faculty of social sciences - university of nigeria, nsukka mallam isgogo.pdfper day, odiaka,( 2006)....

Odimba Rita

IMPACT OF POWER OUTAGE ON THE PERFORMANCE OF

MANU

Digitally Signed by: Content manager’sDN : CN = Weabmaster’s name O= University of Nigeria, Nsukka

OU = Innovation Centre

Odimba Rita

Faculty of Social Sciences

Department of Economics

MPACT OF POWER OUTAGE ON THE PERFORMANCE OF

MANUFACTURING INDUSTRIES IN NIGERIA

MOHAMMED MALLAM ISGOGO

PG/MSC/10/57944

i

: Content manager’s Name

a, Nsukka

MPACT OF POWER OUTAGE ON THE PERFORMANCE OF

FACTURING INDUSTRIES IN NIGERIA

ii

IMPACT OF POWER OUTAGE ON THE PERFORMANCE OF MANUFACTURING

INDUSTRIES IN NIGERIA

MSC (ECONOMICS) DISSERTATION

BY

MOHAMMED MALLAM ISGOGO

PG/MSC/10/57944 [email protected]

08035902699. 08054313408

THE DEPARTMENT OF ECONOMICS, FACULTY OF SOCIAL SCIENCES,

UNIVERSITY OF NIGERIA, NSUKKA.

SUPERVISOR:- PROF.(MRS.) S. I. MADUEME

April, 2013

iii

APPROVAL PAGE

This is to certify that this research project has been read and approved as meeting the

requirement for the award of Master in Science (M.Sc) Economics in the Department of Economics,

University of Nigeria Nsukka.

Prof. (Mrs) Stella I. Madueme _________________

Project Supervisor Sign/Date

_______________________ __________________

Project Coordinator Date

Dr. C.C. Agu __________________

Head of Department Date

____________________________ ___________________

External Examiner Date s

iv

DEDICATION

This project is dedicated to my late mother, Hauwa Kulu Ibrahim.

v

ACKNOWLEDGEMENT

My greatest gratitude goes to God, He made all these possible. He created me and made me

achieve this status. I sincerely want to thank my supervisor, Professor, (Mrs) Stella I. Madueme,

who worked tirelessly incriticizing, correcting and straightening this work.

I want to acknowledge my father Mallam Ibrahim Isgogo, my late mother, HauwaKulu

Ibrahim,my wife Maryam Suleiman, my children, Suleiman, Salma and Saudat, my friends and well

wishers who are too numerous to mention for their prayers, and encouragement.

1

Chapter One

1. Introduction

World Bank (1991:31) defined development as “a sustainable increase in living standard that

encompasses material consumption, education, health and environmental protection”. Social

scientists, particularly economists and sociologists, have for centuries been preoccupied with the

subject matter of development. The economists have traditionally considered an increase in per

capital income to be a good indicator of development (Hewick and Kindleberger, 1984; Kayode,

2002; Obadina, 2004). They assumed that growth in per capital income induced by growing

productivity is the engine of development. As regards the sociologists, development refers to

qualitative and quantitative changes in the structure and performance of the forces of production

through eradication of poverty, disease, hunger, inequality and unemployment among other social

problems (Offiong, 2001; Isamah, 2002). Considering the position of the economists, a critical

question that arises is: what drives productivity? The answer according to the World Bank (1991)

lies in the industrial development and technological infrastructure.

Industrial development is a process by which a nation acquires a competence in the

manufacturing of equipment and products required for sustainable development. Technology is

considered the prime factor in this regard; industrial development and technological development

are interdependent and interrelated, yet, they both depend on adequate energy supply.

Empirical evidence reveals that manufacturing firms in Nigeria have for long been facing

serious challenges leading to their unsatisfactory performance. Selected indicators of manufacturing

performance show that percentage average share of manufacturing sector in Gross Domestic Product

(GDP) from 1980 to 2005 was 7.2%, while percentage average share in total export was below one

percent. In the same vain, capacity utilization was below 50%, with share in the total import over the

same period being over 70%. The outcome of most researches deduced that infrastructural collapse

2

– particularly poor electricity supply being the greatest- have been the major problem confronting

manufacturing sector in Nigeria (Adenikinju, 2005; Anyanwu, 2000).

This situation is exacerbated by a grossly inefficient, poorly maintained distribution system.

The transmission network in Nigeria is characterized by several outages leading to disruption in the

lives of the citizenry. According to Anil et al (2007)” the level of disruption is a function of the over

dependence of people on electric power, which can be very high for a developed and not as much as

developing countries. The available energy generated is not enough to meet the demands of the

users, leading to constant load- shedding and blackouts.” Equipment is damaged by power surges

that usually accompany epileptic power supply and goods at various stages of manufacturing are

damaged.

According to a National Electric Power Authority (NEPA) Technical committee Report, 2004,

the last transmission line in Nigeria was built in 1987 while none of the on-going ones have been

completed. The manufacturing industry’s response has been to run permanently on internal

generating plants while the PHCN supply is used as standby. It is ironical that, in spite of the

enormous power generating potentials, about 60% of the country still has no access to electricity

power supply (NNDP 2001: Ajanaku, 2007, Adegbamigbe, 2007).

The recent survey on power distribution to the industrial sector in Nigeria showed that average

power outage in the sector increased from 12.3hours in January 2006 to 14.5 in March 2006. In a

worsening experience, the outage increased to 16.48 hours per day in June, 2006. In other words,

power distribution in the month of June, 2006 to the industrial sector, on the average, was 7.52 hours

per day, Odiaka,( 2006).

However, outages can be planned or forced. The National Control Centre (NCC), a unit of the

Power Holding Company of Nigeria (PHCN) stipulated in its operational procedure no; 10 ( op, ;

10) (NCC and PHCN,2006), that power stations and transmission stations are required to forward

3

their planned outage schedules for the following year, latest by the end of November. This enables

the NCC to plan a master programme of planned outages properly coordinated to ensure

maintenance of grid integrity after a thorough study and analysis of the outages. Forced outages can

be associated with aging equipment/defects, lightning, wind, birds/animals, vandalization, accidents

and poor job execution by contractors. However forced outages can be minimized if the system is

properly designed and maintained, but this will not completely eliminate interruptions.

1.1 Statement of Problem

The statistics on the power sector have been appalling. Only about 40 per cent of Nigerians

have access to electricity, Adekininju (2005). In terms of efficiency and performance, the Nigerian

electric power sector has been rated by the UNDP/World Bank Report in 2003 as having one of the

highest rate of losses (33%), the lowest generating capacity factor (20%), the lowest revenue at 1.56

c/k Wh, the lowest rate of return (-8%) and the longest average account receivable period (15

months) among a group of 20 low income and upper income countries. World Bank,( 2003). PHCN

is the public utility vested with the responsibilities of electricity supply in Nigeria. However, its

failure to provide adequate and reliable electricity to consumers, despite billions of naira of

investment expenditure has generated a confidence crisis in the industry. Regularpower supply is the

prime mover of technology and social development. There is hardly any enterprise or indeed any

aspect of human development that does not require energy in one form or the other- electricity

power, fuels, e.t.c. Nigeria is richly endowed with various energy sources, crude oil, natural gas,

coal, hydropower, solar energy, fissionable materials for nuclear energy, yet the provision of

sustainable energy has become a mirage. According to the Power Holding Company of Nigeria

(PHCN), the electric demand in February 2011 was 7,600 megawatts (MW), but actual generation

capability was 3,600 MW. The discrepancy between electricity demand and actual generation is

mostly due to low water levels and inadequate maintenance. Oluwole,( 2012).

Figure 1.1 Electricity Generation in Nigeria 1970-2005

Source: CBN Statistical Bulletin (2009)

Source: CBN Statistical Bulletin (2009)

4

5

.Despite changes in the Nigerian electricity sector, the poverty of energy is entrenched in the

country; about 85 million people, representing approximately 60 percent of the population lack

access to electrical services, (Iceed,2006). Less than 20 percent of rural areas have electricity service

coverage. The Nigerian overall electricity per capita is about 100 kwh, (Iceed, 2006). Contrary to the

Nigerian Government plan in 2003 to expand electricity access to 85 percent of the population by

2010 only 40 percent of Nigerians have been able to access electricity. The Nigerian electric

networks operated below its capacity of 9,900 megawatts, but less than 6,000 megawatts has been

generated, (Nnaji, 2008).Less than 2,000 Mw-hours. The Nigerian case is part of the African

problems. Total electricity installed capacity in Africa is less than 103,000 MW, representing 5 % of

the world’s total installed capacity, (Nnaji, 2008). Thus the following research problem statements

- How does power outage affect the production time and total output of the manufacturing

industries in Nigeria?

- How has the use of generators, as alternative source of energy, affected the cost of production of

the manufacturing industries in Nigeria?

-How is the level of labour output coping with the incessant power outage in the manufacturing

sector ?

1.2 Research Questions

In the course of this study, it is expected that answers shall be provided for the following

questions?

(a) To what extent has electricity outage affected the production time and total outputof

the manufacturing firms in Nigeria?

(b) How has generator as alternative source of power affected the performance of

manufacturing firms in Nigeria?

(c) How has power outage affected labour productivity growthof manufacturing firms in

Nigeria?

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1.3 Research Objectives

The central purpose of this work is to produce an in-depth research document that shows

clearly the present dilemma of the manufacturers in the state. To actualize this, the general objective

of this study is to ascertain the impact of power outage on the manufacturing industries in Nigeria.

The following are the specific objectives;

1. To analyze the economic impact of power outage on the performance of the manufacturing firms

in Nigeria.

2. To ascertain the impact of generator as alternative source of poweron the manufacturing firms

performancein Nigeria.

3. To ascertain the impact of power outage effects onlabour productivity growthof

manufacturing firms in Nigeria.

1.4 Research Hypothesis

The following hypothesis shall be tested in this research work;

Ho1: There is no significant impact of power outage on the performance of manufacturing

firms in Nigeria.

Ho2: There is no significant impact ofgenerator as an alternative source of poweron the

performance of manufacturing firms in Nigeria.

Ho3: There is no significant impact of power outage on labour productivity growthof

manufacturing firms in Nigeria.

1.5 Scope of the Study

The scope of this study is limited to the manufacturing industries in Nigeria.This study

investigates the impact of power outage on the performance of manufacturing industries in Nigeria.

The primary sources of data used for this study is the World Bank’s Investment Climate Surveys

(ICS) on manufacturing sectors in Nigeria. The total number of establishments covered in

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the survey is 2,387firms. These firms were also drawn from ten International Standards

Industrial Classification (ISI) industries in 11 Nigerian states (Lagos, Ogun, Kano, Kaduna,

Enugu, Cross River, Abia, Anambra, Abuja, Bauchi and Sokoto States).

1.6 Justification/Significance of the Study

This research work is of great importance to a country crippled by incessant electricity

power fluctuations, a country deemed both internally and externally, as being incapable of providing

a sustained adequate power supply, which has culminated into growth stagnation. Administrators in

Nigeria are now availed with a roadmap towards unraveling some of the remote and immediate

causes of the enormous loss of revenue due to poor maintenance of the various electrical

installations across the country.

This study shall benefit the government with relevant and reliable information on industrial

output, employment fluctuations and businesses inefficiencies as a result of erratic power supply.

This could help to restructure the government’s annual budget towards key issues that hinder the

rapid development of the energy sector. This study, besides providing hints for a viable energy

policy for the country could aid in manpower planning. The research work is equally significant to

the manufacturers who shall be availed with methods of determining and comparing cost of using

the PHCN with that of own-generation. This enables them to decide whether to initiate price

differentiation for products by the use of PHCN and those by own-generation.

To the general public and other researchers, this shall become another body of literature that

provides adequate information on the relationship between power supply and productivity,

employment as well as revenue generation.

8

CHAPTER TWO

LITERATURE REVIEW

2. Introduction

The electrical utility is probably the largest and most complex industry in the world. The

electrical engineer, who researches in this industry, will encounter challenging problems in

designing future power systems to deliver increasing amounts of electrical energy in a safe, clean

and economical manner (Glover and Sarma, 2002). The transmission network in Nigeria is

characterized by several outages leading to disruption in the lives of the citizenry. According to

Anil et al. (2007), the level of disruption is a function of the dependency of people on electricity,

which can be very high for a developed country and not as much as developing countries. In

Nigeria, the available energy generated is not enough to meet the demands of the users leading to

constant load shedding and blackouts.

To provide adequate power to ensure that Nigeria is among the industrialized nations, three critical

activities must be effectively achieved.

� Adequate power must be generated;

� The power must effectively be transmitted to all parts of the country; and

� Finally, be efficiently distributed. Sambo et al (2011).

This chapter is divided into three; theoretical literature, which focuses on the overview of the

electricity supply in Nigeria, the Generation, Transmission, Distribution and Marketing of

electricity, a comparative analysis of consumption of electricity across the world, the consequence

of power outage on the manufacturing industries and causes of power outages in the country:

empirical literature, which discusses similar works done in this area and, the summary of the entire

chapter, which shall also contain value added to the previous works done in the area.

9

2.1 Conceptual Framework

Power outage in this research work refers to fluctuation and persistent cut in electricity supply

by the PHCN. It can also mean total disruption of power supply for a long period of time. What the

term stands for infer the unsustainability of power generation, and distribution, such that supply is

consistently and reliably adequate to foster the creation and growth of small, medium and large

business enterprises necessary for the development of a nation via mass investments.

Production Time here connotes the total number of hours of work used by the industries;

Employment Rate denotes the total number of manpower hired or fired from the industries;

Alternative source of energy refers to any other means (energy) used to power the engines e.g.

premium motor spirit (PMS), Gas, Coal, Solar, e.t.c.; Total output means the sum of all goods and

services produced by the industries and; Closure denotes the total stoppage of any economic activity

at the industrial site, relocation is used in this work to mean change or movement from the initial site

to a new one or from a new site to the initial one.

A power outage (also power cut, blackout, or power failure) is a short- or long-term loss of

the electric power to an area. There are many causes of power failures in an electricity network.

Examples of these causes include faults at power stations, damage to electric transmission lines,

substations or other parts of the distribution system, a short circuit, or the overloading of electricity

mains.

Power failures are particularly critical at sites where the environment and public safety are at

risk. Institutions such as hospitals, sewage treatment plants, mines, and the like will usually have

backup power sources such as standby generators, which will automatically start up when electrical

power is lost. Other critical systems, such as telecommunications, are also required to have

emergency power. Telephone exchange rooms usually have arrays of lead-acid batteries for backup

10

and also a socket for connecting a generator during extended periods of outage. Power outages are

categorized into three different phenomena, relating to the duration and effect of the outage:

• A transient fault is a momentary (a few seconds) loss of power typically caused by a

temporary fault on a power line. Power is automatically restored once the fault is cleared.

• A brownout or sag is a drop in voltage in an electrical power supply. The term brownout

comes from the dimming experienced by lighting when the voltage sags. Brownouts can

cause poor performance of equipment or even incorrect operation. In the Philippines, the

term brownout refers to a power outage, not to a drop in voltage.

• A blackout refers to the total loss of power to an area and is the most severe form of power

outage that can occur. Blackouts which result from or result in power stations tripping are

particularly difficult to recover from quickly. Outages may last from a few minutes to a few

weeks depending on the nature of the blackout and the configuration of the electrical

network.

Power outage, if unattended to, is expected to impact negatively on the total output of

manufacturing industries, distort the employment pattern of the industries, increase their cost and

time of production, or even lead to their closure/ relocation, e.t.c.

2.2 Theoretical Literature

There have been widespread and growing interests in empirical analysis and studies of

firms’ growth dynamics and its determinants especially in the manufacturing industry

because of the forward-backward linkage in promoting growth. The factors that foster the

creation and growth of new and existing enterprises remain the central interest to researchers

and policy-makers. Manufacturing firms are considered vital to economic growth and are

increasingly important laboratories for scholars interested in researching where a variety of

11

market frictions-information, asymmetry, moral hazard, liquidity constraint, integration and

market diversification, for example- are most amplified. At the same time renewed interest in

how firms grow through dynamic structure, mechanisms through which they grow and

significant forces either external or internal that propel the growth rate of firms have drawn

attention to how firms irrespective of size and structure of ownership behave in one industry

to the other. This is as result of their potential for diversification and expansion of industrial

production as well as the role they play in the attainment of the basic objectives of

development. Findings by economists over the years show that firms of different size-micro,

small, medium or large enterprises-play a much more important role in economic growth and

development.

The lesson of the past few years in Nigeria have shown that if local manufacturers are

to survive in a globalized world, the provision of energy and other key infrastructure facilities

cannot be compromised particularly in our peculiar situation where the upgrading of energy

production had suffered almost 30 years of neglect. From all account, the level of investment

required to reverse the decay arising from prolonged neglect would be massive without

establishing the exact factors that determine growth dynamics to ensure survival and play

their expected roles in the economy. Therefore there is a need for extensive survey research

to identify precise factors that determines manufacturing firm’s growth dynamics as carried

out in this study using panel survey data of quoted manufacturing firms under the Nigerian

Stock Exchange (NSE) between 2003 and 2012.

The principle of comparing productivity models is to identify the characteristics that are present in

the models and to understand their differences. This task is alleviated by the fact that such

characteristics can unmistakably be identified by their measurement formula. Based on the model

comparison, it is possible to identify the models that are suited for measuring productivity. A

criterion of this solution is the production theory and the production function. It is essential that the

model is able to describe the production function.

Dimensions of productivity model comparisons (Saari

The principle of model comparison becomes evident in the figure. There are two dimensions in the

comparison. Horizontal model comparison refers to a comparison between business models. Vertical

model comparison refers to a comparison between economi

of business, industry and national economy.

At all three levels of economy, that is, that of business, industry and national economy, a uniform

understanding prevails of the phenomenon of productivity and of how i

measured. The comparison reveals some differences that can mainly be seen to result from

differences in measuring accuracy. It has been possible to develop the productivity model of

business so as to be more accurate than that of n

business the measuring data are much more accurate. (Saari 2006b) In 1955, Davis published a book

titled Productivity Accounting in which he presented a productivity index model. Based on Davis’

model several versions have been developed, yet, the basic solution is always the same (Kendrick &

Creamer 1965, Craig & Harris 1973, Hines 1976, Mundel 1983, Sumanth 1979). The only variable


model is able to describe the production function.

Dimensions of productivity model comparisons (Saari 2006b)



model comparison refers to a comparison between economic levels of activity or between the levels

of business, industry and national economy.


understanding prevails of the phenomenon of productivity and of how it should be modelled and



business so as to be more accurate than that of national economy for the simple reason that in



ersions have been developed, yet, the basic solution is always the same (Kendrick &


12




c levels of activity or between the levels


t should be modelled and



ational economy for the simple reason that in



ersions have been developed, yet, the basic solution is always the same (Kendrick &


13

in the index model is productivity, which implies that the model can not be used for describing the

production function. Therefore, the model is not introduced in more detail here.

PPPV is the abbreviation for the following variables, profitability being expressed as a function of

them:

Profitability = f (Productivity, Prices, Volume)

The model is linked to the profit and loss statement so that profitability is expressed as a function of

productivity, volume and unit prices. Productivity and volume are the variables of a production

function, and using them makes it is possible to describe the real process. A change in unit prices

describes a change of production income distribution.

PPPR is the abbreviation for the following function:

Profitability = f (Productivity, Price Recovery)

In this model, the variables of profitability are productivity and price recovery. Only the productivity

is a variable of the production function. The model lacks the variable of volume, and for this reason,

the model can not describe the production function. The American models of REALST

(Loggerenberg & Cucchiaro 1982, Pineda 1990) and APQC (Kendrick 1984, Brayton 1983,

Genesca & Grifell, 1992, Pineda 1990) belong to this category of models but since they do not apply

to describing the production function (Saari 2000) they are not reviewed here more closely.

The empirical literature on production and cost developed largely independently of the discourse on

frontier modeling. Least squares or some variant was generally used to pass a function through the

middle of a cloud of points, and residuals of both signs were, as in other areas of study, not singled

out for special treatment. The focal points of the studies in this literature were the estimated

parameters of the production structure, not the individual deviations from the estimated function. An

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argument was made that these ‘averaging’ estimators were estimating the average, rather than the

‘best practice’ technology. Farrell’s arguments provided an intellectual basis for redirecting attention

from the production function specifically to the deviations from that function, and respecifying the

model and the techniques accordingly. A series of papers including Aigner and Chu (1968) and

Timmer (1971) proposed specific econometric models that were consistent with the frontier notions

of Debreu (1951) and Farrell (1957). The contemporary line of research on econometric models

begins with the nearly simultaneous appearance of the canonical papers of Aigner, Lovell and

Schmidt (197

Figure 2.1 Input Requirements

Shephard's (1953) input distance function is

It is clear that DI(y,x) ≥ 1 and that the isoquant is the set of xs for which DI(y,x) = 1. The Debreu

(1951) - Farrell (1957) input based measure of technical efficiency is

15

From the definitions, it follows that TE (y,x) ≤ 1 and that TE(y,x) = 1/DI(y,x). The Debreu-Farrell

measure provides a natural starting point for the analysis of efficiency.

The Debreu-Farrell measure is strictly defined in terms of production, and is a measure of technical

efficiency. It does have a significant flaw in that it is wedded to radial contraction or expansion of

the input vector. Consider in Figure 2.2, the implied inefficiency of input vector XA. Figure 2.2 is a

conventional isoquant/isocost graph for a single output being produced with two inputs, with price

ratio represented by the slope of the isocost line, ww′. With the input vector XA normalized to

length one, the Debreu-Farrell measure of technical efficiency would be θ. But, in economic terms,

this measure clearly understates the degree of inefficiency. By scaling back both inputs by the

proportion θ, the producer could reach the isoquant and, thus, achieve technical efficiency. But, by

reallocating production in favor of input x1 and away from x2, the same output could be produced at

even lower cost. Thus, producer A is both technically inefficient and allocatively inefficient. The

overall efficiency or economic efficiency of producer A is only α. Allocative inefficiency and its

implications for econometric measurement of inefficiency are discussed in Section 2.9. Empirically

decomposing (observable) overall inefficiency, 1-α into its (theoretical, latent) components,

technical inefficiency, (1-θ) and allocative inefficiency, (θ - α), is an ongoing and complex effort in

the empirical literature on efficiency estimation.

Output measurement

Conceptually speaking, the amount of total production means the same in the national economy and

in business but for practical reasons modelling the concept differs, respectively. In national

economy, the total production is measured as the sum of value added whereas in business it is

measured by the total output value. When the output is calculated by the value added, all purchase

16

inputs (energy, materials etc.) and their productivity impacts are excluded from the examination.

Consequently, the production function of national economy is written as follows:

Value Added = Output = f (Capital, Labour)

In business, production is measured by the gross value of production, and in addition to the

producer’s own inputs (capital and labour) productivity analysis comprises all purchase inputs such

as raw-materials, energy, outsourcing services, supplies, components, etc. Accordingly, it is possible

to measure the total productivity in business which implies absolute consideration of all inputs. It is

clear that productivity measurement in business gives a more accurate result because it analyses all

the inputs used in production. (Saari 2006b)

The productivity measurement based on national accounting has been under development recently.

The method is known as KLEMS, and it takes all production inputs into consideration. KLEMS is

an abbreviation for K = capital, L = labour, E = energy, M = materials, and S = services. In

principle, all inputs are treated the same way. As for the capital input in particular this means that it

is measured by capital services, not by the capital stock.

The problem of aggregating or combining the output and inputs is purely measurement technical,

and it is caused by the fixed grouping of the items. In national accounting, data need to be fed

under fixed items resulting in large items of output and input which are not homogeneous as

provided in the measurements but include qualitative changes. There is no fixed grouping of

items in the business production model, neither for inputs nor for products, but both inputs and

products are present in calculations by their own names representing the elementary price and

quantity of the calculation material. (Saari 2006b)

The transformation theory, which is based on input, process and output (IPO) is the

dominant production theory in use today. It is reductionist, it breaks down every process into

17

individual tasks performed by specialists. Activities are tightly organized and controlled; it is

consistent with Scientific Management and traditional cost accounting. It seeks to optimize the

entire production phase by optimizing each individual task, assuming that minimizing the effort and

cost of each task translates directly to maximum through put and customer value.

Structuralism is a development theory which focuses on structural aspects which impede the

economic growth of developing countries. The unit of analysis is the transformation of a country’s

economy from, mainly, subsistence agriculture to a modern, urbanized manufacturing and service

economy. Policy prescriptions resulting from Structuralist thinking include major government

intervention in the economy to fuel the industrial sector, known as Import Substitution

Industrialization (ISI). This structural transformation of the developing country is pursued in order

to create an economy which in the end enjoys self-sustaining growth. This can only be reached by

ending the reliance of the underdeveloped country on exports of primary goods (agricultural and

mining products), and pursuing inward-oriented development by shielding the domestic economy

from that of the developed economies. Trade with advanced economies is minimized through the

erection of all kinds of trade barriers and an overvaluation of the domestic exchange rate; in this way

the production of domestic substitutes of formerly imported industrial products is encouraged. The

logic of the strategy rests on the Infant industry argument, which states that young industries

initially do not have the economies of scale and experience to be able to compete with foreign

competitors and thus need to be protected until they are able to compete in the free market. The ISI

strategy is supported by the Prebisch-Singer thesis, which states that over time, the terms of trade for

commodities deteriorate compared to manufactured goods. This is because of the observation that

the income elasticity of demand is greater for manufactured goods than that for primary products.

18

The Structuralists argue that the only way Third World countries can develop is through ‘action by

the state’. Third world countries have to push industrialization and have to reduce their dependency

on trade with the First World, and trade among themselves.

2.3 An Overview of Electricity Supply in Nigeria

To discuss the power sector in Nigeria in a realistic and practical context, some brief review is

necessary to give an insight into the sector since independence.

Electricity supply in Nigeria dates back to 1886 when (2) small generating sets were installed

to serve the then colony of Lagos. By an act of parliament in 1951, the electricity corporation of

Nigeria (ECN) was established, and in 1962, the Niger Dams Authority (NDA) was also established

for the development of Hydro Electric Power. However, a merger of the two was made in 1972 to

form the National Electric Power Authority (NEPA), which as a result of unbundling and the power

reform process was renamed power Holding Company of Nigeria (PHCN) in 2005.

The Nigerian government has made an effort to increase foreign participation in the electric

power sector by commissioning Independent Power Projects (IPPs) to generate electricity and sell it

to PHCN. According to Sambo et al (2011),as part to government’s effort to engage foreign

partners, in April 2005, Agip’s 450 MW plant came on line in Kwale, Delta State. The Nigerian

National Petroleum Corporation (NNPC) and Joint Venture (JV) partners, ConocoPhillips and Agip,

provided the $480 million to construct the plant.

IPPs currently under construction include the 276 MW Siemens station in Afam, Exxon

Mobil’s 388 MW plant in Bonny, ABB’S 450-MW plant in Abuja and Eskom’s 388 MW plant in

Enugu. Several states governments have also commissioned oil majors to increase generation

including Rivers State, which contracted shell to expand the 700 MW Afam station. The Federal

Government also approved the construction of four thermal power plants (Geregu, Alaoji, Papalanto

and Omotosho) with a combined capacity of 1,234 MW to meet its generating goal of 6,500 MW in

19

2006. In addition fourteen hydroelectric and natural Gas plants were planned to kick up but yet to

commence since then. China’s EXXIM Bank, Su Zhong and Sino hydro have committed to funding

the Mambila (3,900mw) and Zungeru (950-MW) hydroelectric projects. Also NNPC, in a Joint-

Venture with Chevron are to construct a 780-MW gas fired thermal plant in Ijede, Lagos state. The

project is expected to be constructed in three phasis, with the first two phases expected to have

capacity of 250 –MW each. The plant is expected to be operational in 2007 but yet to commence

construction. Sambo et al (2011).

2.4 Major Components of power supply in Nigeria

(a) Generation

The total Installed Capacity of the currently generating plants is 7,876 MW , but the Installed

available capacity is less than 4,500MW as at December 2012. Seven of the fourteen generation

stations are over 20 years old and the average daily power generation is below 2,700MW, which is

far below the peak load forecast of 8,900MW for the currently existing infrastructure. As a result,

the nation experiences massive load shedding.(PHCN, 2009)

Through the planned generation capacity projects for a brighter future (Table 2.2.1) shows

that the current status of power generation in Nigeria presents the following challenges.

I) Inadequate generation availability;

II) Inadequate and delayed maintenance of facilities:

III) Insufficient funding of power stations;

IV) Obsolete equipment, tools, safety facilities and operational vehicle.

V) Inadequate and obsolete communication equipment.

VI) Lack of exploration to tap all sources of energy form the available resources; and

VII) Low staff morale.

20

(b) Transmission

The transmission system in Nigeria does not cover every part of the country. It currently has

the capacity to transmit a maximum of about 4,300 MW and it is technically weak thus very

sensitive to major disturbances. Sambo,et al(2011) In summary, the major problems identified are:

I) It is funded solely by the Federal Government whose resource allocation cannot adequately

meet all the requirements.

II) It is yet to cover many parts of the country.

III) It’s current maximum electricity wheeling capacity is 4,000 MW which is awfully below the

required national needs:

IV) Some sections of the grid are outdated with inadequate redundancies as opposed to the

required mesh arrangement.

V) The Federal government lack the required fund to regularly expand, update, modernize and

maintain the network:

VI) There is regular vandalization of the lines, associated with low level of surveillance and

security on all electrical infrastructure:

VII) The technologies used generally deliver very poor voltage stability and profiles:

VIII) There is a high prevalence of inadequate working tools and vehicles for operating and

maintaining the network;

IX) There is a serious lack of required modern technologies for communication and monitoring:

X) The transformers deployed are overloaded in most service areas:

XI) In adequate of spare-parts for urgent maintenance; and

XII) Poor technical staff recruitment, capacity building and training programme.Agbo, (2007)

(c) Power Distribution & Marketing

In most locations in Nigeria, the distribution network is poor, the voltage profile is poor and

the billing is inaccurate. As the department, which inter-faces with the public, the need to ensure

21

adequate network coverage and provision of quality power supply in addition to efficient marketing

and customer service delivery cannot be over emphasized Sambo, et al (2011). In summary some of

the major problems identified are:

I) Weak and inadequate Network Coverage;

II) Overloaded Transformers and bad feeder pillars:

III) Substandard distribution lines;

IV) Poor Billing system;

V) Unwholesome practices by staff and very poor customer relations.

VI) Inadequate logistic facilities such as tools and working vehicle

VII) Poor and obsolete communication equipment:

VIII) Low staff morale and lack of regular training and

IX) Insufficient funds for maintenance activities.

2.5 Comparative Analysis of Consumption of Electricity across the World

Electricity consumption across the world reflect a great imbalance compared to what is

obtainable in Nigeria, for instance, Libya, with a population of only 5.5 million has generating

capacity of 4,600 megawatts, approximately the same as Nigeria, which has a population of about

140 million (Loher and Ezeigbo 2006: Oloja and Orelada 2006). There are plans to build seven

more plants in Nigeria (Atser 2007). All the stations are oil or gas fired and the country is selling

power to other Africa countries. South Africa, with a population of only 44.3million, has a

generating capacity of 45,000MW almost eleven times the generating capacity in Nigeria which has

three times the population of South Africa (Agbo 2007).

Studies and experiences have shown that power generation in the country has been dismal and

unable to compare with what obtains in smaller Africa countries.

The table below shows the consumption of electricity in some countries.

22

Table 2.2.3

Global Comparative analysis of Electricity Consumption

Country Population Power Generation

per capita

Consumption

United States 250,00 million 813,000MW 3.20kw

Libya 1054 million 4,000MW 0.38KW

United Kingdom 57.50 million 76,00MW 1.33kw

Iraq 23.60 million 10,000MW 0.42Kw

South Korea 47,00 million 52,000MW 1.09Kw

South Africa 44.30 Million 45,000MW 1.015Kw

Libya 5.50 million 4,600MW 1.05Kw

Egypt 67.90 million 18,00MW 0.265KW

Nigeria 140,00Million 4,000MW 0.03KW

Source: Agbo (2007)

2.6 Major Causes of Power Outages in Nigeria

Some of the major causes of power outages posing challenges to power engineers according

to Okafor and Eze (2010) include lack of reliable real time data, increase in aging equipment,

management not making time to take decisive and appropriate remedial action against unfolding

events on the system, and lack of proper automated and coordinated controls to take immediate and

decisive action against failure of events. In an effort to prevent cascading many of the problems may

be driven by changing priorities for expenditure on maintenance and reinforcement of most Nigeria

transmission lines” This is from the technical part of view.

23

Similarly, reasons have been adduced on why the various efforts made by government in the

last eight years have not yielded any significant improvement on power supply in Nigeria, some of

these are:-

First, is the constant vandalisation and attack on Escravos gas pipelines especially Chanomi

creek in Delta State by militant groups operating in the Niger Delta, the channel is feeding Egbin

Thermal station. Another pipeline, Escravos Lagos pipeline owned by the Nigeria Gas Company

(NGC) which feed Afam with gas has been vandalized several times over. This has brought power

generation to all time low (Nwachukwu 2007).

Second, PHCN is indebted to the NGC in the sum of N7 billion for gas supplies. To recover

their money NGC several times has to halt supply of gas to the organization (PHCN) to recover the

debts (Atser 2007)

Third, beside the low gas supply to the thermal stations, the worst and major cause is the

activities and conduct of the PHCN personnel. This age long problem in the sector persists in the

organization. For instance, those personnel in the marketing Department hardly read the meter.

Billing in such cases is largely by estimation. The result is often in spurious bills. In some cases

where bills are estimated instead of the actual consumption, most of the consumers are often hostile

to the efficient or personnel of the organization. Some even refuse out rightly to settle such bills

claiming that they cannot pay for services not rendered (Ikechukwu 2005, Agbo 2007; Johnson

2007).

Fourth, the endemic corruption in the sector; it has been argued that beside the Nigeria Police

Force, the next government parastatal that is ridden with the cankerworm is the PHCN.

Further, the problem of power supply is traceable to the usual gross inefficiency and

bureaucracy that are evident in most parastatals. Sabotage is also a significant factor. High tension

lines and transmission and generating equipment, components are stolen regularly. Revenue

24

collection is poor and the greatest debtors are government establishments and parastatals

(Adegbamigbe, 2007).

Another problem confronting the PHCN is the low investment in power generation over the

years. All the plants are very old. The thirty six percent of them are over twenty five years old, 48

percent are over twenty years old and no new plant has been installed in the last fifteen years prior to

the advent of civilian administration in 1999. With this, it is pertinent to note that power supply

situation in the country has not improved in the last eight years despite huge investments

government claimed to have made on it. However because of its dismal performance, plans are

underway to restructure and privatize the PHCN (Agbo, 2007)

Frustrated and provoked by PHCN’s crazy bills, ineptitude, dismal performance plaguing the

organization and the spate of corruption going on, it is understandable why the public

disenchantment against the performance of the sector has increased over the years (Ameh 2006,

Arowolo 2006).

2.7 Economic Consequence of Power Outage on the Manufacturing Sector

In order to capture the seriousness of the matter and present a scope on the economic

consequences of constant power outages, recent developments have shown that some companies in

Nigeria have started relocating elsewhere, especially to neighboring countries, where power is not

only provided constantly, but there is just enough to grant its affordability. Elkan (1995) reports that

these costs could have been indirectly borne by the government if an efficient system of power

infrastructure was provided to these firms. Most companies have to bear the heavy cost of

installation and maintenance of infrastructural facilities in Nigeria. In terms of numbers, and

according to the manufacturing association of Nigeria (MAN) as reported by Mayah (2010), 820

manufacturing companies closed shop between the years 2000 and 2008. In a similar instance

(MAN) again did a survey in January 2010 and Adeloye (2010) reported that a total of 834

manufacturing companies closed shop in the year 2009 alone. This increase is extremely alarming

25

because it surpassed the cumulative 8 years, from 2000-2008 value in just a single year (2009). The

survey which usually covers five manufacturing enclaves into which the country is divided, in terms

of their manufacturing activities, include 214 companies in Lagos, 176 in the North, 178 in the

South East, 46 in the South-South and 225 in the South –West areas.

Examples of big companies that have relocated or closed business include Dunlop Nigeria PLC,

Coca-Cola, Michelin, Cadbury Nigeria PLC, Unilever PLC, Patterson Zochonis (PZ). Guinness

Nigeria PLC, International Institute of Tropical Agriculture, OK Foods Group etc, Mayah (2010).

Apart from squandering the benefits of goods and services produced and/or rendered by this

companies within the shores of the country(Nigeria), in terms of cost and customer utility, it is also

painful to mention the indirect loss of millions of earnings by Nigeria to these other countries who

have Capitalized on these self- induced woes to boost their economy. One survey conducted by the

Central Bank of Ghana revealed that Nigeria was one of the 10 sources of Foreign Direct Investment

(FDI). To this end, Nigeria placed ninth with a contribution of 2.1 percent of the GHC 1.5 billion

invested in Ghana in 2007, Daily Trust (2010).

A closer analysis reveal greater overflow of economic implication, from the statistics of

manufacturing companies closing their businesses in Nigeria. For instance, it was reported by

Mayah (2010) that the 5% quota that the manufacturing sector contributed to Nigeria’s GDP in

1999 shrunk to 4.9% by the year 2000, Also, these large numbers of closed manufacturing

companies in recent times have worsened Nigerian’s growing unemployment rate. An economic

Analyst, quoted in Adeloye (2010) painted a scary picture of it when he was reported to have used a

simple calculation to explain the terrible spillover effect that closing of manufacturing companies

have on employment generation in Nigeria. The estimation revealed that when a company stops

operation, its workforce immediately became frontline victims, like in the 834 firms submitted by

MAN to have closed shop in 2009 alone, it can be speculated that less than 83,400 jobs were lost.

26

This submission is based on the assumption that the firms were medium sized manufacturing firms,

with each having at least 100 workers.

The pointer in all of the submissions by Mayah, Adeloye and Daily Trust (2010) is that poor

power supply has been identified as the major factor responsible for these unfortunate trends which

carries such economic repercussions.

According to the Manufacturing Association of Nigeria’s (MAN) survey in 2005, only 10

percent of industries operated. But then, the 10 percent could, on the average, only function at 48.8

percent of their respective installed capacities, According to the survey, 60 percent of the

companies were in comatose while another 30 percent had completely closed down. The following

year, 2006; a survey conducted by MAN in the first quarter indicated that most of the industrial

areas around the country suffered an average of 14.5 hours of power outage per day as against 9.5

hours of supply. Further, the figure released by the MAN indicated that the cost of generating

power supply accounts for 36 percent of production. About 1500 firms 160 percent; of the

association’s 2,500 members are in dire strait principally because of the additional operating cost of

alternative power generation, (Udeajah 2006, Adegbamigbe 2007).

As a result power supply and other related factors, industrial sector contribution to the Gross

Domestic Product (GDP) has continued to drop since 1990 from 8.2 per cent, got to 4.7 percent in

2003: 4.06 percent in 2004 and 4.2 percent in 2005, the lowest figure since the country got

independence in 1960 (Ajanaku 2007). The poor power supply situation has made almost all

manufacturing companies that have remained in business run private power plant at great cost and

this is evident on the amount spent on the importation of generators into Nigeria. A London based

magazine, African Review of Business and Technology in its April 2006 edition revealed that

Nigeria topped the list of generator-importing countries for the fourth year in a row, having

surpassed others since 2002. According to the report, Nigeria accounted for 35 per cent or $152

million of the total $432.2 million spent by African countries on generator imports in 2005. The

27

Report, which focused on diesel generator of between 2, 000KVA and 5,000KVA capacity, said the

country imported three times as many generators than the closest Africa importers Sudan and Egypt

– that spent $40.6 million and $32 million respectively on the product in 2005 (Atser 2006a: 28).

In buttressing the above report, a survey conducted in Lagos showed that the British America

Tobacco (BAT) Plc spent about N67.5 million in 2005 on diesel and maintenance of its private

power generation plant. Dunlop Nigeria Plc similarly spent N96 million on annual average, while

West African Portland Cement spent N90 million on the average. Others are Friestland Foods PLC,

N50 million; Nigerite PLC, N36million and Cadbury Nigeria Plc: N49 million. By MAN’s

statistics, nine companies within its fold spent a total sum of N69.5 billion to generate their power

(Odiaka 2006; Oke 2006). Against the backdrop of the epileptic power supply and the desire of the

companies to remain in the business, some multinational companies have devised other alternative

sources of power generation. In recent times quite a number of multinational companies operating

in Nigeria generate own power through Independent Power Project (IPP) (Udeajah 2006). However,

even with this situation it is on record that some of these companies have continued to post

impressive profits and meeting the obligations of their shareholders. But such performance is a

reflection of the fact that more and more of production costs are shifted to the final consumers most

of whose disposable incomes have declined steadily as a result of inflation generated by

government’s tough economic policies. This has the tendency to reduce consumers’ effective

demand and may force some companies to close shop or even relocate to a more investment friendly

environment on the long run as recently demonstrated in the case of Michelin (Ogunjobi 2007).

A critical assessment of the performance of the power sector by the World Bank best

captures its implication for industrial sector in Nigeria. The World Bank Report (2004: 135) on the

nation’s difficult investment climate states; Manufacturing firms in Nigeria consider inadequate

infrastructure, particularly power supply, as their most severe constrain. Dealing with the inadequate

28

power supply and other infrastructure problems absorbs for more of management’s attention than

any other business problem.

2.8 Empirical Literature

It is fairly settled in the literature that unreliable power supply results in welfare losses

(Kessides, 1993). But the empirical research on economic costs of power outages and own-

generation in developing countries remain limited, owing to the lack of appropriate microeconomic

panel data that could be used to infer firms’ and households’ response to poor provision of

electricity supply. Only two studies have recently been done on this subject in Africa. Adekininju

(2005) analyzed the economic cost of power outages in Nigeria. Using the revealed preference

approach on business survey data ( Bental and Ravid 1982; Caves, Herriges, and Windle 1992;

Beenstock, Goldin, and Haitovsky 1997), Adekininju estimated the marginal cost of power outages

to be in the range of of $0.94 to $3.13 per kWh of lost electricity. Given the poor state of electricity

supply in Nigeria, Adekininju(2005) concluded that power outages imposed significant costs on

business. Small-scale operators were found to be most heavily affected by the infrastructure failures.

Renikka and Svensson(2002) analyzed the impact of poor provision of public capital goods on firm

performance in Uganda. Using a discrete choice model on business survey data, the authors

predicted that unreliable power supply causes firms to substitute complementary capital (for own

generators) for deficient public services. Estimating investment equations on the same data, they

found that poor complementary public capital significantly reduced private investment.

Reconciling the results of the two studies is difficult. Both rely on limited data tests from

business surveys done in a single country. Both uses only a small number of factors among the

many that firms might consider in choosing to generate their own power. Neither accounts for

effects that may change the provision of power supply. And the estimated marginal costs of

electricity and effects of unreliable power supply on firms’ investment may be biased because of the

failure to address the possible endogeneity in choice of generator. Provision of electricity supply,

29

and other observed explanatory variables, such as firms’ profitability and access to finance, and the

country’s industrial structure.

A diversity of approaches to the estimation of electricity demand can be found in the

literature ranging from aggregative analysis of the relationship between electricity demand,

income and prices (Narayan et al., 2007; Lin, 2003, Holtedahl and Joutz, 2005), to more

detailed disaggregated analysis (Bose and Shukla, 1999;) based on simultaneous model

structure. In the most basic model, the demand for electricity, has been modeled as a function of a

single variable, such as real income (Dincer and Dost, 1997) or temperature (Al-Zayer and Al-

Ibrahim, 1996); real income and prices (Houthakker, et al., 1974; Zachariadis and Pashourtidou,

2006, Ziramba 2008) real income, residential electricity price and price of natural gas (Narayan et

al., 2007); real income, electricity prices, population growth, structural changes in the economy and

efficiency improvement (Lin, 2003); population, income, price of electricity, price of oil,

urbanization, weather (Holtedahl and Joutz, 2005); real income, price of electricity and diesel (used

in for captive power generation to meet the shortages), and reliability of power supply from utilities

(Bose and Shukla 1999); real income, the real price of electricity, and the variable that captures the

seasonal component of the demand for electricity (Chang and Martinez-Combo, 2003).

Empirical evidence on electricity infrastructure and manufacturing performance relationship is

so overwhelming. While there is concern in the literature on the fundamental positive roles that

electricity supply and access have in the growth of manufacturing sector, a wide gap exists on

measuring erratic electricity supply – manufacturing performance relationship particularly in

developing nations.

Some empirical studies have supported a positive impact of infrastructure on sectorial

performance and overall output. Indeed, infrastructure was found to be a significant determinant of

productivity. Studies have revealed that the contributions of telecommunications, roads and power

on industrial output and economic growth cannot be overemphasized (Anand, 2004, Hulme, 1996;

30

Clarke, 2002; Deaton and Ditcher, 1996; Rioja, 2003; Wodon, 2004; Foster, 2003; Seragelding,

2000; Estache, 2002, Fay 2003; Chisani,(1999).

Impact of electricity and petroleum were found to be high and significant on economic

growth in many countries. For example, Ghosh (2002) examined economic growth and electricity

consumption at disaggregate level for India over the period 1950-1997. He finds a unidirectional

causality from economic growth to electricity. Rufael (2006) finds cointegration in nine countries

and Granger causality for twelve countries. He concludes that the causality running from GDP to

electricity consumption in six countries and from electricity consumption to GDP in three countries

and bidirectional causality in three countries.

Aqeel and Butt, (2001), for Pakistan found that economic growth causes total energy

consumption at aggregate level. At disaggregate level, they found unidirectional causality from

economic growth to petroleum consumption, but no causality between economic growth and gas

consumption and unidirectional causality from electricity consumption to economic growth. Ashad

and Ahmed (2009) examined the demand for energy at disaggregated level, also for Pakistan over

period1972-2007 and found that electricity and coal consumptions respond positively to changes in

real income per capita and negatively to changes in domestic price level. The gas consumption

responds negatively to real income and price changes in the short- run, however in the longrun,

they found out that real income exerts positive effects on gas consumption.

It can therefore, be deduced from the foregoing that erratic electricity supply is detrimental

to the growth and industrial performance. On that note, knowing whether the impact of electricity

failure on manufacturing performance is positive or negative is not sufficient but a necessary

condition. The sufficient condition is to ascertain or measure the degree or extent of the impact for

suggestions and policy formulation. However, studies conducted on infrastructure-growth

relationship reveal that poor or infrastructure or specifically electricity failure constitutes the

fundamental problem affecting the growth of most economies.

31

Concerning the constraints to countries’ economic growth and the growth of sectors, there is

a consensus in the literature that has been noticed to be among the leading factors. For example,

poor electricity supply and access made a number of countries’ performances to rank among the

worst in the world. A study conducted by Zeljko (2006), using production function approach,

showed that only 6% of the population of Lesotho have infrastructure access rate. Comparatively,

as at 2002, low-income countries and Sub-Saharan Africa have an average infrastructure access of

31% and 15% respectively.

Specially therefore, electricity failure or power outages have been in the center stage in

inhibiting sectorial and economic performance of many countries. The electricity failure in either

the form of power outages, costs of load shedding to industries, etc, have significant effects on

countries GDP, growth manufacturing capacity utilization and the overall welfare. Studies

conducted by USAID (1988) on Indian economy, Kessides (1993) on Colombian economy, Tsauni

(2005) on Nigeria economy and also Pakistan economy respectively reveal that power outage was

the major factor in low capacity utilization as well as an estimated total production losses of about

1.5% of GDP. For instance, the report shows a fall of 1% of GDP as a result of power rationing in

Colombia. The Nigerian case which resulted into drastic fall in manufacturing productivity,

consequently closure of industries was due to erratic power supply. Power outages also in Pakistan

were reported to have reduced the GDP by 1.8% and the volume of manufactured exports by 4.2%

GDP.

The nature as well as the supply and access of basic infrastructural facilities for industrial

performance in developing nations have been unappreciative. As highlighted earlier, they constitute

the major hurdles to manufacturing sector performance in particular and the growth and

development of many countries, Nigeria inclusive.

It is in line with the above fact that the average costs of manufacturing establishments have

significantly increased beyond competitive level leading to investment crowding out effect. The

32

implication is that poor infrastructure raises cost of production and the sale price. This indeed could

contribute in crowding out investment when the output cannot compete favorably.

In a study on the Nigerian manufacturing industry, Lee (1989) found that the deterioration of

socio-economic infrastructure has forced private companies in the country to provide for substitutes.

Expensive generators have been acquired due to irregular electricity supply. The study shows that

there are clear economies of scale in the provision of utilities, communication and social services

from which private producers derived economic benefits. The non-availability or deterioration of

the infrastructure due to forced reduction in public investment has imposed heavy costs, and shifted

resources away from productive private investment in Nigeria. Another study by Lee and Anas

(1992) report that manufacturing establishments in Nigeria spend on average 9% of their variable

cost on infrastructure, with electric power accounting for half of this share (Also, Lee and Anas,

2002).

Considering the relationship between the volume of infrastructure and the cost function

structure of manufacturing industries in India, Lackshamana, et al (1988) found a positive

correlation between the two major manufacturing outlets and even in the informal sectors,

infrastructure was found to constitute their major expenses. Similarly, Lee and Anas (1992), in their

study found that smaller firms bear relatively higher cost of economic infrastructure. They studied

179 manufacturing establishment in Nigeria and discovered that private/personal infrastructural

provision cost contributed 15% by the larger firms where as it is up to 25% for the smaller firms as a

result of the fact that smaller firms incur higher per unit cost in contrast with the larger firms.

Indeed the level of infrastructural decay in sub-Saharan Africa had adversely affected their

manufacturing growth hence, overall factor productivity in the last two decades, thus resulted into a

poor economic performance and retarded growth. Nigerian’s growth rate for instance, compares

unfavourably with that reported by other countries and particularly those posted by China and the

Asian tigers. (Hong- Kong, Singapore, Taiwan and South Korea).

33

Although, studies on the effect of infrastructures (for instance electricity failure) on

manufacturing performance exist, there is no consensus of opinions on the approach to measure the

cost of power outages (electricity failure) and the characteristics of power outages. However, there

is divergence on the issue of various characteristics of power outages (such as warning time, outage

frequency and partial outages) in assessing the performance of manufacturing companies. Thus,

while some assess the impact of power outages on residential consumers, others do on industrial and

commercial consumers. Power outages have a number of dimensions and features, which include

among others duration, frequency timing, warning time, and interruption depth. Each of these

features essentially results in the alterations of the outage cost incurred by a customer.

For examples, a study on power outage by Ontario (1980) and Billinton (1982) reveal that

residential outages costs are at lower end of the spectrum, when compared with the costs incurred by

industrial and commercial consumers. The implication of this is that, industrial outage costs could

be so significant that production is affected.

A Study by Oyeke (2002) on social infrastructure and economic development reveal that the

supply of adequate power for domestic and commercial users is of utmost importance for sustainable

growth of the national economy. According to the study, it was observed that as people’s level of

education and technology improves, so do the quality of their life’s expectations hence, demand for

modern gadgets most of which depends on some form of energy to function. Contrastingly

however, the growth rate of energy (electricity) generation in Nigeria is far less than the rate of

growth of the population of its consumers.

This tendency is rather a re-occurring phenomenon hence, has become a permanent feature

of the Nigerian society. This had resulted into frequent power outages ranging from longer duration

interruption of (e.g. 6-8 hours of interruptions). It is useful to observe that most of the industrial and

commercial outage costs studies attempt to look at the measurement of the variation in outage cost

using industrial classification. (Billinton , et al, 1982).

34

With this intense low supply, coupled with population explosion, self –generation is found to

be the main alternative for industrial survival. In the same token, back-up power supply was also

found to have increased the probability of outage cost (cave et al, 1990) several strategies have been

used in different cases studies and variables. While some studies were focused on the impact of

power outage, electricity failure or even infrastructure in general on industrial sector, others

highlighted on commercial, household, education sectors and the entire economy.

Using a sample of firms to estimate the power outage cost, Ukpong (1973) adopted a

production function approach in a two year study i.e. 1965 and 1966. In the study, he found that the

unsupplied electrical energy for 1965 was 130kwh, while it was 172kwht of 1966 respectively. The

resultant costs of the power outages to the industrial sector in these two years were estimated to have

involved the sum of N1.68 million and N2.75 million respectively. In a similar study involving a

self- assessment methodology, Iyanda, (1982), estimates the impact of power shortage in high

income area in Lagos state. According to him, there is on the average electricity outage cost of

N1.19 per hour for each house hold.

Arising from the reviewed literature, it is easy to observe most of the efforts were centrered

on aggregate time series; cross section pooled or panel data. Considering Nigeria, most of the

studies used aggregate data to assess the impact of infrastructure on manufacturing sector growth

(Adu-Aka, 1996, Dandago, 2002, Tsauni, 2005 Chete 2005; and Akpokojie, 1997). In the same

token, some studies were undertaken to find out the impact of infrastructure deficiency on

manufacturing establishments in Nigeria using firm level (disaggregated) data (Lee and Anas, 2002;

Lee, 1989; Adenikinju, 2005; Iyanda, 1982; Uchendu, 1993; World Bank, 1993.

2.9 Summary of Literature

This chapter was divided into three sections the first which traced the history of electricity in

Nigeria and the various efforts by the government to address the problems of power supply. This

section also highlighted on power generation. Transmission and distribution in the country with a

35

comparative analysis of consumption of electricity in some selected countries. The economic

consequence of power outages on the manufacturing sector as well as the major causes of power

outage concludes the section.

The second section depicts the empirical literature, which highlight previous studies carried

out on power outages. Most of the studies reviewed proved that power outage is the major lacuna to

industrial development in developing counties.

2.10Limitations of previous studies

Studies have been carried out in the past by various scholars on power outages, some tried to

measure the cost of electricity power shortages e.g Ukpong (1973) Using a sample of firms to

estimate the power outage cost, adopted a production function approach in a two year study i.e. 1965

and 1966. Iyanda (1982). Considering Nigeria, most of the studies used aggregate data to assess the

impact of infrastructure on manufacturing sector growth (Adu-Aka, 1996, Dandago, 2002, Tsauni,

2005 Chete 2005; and Akpokojie, 1997). Ghosh (2002) examined economic growth and electricity

consumption at disaggregate level for India over the period 1950-1997. He finds a unidirectional

causality from economic growth to electricity. Rufael (2006) finds cointegration in nine countries

and Granger causality for twelve countries. (Bose and Shukla 1999) analyzed real income, price of

electricity and diesel (used in for captive power generation to meet the shortages), and reliability of

power supply from utilities Most of the studies did not look at the effect of power outage as it affects

every facet of the operations of the industries, e.g. production time, cost, total output, and relocation

or closure.

This very study is different from the previous ones in several ramifications, the period

covered here shall be 10 years and shall use the “Stochastic Frontier Model” in order to capture the

entire variables under investigation, and study the impact as it affects the manufacturing industries

in the state under the specified variables.

36

CHAPTER THREE

METHODOLOGY

3.1 Analytical Framework

There are a number of methodologies that can be used to estimate productivity, each

with its own strengths and weaknesses. One can use index numbers, parametric and non-

parametric methods, data envelope analysis, and stochastic frontiers. According to

Biesebroeck (2003), index numbers and data envelopment analysis are flexible in the

specification of technology but do not allow for measurement errors in the data. He argued

that parametric methods, which calculate productivity from an estimated production

function, are less vulnerable to measurement errors, certainly in the dependent variable, but

mis-specification of the production function might be an issue. However, for our study, we

propose to use the Stochastic Frontier Analysis (SFA).

The ‘Stochastic frontier analysis’ (SFA) draw its starting point in the stochastic

production frontier models simultaneously introduced by Aigner, Lovell and Schmidt (1977)

and Meeusen and Van den Broeck (1977).The ‘production frontier model’ without random

component can be written as:

yi = f(xi; β) . T Ei-------------------------------------------------------------------- (1)

Where

yi = the observed scalar capacity utilization of the firmi, i=1,..I,

xi = a vector of N inputs used by the producer i,

f(xi, β) = the production frontier, and

= a vector of technology parameters to be estimated.

37

TEi = the technical efficiency defined as the ratio of observed output to maximum

feasible output. TEi = 1 shows that the i-th firm obtains the maximum feasible output,

while TEi< 1 provides a measure of the shortfall of the observed output from

maximum feasible output.

A stochastic component that describes random shocks affecting the production

process is added. These shocks are not directly attributable to the producer or the underlying

technology. These shocks may come from weather changes, economic adversities or plain

luck. We denote these effects with . Each producer is facing a different shock, but

we assume the shocks are random and they are described by a common distribution.

The stochastic production frontier will become:

( ; ). .exp{ }t i i iy f x TE vβ= --------------------------------------------------------- (2)

We assume that TEi is also a stochastic variable, with a specific distribution function,

common to all producers.We can also write it as an exponentialTEi = exp{-µ i}, where ui ≥ 0,

since we required TEi ≤ 1. Thus, we obtain the following equation:

( ; ).exp{ }.exp{ }t i i iy f x u vβ= − ---------------------------------------------------- (3)

Now, if we also assume that f(xi, β) takes the log-linear Cobb-Douglas form, the

model can be written as:

0ln lnt n ni i i

n

y xβ β ν µ= + + −∑ ---------------------------------------------------- (4)

where

vi = the “noise” component, which we will almost always consider as a two-sided

normally distributed variable, and

ui = the non-negative technical inefficiency component.

38

Together they constitute a compound error term, with a specific distribution to be

determined, hence the name of “composed error model” as is often referred.

3.2 Model Specification

Building on the ‘Stochastic Frontier’framework stated above,we analyze the

economic impact of power outage on the capacity utilization (CU) and labour productivity

growth of manufacturing firms in Nigeria using a standard Cobb Douglas production

function as follows:

Y = AKα Lβ Mδ ---------------------------------------------------------------- (5)

where

Y = firms performance proxy by capacity utilization of the firms,

K = capital inputs,

L = labour inputs,

M = material inputs, and

A = the portion of output not explained by inputs and thus called total factor

productivity (TFP).

Therefore, to calculate TFP, we transform the production function into logs and then express

it in terms of TFP as follows:

LogYt = β0 + β1LogLt + β2logMt + β3LogKt + µ t --------------------------------- (6)

where

y = the log of output of manufacturing industries in the economy,

K = the log of stock of capital,

M = the log of material inputs, and

L = the log of number of workers in each firm.

39

Where the log of TFP is proxy by the residuals from the estimated Cobb Douglas

production function as in the next equation[see Biesebroeck (2003), Harris and Trainor

(2005), and Njikam et al (2005), etc].

1 2 3ˆ ˆ ˆ ˆln

t t t t t tTFP y L M kβ β β µ= − − − = --------------------------------------------------- (7)

Equation 7 will help us to obtain estimates of the elasticities of output with respect to

inputs (β1, β2 and β3) and then treat TFP as residuals from equation (6). Hence using this

method, the TFP estimates from equation (7) would need to be regressed using a second

stage model against a set of determinants, such as the quality of power infrastructure

variables, which do not feature when estimating equation (6) and yet clearly are not random

even though they are captured in the random error term, where µ t~ n.i.d (0, σ2) is required

for efficient and unbiased estimation of the model.

Newey and McFadden (1999) and Wang and Schmidt (2002) argue that using the

estimated value of lnTFP , based on equation (7) in a second stage model, results in both

inefficient estimates (in the form of inconsistent standard errors and, hence, inconsistent -

values) of the determinants of TFP. Thus, Wang and Schmidt (2002) argue that this

approach results in potentially biased estimates since by omitting factors from equation (6)

that determine output, theestimates of the estimated elasticities will suffer from an omitted

variable problem and thus LnTFPˆ will beincorrectly measured. The other thing is that two-

stage approaches are inefficient because they ignore any crossequation restrictions since they

do not take into account the correlation of the error terms across equations (Harrisand

Trainor, 2005).

Moreover, a more serious problem associated with this approach is that of omitted

variable bias. Thus thefirst step regression, equation (6) ignores other known determinants of

40

output such as power outage and standard econometric theory saysthat estimated elasticities

from equation (6) will be biased as a result. Thus the estimates obtained in the second

stepregression will also be biased and this is true regardless of whether factor inputs and

those variables that determineTFP are correlated or not. Wang and Schmidt (2002) show that

in the case of two step estimators of technicalefficiency using Stochastic Frontier Production

approach, simulations indicate that the bias due to omitted variableproblem is substantial.

Their results are relevant even when using two step estimations of the determinants of TFP,

atechnique shown by equation (6) and (7) above.

The preferred approach, therefore, is to directly include the determinants of output

and thus TFP intoequation (6) since this will avoid any problems of inefficiency and bias

and helps in directly testing whether suchdeterminants are statistically significant. Since TFP

is defined as any change in output that is not due to changes infactor inputs, we include these

determinants directly into equation (6) as follows:

LogYt = β0 + β1LogLt + β2logMt + β3LogKt + γ1LogQPFt + γ2LogPTGt + ΣXi + εt -- (6)

Where

QPF = the quality of power infrastructure at period t,

PTG = the annual power outage at period t, and

X = a vector of variables that includesall other productivity effects, like

industry’s age, dummy for foreign ownership and exporting, country and sectoraldummies.

We include these variables because some studies have shown that productivity is also

affected by the ageof the firm, as well as exporting and foreign ownership (de Kok et al,

2006; Huergo and Jaumandreu, 2004; Griffithet al, 2004; Harris and Robinson, 2004). We

include generator ownership to ascertain whether such ownership doesminimize the negative

41

effects of power outages on productivity (objective 1) cost of production (objective 2) and

closure and relation of the industries (objective 3).

Variables Measurement/Estimation Procedure

We measure our productivity variables, like capital, using the replacement cost of

plant and machinery while output and material inputs are measured using total sales value

and total cost of raw materials and intermediate goods used in production, respectively. Firm

age is calculated as the difference between the year the firm was established and the year the

survey was done. Foreign ownership is a dummy taking the value of 1 if the firm has at least

10% foreign ownership, and zero otherwise, and the export dummy takes a value of 1 if the

firm exports and zero otherwise. We also measured firm size using the total number of

permanent workers. Power disruptions are measured using the number of days firms go

without power per month, the number of hours without power per day, and the percentage of

output lost due to power outages in a given year.

Dummies are included in the model so as to capture the unobserved sector

heterogeneity because some products may use less electricity than others in their production

and these dummies may also capture sectoral comparative advantage based on the country’s

factor endowment differences (Yoshino, 2008). The manufacturing sectors covered include

textile and garment; machinery and equipment; chemical; electronic, non metallic minerals;

metal sector, other manufacturing; wood and furniture; as well as food sectors. We do our

estimations using ordinary least squares (OLS) and the Stochastic Frontier model.

We estimated out variable of interest ‘Power Outage’ using days without power per

month, hours without power per day, and percentage of output lost due to power disruptions.

This helps us to determine whether our results are robust to model and variable specification.

42

We also divided our firms into small (all firms with less than 20 employees) and large (all

firms more than 20 employees) to learn whether power outages affect firms indiscriminately

or whether the impact depends on size of the firm. In addition, by looking at the food sector,

as well as the Textile and garment sector, we went further to looked at the effect of power

disruptions at sector level.

Moreover, it is generally argued that firms with some foreign ownership are more

productive than those without (Yoshino, 2008; Griffith et al, 2004; Harris and Robinson,

2004) because foreign ownership brings with it skills and technologies that help improve the

productivity of firms (productivity effect).

DataSource and Coverage

The World Bank’s Investment Climate Surveys (ICS) on manufacturing sectors in

Nigeria is the primary source of the data used in this study. The survey in this country

covered 2,387 numbers of establishments. These firms were drawn from 10 International

Standards Industrial Classification (ISI) in 11 Nigerian states (Lagos, Ogun, Kano, Kaduna,

Enugu, Cross River, Abia, Anambra, Abuja, Bauchi and Sokoto States).

43

CHAPTER FOUR

PRESENTATION AND INTERPRETATION OF RESULT

4.1 Trend of Electricity Production, Consumption and distribution in Nigeria

The electricity statistics of Nigeria as presented in Figure 1 shows the relationship between

electricity production and consumption in Nigeria over the period 1971-2010. The graph clearly

shows the wider disparity between the amounts of electricity power production, consumption and

transmitted measured in kWh in Nigeria. For instance, out of the one hundred and forty seven

million two hundred and seventy thousand kilowatt electricity production (14727000000 kWh) in

the year 2000, in Nigeria, five hundred and sixty one million eighty thousand (56180000.00)

kilowatt was lost through the distributing and transmission channels. This represents 38% losses of

electricity transmission and distribution in Nigeria. This losses of electricity transmission and

distribution increased dramatically in 2003to six hundred and seventy three million ninety thousand

kilowatt (6739000000 kWh) as the production increased to two hundred and one million eight

hundred and thirty thousand kilowatt (20183000000 kWh). Though, this implies a decline in the

percentage loss of electricity distributions, from 38% in 2000 to 33.4% in 2003.

Table 4.1: Electricity Infrastructure Problems in Nigeria (2000-2010)

Period

Electricity

production

(kWh)

Electric power

consumption

(kWh)

Electric power

transmission and

distribution losses

(kWh)

2000 14727000000 9109000000 5618000000

2001 15463000000 9476000000 5987000000

2002 21544000000 13459000000 8085000000

2003 20183000000

2004 24275000000

2005 23539000000

2006 23110000000

2007 22978000000

2008 21110000000

2009 19777000000

2010 26121000000

Source: World Bank’s Nigeria Statistic (2012) Data

This size of losses of electricity undoubtedly has great negative impact on the industrial sector

especially the manufacturing sector of the economy.

consumption levels tracked very closely those ofpower p

being maintained.This could indicate that the levels of consumption of electricity were constrained

by what has been produced and supplied. This means that any unexpected increases in demand will

most likely lead to power outages or load shedding.

0

5E+09

1E+10

1.5E+10

2E+10

2.5E+10

3E+10

19

71

197

4

19

77

19

80

198

3

19

86

19

89

19

92

Am

ou

nt in

kW

h

Year

Fig. 1: Electricity Statistics in Nigeria (1971

Source: Plotted from World Bank Nigeria Statistics, 2012

20183000000 13444000000 6739000000

24275000000 16730000000 7545000000

23539000000 17959000000 5580000000

23110000000 15929000000 7181000000

22978000000 20328000000 2650000000

21110000000 19121000000 1989000000

19777000000 18617000000 1160000000

26121000000 21624000000 4497000000

Source: World Bank’s Nigeria Statistic (2012) Data


especially the manufacturing sector of the economy. This also signified that the country’s electricity

consumption levels tracked very closely those ofpower production without any reserve margins

This could indicate that the levels of consumption of electricity were constrained


most likely lead to power outages or load shedding.

19

92

19

95

19

98

20

01

20

04

20

07

20

10

Year

Fig. 1: Electricity Statistics in Nigeria (1971 - 2010)

Electric power consumption

(kWh)

Electricity production (kWh)

Electric power transmission and

distribution losses (kWh)

World Bank Nigeria Statistics, 2012

44


This also signified that the country’s electricity

roduction without any reserve margins

This could indicate that the levels of consumption of electricity were constrained


Electric power consumption

Electricity production (kWh)

Electric power transmission and

45

Thus, this trend in consumption and production of electricity partly explains why the country

experiences serious intermittent power disruptions. According to Babatunde and Shuaibu (2009),

despite Nigeria’s vast oil reserves, much of the country’s citizens do not have access to an

uninterrupted supply of electricity. Thus, Nigeria has approximately 5900MW of installed

generating capacity but is only able to generate 1600MW because most power infrastructure

facilities are poorly maintained. This also explains why the power sector also experiences high

energy losses of about 30%-35% from generation to billing, low access to electricity by population

(36%), as well as intermittent power outages (Babatunde and Shuaibu, 2009).

4.2 Empirical Model Estimation

The results were estimated using both the Ordinary Least Squares (OLS), Stochastic Frontier

Analysis (SFA) techniques and the tobit approach. The adoption of these three estimation

approaches is to enable study identify the best technique that capture the impact of electricity outage

on industrial sector performance captured by electricity capacity utilization. The tobit method

particularly was used because the dependent variable (the electricity capacity utilization) is censored

from below. We estimated out variable of interest power interruptions using days without power per

month, hours without power per day, and percentage of output lost due to power disruptions. This

helps us to determine whether our results are robust to model and variable specification. We also

divided our firms into small (all firms with less than 20 employees) and large (all firms more than

20 employees) to learn whether power outages affect firms indiscriminately or whether the impact

depends on size of the firm. Tables 4.2 and 4.3below present the OLS estimation results on the

impact of power outage on the performance of manufacturing firms in Nigeria.

46

Table 4.2: Summary of OLS Capacity Utilization Estimation

Table 4.3: Summary of OLS Annual Labour Productivity Growth Estimation

It is worthy to note that the variables that are of central interest in this study are those that measures

power outages and industrial sector performances. Our argument is that power is an intermediate

input and any reduction in its costs raises the profitability of production and enhances the marginal

productivity of labour and capital (Kessides, 1993). High number of hours without power, as well as

high percentage of output lost due to electricity disruptions must therefore have a negative effect on

productivity. The aboveresults largely support this expectation. Thus, when using the number of

hours without electricity, power disruptions have a negative and significant effect on productivity.

Specifically, the results presented in tables 4.2 and 4.3 shows the OLS estimation of the impact of

Total 333377779999000077772222....333300008888 111100005555 3333666611110000....22221111222244445555 Root MSE = 11114444....66664444 Adj R-squared = 0000....9999444400006666 Residual 22220000333366660000....4444666600008888 99995555 222211114444....33332222000066664444 R-squared = 0000....9999444466663333 Model 333355558888777711111111....888844447777 11110000 33335555888877771111....1111888844447777 Prob > F = 0000....0000000000000000 F( 10, 95) = 111166667777....33337777 Source SS df MS Number of obs = 111100006666

. regress cu neo adeo aleo fog peg nfec lpg npw age fow, beta

_cons 1111....777788880000555533338888 3333....111133339999000022222222 0000....55557777 0000....555577772222 .... fow ....4444333333337777333344449999 ....2222777799996666666600003333 1111....55555555 0000....111122224444 ....8888555522223333333355559999 age ----1111....44445555888811111111 ....7777222211110000333399993333 ----2222....00002222 0000....000044446666 ----2222....888800001111666655557777 npw ----....0000222200007777555500003333 ....1111666688886666111144449999 ----0000....11112222 0000....999900002222 ----....0000333399990000222288886666 lpg ....0000111133331111777755557777 ....0000222200004444999911117777 0000....66664444 0000....555522222222 ....0000333311111111333300008888 nfec ....5555777733331111333300003333 ....1111666633336666444499995555 3333....55550000 0000....000000001111 1111....000055551111111177775555 peg ----....3333444444444444000044449999 ....3333111122220000444477773333 ----1111....11110000 0000....222277773333 ----....3333222233333333000044448888 fog 1111....000044442222666655555555 ....2222666677779999999922226666 3333....88889999 0000....000000000000 1111....111133335555444477778888 aleo ----....5555999933334444999999991111 1111....222233334444444455558888 ----0000....44448888 0000....666633332222 ----1111....000088882222111144447777 adeo 3333....444444441111444488882222 1111....222299998888444477773333 2222....66665555 0000....000000009999 6666....333322225555444411116666 neo ----2222....111199990000444455557777 ....6666555522225555555566662222 ----3333....33336666 0000....000000001111 ----4444....111122221111777766666666 cu Coef. Std. Err. t P>|t| Beta

_cons ....4444666600003333888811116666 ....555599990000777700003333 0000....77778888 0000....444433338888 ----....777711112222333311112222 1111....666633333333000077775555 fow ....2222444455558888222233335555 ....0000555522221111333399993333 4444....77771111 0000....000000000000 ....1111444422223333111133338888 ....3333444499993333333333331111 age ....1111666688883333333344442222 ....1111333366660000222244442222 1111....22224444 0000....222211119999 ----....111100001111777700008888 ....4444333388883333777766665555 npw ....1111888877776666555533336666 ....0000333311116666111144444444 5555....99994444 0000....000000000000 ....111122224444888899991111 ....2222555500004444111166661111 aeg ....9999888833330000222255556666 ....0000000033337777999966666666 222255558888....99992222 0000....000000000000 ....9999777755554444888888884444 ....9999999900005555666622229999 nfec ----....222233336666222211114444 ....0000333300007777888855555555 ----7777....66667777 0000....000000000000 ----....2222999977773333333300009999 ----....1111777755550000999977771111 peg ....0000000077779999777700007777 ....0000555588887777777700007777 0000....11114444 0000....888899992222 ----....111100008888777700004444 ....1111222244446666444455553333 fog ----....0000111122227777000066668888 ....000055550000444477772222 ----0000....22225555 0000....888800002222 ----....1111111122229999000066663333 ....0000888877774444999922227777 aleo ....0000111122222222666677771111 ....2222333322225555111144449999 0000....00005555 0000....999955558888 ----....4444444499993333333333332222 ....4444777733338888666677775555 adeo ....0000111166667777444444441111 ....2222444444445555555511117777 0000....00007777 0000....999944446666 ----....4444666688887777555522224444 ....5555000022222222444400006666 neo ----....3333888866665555333344442222 ....111122223333111111116666 ----3333....11114444 0000....000000002222 ----....6666333300009999555500004444 ----....1111444422221111111188881111 lpg Coef. Std. Err. t P>|t| [95% Conf. Interval]

47

power outage on the performance of manufacturing industries proxy by capacity utilization (CU) of

manufacturing firms and the annual labour productivity growth (LPG). The results in both tables

proved that power outage (NEO) impacts negatively on both capacity utilization and labour

productivity growth of the manufacturing firms in Nigeria (tables 4.2 & 4.3). The results show that,

other factors kept constant, a unit increase in power outage in Nigeria will decrease manufacturing

firms’ performance (proxy by capacity utilization and labour productivity growth) by 2.19 and 0.38

units respectively.

Also when using the percentage of output lost due to power outage; therefore, the results in Table

4.2 show that power outages reduce productivity by about 59%. However, on the labour productivity

growth (table 4.3), this variable becomes insignificant and positive. The reason is probably that

measuring manufacturing performance with capacity utilization is more appropriate than using the

labour productivity growth.We also associated power infrastructure quality variables with generator

ownership to ascertain whether owning a generator helps in minimizing the negative impact of

power interruptions. Results show that the variable is insignificant and have negative impact to the

performance of manufacturing firms. Thus, generally owning a generator does ameliorate power

outage problems, even though the effect is weak. The reason why the variable is negative to

manufacturing firms performance could be that acquiring a quality generator is an additional cost for

firms with limited funds which may affect their capacity utilization and labour productivity growth.

To further highlight the cost – impact of generator to the performance of manufacturing firm,

another indicator, the percentage of firms owning or sharing a generator (FOG), shows significant

impact on manufacturing firms’ performances (CU). Other variables that have significant impact on

firm’s performances are average duration of a typical electrical outage (ADEO), number of firms

identifying electricity as their major constraint and the annual employment growth.

48

In the 2nd estimation in table 4.3 above, the proportion of foreign ownership in a firm (FOW) is

statistically significant in effect labour productivity growth. It is generally argued that firms with

some foreign ownership are more productive than those without (Yoshino, 2008; Griffith et al,

2004; Harris and Robinson, 2004) because foreign ownership brings with it skills and technologies

that help improve the productivity of firms (productivity effect). Results from the above regressions

show that the foreign ownership is an insignificant determinant of capacity utilization, but

significant in determinant the labour productivity growth (table 4.3). This may be because only

about one percent of surveyed Nigerian firms are foreign owned and most of them are in the food

sector and are mostly large firms.

Other variables that proved significant in determining capacity utilization and average labour

productivity growth in the OLS estimation arenumber of firms identifying electricity as their major

constraint and number of permanent full-time workers. Next we present the ‘Stochastic Frontier

Model’ estimation as stipulated above.

Table 4.4: Summary of Stochastic Frontier Estimation

LR test vs. linear regression: chibar2(01) = 8.4e-12 prob >=chibar2 = 1.0000

_cons 1111....999900008888888855557777 2222....999977772222999999993333 0000....66664444 0000....555522221111 ----3333....999911118888111100001111 7777....777733335555888811115555 fow ....5555111100002222777777776666 ....2222999900005555777777771111 1111....77776666 0000....000077779999 ----....0000555599992222444433331111 1111....000077779999777799998888 age ----1111....333399997777999933337777 ....6666888877779999000066668888 ----2222....00003333 0000....000044442222 ----2222....77774444666622221111 ----....0000444499996666666644445555 npw ....0000333399996666777733336666 ....111188885555777700005555 0000....22221111 0000....888833331111 ----....3333222244443333000011115555 ....4444000033336666444488888888 lpg ----....3333111122225555999988881111 ....5555111144447777222299992222 ----0000....66661111 0000....555544444444 ----1111....333322221111444444449999 ....6666999966662222555522227777 aeg ....3333222200006666999977778888 ....5555000066663333555500004444 0000....66663333 0000....555522227777 ----....6666777711117777333300009999 1111....333311113333111122226666 nfec ....4444999966662222777711116666 ....1111999966665555666655552222 2222....55552222 0000....000011112222 ....111111111111000011111111 ....8888888811115555333322223333 peg ----....3333444422220000444466668888 ....2222999944448888777788889999 ----1111....11116666 0000....222244446666 ----....9999111199999999999988888888 ....2222333355559999000055553333 fog 1111....00003333888888882222 ....2222555533333333000000002222 4444....11110000 0000....000000000000 ....5555444422223333666600004444 1111....555533335555222277779999 aleo ----....5555888844444444555500009999 1111....111166666666555533335555 ----0000....55550000 0000....666611116666 ----2222....888877770000888811117777 1111....777700001111999911115555 adeo 3333....444444442222444499995555 1111....222222226666999933336666 2222....88881111 0000....000000005555 1111....000033337777777744444444 5555....888844447777222244446666 neo ----2222....333311118888555544441111 ....6666444488889999222211115555 ----3333....55557777 0000....000000000000 ----3333....555599990000444400004444 ----1111....000044446666666677779999 cu Coef. Std. Err. z P>|z| [95% Conf. Interval]

Log likelihood = ----444422228888....88887777666655558888 Prob > chi2 = 0000....0000000000000000 Wald chi2(11111111) = 1111888877774444....99998888

49

The result from the stochastic Frontier estimation reconfirmed the results from OLS estimation. It

shows that the number of electricity outages in a typical month has negative and significant impact

on capacity utilization of manufacturing firms in Nigeria.As an implication of this result, the

expectation that the higher the number of times without power (the incessant power outage), which

results to high percentage of output lost due to electricity disruptions must therefore have a negative

effect on capacity utilization and labour productivity of firms. As also observed in OLS estimation

of table 4.2 above, the stochastic frontier estimation shows that the generator variable proxy with the

proportion of electricity from generator is insignificant and have negative impact to the performance

of manufacturing firms. The implication of this is that theowning a generator does ameliorate power

outage problems, but in the other hand, it is an additional cost for firms with limited funds which

may affect their capacity utilization and labour productivity growth.

There is no doubt that Nigerian manufacturing sector performed 'poorly', as experts say the

manufacturing sector contributed only 5 per cent to the nation's Gross Domestic Product (GDP).The

findings of this study is inline with the report of Nigerian Association of Chambers of Commerce,

Industry Mines and Agriculture (NACCIMA),which said that no fewer than 800 companies in

Nigeria closed shop between 2009 and 2011 mainly due to harsh operating business environment

mainly caused by electricity problem. According to this report, more than half of the surviving firms

had been classified as ailing, which poses a serious threat to the survival of the manufacturing

industry in the country. Capacity utilization in industries hovered around 30 per cent and 45 per cent

on the average, with 100 per cent overhead costs.The key impediments to the industry are attributed

mainly to poor infrastructure and epileptic power supply. The industry as a whole operates on more

than 70 per cent of energy it generates, using generators and operating these generators greatly

increases the cost of manufacturing goods.

50

Also corroborating the findings of this study are the Nigerian Association of Small Scale

Industrialists (NASSI), which reported that many companies in Nigeria operated below capacity in

2012 because of unstable power supply, inadequate funds and high labour costs. According to this

report, these has increased businesses' expenses, reduced productivity and hampered economic

growth making many firms to shut down or relocated to neighbouring countries.The blackouts are

negatively impacting the economy, which is grappling with a combination of slow growth, a weak

currency, high inflation and the effect of flooding that is expected to drive up food prices.

Checks on Model Specification Error

In order to ascertain the unbiasness and validity of the presented estimation above, we conduct

omitted variables test (Ramsey RESET test), heteroskedasticity and multicolinearity test. First, we

present the Ramsey RESET test for model adequacy in table 4.5 below.

Test for Specification Error

Under this test, we ascertain if the models estimated and analyzed above is well specified, (no

omitted variables) and fit for this study. Table 4.5 below presents the result for this test.

Table 4.5: Ramsey RESET test using powers of the independent variables

fow 111111117777....0000222244445555 111111118888....0000777733336666 0000 555544448888 FOW age 111111117777....4444222266664444 111111115555....4444444499992222 ....2222 555544445555 AGE npw 111122220000....0000888800002222 111111113333....0000111122223333 ....1111 555544444444 NPW lpg 111144445555....3333888888887777 111144441111....9999666655556666 ----22220000 555555551111....1111 LPG nfec 111144443333....5555444477772222 111111110000....2222000011117777 1111....2222 555544448888 NFEC peg 77773333....11114444000055557777 55556666....44440000333388889999 ....8888 333355552222 PEG fog 99990000....99991111777799992222 66665555....44443333444411116666 1111....2222 444422223333 FOG aleo 111111110000....4444666688889999 111100009999....5555555555551111 ....2222 555500001111 ALEO adeo 111111111111....0000555566666666 111111110000....4444333355558888 ....1111 555500003333 ADEO neo 111122222222....1111555544447777 111111113333....0000666611116666 ....4444 555533336666 NEO____ cu 88881111....66668888000011119999 66660000....00008888555500004444 ....7777 444422223333 CUddddeeeeppppvvvvaaaarrrr Variable Mean Std. Dev. Min Max Label

Estimation sample rrrreeeeggggrrrreeeessssssss Number of obs = 111100006666

51

H0: Model has no omitted variables

The null hypothesis can only be rejected when the power of the dependent variables, measured by

the mean and standard deviation of capacity utilization of manufacturing firms is greater or equal to

the powers of the independent variables included in the model.Thus, the mean and standard

deviation presented in table 4.5 above shows that apart from the proportion of employment growth

(PEG); all the powers (mean and standard deviation) of the independent variables are greater than

that of dependent variable. Based on this, we accept the null hypothesis (no omitted variables in the

model) and conclude that there is no specification error, therefore the mode is adequate and fit for

this type of study.

Test for Heteroskedasticity (unequal variance of the error terms)

The Breusch-Pagan/Cook-Weisberg test for heteroskedasticity was adopted for this test. The test followed F –

distribution with null hypothesis of constant variance of the error terms and 10 and 95 degrees of freedom

respectively. The test statistic (F calculated and tabulated) results for this test are as follow:

F*(10, 95) = 2.30

F tabulated = 4.08

Since the F calculated (2.30) is lesser than F tabulated (4.08) we accept the null hypothesis and

conclude the conditional error terms in the model are constant and have equal variance. Next we test

to make sure that our estimated models have no problem of multicolinearlity (strong linear

relationships among the explanatory variables).

Test for Multicolinearity among the Explanatory Variables

52

As explained earlier, this test is conducted to make sure that there is no multicolinearity among the

variables (inline with the assumption of OLS estimation technique). To achieve this, the study

adopted Correlation Matrix of Coefficients regress model, the summary of result is presented below.

Table 4.6: Correlation Matrix (Test for Multicolinearity)

The results given in table 4.6 suggest the absence of multicollinearity among the variables of the

model. However, a mild but negative correlation exists between electricity from generator and the

percentage of firms owning or sharing a generator. Again, found to be correlated are the number of

electricity outages in a typical month and the number of firms identifying electricity as their major

constraint. This is true since a rise in the electricity outage will trigger rises in both generator usage

and number of firms that observes power outage as major problems.Though, taken a lead from the

rule of thumb that placed the bench mark of 0.8 correlation coefficients, we conclude that

multicolinearity is not a major problem for this study.

4.3 Evaluation of Research Hypothesis

_cons 0000....0000666622228888 ----0000....1111333311116666 0000....1111777733337777 1111....0000000000000000 fow 0000....1111333344448888 ----0000....5555444466664444 1111....0000000000000000 age ----0000....4444888899999999 1111....0000000000000000 npw 1111....0000000000000000 e(V) npw age fow _cons

_cons 0000....1111222255558888 0000....0000888899998888 ----0000....0000999944444444 ----0000....0000999922223333 0000....0000666633335555 ----0000....2222888844445555 ----0000....3333000099993333 fow 0000....0000222200007777 0000....3333111155556666 ----0000....2222666666661111 0000....2222333377779999 ----0000....2222444422225555 0000....0000333366660000 ----0000....5555777799999999 age ----0000....4444555522221111 ----0000....3333777766669999 0000....1111888855555555 ----0000....0000222288888888 0000....0000666633331111 0000....2222999955556666 0000....3333222233336666 npw 0000....0000666699996666 ----0000....0000222288889999 0000....1111333388881111 ----0000....1111777755553333 0000....1111555555555555 ----0000....0000999955559999 ----0000....2222000099994444 lpg ----0000....1111333344449999 ----0000....1111555522221111 0000....1111888811115555 0000....0000555511113333 ----0000....0000333344444444 0000....0000555555550000 1111....0000000000000000 nfec ----0000....8888555566662222 0000....0000999966662222 0000....0000555588882222 ----0000....0000000088881111 ----0000....0000111177775555 1111....0000000000000000 peg 0000....2222777755557777 ----0000....1111555566665555 0000....0000111122224444 ----0000....9999999911118888 1111....0000000000000000 fog ----0000....2222777722226666 0000....1111333333331111 ----0000....0000000044448888 1111....0000000000000000 aleo ----0000....1111999911113333 ----0000....9999222200001111 1111....0000000000000000 adeo 0000....0000222211111111 1111....0000000000000000 neo 1111....0000000000000000 e(V) neo adeo aleo fog peg nfec lpg

Correlation matrix of coefficients of rrrreeeeggggrrrreeeessssssss model

53

The hypotheses stated initially for this study are as follow;

Ho1: There is no significant impact of power outage on the performance of manufacturing firms in

Nigeria.

Ho2: There is no significant impact ofgenerator as an alternative source of poweron the performance

of manufacturing firms in Nigeria.

Ho3: There is no significant impact of power outage on labour productivity growthof manufacturing

firms in Nigeria.

These three hypotheses can easily be answer from the model results presented above. As proved by

the results, the first hypothesis was rejected. This is because power outage variables were seen to be

highly significant in the three estimated model and confirmed with theory ‘a priori’ expectations.

Based on this, we conclude that there is significant impact of power outage on the performance of

manufacturing firm proxy by the capacity utilization of the manufacturing firms in Nigeria. Also the

second hypothesis was rejected and conclusion was drawn that there is indeed significant impact

generator as alternative source of poweron the performance of manufacturing firms in Nigeria.

Undoubtedly, this result is corroborating by the views of the Nigerian Association of Small Scale

Industrialists (NASSI) reports that many manufacturing firms in Nigeria operated below capacity

because of unstable power supply, inadequate funds and high labour operation costs.On the third

hypothesis, the study rejected it and concludes that there is significant impact of power outage on

labour productivity growth of manufacturing firms in Nigeria. Again, this conclusion is corroborated

inSanchis (2007), who state that “electricity as an industry is responsible for a great deal of output”.

The study went on to say that electricity had effects not only on factors of production but also on the

impact it had on capital accumulation.

CHAPTER FIVE

54

SUMMARY, POLICY IMPLICATION AND CONCLUSIONS

5.1 Summary of Research Findings

The electricity sector in Nigeria is presentlycharacterized by chronic power shortages

andpoor power quality supply, just like many African Countries. With an approximated

installedcapacity of 4000 MW (Electric Power sector reform Implementation Committee (EPIC),

2013)), it was stated that the country consumes about half its capacity. With an increasedpopulation

coupled with diversification of economicactivities, energy demand is rising but yet, electricitysupply

is relatively stagnant. On this basis, this very study is designed to investigate the impact of

electricity (power) outage on the performance of manufacturing industries proxy by the capacity

utilization of the manufacturing firms in Nigeria. Ascertain the impact of generator set as alternative

to electricity on the productivity growth. In doing this, data from the World Bank’s Investment

Climate Surveys (ICS) on manufacturing firms which covered 2,387 numbers of establishments in

Nigeria was exclusively used. The survey also covered 11 state selected at random from the 6

geopolitical zones of Nigeria. They includeLagos, Ogun, Kano, Kaduna, Enugu, Cross River, Abia,

Anambra, Abuja, Bauchi and Sokoto States.

The method ofOrdinary Least Squares (OLS) and Stochastic Frontier regression wasadopted to

analyse this economic impact of power outage on the capacity utilization (CU) and labour

productivity growth of manufacturing firms in Nigeria.Thus, the results from both methods

confirmed that electricity outage have serious negative and significant impact on the performance of

manufacturing industries, captured by the capacity utilization and labour productivity growth of

manufacturing firms in Nigeria. The study found that high number of hours without power, as well

as high percentage of output lost due to electricity disruptions exact negative effect on both capacity

utilization and productivity of manufacturing firms. The study observed that, other factors kept

constant, a unit increase in power outage in Nigeria will decrease manufacturing firms’ performance

(proxy by capacity utilization and labour productivity growth) by 2.19 and 0.38 units respectively.

55

Also when examined the percentage of output lost due to power outage, the study found that power

outages reduce productivity by about 59%. However, on the labour productivity growth, this

variable becomes insignificant and positive. The reason is probably that measuring manufacturing

performance with capacity utilization is more appropriate than using the labour productivity growth.

The study also found that power infrastructure quality variables captured by generator ownership

helps in minimizing the negative impact of power interruptions. Results show that the variable is

insignificant and have negative impact to the performance of manufacturing firms. Thus, generally

owning a generator does ameliorate power outage problems, even though the effect is weak. The

reason why the variable is negative to manufacturing firms performance could be that acquiring a

quality generator is an additional cost for firms with limited funds which may affect their capacity

utilization and labour productivity growth.

It is therefore obvious that inefficiency as well as inadequate facilities to boostelectricity supply in

Nigeria is a major cause of the increasinggap between demand and supply of electricity.This could

be due to the fact that there are only 9 workinggenerating stations in Nigeria which comprises 3

hydro and 6 thermal generating stations.Out of the approximated 6000 MW of installed capacity

inNigeria, not more than 4500 MW is ever produced. This isdue to poor maintenance, fluctuation in

water levels poweringthe hydro plants and the loss of electricity in transmission.

It could also be due to the 80 MW export of electricityeach to the republic of Niger and Benin.

“Apart fromserving as a pillar of wealth creation in Nigeria, electricityis also the nucleus of

operations and subsequently theengine of growth for all sector of the economy” (Ayodele,2004). He

has indirectly re-echoed that electricity consumptionis positively related to productivity andthat the

former is a cause factor of the latter. This meansthat electricity consumption have diverse impact in

arange of firms’ activities and consequentiallytheir performances.

5.2 Policy Implications

56

The essence of electricity in a nation is one so pertinentthat generating sets is owned by most

Nigerians. This practice reaffirmed the negative and significant impact electricity outage has on the

economic and industrial activities. Thisequally shows that electricity is not only important for

fuellingeconomic/industrial activities and growth but it is also necessary forthe attainment of

sustained comfort.Uses of Electricity are very numerous and increase industrialactivities in a

country. However, in Nigeria where electricity is in short supply,rational use of energy has been

professed as a measureto enhance consumption of electricity. Engineers and scientistshave also

advocated the potential rational energyuse depending on scientific knowledge and technology.This

will aid energy conservation and sustainability (Jochem,2004). Towards this end, the long term

technicalpotential for rational use of power could be driven by variousefforts. Among these efforts,

increasing energy efficiencyis paramount.

Besides the empirical impact of power outage on manufacturing firms found by this study, there are

numbers of observable obstaclesimpede the generation, distribution and consumption of electricity,

which put together brings about the insistence power outagein Nigeria. As corroborated in the

Central Bank of Nigeria (2000 report), the power sector is constrained by nine associated factors,

which if well tackled willbring to the barest minimum, the incidence of power outage and low

capacity utilization of manufacturing firms and other economic activities.

They include:

1. Lack of preventive and routine maintenance of Power Holding Company of Nigeria (PHCN)

facilities which results in huge energy losses.

2. Frequent major breakdowns, arising from the use ofoutdated and heavily overloaded

equipment.

3. Lack of co-ordination between town planning authorityand PHCN, resulting in poor overall

power system planningand over-loading of PHCN equipment.

57

4. Inadequate generation due to operational/technicalproblems arising from machine

breakdown, low gas pressureand low water levels.

5. Poor funding of the organization.

6. Inadequate budgetary provision and undue delay in releaseof funds to PHCN.

7. PHCN’s inefficient billing and collection system.

8. High indebtedness to PHCN by both public and privateconsumers who are reluctant to pay

for electricity consumedas and when due.

9. Vandalization and pilfering of PHCNequipment.

There is no doubt that any policy that can country all these problems will as well reduced power

outage and improve the performances of manufacturing firms (capacity utilization and labour

productivity growth) in Nigeria.

5.3 Recommendation

Electricity outage has tremendous economic implications for the growth and development of

a nation. Governments across the world have, over the past three decades, spent excessively on the

provision of power, evidently due to its significance in industrialization and technological

advancement. This study has the following recommendations for the Nigerian policy makers.

(1) Intensive funding of researches into alternative sources of energy eg solar, wind it should be

pursued immediately.

(2) Private entrepreneurs, dedicated government agencies and local communities should start

developing micro-hydropower stations and solar home systems at prices that can compete

with kerosene lamps, that currently light and pollute rural homes.

(3) Government should pursue its current privatization of the distribution of electricity in

Nigeria transparently and ensure that the approval investors are reputable technocrats in

power operations.

58

(4) The National policy on power should de-emphasize the “National arid” operation and allow

states that have the ability to generate, distribute and sell power to do so.

(5) The current distributional system is weak and thus, vulnerable to rain, wind and vandals. A

more wind resistant burglary-proof distribution network should be installed and made

operational.

5.4 Conclusion

This study has empirically examined the impact of power outage on manufacturing

firm’sperformances (capacity utilization and productivity)in Nigeria. The significance of power

outage variable both the tow estimated models suggests that there is need for the Nigerian

government to come up with ways of improving energy generation and supply. This could also be

supported by proper maintenance of electricity infrastructure as narrated above. The severity of

power outage problems in Nigeria is ironical in that the country is well endowed with resources to

produce power from crude oil and it is the sixth largest exporter of crude oil in the world, but

electricity black-outs and brown-outs appear to be the order of the day in this country. This can be

achieved either through the commonly used private-public partnership arrangements or privatization

of power utility monopolies as being pursuit by the present government of Dr. Goodluck Janathan.

Proper regulatory mechanisms can be used to minimize abuse of monopoly power by these

privatized utility companies. By so doing, resources will be generated to build and maintain

electricity infrastructure.

As remarked in Busani Moyo (2012), and Wasiu (2008), energy is the engine that drives

industrialization, which improves communication and helps innovation in science and technology,

provides sound health care delivery systems, and improves citizens’ standards of living. In light of

these benefits, a sound energy policy would increase competitiveness and growth, and reduce

poverty and unemployment. This sound energy policy should not be limited to the generation of

electricity from fossil fuel like oil, gas and nuclear sources, but even environmentally-friendly

59

sources like biomass, geothermal, hydro power, ocean waves, solar, and wind. Since generators

appear to be helpful, the government could find ways of ensuring that firms can easily or cheaply

access these machines with high sense of precaution on the usage. This can be done by supporting

firms who produce generators or even encourage more firms to participate in the generator

production sector so as to encourage competition and price reduction.

Again since the place of energy as a contributor to economic growth cannot be overemphasized, it is

therefore paramount that such a sector is not neglected in the country. The government should

ensure that energy supply is beefed up in diversity so that more economic activity can thrive. Energy

is the vital backbone of an economy. Research and development backed up by energy efficiency will

be beneficial to the nation. Also, increased investment will be needed to foster increased energy

production. The private, public or a partnership project could be carried out to see to the increase in

provision of energy.

5.5 Suggestion for Further Research

This study attempted to analyze the impact of power outage on the performance of

manufacturing industries in Nigeria. It has however opened a plat form for further researches, like

(1) The impact of power outage on the growth and development of small and medium

enterprises in Nigeria.

(2) The effect of power outage on technological development in Nigeria.

(3) The impact of electricity on Agriculture, health, etc.

(4) The impact of electricity outage on learning in Nigeria schools.

(5) The impact of power outage on Government or Household expenditure.

61

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