FINAL Kennis.final

CHAPTER ONE

1.0 INTRODUCTION

Nigeria spends about N400 billion yearly to import diesel to run our

homes and industries, but energy experts, some of whom work in the

Ministry of Power, say the real crisis in the sector today is the N1.8 trillion

loss in the nation’s annual Gross Domestic Product, as a consequence of

the current energy policy presided over by Rilwan Lanre Babalola, the

energy minister.(NEXT magazine 20th November 2010)

“The poorest in our community, currently pay more than N80 per kilowatt

burning candles and firewood, while our manufacturers pay in excess of

N60 per kilowatt on diesel generation and everyone else pays around N50

per kilowatt on self-generation” an energy expert in the Ministry of Power

who spoke confidentially to NEXT in Abuja at the weekend said. Gross

Domestic Product represents the total value of goods and services

produced in a country over a period of time, typically a year.

Startling revelations like this could only leave us to imagine what the

future holds for the power sector.It is fairly settled in the literature that

infrastructure plays a critical and positive role in economic development.

Infrastructure interacts with the economy through multiple and complex

processes. It represents an intermediate input to production, and thus

changes in infrastructure quality and quantity affect the profitability of

production, and invariably the levels of income, output and employment.

Moreover, infrastructure services raise the productivity of other factors of

production (Kessides, 1993). The provision of infrastructure in most

developing countries is the responsibility of the government. This is

because of the characteristics of infrastructure investment. First,

infrastructure supply is characterized by high set-up cost. Its lumpiness

and indivisibility precludes the private sector from investment. Second, its

indirect way of pay-off, coupled with its long gestation period, makes it

generally unattractive to private investors. Moreover, provision also

generates externalities that the producer may not be fully able to

internalize in the pricing structure. Thus, in the face of other numerous

competing, less risky and more familiar investment opportunities offering

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the promise of higher and quicker returns, few private investors are willing

to embark on infrastructure investment (Ajayi, 1995). However, the nearly

exclusive concentration of infrastructure provision in the hands of the

public sector, especially in developing countries, has led to failures in the

supply of these services. Faced with declining economic fortunes and

dwindling revenue, most governments in developing countries found it

increasingly difficult to keep pace with adequate provision and

maintenance of infrastructure. Moreover, the perception of government

that economic infrastructure is a social service affected the pricing of its

products and consequently the effectiveness of their provision. Besides

these, the traditional inefficiency associated with public monopolies affects

the quality and reliability of their services. There are five main approaches

used in the literature to infer the welfare losses from power outages.

These are the production function approach, self-assessment analysis,

economic welfare analysis, contingent valuation and, finally, the revealed

preference approach. These methods have their relative strengths and

weaknesses. They have been used widely in both developed and

developing countries, especially the former, to infer outage costs. For the

industrial sector, existing measure of outage costs vary between $1.27 to

$22.46/kWh of unserved electricity. Residential outage costs vary between

$0.02 and $14.61/kWh unserved (Caves, Herriges and Windle, 1992).

1.1 STATEMENT OF THE PROBLEM

In Nigeria, poor electricity supply is perhaps the greatest infrastructure

problem confronting the business sector. The typical Nigerian firm

experiences power failure or voltage fluctuations about seven times per

week, each lasting for about two hours, without the benefit of prior

warning. This imposes a huge cost on the firm arising from idle workers,

spoiled materials, lost output, damaged equipment and restart costs. The

overall impact is to increase business uncertainty and lower returns on

investment. For the aggregate economy, this has seriously undermined

Nigeria’s growth potential and the attractiveness of the economy to

external investors. The Power holding Company of Nigeria (PHCN) is the

public/Private utility vested with the responsibility of electricity supply in

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Nigeria. However, the failure of PHCN to provide adequate and reliable

electricity to consumers despite billions of naira of investment expenditure

has generated a confidence crisis in the industry. Public confidence in

PHCN’s ability to supply uninterrupted and stable electric power is so low

that consumers don’t longer rely on it’s supply they prefer to use own

generation device(s) like generator. The inefficiency of PHCN imposes a

huge cost on the economy. In 1990, the World Bank estimated the

economic loss to the country from then NEPA’s inefficiency at about N1

billion. There are essentially five ways by which firms may respond to

unreliable electricity supply. These are choice of location, factor

substitution, private provision, choice of business and output reduction.

While all these elements are presently observed among Nigerian firms, the

most common approach has been through private provision. Electricity

consumers have responded to PHCN’s inefficiency through self-generation.

Electricity users, both firms and households, now find it necessary to

provide their own electricity in part or in whole to substitute or

complement PHCN supply by factoring generator costs into the overall

investment cost, thus raising significantly the set-up cost for

manufacturing firms operating in the country. Incidentally, indigenous,

small-scale enterprises are worse affected. Lee and Anas (1991) report

that small-scale enterprises spend as much as 25% of the initial

investment on self-provision of a generator. Banks also insist that firms

seeking project loans must make provisions for investments in captive

generating equipment (Ajayi, 1995). This affects the range of profitable

investment available to budding entrepreneurs, raises cost of production,

reduces cost competitiveness of local production and represents a loss of

revenue to the electricity monopoly.

Electricity, strictly speaking, is not a private good. The sector is

characterized by high set-up costs and increasing returns to scale that

permit at most very few producers. However, the legislation setting up

PHCN effectively bars private operators from the markets and thus

prevents such possibilities as joint production and pooled supply, satellite

behaviours by private firms that could have led to shared costs and

guaranteed reliable supply of electricity. Thus, there are big firms with

huge excess scale that are not allowed to sell their excess production to

other firms. One implication of the existing electricity market structure is

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that PHCN, by taking advantage of the huge economies of scale in the

industry, is able to supply electricity at

much lower cost than private provision. This cost differential is large,

sometimes running to over four times. A 1983 joint UNDP/World Bank

study estimated a cost differential of 16–30% for large industrial

establishments in the country with auto-generation. In spite of this large

cost differential, however, over 90% of Nigerian manufacturers make

provision for auto-generation. The relevant question then is, Why are

manufacturers willing to incur such a huge extra costs for self-generation?

Is it possible that the manufacturers are perfectly rational agents who are

willing to incur the extra cost of auto-generation as an insurance against

the larger costs from power outage? An understanding of the behavior of

the firm is important in proffering policy recommendations to solve their

energy problems. In addition, an analysis of outage costs may provide

useful data for measuring the willingness of consumers to pay for reliable

electricity supply, measure the inefficiency of PHCN and hence form a

basis for reform of the public monopoly. A few studies have tried to

measure the cost of electric power shortages in Nigeria. These include

Ukpong (1973), Iyanda (1982), Lee and Anas (1991, 1992), Uchendu,

(1993) and Ajayi (1995). Our study is different from these studies in two

important respects – the methodology and the scope. Our methodology

combines the benefits of the revealed preference and survey techniques.

The principle of revealed preference implies that the impact of an outage

may be inferred from the actions taken by consumers to mitigate losses

induced by unsupplied electricity. Investments in backup generators may

then be used to impute the costs incurred by power outages. In addition,

we also investigate the factors underlying the behaviors of consumers in

the attempt to mitigate outage losses. We complement the results from

the revealed preference methodology with those obtained from subjective

valuation. The survey technique enables us to measure the impact of

outage characteristics on outage costs. In terms of scope of coverage, the

study focuses on a disaggregated analysis of the manufacturing sector of

the Nigerian economy. This enables us to examine the differential impact

of outages across the various subsectors of the manufacturing sector, and

across sizes and locations.

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1.2 PURPOSE OF THE STUDY

The central purpose of this study is to examine the impact of

infrastructure failures on the performance of the Nigerian manufacturing

sector, using the case of the electricity sector, and to understand the

behavior of firms in adapting to the uncertain business environment.

The specific objectives of the study are to:

Determine the severity of infrastructure problems in Nigeria.

To determine firms perception of the factors responsible for the

poor performance in the power sector.

To characterize electricity outages in Nigeria and the impact across

various Nigerian manufacturing sector.

To determine the level of impact that power outages has on the

industry and to also know the factors that contribute to the impact..

To determine the scale of production that suffers most.

To determine firms perception of how to improve the power sector.

1.3 RESEARCH QUESTIONS

Which of the infrastructures is most problematic to manufacturing

firms in Lagos state.

What are the factors responsible for the poor performance of the

power sector.

What impact does power failure has on Nigerian manufacturing

sectors.

What methods can be applied for the improvement of the power

sector.

1.5 SIGNIFICANCE OF THE STUDY

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Technological infrastructure is an enabling environment required

for rapid growth of technological and industrial development and comprises

physical and human variables like energy, water, transport, communication,

financial and human capital (Chenery 1960; Afonja 2003). Ability to provide

and effectively apply these inputs is a direct indicator of the potential for

the development of any nation, and it is primarily differentiating factor

between the various levels of development worldwide.

As this research work examines the impact infrastructural failure

has on a developing economy using the electricity sector of the Nigeria.

1.6 SCOPE OF THE STUDY

The scope of this research work covers the three senatorial district

of Lagos. Lagos is chosen as sample so as to draw conclusion from it and

then make generalization about Nigeria.

1.8 LIMITATIONS OF STUDY:

In the course of this research work, we were faced with several

limitations. Our major limitation was the financial aspect. Due to lack of

finance, we limited the research to Lagos state only.

The respondents which are industries and manufacturing companies

on the other hand did not make things easy as we have to follow ‘due

process’ which in some cases it took weeks to return some questionnaires,

some were not even returned as at the time of the analysis.

Some companies refused to part with some information as they told

us that it is against the company’s policy.

We also had difficulty in obtaining a sample frame for all the

companies, thereby leaving us no option but to use decide the sample size

subjectively.

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CHAPTER TWO

2.0 LITERATURE REVIEW

2.1 REVIEW OF THE LITERATURE ON ELECTRICITY OUTAGES

The poor state of infrastructure supply in developing countries has a

negative impact on their economic performance. For example, Lee and

Anas (1992) report that manufacturing establishments in Nigeria spend on

average 9% of their variable costs on infrastructure, with electric power

accounting for half of this share. Elhance and Lakshamanan (1988) show

that changes in the stock of economic infrastructure have important

implications for the cost structure of manufacturing firms in India. Even in

the informal sector, infrastructure can be a major share of business

expenses (e.g., in Zimbabwe, transport accounted for 26%, the largest

single item, according to Kranton, 1991). Similarly, a 1987 study focusing

on the effects of power outages in Pakistan estimated that the direct costs

of load shedding to industry during a year, coupled with the indirect

multiplier effects on other sectors, resulted in a 1.8% reduction in GDP and

a 4.2 reduction in the volume of manufactured exports. In India, a 1985

study concluded that power outages were a major factor in low capacity

utilization in industry, and estimated the total production losses in 1983/84

at 1.5% of GDP (USAID, 1988).

Similarly, power rationing in Colombia was estimated to reduce overall

economic output by almost 1% of GDP in 1992 (Kessides, 1993). Usually

small firms bear a relatively higher cost of infrastructure failures. Lee and

Anas (1992) in a 1988 study of 179 manufacturing establishments in

Nigeria found that the impact of infrastructure deficiencies of all types was

consistently higher for small firms. Private infrastructure provision (for

generators, boreholes, vehicles for personnel and freight transport, and

radio communications equipment) constituted 15% of total machinery and

equipment costs for large firms (over 50 employees), but 25% for small

firms. Small firms were found to generate a larger percentage of their

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power needs privately than larger firms and to pay a higher premium for

doing so, as measured by the excess costs of privately generated power

over that of publicly provided. Other enterprise level surveys conducted in

several countries have found that infrastructure costs and problems of

unreliability rank high among issues in the business environment. A 1991

survey of small enterprises in Ghana cited power outages, transportation

costs and other infrastructure problems among the top four problems of

operations (behind taxes), with this response strongest among “micro”

and small firms. Electricity outage was ranked by very small firms among

their top four constraints to expansion (Steel and Webster, 1991). Thus,

the issue of infrastructure supply – its adequacy and reliability – is very

important for the overall performance of the business sector and deserves

policy attention.

The theoretical basis for estimating electricity outages is that

there is a consumer welfare loss when there is electric power failure. Quite

a number of studies have examined the cost of outages using the various

approaches noted earlier. However, until recently many of these studies

focused on the developed countries, which have less actual experience of

outage failures. Moreover, there are significant differences in the

methodologies used, leading to highly disparate results regarding the

impact of service interruptions. Finally, fewer studies have focused on the

impact of the characteristics of outage cost such as the warning time,

outage frequency and partial outages.

Table 2.1 provides a summary of the literature on outage costs estimates.

These estimates vary significantly according to the choice of

methodologies and reporting system used. The proxy methods have

yielded estimates that are generally lower than those reported by the

survey methods. For the industrial sector, existing studies put the cost of

interruptions in the range of $1.27 to $22.46/kWh of unserved electricity.

Residential outage costs vary between $0.02 and $14.61/kWh unserved. In

the case of commercial sectors of the economy, outage costs range from

$5.02 in the retail service sector to $21.73kWh for office buildings. The

evidence points to significantly lower outage cost for government agencies

and institutions (Caves et al., 1992).

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Table 2.1 :A typology of selected previous study.

STUDY COUNTARY SECTOR METHODOLOGY

Bental and Ravid

(1982)

USA AND ISREAL INDUSTRIAL PROXY METHOD(GENERATOR)

Bernstein and Heganazy (1988)

EGYPT INDUSRIAL(PRODUCTION)

PROXY METHOD

Billinton, Wacker and Wojczynski (1982)

CANADA RESIDENCIAL SURVEY

Caves, Herriges and Windle (1992)

USA INDUSTRIAL PROXY METHOD

Matsukawa and Fujii

(1994)

JAPAN FINANCIAL AND COMMUNICATION

PROXY METHOD(GENERATOR & UPS)

Ontario Hydro (1977)

CANADA INDUSTRIAL SURVEY

Billinton, Wacker and

Wojczynski (1982)

CANADA INDUSTRIAL SURVEY

Doane, Hartman and

Woo (1988)

CANADA RESIDENCIAL SURVEY

Ukpong (1973 NIGERIA INDUSTRIAL(PRODUCTION)

PROXY METHOD

Iyanda (1982) NIGERIA RESIDENCIAL SURVEY

Uchendu (1993) NIGERIA INDUSTRIAL SURVEY

Lee and Anas (1992) NIGERIA INDUSTRIAL SURVEY

Beenstock, Goldin

and

ISREAL BUSINESS & PUBLI PROXY METHOD(GENERATOR)

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Haitovsky (1997)

Source : compiled by the writers(authors)

Another study reported by Billinton et al. (1982) and Ontario Hydro (1980) on

the sectoral variation in outage costs yielded similar conclusion. Residential

outage costs are found to be at the lower end of the spectrum, with costs less

than one-third of those estimated for industrial and commercial consumers.

Industrial outage costs are consistently lower than those in the commercial

sector, but the differences are not large. However, government and institutional

costs are consistently placed between those for residential and industrial

sectors, while office buildings and large farms have outage costs well in excess

of those for the commercial sectors. Some studies have also considered the

varied impact of outage characteristics on outage costs. Power outages can be

characterized along a number of dimensions, including duration, frequency,

timing, warning time and interruption depth. Each of these characteristics

potentially alters the outage costs incurred by a customer. Billinton et al. (1982)

and Ontario Hydro (1980) report that firms experience high outage costs

initially. However, these average hourly costs diminish rapidly as duration

increases, leveling off at about 50% of a 1-hour interruption.

Table 2.2 : estimated outage cost in selected previous studies.(US$ PER KWH)

COUNTARY COST SOURCEUSA 0.57 Telson 1975ISREAL 1.67 Telson 1975ISREAL 0.21 Bental & Ravid 1982USA 1.16 Bental & Ravid 1982ISREAL 7.20a Beenstock, Goldin and

Haitovsky (1997)

USA 11.20a CAVES,HERRIGES & WINDLE(1992)

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JAPAN 118.149b Matsukawa and Fujii (1994)

Note : a-measured at 1991 price

b-measured at 1988 price.

The relationship between outage costs and outage frequency has

received less attention in the literature. The available results indicate that

total outage costs are not proportional to outage frequency, but rather

decline per interruption as frequency increases. This pattern suggests that

customers may be able to adapt to more frequent outages. Customers also

prefer infrequent outages to frequent outages. Studies by Billinton et al.

(1982) and

Ontario Hydro (1980) among industrial and commercial sectors reveal that

these consumers prefer infrequent long duration interruptions (e.g., one 4-

hour interruption) to frequent short duration interruptions (e.g., four 1 hour

interruptions). However, the reverse result was reported for residential

consumers. Similarly, outage costs have been found to vary with the timing

of the power interruptions. Large industrial firms exhibit little variability in

estimated outage costs, while small firms exhibit a definite seasonal

pattern. Virtually all of the industrial and commercial outage cost studies

attempted to measure the variation in outage costs by industrial

classification. Billinton et al. (1982) reported outage costs for 15 industrial

classifications. One-hour outage costs range from $2.00/ average kWh for

mineral fuels and paper and allied industries to over $60.00/average kWh

for leather industries. Ontario Hydro (1977) reports similar results for 12

industry groups with less variation in the outage costs among groups.

However, Caves et al. (1990) observed some limitations with these studies.

First, industry specific outage costs are typically based on few observations

(i.e., fewer than ten) so that differences between them may not be

statistically significant. Second, much of the variation in outage costs by

industrial category may reflect differences in load

factors across groups.

Industrial firms with self-generation capabilities have lower outage costs.

Self-

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generation is found to lower the probability that customers may assign

positive costs to an outage, and to reduce costs by approximately 40%

given outage costs are positive. Backup power is found to increase the

probability of a positive outage cost, but to reduce the level of outage costs

by 0.03% for each 1% of backup power capabilities. Perhaps the earliest

study on the costs of power outages to the industrial and commercial sector

in Nigeria was carried out by Ukpong (1973). He used the production

function approach to study power outage costs in the two years, 1965 and

1966. Using a sample of 38 firms, he estimated unsupplied electrical energy

to be 130 kWh and 172 kWh in 1965 and 1966, respectively. The

corresponding costs of the power outages to the industrial sectors in the

two years were estimated at N1.68 million and N2.75 million, respectively.

The shortcomings of this study include: first, he used aggregated data for

the manufacturing sector and thus omitted subsector effects of the power

outages; second, the study focused on output loss for unsupplied electricity

and thus ignored other important costs such as raw material and equipment

spoilage and the cost of auto-generation. Iyanda (1982) adopted the self-

assessment methodology to estimate the impact of power shortages on the

household sector. He focused on the high-income area of Lagos Island,

Ikoyi, Victoria Island, Yaba and Surulere areas of Lagos state in Nigeria. He

estimated an average electricity outage cost of N1.19 per hour for each

household. A similar framework of analysis was reported in Uchendu (1993),

but the focus was on the industrial and commercial firms in Lagos state

through a survey covering various industrial sectors. The study estimated

several types of outage costs such as on material and equipment loss and

value of unproduced output. The value of unproduced output was estimated

at N1.3 million, N2.01 million and N1.32 million in 1991, 1992 and mid-

1993, respectively. However, the study suffers from the methodological

limitations of self-assessment data and was limited only to Lagos state.

World Bank (1993b) estimated the adaptive costs of electricity failure

on the Nigerian economy at US$390 million, divided between consumer

backup capacity (US$250 million), operating and maintenance costs of

diesel auto-generators (US$90 million), and fuel and lubrication (US$50

million). The estimate of NEPA revenue lost to unserved consumer energy

amounted to US$40 million. However, the short-term losses incurred by

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consumers such as raw material and equipment spoilage and lost output

were not estimated.

Finally, Lee and Anas (1991) used the self-assessment survey to measure

the adaptive costs to the business sectors in coping with infrastructural

deficiencies in Nigeria. Their study shows that most firms in Nigeria adapt to

the unreliability of publicly provided electricity by investing in backups. The

huge investment costs of the backup increases the set-up costs of these

firms and thus reduces their competitiveness and relative efficiencies. One

important finding of this study is that small firms have borne the brunt of

power failure in Nigeria because they cannot afford personal generators.

The study shares the shortcomings of World Bank (1993b) in that the short-

term losses incurred by consumers arising from raw material and

equipment spoilage and lost output were not estimated.

Our methodology incorporates the strengths of both the revealed

preference approach and the self-assessment approach. Also quite

important is that we investigated the impact of outage characteristics on

outage costs, an issue that was not considered in the previous studies.

2.2 HISTORICAL BACKGROUND OF ELECTRICITY IN NIGERIA

The history of electricity development in Nigeria can be traced back

to the end of the 19th century when the first generating power plant was

installed in the city of Lagos in 1898. From then until 1950, the pattern of

electricity development was in the form of individual electricity power

undertaking scattered all over the towns. Some of the few undertaking

were Federal Government bodies under the Public Works Dept, some by

the Native Authorities and others by the Municipal Authorities.

ELECTRICITY CORPORATION OF NIGERIA (ECN)

By 1950, in order to integrate electricity power development and

make it effective, the then colonial Government passed the ECN ordinance

No. 15 of 1950. With this ordinance in place, the electricity department

and all those undertakings which were controlled came under one body.

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The ECN and the Niger Dam Authority (NDA) were merged to become the

National Electric Power Authority (NEPA) with effect from the 1st of April

1972. The actual merger did not take place until the 6th of January 1973

when the first General Manager was appointed. Despite the problems

faced by NEPA, the Authority has played an effective role in the nation's

socio-economic development thereby steering Nigeria into a greater

industrial society. The success story is a result of careful planning and

hard work. The statutory function of the Authority is to develop and

maintain an efficient co-ordinate and economical system of electricity

supply throughout the Federation. The decree further states that the

monopoly of all commercial electric supply shall be enjoyed by NEPA to

the exclusion of all other organisations. This however, does not produce

privy individuals who wish to buy and run thermal plants for domestic use

from doing so. NEPA, from 1989, has since gained another status-that of

quasi-commercialisation. By this, NEPA has been granted partial autonomy

and by implication, it is to feed itself. The total generating capacity of the

six major power stations is 3,450 megawatts. In spite of considerable

achievements of recent times with regards to its generating capability,

additional power plants would need to be committed to cover expected

future loads. At present, efforts would be made to complete the on-going

power plant projects. Plans are already nearing completion for the

extension and reinforcement of the existing transmission system to ensure

adequate and reliable power supply to all parts of the country. By 1970,

the Military Government appointed a Canadian Consultant firm "Showment

Ltd" to look into the technical details of the merger. The report was

submitted to the government in November 1981. By Decree No. 24 the

ECN were merged to become the NEPA with effect from April 1, 1972. The

actual merger did not take place until the 6th of January 1973 when the

first General Manager was appointed. The day-to-day running of the

Authority is the responsibility of the Managing Director.

In the early 1960s the Niger Dam Authorities (NDA) and Electricity

Corporation amalgamated to form the Electricity Corporation of Nigeria

(ECN). Then, immediately after the Nigerian civil war, the management of

ECN changed its nomenclature to NEPA. What is currently referred to as

the Power Holding Company of Nigeria was formally known as National

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Electric Power Authority. For several years, despite consistent perceived

cash investment by the Federal Government, power outages have been

the standard for the Nigerian populace, and are now seen as normal by

the citizens of the country. Generally, the tariff has been criticised as

being too low compared to the cost of generating power. The federal

government of Nigeria has increased the tariff to attract foreign investors

since the 1st of July, 2010 in order to meet the growing concern for foreign

investors into the electricity sector.

2.3 ELECTRICITY POLICY AND DECISION MAKING PROCESS

The major power utility in the country, the National Electric Power

Authority (NEPA), now known as the Power Holding Company of Nigeria (PHCN) is

owned by the Federal Government. Until recently, it has been the main electric

power monopoly for the generation, transmission and distribution of centralized

grid power. In order to engender greater efficiency and sustainability in the

system, a process of reforms has been started to remove government’s

monopoly on generation, transmission and distribution. Thus, in 2005, the

Electric Power Sector Reform Act (EPSRA) was enacted. The law is aimed at

liberalizing the power sector. A component of the power reform law, was the

creation of the Nigerian Electricity Regulatory Commission (NERC), which is

mandated to regulate the electricity sector based on free market economic

principles and thereby create a level playing field for all interested stakeholders

to participate in the sub-sector.

As a consequence of the reform process, the generation and distribution

areas of the electricity market are being deregulated. The PHCN is being

unbundled into six separate generation companies and eleven distribution

companies for the various parts of the country. These would be individually

licensed, while only the transmission area would remain under government

control in the Transmission Company of Nigeria (TCN).

In light of the current pathetic power situation in the country, and in spite of

the current liberalization process, the Federal Government is still increasing

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investments in the sector to increase the generation capacity to 10,000MW by

2011 and to 20,000MW by 2015 through the National Integrated Power Projects

being implemented across the country. Most of the additional capacity would be

thermal (Gas) plants with renewable such as solar, biomass and wind

contributing a minimal amount. Nuclear power has been factored into the energy

mix, and it is expected that the first nuclear power plant of 1,000MW capacity is

expected to be commissioned not later than 2020.

2.4 STRUCTURE OF ELECTRIC POWER SECTOR

The electric power sector is currently dominated by a government

company, namely, the Power Holding Company of Nigeria (PHCN). It

currently owns all the power generation stations, the transmission network

and the distribution system. It has an installed capacity of 6000MWe

through a number of hydro (Kainji, Jebba, Shiroro), and thermal stations

(Egbin, Ughelli, Afam, Sapele). The Power Holding Company of Nigeria

(PHCN) Generation and Transmission System and its geographical

distribution is graphically depicted in Figure 2. The transmission voltage

levels are 330KV for the grid transmission; 132KV for the sub-transmission

lines, whilst the 33kV, 11KV and lower voltages constitute the distribution

networks. The System normal frequency is 50Hz. With the expected full

implementation of the Electric Power Sector Reform Act of 2005, and the

unbundling of the PHCN into the proposed generation and distribution

companies under a functional regulatory regime, the electricity market will

be liberalized. However, a large number of manufacturing companies

currently generate their own power, which further leads to increased cost

of production.

Although PHCN is the main supplier of public electricity, the state

governments have also participated in the provision of electricity, dating

back to the Third National Development Plan period, 1975–1980. They are

restricted to supplies in the rural areas, where there is no PHCN supply,

through the Rural Electrification Boards (REB). The ultimate goal of these

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isolated supplies, based on fossil fuels, is that they would eventually be

linked to PHCN’s network when feasible. There are many REBs in several

states but their performance has been abysmal. The state governments

could not meet the high costs of operating and maintaining these small

isolated diesel-fired generating plants. Only consumers lucky enough to be

absorbed into the national networks experienced electricity supply for a

larger fraction of the day. Although the legal instrument establishing PHCN

vested it with the sole responsibility of electric power production in

Nigeria, in practice the situation has been different because of the poor

performance of the organization in the provision of adequate and reliable

electricity supply. The response of the private sectors to the poor quality

of PHCN’s electricity supply has been widespread use of private provision

of electricity. Certainly, the need to minimize the enormous cost of

frequent interruptions in public electricity supply as manifested in

considerable loss of output, damage to machinery and equipment, and

idle labour time has aided the sustenance of the de facto situation. In

recent years virtually all major new establishments, whether privately or

publicly owned commercial or individual enterprises, have undertaken

substantial investment in private supply of electricity. Obviously the

impact of this is to increase costs and reduce the competitiveness of the

country’s production both locally and in international trade. There are

essentially five ways by which firms might respond to infrastructure

deficiencies:

Choice of location

Factor substitution

Private provision

Choice of sector

Output reduction

However,

private provision is by far the strategy most widely adopted by Nigerian

firms. Piqued by the extremely poor performance of PHCN, private

providers have had to make alternative private arrangements to reduce

their dependence on PHCN, with the attendant losses from infrequent

electricity supply. Lee and Anas (1991) identify four different private

response strategies pursued by firms:

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Self-sufficiency: In this case, the firm provides its own

infrastructural services to the point where it does not need any

public input.

Stand-by private provision: Here, the firm has its own

infrastructural facilities in place and switches to these facilities

where the quality or reliability of the public service falls below a

critical level.

Public source as standby: The firm relies primarily on its own

facilities but switches to the public supply during those times of the

day when the public source delivers a high quality service.

Captivity: The firm continues to rely on the public source

exclusively despite the very low reliability of such services.

In Nigeria, government regulations in the supply and trading of

infrastructural services prevent the possibilities of three other

mechanisms: joint production, satellite behavior or shared production. The

unreliability of PHCN has led most manufacturers to incur extra costs for

private alternatives. The generator market is very vibrant. Most small gas-

powered electric generating sets in use are Japanese products (e.g.,

Honda, Suzuki, Yamaha). Most of these products are imported from Japan,

while some are assembled in Nigeria. Holt Engineering Limited, for

example, is the company assembling Yamaha generators. Moreover, many

small-scale industrialists now prefer locally fabricated generating sets,

which are considerably cheaper than the imported brand names. The

Federal Ministry of Mines, Power and Steel is empowered to register all

electricity generating sets being used in the country, but few users

register with them.

18 | P a g e

CHAPTER 3

3.0 RESEARCH METHODOLOGY

3.1 RESEARCH PROCEDURE

Here we look at the various sources of data, the rationale for the chosen

research methodology, and the methodology for the analysis.

3.2 METHODOLOGY OF THE SURVEY

The sample framework of the Federal Office of Statistics (FOS) provided the basis

for the selection of the firms used in the survey. The sampling frame is not

possible get due to inability to get the total number of manufacturing

establishments in Lagos state. The study used a stratified random sampling

method to select specific firms. The stratification was necessary to reflect the

following variation: size, industry and location. The study covers three main

industrial zones: the Lagos west (Somolu) axis, the Lagos central(Lagos Island)

axis and the Lagos east(Ikeja) axis. Besides contributing over 90% of

manufacturing output in Lagos state, these zones also represent more than 66%

of electricity consumption in the State. In all, 120 manufacturing enterprises

were included in the survey 40 firms from each axis but 104 questionaires was

successfully retrieved. These are distributed as shown in Table

Table 3.1 : Distribution of respondents by sector, scale of production and location

Freqeuncy PercentageSector

Food and Manufacturig 15 14.4

Wood and Product 10 9.6

Beverages and Tobacco 20 19.2

Paper and Product 9 8.7

Textile and Leather 8 7.7

Rubber and Chemical 16 15.4

Metal and Product 20 19.2

Others 6 5.8

Total 104 100.0Location (senotarial district)

Lagos west(Somolu) 20 19.2

Lagos central (Lagos island) 42 40.4

Lagos east (Ikeja) 42 40.4Scale of production

19 | P a g e

Small scale 52 50.0

Medium scale 32 30.8

Large scale 20 19.2Source: Authors sample survey

For the purpose of the survey, firms that employed fewer than 50 workers were

classified as small scale, and those with more than 100 as large scale. Hence

firms that employ between 50 and 100 were regarded as medium-scale

enterprises.

Table 3.1 shows the surveyed companies encompassed producers of a

wide range of commodities, including capital goods, intermediate goods

and final goods. Specifically, the sectors covered include: Food

manufacturing

Beverages and tobacco

Textiles and leather

Wood and furniture

Paper and printing

Rubber and chemicals

Metals and products

Others

Thus the firms eventually selected spread across sectors, scales of

operations and the major industrial clusters of Lagos state, and should

therefore reflect a broad spectrum of the manufacturing firms in Lagos

state. The analysis of the survey data was carried out with the aid of a

computer software packages designed for the analysis of qualitative data.

The package is SPSS VERSION 17 (Statistical package for social sciences),

which is a very versatile tool for the analysis of qualitative data.

3.3 TOOL OF ANALYSIS

3.4 MULTINIMOIAL LOGISTIC REGRESSION

Logistic regression is the linear regression analysis to conduct when

the dependent variable is dichotomous (binary). Like all linear

20 | P a g e

regressions the logistic regression is a predictive analysis. Logistic

regression is used to describe data and to explain the relationship

between one dependent binary variable and one or more metric (interval

or ratio scale) independent variables.

Standard linear regression requires the dependent variable to be of

metric (interval or ratio) scale. How can we apply the same principle to a

dichotomous (0/1) variable? Logistic regression assumes that the

dependent variable is a stochastic event. That is that for instance if we

analyze a pesticides kill rate the outcome event is either killed or alive.

Since even the most resistant bug can only be either of these two states,

logistic regression thinks in likelihoods of the bug getting killed. If the

likelihood of killing the bug is > 0.5 it is assumed dead, if it is < 0.5 it is

assumed alive.

Both logistic regression and least squares regression investigate the

relationship between a response variable and one or more predictors . A

practical difference between them is that logistic regression techniques are

used with categorical response variables, and linear regression techniques are

used with continuous response variables.

Minitab provides three logistic regression procedures that you can use to assess

the relationship between one or more predictor variables and a categorical

response variable of the following types:

Variable

type

Number of

categories Characteristics Examples

Binary 2 two levels success, failure

yes, no

Ordinal 3 or more natural ordering of

the levels

none, mild, severe

fine, medium,

coarse

Nominal 3 or more no natural ordering of

the levels

blue, black, red,

yellow

sunny, rainy, cloudy

Both logistic and least squares regression methods estimate parameters in the

model so that the fit of the model is optimized. Least squares minimizes the

sum of squared errors to obtain parameter estimates, whereas logistic

21 | P a g e

mk:@MSITStore:C:%5CProgram%20Files%5CMinitab%5CMinitab%2016%5Cresources%5C1033%5CMtbst.chm::/ST_Regression/Nominal_Logistic_Regression/Nominal_Logistic_Regression.htm

mk:@MSITStore:C:%5CProgram%20Files%5CMinitab%5CMinitab%2016%5Cresources%5C1033%5CMtbst.chm::/ST_Regression/Ordinal_Logistic_Regression/Ordinal_Logistic_Regression.htm

mk:@MSITStore:C:%5CProgram%20Files%5CMinitab%5CMinitab%2016%5Cresources%5C1033%5CMtbst.chm::/ST_Regression/Binary_Logistic_Regression/Binary_Logistic_Regression.htm

regression obtains maximum likelihood estimates of the parameters using an

iterative-reweighted least squares algorithm [29].

Multinomial Logistic Regression is useful for situations in which you want

to be able to classify subjects based on values of a set of predictor

variables. This type of regression is similar to logistic regression, but it is

more general because the dependent variable is not restricted to two

categories.

Linear regression is not appropriate for situations in which there is no

natural ordering to the values of the dependent variable. In such cases,

multinomial logistic regression may be the best alternative.

Model

SPSS calculates K - 1 logit functions for a model with K response categories. For

example, a response with three categories (1, 2, 3) has two logit functions

(reference event = 3):

gk(x) = loge pk (x) = qk +

x'bkpK (x)

where:

gk(x) = the logit link function

qk = the constant associated with the kth distinct response

category

xk = a vector of predictor variables

bk = a vector of coefficients associated with the kth logit

function

Event probability

Denoted as p. For a three-category model with categories 1, 2, and 3 (reference

event 3), the conditional probabilities are:

P(y = 1| exp(x'b1)

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mk:@MSITStore:C:%5CProgram%20Files%5CMinitab%5CMinitab%2016%5Cresources%5C1033%5CMtbst.chm::/ST_Regression/Regression/References_for_Regression.htm

RE

SU

LT

x) = 1 + exp(x'b1) + exp(x'b2)

P(y = 2|

x) =

exp(x'b2)

1 + exp( x'b1) + exp(x'b2)

P(y = 3|

x) =

1

1 + exp(x'b1) + exp(x'b2)

where p k(x) = P(y = k|x) for k = 1, 2, 3. Each probability is a function of the

vector of 2(p + 1) parameters, b' = (b'1, b'2)

3.5 CHI-SQUARE TEST FOR INDEPENDENCE (CONTINGENCY TABLE

ANALYSIS)

The Chi-square statistic discussed in Section 8.7.1 and 8.7.2 can also be

used to test the independency of two factors or attributes in which the

data classified or summaized in a two-way table called a contingency

table.

An r x c Contingency Table

A contigency, table containing ‘r’ rows and ‘c’ columns is referred to as an

r c table.

Table 3.1

I.Q LEVEL

10% 30% 60% 100% Totals

High n11 n12 n13 n14 r1

med n21 n22 n23 n24 r2

Low n31 n32 n33 n34 r3

Total c1 c2 c3 c4 N

23 | P a g e

Table 3.1 can then be said to be of 3 4 contingency table, where

ri are row totals

cj are column totals

nij are observed frequencies and

N is the grand total

Therefore the procedure to test whether two factors are independent or not for a

r c contingency table is as follows:

(a). H0 : The two factors are independent

(b). Calculating expected frequencies (ei) for each cell i.e.

E(nij) =

=

(c). Compute the test statistic

2 = with 2,[(r-1)(c-1)] degrees of freedom

(d). Reject H0 if 2 > 2,[(r-1)(c-1)]

24 | P a g e

CHAPTER FOUR

4.0 PRESENTATION OF DATA AND ANALYSIS

4.1 ANALYSIS OF SURVEY FINDINGS

Table 4.1 .1 How do you view electricity supply in your industry?

Frequency Percent Valid Percent

Cumulative

Percent

Valid No obstacle 10 9.6 9.6 9.6

Moderate obstacle 42 40.4 40.4 50.0

Major obstacle 52 50.0 50.0 100.0

Total 104 100.0 100.0

Source : SPSS output

Figure 4.1.1

25 | P a g e

The table 4.1.1 and Figure 4.1.1 shows that 50% of the respondents view electricity

consumption as major obstacle in their firm,40.3% viewed it as moderate obstacle and 9.62%

viewed as minor obstacle .

Table 4.1.2 How important is Electricity in your factory?


Cumulative

Percent

Valid Most important 42 40.4 40.4 40.4

Important 42 40.4 40.4 80.8

Less important 20 19.2 19.2 100.0

Total 104 100.0 100.0


Figure 4.1.2

26 | P a g e

The table 4.1.2 and Figure 4.1.2 Shows that 40.38% viewed electricity as most important,also

40.38% viewed electricity as important and the remaining 19.23% viewing electricity as less

important.

Table 4.1.3 Years of the use of Generator


Cumulative

Percent

Valid Less than 1 year 20 19.2 19.2 19.2

1-5 Years 21 20.2 20.2 39.4

5-10 Years 33 31.7 31.7 71.2

More than 10 years 30 28.8 28.8 100.0

Total 104 100.0 100.0

Source : SPSS output27 | P a g e

Figure 4.1.3

From Table 4.1.3 and Figure 4.1.3 above, firms that purchased their Generator 5 to 10 years

ago have the largest percentage with 31.73% while those with less than 1 year old has the

least percentage with 19.23%.

Table 4.1.4

The cost of the Generator


Cumulative

Percent

Valid Less than N250,000 20 19.2 19.2 19.2

N250,001-N1,000,000 32 30.8 30.8 50.0

N1,000,000-N5,000,000 42 40.4 40.4 90.4

More than N5,000,000 10 9.6 9.6 100.0

Total 104 100.0 100.0

28 | P a g e

Source : SPSS outputFigure 4.1.4

From table 4.1.4 and Figure 4.1.4,those that have the cost of their generator to be between

N1million and N5million have the largest percentage with 40.38% while those that have the

cost of their generator to be more than 5 million has the least with 9.63%.

Table 4.1.5

29 | P a g e

What is the proportion of total investment at start up level is devoted to own

electricity by your firm?


Cumulative

Percent

Valid 0-10% 22 21.2 21.2 21.2

11-20% 41 39.4 39.4 60.6

21-30% 31 29.8 29.8 90.4

ABOVE 30% 10 9.6 9.6 100.0

Total 104 100.0 100.0


From Table 4.1.5 and Figure 4.1.5, 39.42% of the firms studied used 11% to 20% of their

total investment on supply of own electricity,29.81% used 21% to 30%,21.15 used only 0%

to 10% and 9.62 firms used over 30% of their total investment for the provision of own

electricity.

30 | P a g e

Table 4.1.5

How often do you fuel your generating set ?


Cumulative

Percent

Valid Daily 40 38.5 38.5 38.5

2 Times in a week 20 19.2 19.2 57.7

3 Times in a week 10 9.6 9.6 67.3

Weekly 23 22.1 22.1 89.4

Monthly 11 10.6 10.6 100.0

Total 104 100.0 100.0


31 | P a g e

From Table and Figure 4.1.5, 38.46%(largest) fuel their generator daily,followed by weekly

with 22.12% and those that fuel their generator 3 times in a week have the least percentage

with 9.62%.

Table 4.1.6

How much does it cost you to pay electricity bill to PHCN in a month


Cumulative

Percent

Valid less than N100,000 40 38.5 38.5 38.5

N100,001-N500,000 34 32.7 32.7 71.2

N500,001-N1,000,000 20 19.2 19.2 90.4

More than N1,000,000 10 9.6 9.6 100.0

Total 104 100.0 100.0


32 | P a g e

Figure 4.1.6.

From figure and object 4.1.6,32.69%(largest) of the respondents pay between N100,001 and

N500,000 as PHCN bill every month while those that pay more than N1million have the least

percentage with 9.62%.

Table 4.1.7

33 | P a g e

How much does it cost you to fuel and maintain your generating set in a month?


Cumulative

Percent

Valid Less than N100,000 40 38.5 38.5 38.5

N100,001-N500,000 23 22.1 22.1 60.6

N500,001-N1,000,000 21 20.2 20.2 80.8

N1,000,000-N5,000,000 10 9.6 9.6 90.4

More than N5,000,000 10 9.6 9.6 100.0

Total 104 100.0 100.0


Figure 4.1.7

From Table and Figure 4.1.7, the largest percentage(38.46%) goes for those that spend less

than N100,000 for the fueling and maintenance of their generating set while those that

spends between N1million and N5million and those that spend more than N5,000,000 share

the same percentage(9.62%) as the least.

34 | P a g e

Table 4.1.8

Respondents perception of the frequency of power outages per week


Cumulative

Percent

Valid Fewer than 5 times 10 9.6 9.6 9.6

Between 5 and 10 times 32 30.8 30.8 40.4

More than 10 times 62 59.6 59.6 100.0

Total 104 100.0 100.0


Figure 4.1.8

From Table and Figure 4.1.8, 59.62% of the firms studied experience power outages more

than 10 times a week,30.77% experience power outages between 5 and 10 times a week

while the remaining 9.62% experience power outages fewer than 5 times a week.

35 | P a g e

Table 4.1.9

Respondents perception of the average duration of power outages


Cumulative

Percent

Valid 1-6 hours 31 29.8 29.8 29.8

More than 6 hours 73 70.2 70.2 100.0

Total 104 100.0 100.0


Figure 4.1.9

36 | P a g e

Table and Figure 4.1.9 shows that the larger percentage(70.19%) of the firms studied

experience more than 6 hours of power outages while the remaining 29.81% of the firms

experience power outages for 1hour to 6 hours.

Table 4.2.0

How has electricity shotages affected your business?


Cumulative

Percent

Valid No effect 6 5.8 12.0 12.0

Low effect 10 9.6 20.0 32.0

High effect 34 32.7 68.0 100.0

Total 50 48.1 100.0

Missing System 54 51.9

Total 104 100.0


Figure 4.2.0

37 | P a g e

Multiple Response

Table 4.2.1

38 | P a g e

$Infracstructure Frequencies

Responses

N Percent Percent of Cases

Severity of infracstructure

problema

Land 10 4.8% 9.6%

Elecricity 104 49.8% 100.0%

Water 20 9.6% 19.2%

Telecommunication 20 9.6% 19.2%

Road 32 15.3% 30.8%

Petroleum shotages 23 11.0% 22.1%

a Total 209 100.0% 201.0%

a. Dichotomy group tabulated at value 0.


MULT RESPONSE GROUPS=$Problems 'Problem of electricity outages'

(lowelectricitytarrif inefficiency toomuchgovt poorfunding corruptio n (0))

/FREQUENCIES=$Problems.

Multiple Response

39 | P a g e

Table 4.2.2

$Problems Frequencies

Responses


Problem of electricity

outagesa

Low electricity tarrif. 10 6.8% 18.2%

Inefficiency of PHCN 46 31.1% 83.6%

Too much government

control

31 20.9% 56.4%

Poor funding 21 14.2% 38.2%

Corruption 40 27.0% 72.7%

a Total 148 100.0% 269.1%



Crosstabs

Scale of Production * Respondents perception of the frequency of power

outages per week

Table 4.2.3

Crosstab

Count

Respondents perception of the frequency of power

outages per week

Fewer than 5

times

Between 5 and

10 times

More than 10

times Total

Scale of Production Small scale 10 22 20 52

Medium scale 0 0 32 32

Large scale 0 10 10 20

Total 10 32 62 104


40 | P a g e

Table 4.2.4

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 37.468a 4 .000

Likelihood Ratio 49.641 4 .000

Linear-by-Linear Association 9.223 1 .002

N of Valid Cases 104

a. 2 cells (22.2%) have expected count less than 5. The minimum

expected count is 1.92.


Scale of Production * Respondents perception of the average duration of

power outages

Crosstab

Table 4.2.5

Respondents perception of the

average duration of power outages

1-6 hours

More than 6

hours Total

Scale of Production Small scale 10 42 52

Medium scale 11 21 32

Large scale 10 10 20

Total 31 73 104


41 | P a g e

Table 4.2.6

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)




N of Valid Cases 104

a. 0 cells (.0%) have expected count less than 5. The minimum expected

count is 5.96.


Crosstabs

Scale of Production * How has electricity shortages affected your business? Crosstabulation

Table 4.2.6

How has electricity shotages affected your business?

No effect Low effect High effect Total

Scale of Production Small scale 6 5 15 26

Medium scale 0 5 9 14

Large scale 0 0 10 10

Total 6 10 34 50


42 | P a g e

Table 4.2.7

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)




N of Valid Cases 50

a. 5 cells (55.6%) have expected count less than 5. The minimum

expected count is 1.20.


Nominal Regression

Table 4.2.8

Model Fitting Information

Model

Model

Fitting

Criteria Likelihood Ratio Tests

-2 Log

Likelihood Chi-Square df Sig.

Intercept Only 83.857

Final .000 83.857 18 .000


Table 4.2.9

43 | P a g e

Goodness-of-Fit

Chi-Square Df Sig.

Pearson .000 0 .

Deviance .0

00

0 .


Table 4.3.0

Pseudo R-Square

Cox and Snell .713

Nagelkerke .762

McFadden 1.000


44 | P a g e

Table 4.3.1

Likelihood Ratio Tests

Effect

Model

Fitting

Criteria Likelihood Ratio Tests

-2 Log

Likelihood

of Reduced

Model Chi-Square df Sig.

Intercept .000a .000 0 .

Years of the use

Generator*

83.857 83.857 18 .080

The cost of generator 77.545 65.931 19 .010

Proportion of total

invest on elec

177.547 84.899 16 .000

Cost of electricity bill 91.324E2 39.741 18 .092

Maintenance and

fueling of gen81.276E2 34.968 13 .020

How often do you fuel

gen.

9.000a .000 17 .000


Table 4.3.2

How has electricity outages affected your business

Observed

Predicted

No effect Low effect High effect Percent Correct

No effect 6 0 0 100.0%

Low effect 0 10 0 100.0%

High effect 0 0 34 100.0%

Overall Percentage 12.0% 20.0% 68.0% 100.0%


45 | P a g e

Multiple Response

Table 4.3.3

$Solution Frequencies

Responses


Solution to power problemsa Complete Privatization 25 25.0% 55.6%

Increase funding 20 20.0% 44.4%

Introduce competition 45 45.0% 100.0%

Increase tarrif 10 10.0% 22.2%

a Total 100 100.0% 222.2%


Source : SPSS output.

Ranking of infrastructure problems in Nigeria

Respondents were asked to rank the severity of infrastructure problems in

Nigeria on a scale of 1 to 3 – no obstacle, moderate obstacle, major obstacle (see

Table 4.1.1 for the results). The responses we received show that a large

percentage of the firm regarded power and voltage fluctuations as major

obstacles to their operations. Most of the respondents also ranked electricity as

their number one problem with 49.8%. This is followed by Road in a distant

second(15.3%) and Land as the least(4.8%). More small-scale firms ranked

electricity as a problem (52%) compared with large firms (20%).

Electricity consumption Table 4.1.5 further shows the share of total investment

devoted by firms to their own provision of electricity facilities. This cost, as

expected, varies inversely with the scale of operations of the firms. Small-scale

firms spend on average between 11 and 20% of initial investment on self-

generation compared with large-scale firms, which spend less than 10%. Across

46 | P a g e

all the firms, however, the additional investment costs incurred to mitigate the

unreliability of PHCN is an avoidable cost that simply increases the costs of

business operations in Nigeria.

Power outage impacts

The poor reliability of publicly supplied power in Nigeria has imposed a lot of

impacts on manufacturing firms in the country. As a result of power outages

firms lost an average of 1382 working hours in 2005. Assuming a nine-hour

working day, this translates to about 88 working days in 2005. Also, about 35%

of the firms reported having to shut down production at one time or the other in

the year as a result of power outages. The dimension of the poor state of

electricity supply in the economy can be seen in

Tables 4.2.0 and 4.3.2, which confirm the seriousness of the power outage

problem in Nigeria. On the average, firms experience outages between five and

ten times in a week, with each outage lasting between 1and 6 hours hour. If

these outages occur during the working period of the firms (which they do), then

the potential losses would be so much and thus firms would need a form of

insurance.

Finally, the average cost for auto-generation with maintenance cost and for

Publicly provided electricity is presented in Table 4.1.7 and 4.1.6 respectively.

There is a sharp division in the perception of consumers and PHCN on the causes

of the poor supply of electricity in Nigeria. While PHCN emphasized inadequate

funding, low tariff rates, and technical problems arising from illegal connections

and tampering with PHCN installations, consumers are convinced that the tariff

rate has little or nothing to do with it. The general perception is that PHCN is

corrupt and inefficient. Hence, the consumers’ solution is to deregulate the

sector.

CHAPTER FIVE

5.0 FINDINGS, CONCLUSION AND RECOMMENDATIONS:

5.1 FINDINGS

The following findings were observed from the analysis:

It was observed that electricity is ranked among the first in terms of

47 | P a g e

severity problems in Nigeria with virtually all the sampled

companies agreeing to the fact, followed by road. While the least of

them is land.

It was observed that inefficiency and corruption are ranked among

the highest as regards to factors responsible for poor performance

in the power sector, while low tariff and poor funding is ranked

among the lowest.

It was observed that 90% of the respondents were of the opinion

that average duration of power outage is more than 6 hours.

Moreover this rate of power outages is spread across all the sectors

and scale of production.

It was observed from the multinomial logistic regression that 68% of

the respondents have suffered greatly as a result of power outages,

20% are have suffered with a negative low effect, while the

remaining 12% have not suffered at all. The factors that influenced

their opinion are cost of maintenance and fuelling of generator, cost

of generator, frequency of power outages and proportion of total

investment on auto-generation.

It was observed that the scale of production that suffered most is

the small scale industries.

It was also observed that introduction of competition is ranked

highest among the steps to be taken in order to improve the power

sector followed by complete privatization of the power sector, while

increasing funding and tariff were ranked among the lowest.

5.2 CONCLUSION;

This study analysed the impact of power outages to the business sector of

the Nigerian economy. The study applied both the survey technique. The

characteristics of the electricity market in Nigeria were also analysed. One strong

outcome of the study is that the poor state of electricity supply in Nigeria has

48 | P a g e

exerted a negative effect in terms of the cost of operating in the business sector

of the Nigerian economy. The bulk of these costs come in the form of acquisition

of very expensive backup power. However, the decision to acquire a backup is

actually a rational decision on the part of the firm in order to insure it from larger

losses arising from frequent and long power fluctuations.

The continuation of the existing state of power supply will no doubt

continue to have a negative impact on the attempt by the government to

diversify the production and export base of the economy away from oil. A

situation where firms spend as much as 20% to 30% of initial investment on the

acquisition of facilities to enhance electricity supply reliability has a significant

negative impact on the cost competitiveness of the manufacturing sector.

Furthermore, as the results of our analysis have shown, small-scale

operators are more heavily affected by the infrastructure failures. In many

instances they are unable to finance the cost of backup necessary to mitigate

the negative impact of frequent outages. Hence, they have to bear the full

burden of electricity failures. Small-scale operators that could afford to back up

their operations have to spend a significant proportion of their Investment outlay

on this.

5.3 RECOMMENDATION AND FURTHER STUDY

From the statement made by the president Dr. Goodluck Jonathan GCFR that

he will sell of the monopoly state of PHCN (Next magazine), this is also in line with the

outcome of the research that a complete privatization will be the best option to solve the

crippling effect of power outages in Nigeria.

One important area of further research is to examine the institutional

reforms that can enhance the public sector delivery of electricity. It is very

obvious from the study that private generation is inefficient relative to that by

the public sector. Private provision is, therefore, not the optimal way for the

economy to go. There is a need for an in-depth study of the institutional

structure of PHCN in Nigeria and how effective reforms could be carried out to

ensure its effectiveness.

REFERENCES:

ABE J.B (2002) Sampling Techniques One, New Waves publishers Festac Lagos.

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ABE J.B (2004) Sampling Techniques Two, New Waves publishers Festac Lagos.

ADEBOWALE A.S (2003) “STATISTICS for Engineer, managers and scientists”,

Andkolad ventures Nig Ltd.

Agresti, A.(2002). Categorical Data Analysis, 2nd ed. New York: John Wiley and

Sons.

Bernstein, M. and Y. Heganazy. (1988). “Economic costs of electricity

shortages: Case study of Egypt”. The Energy Journal, Special Electricity

Reliability Issue, 9:173–88.

Caves, D.W., J.A. Herriges and R.J. Windle. (1992). “The cost of electric power

interruptions in the industrial sector: Estimates derived from interruptible

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No. 213. The World Bank.

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deficiencies in Nigeria: Private alternatives and policy options”. In A. Chibber

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http://www.nigerianinquirer.com/2010/08/26/president-jonathan-announces-plans-to-privatize-phcn/

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YABA COLLEGE OF TECHNOLOGY,YABA LAGOS. P.M.B.2011,YABA LAGOS Department of StatisticsQUESTIONAIRE ON THE COST OF INFRASTRUCTRE FAILURE ON LAGOS STATE ECONOMY. (A case study of Electricity).

Dear Respondents,

We are Statistics students of Yaba college of Technology, we are carrying out a statistical survey on the cost of infrastructure failure on the Economy of Lagos state, In partial fulfilment of the requirement for Higher National Diploma award.

Answering each of the questions sincerely and correctly would really be appreciated as we promise to make every information provided confidential. In order to prove our anonymity, please do not involve you name or organisation name. Thank you for spearing part of your busy hours to attend to our questionnaire.

SOLAWON KEHINDE A. & ONYEKA VICTOR U.

1. SECTOR: Food and manufacturing Wood and Product.

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Beverages and Tobacco Paper and Product.

Textile and Leather Rubber and Chemical.

Metal and Products. Others(specify)

……………………………

2 Location(Senatorial District) : Lagos west Lagos central

Lagos east.

2 Scale of Production: Small scale Medium scale Large scale.

3 How often do you have light in your industry? Less often Often

More often.

4 How do you view electricity supply in your industry?

No obstacle Moderate obstacle Major obstacle.

5 Rank the following infrastructure in order of their inportance in your factory

a. Land b. Electricity c. Water d. Telecommunication e. Road f. Petroleum shortages

6 How important is Electricity in your factory?

Most important Important Less important.

7 What is the source of elecricity in your factory?

PHCN only PHCN main Own generator only Own

generator main

8 Years of the use of Generator : less than 1 yr. 1-5 yrs

5-10yrs More than 10 yrs

.

9 The cost of the Generator : less than N250,000 N250,001-N1,000,000

N1,000,001N-5,000,000. More than N5,000,000

10 What proportion does provision of own electricity facilities from the total investment at

start?.

0-10% 11-20% 21-30% more than 30%

11 How often do you fuel your generating set ? Daily 2 times in a week

. 3 times a week weekly monthly

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12 What does it cost you to pay electricity bill to PHCN in a month?

Less than N100,000 N100,001-N500,000 N500,000-N1,000,000

N1,000,001-N5,000,000 More than N5,000,000

13 What does it cost you to fuel and maintain your generating set in a month?

Less than N100,000 N100,001-N500,000

N500,001-N1,000,000 N1,000,000-N5,000,000

n More than N5,000,000.

14 What other materials do you use for the set’s maintenance?

Capital Cost : Generator house Operating cost : Fuel and grease

Stabilizer Wages and salary

Oil tank Maintenance cost

Others (specify)……………………. Others…………

15 How often do you have power failure in a week? Less than 5 times

5-10 times more than 10 times

16 What is the average of the duration of the power failure for each time?

Less than 30 mins 1-6 hrs more than 6 hrs

17 What is your perception on the factors responsible for the poor performance of PHCN?

Low electricity tariff Inefficiency of PHCN Too much govt.

control Poor funding Corruption.

18 Firms perspective of how to improve PHCN.

Increase funding Introduce competition Increase tariff.

19. How has electricity outages affected your business? No effect

Low effect High effect.

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