




































American International Journal of Humanities, Arts and Social Sciences  

Vol. 1, No. 2; 2019 

ISSN 2643-0061   E-ISSN 2643-010X 

Published by American Center of Science and Education, USA 

 

47 

 

 

Impact of Disaggregated Public Expenditure on Unemployment 

Rate of Selected African Countries: A Panel Dynamic Analysis 

Approach 

 
Aphu Elvis Selase 

College of Public Administration 

Huazhong University of Science and Technology 

Wuhan, Hubei, 430074, China 

E-mail: elvis.qapito@yahoo.com 
 

 

Abstract 

The study demonstrated the impact of disaggregated public expenditure on unemployment rate in selected African 

countries with panel data spanning from 2000 to 2017. The data were majorly sourced from the World Bank 

Indicator. The study employed Generalized Method of Moments (GMM) techniques for empirical analysis. The 

findings of two-step system GMM showed that expenditure on infrastructure and education reduce unemployment 

rate, while expenditure on defense and health increase unemployment rate in the region. The short-run elasticity 

estimate showed that infrastructure and education expenditures reduce unemployment rate by 9% and 1.83%. A unit 

rise in defense and health expenditure increase unemployment rate by 5.2% and 84.5%. The long-run elasticity of 

infrastructure and education expenditure reduce unemployment rate by 3.8% and 7.89 %, while the long-run defense 
and health expenditure elasticity’s increase unemployment rate by 22.22% and 364.58% in the selected African 

countries. The policy implication is that, the positive relationship between expenditure on health and unemployment 

could be attributed to mismanagement of government funds due to corruption, while that of defense and 

unemployment could be high rate of insecurity and crimes in the region. Therefore, the study recommended among 

others a drastic measure to further improve the education sector through adequate investment in education that will 

help in skills, development and training. 

 

Keywords: African Countries, Expenditure Rate, Health, Defense, Education, Infrastructure, Unemployment. 

 

1. Background to the study 

Public expenditure plays an important role in aggregate economy in multiple dimensions and has remained a crucial 

issue in economic development, and most especially in the less developing countries of Sub-Saharan Africa (Peter, 

2015).Public expenditure has occupied a strategic position in various economies of the world and it is an important 

instrument in public sector policy. No economy exists without incurring public spending for the benefit of its 

citizens and to stimulate economic activities. In an underdeveloped country, public expenditure has an active role to 

play in reducing regional disparities, developing social overheads, creation of infrastructure of economic growth in 

the form of transport and communication facilities, education and training, growth of capital goods industries, basic 

and key industries, research and development, reducing unemployment rate and so on (Bhatia, 2002). 

Government role in the economy has been subjected to series of debate over the years. Some argue against large 
governments others believe that without government’s participatory role to guides the economy, countries could be 

endangered with unstable growth which may lead to prolonged recessions and massive rates of unemployment. 

Nwosa (2014) opined that the role of government includes the financial bail-outs of the entire economy or a 

particular sector of the economy which is to increase the government expenditure. But challenges still remain, 

despite increase in government spending especially for the structural transformations to create more jobs and reduce 

poverty by deepening investment in agriculture and developing agricultural value chains to spur modern 

manufacturing and services in African countries. 

African Economic Outlook (2018) portrayed that African countries growth rate have not been accompanied 

by high job growth rates, employment grew at an annual average of 2.8 percent between 2000 and 2008 roughly half 

the rate of economic growth. Algeria, Burundi, Botswana, Cameroon, and Morocco experienced employment 

growth of more than 4 percent. Between 2009 and 2014, annual employment growth increased to an average of 3.1 



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percent despite slower economic growth. But this figure was still 1.4 percentage points below average economic 

growth. Slow job growth has primarily affected women and youth (ages 15–24). Africa is estimated to have had 226 

million youth in 2015, a figure projected to increase 42 percent, to 321 million by 2030. The lack of job growth has 

retarded poverty reduction. Although the proportion of poor people in Africa declined from 56 percent in 1990 to 43 

percent in 2012, the number of poor people increased. Inequality also increased, with the Gini coefficient rising 

from 0.52 in 1993 to 0.56 in 2008. 
The effect of government expenditure on employment generation has been subject to considerable interest 

in recent years. There has been growing concern about the extent to which government expenditure has impacted the 

unemployment rate in African countries. The rising cost of governance remained a challenge by African countries; 

the public expenditure size has expanded which has generated interest in both developed and developing world to 

optimize the size of government. The need to provide and expand the tentacles of public goods becoming too 

obvious and unavoidable recognized, mismanagement and misappropriation of public expenditure in the economy 

cannot be underestimated, coupled with the pressing demand to expand and cater for the rising population via 

provision of employment opportunities. Employment is generated when job opportunities are provided by the 

government through their expenditure arm of the provision of social and economic infrastructural amenities in the 

economy. Hence, Jhinghan, (2008) opined that the provision of infrastructural facilities through public funds has 

dual purpose of generating employment opportunities directly while at the same time using the amenities towards 

encouraging the productive sectors in order to produce and provide employment opportunities for the 
populace/labour force (Araga, 2016).  Although, high rate of unemployment is not peculiar to less developed 

countries but also developed ones. The macroeconomic problem is severe in LDCs’ including African countries. 

Lack of employment opportunities aggravates unemployment situation in which some employable persons, 

in the labour force, with requisite qualifications, skills and ability are willing and seeking to work but cannot get 

jobs (Adawo, Essien and Ekpo, 2012). In related terms, deficiency in employment opportunities (Jhingan, 2008) 

leads to involuntary idleness of persons who are willing to work at the prevailing wage rate but unable to find work. 

The level of employment (Nwosa, 2014) measures the proportion of the available labour force that is employed in 

the economy. Amidst the unresolved foregoing controversies, most African countries are still faced with rising rate 

of unemployment where employable persons, in the labour force, with required qualifications, skills and ability are 

willing and seeking to work but cannot get jobs (Adawo, Essien and Ekpo, 2012). Therefore, the policy makers 

emphasized on the roles of public sector expenditure as important instrument which the government can apply to 
restore some economic problems such as reduction in inequality, poor living standards, high rate of unemployment, 

dwindling oil price and the desire to restore the economy on the part of full employment, increase in economic 

growth etc.  However, it has been argued that, the rising state of public expenditure contributed to employment 

generation, this has continued to generate series of debate among scholars, the empirical and theoretical positions on 

the subject is quite diverse and still remain mixed.  

According to empirical evidences of Estache, Ianchovichina, Bacon and Salamon, (2013); Holden and 

Sparrman, (2013); Faramarzi, Avazalipour, Khaleghi and Hakimipour, (2014); Carmignani, (2014), government 

expenditure can enhance the level of employment and reduce unemployment in both developed and developing 

countries. However in spite of the huge government expenditure being spent on productive sectors such as 

infrastructures, defense of the citizenry, education and healthcare in Africa, there has been continuous rise in the 

level of unemployment in the continent. Therefore, it is against these issues raised above that this study examine 

whether gross public expenditure has any impact on unemployment rate in selected African countries. Hence, the 
study provides answers to the impact of public expenditure of selected African countries on the unemployment. The 

study is structured to the following arrangement, section one captures the background to the study, section two 

focuses on detailed theoretical propositions and empirical review. Section three explains the method adopts to 

analyze the data while section four shows outcome of results and interpretations. Finally, section five entails 

summary, conclusion and policy recommendations. 

 

2. Literature Review 

2.1Theoretical Review 

The theory of employment has always centered on two major arguments and strand of literature, among them are 

classical and Keynesian theories of employment. At the forefront of this theory, the classical economists assumed a 

full employment of labour and the flexibility of prices and wages to bring about the full employment in the case of 
any deviation. The classical assumption of full employment is based on the belief that over-production and general 

unemployment are impossible. In case of any unemployment, it is believed to be abnormal and will not continue for 

long since there are economic factors (self-adjusting mechanism) that inherently work towards bringing it back to 



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equilibrium (Onodugoet al, 2017). To this end therefore, the economy does not need government intervention 

through spending to achieve full employment since there is the existence of full employment. 

Another strand of argument follows the Keynesian theory of employment which states that in the short run, 

economic growth through full employment is strongly influenced by total spending in the economy. Hence, the 

economy is being regarded as inherently unstable and required active government intervention through spending to 

achieve full employment. He is also of the view that public expenditures can contribute positively to economic 
growth by increasing government consumption through increase in employment, profitability and investment. This 

theory believes that active government intervention in the market place through government expenditure was the 

only method for ensuring full employment by ensuring efficiency in resources allocation and regulation of markets 

(Sangkuhl, 2015). 

In support of this theory, Abu and Abdullahi (2010) asserted that in the Keynesian model, an increase in 

government expenditure leads to a higher economic growth. Hence, fiscal policy is a technique to attain and 

maintain the level of full employment by manipulating public expenditure and revenue in such a way so as to keep 

equilibrium between effective demand and supply of goods and services. In like manner, Dewett and Navalur (2012) 

posit that if depression occurs, fiscal policy should help in increasing demand and an increase in demand leads to 

increase in output. As such, the government can increase its expenditure and spend more on public works which will 

provide employment to more people. And a budget deficit during a depression they believe is a positive help in 

fighting unemployment and stimulating output growth. 
This work will adopt Keynesian theory of employment just like Araga (2016), because (a) most empirical 

evidence revealed that government intervention is inevitable in every economy around the world today. This was 

demonstrated during the recent economic recession that lead government providing funds to bail out some failed 

banks in UK, USA, Nigeria, etc.(b) Government intervention is required in providing basic social and economic 

infrastructural facilities such as roads, schools, hospitals, etc. for the development of the economy(c) Government 

expenditures in capital public projects bring about the development of infrastructural facilities which can improve 

productive sectors of the economy and as such create employment opportunities for the populace, to mention but a 

few. 

 

2.2 Empirical Review 

2.2.1 Studies on the Relationship between Unemployment and Government Expenditures in Non-Africa 
Holden and Sparrman (2013) empirically analyzed the effect of government purchases on unemployment in 20 

OECD countries from 1980 to 2007. Using ex post factor methodology, the findings revealed that an increase in 

government purchases reduced unemployment by about 0.3 percentage point in the same year. The effect was also 

observed to be greater in downturns than in booms, while greater under a fixed exchange rate regime than a floating 

regime. Faramarziet al (2014) examined the long run impact of government expenditure and tax on liquidity and 

employment in Iranian economy with time series data spanning 1976-2009. Employing Vector Auto regressive 

model (VAR), Vector Error Connection (VECM) and co-integration techniques, the results indicate that government 

expenditure have positive impact on both employment and liquidity while tax has negative effect on employment.  

Monacilliet al. (2010) analyzed the effect of fiscal policy on labour market variables in the United States. 

Using a VAR model, the result showed that hour and employment also rise significantly in response to a government 

spending stock. Also, increase in government spending of 1 percent of GDP generated output and unemployment 

multiplier around 1.3 and 0.6 respectively, implying that each percentage point increase in GDP produces an 
increase in employment of about 1.3 million jobs. Kasau, et al (2015) examined the effect of government spending 

and investment towards job opportunities in Eastern and at the KBI both direct and indirect as well as the total 

influence in both regions from 2007 to 2013. The panel data was analyzed using SEM (Structural Equation 

Modeling) and the result revealed that government spending has significant positive effect on the Investment and 

Employment either indirectly or in total 

Aziz and Leruth (1997) analyzed the effect of changes in the composition of government expenditure 

between consumption and investment goods on the long run and short run fluctuations of the U.S economy. Using 

quantitative research methodology, the result revealed that the effects of changing the composition of government 

spending through government purchases can have efficiency effects as well as affect short run volatility of 

macroeconomic variables such as output and employment. Anthanasios (2013) using the SVAR methodology to 

analyze unemployment effects of fiscal policy in Greece, found a negative relationship between unemployment and 
government purchases and a positive relationship between tax and unemployment. In like manner, Tagkalakis 

(2013) examined the unemployment effects of fiscal policy changes in Greece from 2000-2012. Adopting the 

Blanchard and Perotti (2002) SVAR methodology, he found that unemployment reduced when there was an increase 



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50 

 

 

in government purchases, government consumption, the government wage bill and government investment, but it 

increased when there was a cut in government purchases and  its components. 

Mahmoodet al. (2014) investigated the causes of unemployment in Pakistan. They discovered that budget 

deficit significantly increased unemployment. The study had employed variance inflation factor analysis and 

Stepwise regression. Their results were similar to his conclusion as they found out that fiscal expansion increased 

output, private consumption and private investment and reduced unemployment. Battaglini and Coate (2011) 
explored the interaction between fiscal policy and unemployment in OECD countries with panel data from 2006 to 

2010. Using OLS of fixed effect technique, the result revealed that government spending has positive relationship 

with unemployment. Laokulrach (2013) examined the effect of fiscal policies on service sector employment in 

Thailand.  Adopting multiple regression method, he found out that fiscal policy had no significant relationship with 

employment rate. 

Umut (2015) examined the effect of fiscal policy in Netherland, adopted a VAR technique. The result 

showed that fiscal shocks exert significant impact on GDP, Unemployment rate, Consumption and Investment. 

Hence, unemployment rises in response to a fiscal contraction and falls to fiscal expansion. Samira and Khalil 

(2015) studied the effect of government civil expenditures on unemployment rate in Iran from 1997-2013. 

Employed Johansen co-integration test, (VAR) and VECM techniques. The result showed long run relationship and 

a negative impact on unemployment rate. 

 

2.2.2 Studies on the Relationship between Unemployment and Government Expenditures in Africa 

Nwosa (2014) explored the impact of government expenditure on unemployment and poverty rates in Nigeria for the 

period 1981 to 2011. Employing the OLS estimation technique, he observed that government expenditure 

significantly and directly influences unemployment rate but inversely and insignificantly affects poverty rate. 

Okoye, Evbuomwan, Modebe and Ezeji (2016) investigated the effect of fiscal deficit on unemployment in Nigeria 

from u used the vector error correction model (VECM) and granger causality test and found a significant negative 

and causal relationship. The study also applied the Ordinary Least Square econometric technique. Araga (2016) 

examined the implications of public expenditure pattern particularly in road infrastructure, agriculture sector, road 

construction, and education sector on employment rate in Nigeria from 1980-2014 by adopting the VECM and Co-

Integration. The result revealed that agriculture expenditure (AGREX) and road construction expenditure (RCEXP) 

have significant negative effect on employment (EMPR) while transport expenditure (TREXP) and education 
expenditure (EDEXP) have positive significant effect on rate of employment (EMPR). 

Emeka (2018) analyzed the Budget Deficit and Unemployment Nexus in Nigeria with a time series data 

spanning1997 - 2017. Employing linear regression and Vector Error Correction Mechanisms (VECM), the findings 

revealed that Government Annual Deficit has a significant positive effect on the Unemployment Rate in Nigeria. 

Murwirapachena, et al (2013) investigated the effect of fiscal policy on unemployment in South Africa from 1980 to 

2010. Employing vector error correction model and co-integration techniques, the findings showed that government 

recurrent expenditure and tax has positive relationship on unemployment whereas capital expenditure had a negative 

effect. 

Chimeziri (2016) examined the Effect of Federal Government Expenditure on Unemployment in Nigeria 

from 1981 to 2014. Using OLS technique, the result indicated that federal government expenditure variables 

(Expenditure on Administration, economic service, social and community service, and transfer) jointly have positive 

and significant impact on unemployment in Nigeria. Individually, only government expenditure on economic 
services affected unemployment significantly and negatively. Ubi and Inyang (2018) analyzed the fiscal deficit and 

its implication on Nigeria’s economic development from 1980 to 2016. Using quantitative technique, they observed 

that fiscal deficit did not reduce unemployment rate. 

Egbulonu and Amadi (2016) investigated the relationship between fiscal policy and unemployment rate in 

Nigeria for the period 1970 to 2013. Using co-integration test and a parsimonious Error Correction Model (ECM), 

the result showed a long run relationship between unemployment rate and fiscal policy tools (Government 

Expenditure, Government Debt Stock and Government Tax Revenue). Also there existed a negative relationship 

between expenditure and government debt and unemployment rate in Nigeria while government tax revenue 

indicated a positive relationship with unemployment rate. However, the granger causality test showed that there was 

no causality running from either of government expenditure or unemployment. 

Wosowei (2013) empirically studied the link between fiscal deficit and unemployment rate in Nigeria with 
time series data spanning 1980-2010. Using Ordinary Least Square and co integration techniques, the findings 

revealed a bi-directional causal relationship between unemployment and deficit. In a similar study employing the 

same method of analysis, Egbulonu and Amadi (2016) analyzed the fiscal policy and unemployment rate association 

in Nigeria from1970 to 2013. Their findings revealed a negative relationship between unemployment and fiscal 



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policy in long-run. Onodugo, et al. (2017) empirically examined the impact of public sector expenditures (CEXP 

and REXP) together with private sector investment (PINV) on unemployment in Nigeria from 1980 to 2013. Using a 

regression model Capital expenditure and private sector investment have negative effect on unemployment in the 

medium and long-run. 

Abubakar (2016) investigated the effect of fiscal policy shocks on output and unemployment in Nigeria 

under the Keynesian framework from 1981-215. Using the Structural Vector Auto regression (SVAR) methodology 
and co-integration, the result revealed that shocks to public expenditure have a long-lasting positive effect on output 

growth. Also revenue is found to reduce unemployment in the short run, while public expenditure is found to 

produce no significant effect on unemployment. Finally, there exist long run equilibrium relationships among the 

variables. Fagbohun (2017) examined the impact of budget deficit on economic performance in Nigeria from 1970 

to 2013.  Employing the least square method, he found that budget deficits did not increase the employment rate in 

Nigeria. In same manner a study carried out by Ayogueze and Anidiobu (2017) revealed that government budget 

deficit had had a positive and insignificant impact on unemployment rate in Nigeria within1986 – 2015. The 

methodology used was Ordinary Least Square Method. 

 

3. Data and Methodology 

3.1 Data and Measurement 

The selection of the sample period and countries are based on the availability of annual data, ranging from 2000 to 
2017. The selected African countries are classified by World Bank. Hence this work makes use of a balanced panel 

data of 20 African countries (four from each sub-region); Angola, Benin, Botswana, Cameroun, Central African 

Republic, Chad, Egypt, Equatorial Guinea,  Ethiopia, Ghana, Kenya, Mauritius, Morocco, Namibia, Nigeria, South 

Africa, Sudan, Tanzania, Togo and Tunisia. 

The study considered panel series data on real unemployment rate, defense expenditure, health expenditure 

and education expenditure obtained from World Development Indicator (WDI) online database which was published 

by the World Bank. The variables above are measured as follows; Unemployment Rate (UNEMP):   Unemployment 

refers to the condition of having no job. The International Labour Organization (ILO) defines the unemployed as 

numbers of the economically active population who are without work but available for and seeking work, including 

people who have lost their jobs and those who have voluntarily left work (World Bank, 1998). Unemployment rate 

is the percentage of the working population that is not currently employed. The percentage only takes into account 
the number of unemployed persons who are actively seeking employment. Those who are unemployed and not 

seeking jobs are considered to be “voluntarily” unemployed.  Annual growth of gross fixed capital formation 

(GFCF) based on U.S dollar. This includes plant, machinery, and equipment purchases; and the construction of 

roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and 

industrial buildings. Defense expenditure (DEXP) measured in U.S dollar, this is the military expenditure (% of 

general government expenditure). This includes all current and capital expenditures on the armed forces, including 

peacekeeping forces, defense ministries and other government agencies engaged in defense projects. Health 

expenditure (HEXP), this is the general government expenditure on education (current, capital, and transfers), is 

expressed as a percentage of total general government expenditure on all sectors (including health, education, social 

services, etc.). It includes expenditure funded by transfers from international sources to government. General 

government usually refers to local, regional and central governments. (Onuoha and Agbede, 2019). 

 

3.2 Model Specification 
Given that the goal is to investigate the dynamic relationship between public expenditures and unemployment rates 

in Africa.  Building on the works of Nwosa (2014) and Araga (2016), we exploit the cross section and time series 

dimension of our data by using the Generalized Method of Moments (GMM) estimation. The GMM developed by 

Hansen (1982), provides a convenient framework for obtaining asymptotically efficient estimators in this context, 

and first-differenced GMM estimators for the AR(1)panel data model were developed by Holtz-Eakin, Newey and 

Rosen (1988) and Arellano and Bond (1991). Hence, unemployment rate (unemp) depends on expenditure variables 

(expenditure on infrastructures-gfcf, defense expenditure-dexp, health expenditure-hexp and education expenditure-

edexp). The initial dynamic model which is autoregressive in nature is specified as; 

 

Yit = Yit−1 + βX’it + (  + εit)i=1,2……..,N,t=1,2……, T.(1) 

Re-writing with our variables, we have; 



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UNEMP it = UNEMPit−1 + βi1GFCFyit + βi2DEXPpit + βi3HEXPdit + βi3EDEXP +ditvi + ψt + εit                  

        (2)   

Where i denotes the country (i=1,y,…….20) and t denotes the time period (t=2000, y, 2017). Eq. (1) is a fairly 

general specification which allows for dynamic macroeconomic (unemp) effect, individual fixed country effects (v), 

fixed time effects (ψ), and a stochastic error term (ε), 

By apriori, 

Β1, β2, β3, β4<0 

Eq. (1 and 2) are examples of linear dynamic panel model (Arellano and Bond, 1991). This model contains 

unobserved panel-level effects which may be either fixed or random. By construction, the unobserved panel-level 
effects are correlated with the lag(s) of the dependent variable and this makes most standard estimation approaches 

inconsistent (Arellano and Bond, 1991).   

From the aforementioned details, to handle the econometric issues and control for the potential endogeneity of 

unemployment rate we have applied the dynamic panel estimator of Arellano and Bover (1995) and Blundell and 

Bond (1998). Although we could use an instrumental variable estimator for this purpose, this dynamic panel 

estimator also allows us to control for the endogeneity of all the other regressors in the model and at the same time 

control for the econometric problems that arise from the inclusion of the initial selected unemployment rate 

variables as an explanatory variable. This estimator involves estimating the equations in levels and in differences.   

For the levels equations lagged values of all explanatory variables are used as instruments while for the differenced 

equation we use the lagged values in levels of all explanatory variables as instruments. The two equations levels and 

differenced are then combined to give the GMM system estimators. These instrumental variables are called internal 

instruments because they rely on previous realizations of the explanatory variables and we test their validity using 
the Sargan test and their consistency using the second-order serial correlation test. 

 

3.3 The Long-run GMM Estimates 

The mathematical computation of the long run elasticity coefficient for the Kth parameter is specified as; βit  (1- ) 

where β is the short run coefficient of the explanatory variables,  is the coefficient of the lagged dependent 

variable. 

 

3.4 Justification of the utilization of the model 

The method of GMM is chosen because our panel is of N>T (N=20, T=18) size. However, two-step system GMM 

was chosen over one-step system GMM for the following reasons; 

 It is the augmented two-step difference GMM 

 It is more robust to one-step system GMM 

 It is more efficient and robust to treating heterosckedasticy and autocorrelation  

 

Following Bond (2001)’s rule of thumb for selection between Difference GMM or System GMM, decision is based 
on the following criteria: 

 Pooled OLS->  estimate biased upwards 

 FE->  estimate biased downward 

 Diff. GMM-> estimate lies below or close to FE estimate. It is biased downward and  

 Use system GMM estimator, 
Our model indicate that system GMM is preferable for analyzing our dynamic model. 

4. Empirical Results 

4.1 Selection between Difference GMM and System GMM 

Based on Blundell-Bond (2001) rule of thumb, the estimated one-step and two-step difference GMM are both less 

than fixed effect estimate. This implies that difference GMM is downward biased and as such Blundell and Bond 

(1998) proposed use of system GMM. 

 



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Table 1:  Bound test Estimators (involving Pool, FE, Diff. GMM and Sys. GMM) 

 

Estimators 

  Coefficients 

Pooled OLS 0.97345 

   

Fixed Effects 0.88352 

   

One-step Diff.GMM 0.72452 

   

Two-step Diff. GMM 0.60144 

   

One-step Sys. GMM 0.78624 

   

Two-step Sys. GMM 0.76818 

   

`Source: Author’s computation 

 

4.2 Two-Step System GMM Estimation Regression Results 

The results of the two-step system GMM estimation is considered more appropriate as indicated by the bound 
testresult in table 1proposed by Bond (2001).  The result indicates that a unit increase in gfcf and edexp bring about 

0.009 and 0.0183 decrease in unemp respectively. Also, a unit increase in dexp and hexp bring about 0.0515and 

0.8451increase in unemp respectively. Statistically, all the explanatory variables significantly influenced unemp 

(unemployment rates) in the selected countries of Africa. This implies that expenditure on infrastructure (gfcf) and 

education (edexp) reduce unemployment rates rate in the region under study, while expenditure on defense and 

health increase unemployment rate. The overall statistics is significant which implies that the variables are stable. In 

like manner, number of groups is greater than the number of instruments which means that the model is good.  

However, Sargan and Hansen tests of over identification restrictions indicate that p-values are not 

significant (0.78 and .803). This implies that we will not reject the null hypothesis and so we conclude that all 

instruments as a group are pure exogenous. Hence, the instruments used in the model are desirable.   

Finally, the Arellano-Bond tests for AR (2) in second order autocorrelation tests is insignificant (0.129). 

This means acceptance of null hypothesis and we conclude that error term of the differenced equation is not serially 
correlated at 2nd order. 

 

Table 2: Comprehensive GMM results 

Variable Pool 

Regression 

Fixed 

Effect 

One-step 

D.GMM 

Two-step 

D.GMM 

One-step 

sys. GMM 

Two-step System 

GMM 

unemp(-

1) 

0.9734*** 0.884*** 0.7245*** 0.6014** 0.7862*** 0.7682*** 

 (0.0000) (0.0000) (0.005) (0.041) (0.000) (0.000) 

Gfcf -0.0086*** -0.0089* -0.0067 -0.0049 -0.008* -0.009** 

 (0.001) (0.07) (0.176) (0.318) (0.076) (0.044) 

Dexp 0.0053 0.0182 0.0062 0.0102 0.046 0.0515** 



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 (0.331) (0.257) (0.657) (0.459) (0.135) (0.043) 

Hexp 0.1679*** 0.1338*** -0.2525** -0.2514** 0.8068* 0.8451*** 

 (0.012) (0.008) (0.024) (0.037) (0.07) (0.002) 

Edexp -0.0072* -0.0099 0.0184 0.0096 -0.0181** -0.0183*** 

 (0.076) (0.432) (0.185) (0.451) (0.054) (0.010) 

Diagnostic test      

AR(1)   0.126 0.205 0.004 0.004 

AR(2)   0.075 0.091 0.092 0.129 

Sargan test  0.316 0.316 0.780 0.78 

hansen test  0.335 0.335 0.803 0.803 

Obs 323 323 304 304 323 323 

Prob>F 0.000  0.000 0.0015 0.000 0.000 

No of Groups  19 19 19 19 

No of instruments   6 6 8 8 

***designate the significance at 1% significance level, **designate the significance at 5% significance level while 

*designate the significance at 10% significance level. The regression coefficients are estimated using the Arellano 

and Bover (1995) and Blundell and Bond (1998) Two-step System GMM estimation approach. AR(1) and AR(2) are 

Arellano and Bond (1991) tests for autocorrelation indifferences. Sargan test (Arellano and Bond (1991)) and 

Hansen test for over-identification restrictions. p values for these tests shown in parenthesis. Estimation uses the 

xtabond2 (Roodman, 2009) and two-step robust no diff sarganin stata 15. GMM type instruments for the difference 
equation include fourth and fifth lags of unemployment rate and collapse.  Standard-type instruments for the 

difference equation include the first differences of  gfcf, dexp, hexp, edexp, variables. GMM-type instruments for the 

level equation include the lagged first difference of unemployment rate variable and collapse option. 

Source: Authors Computations 

4.3 Unemployment rate variable Elasticity Estimate Calculated Using the Estimates of Table 2 

Table 3:  Long run GMM Elasticity Estimates 

 Unemp prob* 

Short 

run 

  

Gfcf -0.009** (0.044) 

   

Dexp 0.0515** (0.043) 

   

Hexp 0.8451*** (0.002) 

   

Edexp -

0.0183*** 

(0.010) 

    

Long 

run 

  

Gfcf -0.0388  



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Dexp 0.2222  

   

Hexp 3.6458  

   

Edexp -0.0789   

***designate the significance at 1% significance level, **designate the significance at 5% significance level while 

*designate the significance at 10% significance level. 

Source: Author’s computation 

 

4.4 Analysis of Short and Long-run Elasticity 
The short-run unemployment rate elasticity indicates that a 1% increase in gfcf and edexp reduced unemp by a value 

of 9% and 1.83% respectively. Also, the short run dexp and hexp elasticicies are 0.0515 and 0.8451 which implies 

that a 1% increase in dexp and hexp increase unemp by a value of 5.2% and 84.5%respectively. The long-run 

elasticities are obtained by dividing the short-run elasticities by one minus the estimated coefficient on the lagged 

UNEMP variable. The long-run gfcf and edexpelasticitiesare0.0388 and 0.0789indicatingthat a 1% increase in gfcf 

and edex produced unemp by a value of 3.8% and 7.89 % respectively. Also, the long-run dexp and hexp elasticities 

are 0.2222and 3.6458which indicate that a 1% increase in dexp and hexp increased unemp by 22.22% and 364.58% 

respectively.  

 

4.5 Discussion of Findings  

The short-run unemployment rate elasticity indicates that a 1% increase in gfcf and edexp reduced unemp by a value 

of 9% and 1.83% respectively. The finding corroborates with the study of Okoye et al (2016). Also, the short run 
dexp and hexp elasticicies are 0.0515 and 0.8451 which implies that a 1% increase in dexp and hexp increase unemp 

by a value of 5.2% and 84.5% respectively, this finding is in line with the work of Chimeziri (2016). Also, the long 

run effects of gfcf and edexp on unemp are 0.0388 and 0.0789. This means that a percent change in infrastructural 

expenditure (gfcf) and education expenditures (edexp) are associated with 0.0388% and 0.0789% reduction in 

unemployment rate in the long run. This finding is in agreement with the studies of Mahmood et al(2014) and 

Samiral and Khalil (2015) but against the work of Araga (2016) in terms of infrastructural expenditure. Hence, 

infrastructural and educational expenditures have larger inverse effect on unemp in the long run (0.0338 and 0.0789) 

than in the short run (0.009 and 0.0183). On the other hand, the long run effects of dexp and hexp on unemp are 

0.2222and 3.6458. This means that a percent change in defense expenditure (dexp) and health expenditures (hexp) 

are associated with 0.2222%and 3.6458% increase in unemployment rate in the long run, as established  by 

Faramarzi et al (2014) study. Hence, defense and health expenditures have larger positive effect on unemp in the 
long run (0.2222and 3.6458) than in the short run (0.0515and 0.8451), this result is in line with the studies of 

Murwirapachena et al (2013) and Emeka (2018). 

 

5. Summary, Conclusions and Recommendations 

The major objective of this research work is to examine the impact of gross public expenditure on unemployment 

rate in selected African countries with panel data from 2000 to 2017. The study employed dynamic panel Approach 

of two-step system Generalized Method of Moments (GMM) techniques for empirical analysis. The findings from 

the two-step GMM result shows that gross fixed capital formation and education expenditure have an inverse 

relationship with the unemployment rate in selected African countries. The study also finds that expenditure on 

defense and healthincrease unemployment rate in the region. However, all the variables investigated are statistically 

significant.The inability of defense and health expenditure to meet up with a priori could be attributed to high rate of 

insecurity and crime as a result of joblessness, and mismanagement of funds meant for health sector due to 
corruption in the region. In conclusion, the study unravelled that unemployment rate in selected African countries 

had created the emergence of militants groups, constituting hiccups to security of lives and properties in the region. 

Therefore, the study recommends stiffer constraints for cases of mismanagement of government funds by 

economic managers in order to limit the occurrence of repeated cases. Also, adequate attention should be given to 

infrastructural development in order to build up productive capacity through government expenditure. There is need 

for drastic measures to improve the educational sector through adequate investment in education that will help in 



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skills development and training. Finally, more effort should be given to the health sector at all levels with the 

government and private sector in order to improve the capacity for additional opportunities.  

 

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