







































 Humanities and Social Science Research; Vol. 1, No. 1; 2018 

ISSN 2576-3024   E-ISSN 2576-3032 

https://doi.org/10.30560/hssr.v1n1p30 

 30 Published by IDEAS SPREAD 

 

Population Growth and Life Expectancy in Nigeria: Issues and Further 

Considerations 
Oladayo Timothy POPOOLA1 

1 Department of Economics, Ahmadu Bello University, Zaria, Nigeria 

Correspondence: Popoola, Oladayo Timothy, Department of Economics, Ahmadu Bello University, Zaria, Nigeria. 

Tel: 234(0)813-574-5100. E-mail: poladayo@gmail.com  

 

Received: April 25, 2018; Accepted: May 15, 2018; Published: May 19, 2018 

 

Abstract 

This study empirically investigates the effects of population growth on average life expectancy in Nigeria taking 

into account the explicit role of healthy citizens in economic development as well as other control variables not 

considered in prior studies. Predicted on country-specific regression and Granger Causality test using time series 

data between 1986 and 2015, the findings reveal that rising population growth have positive and insignificantly 

impacts life expectancy; but 1% decrease in fertility rate and population of 65-and-above dependency ratio could 

positively stimulates an improvement in longevity by 5.84, and 81.5 respectively in Nigeria. Furthermore, the 

granger causality test shows that population growth could granger cause low life expectancy in Nigeria at least at 

10% level of significant. The findings therefore make a case for strengthening efforts towards reducing both 

fertility rate and age 65 and above dependency ratio with priority given to the welfare of ages 65 and above 

population in Nigeria. 

Keywords: Population Growth, Health and Development, Nigeria, Granger Causality, Regression  

JEL Classification: J11, I15, C13  

1. Introduction 

Is rapid population growth good or bad for longevity? Both theories and empirical studies are yet to provide 

satisfying explanation about the implications of rising population growth on average life expectancy even in 

developed countries. Specifically, some theories suggest that more increasing population growth could be bad for 

health status of citizens and their life expectancy because with a larger population there would be more pressure 

on health facilities especially in developing countries. While others explain that rising population growth could 

lead to greater productivity either by inducing innovation, producing innovation or through creating greater 

economies of scale even in medical-care services (Turner, 2014; and Mateo, 2016). 

In recent years, growing population has become a key issue of concern to Nigerian and policy makers alike. This 

is because, rising population in terms of its composition and size, has far-reaching implication for citizens’ quality 

of life. As Odusina (2011) observes, population is a major asset of the developing countries, as resource for 

economic growth and development, and is also the prime beneficiary of development. It often constitutes the bulk 

of the producers of goods and services as well as the major consumers of the goods and services. However, the 

impact of population on development depends not only on the absolute size but also on its quality and implications. 

Obviously, Nigeria’s population is large with appropriately 194 million people (United Nations, 2017). Her 

population is also about 3% of the world population with a population growth rate of about 2.62% annually 

alongside with low GDP growth rate of -1.54% (during economic recession) respectively in 2016 (World Bank, 

2017). This implies that the current population growth rate in Nigeria exceed the GDP growth rate. With rising 

population, it become increasingly important to also increase health financing and infrastructure; however, the 

total health financing (percentage of GDP) is 3.67% in 2016 which is low compare to health spending (% of GDP) 

of 4.32% and 4.47% in 2004 and 2007 respectively (World Bank, 2017). 

Consequently, the average life expectancy at birth in Nigeria is merely 53 years in recent years, while it is above 

80 years for countries like France, Japan, Singapore, and Hong Kong. For Ghana and Niger, their average life 

expectancies were 64 and 61 years respectively in 2015 (World Bank, 2017). All these perhaps explain why Nigeria 

was far from reaching the health-related targets of the recent past Millennium Development Goals (Novignon et 

al., 2015; WHO, 2015). Another worrisome issue is the high prevalence of; malaria, cholera, acute hepatitis E, 

stroke, hypertension, typhoid, and all forms of cancer that often constraint longevity (WHO, 2017). Evidence from 



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prior studies have also shown that life expectancy at birth is low in Nigeria (see Novignon et al., 2015; World 

Bank, 2017; Karimo et al., 2017). 

The foregoing suggests that probably insufficient health facilities to meet the population needs, in addition to 

inadequate health financing and infrastructure resulted from poor governance might be accounting for low life 

expectancy in Nigeria. Although the impact of population growth towards education, urbanization, public finance 

and economic growth has been established (see Mateo, 2016; Bussolo et al. 2015; and Bloom et al., 2011), its 

longevity-impact has not received adequate attention from previous studies. Therefore, this paper relies on the 

country-specific regression to comparatively ascertain the effects of population growth, infrastructure and health 

financing on life expectancy in Nigeria. This is with a view to proffer sustainable strategies for health sector 

development in Nigeria. Following this introductory section, review of related literature is presented in section 

two. Section three present methodology while section four focus on findings. Finally, section five concludes and 

draws lessons for Nigeria. 

2. Review of Related Literature  

Population growth rate is an increase in the number of people that reside in a country, state, or city over time. The 

global population rise from 2.5 billion to 5.7 billion people between 1950 and 1995, and presently the statistics is 

nearly 7.6 billion in 2017 up from 7.4 billion in 2015; the figure is also expected to grow to 9.8 billion people by 

2050 (United Nations, 2017). As the report noted, despite nearly universal lower fertility rates globally, the 

increase is still spurred by the relatively high levels of fertility in developing countries. For Nigeria, the report of 

United Nations’ World Population Reviews (2018) further indicate that among the ten largest countries globally, 

Nigeria is growing the most rapidly. Specifically, as of 2018 the estimated population of the country is 195.88 

million (United Nations, 2018). As Figure 1 indicate, the population growth of Nigeria has been on the rising since 

1997 from 2.48% to 2.69% in 2015 (World Bank, 2017). 

 

Figure 1. Population Growth Rates in Nigeria (1981 – 2015) 

Source: World Bank Development Indicator (2017) 

 

Figure 2 indicate that in recent years the percentage of 0 to 14 years old population to the total population in 

Nigeria is about 44% in 2016. 

 
Figure 2. Percentage of Population ages 0-14 to Total in Nigeria (1981 – 2016) 

Source: World Bank Development Indicator (2017) 



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While Figure 3 show that the percentage of 15 to 64 years old as been rising over time in Nigeria. The population 

of ages 15-64 years to the total population in Nigeria increase from 51% in 1986 to 53% in 2016 (see World Bank, 

2017). 

 

Figure 3. Percentage of Population ages 15-64 to Total in Nigeria (1981 – 2016) 

Source: World Bank Development Indicator (2017) 

 

Conversely, the population of Nigerian from ages 65 years and above to percentage of the total population declines 

over decades. For instance, in 1986 the figure was about 2.9%, while in 2016 the statistics reduce to 2.74% to total 

population as Figure 4 revealed. This imply that longevity in Nigeria is decreasing. 

 

Figure 3. Percentage of Population ages 65 and above to Total in Nigeria (1981 – 2016) 

Source: World Bank Development Indicator (2017) 

 

For longevity or life expectancy (LEB), which refers to the average number of years an infant is expected to live 

if mortality patterns at the time of birth remains constant in the future (World Bank, 2016). It is the average-period 

that a person is expected to live as determined taking account of current economic situation. This also reflects the 

overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups 

(WHO, 2006). It also indicates the number of years an infant would live provided the patterns of mortality 

continues at the time of birth were to stay the same throughout his life. Hence, it is an important index of long life 

and quality of living (Grepin and Bharadwaj, 2015). Table 1 reveals that LEB in Nigeria increased marginally 

from 46 years in 1995 to about 53 years in 2015, while Rwanda’s LEB increased from 31 years to about 65 years 

during the same period (World Bank, 2017). In comparison, LEB in Nigeria is lower compared with Ghana and 

South Africa. 

 

 

 



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Table 1. Average Life Expectancy at birth (total) in Nigeria compare to other countries 

    1980  1985  1990  1995  2000  2005  2010  2015 

Cote d’Ivoire  50.7  52.6  52.6  49.5  46.7  47.7  50.1  51.9 

Ghana   52.3  54.1  56.7  57.5  56.9  58.7  60.6  63  

South Africa  56.9  59.9  62.1  61.4  55.8  51.5  54.4  57.4  

Rwanda   47.9  50.4  33.4  31.6  48.1  54.7  61.4  64.5  

Nigeria   45.5  46.3  46.1  46.1  46.6  48.6  51.3  53.0  

 Source: Compiled from World Development Indicator, 2017  

 

Theoretically, the debate over population growth and its significance for human welfare and their longevity was 

raised in Robert Malthus studies on population, in the sense that growth of population depends upon the means of 

subsistence, primarily the food supply (Thompson, 2010). Malthusian explanation predicts a fast and steady 

population growth for countries with an abundance of natural resources until the points where abundance turns to 

scarcity since the production growth would be linear and population growth exponential (Bussolo et al. 2015, and 

Thompson, 2010). With time, the exponential function would return the linear production function and lead to 

famine or other disasters. Other economists like Adam Smith (1937), Schumpeter (1954), and Rostow (1990) 

further provided insight about the relationship between population growth and subsistence resources. 

These theories led to various empirical findings. For instance, Istaiteyeh (2017) investigate the impact of socio-

economic determinants (including per capita GDP, public spending on health, urban population, and secondary 

school enrolment) on life expectancy in Jordan. The study covers the period from 1990 to 2014 and employed 

Vector Auto-Regression technique (VAR). The empirical findings indicate that unemployment and secondary 

school enrolment explains longevity in Jordan. In Nigeria, Ilori et al. (2017) examines an empirical evidence of 

the specific impact of public health expenditure on life expectancy in Nigeria using time series data spanning 

between 1981 and 2014. Their study employ bounds testing co-integration and Autoregressive Distributed Lag 

(ARDL) procedures to determine the relationship between public spending on health and life expectancy in Nigeria. 

The results indicate that School Enrolment and carbon-dioxide emission significantly and directly influenced life 

expectancy in Nigeria, while School Enrolment was found to be insignificant in both short and long runs contrary 

to economic theory. 

Ratna and Sari (2016) examine empirically the relationship between the health budget, human capital and 

population growth in Indonesia by using both quantitative and qualitative analysis. Their quantitative results from 

regression technique support the theory that there is significant no relationship between life expectancy and 

population growth, while their qualitative analysis is used to describe the role of formal and informal institutions, 

including financial institutions in reducing birth rates in an effort to improve human capital. However, Shahbaz et 

al. (2016) focused on life expectancy drivers in Pakistan. Their findings conclude that rural-urban inequality in 

income and economic misery have substantial inverse impact on life expectancy, but urbanization support life 

expectancy, while illiteracy declines it. 

The study of Hansen and Lonstrup (2013) indicates that increase in longevity decreased per capita GDP growth 

and rising population growth. These findings are robust to the inclusion of initial life expectancy and initial GDP 

per capita. Currais (2000) take into account the extent to which fertility and mortality affect the population growth 

rate. His findings indicate that mortality depends on health expenditure and fertility rate, and that household often 

take into account the welfare and resources of their current and future that concerns population growth rate. In 

sum, most of the empirical studies conducted especially in developed and developing countries including Nigeria 

with emphasis on public health spending, per capita GDP, rural-urban inequality in income, and unemployment 

on life expectancy. However, these studies failed to appropriately account for the impact of population growth on 

longevity and the effect of fertility rate and age 65 and above dependency ratio. Hence, this study will bridge the 

gap in the empirical literature by investigating the impact of population growth on life expectancy in Nigeria. 

3.1 Theoretical Framework 

Grossman (1972) theorized that health status (H) is determined by various factors (X). This implies that: 

  H = f (X)          (1) 

where X are population growth ratio (PG), age 65 and above dependency ratio as percentage of working-age (AD), 

fertility rate (FR), and population ages 65 and above as the percentage of the total population (PA) in Nigeria. 

Thus,  



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LE = f (PG, AD, FR, PA)          (2) 

From the foregoing, the model utilized for this study is in the spirit of Istaiteyeh (2017). However, the model 

differs in that it considers the impacts of population growth rate, age 65 and above dependency ratio as percentage 

of working-age, fertility rate, and population ages 65 and above as the percentage of the total population in Nigeria.  

From equation (2), the econometric model is specified as: 

𝑳𝑬𝒕 = 𝜶 + 𝜷𝟏𝑷𝑮𝒕 + 𝜷𝟐𝑨𝑫𝒕 + 𝜷𝟑𝑭𝑹𝒕 + 𝜷𝟑𝑷𝑨𝒕 + 𝞵    (3) 

where 𝝻 is the error term assumed to satisfy the Gaussian white noise condition. Apriori, the ADW, FRT and PA 

are expected to exert inverse influence on longevity or average life expectancy (LE), while PG could either be 

positive or negative. 

3.2 Scope and Data Sources 

The study employs annual data spanning 1981 to 2016. The description and definitions of variables, including the 

sources are given in Table 2. 

 

Table 2. Descriptive Statistics of Variables in Nigeria 

Variable Descriptions  Sources 

LE Longevity or average life expectancy at birth World Bank Development Indicators (WDI, 2017) 

PG Population Growth Rate WDI, 2017 

AD Age 65 and above dependency ratio as 

percentage of working-age 

WDI, 2017 

FR Fertility Rate WDI, 2017 

PA Population ages 65 and above as the 

percentage of the total population 

WDI, 2017 

Source: Author Compilation, 2018 

 

4. Empirical Analysis 

4.1 Descriptive Statistics 

The descriptive statistics of variables used in the estimations is presented in Table 3. From the table, the average 

LE is 48 years old with standard deviation of 2.57 years. The minimum population growth was 2.49 percent, while 

the average fertility rates is 6 children per woman in Nigeria for the periods under study. Again, the correlation 

matrix table presented in Table 4 reveal that, high inverse correlation exists among all the variables and LE except 

PG. For instance, the correlation between LE and FR is about 86 percent.  

 

Table 3. Descriptive Statistics of Variables in Nigeria 

Variables LE PG AD FR PA 

Mean  48.06 2.58 5.31 6.19 2.81 

Standard Deviation 2.57 0.07 0.15 0.37 0.05 

Maximum 54 2.49 5.09 5.52 2.73 

Minimum 45.85 2.72 5.54 6.78 2.89 

Observations 36 36 36 36 36 

Source: Own Computations with Stata 13 

 

Table 4. Correlation Matrix (1981-2016) 

 LE PG AD FR PA 

LE 1.00     

PG 0.65 1.00    

AD -0.75 -0.39 1.00   

FR -0.86 -0.28 0.81 1.00  

PA -0.83 -0.57 0.97 0.77 1.00 

Source: Own Computations with Stata 13 



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4.2 Unit Root Test 

The study tests for unit roots for all the variables. Augmented Dickey-Fuller (ADF) and Phillip-Perron (PP) are 

used to perform the tests. The results of the stationarity tests of variables at levels are presented in Table 5. 

 

Table 5. Results of Unit Root Tests (1981-2016) 

Variables ADF test 

at level 

90% ADF 

Critical  level 

Order of 

Integration 

PP test at 

level 

90% PP 

critical level 

Order of 

Integration 

LE 5.993 -2.618 I(0) 2.547 -10.40 I(1) 

PG -2.027 -2.618 I(1) -4.820 -10.40 I(1) 

AD -0.234 -2.618 I(1) -0.315 -10.40 I(1) 

FR -2.041 -2.618 I(1) -0.116 -10.40 I(1) 

PA -0.153 -2.618 I(1) -2.945 -10.40 I(1) 

Source: Own Computations with Stata 13 

 

The findings in Table 5 indicate that all the variables except LE are non-stationary at levels for ADF. The unit root 

tests applied to the variables at levels reject the null hypothesis of stationarity with evidence from PP test for all 

the variables. The variables are therefore, differenced once and they are confirmed to be stationary. The ADF and 

PP tests applied to the first difference of the data series accept the null hypothesis of stationarity for all the variables. 

Thus, the variables are integrated of order one I (1). 

4.3 Co-integration Test 

Considering the unit root tests of the variables, the study proceeded to establish whether or not there is a long-run 

co-integrating nexus among the variables (LE, PG, AD, FR, and PA) by using the Johnasen full information 

maximum likelihood method. The Johnasen tests (as presented in Table 6) indicated that the trace and maximal 

eigenvalue statistics revealed the existence of four co-integrating relationships between LE and other variables at 

the 5 percent level of significance. The conclusion drawn from this result is that there exists a unique long-run 

relationship between LE, PG, AD, FR, and PA. 

 

Table 7. Johnasen Co-Integration Test Results 

Hypothesized Eigenvalue Trace Statistic 5% Critical value Prob. 

None* 0.97 200.7 69.8 0.0000 

At most 1* 0.74 86.1 47.9 0.0000 

At most 2* 0.54 40 29.8 0.0024 

At most 3 0.25 13.9 15.5 0.0857 

At most 4* 0.118 4.27 3.8 0.0384 

Source: Own Computations with Stata 13 

 

The long-run longevity (LE) can be obtained by normalizing the estimates of the unconstrained co-integrating 

vector for the long-run LE are presented in equation 4. 

 

Table 8. Normalized Co-integration Coefficients 

LE PG AD FR PA 

1.0000 -79.98 

(3.22) 

107.06 

(9.96) 

-5.79 

(1.78) 

-268.74 

(21.96) 

Source: Own Computations with Stata 13 

 

 LE = -79.98PG + 107.06AD – 5.79FR – 268.74PA      (4) 

4.4 Dynamic Specification of Longevity 

In the short-run, deviations from this relationship could occur due to changes to any of the variables (PG, AD, FR, 

and PA). Due to the differences between dynamics governing the short-run and long-run behaviour, the short-run 



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interactions and the adjustments to long-run equilibrium are paramount because of the policy implications. The 

Error-Correction Model (ECM) is expected to be negatively signed and significant in the LE function. This result 

as presented in Table 9 substantiates the finding of co-integration among the variables reported earlier. Table 9 

also indicate that the error-correction term of 18 percent, suggesting that the disequilibrium in the LE model is 

offset by annual short-run adjustment.  

 

Table 9. Error Correction Model Test 

Variable Coefficient Standard Error t-statistic Probability 

ECT(-1) -0.1756 0.1010 -1.7391 0.0926 

  Source: Own Computations  

 

4.5 Granger Causality 

LE and PG are inter-linked and co-related through various links. Although there seem to be no empirical or 

theoretical insight that could conclusively indicate sequencing from either direction. Therefore, the Granger 

Causality test for this study was carried out on LE and PG. The results (as presented in Table 10) indicates the 

existence of a bi-directional causality at 10 per cent level of significant which runs from LE to PG and vice versa 

at lag 2. 

 

Table 10. Granger Causality Test 

Null Hypothesis Lag Observation F-Statistics Prob. Decision 

LE does not granger cause PG 1 35 10.03 0.0034 REJECT 

2 34 5.31 0.0108 REJECT 

PG does not granger cause LE 1 35 1.63 0.2104 ACCEPT 

2 34 2.99 0.0659 REJECT 

  Source: Own Computations  

 

4.6 Regression Results 

The regression estimation results are reported in Table 11; although, rising population growth have positive and 

insignificantly impacts life expectancy in Nigeria, but FR and PA have negative and significant impacts on 

longevity in Nigeria. This means that 1 percent decrease in FR, AD, and PA could positively stimulates an 

improvement in life expectancy by 5.84, 27.7, and 81.5 respectively. These findings confirmed that of Ratna and 

Sari (2016) in Indonesia. Therefore, increase in any of these variables could trigger longevity in Nigeria. The R2 

as shown in the result is 97.5 per cent, this value indicates that the model explains about 97.5 per cent of the 

behaviour of life expectancy in Nigeria. At 0.0000, the probability value indicates that the regression estimation 

result is highly and statistically significant. 

 

Table 11. Regression Results Table 

Variables Coefficient Standard Deviation t-statistics Probability Value 

Constant 162.302 16.031 10.12 0.0000 

PG 1.643 2.131 0.770 0.4460 

FR -5.846 2.131 -17.06 0.0000 

AD 27.678 3.235 8.55 0.000 

PA -81.530 9.560 10.12 0.000 

    Source: Own Computations with Stata 13 

 

5. Conclusion and Recommendations 

This study investigates the dynamics of population growth and life expectancy in Nigeria. The empirical results 

suggest a long-run relationship between population growth and longevity for a 35-years data period spanning 1981 

to 2016. This result is consistent with Istaltayeh (2017) that investigated the impact of socio-economic 

determinants that includes per capita GDP, government expenditure on health, secondary school enrolment, and 



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urban population on life expectancy in Jordan. Although, the regression estimated results of this study indicates 

that population growth is insignificant in predicting life-expectancy; however, it indicates that rising fertility rate 

and population of 65 and above dependency ratio would reduce life expectancy in Nigeria.  

The implication of the findings is that rising population growth have positive and insignificantly impacts life 

expectancy; but 1% decrease in fertility rate and population of 65-and-above dependency ratio could positively 

stimulates an improvement in longevity by 5.84, and 81.5 respectively in Nigeria. Furthermore, the granger 

causality test shows that population growth could granger cause low life expectancy in Nigeria at least at 10% 

level of significant. These results therefore suggest that for policy makers in Nigeria to increase average life 

expectancy of her citizens, attention should be given to reducing fertility rate. More importantly, a higher level of 

attention should be given to timely payment of pensions and gratuity, this will reduce over dependency ratio in 

Nigeria. Also, policies aimed at improving the welfare of population ages 65 and above should be given more 

prominent attention. 

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