







































 
 

48 
© 2020 by the authors; licensee Eastern Centre of Science and Education, USA 

 

Asian Business Research Journal 
Vol. 5, 48-54, 2020 
ISSN: 2576-6759 
DOI: 10.20448/journal.518.2020.5.48.54 
© 2020 by the authors; licensee Eastern Centre of Science and Education, USA 

 
 

 

 
Impact of Population Growth, Poverty and Unemployment on Economic Growth 

 
Umar Bala1     
Aminu Ibrahim2 
Nazeef Bala Hadith3 
  

( Corresponding Author) 
 
 
 

 

1,2Department of Economics, Faculty of Social and Management Sciences Bauchi State University, Gadau Nigeria. 

 
3National University Commission (NUC) Garki GPO, Maitama Abuja, Nigeria. 

 
 

 
Abstract 

This research explores the impact of population growth, poverty and unemployment on economic 
growth in Nigeria using Auto Regressive Distributed Lag (ARDL). The study employed an 
econometric procedure; unit root test which involved the use of Augmented Dickey Fuller test 
(ADF) and Phillip-Perron test (PP). The cointegration test technique used in the study is Auto 
Regressive and Distributed Lag (ARDL). The study variables are real GDP, population, poverty, 
unemployment and foreign direct investment has control variable.  The null hypothesis stated 
that there is presence of a unit root was failed to be rejected at levels but rejected at first difference 
according to the two tests (ADP and PP) employed. The study found that some of the variables 
are stationary at level I(0) while others are stationary at first difference I(1).The results of the 
cointegration test showed that there exist cointegrating equation between explanatory variables 
and economic growth. The ECT speed of adjustment to the normal equilibrium confirms their 
long run relationship of the variables. Finally, the study found that population and FDI have a 
positive impact while poverty and unemployment has negative impact on GDP. Based on these 
findings recommend that policy makers should grow the real economic sectors to improve and 
enhance productivity, exports, job creation, curb inflation and reduce poverty and rapid economic 
growth and substitute the non-productive imports with domestic products and develop enabling 
environment to attract foreign private investors. 

 
Keywords: Population growth, Poverty, Unemployment, Economic growth, foreign direct investment, ARDL bound test. 

JEL Classification: I39. 

 
1. Introduction 

There are so many problems recently in problem in Nigeria which associated with high population grow 
especially in urban areas. Increases in growing population causes high level of unemployment in the urban area and 
translate to poverty. Moreover, the presence of increases numbers of poorer and jobless individuals in the society 
generates insecurity (Frank, 2010). In most of the populated area has deficiency to acquire basic human need and 
social amenities. Most of the people are farmers which categories as seasonal unemployment. When the level of 
unemployed people are high without corresponding human basic need the rate of crime increases. In Nigeria there is 
still high rate of unemployment with translate to poverty and infrastructural deficit (Okolobah & Ismail, 2013). 
Several decades ago in Nigeria, agriculture seemed to be the major source of livelihood and as a result population 
growth was positively linked to production. It was believed that more people imply greater productivity and security 
since more workers or laborers working efficiently would be expected to immensely improve productivity and the 
overall output of the nation (Tartiyus, Dauda, & Peter, 2015). When the societies and economies began 
to flourish, success was dependent upon a productive agricultural sector and attributed to 
large population. The economy inevitably expanded and the society reaped the financial benefits with more efficient 
labor. The high fertility rates allowed for increased laborers, enhanced productivity, facilitated economic activities 
and helped overcome the previously recorded exorbitant death rates as a result of combined effects of famine, disease, 
malnutrition, plague and war (Latimer & Kulkarni, 2008). The level of unemployment and poverty have continue to 
be core obstacle confronting the Nigerian economy (Adelowokan, Maku, Babasanya, & Adesoye, 2019).  

According to world bank development indicators Nigeria has a population of about 200,963,599 million people in 
2019 and a land area of 923,768 km2, Nigerian economy is the largest economy in African region  (OPEC, 2015). The 
measurement of size and growth of the economy Nigeria is re-basing its GDP data from 1990 to 2008. A preliminary 
forecast of the rebasing is about a 40 percent increase in the total GDP; i.e. from a 2012 IMF estimate of USD 270 
billion to USD 375 billion. This would bring Nigeria’s GDP behind South Africa’s GDP of USD 390 billion, making 
Nigeria the 30th largest economy in the world from the previous 40th position. The structure of the Nigerian 
economy is predominantly primarily product oriented (agriculture and crude-oil production). The projected growth 
rate of 6.75 percent appears conservative given the potential for double digit growth. Also there necessity to achieve 

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higher growth rates in order to actualize it goal to become one of the top 20 economies in the world by the year 
2020. The planned rebasing of the Gross Domestic Product (GDP) is anticipated to boost the size of the economy 
possibly to become the largest in Africa. But it will lead to a reduction in future GDP growth. Irrespective of the 
potential increase in the size of the Nigerian economy, Nigeria is still facing some setback in economic sector such as 
militants, inflation, unemployment, poverty, insecurity problem and corruption.  

The 2020 budget shows that the federal government will remain focusing on major actions to control the 
disorder in the Country particularly in Northern part and to consolidate on the relative peace in the Niger-Delta 
post-amnesty. In 2020, the effect of Covid19 turn the real GDP grow into negative accomplish by the declining 
global crude oil price and production cut challenges in an economy that is the still predominantly oil based. The 
alternative way to compensate the deficit growth from the oil sector is by creating alternative sources that will drive 
the economy forward. To achieve this, government must pay attention on fiscal and monetary policies that will drive 
the non-oil sectors of the economy. The sectors driving the economic growth were not sufficiently high job creating 
sectors. Furthermore, the oil industry is a capital-intensive virtual enclave that generates very little employment and 
the rate of poverty still increases among the citizens. Figure 1 we can see that population, unemployment and 
poverty are still increasing which may affect economic growth. It can be observed that all the trends of the variable 
are increases especially the unemployment rate from 2015 to 2019 which has direct corresponding effect on poverty 
rate has shown in the chart.  
 

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

1990 1995 2000 2005 2010 2015

GDP

80,000,000

100,000,000

120,000,000

140,000,000

160,000,000

180,000,000

200,000,000

220,000,000

1990 1995 2000 2005 2010 2015

POP

51

52

53

54

55

56

1990 1995 2000 2005 2010 2015

POV

3

4

5

6

7

8

9

1990 1995 2000 2005 2010 2015

UNEM

 
Figure-1. Real GDP, Population, Poverty and Unemployment. 

 

2. Literature Review 
The relationship between population growth, unemployment and poverty on economic growth, is a very 

common debate in the economic literature. Numerous empirical and theoretical studies have been carried out on the 
areas. Mamingi and Perch (2013) examined the effect of population growth on economic growth/development in 
Barbados for the period 1980-2010. Using the Autoregressive distributed lag approach to cointegration, the study 
revealed that population growth and population density positively and significantly affects economic growth, 
economic growth negatively and significantly affects population growth, natural increase rate positively and 
significantly affects population growth, also that net international migration negatively and significantly impacts 
population growth. The study failed to give any recommendations. The results from this study have policy 
implications on the grounds that, with increases in population comes a larger market and these are transformed into 
growing consumption accompanied by rise in investment which is the needed prerequisite for economic growth. 

 Njoku and Ihugba (2011) found that agriculture activities have the major contribution and the main source of 
employment in the Nigeria contributed more than the oil sector. Ukpong, Ekpebu, and Ofem (2013) study about 
poverty and population growth in Nigeria found that there is positive relationship between poverty rate and 
population growth, and negative relationship between GDP real growth rate and poverty rate in Nigeria while 
Maijamaa, Saidu, Muktari, and Nafisa (2019) studies the relationship between population growth and unemployment 
found that that population positively increase unemployment whereas foreign direct investment reducing the rate of 
unemployment in the long-run. Kurnianto, Rakhmasari, Ikhsan, Apriyanto, and Nurdin (2018) used secondary data 
source found that population growth, economic growth and unemployment, have significant impact on the poverty 
level in Indonesia. Mamingi and Perch (2013) study Barbados population growth found that population density have 
positive and significantly effect on economic growth. Tartiyus et al. (2015) used regression analysis the result reveals 
that there is a positive relationship between population and economic growth.  

Aiyedogbon (2012) used time series data found that unemployment, agricultural and services contributions to 
real GDP as well as population is the responsible increase the level of poverty in Nigeria. Osinubi (2005) argue that 
economic growth has not grantee to reduce the level of poverty and unemployment. Chisom and Oluchukwu (2017) 
study the relationship between poverty and unemployment found that poverty and unemployment have positive and 
significant correlation in Nigeria. Odeh and Okoye (2014) studies that poverty reduction policies in relation to youth 
unemployment found that that majority of the youth portion of the Nigerians lives below the poverty line, due to 
unemployment rate. Nwosa (2014) apply an Ordinary Least square (OLS) estimation technique found that 



Asian Business Research Journal, 2020, 5: 48-54 

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© 2020 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

government expenditure has positive and significant impact on unemployment rate while it has a negative and 
insignificant impact on poverty rate. Adewale and Ademola (2017) apply causality test found is two ways causality 
between unemployment rate and poverty in Nigeria. Also in Pakistan Meo et al. (2018) apply ARDL bound test 
found that there is asymmetries relationship between inflation, unemployment and poverty. Bala, Ayatu, and 
Maijamaa (2020) and Mansoor (2018) found that population growth has direct impact on energy generation and 
demand in Nigeria and Pakistan.  
 

3. Methodology 
This paper builds on Okun’s model by adding population and poverty to find their effect on economic growth of 

Nigeria. The study used annual time series data between 1980 and 2017. In addition, Eviews9 was used as statistical 
software for analyzing the available data, which has been transformed to their natural logarithm. All the data are 
sourced from World Development Indicators of World Bank (WDI).  
 

3.1. Unit Root Test  
Unit root test: in order to determine the level of stationarity of the variable unit root test has to be conducted 

which most usual and accepted test by (Augmented Dickey Fuller (ADF) and Philips Perron (PP). If the null 
hypothesis was not rejected of non-stationary at level, then the study will further test the variables in the first 
difference. 

Assume X to be any variable and the Augmented Dickey-Fuller (ADF) model can be defined as follows: 

                   

      

Where  is a pure white noise error term and , , 

, and i represents the number of recent time and j as the number of previous times or years. 

The hypothesis of Augmented Dickey Fuller ADF is  

 is non-stationary, (unit root) 

            is stationary, (no unit root) 

The first differencing in unit root test is to be tested if non-stationary time series Y need to be “differenced” at 
the times to make it stationary. Then the result can be stationary and correct, hence one can proceed to test for the 
co-integration. 
 

3.2. Autoregressive and Distributed Lag Co-Integration Bound Test 
After the test for the order of integration, the next step is to test for co-integration. This test is used to check if 

long run relationship exists among the variables in the model. This will be carried out using the ARDL Bound 
technique. 
 

3.3. Decision Rule 
Ho: α1 = α2 = α3 = α4 = α5 = 0 (there is no co-integration among the variables) 

H1: α1≠ α2 ≠ α3 ≠ α4 ≠ α5≠ 0 (there is co-integration among the variables) 
If the F statistics is below the I(0) we cannot reject Ho but if the F statistics is higher than the I(1) bound, then 

we reject Ho and accept the H1. This means that there is co-integration among the variables of study. 
The generalized ARDL (p, q) model is specified as: 

𝑌𝑡 =  𝛾0𝑖 +  Ʃ𝑖 = 1𝑝𝛿𝑖  𝑌𝑡 = 𝐼 + Ʃ𝑞𝑖 = 0 𝛽𝑖𝑋𝑡 = 𝐼 +  𝜇𝑖𝑡  

Where Y is a vector and the variables in (X) are allowed to be purely I (0) or I (1) or cointegrated; β and δ are 

coefficients; γ is the constant; i=1, p, q are optimal lag orders; μ is a vector of error terms unobservable zero mean 
while noise vector process (serially uncorrelated or independent). 
 

3.4. Model Specification 
From Okun’s model we develop a simple equation to capture the objective of this paper; 

𝐺𝐷𝑃𝑡  =  𝑓 (𝑃𝑂𝑃𝑡 , 𝑃𝑂𝑉𝑡, 𝑈𝑁𝐸𝑀𝑡 ,  𝐹𝐷𝐼𝑡) 
Transforming the function in to econometric model, we have the following: 

𝐺𝐷𝑃𝑡  =  𝛽0  +  𝛽1𝑃𝑂𝑃𝑡 +  𝛽2𝑃𝑂𝑉𝑡 + 𝛽3𝑈𝑁𝐸𝑀𝑡 + 𝛽4𝐹𝐷𝐼𝑡 +  휀𝑡 
If we transform it in to a log form, we have; 

𝑙𝑛𝐺𝐷𝑃𝑡  = 𝛽0 + 𝛽1𝑙𝑛𝑃𝑂𝑃𝑡 +  𝛽2𝑙𝑛𝑃𝑂𝑉𝑡 + 𝛽3𝑙𝑛𝑈𝑁𝐸𝑀𝑡 + 𝛽4𝑙𝑛𝐹𝐷𝐼𝑡 + 휀𝑡 

Where, 𝐺𝐷𝑃𝑡  is gross domestic product, which is the dependent variable 𝛽0 is expected to be positive. 𝛽0>1 

𝑃𝑂𝑃𝑡 is population growth with the rate at which the Nigerian population growing. Thus, its coefficient β1, is 

expected to be positive on economic growth i.e., β1 >0.  

𝑃𝑂𝑉𝑡 is poverty which is not having enough material possessions or income for a person's needs. Therefore, the 

coefficient is expected to be negative (β2 < 0). 

𝑈𝑁𝐸𝑀𝑡  is unemployment, this is number of people that have no job to do in Nigeria. Thus, its coefficient β3, is 

expected to be negative i.e., β3 <0.  

 𝐹𝐷𝐼𝑡  is foreign direct investment it is an investment in the form of a controlling ownership in a business in one 

country by an entity based in another country. Thus, its coefficient β4, is expected to be positive i.e., β4>0 
The unrestricted error correction model, (ECM) for ARDL is specified below:                                                                                                                                                                                                

tit

m

i

itt XXtX  ++++= −

−

− 
1

121

t )( 211 −−− −= ttt XXX )( 322 −−− −= ttt XXX

)( jtitit XXX −−− −=

tXH ,0:0 =

tXH ,0:0 



Asian Business Research Journal, 2020, 5: 48-54 

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© 2020 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

∆𝑙𝑛𝐺𝐷𝑃𝑡 = 𝛽1𝑡 + ∑ 𝛼1𝑖

  i=1

∆𝑙𝑛𝐺𝐷𝑃𝑡−𝑖 + ∑ β1𝑖

  i=0

∆𝑙𝑛𝑃𝑂𝑃𝑡−𝑖 + ∑ 𝛿1𝑖

  i=0

∆𝑙𝑛𝑃𝑂𝑉𝑡−𝑖 + ∑ 𝜒1𝑖

  i=0

∆𝑙𝑛𝑈𝑁𝐸𝑀𝑡−𝑖

+ ∑ 𝜕1𝑖

  i=0

∆𝑙𝑛𝐹𝐷𝐼𝑡−𝑖 + 𝜃1𝑙𝑛𝐺𝐷𝑃𝑡−1 + 𝜃2𝑙𝑛𝑃𝑂𝑃𝑡−𝐼  + 𝜃4𝑙𝑛𝑃𝑂𝑉𝑡−1 + 𝜃3𝑙𝑛𝑈𝑁𝐸𝑀𝑡−𝐼

+ 𝜃5𝑙𝑛𝑙𝑛𝐹𝐷𝐼𝑡−𝐼 + 휀𝑡   
 Where Δ is first difference operator and k is optimal lag length. 

Long-run equation  

𝑙𝑛𝐺𝐷𝑃𝑡 = 𝛽2𝑡 + ∑ 𝛼2𝑖

  i=1

𝑙𝑛𝐺𝐷𝑃𝑡−𝑖 + ∑ β2𝑖

  i=0

𝑙𝑛𝑃𝑂𝑃𝑡−𝑖 + ∑ 𝛿2𝑖

  i=0

𝑙𝑛𝑃𝑂𝑉𝑡−𝑖 + ∑ 𝜒2𝑖

  i=0

𝑙𝑛𝑈𝑁𝐸𝑀𝑡−𝑖

+ ∑ 𝜕2𝑖

  i=0

𝑙𝑛𝐹𝐷𝐼𝑡−𝑖 + 휀2𝑡  

Short-run equation  

∆𝑙𝑛𝐺𝐷𝑃𝑡 = 𝛽3𝑡 + ∑ 𝛼3𝑖

  i=1

∆𝑙𝑛𝐺𝐷𝑃𝑡−𝑖 + ∑ β3𝑖

  i=0

∆𝑙𝑛𝑃𝑂𝑃𝑡−𝑖 + ∑ 𝛿3𝑖

  i=0

∆𝑙𝑛𝑃𝑂𝑉𝑡−𝑖 + ∑ 𝜒3𝑖

  i=0

∆𝑙𝑛𝑈𝑁𝐸𝑀𝑡−𝑖

+ ∑ 𝜕3𝑖

  i=0

∆𝑙𝑛𝐹𝐷𝐼𝑡−𝑖 + 𝜆𝐸𝐶𝑇𝑡−1 

 
Error correction term (ECT) in equation 

𝐸𝐶𝑇𝑡 = 𝑙𝑛𝐺𝐷𝑃𝑡 − 𝛽2𝑡 − ∑ 𝛼2𝑖

  i=1

𝑙𝑛𝐺𝐷𝑃𝑡−𝑖 − ∑ β2𝑖

  i=0

𝑙𝑛𝑃𝑂𝑃𝑡−𝑖 − ∑ 𝛿2𝑖

  i=0

𝑙𝑛𝑃𝑂𝑉𝑡−𝑖 − ∑ 𝜒2𝑖

  i=0

𝑙𝑛𝑈𝑁𝐸𝑀𝑡−𝑖

− ∑ 𝜕2𝑖

  i=0

𝑙𝑛𝐹𝐷𝐼𝑡−𝑖 

Where λ is the coefficient of the error correction term which measures the speed of adjustment of the variables 
toward convergence to equilibrium In addition, the coefficient provides information about the long-run relationship 
among the variables. To complete the estimation process, diagnostic tests will be conducted to assess the reliability 
and efficiency of the estimates.  
 

4. Results and Discussions 
The major concern of this part is to presents analytical discussions on the results effort was made to establish a 

results and at the same time justify the authenticity of the statement analysis and interpretations of the data. Table 1 
shows the conducted Augmented Dickey-Fuller (ADF) test and the Phillips-Perron (PP) test results for more 
reliability and stationarity of the data. The T-statistic and the p-value of each of the tests must be observed. If the p-
value is less than (0.05) reject the null hypothesis of non-stationarity and accept the alternative hypothesis of 
stationarity, otherwise accept the null hypothesis of non-stationarity. The results of the tests revealed that GDP, 
poverty and Foreign Direct Investment are stationary after first difference that is I(1), while population and 
unemployment are stationary at level that is I(0). This indicate a mixture of order of integration that is I(0) and I(1). 
As such ARDL methodology is the most fitted methodology to apply. 
 

Table-1. Unit root test. 

 
 

ADF Test Statistics PP Test Statistics 

Constant Trend Constant Trend 

Level First 
Difference 

Level First 
Difference 

Level First 
Difference 

Level First 
Difference 

lnGDPt -0.783 
(0.811) 

-4.121 
(0.002)*** 

-2.223 
(0.462) 

-3.317 
(0.080)* 

0.866 
(0.993) 

-4.159 
(0.002)*** 

-3.946 
(0.019)** 

-3.843 
(0.025)** 

lnPOPt -6.102 
(0.000)*** 

-3.565 
(0.013)** 

-6.119 
(0.000)*** 

-1.731 
(0.710) 

-3.417 
(0.016)** 

-4.046 
(0.003)*** 

-3.931 
(0.020)** 

-3.445 
(0.061)* 

lnPOVt 0.573 
(0.986) 

3.202 
(1.000) 

4.079 
(1.000) 

2.421 
(1.000) 

5.816 
(1.000) 

-1.825 
(0.362) 

3.381 
(1.000) 

-4.869 
(0.002)*** 

lnUNEMt 

 

-3.786 
(0.006)*** 

-5.692 
(0.000)*** 

-4.138 
(0.012)** 

-5.672 
(0.000)*** 

-2.020 
(0.277) 

-6.370 
(0.000)*** 

-2.140 
(0.507) 

-6.903 
(0.000)*** 

lnFDIt -1.620 
(0.462) 

-11.383 
(0.000)*** 

-5.474 
(0.000)*** 

-11.245 
(0.000)*** 

-1.396 
(0.573) 

-11.169 
(0.000)*** 

-3.772 
(0.029)** 

-11.245 
(0.000)*** 

Note: ***, ** and * Denotes 1%, 5% and 10% significance level respectively. 

 

4.1. Cointegration Tests 
After determining the stationarity of the data, the next step is to estimate the long relationship among the 

variables. But before then, we have to determine the optimal lag to be used. Prior to carrying out the task, an 
optimum lag length for the model was selected based on Akaike Information Criterion (AIC) which suggested Lag 4 
for the model. The cointegration result presented in Table 2 shows that the model has cointegration as the 
calculated F statistics (9.261731) based on the optimum lags selected exceed the upper bound of the critical values 
table developed by Narayan and Narayan at 5%. This result indicates that the variables in the model have a long-run 
equilibrating relationship. As such, we could go on and estimate the long-run model of the ARDL specification. 
 
 
 
 
 



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Table-2. ARDL bound test results. 

                  Model F – Statistics Lag Level Of   
Significance 

Bound Test Critical Values 
(Constant Level) 

GDP    I(0) I(1) 

(POP, POV, UNEMP, FDI) 9.2617 4 10% 2.45 3.52 

   5% 2.86 4.01 
   2.5% 3.25 4.49 
   1% 3.74 5.06 

Note: Based on Narayan Table Case III  (Narayan, 2005). 

 

4.2. Long-Run and Short-Run Results  
Table 3 below presents the results of the long-run and short-run equations for the Model. The presence of long-

run equilibrium relationship among the variables as found from the ARDL bound test led to the application of 
ARDL. With this approach, both the long-run equilibrium and short-run dynamic relationships associated with the 
variables under the study are established. In the long run, population has a positive impact on GDP. A unit change in 
population will result in increase in GDP by 5.956. Population is remain the main factor of human capital accomplish 
which education, health and productivity. Poverty on the other hand has a negative impact on GDP and the 
relationship is statistically significance. Precisely 1 per cent increases in poverty will cause GDP to decrease by -
6.652. The level of poverty in Nigeria is high accomplished which inequality and corruption.  

Similarly, Unemployment has a negative and significant impact on GDP, 1 per cent increases in unemployment 
will result GDP to decrease by -0.312. This result sounds counterintuitive because unemployment leads to lower 
productivity, malnutrition, and high cost of living. Foreign Direct Investment has a negative and statistically 
significant impact on GDP. That is, 1per cent increases in Foreign Direct Investment will result in increase in GDP 
by 0.138. The FDI result similarly sounds counter intuitive like unemployment as FDI leads to more job 
opportunities, contribute to country’s GDP among the others. 

In the short run, population has a positive and significant impact on economic growth. That is a unit change in 
population will result increase in GDP by 11.053. Poverty on the other hand has a negative and insignificant impact 
on economic growth unlike in the long run case where the relationship is similarly negative but significant. That is 
1% increase in poverty rate will lead to -8.045 decreases in GDP. Unemployment has a positive and significant 
impact on economic growth which became counterintuitive to a priory expectation. That means 1% increase in 
unemployment will decrease GDP by 0.067. At last, Foreign Direct Investment has a negative and insignificant 
impact on economic growth unlike the long run case where the relationship is negative but significant. Similarly, it’s 
contrary to our expectation. That is 1% increase in FDI will result decrease in GDP by -0.002. Thus, some of the 
variables are insignificant in the short run. This shows that those variables take time before they can affect the 
economy. Further, the case of FDI might be because the amount of Foreign Direct Investment inflow in to the 
country is too low to affect the economic activities of the country in the shut run. All the variables conform with a 
priori expectation in the long run, so also in the short run except for unemployment and foreign direct investment 
where they have been explained above. 

The ECT coefficient (-0.757) substantiates the long-run relationship among the variables and denotes that the 
speed of adjustment of the variables’ convergence to equilibrium is 75.7%. The P-value of the F-statistics is less than 
5% (i.e. 0.001<0.05). This means the F-statistics is significant we therefore reject the null hypothesis and conclude 
that the explanatory variables are jointly significant in influencing the dependent variable GDP. The adjusted R2 is 
0.699%. This means that about 69.9% of the proportion of total variation in economic growth is explained by the 
explanatory variables, while only 30.1% of the variation is captured by the error term in the model. The Durbin 
Watson statistics is 2.320. The DW statistic reveals that there is no problem of first order autocorrelation because it 
falls between the range of 1.5 and 2.5.  
 

Table-3. Estimated long-run and short-run coefficients. 

Dependent Variable GDP Coefficient T Ratio (P-Value) 

Regressors   

Long Run Results   

LPOP 5.956 9.071(0.000)*** 
LPOV -6.652 -2.504(0.025)** 
LUNEM -0.312 -3.366(0.004)*** 
LFDI 0.138 3.441(0.004)*** 
C 52.792 4.994(0.000)*** 

Short Run Results   

∆LPOP 11.053 5.143(0.000) *** 
∆LPOV -8.045 -1.524(0.149) 
∆LUNEM 0.067 1.925(0.074)* 
∆LFDI -0.002 -0.179(0.859) 

ECT (-1) -0.757 -5.402(0.000)*** 

Adjusted R2 0.699  

Durbin Watson statistics  2.320  

F statistics 5.147(0.001)***  
Note: *Significant at 10% level. **Significant at 5% level. ***Significant at 1% level. 
 

 

4.3. Diagnostic Tests 
Like any other time-series analysis, diagnostic tests are imperative to assessing the validity, efficiency, and 

reliability of our model estimate. If the model passes the major diagnostic tests, then the estimate could be reliable. 
On the contrary, if the models could not surpass the major diagnostic tests, then the deduced inference would be 
void. The results in Table 4 denote that the Model have passed all the diagnostic tests. Therefore, on the general 



Asian Business Research Journal, 2020, 5: 48-54 

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© 2020 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

note, our models could produce a reliable outcome having passed all the diagnostic tests of autocorrelation, 
heteroscedasticity, normality and stability. 
 

Table-4. Diagnostic tests result. 

Type of Test F-statistics Probability 

LM 0.550 0.590 
Breusch-Pagan-Godfrey 0.903 0.586 

JB 0.459 0.794 
RESET 1.793 0.203 

 
As suggested by Pesaran, Shin, and Smith (2001) we have conducted a stability test for the model based on 

CUSUM and CUSUM of Squares tests. It is suggested that for a model to be stable along the sampled period, the 
residuals must be within the straight lines of the critical bounds at 5% significance level. The stability results are 
depicted in Figure 2. All the figures show that the model is fairly stable over the studied period because the residual 
lies within the straight line critical bounds at 5% level of significance. 
 

-12

-8

-4

0

4

8

12

04 05 06 07 08 09 10 11 12 13 14 15 16 17

CUSUM 5% Significance

-0.4

0.0

0.4

0.8

1.2

1.6

04 05 06 07 08 09 10 11 12 13 14 15 16 17

CUSUM of Squares 5% Significance  
Figure-2. CUSUM and CUSUM of squares test result. 

 

5. Summary and Conclusion 
This research explores the impact of population growth, poverty and unemployment on economic growth in 

Nigeria. The empirical review showed a strong link between population and unemployment and poverty on GDP. 
The study found that some of the variables are stationary at level, that is, I(0) while others are stationary at first 
difference, that is, I(1). The ARDL bound Cointegration test were applied and found the existence of long run 
relationship between variables. The results of the cointegration test showed that there exist cointegrating equation 
between explanatory variables and GDP. The ECT speed of adjustment to the normal equilibrium confirms their 
long run relationship of the variables. Finally, the study found that population and FDI have a positive impact while 
poverty and unemployment has negative impact on GDP. Based on these findings recommend that policy makers 
should grow the real economic sectors to improve and enhance productivity, exports, job creation, curb inflation and 
reduce poverty and rapid economic growth and substitute the non-productive imports with domestic products and 
develop enabling environment to attract foreign private investors. The diagnostic tests were also carried out to 
check the reliability of the model and the results shows that the model has passed all the diagnostic tests. That is, 
auto-correction, heteroscedasticity, normality and stability.  
 

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Citation: Umar Bala; Aminu Ibrahim; Nazeef Bala Hadith (2020). 
Impact of Population Growth, Poverty and Unemployment on 
Economic Growth. Asian Business Research Journal, 5: 48-54. 
History:  
Received: 22 September 2020 
Revised: 14 October 2020 
Accepted: 26 October 2020 
Published: 12 November 2020 
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Eastern Centre of Science and Education 
 

Acknowledgement: All authors contributed equally to the conception and 
design of the study. 
Funding: This study received no specific financial support.    
Competing Interests: The authors declare that they have no competing 
interests. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study was reported; that no vital 
features of the study have been omitted; and that any discrepancies from the 
study as planned have been explained. 
Ethical: This study follows all ethical practices during writing.  
 

Eastern Centre of Science and Education is not responsible or answerable for any loss, damage or liability, etc. caused in relation to/arising out of the use 
of the content. Any queries should be directed to the corresponding author of the article. 

 

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