



































Impact of Electricity Loss on Gross Domestic Product in Nigeria


American Interdisciplinary Journal of Business 

and Economics 
ISSN: 2837-1909| Impact Factor : 4.6 

Volume. 9, Number 2; April-June, 2022; 

Published By: Scientific and Academic Development Institute (SADI) 

8933 Willis Ave Los Angeles, California 

https://sadipub.com/Journals/index.php/aijbe 

 

 

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IMPROVING TRANSMISSION AND DISTRIBUTION 

INFRASTRUCTURE TO REDUCE ELECTRICITY LOSS AND BOOST 

GDP IN NIGERIA 

 

 

Akindele Samson 

Department of Economics, Trinity University, Lagos, Nigeria 

 

Abstract: This study examines the impact of electricity loss on the economic growth of Nigeria from the 

period 1981 to 2020. The study employs a Vector Autoregressive (VAR) model to estimate the effects of 

electricity loss, electricity demand, and electricity supplied from hydropower on the Gross Domestic Product 

(GDP) of the country. The results of the study reveal that electricity loss has a negative impact on the GDP, 

while electricity demand has a positive impact on GDP, and electricity supplied from hydropower negatively 

affects GDP. However, the study finds that there is no causal relationship between electricity loss, electricity 

demand, and electricity supply, and GDP in Nigeria. The study recommends a deliberate policy to stimulate 

investment in the transmission and distribution infrastructure to reduce electricity losses in the country. The 

study highlights the different forms of electricity losses, which can be technical or non-technical, and 

categorizes them into transmission and distribution losses. The study suggests that decreasing electricity losses 

leads to an increase in GDP through an increase in distribution company revenue and an increase in the 

quantity of electricity reaching the end-users. 

Keywords: electricity loss, economic growth, Gross Domestic Product (GDP), Vector Autoregressive (VAR) 

model, transmission and distribution losses, Nigeria. 

 

Introduction 

Electricity is a vital input for economic growth and development in all sectors of the economy. However, it is 

a well-known fact that many countries, especially developing countries, experience significant losses in the 

electricity sector due to various reasons such as outdated infrastructure, vandalism, and theft. These losses not 

only impact the quality and availability of electricity to households and businesses but also impede the 

economic growth of these countries. Nigeria, being the most populous country in Africa, has experienced 

frequent electricity outages that have had severe consequences on the economy. This study aims to investigate 

the impact of electricity loss on the Gross Domestic Product (GDP) of Nigeria from 1981 to 2020, using a 

Vector Autoregressive model. The study considers electricity loss, electricity demand, and electricity supplied 

from hydropower as the primary variables affecting GDP. The study provides policy recommendations to 

reduce electricity losses and increase economic growth in Nigeria. The remainder of the study is divided into 

five sections. The next section presents a review of the empirical literature on electricity loss and economic 

growth. The third section outlines the theoretical framework underpinning this study, while Section four 

presents the methodology and model specification. Section five provides sources and data for the study, and 

the final section presents and discusses the results alongside the policy recommendations for reducing 

electricity losses and increasing economic growth in Nigeria. 

There are different degrees of electricity loss along the transmission lines. Chint (2020) showed that as the 

electricity is being generated and stepped up by the transformer, about 1 to 2 percent of generated energy is 

lost. Another 2 to 4 percent is again lost in the transmission lines, while another 1 to 2 percent is lost during 

step down from the high tension wires. More importantly, about 4 to 6 percent is lost in the distribution process 



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to the end users. On the whole, about 8 to 15 percent of electricity is lost between the power plants and the 

end users. This overall percentage loss implies a heavy dampening effect on Gross Domestic  

Product through firms’ productivity. Dakpogan and Smit (2018) estimated the effect on GDP from 0.5 to 1.2 

percent in some Sub-Saharan African countries. 

A comparison of electricity loss in Nigeria with some other countries can be observed in figure one. The 

country with the highest volatility of losses is Nigeria followed by Ghana. The highest for electricity loss in 

Britain was 9.8 percent in 1994 while the highest loss in Nigeria was 49.27 percent in 1981. The lowest loss 

that Nigeria has ever had was 4.5 in 2009. This amount of electricity loss in Nigeria is quite worrisome and 

could be a factor responsible for the incessant power outages in the country and this has grave consequences 

on productivity in the country and attendant effect on GDP growth. 

Figure 1: Electricity Loss in some selected countries  

 
Source: Author generated using Excel 2010  

Losses in electricity transmission and distribution in the Latin American and the Caribbean countries was 

estimated at between 0.19 and 0.3 of GDP for all the countries and the losses in terms of monetary value 

ranges between $11 to $17 billion (Jiménez, Serebrisky and Mercado, 2014). On a country by country analysis, 

electricity loss in Brazil cost about 0.26 of GDP, while that of Mexico is between 0.12 and 0.3 (Jiménez, et al, 

2014). These authors estimated technical losses in Uruguay at $80 million as at 2010. The implication of these 

is that as the monetary losses increase, it finds its expression in reduction of productivity and consequently on 

economic growth. 

A reduction in electric power losses leads to increase in GDP through increase in the revenue of the distribution 

companies, as a result of increase in the quantity of electricity that reaches the end users. This increase in 

electricity supply will increase its consumption and a reduction in the use of alternative sources of electricity, 

like generators, which in turn reduces the cost of production and raises profits of firms and expands investment 

potentials. 

This work is particularly important because, to the best of the author’s knowledge, most of the works done on 

electricity loss in Nigeria have only considered the effect of the loss on power outages in the country using 

descriptive analysis without a sound methodological approach. None of the works have investigated the impact 

of electricity loss on the Gross Domestic Product of the country. The remaining part of this work will be 

divided into six sections. The following section will handle the recent empirical literature, while the following 

section three will present the theoretical framework underpinning this study. Section four will show the 

methodology and the model specification while section five will show the sources of data for the work and 

section six will present the result and then analyze. Section seven will then summarize and give policy 

recommendations. 

Literature Review 

Das and McFarlane (2021) investigated the effect of remittances on electricity consumption and electricity 

losses in Jamaica between 1976 and 2014. The work employed Vector Error Correction model and Granger 

causality to analyze the work. The findings revealed that cointegration existed between remittances and 

electricity consumption and losses. It was also discovered that there was a bi-directional causality among the 

variables; however, the relationship between electricity losses and remittances was negative while that 

between energy consumption and remittances was positive. It was therefore recommended that social policies 

be designed to make use of remittances to reduce electricity theft in the country. 

Adams, Atsu, Klobodu and Lamptey (2020) employed Autoregressive Distributed Lag model to evaluate the 

effect of transmission and distribution losses on the growth of South African economy using time series data 



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between 1971 and 2014. Their findings showed that there existed a long run relationship among the variables 

by using Foreign Direct Investment as a control variable. By the time the robustness check was carried out it 

was found that 1 percent change in electricity loss in the country results in a decline in economic growth from 

about 3.8 percent to 2.2 percent. It was therefore recommended that policy makers should invest more in 

energy production and infrastructure to help reduce electricity loss due to technical factors. 

George-Anokwuru and Ekpenyong (2020) did an empirical study on relationship between electricity and 

economic growth in Nigeria between 1971 and 2018 and using consumption of electricity, generation along 

with transmission and distribution losses. The work employed Autoregressive Distributed Lag model, the 

findings revealed that electricity generation and distribution losses have negative relationship with economic 

growth and electricity generation has a negative relationship with economic growth. It was therefore suggested 

that production and transmission infrastructure expenditure be encouraged to boost energy consumption and 

reduce the losses. 

Dakpogan and Smit (2018) looked at the effect of electricity loss on Gross Domestic Product (GDP) in Benin 

Republic between 1980 and 2014 by employing the methodology of Autoregressive Distributed Lag model. 

The findings of the study showed that a 1 percent loss in electricity in the country would cost about 0.16 

percent decline in GDP. It was therefore proposed that a financing mechanism on electricity infrastructure to 

reduce the losses from electricity in order to increase GDP be designed and implemented.  

Adams, Klobodu and Lamptey (2017) carried out an examination of how electricity loss from transmission to 

distribution affects GDP growth in Ghana between 1971 and 2012. The work employed Autoregressive 

Distributed Lag model using Bounds test for cointegration and their findings revealed that there was a long 

run relationship between electricity loss and economic growth in the country. It was further discovered that 

electricity loss did not show any significant effect on economic growth but when the urban population growth 

was factored in, it was discovered that electricity loss became significant in affecting economic growth in 

Ghana. 

Costa-Campi, Daví-Arderius and Trujillo-Baute (2016) carried out an analysis of the impact of consumption 

and generation on electricity losses in Spain. The study used a quantitative analysis of the marginal effect of 

losses in Mega Watts from an additional Mega Watts produced or consumed. It was found that there is a huge 

amount of saving that will be made when losses can be reduced by using smart meters. It was found out from 

the result that a 1 percent reduction in loss of electricity will lead to about 1.25 percent in saving. It was also 

discovered that in increase in solar and wind capacity would reduce energy losses. It was therefore 

recommended that a stronger coordination between the transmission and distribution system operators be 

encouraged. 

Theoretical Framework 

The work of Samuelson and Nordhaus (1989) on growth will be the framework on which this study will rest. 

The mainstream theory of economic growth claims that production plays the most important role of 

determining the growth of an economy, and every production process requires energy to be able to transform 

any matter into finished product. The theory classifies capital, labour and land into primary factors that must 

be obtained at the beginning of any production and they are not used up although they can be degraded or 

improved upon. But the theory asserts that energy resources or commodities like oil, gas fuels, electricity and 

coal are classified as intermediate inputs and can be completely used up in the production process. This theory 

helps us to explain that production can be influenced by the availability of electricity. The more the productive 

agents have access to electricity the more productivity can be enhanced. Increase in energy loss will imply 

shortage of electricity supply and consequently the negative implication on productivity and growth. 

Methodology and Model Specification 

This study employed an ex post facto research design by trying to interrogate the relationship between 

electricity loss and economic growth in Nigeria. An econometric method of Vector Autoregressive (VAR) 

model will be employed. The reason is that this method of estimation is to be used when all the variables are 

stationary at first difference or integrated at order one, I(1). The data were subjected to unit root test using 

Augmented Dickey Fuller test method and it was found that all variables were integrated at I(1). The first step 

after confirming the order of integration is to test for the long run relationship among the variables and this 

was done using Johansen cointegration test and it was found that there was no cointegration, and so the work 

has to employ Vector Autoregressive model. 



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The model for this work is hinged on the growth model proposed by Samuelson and Nordhaus (1989) where 

the Cobb-Douglas production function depends on labour and capital and energy as specified below: 

Output = f (Labour, Capital, Energy) …  (1)  

Qt = wLa CB E1-a-B  … (2)  

Where is the output, is the total factor productivity, L is the labour input, C is the capital input and E is the 

energy input, , and are the share of the factor inputs in income. 

Adapting model (2) and incorporating the electricity components that is the focus of this study, we then have: 

GDP=f(ELELOS,ELEDD,ELEHP)     … (3) 

Expressing (3) econometrically, we have:  

RGDPt = a + B1ELELOSt + B2ELEDDt + B3ELEHPt + ut   …   (4)  

Where RGDP is the real Gross Domestic Product, ELELOS is the electricity loss both at the transmission and 

distribution lines, ELEDD is electricity demand in total and ELEHP is electricity supplied from hydropower 

source, is the error term. 

4.1 Specification of Vector Autoregressive Model 

The basic form of a VAR model consists of a set of M variables that are endogenous: 

gt = g1t , ..,gmt, For m = 1, …M. When the lags p of the endogenous variables is included, we have VAR(p) 

expresses as: 

gt = B1gt-1 + … + Bpgt-p + EFt + Et …   (5)  

Where are (M x M) coefficient matrices for i =1, …., p, is an M-dimensional white noise with time invariant 

positive definite covariant matrix , where 

E[Et] = 0  

  whose value is positive and definite 

The matrix E is the matrix of coefficients of possible deterministic regressors having dimension (M x N), 

while F is a column vector containing deterministic regressors like constant, trend, dummy and seasonal 

variables. 

Equation (5) can be expressed VAR with the variables of interest in this work as: 

𝑅�𝐺�𝐷�𝑃�𝑡��=�∑𝒌�𝒊�=𝟏��𝛽�𝑖��𝑅�𝐺�𝐷�𝑃�𝑡�−1�+ ∑𝒌�𝒋�=𝟏��𝛽�𝑗��𝐸�𝐿�𝐸�𝐿�𝑂�𝑆�𝑡�−1 + ∑𝒌�𝒏�=𝟏��𝛽�𝑛��𝐸�𝐿�𝐸�𝐷�𝐷�𝑡�−1 + 

∑𝒌�𝒎�=𝟏��𝛽�𝑚���𝐸�𝐿�𝐸�𝐻�𝑃�𝑡�−1�+� u1t      …          (6)  

    …          (7)  

𝐸�𝐿�𝐸�𝐷�𝐷�𝑡��=�∑𝒌�𝒊�=𝟏��𝛽�𝑖��𝑅�𝐺�𝐷�𝑃�𝑡�−1�+ ∑𝒋�𝒌�=𝟏��𝛽�𝑗��𝐸�𝐿�𝐸�𝐿�𝑂�𝑆�𝑡�−1 + ∑𝒌�𝒏�=𝟏��𝛽�𝑛��𝐸�𝐿�𝐸�𝐷�𝐷�𝑡�−1 

+ ∑𝒌�𝒎�=𝟏��𝛽�𝑚���𝐸�𝐿�𝐸�𝐻�𝑃�𝑡�−1 + u3t     …          (8)  

𝐸�𝐿�𝐸�𝐻�𝑃�𝑡��=�∑𝒌�𝒊�=𝟏��𝛽�𝑖��𝑅�𝐺�𝐷�𝑃�𝑡�−1�+ ∑𝒋�𝒌�=𝟏��𝛽�𝑗��𝐸�𝐿�𝐸�𝐿�𝑂�𝑆�𝑡�−1 + ∑𝒌�𝒏�=𝟏��𝛽�𝑛��𝐸�𝐿�𝐸�𝐷�𝐷�𝑡�−1 

+ ∑𝒌�𝒎�=𝟏��𝛽�𝑚���𝐸�𝐿�𝐸�𝐻�𝑃�𝑡�−1 + u4t  
    …          (9)  

where the variables remained as defined earlier. 

Data and Sources 

Annual time series data will be used for this study and it will cover the period between 1981 and 2014. Real  

Gross Domestic Product data is obtained from the National Bureau of Statistics of the country while data for 

Electricity Loss, Electricity demand, and Electricity from hydropower are all sourced from the World Bank 

Development Indicator. Electricity loss, electricity demand and supply are expressed as percentage of output. 

Table 1: Variables, Definition and Source 

No  Variable  Definition  Source  

1  RGDP  Real Gross Domestic product  National Bureau of Statistics 

2  ELELOS  Electricity Loss  WDI  

3  ELEDD  Elecricity Demand  WDI  

4  ELEHP  Elecridity supply from Hydropower  WDI  

Note: WDI is World Development Indicator Data base of World Bank  



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Results and Discussion  

6.1 Unit Root Test 

The variables were tested for the presence of unit root, because time series are assumed to be stationary.  

Table 2: Stationarity Test (Augmented Dickey Fuller) 

  At Level  At First Difference  

Variable  ADF Stat  5 % Level  Prob. Value  ADF Stat  5 % Level  Prob. Value  Order of Integration 

RGDP  0.650415  -2.92245  0.9898  -5.95836  -2.92378  0.0000  I(1)  

ELELOS  -1.45922  -2.92517  0.5453  -6.54566  -2.92517  0.0000  I(1)  

ELEDD  -1.48747  -2.92245  0.5316  -9.39097  -2.92378  0.0000  I(1)  

ELEHP  -2.70943  -2.92245  0.0797  -8.98493  -2.92378  0.0000  I(1)  

Source: Author generated using Eviews 10 

The result of the test is in table 1. It showed that all the variables are stationary at first difference with all the 

probability values less than 5 percent. 

6.2 Optimum Lag Selection 

The lag selection criteria were used and all the criteria selected lag length one as indicated in table 3.  

Table 3: Optimum lag length selection  

Lag  LogL  LR  FPE  AIC  SC  HQ  

0  -1080.21  NA  4.89e+14  45.17536  45.33129  45.23428  

1  -908.892  306.9428*  7.59e+11*  38.70382*  39.48348*  38.99845* 

2  -895.812  21.25386  8.70e+11  38.82551  40.22891  39.35586  

*Lag length selected.  

Source: Author generated using Eviews 10  

6.3 Cointegration test  

From the result of the unit root test results, the test for the existence of long run relationship among the 

variables was carried out using Johansen cointegration test method as shown in table 3. 

Table 4: Cointegration (Johansen) 

  Trace Test  Max-Eigen Test  

  

Null: No of Coint.  

      
 5 % Prob.  

Trace Statistic  

Level Value  

 5 %    

Max-Eigen Level Prob.  

Statistic Value  

None  
32.62383  47.85613  0.5777  14.44681  27.58434  

0.7906  

At most 1  
18.17702  29.79707  0.5530  10.56215  21.13162  

0.6907  

At most 2  
7.614869  15.49471  0.5074  7.600756  14.26460  

0.4207  

Source: Author generated using Eviews 10 

The condition for accepting the null hypothesis is that if the probability value of the first null hypothesis is 

greater than 5 percent critical value, we accept the null hypothesis that there is no cointegration. The 

probability is 79 percent, so we conclude that there is no cointegration among the variables and we can only 

run the VAR at level. 

6.4 VAR Impulse Response Function  

The VAR models (6) to (9) were estimated and the impulse response functions were generated as shown in 

figure 2 to 4. 

Figure 2: Response of RGDP to electricity loss 



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Source: Author generated using Eviews 10 

The response of GDP to a shock in electricity loss as shown in figure 2 assumes a negative dimension from 

period one to period 10 of the horizon. This implies that GDP and electricity loss are negatively related and 

significant. It also means that as more electricity is lost through the technical and non-technical channels, it is 

manifesting a dampening effect on the nation’s productivity. This finding is in support of most of the works 

from other countires, such as Dakpogan and Smit (2018) and George-Anokwuru and Ekpenyong (2020). 

Figure 3 on the other hand showed the response of GDP to a shock in electricity demand. It showed that GDP 

is positively responding to electricity demand. This implied that if more electricity is available for 

consumption it has a positively strong impact on GDP. It is obvious that the more the available electricity for 

consumption, the more positive ripple effect it will have on GDP of the country. 

The response of GDP to a shock in electricity supply from hydropower is shown in figure 4 and it revealed 

that GDP is negatively related to electricity supply from hydropower. It is expected however, since electricity 

loss is huge, it means the available electricity supplied will not be able to influence GDP significantly.  

Figure 3: Response of RGDP to electricity demand 

 
Source: Author generated using Eviews 10 

Figure 4: Response of RGDP to electricity supplied from hydropower  

 
Source: Author generated using Eviews 10 

6.5 Residual Diagnostics 

The VAR model was checked if the assumptions of Ordinary Least Squares are satisfied. Table 4 showed that 

the model is free from serial correlation among the residuals and that is makes the model suitable for policy 



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prescription and forecast. But the errors in the model are not free from heteroscedasticity and they are not 

normally distributed. 

Table 5: Residual Diagnostic tests 

VAR Residual Serial Correlation LM Tests  

Lag  LRE stat  df  Prob.  Rao F-stat  df  Prob. value 

1  21.23046  16  0.1698  1.366627  (16, 13.7)  0.1711  

VAR Residual Heteroskedasticity Tests (Levels and Squares)  

Joint test:        

Chi-sq  df  Prob.    

139.5899  80  0.0000    

VAR Residual Normality Tests      

Component  Jarque-Bera  df  Prob.    

RGDP  701.0944  2  0.0000    

ELELOS  3.775582  2  0.1514    

ELEDD  3.806633  2  0.1491    

ELEHP  33.81916  2  0.0000    

Joint  742.4958  8  0.0000    

Source: Author generated using Eviews 10 

6.6 Granger Causality test 

It was shown in table 5 that none of the independent variables could cause GDP with all their probability 

values more than 5 percent significant level. This implies that even though electricity loss is negatively 

impacting GDP, there is no causality running from electricity loss to GDP. Similarly, electricity demand and 

supplied from hydropower could not Granger cause GDP. 

Table 6: VAR Granger Causality test 

Dependent variable: RGDP     

Excluded  Chi-sq    df  Prob.  

ELELOS  0.238600   1  0.6252  

ELEDD  1.609738   1  0.2045  

ELEHP  0.335856   1  0.5622  

All  3.975535   3  0.2641  

Source: Author generated using Eviews 10 

Conclusion and Policy Implications  

This study investigated the impact of electricity loss on Real Gross Domestic Product in Nigeria along with 

electricity demand and supplied from hydroelectric power. It was found that there was no long run relationship 

among the variables, Vector Autoregressive impulse response analysis was employed to check the how the 

independent variables stimulate the response of RGDP and it was revealed that GDP responds negatively to 

electricity loss significantly through the period under consideration. Although the period covered was limited 

due to availability of data on electricity loss, the situation described in this work has not changed, but rather 

grown worse. Similarly, electricity demand impacted GDP positively and electricity supplied from 

hydropower impacted GDP negatively. This negative relationship between electricity demand and GDP shows 

that if only more electricity could be supplied, it would boost GDP. It was also shown that electricity supply 

showed a negative impact on GDP simply due to the huge loss of electricity through transmission and 

distribution channels. 

The major recommendation therefore is that there should be a sound energy policy that would be designed to 

encourage investment in the transmission and distribution lines to upgrade the infrastructure with the purpose 

of reducing electricity loss. It is certain that if the losses can be reduced, it will increase the quantity being 



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supplied and consequently increase the quantity demanded and eventually translate to higher productivity and 

GDP. 

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