International Studies. Interdisciplinary Political and Cultural Journal
Vol. 30, No. 2/2022, 5–30
https://doi.org/10.18778/1641-4233.30.01

Governance, Health Financing and Health Outcomes in Nigeria: A Quantitative Assessment

Praktyki zarządzania, finansowanie ochrony zdrowia i wyniki zdrowotne w Nigerii: ocena ilościowa

Temitope Sade Akintunde ^*

https://orcid.org/0000-0002-4713-7560

Osun State University, Osogbo, Nigeria
Department of Economics
e-mail: temitope.akintunde@uniosun.edu.ng

Kehinde Elizabeth Adesina ^*

https://orcid.org/0000-0003-0444-3004

Obafemi Awolowo University, Ile-Ife, Nigeria
Department of Economics
e-mail: kehindeelizabeth90@gmail.com

Olawale Olufemi Akinrinde ^*

https://orcid.org/0000-0001-7350-2376

University of Johannesburg, South Africa

Johannesburg Business School

e-mail: oakinrinde@uj.ac.za

Introduction

Investing in health is inevitable because protecting communities from infectious diseases brings greater security and stability. Health is not necessarily the absence of illness and diseases but the presence of the populace’s adequate (psychological, social and physical) well-being. However, most of the West African Countries are faced with different levels of crises in health financing even though the region has witnessed the outbreak of global infectious diseases in recent times, such as Ebola. Public sector spending on health is low in most West African countries, Nigeria inclusive. Most of the time, making health a priority has been a daunting task. Healthcare financing implies mobilising funds for healthcare services (Oyefabi et al., 2014). It could also mean providing medical and related services to maintain good health. So, the essence of financing health is to ensure that funds and proper financial incentives are available and channelled so that public and personal health care is accessible to all individuals.

Health financing covers three essential functions. These are revenue collection, risk pooling, and purchase of health services. Revenue collection is how funds are raised from different sources to finance the health system. Risk pooling is how revenue collected is managed while purchasing implies transferring the pooled funds to the providers of health services to enable them to provide health services to the population (Asante, Wasike and Ataguba, 2020). Per capita health spending in low-income countries was $US110 in 2015, which is behind target according to the World Health Organization (WHO). On average, the total health expenditure that comes from the government is less than 30%. This is low when compared to high-income and middle-income countries. This compels the populace to depend heavily on out-of-pocket expenditures (Asante, Wasike and Ataguba, 2020; Aregbeshola, 2021). With this, millions of people do not have access to health services due to poverty. Out-of-pocket expenditure (OOP) constitutes almost 70% of total national healthcare expenditure (Alfred et al., 2016; Micheal, Aliyu and Grema, 2020). This high OOP has pushed poor households to self-medication, quack practitioners, and postponing medical treatment. All these harm individuals’ and households’ health and impoverish people with low incomes even more. Despite the importance of health financing, some studies found health financing to improve health outcomes (Kulkarni, 2016; Edeme, Emecheta, and Omeje, 2017), while in some countries, health financing does not promote health outcomes (Nathaniel and Khan, 2019).

One of the following three models describes the health system of most countries—national health, social, and private insurance models (Kulesher and Forrestal, 2014). The national health model is seen as the universal health coverage of all citizens by the federal government financed through tax revenue; the social insurance model is funded by any employer, individual or private insurance funds; the private insurance model is employment-based or when individuals purchase health insurance (private) which is financed by employers or individuals. The governments of developing countries adopt different health financing models to achieve universal health coverage. Standard models in developing countries are private, community-based, and social health insurance (Domapielle, 2020). In low-income countries, the informal sector population is found to be excluded from the social health insurance scheme. It majorly captures the formal sector. In Nigeria, the Federal Government introduced the National Health Insurance Scheme (NHIS) to guarantee better access to efficient healthcare services and reduce household out-of-pocket expenditure. However, statistics revealed that as of 2016, approximately 4.2% of the population were covered. These are mainly the federal government’s civil servants (including their dependents) (Health and Managed Care Association of Nigeria, 2017). As a result, 75.2% of total health expenditure was accounted for by Out-Of-Pocket spending, which implies that 25% of the households spend over 10% on health, which exposed those households to catastrophic health expenditure.

Likewise, in 2016, total government health expenditure was 0.6 per cent of GDP (World Bank, 2017). This low level of government health expenditure was due to the economic downturn. However, it was further argued that even when the country had good economic terms, there has been a lower investment in health over two decades compared to countries with similar economic status to Nigeria (World Bank, 2017). There are also disparities in health system financing, such as uneven distribution of resources and budget constraints in different areas across countries. The health outcomes in Nigeria are not so encouraging. Life expectancy for males is 54.7, female is 55.7, while total life expectancy is 55.2 in Nigeria (WHO, 2018), infant mortality rate for Nigeria in 2019, 2018, 2017 were 60.662, 62.142, 64.708 deaths per 1000 live births respectively (United Nation-World Population prospect, 2019), maternal mortality rate for Nigeria in 2015, 2014, 2013 were 814.00, 820.00, 821.00 per 100,000 live births respectively (World Bank, 2012).

The government cannot be left out in making concrete decisions related to the well-being of its populace. Some empirical studies opined that good governance is needed to improve health outcomes through health financing (Holmberg and Rothstein, 2011; Osakede, 2020). Therefore, good governance has a role to play in promoting health outcomes. Nigeria has been described as experiencing political instability, corruption, an ineffective government, and a lack of rule of law (Fayissa and Nsiah, 2010).

Good governance provides checks and balances in health financing for the system’s proper functioning (Phua et al., 2015). Corruption is experienced by 80% of the population in the health sector in developing countries (Holmberg and Rothstein, 2011). Corruption could involve bribing health professionals and diverting medical supplies meant for public use into personal use (Vian, 2008; Mackey and Liang, 2012). According to (Easterly, 2006), it is possible for donors also to influence bad governance when they support corrupt governments or programs that are not successful, and these all have effects on global health outcomes. More so, fungibility exists if external aid for health care reduces government health spending from domestic sources (WHO, 2011). Several studies have studied government spending and health outcomes, but few have investigated health financing and governance. It is, therefore, against this background and given the importance of health financing that this study aims to assess the effect of health financing and good governance on health outcomes in Nigeria. Section two presents the relevant literature review, while data and methodology are specified in section three. In section four, the variables are analysed, results are discussed, and conclusions are drawn in section five.

Empirical review

Governance and health outcomes in Nigeria

Faraq (Faraq et al., 2013) examined the relationship between health expenditure and health outcomes and the role of good governance in 133 low-income and middle-income countries. The study proxied health outcomes with child and infant mortality, while the health expenditure proxy was total and public health spending and GDP per capita. Also, government effectiveness was used to measure good governance. The fixed effect model result shows that health spending significantly reduced child and infant mortality and government health spending. The study concluded that health spending is adequate with good governance. Likewise (Hu and Mendoza, 2013) examined the determinants of child health in developing countries in 136 countries between 1960 and 2005. Their empirical analysis suggests that quality of governance (control of corruption and bureaucracy quality) and public spending on health services reduce child mortality. Sirag et al. (2017) examined the determinants of health financing in 177 developed and developing countries. They formulated three public health financing models in their study for the countries. The first model does not include the institutional variables; the second model includes one of the two institutional variables, government effectiveness, while the third model includes the other institutional variable, control of corruption. Using GMM estimators, the empirical findings showed that income improves health financing with an elasticity of less than one. Public expenditures as a share of GDP improve total and public health financing while lowering private health financing. Also, developed countries are characterised by a high quality of governance, while low quality of governance is peculiar to developing countries. Their study concluded that GDP per capita and government expenditure are important factors that influence health financing. Governance quality is also an essential factor that helps achieve sustainable health financing, which will drastically reduce catastrophic health expenditures and out-of-pocket expenses, which could further impoverish people experiencing poverty.

Bashir (2016) evaluated the role of government expenditure in Nigeria’s health sector. They measured life expectancy and infant mortality rates against government expenditure between 2000 and 2013. Pearson’s moment correlation indicates that when government spending on health increases, the infant mortality rate reduces, significantly improving the government’s life expectancy and expenditure. In selected middle-income countries, Salatin and Noorpoor (2015) examined the theoretical relationship between quality of governance effectiveness and health economics between 2002 and 2011. The methods of estimation used were Generalised least squares (GLS) and Generalised method of moments (GMM), and their findings showed that governance quality has a positive and significant effect on life expectancy. Some studies (Thornton, 2002; Baltigi and Moscone, 2010; Santias et al., 2011; Moscone et al., 2012; Fayissa and Train, 2013; Ravangard, 2014) have used the production function approach to examine the performance of health care system, and this is mainly estimated using the ordinary least square (OLS) techniques. This approach consists of input and output variables, where the input variables contribute to output production. The output variables could be life expectancy at birth and infant mortality rate. In contrast, the input variables could be health care resources both in monetary and non-monetary terms.

Grossman (1972) developed a demand for a good health model. This model assumes that health is a durable capital stock which produces healthy output time. It is also assumed that there is a first stock of health inherited by individuals that reduces with age at an increasing rate, which could be increased by investment. The rate of depreciation increases when humans grow older, and thus, expenditure on health and medical care increases for elderly people. The model stated that a person’s accumulated knowledge affects productivity (market or non-market). At the same time, the time he spends on earning and commodities depends on his health stock. When the stock falls below a certain level, death occurs. Consumers demand health as a commodity (investment and consumption commodity).

Similarly, a demand for a health model was developed by Galama (2011). The model predicted a negative relationship between health status and showed that a healthier population demands fewer medical services than a less healthy population. Keynesian fiscal policy has a direct influence on consumption and investment. It depends on tax policy and how public expenditures are administered. In the healthcare system, fiscal policy is essential to ensure adequate health sector financing, equity in health services distribution, and easy access to the populace to reduce out-of-pocket expenditures. Tax policy is the primary source of public revenue used to finance public expenditure, which increases the income level, expands effective demand and enhances the equal distribution of income. The policy also has a macroeconomic role in determining economic growth and income distribution. This should be implemented over time to prevent the occurrence of peaks and slumps in investment (Galama, 2011).

Health financing and health outcomes in Nigeria

Anton and Onofrei (2012) investigated the relationship between health system performance and total health spending in some selected countries from Central and Eastern Europe. The regression analysis performed with cross-sectional data explained the differences in health expenditure and the implications this has on the system efficiency. Their findings show that the total health spending and GDP per capita are essential factors explaining the differences in the health sector, apart from other lifestyle factors that could also play essential roles. Using the fixed effect model, Kim and Lane (2013) used two health outcome indicators to analyse the relationship between national outcomes and public health expenditure among seventeen OECD countries between 1973 and 2000. The results showed that government health expenditure reduces infant mortality and life expectancy. Their findings also discovered that men consume health services more regularly than women. Likewise, there is an essential improvement in health outcomes for women when spending on health expenditure increases. This effect is more pronounced than in men. The study (Oladosu, Chanimbe, and Anaduaka, 2020) examined the impact of public health expenditure on health outcomes in Ghana and Nigeria. Health outcomes were captured with HIV/AIDS, maternal, malaria and infant mortality. The study finds that public health expenditure needs to be augmented in Ghana and Nigeria to improve health outcomes. Using pooled regression and pairwise correlation, Sango-Coker and Bein (2018) investigated West Africa’s private, public, and public-private healthcare sectors from 1999 to 2014. They found out that the women in the population lived longer than men. Also, in the public health sector, healthcare spending improves life expectancy, while healthcare spending does not significantly improve life expectancy in the private health sector. The findings of Bein et al. (2017) on the relationship between healthcare expenditures and health outcomes in eight East African countries with the use of regression techniques show that healthcare expenditures contribute positively to life expectancy (both male and female) while healthcare expenditures reduce the number of neonatal, infant and under-five deaths.

In addition, Kulkarni (2016), in his study on examining the differences in the healthcare systems of emerging economies of BRICS, found that health outcome improves the Adult Literacy rate, GDP per capita, and out-of-pocket expenditure while the public health expenditure is positively related to Infant Mortality Rate. Boachie et al. (2018) re-examines the connection between government health outcomes and health expenditures with the use of annual data for the period of 1980–2014 on Ghana and Ordinary Least Square estimates (OLS) and two-stage Least Squares (2SLS) estimators. Their findings show that income and public health expenditure contribute to improving health outcomes within the study period. The health sector can be measured using four measures: equity, sustainability, accountability and performance (Health Finance and Governance Briefing Kit, 2015). Equity as a measure of the health sector aids better health outcomes, and it is simply the gap between health quality and peoples’ access to health care services in a country. When the population have access to quality healthcare services, these services should be sustainable in the sense that the present and future amount of resources must be sufficient to meet the health needs of the present and future population. The sustainability of these resources also depends on the accountability and performance of the health sector. This will ensure checks and balances in the health sector and enable the government to implement effective health policies and deliver quality health services to the public in rural and urban areas (Sango-Coker and Bein, 2018).

Data and methodology

Secondary data were employed for the study. The data were sourced from the National Health Account (NHA) from the WHO database for public, private and total health financing, the World Development Indicator (WDI) of the World Bank, the World Governance Indicator (WGI) and ICRG for Governance variables. The dependent variables are life expectancy and infant mortality. In contrast, the explanatory variables are government effectiveness, bureaucracy quality, democracy index, GDP per capita, government spending on health, gross capital formation, and carbon emissions. The study made use of the health production function, which was postulated by Grossman (1972) and was also used in studies by Galama (2011), Riman and Akpan (2012), and Boachie et al. (2018). The health production function model is specified below;

\( Y_t = ( Z_t, X_t ) \)

(1)

Where t = 1, 2, 3, ………T; Y_t is a vector for the two dependent variables, that is, life expectancy at birth (LEAB) and infant mortality rate (IMR); Zt is a vector for GDP per capita (GDPC), domestic government health expenditure (DGHEXP), gross capital formation (GCF) and Carbon emission (CAEM); Xt is a vector for governance variables that is, government effectiveness (GVEF), Bureaucracy quality (BUQU) and democracy index (DMIN).

\( Y_t = \alpha Z_t + \beta X_t + \varepsilon_t \)

(2)

𝛼 and 𝛽 are the coefficients of health financing and governance variables, while is the error term. By disaggregating the components of Z_t and X_t. Equation 2 becomes

𝐿𝐸𝐴𝐵_𝑡 = 𝛼₁𝐺𝐷𝑃𝐶_𝑡 + 𝛼₂𝐷𝐺𝐻𝐸𝑋𝑃_𝑡+ 𝛼₃𝐺𝐶𝐹_𝑡 + 𝛼₄𝐶𝐴𝐸𝑀_𝑡 + 𝛽₁𝐺𝑉𝐸𝐹_𝑡+ + 𝛽₂𝐵𝑈𝑄𝑈_𝑡 +𝛽₃𝐷𝑀𝐼𝑁_𝑡 + 𝜀_𝑡

(3a)

𝐼𝑀𝑅_𝑡 = 𝛼₁𝐺𝐷𝑃𝐶_𝑡 + 𝛼₂𝐷𝐺𝐻𝐸𝑋𝑃_𝑡+ 𝛼₃𝐺𝐶𝐹_𝑡 + 𝛼₄𝐶𝐴𝐸𝑀_𝑡 + 𝛽₁𝐺𝑉𝐸𝐹_𝑡+ 𝛽₂𝐵𝑈𝑄𝑈_𝑡 +𝛽₃𝐷𝑀𝐼𝑁_𝑡 + 𝜀_𝑡

(3b)

To specify the long-run model for the VECM, we have the following;

𝐸𝐶𝑇_𝑡−1 = 𝑌_𝑡−1 - 𝛼𝑗𝑋_𝑡−1 + 𝛽𝑖𝑅_𝑡−1 - ∅𝑚𝑈_𝑡−1 - ∞𝑛𝑉_𝑡−1 - 𝜇𝑏𝑊_𝑡−1 - 𝜋𝑐𝑍_𝑡−1 - 𝜑𝑑𝑆_𝑡−1

(4)

To specify the short-run model, we have;

(5)

Results and discussion

Note that LLEAB means log of life expectancy at birth, LIMR is a log of infant mortality rate, GVEF is government efficiency, BUQU is bureaucracy quality, LGDPC is a log of GDP per capita, DGHEXP is the domestic government health expenditure, LGCF is the log of gross capital formation, LCAEM is a log of carbon emission, DMIN is democracy index. We used the three-year moving average method to fill up the missing data in the Excel spreadsheet for this analysis so that we would not have any missing data.

Summary statistics

Table 1 below presents the summary statistics of both the dependent and explanatory variables. Descriptive statistics lets us determine whether the data are consistent and normally distributed. LIMR, LCAEM, GVEF, and DMIN are negatively skewed, while LLEAB, LGDPC, LGCF and DGHEXP are positively skewed. The kurtosis tells us that GVEF, DMIN, and DGHEXP are peaked relative to the normal distribution while LLEAB, LIMR, LGDPC, LGCF and LCAEM are flat relative to the normal distribution. Also, all the variables have a positive mean value except GVEF, which has a negative mean value. The positive mean value implies an upward trend in the time series where LGDPC has the highest mean value while DGHEXP has the lowest.

Table 1. Descriptive statistics of the data

Source: authors’ computation

Unit root test

The results from the unit root test are presented in Tables 2a and 2b below. Augmented Dickey-Fuller (ADF) and Philip Perron (PP) are used to test for the Unit root. The result in Table 2a shows the unit root test with intercept, while Table 2b shows the unit root test with intercept and trend. In Table 2a, BUQU, LCAEM, DGHEXP, DMIN, and LGDPC are significant at 1% for both the ADF and PP. The LGCF is significant at levels for ADF and PP at 10% and 1%, respectively; LLEAB was significant at levels at 10% for ADF, while LIMR was significant at the first difference for ADF at a 5% significance level. In Table 2b, BUQU, LCAEM, DGHEXP, DMIN, and GVEF are significant at a 1% level for both the ADF and the PP test. LGDPC was significant at 5% level both for the ADF and PP test, LGCF was significant at levels for ADF at 5% level and significant at first difference for PP at 1%.

Table 2a. Unit root test (Augmented Dickey-Fuller [ADF] and Phillip Perron [PP] test with intercept)

Note: *, **, *** denote 1%, 5% and 10% level of significance, respectively
Source: authors’ computation

Table 2b. Unit root test (Augmented Dickey-Fuller [ADF] and Phillip Perron [PP] test with intercept and trend)

Note: **, *** denote 1% and 5% level of significance, respectively
Source: authors’ computation

Co-integration test

The co-integration test was introduced by Granger (1981) and Engle and Granger (1987). It implies stationarity due to a linear combination of two integrated variables with a common stochastic trend. In Tables 3a and 3b below, the test statistic’s result shows that at least eight equations are co-integrated among the variables at the significance level of 5%. It could be deduced from this that a long-term relationship exists between the model’s short-term and long-term equilibrium dynamics.

Table 3a. Co-integrating results with LLEAB as the dependent variable

The trace test indicates eight co-integrating equations at the 0.05 level.
Source: authors’ computation

Table 3b. Co-integrating results with LIMR as the dependent variable

The trace test indicates eight co-integrating equations at the 0.05 level.
Source: authors’ computation

Vector Error Correction Model (VECM)

VECM is appropriate for variables that have a co-integrating relationship. In the VECM model, the equation is different, and it also includes an error correction term (ECT), which implies the deviation from long-term equilibrium in the previous period. In a VECM model, we have both the long run (ECT) and the short run relationship. Tables 4a and 5a explain the long run relationship with LLEAB and LIMR as dependent variables, respectively, while Tables 4b and 5b explain the short run relationship with LLEAB and LIMR as the dependent variables respectively. In Table 4a, GCF, CAEM, GVEF, DGHEXP and BUQU have a positive relationship with LLEAB, while GDPC and DMIN have a negative relationship with LLEAB, on average, ceteris paribus. Our result supports the findings of Kim and Lane (2013) that a positive relationship exists between government health expenditure and LLEAB. It is also partially in support of the findings of Anton and Onfrei (2012), who found that total health spending (DGHEXP) and GDP per capita (GDPC) are the most critical factors that explain the differences in health status. Also, two of our measures of governance quality (GVEF and BUQU) have a positive effect on LLEAB, which supports the findings of (Salatin and Noorpoor, 2015) that governance quality has a positive and significant effect on LLEAB.

In Table 4b, the co-integrating equation value implies that the deviation from the long-run equilibrium in the previous year is corrected for the current period as an adjustment speed of 0.37%. Also, a change in percentage in LGDPC, LGCF, LCAEM, DMIN, and DGHEXP is associated with 1.1, 0.08, 0.11, 0.07, and 0.26 percentage increase in LLEAB on average, respectively. In contrast, a percentage change in GVEF and BUQU is associated with an average 0.50 and 0.02 percentage decrease in LLEAB, respectively, in the short run. This supports the study of (Kim and Lane, 2013 Bashir, 2016; Bein et al., 2017), that a government expenditure improves health (DGHEXP) and LLEAB. More so, the result also supports the findings of (Boachie et al., 2018) that public health expenditure improves health outcomes.

In Table 5a, GCF, CAEM, GVEF, DGHEXP and BUQU negatively affect LIMR, while GDPC and DMIN positively affect LIMR. This supports the findings of Kulkarni (2016) that GDPC has a positive effect on LIMR while CAEM has a negative effect on LIMR. It also supports the study of Bashir (2016) that an increase in the government budget for the health sector (DGHEXP) can reduce the mortality rate (infant).

In Table 5b, the co-integrating equations imply an adjustment speed of 102% from the previous year to the current period. Also, a one per cent change in LGDPC, LGCF, LCAEM, and DGHEXP is associated with 4.2, 925, 0.27 and 0.59 percentage decreases, respectively, in LIMR, while a percentage change in GVEF, DMIN, and BUQU is associated with 1.09, 0.15, 0.21 increase in LIMR in the short run. An increase in LGDPC and LGCF percentages will have a very significant influence on reducing IMR. This supports the findings of (Anton and Onfrei, 2012) that the most critical factors that significantly explain the differences in health status are LGDPC and DGHEXP.

Tables 4c and 5c explain the response of LLEAB and LIMR to shocks, respectively. In Table 4c, the response of LLEAB to GVEF and GCF is adverse to a positive shock of one standard deviation. This implies that LLEAB responds negatively to shocks in GVEF and GCF. Also, if one standard deviation positive shock is given to BUQU, GDPC, DGHEXP and CAEM, life expectancy will react to these shocks in all ten periods. This means life expectancy reacts to positive shocks in BUQU, GDPC, DGHEXP and CAEM.

In Table 5c, the infant mortality rate (IMR) responds negatively to a shock in GVEF, GDPC, DGHEXP, GCF and DMIN through the ten periods, while IMR responds positively to a shock in BUQU and CAEM through the ten periods.

Tables 4d and 5d give the variance decomposition for LLEAB and LIMR, respectively. The variance decomposition scale is the variation (in percentage) in the endogenous variables induced by the shocks (innovations) that result from any variables in the system (Akintunde and Satope, 2013). The variance decomposition also tells the level of information that each variable adds to the other in the auto-regression and predicts the error variance of each variable that other variables’ exogenous shocks can explain.

In Table 4d, GVEF, BUQU, LGDPC, DGHEXP, LGCF, LCAEM, and DMIN do not explain variations in LLEAB in period one. The shock from each variable increased from zero in the first period to 2.00, 0.13, 4.76, 0.08, 0.61, 1.59 and 0.01 per cent, respectively, in the tenth period. GDPC and GVEF offer significant explanations for LLEAB, while DMIN has the lowest power to explain the variations in LLEAB. This supports the findings of Sirag, Nor and Abdullah (2017) that GDPC is a crucial factor to be considered in health financing and that a high level of government effectiveness (GVEF) stimulates public health financing and reduces private health financing.

In Table 5d, GVEF, BUQU, LGDPC, DGHEXP, LGCF, LCAEM, and DMIN did not explain variations in LIMR in the first period. The shock from each variable increased from zero in the first period to 0.48, 0.72, 0.24, 0,.33, 1.79, 3.42 and 1.82 per cent, respectively, in per cent, nth period. C, AEM and DMIN offer significant explanations for LIMR, while LGDPC has the lowest power to explain the variations in LIMR. This supports Kulkarni’s (2016) study that CAEM is significant in explaining the variations in LIMR.

The co-integrating equation and the long-run model can be expressed as

ECT_t-1 = Y_t-1 – αjX_t-1- βiR_t-1 - ømU_t-1 - ∞nV_t-1 - µbW_t-1 – πcZ_t-1 – φdS_t-1

therefore, the long-term result for Table 4a can be expressed as

ECT_t-1 = 1.0000LLEAB_t-1 – 9.59E-05GDPC_t-1 + 1.43E-1GCF_t-1 + 5.63E-05CAEM_t-1 + 20.8839GVEF_t-1 – 2.0619DMIN_t-1 + 7.5530DGHEXP_t-1 + 0.6757BUQU_t-1 – 11.4384

Table 4a. Long run relationship with LLEAB as the dependent variable

Source: authors’ computation

Table 4b. Short run relationship with LLEAB as the dependent variable

Source: authors’ computation

Table 4c. Response of LLEAB to shocks

Table 4d. Variance decomposition result for LLEAB

The co-integrating equation and the long-run model for Table 5a can be expressed as

ECT_t-1 = Y_t-1 – αjX_t-1- βiR_t-1 - ømU_t-1 - ∞nV_t-1 - µbW_t-1 – πcZ_t-1 – φdS_t-1

therefore, the long-term result below can be expressed as

ECT_t-1 = 1.0000LIMR_t-1 + 0.0013GDPC_t-1 -2.14E-11GCF_t-1 – 0.0007CAEM_t-1 -334.0696GVEF_t-1 + 1.0228DMIN_t-1 – 87.9806DGHEXP_t-1 -7.7408BUQU_t-1 – 727.2442

Table 5a. Long run relationship with LIMR as the dependent variable

Source: authors’ computation

Table 5b. Short-term relationship with LIMR as the dependent variable

Source: authors’ computation

Table 5c. Response of LIMR to shocks

Source: authors computation

Table 5d. Variance decomposition result for LIMR

Source: authors computation

Conclusion

Quality health is vital for everyone because of its effect on economic and human welfare. Adequate financing of the health sector and good governance quality could improve the delivery of health services and health outcomes in less developed countries. Therefore, we have examined how health financing and quality of governance impacted health outcomes in Nigeria over 39 years (1980–2018).

The findings show that in the long run, gross capital formation (GCF), carbon emission (CAEM), government effectiveness (GVEF), domestic government expenditure (DGHEXP) and bureaucracy quality (BUQU) have a positive effect on life expectancy at birth (LEAB). In contrast, GDP per capita (GDPC) and democracy index (DMIN) adversely affect LEAB. This implies that an increase in GCF, CAEM, GVEF, DGHEXP and BUQU will increase LEAB and vice versa, while an increase in GDPC and DMIN will reduce LEAB and vice versa in the long run. Also, gross capital formation, carbon emission, government effectiveness, domestic government expenditure and bureaucracy quality have reduced the infant mortality rate (IMR). At the same time, GDP per capita and the democracy index have a positive relationship with IMR in the long run. This suggests that an increase in GCF, CAEM, GVEF, DGHEXP, and BUQU will reduce the infant mortality rate in the long run, while the increase in GDPC and DMIN will increase IMR. The short-term effect shows that GDPC, GCF, CAEM, DMIN, and DGHEXP increase LEAB while GVEF and BUQU decrease LEAB. Also, GDPC, GCF, CAEM and DGHEXP decrease IMR while all the governance measures (DMIN, GVEF and BUQU) increase IMR in the short run. The impulse response result shows that LLEAB responds negatively to a shock in GVEF and GCF, while LLEAB responds positively to a shock in BUQU, GDPC, DGHEXP and CAEM. IMR respond positively to a shock in BUQU and CAEM, while IMR responds negatively to a shock in GVEF, GDPC, DGHEXP, GCF and DMIN. More so, the variance decomposition result tells us that GDPC and GVEF offer significant explanations to LEAB, while CAEM and DMIN offer significant explanations to IMR.

From the study, it can be gathered that good governance promotes health outcomes. Hence, the government should strengthen institutions and ensure political stability, especially in Nigeria, since we now practise democracy. Also, the government should increase its spending on the health sector in the country to improve people’s longevity and reduce death so that when people are healthy, they can contribute to the country’s productivity level positively.

^* Dr (Mrs) Temitope Sade Akintunde
Is an Associate Professor at the Department of Economics, Osun State University, Osogbo (Okuku Campus), Nigeria. She has published more than 40 articles in both local and international journals in health economics and development economics in developing countries, particularly Nigeria. She has attended several local and international conferences and presented papers and abstracts. She has also won international fellowships, for example, the IIE/Hewlett Foundation fellowship award. She has served as the principal investigator of a TETFUND Research Grant. She is supervising MSc and PhD students in Health and Development Economics.

^* Kehinde Elizabeth Adesina
Is a doctoral student in the Department of Economics at Obafemi Awolowo University, Ile-Ife, Nigeria. She has a BSc. and MSc. in Economics. Her area of interest is development and monetary economics. She has published in local and international journals in health economics, development economics and monetary economics. She has attended local conferences and virtually attended international conferences and presented papers. She is a fellow of the Centre for Economic Policy and Development Research (CEPDeR), Covenant University, Ota, Nigeria. She has taught economics in senior secondary schools and currently lectures to university distance learning students at one of the prestigious universities in Nigeria. She is dedicated to learning that improves her personal growth and development. Driven to succeed and be globally relevant, she is open to engagement that provides opportunities to learn and gain more skills as a world-class economist and researcher.

^* Dr. Olawale Olufemi Akinrinde
Currently a Lecturer I, holds a permanent position in the Department of Political Sciences of Osun State University. Dr. Akinrinde is also a Postdoctoral Research Fellow in Sustainable Development Goals at the University of Johannesburg, South Africa. Dr. Akinrinde is a published scholar with over fifty refereed articles published in refereed university-based journals across the globe, with many articles being Scopus-indexed. Dr. Akinrinde is a recipient of several local and international fellowships and grants.

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