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Electricity Supply Dynamics and Economic Growth in Nigeria:

Evidence from an Error Correction Model (1980-2021).

Nweze Paul Nweze

1,3

, Greg Ekpung Edame

, Okoi, Willie Wilfred

, Otemdam Jombo Okey

, Inaku,

Jacob Sunday

, Ogbaji, Felicia Omonya

, Muhammad Kabir Lawal

, Patrick Awok Mbum

1,2

Department of Economics, Faculty of Social Sciences & Humanities, Ebonyi State University,

Abakiliki, Nigeria.

Department of Economics, Faculty of Social Sciences, University of Calabar, Calabar-Nigeria.

Department of Economics, Faculty of Social Sciences, University of Calabar Calabar – Nigeria

Department of Economics, Faculty of Social Sciences, University of Calabar Calabar, Nigeria.

Department of Economics, Faculty of Social Sciences, University of Calabar Calabar – Nigeria

Department of Finance, Faculty of Management, ABU, Zaria-Nigeria

DOI:

https://doi.org/10.51583/IJLTEMAS.2026.150600260

Received: 10 July 2026; Accepted: 15 July 2026; Published: 04 August 2026

ABSTRACT

This research study investigates the impact of electricity supply dynamics and economic growth in Nigeria:

evidence from an Error Correction Model (1980 – 2021). The study examined the effects of electricity demand-

supply gaps, electricity pricing per capita, lending interest rates, and disposable income on GDP. Result of ADF

unit root test confirm that all variables were integrated of order one (I(1)); and this necessitated the test for long-

run relationship. Thus, the cointegration result shows that there is one cointegrating equation ie trace statistics

(83.73255) is greater than their respective critical value (69.81889) at 5% level of significance, hence there is a

long-run equilibrium relationship among the variables. The ECM results reveal that power outages significantly

reduce economic growth as a million MWh of deficit in electricity demand reduces GDP by 7600.8billion Naira,

while disposable income positively and significantly drives growth. Interestingly, lending interest rates show a

positive relationship with GDP, contrary to conventional expectations, possibly reflecting credit expansion

during periods of economic optimism. Electricity pricing exhibited a negative but statistically insignificant

effect. The findings highlight the importance of reliable power infrastructure, income-enhancing fiscal policies,

and balanced energy pricing strategies for fostering inclusive economic growth in Nigeria. Therefore, the study

recommends for governments’ increasing investments in Energy/Electricity infrastructure in order to enhance

energy supply and hence reduce power outages, benefiting all sectors of the economy. Also, there is need to

advocate for full privatization of the Nigeria’s energy sectors including electricity generation and the

downstream.

Keywords: Electricity supply, Economic growth, Power outages, Energy pricing, Error Correction Model,

Nigeria. JEL Classification: B41, C22, C32, O47, F43, P22, Q43, Q47.

Contribution / Originality: The study uniquely employs Error Correction Model (1980-2021), to examine

electricity supply dynamics and economic growth evidence from Nigeria. The ECM results shows that power

outages significantly reduce economic growth, while disposable income positively and significantly drives

growth in the Nigerian economy.

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INTRODUCTION

Energy is a cornerstone of modern civilization, playing a vital role in powering industries, sustaining households,

facilitating transportation, and enabling virtually all technological and economic activities. It is widely

recognized as one of the fundamental inputs for sustainable development and economic progress. A well-

functioning energy system not only drives industrial output and infrastructure development but also enhances

education, healthcare, and overall quality of life. The global experience affirms that a stable and efficient energy

supply is intricately linked to higher productivity, improved welfare, and long-term macroeconomic stability

(Erbaykal, 2008; Nathaniel & Bekun, 2020).

While aspects of most of the research either narrowly focused or limited in temporal scope. Importantly, there

remains a significant gap in empirical studies that comprehensively examine the multifaceted relationship

between electricity supply variables, such as per capita pricing, frequency of outages, financial intermediation

(via bank lending rates), and consumer capacity (via disposable income) and Nigeria’s macroeconomic

performance over a long-term horizon.

Moreover, many existing studies have not sufficiently accounted for how these variables interact within

Nigeria’s unique political economy, nor have they proposed integrated policy responses grounded in the realities

of implementation.

This study seeks to fill the critical gap by offering robust and nuanced electricity supply dynamics and economic

growth in Nigeria, evidence from error correction model (1980 - 2021). In this context, despite its vast

endowment of both conventional and renewable energy resources - including petroleum, natural gas, coal, hydro,

solar, wind, and biomass; Nigeria suffers from a chronically underperforming energy sector. Nigeria ranks

among the lowest globally in electricity access, quality of supply, and energy affordability. The energy crisis has

become a problem of socio-economic development, stifling industrial, discouraging foreign direct investment,

and exacerbating poverty and inequality (Awodumi & Adewuyi, 2020).

The mismatch between electricity supply and demand in Nigeria is severe. Recent estimates, shows that the

nation generates less than 7,500 megawatts (MW) of electricity - despite a peak demand of over 170,000 MW

for its population of more than 200 million. For instance, South Africa, with just over 60 million people,

produces more than 50,000 MW, illustrating the gravity of Nigeria’s energy deficit. The implications are

profound: frequent power outages disrupt manufacturing, hinder service delivery, and impose substantial costs

on businesses and households. The industrial sector, in particular, has suffered significant productivity losses,

with many firms resorting to costly fossil fuel generators to maintain operations (Joseph, 2019 & Inuwa et al.,

2022).

These inefficiencies are not confined to the economic realm alone—they ripple across society. Unreliable power

supply undermines healthcare delivery, compromising critical services such as diagnostics, surgical procedures,

and vaccine storage. Educational institutions struggle to operate effectively without consistent electricity, while

communities face safety hazards and limited access to clean water due to the power-dependent nature of water

systems (see Imadojemu & Tonuchi, 2021; Galadima & Aminu, 2020).

The structural issues underlying Nigeria’s energy sector are manifold. They include underinvestment in power

infrastructure, inadequate maintenance of existing facilities, limited diversification of energy sources, and weak

regulatory and institutional frameworks. The electric power supply chain; spanning generation, transmission,

and distribution is marked by inefficiencies, poor metering, high transmission losses, and low revenue collection.

Additionally, politicization in energy policy further complicated reform efforts (Akuru & Okoro, 2011; Yang &

Khan, 2022). The findings from this study aim to inform both national policy and regional development strategies

by highlighting actionable interventions; including renewable energy adoption, pricing reform, infrastructure

investment, and institutional strengthening - that can help Nigeria transition from energy poverty to energy

security.

LITERATURE

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Romer Endogenous Growth Theory

Energy and economic growth have long been examined through various theoretical lenses, notably the Solow-

Swan Growth Theory. However, to better capture the dynamic role of technological innovation, infrastructure,

investment on modern economies, Paul Romer's Endogenous Growth Theory (1990) provides a more robust

framework for understanding how electricity and energy infrastructure contribute to sustained economic growth.

Romer’s posits long-term economic growth arises from internal factors, particularly investments in knowledge,

innovation, and human capital. According to Romer (1990), public and private investments in infrastructure;

including energy can yield increasing returns by enhancing productivity across all sectors of the economy.

Electricity plays dual function: it is both a productive input and a catalyst for innovation and technology

diffusion. Electricity enhances allowing for higher output per unit of input across industries. It also facilitates

technological innovation, digitalization, and knowledge dissemination, especially when deployed in education,

healthcare, ICT, and manufacturing. As such, a stable and efficient electricity supply is a non-trivial driver of

knowledge-based capital accumulation, reinforcing the positive feedback loops central to endogenous growth

dynamics (Romer, 1990; Aghion & Howitt, 1998).

In developing economies like Nigeria, however, electricity deficits act as a binding constraint on endogenous

growth channels. Studies by Akinlo (2009) affirm electricity availability significantly influence output levels.

Electricity supply failures reduce firm-level productivity, discourage innovation, and disincentivize capital

accumulation—mechanisms that are central to Romer’s growth formulation.

Moreover, the externalities associated with electrification—such as better health outcomes, improved

educational attainment, and enhanced communication—contribute indirectly to economic growth by fostering

human capital development, a key driver in endogenous growth models (Barro, 1991; Lucas, 1988). These

indirect channels are particularly important in rural and peri-urban regions where electricity access correlates

strongly with social mobility and access to public services.

According to Calderón and Servén (2010), infrastructure quality including electricity have strong and statistically

significant effect on long-term growth and inequality reduction. Their work reinforces the endogenous argument

that public infrastructure investment in electricity not only boosts current output but also creates cumulative

productivity effects through time. In the Nigerian context, where the power sector suffers from underinvestment

and inefficiency, these theoretical insights underscore the urgency of electricity reform as a pathway to unlock

endogenous growth potentials. Additionally, UNIDO (2016) emphasizes the importance of electricity for

industrial development and structural transformation in Africa. They note that without reliable power, economies

remain stuck in low-productivity equilibria, unable to transition from primary commodities to value-added

sectors—again reflecting Romer’s model where technological diffusion and capital deepening are prerequisites

for growth. Therefore, Romer’s compelling foundation centrality on electricity to progress, particularly in

economies aiming for structural transformation. Energy infrastructure, far from being a passive input, is a

dynamic enabler of innovation, human development, and capital efficiency. As such, addressing electricity

challenges in Nigeria merely service delivery, unlocking sustained, self-reinforcing economic growth.

Empirical Literature

Literature abounds on the significant relation between energy dynamic – energy supply, energy consumption,

electricity pricing, technology all over the globe. The increased drastically especially from the early 21

century

when technology was seen as a major driver of any economy. A number of works has a kind of conflicting

results or findings. While some researchers found positive relationship between energy consumption, others

found negative relationship with energy consumption especially from power outage. Some findings however

revealed causality on energy dynamics in Nigeria some found no causality between the both concepts.

Dai, Jia and Wang (2022), looked at relationship between energy consumption and economic growth and the

Kuznets energy curve of the United States and Germany. The results show that China’s energy economic rate is

higher than that of Germany and the United States at the turning point of energy consumption. The research

makes an empirical analysis of the relationship between energy consumption and economic growth in OECD

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countries and finds out the turning point of energy consumption to provide a theoretical basis for coordinating

China’s energy consumption and economic growth.

Ali, et al. (2020) explores the relationship between electricity consumption, and economic growth in Nigeria.

Using various statistical tests and estimation methods such as the Maki cointegration test, Ng-Perron test, Zivot-

Andrew’s test, Kwiatkowski unit root test, FMOLS, DOLS, and CCR regression, the study analyzes annual data

from 1971 to 2014. The findings suggest a positive relationship between electricity consumption and economic

growth, indicating that electricity consumption plays a significant role in driving growth in Nigeria. Given

Nigeria's heavy reliance on energy (electricity) consumption, this finding is desirable. The researchers employed

the bounds test and the Bayer and Hanck cointegration tests to confirm the existence of a cointegrating

relationship among the work consolidated the empirical insights in this research axis by using variables. The

findings indicate that electricity consumption has a positive impact on economic growth. The robustness of the

findings was confirmed through the application of the fully modified OLS, dynamic OLS, and canonical

cointegrating regression techniques. Furthermore, the vector error correction model and Granger causality test

supports the neutrality hypothesis in the short run and the feedback hypothesis in the long run, suggesting

interdependencies among the variables over time. Based on these findings, the study suggests implementing

policies that ensure efficient electricity supply, manage rapid urbanization, and promote sustainable economic

growth in Nigeria.

Galadima and Aminu (2020) explored natural gas consumption and economic growth in Nigeria using

econometric techniques. The main objectives of the study were to compare linear and nonlinear methods in

determining the unit root property of the series, assess the impact of natural gas consumption on economic

growth, and analyze the causal effects of positive and negative changes in natural gas consumption on economic

growth. The findings of the study indicate that both natural gas consumption and economic growth in Nigeria

exhibit nonlinear trends. This suggests that linear techniques may not fully capture the complexities of their

relationship. The analysis reveals a nonlinear relationship between natural gas consumption and economic

growth, where an increase in natural gas consumption leads to higher economic growth. The study also identifies

bidirectional causality, indicating that positive changes in natural gas consumption have a causal effect on

economic growth, and vice versa. Furthermore, the study uncovers evidence of unidirectional causality from

economic growth to the negative impact of natural gas consumption. This implies that economic growth can

influence the consumption of natural gas in a way that negatively affects the economy. The study concludes by

suggesting further research on the trend process of energy consumption, particularly in the context of the

Organization of Petroleum Exporting Countries (OPEC) or other regional energy organizations, using panel data

techniques. This would provide a broader understanding of the relationship between energy consumption and

economic growth beyond the scope of Nigeria.

Inuwa et al. (2022) focused their own study on examining the disaggregated effect of natural resource rents on

economic growth in Nigeria from 1981 to 2017. The study utilizes traditional and modern unit root tests, as well

as cointegration techniques such as the Bayer and Hanck combined cointegration test, canonical cointegration

regression (CCR), and fully modified OLS (FMOLS). The findings of the study indicate the presence of a long-

run equilibrium relationship among the variables. In terms of the impact on economic growth, the analysis

reveals that oil and forest rents have a negative effect, supporting the resource curse hypothesis. On the other

hand, natural gas, coal, and mineral rents have a positive effect on economic growth. Additionally, the study

highlights that financial development stimulates economic growth in Nigeria. The policy implications of the

findings suggest that different types of resource rents have varying impacts on economic growth. Therefore, the

study recommends that the government utilize these rents judiciously to stimulate growth. This can be achieved

by reinvesting in other sectors of the economy, such as the financial sector. The study also emphasizes the

importance of designing and implementing policies to ensure the sustainable and beneficial use of rents obtained

from resources like coal, mineral, and natural gas, so that they contribute positively to the Nigerian economy

and avoid becoming a curse.

Awodumi and Adewuyi (2020) investigated the role of non-renewable energy consumption in economic growth

and carbon emissions in the top oil-producing economies in Africa from 1980 to 2015. The study addresses the

dilemma of balancing economic growth and pollution reduction, given that non-renewable energy consumption

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facilitates output production but also contributes to carbon emissions. Using the non-linear autoregressive

distributed lag (NARDL) technique and accounting for nonlinearity and structural breaks, the study finds

evidence of asymmetric effects of per capita consumption of petroleum and natural gas on economic growth and

carbon emissions in all the selected countries, except Algeria. In Nigeria, a positive change in non-renewable

energy consumption hampers growth but reduces emissions. In Gabon, increased consumption of these energy

sources promotes growth and improves environmental quality. Egypt experiences negligible impact on

environmental pollution as non-renewable energy consumption enhances economic growth. In Angola, positive

changes in non-renewable energy consumption contribute to economic growth, but the effects on carbon

emissions vary across time and energy types. Negative changes in petroleum and natural gas consumption have

similar effects to positive changes in Egypt and Nigeria. Wang et al. (2020) examined the relationship between

economic growth and life expectancy in Pakistan, taking into account the potential role of financial development

and energy consumption. The researchers employed both traditional and advanced unit root tests to assess the

stationary properties of the variables. They also utilize the ARDL bounds testing technique to explore the

cointegration levels among life expectancy and its determinants, considering the presence of structural breaks in

the data. The empirical findings confirm the presence of cointegration among the variables, indicating a long-

term relationship. The results suggest a positive association between economic growth and life expectancy,

indicating that as the economy grows, life expectancy tends to increase. However, financial development has a

negative effect on life expectancy in the context of Pakistan, suggesting that factors related to financial

development may hinder improvements in life expectancy. Additionally, the study reveals that energy

consumption has a detrimental effect on life expectancy through environmental degradation. The findings of this

empirical analysis provide valuable insights for policymakers, highlighting the potential of financial

development and energy consumption as economic tools to improve life expectancy. The study suggests that

policymakers should consider directing energy and finance policies towards promoting sustainable development

and environmental protection in order to enhance life expectancy in Pakistan.

Lawal, Ozturk, Olanipekun and Asaleye (2020) investigated the relationship between electricity consumption

and economic growth in sub-Saharan African economies from 1971 to 2017. The researchers utilize the System

Generalized Methods-of-Moments (System GMM) techniques to account for endogeneity in the data generation

process. Additionally, they examined whether the impact of electricity consumption differs based on the level

of energy intensity. They employed an advanced dynamic panel threshold regression model to determine the

threshold level of energy intensity and explore the potential asymmetric effect of energy consumption on

economic growth. The findings reveal a significant positive relationship between electricity consumption and

economic growth. Furthermore, they identified a threshold level of energy intensity at 0.48%, indicating that

energy consumption beyond this level hampers growth, while consumption below this level stimulates growth.

Numerous studies have examined the intricate link between energy consumption dynamics and economic growth

across diverse global contexts. Notably, researchers such as Dai, Jia and Wang (2022), Ali, et al. (2020), and

Nathaniel and Bekun (2020), has consistently shown a positive correlation between electricity consumption and

economic growth by employing multiple regression techniques and cointegration tests to establish the positive

relationship. Additionally, investigations into the connection between natural resource rents and economic

growth showcased mixed effects. (see Inuwa, et al. (2022), Galadima and Aminu (2020), Yang and Khan (2022),

and Nathaniel (2020). These studies collectively emphasize the importance of robust methodologies in

unraveling these complex dynamics in offering insights to guide sustainable economic development strategies

and energy consumption.

RESEARCH METHODOLOGY

Model Specification

In conventional research like this and any other related fields of econometric research, the first steps are to

specify the functional model that will guide the research based on the theoretical and empirical evidence. This

involves selecting an appropriate functional form for the model and choosing which variables to include in the

model, of which it is paramount as wrong specification of the model can produce spurious and misleading result.

This research will be anchored on the Romer endogenous growth theory as reviewed in Section two. Based on

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the general model specified by the endogenous growth model, the general form of Romer endogenous production

model is: Y = A f(K, L ) (1)

Where: Y = Output (gross domestic product)

A = Total factor productivity or efficiency parameter, K = Capital stock and L = Labour.

For this study, it is assumed that the impact of energy dynamic on Nigeria economic growth (i.e., industrial

output or gross domestic product) operates through total factor productivity (A). Since this study intends to

examine the impact of energy dynamic on economic growth in Nigeria, we thus assumed that total factor

productivity (A) is a function of electricity power outage (PO – difference between electricity power demand

and supply), electricity pricing per capita (EPPC), Real interest rate (INT), and Disposable income (dY).

Hence, following the model of Romer endogenous growth and the empirical study of Sarwat, Faiz and Saadia

(2011) that investigated the dynamic relationship between energy and economic growth of eight developing

countries, this research study will modify the model by incorporating other key variables like electricity pricing,

disposable income, commercial bank lending rate and power outage in other to have a better result. Thus, the

model is represented symbolically in its functional form as: GDP = f(PO, EPPC, INT, dY)

3.1

Where;

GDP = Gross Domestic Product

EPPC = Electricity Pricing Per Capita

PO = Power outage (Gaps in Electricity Demand and Supply)

INT = Commercial bank lending rate

dY = Disposable Income

This relationship can be captured in its linear function as represented below,

GDP = b

+ b

PO + b

EPPC+ b

INT + b

dY + U

3.2

Where;

= constant term/parameter intercept

, b

and b

= coefficients of the parameters estimate.

= Error Term

As efforts will be made to rescale the data, the log function is thus expressed as follows:

L(GDP) = b

+ b

LPO + b

LEPPC + b

LINT + b

LdY + U

3.3

Estimation Procedure

The Estimation Procedure focused on ECM.

Following the results of unit root and cointegration tests, the Error Correction Mechanism (ECM) is employed

to estimate the short-run dynamics while preserving the long-run equilibrium relationship among the variables.

The ECM is suitable when the variables are integrated of order one, I(1), and cointegrated, as it allows for the

modeling of both short-run deviations and the speed at which they converge back to long-run equilibrium.

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The rationale for choosing the ECM lies in its ability to correct disequilibrium in the dependent variable caused

by shocks in the independent variables. Specifically, the ECM integrates both the differenced (short-run)

components and the lagged residuals (error correction term) derived from the cointegration equation. The

coefficient of the error correction term (ECM_{t-1}) represents the speed of adjustment—indicating how quickly

deviations from long-run equilibrium are corrected in subsequent periods. This approach aligns with the

theoretical framework of Engle and Granger (1987), which advocates for the use of ECM in the presence of

cointegrated series to ensure valid inference and avoid spurious regression results. By including both lagged

differences and the error correction term, the ECM offers a robust specification that captures temporal dynamics

without losing long-run structural information.

Therefore, the ECM is appropriate for this study because it enables us to disentangle short-run fluctuations from

long-run trends in Nigeria’s energy-economic growth relationship, particularly in the context of electricity

pricing, outages, bank lending rates, and disposable income.

3.3 Data Collection Source.

Secondary source of data is used as the main method of data collection. The relevant data for this study made

used of annual time series data covering the period (1980 – 2021), obtained from Central Bank of Nigeria (2000),

Central Bank of Nigeria Statistical Bulletin (2021), National Bureau of Statistics (NBS) and World Development

Indicator (WDI 2022). OECD/IEA (2022)

(http://www.iea.org/stats/index.asp),subjecttohttps://www.iea.org/t&c/termsandconditions/

Definitions:

Definition of Electricity consumption: This entry consists of total electricity generated annually plus imports

and minus exports, expressed in kilowatt-hours. The discrepancy between the amount of electricity generated

and/or imported and the amount consumed and/or exported is accounted for as loss in transmission and

distribution.

Definition of Electric power consumption: This refers to the price measures of the production of power plants

and combined heat and power plants less transmission, distribution, and transformation losses and own use by

heat and power plants (

International Monetary Fund, World Bank Indicator, 2022).

Definition/Discussion of data used in the Study:

Gross Domestic Product (GDP): Gross Domestic Product represents the total monetary value of all goods and

services produced within a country's borders in a specific time period. It's a comprehensive scorecard of a

nation’s economic health and an indicator of its standard of living. GDP is commonly used to compare the

economic performance of different countries and to track economic growth over time.

Power Outage: A power outage, also known as a blackout, is the temporary loss of electrical power in a specific

area. For instance; from ‘Appendix 1’ in 1980, Energy demand was 7.38 billion kilowatt-hours (KWH) less

Energy supply which was 6.62 billion KWH which gives us 0.76 billion KWH (which is the Energy Gap or

PO). Also, if we check that 2015; Energy demand is 25.67 KWH less Energy supply at 21.76KWH, then PO

is3.91KWH. This can be caused by various factors such as natural disasters, technical faults in the power grid,

or maintenance work. Power outages disrupt daily life and business operations, often leading to economic losses,

decreased productivity, and inconvenience for both households and businesses.

Electricity Pricing Per Capita: Electricity pricing per capita refers to the average amount spent by each

individual on electricity within a country. This metric varies widely depending on the country's energy resources,

efficiency of its electricity infrastructure, and government policies. High electricity costs per capita can indicate

either high consumption rates or inefficiencies and higher prices in the energy sector.

Structural Breaks.

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A structural break refers to a sudden and unexpected shift in the underlying pattern, mean, or parameters of a

time series, typically triggered by significant events such as policy changes, shifts between military and

democratic rule, wars, or economic crises. Such breaks can render previous models invalid, leading to forecasting

errors. The term' e

represents the pooled unexplained variations across two periods: 1980–1986 and 1987–

1993, and between 1994 and 2005, corresponding to military and democratic governments, respectively. e

denotes the unexplained variations in electricity supply during the military government (1986–1999 and between

1985–1999), while e

refers to unexplained variations during the democratic government (1999–2005). The

variable n1 indicates the number of observations during the military period (1980–1983 and 1985–1999), and

n2 represents the number of observations during the democratic period (1983 and 1999–2005). The parameter k

stands for the total number of coefficients, including the intercept, and the degrees of freedom are given by n1 +

n2 – 2k. For non-parametric testing, the Geary test and the kernel density test (k-density) are used to assess the

stability of structural breaks in electricity supply dynamics and economic growth in Nigeria, utilizing

STATA10.0.(https://coda.grammarly.com/d/STRUCTURALBREAK_dE2NRXdVNmc/Untitled-

page_suYnJj0m#_luC8MbVg)

RESULTS AND DISCUSSION

This section focuses on the presentation and analysis of the result to address the issues of Energy dynamic and

economic growth in Nigeria. This analysis is based on standard econometric techniques such as;

 Descriptive statistics: Means, Median, Skewness, Kurtosis, standard deviation, probability and Janqen-

Beran normality test (Pre-estimation test).

 Series and Group test: Unit root test, Cointegration tests, and Error Correction Mechanism test.

 Residual diagnostic test: Breush-Godfrey Serial autocorrelation test, Heteroscedasticity test, Cusum stability

test etc.

Descriptive Statistics:

The descriptive statistics test was conducted to check the degree of variation in the data used in this research

study.

Table 1:Descriptive Result

LGDP

LPO

LEPPC

INT

LDY

Mean

8.693707

0.468776

4.642035

17.05524

7.935906

Median

8.939338

0.157004

4.590430

17.38000

7.794341

Maximum

12.07867

2.094330

5.096813

29.80000

8.822377

Minimum

4.493680

-0.776529

3.926123

6.780000

7.031534

Std. Dev.

2.495879

0.778126

0.297883

4.865736

0.599751

Skewness

-0.249613

0.677369

-0.154911

0.132207

0.181402

Kurtosis

1.617549

2.384036

2.120396

3.396526

1.492722

Jarque-Bera

19.98088

27.15341

1.521962

0.397508

4.206150

Probability

0.000046

0.000001

0.467208

0.819751

0.122080

Sum

365.1357

19.68857

194.9655

716.3200

333.3081

Sum Sq. Dev.

255.4059

24.82467

3.638094

970.6908

14.74773

Observations

Source: Researchers Computation

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From table 1 above there seems to be evidence of significant variations as shown by the huge difference between

the minimum and maximum values for the variables under consideration.

 The skewness of the data series indicates an asymmetric or non-normal data distribution as the series

relatively deviates from normality maintaining positive skewness (values above 0.5), as evidenced by the

variation in the minimum and maximum values of the variables.

 The kurtosis statistic equally shows that all the variables have leptokurtic distribution. This means that GDP,

PO, EPPC, INT and DY has higher influence on Nigeria’s economic growth.

 The Jarque-Bera test is a test of normality. Based on our results using the P-values associated with the Jarque-

Bera statistics, GDP and PO are normally distributed while EPPC, DY, and INT are not.

Proper Estimation

In order to avoid a situation of spurious and misleading result various test were conducted to examine the order

of integration of each of the variables in the model, their long run relationships and their short-term speed of

adjustment to fluctuation in the short run to long run equilibrium.

The unit root test, Johansson cointegration test, and ECM test will be conducted accordingly in this subsection.

Unit Root Test

In other to test for the presence or absence of unit root in the data used for the empirical analysis, Augmented

Dickey-Fuller (ADF) test was employed to check the trend in the data. The optimal lag length is automatically

determined based on Akaike Information Criterion (AIC) or Schwartz Bayesian Criterion (SBC), or more

usefully by the lag length necessary to whiten the residuals (Asteriou and Hall, 2007). The test result of the ADF

is as presented below:

Table 2: Unit Root

Augmented Dickey Fuller Result at Level and First Difference, Trend and Intercept

Variables

ADF @ Level

difference

Critical value (5%)

Order of integration

Remarks

D(LGDP)

0.141878

4.293894

3.526609

I(1)

Stationary

D(LPO)

2.041428

5.366100

3.540328

I(1)

Stationary

D(LEPPC)

3.515006

9.429010

3.526609

I(1)

Stationary

D(LDY)

2.924916

5.809312

3.526609

I(1)

Stationary

D(INT)

3.054882

6.456097

3.529758

I(1)

Stationary

Source: Researchers Computation

The unit root test results as shown in Table 2 above shows that none of the variables were stationary at level, as

their ADF statistics in absolute terms are less than at the 5% critical values (e.g., |0.141878| < |3.526609| for

GDP). However, after first differencing, all variables became stationary, with ADF statistics exceeding the 5%

critical values (e.g., |4.293894| > |3.526609| for GDP), indicating they are integrated of order one, I(1). This

confirms the absence of unit roots and eliminates the risk of spurious regression.

Stationarity is essential for valid time series modeling, and without addressing it, forecasts become unreliable

(Iyoha, 2004; Gujarati, 2003). As noted by Pesaran and Shin (2001), when variables are I(1) and cointegrated,

the appropriate approach is to estimate using an Error Correction Model (ECM). Therefore, since all variables

are I(1), the study proceeds with cointegration and ECM analysis to capture both long-run relationships and

short-run adjustments.

Cointegration Test

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Cointegration test was used to test for the long run relationship among the variables. For this purpose, the

Johansen cointegration test was adopted. In Johansen’s method, the Eigen value statistic is used to determine

whether cointegrated equations exist among the variables. Cointegration is said to exist if the values of computed

statistics are significantly different from zero or if the trace statistics is greater in absolute value than the critical

value at 5 percent level of significance. The model with lag 1 was chosen with the linear deterministic test

assumption and the result is presented below.

Table 3: Unrestricted Cointegration Rank Test (Trace)

Hypothesized

Trace

0.05

No. of CE(s)

Eigenvalue

Statistic

Critical Value

Prob.**

None *

0.668511

83.73255

69.81889

0.0026

At most 1

0.338922

39.56616

47.85613

0.2384

At most 2

0.285571

23.01085

29.79707

0.2455

At most 3

0.165763

9.559992

15.49471

0.3161

At most 4

0.056125

2.310462

3.841466

0.1285

Source: Researchers Computation

Recall that according Pasaran and Pasaran (1998) and pasaran and Shin (2001), if variables are stationary at level

normal OLS can be used to estimate the parameters which is the most desirable form of estimating the

relationship between series under consideration. But if series are not stationary at level but are stationary at same

order, they recommend that co-integration test and error correction mechanism test should be conducted to

determine the longrun and short speed of adjustment disposition.

The reason is because as a result of differencing the data, some vital information about the variable is lost during

the process. In this study, the most desirable case of all variables being integrated of the same order was evident.

The unit root test result shows that all the variables are integrated of the order, I(1). The research therefore

proceeded with the cointegration test. From table 3 above, since the computed trace statistic i.e. (83.73255) is

greater than the critical value (69.81889) at 5% levels of significance the null hypothesis (H

) is rejected and

concludes that there is at least one co-integrating equation and that all the variables are cointegrated. Hence,

putting it differently, there is a sustainable long-run relationship (i.e. steady-stated path) between gross domestic

products (GDP), Power outage (PO), Electricity pricing per capita (EPPC), Commercial bank lending rate (INT),

and Disposable income (dY).

The long run Equation

GDP = 34.97088 - 0.669923PO + 5.092949EPPC+ 0.144729INT - 5.661470dY

(0.44247) (1.44756) (0.04075) (0.77251)

The result above is the coefficient of the explanatory variables which indicate the direction of strength of the

relationship between explanatory variables and economic growth in the long run. The figures in the parenthesis

were the asymptotic standard error.

The long-run relationship displayed above revealed that there is a negative long-run relationship between the

change in power outage (PO) and Nigeria’s Gross Domestic Product (GDP), in the economy during the period

of observation. The implication is that, in the long-run, a unit change (increase) in power outage (PO), will

decrease the value of the Nigerian economic growth by (0.669923 billion) respectively, over this period of

observation (1980-2021).

The result further revealed that one billion increases in Nigeria electricity price per capita will bring about

N5092949 increase on the gross domestic product. One billion increase in disposable income will lead to (bring

about) 5.66 decrease in Gross Domestic Product (GDP). And lastly, an increase in commercial bank lending rate

will bring about 0.1447 unit increase on the Nigeria’s gross domestic product, all other things being equal.

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Error Correction Mechanism

The existence of a long- run co-integrating equilibrium provides for short-term fluctuations. In order to

strengthen out or absolve these fluctuations, an attempt was made to apply the Error Correction Mechanism

(ECM). As noted, the ECM is meant to tie the short-run dynamics of the co-integrating equations to their long-

run static dispositions. Table 4 below shows the error correction mechanism result.

Table 4. Error Correction Model Result

Variable

Coefficient

Std. Error

t-Statistic

Prob.

-22.90436

2.160646

-10.60070

0.0000

LPO

-0.760818

0.240829

-3.159168

0.0020

LEPPC

-1.319743

0.670076

-1.969542

0.0573

INT

0.051503

0.019903

2.587664

0.0143

LDY

4.659579

0.349460

13.33367

0.0000

ECM(-1)

-0.113962

0.019207

-5.933358

0.0311

R-squared

0.960758

Mean dependent var

8.992334

Adjusted R-squared

0.954812

S.D. dependent var

2.331482

S.E. of regression

0.495614

Akaike info criterion

1.574600

Sum squared resid

8.105898

Schwarz criterion

1.830532

Log likelihood

-24.70469

Hannan-Quinn criter.

1.666426

F-statistic

161.5863

Durbin-Watson stat

1.978261

Prob(F-statistic)

0.000000

Source: Researchers Computation

The result of the Error Correction Model (ECM) in Table 4 shows that the error correction term (ECT) has a

coefficient of –0.113962, which is statistically significant. This implies that approximately 11.4% of the short-

run disequilibrium in GDP is corrected each year, confirming the presence of a stable long-run relationship. The

negative sign of the ECT aligns with theoretical expectations, indicating that any deviation from equilibrium is

adjusted over time by the explanatory variables.

The coefficient of determination (R²) is 0.9608, suggesting that about 96% of the variation in GDP is explained

by the model’s independent variables—namely, gaps in electricity demand and supply (LPO), electricity price

per capita (LEPPC), lending interest rate (LINT), and disposable income (LDY). This reflects a high model

goodness-of-fit, with only 4% of variations attributed to external factors not captured in the model.

Key Variables

 Electricity Demand-Supply Gap (LPO): The coefficient is negative and statistically significant (p =

0.0020), indicating that larger electricity shortfalls reduce GDP. Specifically, each million MWh of

unmet electricity demand is associated with a GDP decline of approximately ₦760.8 billion.

 Electricity Price Per Capita (LEPPC): Though negatively signed, this variable is not statistically

significant (p = 0.0573), suggesting that electricity pricing policy reforms could influence growth, but

current pricing effects are weak or delayed.

 Interest Rate (LINT): Contrary to Keynesian theory, the coefficient is positive and significant (p =

0.0143), indicating a direct relationship with GDP. This suggests credit availability may be growth-

inducing despite higher rates, possibly due to inflationary pressures or structural banking constraints.

 Disposable Income (LDY): The coefficient is positive and highly significant (p < 0.0001), confirming

that higher household income levels foster economic growth—consistent with theoretical expectations

regarding consumption and capital formation.

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The DW statistics of 1.98 falls within the expected range of between 1.9 and 2.1. Thus, the model is free from

the problem of serial autocorrelation. This was further confirmed by Breush-Godfrey Serial autocorrelation test.

Table 5: Breusch-Godfrey Serial Correlation LM Test

F-statistic

0.222050

Prob. F(2,28)

0.8023

Obs*R-squared

0.562071

Prob. Chi-Square(2)

0.7550

Source: Researchers Computation (See Appendix)

From the result in Table 5 above, since F-statics P-value 0.8023, also Obs*R-squared had a P-value of 0.7550

are greater than 5% (0.05) level of significance, we conclude that there is no autocorrelation in the series and as

such the findings can be helpful in making policy decision. The absence of serial autocorrelation in the data

makes it fit for policy option by the economic and political actors. This assertion is further collaborated by the

result of the Cusum test.

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

CUSUM of Squares

5% Significance

The Cusum square test revealed that the model exhibits a great level of stability as the blue line falls between

the red lines. What this means is that the relationship between energy dynamics variable captured by gaps in

electricity demanded and supplied [LPO], electricity price per capita [LEPPC], average lending interest rate

[INT] and Disposable Income [DY] and Nigeria economic growth is stable over long period of time. A stable

relationship is necessary to enable prediction of economic phenomenon.

Heteroskedasticity Test

Table 6: Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic

1.895730

Prob. F(5,33)

0.1218

Obs*R-squared

8.702426

Prob. Chi-Square(5)

0.1215

Scaled explained

3.256025

Prob. Chi-Square(5)

0.6606

Source: Researchers Computation

The result presented in Tables 6 shows the Obs*R-squared (8.702426) had a P-value of 0.1215. Also, the F-

statistics (1.895730) had a P-value of 0.1218. The rule of thumb is that if the probability statistics is less than

0.05, then there is a problem of heteroscedasticity in the estimated model. Since the probability statistics is

greater than 0.05, we conclude that the model is free from the problem of heteroscedasticity. As such, the

estimated model can be used for making decisions and policy recommendations.

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Impulse Response test of Energy Dynamic

Impulse response functions (IRFs) are used to test for the effects of shocks on the adjustment path of the variables

under consideration. Impulse response functions is mostly represented by and can also be graphically presented

showing the effect of shocks on the current and future path of the variables under consideration. In essence, IRs

show how these variables react to different shocks in the model.

.00

.05

.10

.15

.20

.25

1 2 3 4 5 6 7 8 9

Response of LGDP to LGDP

-.2

-.1

1 2 3 4 5 6 7 8 9

Response of LPO to LGDP

-.04

-.02

.00

.02

.04

.06

.08

1 2 3 4 5 6 7 8 9

Response of LEPPC to LGDP

-2

-1

1 2 3 4 5 6 7 8 9

Response of INT to LGDP

-.02

.00

.02

.04

.06

1 2 3 4 5 6 7 8 9

Response of LDY to LGDP

Response to Cholesky One S.D. Innovations ± 2 S.E.

The impulse response test above revealed that GDP negatively respond to gaps between electricity demand and

supply but positively respond to disposable income. What this means is that increase in the power outage in

Nigeria will drastically have negative influence on the economy. However, the result revealed that gross

domestic product does not have any significant either positive or negative on electricity price per capita and

lending interest rate.

The IRF test further revealed that power outage captured by gaps between electricity price has an inconsistent

response to GDP, while electricity price par capital revealed low or no response at all to GDP. In overall, one

can conclude that Nigeria economy captured by gross domestic product has positive relationship with energy

dynamics.

CONCLUSION AND POLICY RECOMMENDATIONS

This study examined the impact of electricity supply dynamics and economic growth in Nigeria, evidence from

an error correction model (1980-2021); focusing on electricity pricing, power outages, interest rates, and

disposable income. The findings highlight that electricity pricing per capita showed an inverse relationship with

growth, the result was statistically insignificant, suggesting the need for cautious pricing reforms that balance

cost recovery with affordability.

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The strong, statistically significant negative impact of power outages confirms the critical role of reliable

electricity in driving productivity and economic performance. Addressing outages is essential for industrial

competitiveness and national development. Interestingly, lending interest rates showed a positive correlation

with economic growth—contrary to theoretical expectations—possibly reflecting increased credit demand

during periods of economic optimism. Additionally, the strong positive impact of disposable income aligns with

expectations, reinforcing the importance of household financial health in stimulating economic activity.

Overall, the study underscores the urgent need for reforms in Nigeria’s electricity and financial sectors to achieve

sustainable, inclusive growth.

i) It is recommended that urgent attention be paid to investment in Nigeria’s electricity infrastructure

with a balanced pricing reform, and income-enhancing fiscal and monetary policies in Nigeria to

boost increase in power supply to meet the demand in the country.

ii) Increase on investment in the energy sector will reduce power disruptions and support industrial

productivity as well as promote inclusive economic growth in Nigeria.

iii) power outage otherwise known as a blackout, is the temporary loss of electrical power in a specific

area, for instance in 1980, Energy demand was 7.38 billion kilowatt-hours (KWH) less Energy supply

which was 6.62 billion KWH which gives us 0.76 billion KWH (which is the Energy Gap or PO).

Also, in 2015; Energy demand was 25.67 KWH less Energy supply at 21.76KWH, then PO

is3.91KWH. This can be caused by various factors such as natural disasters, technical faults in the

power grid, or maintenance work. Power outages disrupt daily life and business operations, often

leading to economic losses, decreased productivity, and inconvenience for both households and

businesses. It is therefore recommended that government should invest more on solar energy in the

country in order to improve on the power outage /blackout all over in the country.

iv) Government should encourage public – private -partnerships (PPP) by allowing the private investors

to participate in the energy sector. This should be encouraged in order to reduce the Electricity

pricing per capita, which is the average amount spent by each individual on electricity within a

country.

v) The government should expand the country’s energy resources, efficiency of its electricity

infrastructure, and its policies. This can be done to reduce the high electricity costs per capita which

indicate either high consumption rates or inefficiencies and higher prices in the energy sector. It is

therefore recommended that through the increase in the solar energy investment government can

reduce the pricing per capita in the country.

Funding Statement: it is Not applicable as it is self-funding by all the authors.

International Review Board Statement: Not applicable.

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APPENDIX ‘I’

DATA FOR REGRESSION

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YEARS

Energy

Demand

Billion

(KWH)

Energy

Supply

Billion

(KWH)

Energy Gap

Billion

(PO)

(KWH)

Electricity

Price

(KWH)

Per Capita

Disposable

Income

GDP

(N’Billion)

INT

Rate

1980

7.38

6.62

0.76

67.80

1131.765

89.45

6.78

1981

8.36

7.37

0.99

50.71

1454.079

139.31

7.75

1982

8.34

7.88

0.46

81.58

1487.247

149.05

10.25

1983

7.68

6.68

1.00

81.41

1429.263

158.75

10.00

1984

9.63

8.45

1.18

61.82

1411.542

165.85

12.50

1985

9.45

8.98

0.47

80.13

1534.983

187.83

9.25

1986

10.62

9.45

1.17

90.52

1388.954

198.12

10.50

1987

11.72

10.23

1.49

88.93

1235.195

244.68

17.50

1988

10.44

9.56

0.88

86.78

1335.547

315.62

16.50

1989

11.86

10.56

1.30

96.66

1435.065

414.86

26.80

1990

12.63

11.25

1.38

86.71

1630.702

494.64

25.50

1991

13.45

12.89

0.56

89.22

1674.026

590.06

20.01

1992

13.48

12.79

0.69

89.67

1719.787

906.03

29.80

1993

13.23

12.23

1.00

100.45

1796.384

1,257.17

18.32

1994

12.70

11.56

1.14

95.15

1848.887

1,768.79

21.00

1995

12.33

13.23

1.00

91.09

1877.957

3,100.24

20.18

1996

14.78

13.56

0.78

85.52

2002.549

4,086.07

19.74

1997

14.50

13.39

1.11

81.63

2087.287

4,418.71

13.54

1998

14.75

13.75

1.00

76.61

2159.163

4,805.16

18.29

1999

14.70

14.80

1.10

75.41

2193.223

5,482.35

21.32

2000

14.75

13.72

1.03

74.13

2351.152

7,062.75

17.98

2001

18.76

17.37

1.39

75.20

2504.941

8,234.49

18.29

2002

15.94

14.77

1.17

104.13

2645.992

11,501.45

24.85

2003

15.67

14.55

1.12

101.40

2938.698

13,556.97

20.71

2004

15.67

14.55

1.12

122.98

3196.949

18,124.06

19.18

2005

19.85

18.43

1.42

128.64

3490.059

23,121.88

17.95

2006

15.59

14.46

1.13

111.14

3792.451

30,375.18

17.26

2007

19.06

17.71

1.35

138.14

4132.512

34,675.94

16.94

2008

22.11

15.85

6.26

126.53

4429.456

39,954.21

15.14

2009

22.11

15.85

6.26

119.95

4733.295

43,461.46

18.99

2010

21.92

19.21

2.71

135.64

5127.719

55,469.35

17.59

2011

21.92

19.21

2.71

149.31

5342.438

63,713.36

16.02

2012

20.13

18.14

1.99

155.85

5519.3

72,599.63

16.79

2013

18.82

17.66

1.16

141.87

5745.702

81,009.96

16.72

2014

24.87

20.38

4.49

142.67

6031.42

90,136.98

16.55

2015

25.67

21.76

3.91

145.98

6100.21

95,177.74

16.85

2016

26.35

21.08

4.47

146.33

6206.34

102,575.42

16.87

2017

28.32

22.46

4.51

147.21

6442.35

114,899.25

17.56

2018

28.64

23.01

4.73

147.68

6784.37

129,086.91

19.33

2019

29.56

24.54

5.02

148.23

6587.49

145,639.14

15.53

2020

27.42

19.65

7.77

162.05

6416.10

154,252.32

12.32

2021

32.35

24.23

8.12

163.50

6383.34

176,075.50

11.37

(http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/

DEFINITIONS

Definition of Electricity - consumption: This entry consists of total electricity generated annually plus imports

and minus exports, expressed in kilowatt-hours. The discrepancy between the amount of electricity generated

and/or imported and the amount consumed and/or exported is accounted for as loss in transmission and

distribution.

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Page 3552

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

Definition: Electric power consumption price measures the production of power plants and combined heat and

power plants less transmission, distribution, and transformation losses and own use by heat and power plants

(International Monetary Fund, World Bank Indicator, 2022).

Definition/Discussion of data used in the Study:

i) Gross Domestic Product (GDP): Gross Domestic Product represents the total monetary value of all

goods and services produced within a country's borders in a specific time period. It's a comprehensive

scorecard of a nation’s economic health and an indicator of its standard of living. GDP is commonly used

to compare the economic performance of different countries and to track economic growth over time.

ii) Power Outage: A power outage, also known as a blackout, is the temporary loss of electrical power in

a specific area. For instance; from ‘Appendix 1’ in 1980, Energy demand was 7.38 billion kilowatt-hours

(KWH) less Energy supply which was 6.62 billion KWH which gives us 0.76 billion KWH (which is

the Energy Gap or PO). Also, if we check that 2015; Energy demand is 25.67 KWH less Energy supply

at 21.76KWH, then PO is3.91KWH. This can be caused by various factors such as natural disasters,

technical faults in the power grid, or maintenance work. Power outages disrupt daily life and business

operations, often leading to economic losses, decreased productivity, and inconvenience for both

households and businesses.

iii) Electricity Pricing Per Capita: Electricity pricing per capita refers to the average amount spent by each

individual on electricity within a country. This metric varies widely depending on the country's energy

resources, efficiency of its electricity infrastructure, and government policies. High electricity costs per

capita can indicate either high consumption rates or inefficiencies and higher prices in the energy sector.

iv) Commercial Bank Lending Rate: The Commercial Bank Lending Rate is the average interest rate that

commercial banks charge on loans to customers. This rate is crucial for economic activity as it influences

consumer and business borrowing costs. A higher lending rate can dampen economic growth by making

loans more expensive, while a lower rate can stimulate growth by reducing borrowing costs.

v) Disposable Income: Disposable income is the amount of money an individual or household has available

for spending and saving after income taxes have been deducted. It is a key indicator of financial health

and consumer spending potential. Higher disposable income usually suggests greater economic well-

being and can lead to increased consumer spending, boosting economic growth.