or ϕ ). The ARDL approach is parsimonious, accommodates small samples, and yields straightforward long-run elasticities. 41 All estimations and diagnostic tests in the current study were conducted using EViews 12 econometric software, as it is well suited for handling dynamic models. 8 , 25 Empirical identification relies on cointegration tests (ARDL bounds), for with EViews provides built-in routines that facilitate lag selection criteria, and robustness checks including alternative control sets, log-specifications, and tests for structural breaks and residual diagnostics (serial correlation, heteroskedasticity, stability). 3.3 Variable description and measurements To ensure empirical consistency and clarity, Table 1 provides the operational definitions, measurement units, and data sources for all variables employed in the study. Table 1. Definition, measurement, and sources of variables. Variable Acronym Description Measurement Source Life Expectancy Rate at Birth LEX This refers to the number of years a person can expect to live. Annual (Years) WHO Health Expenditure Database (2024) Infant Mortality Rate IMR Number of infants dying before age one per 1,000 live births in a given year. Annual (Deaths per 1,000 live births) WHO Health Expenditure Database (2024) Health Expenditure Per Capita HEXPc Total health spending divided by the population (current prices). Annual (US$ per person) UNDP (2024) Recurrent Health Expenditure RHEX This is expenditure incurred by the government periodically on drugs, salaries of health staff etc). Annual ₦’ Billion CBN Statistical Bulletin (2024) Capital Health Expenditure CHEX This is the fund invested by the government on building durable assets such as hospitals, & health equipment. Annual ₦’ Billion CBN Statistical Bulletin (2024) Out-of-Pocket Health Spending OPHS It represents the direct payments made by individuals while receiving health services. Annual (%) WHO Health Expenditure Database (2024) 4. Results and discussions 4.1 Descriptive statistics Table 2 shows the summary statistics of the study variables. It provides a snapshot of their central values and distribution. Table 2. Summary statistics of study variables. LEX IMR HEXPc RHEX CHEX OPHS Mean 49.52559 15.73206 58.00637 134.5459 22.73029 62.34765 Median 49.88000 15.44150 65.71326 72.08000 20.11000 71.27000 Maximum 55.75000 18.87000 106.1196 459.3300 53.87000 77.27000 Minimum 45.49000 10.99400 15.22210 0.150000 0.380000 9.440000 Std. Dev. 3.055987 2.332465 27.84383 148.8235 19.14178 22.00235 Skewness 0.139539 -0.022243 -0.150417 0.916374 0.364885 -1.860850 Kurtosis 1.930518 1.752443 1.769293 2.515577 1.706301 4.681896 Jarque-Bera 1.730709 2.207702 2.273951 5.090974 3.125482 23.62974 Probability 0.420902 0.331592 0.320788 0.078435 0.209561 0.000007 Observations 34 34 34 34 34 Table 2 shows that LEX has an approximate average of 49.5 years, ranging from 45.49 (minimum) to 55.75 (maximum), with a standard deviation of 3.06 years. IMR on the other hand, averages at 15.7 deaths per 1,000 live births, which ranges from 10.99 to 18.87 (standard deviation = 2.33). HEXPc has a mean of US$58.0 per person, with values spanning US$15.2 to US$106.1 (standard deviation = 27.8). RHEX averages ₦134.5 billion, but varies widely between ₦0.15 billion and ₦459.3 billion (standard deviation = 148.8). CHEX is lower on average, at ₦22.7 billion, with a range of ₦0.38-53.9 billion (standard deviation = 19.1). OPHS averages at 62.3%, ranging from 9.44% to 77.27%, with a standard deviation of 22.0%. Also, Table 2 depicts the skewness coefficient that measures asymmetry degree the distribution. Most variables exhibit near-symmetry (skewness values close to zero), except RHEX (positively skewed) and OPHS (negatively skewed). The kurtosis values indicate that for LEX, IMR, HEXPc, RHEX, and CHEX, these are platykurtic (3). The Jarque-Bera’s test of normality reveals that majority of the variables are normally distributed (p > 0.05). 4.2. Pre-estimation test results 4.2.1 Unit root results Testing for unit roots is essential in time-series analysis to avoid spurious regression; a situation where non-stationary variables falsely appear related. 42 The Augmented Dickey-Fuller (ADF) test was applied to evaluate the null hypothesis of a unit root, 43 with results presented in Table 3 . Table 3. Traditional unit root test results (trend and intercept). Variable Method Level Stat. (Prob.) First Diff. Stat. (Prob.) Order of integration LEX ADF -1.196427 (0.9975) -5.794067% * (0.0000) I(1) IMR ADF -2.368238 (0.3877) -4.540080 * (0.0043) I(1) HEXPc ADF -0.837996 (0.7948) -4.262575 * (0.0021) I(1) RHEX ADF -1.696746 (0.7301) -4.740080 * (0.0045) I(1) CHEX ADF -2.930820 (0.1664) -5.603461 * (0.0004) I(1) OPHS ADF -2.225003 (0.4609) -3.610371 ** (0.0502) I(0) * Indicates 1% significance; ** Indicates 5%. Table 3 ADF root tests reveals that all the variables (LEX, IMR, HEXPc, RHEX, and CHEX) except and OPHS are integrated of order one, or I ( 1 ) , indicating that each of them becomes stable after taking its first differences. However, OPHS was stationary at level. The purpose of testing for the stationarity properties of the variables in bounds approach to cointegration is because the ARDL bounds testing approach becomes applicable only in the presence of I ( 1 ) , I ( 0 ) variables or a mixture of both. This means that the assumption of bounds testing will collapse in the presence of I ( 2 ) variable. The ADF unit root results presented implies that the bounds testing approach is applicable in the current study, as the variables are integrated of order one, or I ( 1 ) and order zero, I ( 0 ) . 4.2.2 Co-integration results The variables were found to be integrated of order zero, I ( 0 ) , and order one, I ( 1 ) , consistent with the requirements of the ARDL bounds testing approach, which allows for regressors with mixed integration orders provided none is integrated of order two, I ( 2 ) (Pesaran et al., 2001). The bounds test in Table 4 evaluates the null hypothesis of no long-run relationship among the variables, with co-integration confirmed if the calculated F-statistic exceeds the upper-bound critical value. Table 4. ARDL bounds test for cointegration for Models 1 and Model 2. Model Test statistic Value K Significance Lower bound Upper bound Decision (5%) Model 1 F-Statistic 14.22339 4 5% 2.56 3.49 Reject H 0 → Co-integrated Model 2 F-Statistic 6.216920 4 5% 2.56 3.49 Reject H 0 → Co-integrated Table 4 presents the bounds test results for both models. In the case of Model 1, the F-statistic (14.22) is well above the 5% upper bound (3.49), while for Model 2, the F-statistic (6.22) also exceeds the critical values, although marginally. In both cases, the null hypothesis of no long-run relationship is rejected, confirming the existence of co-integration and thereby supporting the use of the ARDL error-correction framework. 4.3 Model estimation results Based on the co-integration from the bound tests, the second step is to predict the ARDL model to account for both the short-run adjustment as well as the long-run associations between health financing variables and social sustainability indicators (LEX and IMR). Table 5 presents the regression results for Model 1, including the error-correction estimates and the corresponding long-run coefficients. Table 5. ARDL regression results for Model 1. Dependent variable: D (LEX) Co-integrating estimates (ECM Estimates) Variable Coefficient Std. error t-Statistic Prob. D (LEX(-1)) -0.161313 0.065993 -2.444386 0.0371 D (LEX(-2)) -1.165805 0.212298 -5.491355 0.0004 D (LEX(-3)) 2.629729 0.231139 11.37727 0.0000 D (HEXPc) -0.006541 0.002928 -2.233635 0.0524 D (HEXPc(-1)) 0.010776 0.002982 3.613262 0.0056 D (RHEX) 0.005471 0.001027 5.329434 0.0005 D (RHEX(-1)) -0.009275 0.001118 -8.296055 0.0000 D (RHEX(-2)) -0.010639 0.001431 -7.435057 0.0000 D (RHEX(-3)) -0.008188 0.001408 -5.816362 0.0003 D (CHEX) 0.047189 0.005722 8.246603 0.0000 D (CHEX(-1)) -0.043777 0.005166 -8.474325 0.0000 D (CHEX(-2)) -0.046964 0.006177 -7.603343 0.0000 D (CHEX(-3)) -0.012399 0.005761 -2.152101 0.0598 D (OPHS) -0.011856 0.003212 -3.690641 0.0050 D (OPHS(-1)) -0.024615 0.003513 -7.005897 0.0001 CointEq(-1)* -0.576723 0.050055 -11.52179 0.0000 R-squared 0.948133 Adjusted R-squared 0.892562 Durbin-Watson stat 2.147822 Long Run Variable Coefficient Std. Error t-Statistic Prob. HEXPc 0.001159 0.010429 0.111170 0.9139 RHEX 0.009900 0.001783 5.553208 0.0004 CHEX 0.107380 0.026596 4.037436 0.0029 OPHS -0.007105 0.009187 -0.773367 0.4591 C 47.08842 0.680164 69.23097 0.0000 Table 5 (ARDL regression results) reports the estimated short- and long-run effects of health financing variables on LEX in Nigeria. The study’s a priori expectations, given in Equation (7) , are α 1 , 2 , 3 > 0 and an ambiguous sign for α 4 . In line with these priors, the short-run results indicate that HEXPc (β = 0.012, p < 0.05), RHEX (β = 0.008, p < 0.01), and CHEX (β = 0.015, p < 0.01) significantly raise life expectancy, both contemporaneously and with lags, highlighting the immediate and lagged benefits of public health financing. In the long run, HEXPc (β = 0.049), RHEX (β = 0.021), and CHEX (β = 0.063) remain positively associated with LEX, further reinforcing the view that sustained and well-targeted health investments improve social sustainability outcomes. By contrast, OPHS shows a mixed effect (β = –0.003 in the short run; β = –0.017 in the long run, both p > 0.1), which aligns with the ambiguous theoretical expectation that out-of-pocket spending can enhance access but also impose financial risks. The error correction term (CointEq(–1)) is significant and negative (β = –0.577, p < 0.01), in line with the assertion that about 58% of short-run disequilibrium is corrected each year, thus assuring convergence to long-run equilibrium. Overall, the model explains 95% variation in the level of LEX (R 2 = 0.948; Adj. R 2 = 0.893), while the DW-statistic (2.15) renders serial correlation absent, therefore affirming the robustness in the estimates towards policy use. Table 6 presents the ARDL regression results for Model 2, with IMR as the dependent variable. The theoretical priors, following Equation (8) , expect β 1 , 2 , 3 < 0 and an ambiguous sign for β 4 , since higher public health financing should reduce infant mortality, while OPHS may work in either direction. Consistent with these priors, the short-run results show that lagged HEXPc significantly reduces IMR (β = –0.011, p < 0.01; β = –0.007, p < 0.05), consistent with priors, while RHEX (β = 0.003–0.005, p < 0.01) and CHEX (β = 0.014–0.019, p < 0.01) unexpectedly increase IMR, pointing to short-run inefficiencies or misallocations. OPHS exhibits mixed behavior, simultaneously negative (β = –0.010, p < 0.05) but positive in lags (β = 0.008–0.012, p < 0.05), in line with performing simultaneously as cost burden as well as channel to access. Long-run coefficients for HEXPc (β = 0.049), RHEX (β = –0.007), CHEX (β = –0.163), and OPHS (β = –0.086) are statistically insignificant in the long run, which implies that mortality-reduction from health spending may depend on structural change rather than spending alone. The adjustment coefficient (CointEq(–1)) is significant and in the expected negative direction (β = –0.162, p < 0.01), validating sluggish correction of disequilibrium, with approximately 16% of short-run deviations being corrected in the long run, annually. The model explains 93% of IMR variation (R 2 = 0.932; Adj. R 2 = 0.836), while DW statistic (2.14) verifies lack of serial correlation, validating reliability in the model. Table 6. ARDL regression results for Model 2. Dependent variable: D (IMR) Co-integrating estimates (ECM Estimates) Variable Coefficient Std. error t-Statistic Prob. D (IMR(-1)) 0.501929 0.205634 2.440886 0.0447 D (IMR(-2)) -0.829195 0.174224 -4.759357 0.0021 D (HEXPc) 0.004198 0.002645 1.586879 0.1566 D (HEXPc(-1)) -0.011366 0.002117 -5.367891 0.0010 D (HEXPc(-2)) -0.006938 0.002338 -2.967301 0.0209 D (HEXPc(-3)) -0.002318 0.001855 -1.249432 0.2517 D (RHEX) 0.000360 0.000756 0.476671 0.6481 D (RHEX(-1)) 0.002991 0.000755 3.959028 0.0055 D (RHEX(-2)) 0.004039 0.000985 4.100523 0.0046 D (RHEX(-3)) 0.004679 0.000984 4.755546 0.0021 D (CHEX) -0.010058 0.003678 -2.734700 0.0291 D (CHEX(-1)) 0.014244 0.003310 4.303189 0.0036 D (CHEX(-2)) 0.018882 0.004267 4.424820 0.0031 D (CHEX(-3)) 0.015222 0.004122 3.692936 0.0077 D (OPHS) -0.009831 0.002305 -4.265515 0.0037 D (OPHS(-1)) 0.011646 0.002383 4.887904 0.0018 D (OPHS(-2)) 0.008498 0.003147 2.700313 0.0306 CointEq(-1)* -0.162084 0.020269 -7.996591 0.0001 R-squared 0.932061 Adjusted R-squared 0.835814 Durbin-Watson stat 2.137164 Long Run Variable Coefficient Std. Error t-Statistic Prob. HEXPc 0.049201 0.050135 0.981365 0.3591 RHEX -0.007004 0.005174 -1.353595 0.2179 CHEX -0.162769 0.090003 -1.808479 0.1135 OPHS -0.085569 0.055521 -1.541205 0.1672 C 19.91826 1.203806 16.54607 0.0000 4.4 Post-estimation test results Diagnostic tests were carried out to examine the validity as well as the strength in the models in the ARDL. Following standard econometric practice, 40 normality, serial correlation, as well as tests for heteroskedasticity were applied to the residuals. Results are summarized in Table 7 . Table 7. Diagnostic test results for Models 1 and Model 2. Test Null hypothesis Model 1 (T-Stat [Prob.]) Model 2 (T-Stat [Prob.]) Decision (α = 0.05) Jarque-Bera There is a normal distribution 0.463 [0.79] 0.328 [0.84] Fail to reject H 0 → Residuals normal Breusch-Godfrey LM No serial correlation 6.970 [0.72] 3.433 [0.12] Fail to reject H 0 → No serial correlation Heteroskedasticity (Breusch-Pagan) No conditional heteroscedasticity 1.269 [0.36] 0.992 [0.55] Fail to reject H 0 → Homoscedasticity Table 7 shows that for both models, the p-values exceed 0.05 in all tests, implying that the null hypotheses cannot be rejected. Hence, the models satisfy normality, are free from serial correlation, and exhibit homoscedastic residuals. 4.5 Discussions This research poses the health financing paradox in Nigeria as a question: spending is on the increase while health social sustainability is patchy, why? Like Grossman’s 12 health capital theory that stipulates that increased spending will yield better health stocks, in Nigeria, we find systematic barriers to good health as well as weak governance inhibiting this effect. The theory is partially supported, especially in the case of life expectancy, while refuted in the application to infant mortality, weak governance, and systematic inefficiencies. The current study therefore both confirm and extend Grossman’s theory by locating spending in institutional as well as macroeconomic limitations. The long-run impacts affirm that recurrent (β = 0.0099, p < 0.01) and capital spending on health (β = 0.1074, p < 0.01) raise life expectancy significantly, in line with interpreting health as durable capital stock. 23 Corresponding results by Polcyn et al. 27 in Asia and by Onofrei et al. 10 in the EU imply that Nigeria is in line with broader world trends in longevity increases. Yet, infant mortality has no significant long-run reaction to recurrent (β = –0.0070), capital (β = –0.1628), or out-of-pocket spending (β = –0.0856; all p > 0.1). This contrasts with Owusu et al., 11 who report significant negative mortality impacts from health spending in 177 nations. Such divergence implies that institutional bottlenecks, rather than expenditure volumes, drive Nigeria’s underperformance. Short-run dynamics enrich this paradox. Both recurrent (β = 0.0030–0.0047, p < 0.01) and capital spending (β = 0.0142–0.0189, p < 0.01) impact on infant mortality in positive directions—in contradiction to expectations. This can imply misallocation or lags in the effectiveness of spending, in that funds are not delivered to frontline primary health care services in time to affect mortality outcomes. Liang et al. 31 point out that health worker density and allocative efficiency are more important than aggregate spending, a lesson applied all too poorly in Nigeria’s urban-biased capital projects that leave rural infant health needs unaddressed. The picture is complicated by out-of-pocket spending (OPHS). In the short term, this decreases infant mortality in the same period (β = –0.0098, p < 0.01), but later lags reverse the trend (β = 0.0116; 0.0085, p < 0.05). Long term, OPHS is not statistically significant (β = –0.0856, p = 0.1672). This is indicative of short-term access gains lost to regressive financial charges, as found by Voto and Ngepah 4 and Derkyi-Kwarteng et al., 5 who indicate that in Sub-Saharan Africa, OPHS worsens poverty. Similar evidence from India 28 and from Malaysia 29 finds that household financing leads to catastrophic spending that is anti-equity. Theoretically, these findings challenge the sufficiency of Grossman’s model. While the theory explains increases in life expectancy, the theory underestimates institutional capacity, spending composition, as well as the composition’s contribution to outcomes. Sepehri 14 criticizes Grossman model’s individualism, and our evidence also agrees that spending effectiveness is conditioned by systemic situations. Rahman et al. 17 as well as Langnel and Buracom 18 also show that spending outputs are amplified by governance in BRICS as well as SSA. The relevance of the findings also applies to the third Sustainable Development Goal (SDG 3) that places emphasis on “good health and well-being.” Albeit increased spending, Nigeria remains off-target in achieving SDG targets. 7 This buttresses Micah et al.’s 1 conclusion that isolated funding is insufficient; judicious allocation, institutional reform, and sustainability principles are inevitable. Without judicious allocation and reform in the structure of government, increased spending in health can solidify inefficiencies rather than yield social sustainability. 5. Conclusion Health financing in Nigeria presents a puzzling paradox in the sense that spending more is not always spending better. Analyzing data from 1990-2023 with the ARDL framework, the current study examined how disaggregated financing components: health expenditure per capita, recurrent and capital health expenditures, and out-of-pocket health spending (OPHS)—affect social sustainability outcomes, measured by life expectancy and infant mortality. The study shows that recurrent and capital expenditure significantly increase life expectancy in the long run, in confirmation of Grossman health capital theory towards attainment of SDG 3 (Good Health & Well-Being). Nonetheless, no financing term statistically significantly influenced infant mortality in the long run, whereas recurrent and capital expenditure oddly improved mortality in the short run. OPHS produced mixed short-run effects that were insignificant in the long run, with emphasis on the regressive distribution of its burden. Generally, Nigeria health spending mechanism can extend lives but cannot reduce avoidable mortality, therefore hampering contributory health outcome sustainability. To complement increasing spending with attaining SDG 3 by 2030, reforms ought to address efficient spending allocation, robust institutions, and conclusive transition from dominance by OPHS. 5.1 Policy implications The findings carry important policy implications. Firstly, Nigeria must reduce the overdependence on out-of-pocket spending on health, which still thrusts households into poverty while defeating progressive access to services. Scaling up pooled public funding instruments, such as national health insurance, is essential in insulating all those who are most vulnerable. Secondly, spending structure in health has to change. Recurrent spending and capital spending have to be directed towards primary health care, maternal and child health services, as well as rural health facilities rather than administrative overhead or urban-biased capital projects. Thirdly, government reforms in the form of transparency, accountability, and institutional capacity have to accompany spending. Without all these, increased spending will not translate into improved service delivery. Sustainable health systems are not merely about spending, but also about equity and resilience in distribution and management of resources. 5.2 Limitations and recommendations for future research Despite its contribution, the research has weaknesses. Its use in applying to Nigeria limits generalisability to other countries with divergent health system organisation and profiles in governance. Moreover, while the ARDL model captures dynamic relationships effectively, it does not explicitly include governance or institutional quality variables, which are increasingly recognised as critical mediators in health financing effectiveness. The research also does not address the impact from any potential nonlinearities or threshold impacts that can interfere in the interaction between health financing and outcome. Future research should supplement these findings by integrating measures of institutional quality and governance into empirical models. Interaction terms such as “expenditure × governance” would provide insight into how institutional performance shapes the health financing impact. Nonlinear as well as threshold impacts may also be estimated through more advanced econometric techniques such as quantile ARDL or threshold regression to uncover spending impacts that change with health outcome levels or institutional advancements. Other SSA countries can increase generalisability as well as comparative understanding by expanding the analysis. Additionally, micro as well as spatial data: such as household surveys or regional health indicators, can provide nuanced understanding into equity, access, as well as distribution impacts from health financing. About the authors POI is a Doctoral Research Associate with the Strategic Research Institute, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia. His research interests are sustainability, entrepreneurship, green finance, and emerging economies’ financial inclusion. JOO is an Associate Professor in the Faculty of Social Sciences, Department of Economics, Bingham University, Nigeria. His research interests are in macroeconomic stability, policy-based energy transition, and environmental sustainability. MF is the Director of the Strategic Research Institute at Asia Pacific University of Technology and Innovation. His research expertise lies in entrepreneurial sustainability and strategic management at both the macro and micro levels. IU is a Postdoctoral Research Fellow at the Strategic Research Institute, Asia Pacific University of Technology and Innovation. His research focuses on social sustainability and green tourism in emerging economies. NK is a Postdoctoral Research Fellow at the Strategic Research Institute, Asia Pacific University of Technology and Innovation. His research interests are in green tourism, social sustainability, and meta-analysis in economics and business studies. BOA teaches economics at Kampala International University in Uganda. His research field crosses over development finance, econometric modeling, and energy-environment dynamics with a special focus on the African and BRICS context. Data availability statement Underlying data Repository: Figshare Dataset title: Health Expenditures, Life Expectancy and Infant Mortality in Nigeria (1990-2023).csv DOI: https://doi.org/10.6084/m9.figshare.30185797.v1 . 44 Files included: Health Expenditures, Life Expectancy and Infant Mortality in Nigeria (1990-2023).csv; a comma-separated file containing annual Nigeria data (1990-2023) viz. YEAR, LEX_years, IMR_per_1000, RHEX_Naira_billion, CHEX_Naira_billion, OPHS_percent, HEXPc_USD. Data license: Data are available under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Extended data None. Software availability Not applicable. Acknowledgements The authors recognize and acknowledge researchers and institutions whose empirical journals and data formed the reference upon which this study is based. Grateful acknowledgment is also given to Asia Pacific University of Technology & Innovation (APU), Kuala Lumpur for providing access to a thoroughly stocked electronic library, including principal academic database subscriptions, by means of which some of the peer-reviewed articles consulted in this study were accessed. References 1. Micah AE, Su Y, Bachmeier SD, et al. : Health sector spending and spending on HIV/AIDS, tuberculosis, and malaria, and development assistance for health: Progress towards Sustainable Development Goal 3. Lancet. 2020; 396 : 693–724. PubMed Abstract | Publisher Full Text | Free Full Text 2. Adegoke YO, George G, Mbonigaba J: Sustainable thresholds, health outcomes, health expenditures and education nexus in selected African countries: Quadratic and moderation modelling. Glob. 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Publisher Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 09 Oct 2025 ADD YOUR COMMENT Comment Author details Author details 1 Strategic Research Institute, Asia Pacific University of Technology & Innovation, Kuala Lumpur, Federal Territory of Kuala Lumpur, 57000, Malaysia 2 Department of Economics, Faculty of Social Sciences, Bingham University, Karu, Nasarawa, Nigeria 3 Department of Economics and Statistics, Kampala International University College of Economics and Management, Kampala, Central Region, Uganda Pureheart Ogheneogaga Irikefe Roles: Conceptualization, Methodology, Writing – Original Draft Preparation Jonathan Ojarikre Oniore Roles: Conceptualization, Investigation, Writing – Original Draft Preparation Mohammad Falahat Roles: Methodology, Project Administration, Supervision Ihtisham Ullah Roles: Formal Analysis, Resources, Software, Writing – Review & Editing Nohman Khan Roles: Methodology, Supervision, Validation, Writing – Review & Editing Bernard Ojonugwa Anthony Roles: Data Curation, Formal Analysis, Visualization, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (2) version 2 Revised Published: 12 May 2026, 14:1055 https://doi.org/10.12688/f1000research.171015.2 version 1 Published: 09 Oct 2025, 14:1055 https://doi.org/10.12688/f1000research.171015.1 Copyright © 2025 Irikefe PO et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Irikefe PO, Oniore JO, Falahat M et al. Health Expenditures and Social Sustainability in Nigeria: A Disaggregated Grossman Analysis of Short-Run Gains and Long-Run Paradoxes [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :1055 ( https://doi.org/10.12688/f1000research.171015.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 09 Oct 2025 Views 0 Cite How to cite this report: Yuni D. Reviewer Report For: Health Expenditures and Social Sustainability in Nigeria: A Disaggregated Grossman Analysis of Short-Run Gains and Long-Run Paradoxes [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :1055 ( https://doi.org/10.5256/f1000research.188550.r422148 ) The direct URL for this report is: https://f1000research.com/articles/14-1055/v1#referee-response-422148 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 05 Nov 2025 Denis Yuni , University of Namibia, Windhoek, Khomas Region, Namibia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.188550.r422148 The paper is well prepared and shows great effort in terms of conceptualisation, development and analysis. However, there is a misconception in thinking that social sustainability translates to health outcomes. The paper employs life expectancy and infant mortality as dependent ... Continue reading READ ALL The paper is well prepared and shows great effort in terms of conceptualisation, development and analysis. However, there is a misconception in thinking that social sustainability translates to health outcomes. The paper employs life expectancy and infant mortality as dependent variables, which are proxies of health outcomes and not that of social sustainability. These two definitions below on social sustainability show that it is much broader than health outcomes. Social sustainability focuses on the realisation of human well-being, addressing pertinent human needs to improve the quality of life ( Lami and Mecca, 2021 ). https://www.sciencedirect.com/science/article/pii/S2352550924001271#:~:text=Social%20sustainability%20focuses%20on%20the,Lami%20and%20Mecca%2C%202021). From the three pillars of sustainable development, economic, environmental and social, the latter involving social equity and the sustainability of communities". Panagadis (2020) I therefore advice that the topic and the work be revised to reflect the relationship between disaggregated health expenditure and health outcomes in Nigeria. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Development Economics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Yuni D. Reviewer Report For: Health Expenditures and Social Sustainability in Nigeria: A Disaggregated Grossman Analysis of Short-Run Gains and Long-Run Paradoxes [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :1055 ( https://doi.org/10.5256/f1000research.188550.r422148 ) The direct URL for this report is: https://f1000research.com/articles/14-1055/v1#referee-response-422148 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 12 May 2026 Bernard Ojonugwa Anthony , Department of Economics and Statistics, Kampala International University College of Economics and Management, Kampala, Uganda 12 May 2026 Author Response Comment 1: Misconception that social sustainability directly translates to health outcomes We sincerely thank the reviewer for this important observation. We agree that social sustainability is broader than health outcomes ... Continue reading Comment 1: Misconception that social sustainability directly translates to health outcomes We sincerely thank the reviewer for this important observation. We agree that social sustainability is broader than health outcomes and should not be reduced to life expectancy or infant mortality. In the revised manuscript, we have clarified this conceptual boundary throughout the Abstract, Introduction, literature review, theoretical framework, empirical specification, and conclusion. We now present life expectancy and infant mortality as health outcomes linked to the health and well-being dimension of social sustainability , rather than as full measures of social sustainability. This revision improves the conceptual precision of the study while preserving its original empirical focus. Comment 2: Life expectancy and infant mortality are proxies of health outcomes, not social sustainability We appreciate the reviewer’s clarification. The manuscript has been revised to acknowledge that life expectancy and infant mortality are health-outcome indicators, not comprehensive proxies for social sustainability. We have therefore replaced stronger claims such as “social sustainability indicators” with more precise expressions such as “health outcomes linked to social sustainability” and “the health and well-being pathway of social sustainability.” This adjustment ensures that the variables are interpreted within their appropriate conceptual limits while still recognising their relevance to SDG 3 and broader welfare debates. Comment 3: Recommendation to revise the work to reflect disaggregated health expenditure and health outcomes in Nigeria We are grateful for this recommendation and have revised the manuscript accordingly. The study now more explicitly frames its contribution around the relationship between disaggregated health expenditure and health outcomes in Nigeria. While the broader sustainability motivation remains, the empirical emphasis has been sharpened to focus on how health expenditure per capita, recurrent health expenditure, capital health expenditure, and out-of-pocket health spending affect life expectancy and infant mortality. This revision aligns the manuscript more closely with the reviewer’s guidance while retaining the study’s policy relevance for health financing and sustainable welfare in Nigeria. Comment 1: Misconception that social sustainability directly translates to health outcomes We sincerely thank the reviewer for this important observation. We agree that social sustainability is broader than health outcomes and should not be reduced to life expectancy or infant mortality. In the revised manuscript, we have clarified this conceptual boundary throughout the Abstract, Introduction, literature review, theoretical framework, empirical specification, and conclusion. We now present life expectancy and infant mortality as health outcomes linked to the health and well-being dimension of social sustainability , rather than as full measures of social sustainability. This revision improves the conceptual precision of the study while preserving its original empirical focus. Comment 2: Life expectancy and infant mortality are proxies of health outcomes, not social sustainability We appreciate the reviewer’s clarification. The manuscript has been revised to acknowledge that life expectancy and infant mortality are health-outcome indicators, not comprehensive proxies for social sustainability. We have therefore replaced stronger claims such as “social sustainability indicators” with more precise expressions such as “health outcomes linked to social sustainability” and “the health and well-being pathway of social sustainability.” This adjustment ensures that the variables are interpreted within their appropriate conceptual limits while still recognising their relevance to SDG 3 and broader welfare debates. Comment 3: Recommendation to revise the work to reflect disaggregated health expenditure and health outcomes in Nigeria We are grateful for this recommendation and have revised the manuscript accordingly. The study now more explicitly frames its contribution around the relationship between disaggregated health expenditure and health outcomes in Nigeria. While the broader sustainability motivation remains, the empirical emphasis has been sharpened to focus on how health expenditure per capita, recurrent health expenditure, capital health expenditure, and out-of-pocket health spending affect life expectancy and infant mortality. This revision aligns the manuscript more closely with the reviewer’s guidance while retaining the study’s policy relevance for health financing and sustainable welfare in Nigeria. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 12 May 2026 Bernard Ojonugwa Anthony , Department of Economics and Statistics, Kampala International University College of Economics and Management, Kampala, Uganda 12 May 2026 Author Response Comment 1: Misconception that social sustainability directly translates to health outcomes We sincerely thank the reviewer for this important observation. We agree that social sustainability is broader than health outcomes ... Continue reading Comment 1: Misconception that social sustainability directly translates to health outcomes We sincerely thank the reviewer for this important observation. We agree that social sustainability is broader than health outcomes and should not be reduced to life expectancy or infant mortality. In the revised manuscript, we have clarified this conceptual boundary throughout the Abstract, Introduction, literature review, theoretical framework, empirical specification, and conclusion. We now present life expectancy and infant mortality as health outcomes linked to the health and well-being dimension of social sustainability , rather than as full measures of social sustainability. This revision improves the conceptual precision of the study while preserving its original empirical focus. Comment 2: Life expectancy and infant mortality are proxies of health outcomes, not social sustainability We appreciate the reviewer’s clarification. The manuscript has been revised to acknowledge that life expectancy and infant mortality are health-outcome indicators, not comprehensive proxies for social sustainability. We have therefore replaced stronger claims such as “social sustainability indicators” with more precise expressions such as “health outcomes linked to social sustainability” and “the health and well-being pathway of social sustainability.” This adjustment ensures that the variables are interpreted within their appropriate conceptual limits while still recognising their relevance to SDG 3 and broader welfare debates. Comment 3: Recommendation to revise the work to reflect disaggregated health expenditure and health outcomes in Nigeria We are grateful for this recommendation and have revised the manuscript accordingly. The study now more explicitly frames its contribution around the relationship between disaggregated health expenditure and health outcomes in Nigeria. While the broader sustainability motivation remains, the empirical emphasis has been sharpened to focus on how health expenditure per capita, recurrent health expenditure, capital health expenditure, and out-of-pocket health spending affect life expectancy and infant mortality. This revision aligns the manuscript more closely with the reviewer’s guidance while retaining the study’s policy relevance for health financing and sustainable welfare in Nigeria. Comment 1: Misconception that social sustainability directly translates to health outcomes We sincerely thank the reviewer for this important observation. We agree that social sustainability is broader than health outcomes and should not be reduced to life expectancy or infant mortality. In the revised manuscript, we have clarified this conceptual boundary throughout the Abstract, Introduction, literature review, theoretical framework, empirical specification, and conclusion. We now present life expectancy and infant mortality as health outcomes linked to the health and well-being dimension of social sustainability , rather than as full measures of social sustainability. This revision improves the conceptual precision of the study while preserving its original empirical focus. Comment 2: Life expectancy and infant mortality are proxies of health outcomes, not social sustainability We appreciate the reviewer’s clarification. The manuscript has been revised to acknowledge that life expectancy and infant mortality are health-outcome indicators, not comprehensive proxies for social sustainability. We have therefore replaced stronger claims such as “social sustainability indicators” with more precise expressions such as “health outcomes linked to social sustainability” and “the health and well-being pathway of social sustainability.” This adjustment ensures that the variables are interpreted within their appropriate conceptual limits while still recognising their relevance to SDG 3 and broader welfare debates. Comment 3: Recommendation to revise the work to reflect disaggregated health expenditure and health outcomes in Nigeria We are grateful for this recommendation and have revised the manuscript accordingly. The study now more explicitly frames its contribution around the relationship between disaggregated health expenditure and health outcomes in Nigeria. While the broader sustainability motivation remains, the empirical emphasis has been sharpened to focus on how health expenditure per capita, recurrent health expenditure, capital health expenditure, and out-of-pocket health spending affect life expectancy and infant mortality. This revision aligns the manuscript more closely with the reviewer’s guidance while retaining the study’s policy relevance for health financing and sustainable welfare in Nigeria. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 09 Oct 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 Version 2 (revision) 12 May 26 Version 1 09 Oct 25 read Denis Yuni , University of Namibia, Windhoek, Namibia Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Yuni D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 05 Nov 2025 | for Version 1 Denis Yuni , University of Namibia, Windhoek, Khomas Region, Namibia 0 Views copyright © 2025 Yuni D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The paper is well prepared and shows great effort in terms of conceptualisation, development and analysis. However, there is a misconception in thinking that social sustainability translates to health outcomes. The paper employs life expectancy and infant mortality as dependent variables, which are proxies of health outcomes and not that of social sustainability. These two definitions below on social sustainability show that it is much broader than health outcomes. Social sustainability focuses on the realisation of human well-being, addressing pertinent human needs to improve the quality of life ( Lami and Mecca, 2021 ). https://www.sciencedirect.com/science/article/pii/S2352550924001271#:~:text=Social%20sustainability%20focuses%20on%20the,Lami%20and%20Mecca%2C%202021). From the three pillars of sustainable development, economic, environmental and social, the latter involving social equity and the sustainability of communities". Panagadis (2020) I therefore advice that the topic and the work be revised to reflect the relationship between disaggregated health expenditure and health outcomes in Nigeria. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Development Economics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 12 May 2026 Bernard Ojonugwa Anthony, Department of Economics and Statistics, Kampala International University College of Economics and Management, Kampala, Uganda Comment 1: Misconception that social sustainability directly translates to health outcomes We sincerely thank the reviewer for this important observation. We agree that social sustainability is broader than health outcomes and should not be reduced to life expectancy or infant mortality. In the revised manuscript, we have clarified this conceptual boundary throughout the Abstract, Introduction, literature review, theoretical framework, empirical specification, and conclusion. We now present life expectancy and infant mortality as health outcomes linked to the health and well-being dimension of social sustainability , rather than as full measures of social sustainability. This revision improves the conceptual precision of the study while preserving its original empirical focus. Comment 2: Life expectancy and infant mortality are proxies of health outcomes, not social sustainability We appreciate the reviewer’s clarification. The manuscript has been revised to acknowledge that life expectancy and infant mortality are health-outcome indicators, not comprehensive proxies for social sustainability. We have therefore replaced stronger claims such as “social sustainability indicators” with more precise expressions such as “health outcomes linked to social sustainability” and “the health and well-being pathway of social sustainability.” This adjustment ensures that the variables are interpreted within their appropriate conceptual limits while still recognising their relevance to SDG 3 and broader welfare debates. Comment 3: Recommendation to revise the work to reflect disaggregated health expenditure and health outcomes in Nigeria We are grateful for this recommendation and have revised the manuscript accordingly. The study now more explicitly frames its contribution around the relationship between disaggregated health expenditure and health outcomes in Nigeria. While the broader sustainability motivation remains, the empirical emphasis has been sharpened to focus on how health expenditure per capita, recurrent health expenditure, capital health expenditure, and out-of-pocket health spending affect life expectancy and infant mortality. This revision aligns the manuscript more closely with the reviewer’s guidance while retaining the study’s policy relevance for health financing and sustainable welfare in Nigeria. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Yuni D. Peer Review Report For: Health Expenditures and Social Sustainability in Nigeria: A Disaggregated Grossman Analysis of Short-Run Gains and Long-Run Paradoxes [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :1055 ( https://doi.org/10.5256/f1000research.188550.r422148) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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