Why does nigeria have a low life expectancy

These healthcare improvements made it possible for more than nine million people to gain access to improved healthcare facilities. More specifically, pregnant women in these regions now have access to healthcare facilities.

This is significant because one of the leading causes of death in Nigeria is attributed to infant mortality.

With pregnant women and mothers gaining better access to healthcare services, there is an increased chance that their children will be able to receive more advanced medical attention that could potentially save their lives. An additional factor potentially leading to the recent increase in the life expectancy in Nigeria is improved sanitation policies and practices.

In Nigeria, more than , children under the age of five die because of diarrhea, mainly due to unsafe water, sanitation and hygiene. One advancement made recently to combat this is the eradication of guinea worm disease ; in , Nigeria was certified as being free of the disease. In addition to the strides being made in water sanitation in Nigeria, there has also been an emphasis placed on ending open defecation. It is estimated that close to 90 million Nigerians live in extreme poverty. This means that Nigeria is the poverty capital of the world with Interestingly, Nigeria has one of the highest economic growth rates in the world but this has been unable to stem the tide of poverty in the country.

Some of the major causes of poverty in Nigeria are political instability, income inequality and ethnic conflict. Additionally, the lack of a stringent regulatory and monitoring system has allowed for rampant corruption which has effectively hindered past poverty alleviation efforts to a large extent, since the resources which could pay for public goods or directed towards investment and so create employment and other opportunities for citizens are being misappropriated.

The unemployment rate in Nigeria is estimated at This has contributed to the high level of poverty in the country. The government can improve the literacy level in the country by providing free and affordable education at least secondary level. Another major cause is the literacy level in the country.

Often times, when people fall ill, the resorted to native medicine which may not be able to cure their illness and they end up losing their lives. Also, uneducated Nigerians are often skeptical about vaccination which has led to high infant mortality in some parts of the country.

They noted that it has crucial effects on fertility behavior, economic growth, human capital investment, intergeneration transfers and incentives for pension benefits. Life expectancy is very crucial to the developing worlds who are earnestly striving for achieving socio-economic progress through investing significantly in social sectors like health, education, sanitation, environmental management and sustainability, and social safety nets [ 5 ]. In Nigeria, as in other developing countries, variations in morbidity and mortality have been associated with a wide variety of measures of socio-economic status including per capita GDP, fertility rate, adult illiteracy rate, per capita calorie intake, health care expenditure, access to portable drinking water, urban inhabitants, unemployment rate and the nominal exchange rate.

Studies have shown that there is a significant tendency for mortality to be lower in countries with a more even distribution of income see Wilkinson, [ 6 ]; Rodger, [ 7 ]; Le Grand [ 8 ] , but Nigeria is said to be highly non-egalitarian in income distribution.

Per capita income of developing countries has improved significantly and translated into higher level of health care expenditure [ 5 ]. For instance there has been remarkable improvement in the incidence of income and non-income poverty overtime that have impacted positively on life expectancy. However, in many of the countries in Sub-Saharan Africa and Nigeria in particular, although income and health expenditure is increasing Figure 1 , life expectancy has been unsteady.

An analysis of three decile averages show that between and , in Nigeria, life expectancy averaged Bello-Imam [ 9 ] compared the Nigerian data with the sub-region and concluded that maternal mortality rate per , live births in Nigeria averages 1, as against Sub-Saharan African average; malaria mortality rate per , population of as against Sub-Saharan African average; tuberculosis mortality rate among HIV negative people per , population of 63 as against 51 Sub-Saharan African SSA average.

Again a thirteen year average data on life expectancy, under five infant mortality rate, per capita income and unemployment rate for Nigeria, Ghana, Kenya, China, India a shows that Nigeria performed poorly on all these indicators Table 1.

The line graph of health expenditure indicators as percentage of GDP. In an attempt to improve the aforementioned indicators and work towards the attainment of 70 years life expectancy by , the Nigerian government has since stepped up her policy focused on the health sector through reforms and several health intervention programmes including the primary health care PHC intended to impact positively on life expectancy, the commercialization policy which was aimed at injecting some measure of efficiency into the public hospitals, the National Health Insurance Scheme NHIS initiated to mitigate the cost of access and the efficient health service delivery monitory policy Ministry of Health, [ 10 ] etc.

However, Sede and Ohemeng [ 11 ] noted large scale inefficient utilization of available resources in most public hospitals in Nigeria. This culminates into technical and scale inefficiencies, notwithstanding the upwards trends in percentage shares of total health expenditure in GDP and total health expenditure in total government expenditure in Nigeria over time Figure 1.

The pertinent questions therefore are:. To what extent have health policy efforts and other socio-economic variables influenced life expectancy of Nigerians? These are the issues this study investigates since previous studies in this area were focused on other countries, at different times, and with different measures of socio-economic indicators suggesting that the underlying subjects may be important and should be pursued further.

Nevertheless, most of the studies concentrated more on the biological, health behavioural and cultural factors. It is on the basis of these, the present study opted to investigate the effects of the socio-economic environment as constituted by per-capita income, health policy, literacy, the naira exchange rate and unemployment rates, on life expectancy of Nigerian. The theoretical foundation of the study hinges on Grossman [ 12 ] who asserted that economic disposition of an individual is critical to affordability of health consumption.

He also affirmed that the socio disposition of the individual as shown in the level of education, sense of awareness of health practices and access to health determines the health of the individual. For one thing, socio-economic variables are those factors that bother upon the social and economic conditions prevailing in the economy where the individual subsists. Bichaku et al. Overall, the study showed that health policy that may focus on the provision of health services, family planning programmes and emergency aids to exclusion of other demographic issue may serve little in the schemes aimed at improving the current health status and for that matter the life expectancy at birth of the region.

In this section, special attention is devoted to the time series component of the data series under consideration. When dealing with time series data, it is important to investigate whether the series are stationary or not because the regression of non-stationary series on another may yield spurious results.

According to Engle and Grange [ 14 ], the parameter estimates from such regression may be biased and inconsistent. A concurrent test to determining the long-run relationship among variables under investigation is conducted by employing the Johansen co-integration test [ 17 ]. This is important because variables that fail to converge in the long-run may be hazardous to policy making. Another common problem with empirical investigations is that they often ignore the feedback effect among variables in the model.

In order to address this problem, vector auto-regression VAR is used in this study. In a VAR, each variable is regressed on its own lag and lags of other variables in the model. In this way, the procedure allows each to be affected by its own history and the history of each variable thus minimizing the problem of simultaneity [ 18 ].

The VAR contains several procedures for evaluating relationships. Two of the procedures are adopted in this study namely causality test and variance decomposition. The causality test is used to determine whether the impact of expanse in socio-economic variables on life expectancy is statistically significant. While the causality test indicates this, it may not show the relative magnitude of the impact. Therefore, the variance decomposition is used to determine the relative magnitude of such impact.

More specifically, it indicates the percentage change in life expectancy that may be attributed to the effect of expansion in socio-economic variables. Such estimates are mostly useful for analyzing impacts in a multivariate system as clearly demonstrated by [ 19 ] and [ 20 ]. The study covers the period 31years which has sufficient degree of freedom to capture a considerably large proportion of the effect of socio-economic variables on life expectancy in Nigeria over time.

Recent studies dedicated to examining possible determinants of life expectancy have considered varied variables like income, education, expenditure on health care and composite consumables, access to portable water and safe sanitation, quality energy, employment rate, residential tenure and many others. In this study variables considered to constitute socio-economic variables are: per capita income, health expenditure, literacy, and the nominal exchange rate and unemployment rate.

Income has been reported as a determinant of life expectancy in most studies. It has been established that absolute level of income measured by per capita GDP seems to impact significantly on mortality as income increases from the lowest towards the middle range of income bracket, and no further gains in life expectancy accompanies increases in income beyond certain threshold of income bracket [ 6 ].

Accordingly, Wilkinson [ 6 ] proposed that if there are diminishing health returns to increases in income, income redistribution might be pareto improvement since redistribution of income makes at least the poor better off without making those within the higher income bracket worse off.

He also noted that, such income life expectancy relationship constituted a non-linear relation. However, Anand and Ravallion [ 21 ] found a significant positive linear relationship between per capita GNP and life expectancy, which is transmitted through public expenditure on health.

But when poverty was introduced into their model, the relationship between per capita GNP and life expectancy became insignificant. Sen [ 22 ] reported impressive high life expectancy in the Indian state of Kerala even at low level of per capita income. Literacy provides the individual with common social virtue of writing, reading and cultivation of health ethics which has a bearing on improving life expectancy. Education augments labour market productivity and income growth, and an educated woman has beneficial effects on child health and social well-being [ 5 ].

In analyzing 40 to 97 countries, Williamson and Boehmer [ 23 ] concluded that education impact positively on female life expectancy. There is however, contention in the literature about significant differences between mortality and life expectancy in relation to education see Kalediene and Petrauskiene, [ 24 ]; Grabauskas and Kalediene, [ 25 ].

Rogers and Wofford [ 26 ] investigated life expectancy for 95 developing countries and came to a conclusion that literacy significantly explained the variation in life expectancy in these countries. This assertion was upheld by Gulis [ 27 ] when he employed multivariate regression analysis on countries. In Nigeria, secondary school education to a very large extent is free and accessible to all children of that age in all the states of the federation.

The standard of secondary educations in Nigeria is sufficient to accomplish the targets of writing, reading and access to health ethics awareness. It is the best statistic that yields the literacy level in Nigeria compared to literacy rate that might be perverted by inclusion of tertiary enrolment that might not be all encompassing. Health policy on its part is government systematic control of important health variable, such as government expenditure on health, so as to make healthy life available and accessible to the individuals.

Such policy efforts might have significant influence on life expectancy since they directly help in reducing morbidity and mortality. Cremieux et al. Evidence abounds in the literature concerning the positive relationship between right health policy and life expectancy.

Kabir [ 5 ] reports of Costa Rica attainment of the highest life expectancy among the developing world, 74 years and 78 years in and respectively. This remarkable feat was achieved by the right health interventions, notably a primary health care programme [ 5 ].

Evidence also shows that there is positive relationship between health care inputs such as number of doctors, hospital beds, government health expenditure and health outcome Grubaugh and Santerre, [ 29 ]; Elola et al.

Hitiris and Posnet [ 32 ] found a relatively small negative relationship between health expenditure and mortality rates in a cross country study. The Naira exchange rate indirectly affects affordability of the health bills since part of the health goods and services have foreign content, whose import charges would always feed into individual final bills. Unemployment rate would affect the affordability of hospital bills negatively.

It can also affect social disposition of individual and the grade of health facilities patronized see [ 6 ]. Based on the above reasons and availability of data these variables were adopted. According to Sims [ 19 ] and Todd [ 20 ], if there is true simultaneity among a set of variables, there should not be a-priori distinction between endogenous and exogenous variables. The VAR model of life expectancy and the socio-economic variables in Nigeria posits that the variables are inter-related.

Government health expenditure can be used to proxy government health policy. The inter-relationship of the variables is shown in the model below:. In order to avoid producing spurious results that would make estimate biased and inconsistent, the time series data for all the variables in the study were tested within the period of to determine their stationarity status.

Under each test type, there are two dimensions of test results. They are, test results conducted without intercept and test results conducted with intercept.

Also, under each dimension, the test was conducted at levels and at first difference. Results showed that all the variables were not stationary at levels but after first difference they were all stationary. In other words, the variables are integrated of order 1 i. I 1 series. For the purposes of reasonable policy making, the relationship between macroeconomic variables in the long-run is very important.

If variables have a causal relationship that allows them to move in perfect harmony in the long-run, the confidence level of the consistency of the formulated policy in their short and long run dynamics will be robust. It was against this backdrop that the co-integration test was conducted, so as to determine if there is a convergence between the long run equilibrium and the short run dynamics of the time-series data.

From the test statistic of trace and maximum eigen-values, result shows that there is at least one cointegrating equation among the variables. This confirms the existence of a long run relationship between the short-run dynamics and the long run equilibrium of the model. See Table 4 below for the detailed results:. The estimation of a VAR-model requires the explicit choice of lag-length in the equation of the model. This result and that of Schwarz and Hannan-Quinn information criteria are shown in Table 5.

The Akaike information criterion AIC is minimized for order 2. This implies that the optimal lag length of this study is order 2.

One of the requirements of regression model is that the error terms of the observations are normally distributed. The study employed the Cholesky Lutkepohl test to ascertain this.

The results are presented below. Another test to be wary of is the collinearity of the regressors. If the regressors are correlated, the BLUE property of the model holds but, it becomes difficult to decipher the distinct impact of each of the covariates on the regressand. Collinearity becomes very worrisome when it becomes severe. If it is mild it is acceptable.

Gujarati [ 34 ] and Greene [ 35 ] asserted that a VIF of more than 10 is severe while a tolerance index of zero indicates severe multicollinearity. The closer the tolerance index to 1 the milder the case. Table 7 below shows the Variance Inflation Factor and tolerance test statistics from the study data.