Energy Economics 32 (2010) 505–514
Contents lists available at ScienceDirect
Energy Economics
j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / e n e c o
When do firms generate? Evidence on in-house electricity supply in Africa☆
J. Steinbuks a,⁎, V. Foster b
a
b
Electricity Policy Research Group, University of Cambridge, Austin Robinson Building, Sidgwick Avenue, Cambridge, CB3 9DD, UK
The World Bank, 1818 H Street, Washington, D.C., 20433, USA
a r t i c l e
i n f o
Article history:
Received 17 August 2008
Received in revised form 2 September 2009
Accepted 19 October 2009
Available online 31 October 2009
JEL classification:
O13
Q41
Keywords:
Generators
Ownership
Electricity
Reliability
Africa
a b s t r a c t
This paper attempts to identify the underlying causes and costs of own generation of electric power in Africa.
Rigorous empirical analysis of 8483 currently operating firms in 25 African countries shows that the
prevalence of own generation would remain high (at around 20%) even if power supplies were perfectly
reliable, suggesting that other factors such as firms' size, emergency back-up and export regulations play a
critical role in the decision to own a generator. The costs of own-generation are about three times as high as
the price of purchasing (subsidized) electricity from the public grid. However, because these generators only
operate a small fraction of the time, they do not greatly affect the overall average cost of power to industry.
The benefits of generator ownership are also substantial. Firms with their own generators report a value of
lost load of less than US$50 per hour, compared with more than US$150 per hour for those without.
Nevertheless, when costs and benefits are considered side by side, the balance is not found to be significantly
positive.
© 2009 Elsevier B.V. All rights reserved.
1. Introduction
The performance of Africa's power supply sector on the continent is
woefully unsatisfactory. Most of the continent's power companies are
unreliable sources of supply, inefficient users of generating capacity,
deficient in maintenance, erratic in the procurement of spare parts, and
unable to staunch losses in transmission and distribution. They also have
failed to provide adequate electricity services to the majority of the
region's population, especially to rural communities, the urban poor, and
small and medium enterprises.
In response to the endemic unreliability of Africa's national electric
power utilities, self-generated electricity has become an increasingly
important source of power. Many end users of electricity, from
households to large enterprises, now generate their own power by
operating small to medium-sized plants with capacities ranging from
1 MW to about 700 MW (Karekezi and Kimani, 2002). For small-scale
enterprises, protection against erratic supply from public utilities often
means installing small (less than 5 MW) thermal generators.
☆ The authors would like to thank Rob Mills, Mohua Mukherjee, David Newbery, and
two anonymous referees for helpful comments. They appreciate funding and other
support from (in alphabetical order): the African Union, the Agence Francaise de
Développement, the European Union, the New Economic Partnership for Africa's
Development, the Public–Private Infrastructure Advisory Facility, the U.K. Department
for International Development, and the U.K. Engineering and Physical Science Research
Council (Grant Supergen Flexnet). This paper was commissioned as a part of the Africa
Infrastructure Country Diagnostic project (www.infrastructureafrica.org). The views
represented in this paper do not necessarily correspond to the views of the World Bank.
⁎ Corresponding author. Tel.: +44 1223 335283; fax: +44 1223 335475.
E-mail address: [email protected] (J. Steinbuks).
0140-9883/$ – see front matter © 2009 Elsevier B.V. All rights reserved.
doi:10.1016/j.eneco.2009.10.012
Overall, own generation by firms – which has been on the rise in recent
years – accounts for about 6% of installed generation capacity in SubSaharan Africa (equivalent to at least 4000 MW of installed capacity).
However, this share doubles to around 12% in the low-income countries,
the post-conflict countries, and more generally on the Western side of the
continent. In a handful of countries (including Democratic Republic of
Congo and Nigeria) own generation represents more than 20% of capacity
(Foster and Steinbuks, 2009).
These adjustments are not without cost, however. Self-generated
electricity is generally more expensive than electricity from the public
grid, as we shall see, which limits its potential as a permanent substitute
for unreliable public supply. Because it adds to the capital and operating
costs of doing business, in-house generation affects the range of
investment available to budding entrepreneurs, raises production
costs, lowers the competitiveness of local products, and blocks the
achievement of economies of scale.1
1
These limitations of own-generation do not mean that there are no gains to be had
from the decentralization of power generation. But in no African country have the legal and
institutional conditions for decentralized power generation yet reached the point where
decentralization can provide an alternative to unreliable supply from monopolistic public
providers or to the use of expensive generators at the firm level. Some 20 African countries
are currently initiating some form of power sector reform. Although most are still in the
initial phases of privatization and restructuring, the contemplated changes reflect a
profound questioning of the principles that have governed the sector since the early 1960s.
Some countries, including the Arab Republic of Egypt and South Africa, have had
unbundled power sectors for a long time and are now thinking of introducing private
participation. Cōte d'Ivoire and Ghana introduced reforms (privatization and restructuring) in the early and mid-1990s, respectively (Karekezi and Mutiso, 1999).
506
J. Steinbuks, V. Foster / Energy Economics 32 (2010) 505–514
This paper attempts to identify the underlying causes and costs of
own generation of electric power in Africa. Our analysis is based on
the World Bank's Enterprise Survey Database, which captures
business perceptions of the obstacles to enterprise growth for 8483
currently operating firms in 25 African countries sampled between
2002 and 2006. Rigorous empirical analysis shows that unreliable
public power supplies is far from being the only or even the largest
factor driving generator ownership. Firm characteristics have a major
influence, and in particular, the probability owning a generator
doubles in large firms relative to small ones. Our model predicts that
the prevalence of own generation would remain high (at around 20%)
even if power supplies were perfectly reliable, suggesting that other
factors such as emergency back-up and export regulations play a
critical role in the decision to own a generator.
The costs of own-generation are high, driven mainly by the variable
cost of diesel fuel. They fall in the range US$0.30–0.70 per kilowatthour; about three times as high as the price of purchasing (subsidized)
electricity from the public grid.2 However, since these generators only
operate a small fraction of the time, they do not greatly affect the
overall average cost of power to industry.3 The benefits of generator
ownership are also substantial. Considering only lost sales resulting
from periods of power outages, firms with their own generators report
a value of lost load of less than US$50 per hour, compared with more
than US$150 per hour for those without. Nevertheless, according to
our conservative estimates, the balance between the costs and the
benefits of own generation is not found significantly positive. This is
likely because the analysis is only able to capture one dimension of the
benefits of generator ownership — namely reduction in lost sales.
The rest of this paper is structured as follows. The second section
reviews existing literature on unreliable power supply. The third
section describes the data used in the empirical analysis. The fourth
section analyzes the effect of power outages and firm characteristics
on the decision to generate electricity in-house. The fifth section
illustrates the results of cost–benefit analysis of own generation. The
final section concludes and presents policy recommendations.
2. The economic literature on unreliable power
It is fairly settled in the literature that unreliable power supply
results in welfare losses (see Kessides, 1993 and references therein).
But the empirical research on the economic costs of power outages
and own-generation in developing countries remains 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.4 Only two studies have recently been done on this
subject in Africa. Adenikinju (2005) analyzed the economic cost of
power outages in Nigeria. Using the revealed preference approach on
business survey data (Bental and Ravid, 1982; Caves et al., 1992;
Beenstock et al., 1997), Adenikinju estimated the marginal cost of
power outages to be in the range of $0.94 to $3.13 per kWh of lost
electricity. Given the poor state of electricity supply in Nigeria
Adenikinju (2005) concluded that power outages imposed significant
costs on business. Small-scale operators were found to be most
heavily affected by the infrastructure failures.5
2
The operating costs of own-generation are even higher in countries with
unreliable diesel and gasoline supply. During fuel supply shortages firms are either
unable to run their generators or have to run them at very high operating costs.
3
Operating costs of running a generator have a significant effect on average cost of
power to the industry in a few countries (e.g. Democratic Republic of Congo and
Nigeria), where outages are very frequent and sometimes of long duration. Some
commercial companies in these countries (e.g. Nestle Nig. Plc. in Nigeria) run entirely
on autogeneration.
4
Cross-country aggregate data analysis, widely used in the development economics
literature, cannot avoid simultaneity between poor infrastructure and welfare.
5
In earlier work Lee and Anas (1992) also found that poor infrastructure had a
negative effect on small enterprises in Nigeria.
Reinikka 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 backup 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 datasets from business surveys done in a single country. Both use
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, and
other observed explanatory variables, such as firms' profitability and
access to finance, and the country's industrial structure.
This paper combines the advantages of cross-country data analysis
with microeconomic analysis of business survey data. Use of a crosscountry dataset can help to identify the effects that affect the provision of
power supply, and to some extent, changes in the industrial structure.
Microeconomic data can be used to infer firms' and households' response
to provision of electricity supply. Because our study relies on crosssectional data, the bias in estimates cannot be fully avoided. However,
cross-country comparisons will still be valid, given the one-dimensional
direction of bias.
3. The data on self-generated electricity
Our main data source is the World Bank's Enterprise Survey Database,
which captures business perceptions of the obstacles to enterprise
growth, the relative importance of various constraints to increasing
employment and productivity, and the effects of a country's investment
climate on the international competitiveness its firms.6 Our database
comprises 8483 currently operating firms in 25 North and Sub-Saharan
African countries sampled from the universe of registered businesses
between 2002 and 2006. Though data on all African countries were not
available at the time of research, the available data are representative of
the continent. The surveys are based on a stratified random sampling
methodology. Because in most countries the number of small and
medium enterprises is far greater than the number of large firms, surveys
generally oversample large establishments. The main advantage of the
Enterprise Survey Database is that it provides both managers' opinions
concerning the reliability of power supplies and the accounting data
needed for structural microeconomic analysis. Table 1 (Appendix A)
presents the summary statistics of the variables used in the empirical
analysis.
The data from the World Bank's Enterprise Survey Database are
complemented by engineering data from generator manufacturers
and other sector sources that help to capture the cost structure of
generating power on-site. For example, the enterprise survey does not
ask for information on generating capacity, which is estimated at firm
level by taking the ratio of the deflated generator's acquisition cost to
the average capital cost per kW of the generator's capacity (assuming
thermal generation). The data for the capital cost per kW of installed
capacity comes from the World Bank's Energy and Water Department
(2005). The GDP deflator data comes from the World Bank's World
Development Indicators database.
4. What drives firms to generate their own power?
To evaluate the extent to which reliability of power supply and
firm characteristics affect the decision to generate electricity in-house,
6
Detailed information on the World Bank's Enterprise Survey Database can be
found at http://www.enterprisesurveys.org.
J. Steinbuks, V. Foster / Energy Economics 32 (2010) 505–514
507
Table 1
Probit and Tobit regression results.
Probit regression
(generator ownership)
Variable
Days of power
outages (log)
Age (log)
Employment (log)
PKM (log)
Size1 lost days (log)
Size2 lost days (log)
Size4 lost days (log)
Size5 lost days (log)
Exporter
Estimated
coefficient
Elasticity
Tobit regression
(generator capacity)
P-value
Estimated
coefficient
(Elasticity)
P-value
0.06⁎⁎⁎
0.02
0.01
0.23⁎⁎
0.05
0.06⁎⁎
0.37⁎⁎⁎
–0.001
–0.14⁎⁎⁎
–0.04
0.001
–0.06⁎⁎
0.29⁎⁎⁎
0.02
0.12
0.00
–0.04
–0.01
0.00
–0.02
0.10
0.03
0.00
0.31
0.00
0.14
0.99
0.05
0.00
0.33⁎⁎
1.74⁎⁎⁎
0.001
0.39⁎⁎
0.14
0.01
0.22
0.56⁎
0.02
0.00
0.69
0.03
0.24
0.96
0.15
0.07
⁎⁎⁎ statistically significant at 1 percent level; ⁎⁎ statistically significant at 5 percent
level; ⁎ statistically significant at 10 percent level.
we employed two modelling approaches. The first approach follows
the binary choice model of Reinikka and Svensson (2002) is based on
the following stochastic specification:
!
′
α1 xi ; α2 Zi
;
PrðYi = 1Þ = Φ
σ
ð1Þ
where Yi is the probability that firm i invests in a generator, Φ is the
standard normal distribution function, xi is the frequency of power
outages, Zi is a vector of controls, including country and firm
characteristics, and σ is a standard deviation of normally distributed
error term. The second approach employs censored regression
approach:
PrðYi N 0Þ = 1−Φ −
!
′
α1 xi ; α2 Zi
;
σ
ð2Þ
where Yi is the logarithm of the capacity of firm's i generator (leftcensored at zero), and the explanatory variables are the same as in
Eq. (1).
Eq. (1) is estimated using the probit method. Eq. (2) is estimated
using the tobit method. Selected estimated parameters are presented
in Table 1, with the complete set appearing in Appendix A, Tables 2
and 3. Reliability of power supply and various firm characteristics —
age, size (as measured by number of employees), and export
orientation all have a significant positive impact on the estimated
probability of investing in generating capacity and the size of the
generator.7
The probability that a firm will own a generator can be expressed as a
function of the reliability of power supply and the firm's size. The
probability of finding a generator on the premises increases by nearly 50%
as one moves from small firms (less than 10 employees) to very large
ones (more than 500 employees) (Fig. 1). The probability of having a
generator remains high (about 20%) even where power supply is
completely reliable.8 For large firms the probability of having a generator
in the absence of power outages is even higher (about 50%).
The evidence thus suggests that both generator ownership and its
capacity are greatly affected by firm characteristics, such as size, sector,
corporate structure, and export orientation. Large firms that operate 24 h
7
The price-cost margin (PKM) variable was not statistically significant, possibly
because of simultaneity bias between firm's profitability and decision to own a
generator.
8
This finding is consistent with firms' insurance behavior. Large and risk-averse
firms incurring high marginal costs from power outages will invest in back-up
generator even if current power supply is completely reliable to keep the option value
of insurance from future outages.
Fig. 1. Probability that a firm will own a generator, by number of employees.
per day are more likely than smaller firms to install backup generation
capacity compared to smaller firms, which operate only during daylight
hours and therefore are less affected by evening blackouts. Mining firms
tend to require own power to keep elevators, air pumps, and other safety
devices fully operational regardless of the power supply from public grid.
Petroleum firms have very sensitive and delicate equipment that must
be protected from damage stemming from power outages. Exporters
may need to generate their own power to meet ISO standards (e.g.,
relating to cold chains). Informal firms may be unable to accumulate
significant generating capacity because of security concerns, which may
include police raids, unstable land or lease tenure, and other factors.
The composite effect of size and reliability of power supply is
generally not significant across firms, except for small firms and
microenterprises (Table 1). The significant negative coefficient on the
product of small firms and large power outages indicates that small
firms suffer the most from unreliable power supply, because they lack
the resources to invest in own-generation.9
5. The costs and benefits of own-generation
5.1. Costs
We use the revealed preference approach to analyze the economic
costs of own-generation.10 This approach is based on the presumption
that rational, profit-maximizing firms will insure themselves against the
risk of frequent power outages. Because insurance contracts for
unreliable power supply are not available in developing countries, the
only way to minimize losses is to acquire backup generating power. The
firm's problem is to choose the optimal amount of backup power that
minimizes the sunk costs incurred by acquiring generation capacity as
well as the damage that unsupplied power would cause.
A competitive, risk-neutral firm will maximize expected profits by
equating at the margin the expected cost of generating a kWh of its
own power to the expected gain due to that kWh. That gain consists of
the continued production (even if partial) that the self-generated
electricity makes possible, and the avoided damage to equipment that
would have been caused by a power failure. Under profit-maximizing
conditions, the expected marginal gain from a self-generated kWh is
also the expected marginal loss from the kWh that is not supplied by
the utility. Therefore the marginal cost of self-generated power may
serve as an estimate for the marginal cost of an outage.
9
Further discussion of this subject becomes complicated because access to
electricity and access to finance are frequently simultaneous. For example, in Nigeria
small firms may lack internal funds to obtain a generator, and owning a generator may
be a prerequisite to secure loan from the bank.
10
See earlier citations to the revealed preference approach in the literature review
section of this paper.
508
J. Steinbuks, V. Foster / Energy Economics 32 (2010) 505–514
Table 2
The comparative costs of self generated and publicly supplied electricity, and the effect of own generation on the marginal cost of electricity, in Africa.
Country
Average variable cost
of own electricity (A)
Average capital cost of
own electricity (B)
Average total cost of own
electricity (C = A + B)
Price of kWh purchased
from public grid (D)
Weighted average cost of
electricity (E = ∂C + (1–∂) D)
Algeria
Benin
Burkina Fasoa
Cameroona
Cape Verdea
Egypt, Arab Rep.
Eritrea
Kenya
Madagascar
Malawi
Mali
Mauritius
Morocco
Nigera
Senegal
Senegalb
South Africa
Tanzania
Uganda
Zambia
0.04
0.36
0.42
0.41
0.46
0.04
0.11
0.24
0.31
0.46
0.26
0.26
0.31
0.36
0.25
0.28
0.18
0.25
0.35
0.27
0.11
0.10
0.32
0.04
0.04
0.26
0.03
0.06
0.08
0.03
0.26
0.35
0.32
0.04
0.09
0.40
0.36
0.04
0.09
0.18
0.15
0.46
0.74
0.46
0.50
0.30
0.13
0.29
0.39
0.50
0.52
0.61
0.62
0.41
0.34
0.68
0.54
0.29
0.44
0.45
0.03c
0.12c
0.21c
0.12c
0.17c
0.04c
0.11
0.10
–
0.05c
0.17
0.14c
0.08c
0.23c
0.16
0.16
0.04
0.09
0.09
0.04
0.05
0.27
0.23
0.16
0.26
0.12
0.12
0.14
–
0.09
0.21
0.25
0.15
0.26
0.18
0.30
0.05
0.13
0.14
0.06
∂ Share of total electricity consumption coming from own generation.
– = data not available.
a
Tourism industry (hotels and restaurants sector) only.
b
Survey of informal sector.
c
Data not reported in the enterprise surveys (obtained from the public utilities).
The cost to the firm of generating its own power consists of two
elements. The first is the yearly capacity cost of the generator and other
capital outlays. Following earlier literature, that cost will be denoted by b
(Kg), where Kg is the generator's capacity measured in kW. The second is
the variable cost per kWh-chiefly fuel cost, which is practically constant.11
If the generator is used to capacity during power cuts, the variable cost per
year will then be v · H · Kg, where v is the fuel cost per kWh, and H is the
expected total duration of outages, measured in hours per year. The total
expected yearly cost per kW of backup generating power is then
CðKgÞ = bðKgÞ + v⋅H⋅Kg:
ð3Þ
The expected respective marginal cost is
′
′
C ðKgÞ = b ðKgÞ + v⋅H
ð4Þ
and the expected marginal cost of a kWh generated is simply given by
′
C ðKgÞkWh =
′
b ðKgÞ
+ v:
H
ð5Þ
Applying Eq. (5) to the enterprise survey data allows us (using
reasonable assumptions) to estimate the (marginal) cost of owngeneration from observed information about the acquisition and running
costs of in-house generating capacity, and from data on the frequency of
power outages.12 For these purposes, values for b′, H, and v must be
obtained.
The operating cost, v, is calculated as a product of the unit cost of
fuel and the generator's fuel efficiency (fuel consumption per kWh).
Assuming that most firms in the enterprise survey dataset rely on
thermal generation, the unit cost of fuel is approximated by an average
price per liter of diesel fuel.13 Fuel efficiency data was obtained from
the Web sites of leading manufacturers of generators.14
The unit capital cost of self-generated electricity, b′, depends on
price schedules for generators, tax and depreciation rules, and the
interest rates. Original price schedules (in national currencies) and
data on year of acquisition are reported in the enterprise surveys. We
converted the original price schedules into current U.S. dollars. First,
we deflated the price schedules, applying the corresponding value of
the country's GDP deflator and then converting into dollars at the
prevailing exchange rate. 15 We then obtained the capital cost per kW
of installed capacity (in 2004 dollars, assuming thermal generation, no
tax rules, and an internal rate of return of 10%)16 using the data from
the World Bank's Energy and Water Department (2005).
Our data on the duration of power outages, H, came from the
enterprise surveys. Data on the average duration of power outages
were generally not available. We assumed a value of 8 h per day.17
In most of the countries of Africa, the average cost of generating
electricity in-house is significantly higher than the cost of electricity
from the public grid (Table 2). This finding reflects the differences in
efficiency between the small backup generators used by commercial
firms and the large plants that produce electricity for the public grid.
The major exceptions are the countries in which fuel is heavily
subsidized (Algeria, Arab Republic of Egypt, and Eritrea), where the
average cost of self-generated electricity is close to the cost of the
electricity from the public grid.
13
The fuel prices came from GTZ International Fuel Prices 2005.
These manufacturers included Wärtsilä and Cummins.
15
Nominal exchange rates were adjusted for price volatility using the World Bank
Atlas method.
16
The results from the enterprise surveys show that most firms in Africa rely on
internal rather than external financing. Therefore, given limited access to finance, the
internal rate of return is preferred to interest rates.
17
Other assumptions about the duration of power outages were considered,
including 4 and 12 h. It follows from Eq. (4) that under these assumptions the
estimates of the unit capital cost of self-generated electricity will vary within the 50%
confidence interval.
14
11
This measure does not account for other variable costs, such as rental costs of
generator house, maintenance, wages, and salaries. Omission of these costs does not
significantly bias our estimates, because they are likely to be substantial for firms with
huge back-up capacity. In our dataset among 1352 firms that owned electric
generators, only 2 firms had large (1 MW or larger) back-up capacity.
12
This measure of marginal cost does not account for incomplete backup that may
result in additional losses such as destruction of raw materials and damage to
equipment. These losses are inversely related to the percentage of backup and the
reliability of the firm's backup equipment.
J. Steinbuks, V. Foster / Energy Economics 32 (2010) 505–514
Table 3
Losses due to outages (“lost load”) for firms with and without their own generator.
509
Table 4
Marginal benefit of owning a generator.
Country
Lost load (no generator, $/h)
Lost load (with generator, $/h)
Reduction in lost sales
Algeria
Benin
Burkina Fasoa
Cameroona
Cape Verdea
Egypt, Arab Rep.
Eritrea
Kenya
Madagascar
Malawi
Mali
Mauritius
Morocco
Nigera
Senegal
Senegalb
South Africa
Tanzania
Uganda
Zambia
155.8
38.4
114.1
403.6
177.7
201.5
31.9
113.1
434.5
917.3
390.3
468.6
377.5
81.3
166.0
12.9
1140.1
–
27.6
286.6
52.2
23.1
13.0
12.3
36.4
30.4
10.2
37.1
153.0
401.4
9.5
13.9
22.9
22.6
19.2
1.9
66.1
444.3
191.4
39.2
Variable
Estimated coefficient
P-value
Days of power outages (log)
Generator ownership
Constant
F-statistic
R2
N
1.94⁎⁎⁎
– 1.16⁎⁎⁎
0.00
0.01
0.11
0.00
⁎⁎⁎ statistically significant at 1 percent level.
According to Eq. (6), the value of lost load (y) measured in dollars
per hour depends on variety of parameters (X), including costs and
frequency of outages, seasonal characteristics, and advance notice.
Because data on seasonal characteristics and advance notice were not
available, the lost load value was based on the costs and frequency of
power outages. Lost load values were computed separately for firms
with and without their own generators. For firms owning a generator
the lost load value was calculated as
– = data not available.
a
Survey of tourism sector.
b
Survey of informal sector.
y = v⋅Kg
The second column of Table 2 reports the first term of Eq. (5), the
estimated average capital cost of self-generated electricity, adjusted by
the frequency of power outages. As might be expected, the average
capital cost of self-generated electricity is higher in countries with a
reliable power supply (Egypt, Mali, Mauritius, Morocco, and South
Africa), and in the informal sector, which uses inefficient low-capacity
generators (Senegal).
The third column of Table 2 shows the average total cost of selfgenerated electricity, calculated as a sum of the average capital cost and
the average variable costs. Overall, the average total cost of selfgenerated electricity is about five times the price of electricity purchased
from the public grid and can be as much as ten times more (in Malawi,
South Africa, and Zambia). Eritrea is the only country in which the
average total cost of self-generated electricity is comparable to the price
of purchasing electricity from the public grid.
The last column of Table 2 shows the weighted average cost of
consumed energy, taking into account the share of self-generated
electricity reported in the enterprise surveys.18 It can be seen that while
the average total cost of own-generation is very high, its effect on the
weighted average cost of power for most countries is not very large,
owing to limited use of own-generation. The impact of own-generation
costs is higher in countries where electricity from public utilities is
subsidized (Egypt, Malawi, and Morocco), where the supply of public
power is reliable (Egypt, Mauritius, and Morocco), and where the share
of own-generation is large (Benin, informal sector in Senegal).
5.2. Benefits
Our analysis of the economic benefits of own-generation takes two
paths. The first follows the literature on the reliability of electricity
supply (see, for example, Kariuki and Allan, 1995) and computes the
value of lost load, defined as the value an average consumer puts on an
unsupplied kWh of energy. The value of lost load is represented by the
customer damage function
y = f ðXÞ:
–1.71
26.25⁎⁎⁎
0.21
4254
ð6Þ
where v is the generator's operational cost (in dollars per kWh), as
described earlier, and Kg is generator's capacity in kW. For firms
without a generator the lost load value was calculated as
y=
Z
t
ð8Þ
where Z is reported sales lost from power outages and t is reported
frequency of power outages, multiplied by their average duration19
The economic benefit of owning a generator is thus expressed as the
reduced loss per interrupted kW. The average values of lost load for
firms with and without generators are reported in Table 3. In all
countries, except Uganda, the lost load is considerably higher for firms
without a generator, especially in countries with infrequent (Mauritius, South Africa) or costly (Malawi) power outages.
The second way to determine the benefit of own-generation is to
estimate the marginal benefit of owning a generator by regressing the
percentage of sales lost from power outages against the duration of
power outages, generator ownership, and salient characteristics of
countries and firms20 Selected estimated parameters of such a regression
are reported in Table 4; the complete set of parameters appears in
Appendix A, Table 3. The estimated parameters are jointly statistically
significant (the p-value associated with the computed F-statistic is less
than 0.01), and account for 20% of the variance in the response variable.
As expected, the sign of the coefficient for duration of power outages is
negative and significant, and the sign of the coefficient for generator
ownership is positive and significant. The size of the estimated
coefficient for generator ownership suggests that, when controlling for
other factors, owning a generator decreases losses from power outages
by approximately 1% of a firm's sales.
5.3. Costs vs. benefits
5.3.1. With no improvement in quality of public power supply
Here we summarize the costs and benefits of own-generation by
integrating them at the firm level, assuming no improvement in
quality of public power supply. The costs are computed as the sum of
the annualized fixed costs of acquiring a generator and the net annual
19
Power outages were assumed to last 8 h on average.
An attempt to estimate the marginal benefit of owning a generator with respect to
physical capital losses on a smaller sample of firms did not yield significant results. The
details are available from authors upon request.
20
18
This indicator is based on the assumption that the electricity tariffs charged by the
utilities are set according to marginal-cost pricing schedules.
ð7Þ
510
J. Steinbuks, V. Foster / Energy Economics 32 (2010) 505–514
Table 5a
Cost–benefit analysis of own-generation, by country.
Percent
Country
Investment
costs
Own-generation
costs
Total costs
Reduced sales
losses
Benefit–cost
margin
Lost sales without
generator
Algeria
Benin
Egypt
Eritrea
Kenya
Madagascar
Malawi
Mali
Mauritius
Morocco
Senegal
Senegala
South Africa
Tanzania
Uganda
Zambia
3.12
0.43
2.34
1.54
0.14
0.28
0.09
0.31
0.17
0.06
0.32
0.98
0.10
0.43
0.45
3.02
–
1.62
0.86
0.41
–
–
0.78
0.17
0.01
0.39
0.50
–
0.09
1.01
0.49
0.95
–
2.05
3.20
1.95
–
–
0.87
0.48
0.18
0.45
0.82
–
0.19
0.94
0.94
3.96
0.61
1.79
1.33
1.28
0.49
1.03
0.23
0.51
0.84
0.08
1.23
0.41
1.19
–
1.35
0.07
–
0.99
2.25⁎⁎
1.43
–
–
0.23
0.04
1.10
0.12
0.18
–
0.90
–
0.62
–3.90⁎⁎⁎
5.44
7.70
6.47
6.55
10.07
7.48
24.10
2.77
3.93
0.87
6.96
5.66
0.84
5.41
4.35
– = data not available.
a
Survey of informal sector.
⁎⁎⁎ Statistically significant at 1 percent level.
⁎⁎ Statistically significant at 5 percent level.
operating costs of generation. The fixed costs of own-generation were
annualized assuming linear depreciation. The net annual operating
costs were calculated as the product of the firm's consumption of selfgenerated electricity and the difference between the costs per kWh of
self-generated electricity and of electricity from the public grid.21
We use two approaches to calculate the benefits of own-generation.
In the first, more conservative approach we assume that firms that own
a generator may incur lost sales from power outages, for example,
because of disruptions in generator's fuel supply. The marginal benefit of
own generation is calculated as the reduced sales loss from the
regression analysis in the previous section, adjusted for a corresponding
fixed-effect.22 In the second approach we assume that firms that own a
generator do not lose sales from power outages. The marginal benefit of
own generation is computed as an opportunity cost of not having a
generator, e.g. the lost sales from power outages by firms without
generator.23 Both costs and benefits of owning a generator are
expressed as percentages of sales. The resulting difference between
the benefits from the first approach and the costs of own-generation
(the benefit-cost margin) was tested for statistical significance from
zero using the Student's t test.24 The results are summarized in
Tables 5a–5c below.
The results from the first approach indicate that in most countries,
the costs of own-generation outweigh the benefits (Table 5a).
However, only in Egypt and Zambia this difference is statistically
significant. In four countries (Mauritius, Senegal, South Africa, and
Uganda) the benefits outweigh the costs, and the difference between
the benefits and the costs is not statistically significant from zero.
These results imply that investment in in-house generation at least
allows firms to break even. The results from the second approach
indicate that benefits of own generation outweigh the costs for all
21
The exact computational procedure is outlined in a technical appendix, which is
available upon request.
22
This measure represents a lower bound estimate of own-generation benefits,
because firms that own generators have little lost load, and given various
considerations not related to reliability of power supply as discussed earlier in this
paper.
23
This measure represents an upper bound estimate of own-generation benefits
given that some generator owners reported lost sales from power outages, or because
firms without generator can be less productive and incur higher losses from power
outages. The degree of this bias cannot be determined without proper counterfactual.
24
One cannot statistically compare the benefits from the second approach and the
costs of own generation, because these estimates are based on different set of firms.
countries. The results from two approaches can be reconciled under
less restrictive assumptions. First, the fixed costs of own-generation
can be sunk or depreciated nonlinearly. In Egypt and Zambia the
negative and statistically significant difference between benefits and
costs of own generation is driven by fixed costs. Second, the analysis
presented above does not account for other potential gains from owngeneration, such as reductions in damaged equipment. This is
especially important in Benin, where the average losses from
damaged equipment account for 1.5% of sales (see Appendix A,
Table 2). Third, the results of the analysis do not incorporate the
option value of lost load due to future shocks to power supply (e.g.
unexpected droughts or power infrastructure damages).
The costs and benefits of own-generation differ according to firm
size (Table 5b). The total costs of own-generation vary nonlinearly by
firm size, being most efficient for medium-sized firms. For small firms
own-generation imposes relatively low fixed costs but higher variable
costs. Larger firms have relatively high fixed costs, and increasing
variable costs. The total benefits of own-generation calculated as
reduced sales losses increase non-linearly with firm size, being the
highest for medium-size firms. Larger firms without generator are
found to incur smaller losses from the power outages. The difference
between the costs and benefits of own-generation calculated as
reduced sales losses is negative across all size categories, except for
the medium size. However, it is statistically significant only for large
firms. The difference is insignificant or marginally significant for
microenterprises, small medium, and very large firms. For all size
categories the average sales losses of firms without generator are
higher than total costs of own generation.
The costs and benefits of own-generation vary significantly across
industries (Table 5c). The costs are highest in energy-intensive
industries — chemicals, nonmetal and plastic materials, and mining,
and lowest in light industries, such as textiles and wood. Chemicals and
construction have the highest fixed costs of own-generation, whereas
nonmetal and plastic materials and mining have the highest operational
costs. The highest gains from own-generation calculated as reduced
sales losses are observed in mining, chemicals, and construction. In most
industries, the difference between the costs and the benefits of own
generation is not statistically significant. The costs of own-generation
significantly outweigh the benefits in food and beverages and metals
and machinery. However, these two industries, as well as nonmetallic
and plastic products also have the highest average sales losses for firms
without generator. Therefore, the difference between two approaches is
J. Steinbuks, V. Foster / Energy Economics 32 (2010) 505–514
511
Table 5b
Cost–benefit analysis of own generation, by size of firm.
Percent
Size
Investment
costs
Own-generation
costs
Total costs
Reduced sales
losses
Benefit-cost
margin
Lost sales without
generator
Micro
Small
Medium
Large
Very large
0.49
1.19
0.43
1.64
1.20
0.67
0.51
0.36
0.77
1.22
1.16
1.71
0.78
2.41
2.42
0.45
0.82
1.48
0.05
0.54
0.72
0.67⁎
0.66
1.56⁎⁎
5.97
6.09
4.93
3.80
3.27
1.97
⁎⁎ Statistically significant at 5 percent level.
⁎ Statistically significant at 10 percent level.
Table 5c
Cost–benefit analysis of own-generation, by sector.
World Bank, Enterprise Survey Database.
Percent
Industry
Investment
costs
Own-generation
costs
Total costs
Reduced sales
losses
Benefit–cost
margin
Lost sales without
generator
Textiles
Food and beverages
Metals and machinery
Chemicals
Construction
Wood and furniture
Nonmetallic and plastic materials
Mining and quarrying
0.42
0.79
1.08
2.92
2.08
0.42
0.91
0.11
0.29
0.77
0.13
0.14
0.46
0.37
2.30
3.10
0.71
1.56
1.21
3.06
2.54
0.79
3.20
3.22
0.84
0.28
0.41
1.98
1.37
1.33
0.99
2.41
0.10
1.41⁎⁎⁎
1.72⁎⁎
0.44
0.22
0.53
2.21
2.40
4.81
6.12
7.00
4.84
3.57
5.30
6.79
3.28
⁎⁎⁎ Statistically significant at 1 percent level.
⁎⁎ Statistically significant at 5 percent level.
likely to be caused by industry's idiosyncratic factors (for example, sales
losses because of reductions in damaged equipment are important in
the food processing industry).
5.3.2. With improvements in quality of public power supply
The results of the probit model discussed earlier can be used to
evaluate the extent to which an improvement in the reliability of
power supply will affect generator ownership. The marginal effects
of the probit model (Eq. (1)) suggest that the probability of a firm's
owning a generator is not very sensitive to power supply reliability.
Reducing power outages by half the mean outage reduces generator
ownership by less than 2% (Table 6). It appears that thoroughly
reliable power would reduce generator ownership by no more than
12%.
The predictions of the probit regression are extended to individual
countries in Appendix A, Table 4. Raising the reliability of power
supply to the level of South Africa results in a mere 3–5% reduction in
generator ownership.
Although the regression results suggest that improving the
reliability of power supply would have a relatively small effect on
generator ownership, there are several reasons to expect that the
Table 6
Simulated change in generator ownership.
Change in probability that firm will own a
generator
Variable
Mean
Min N Max
½ mean change
½ std change
Days of power outages
Age
Employment
Exporter
37.9
18.3
111.7
0.19
0.12
0.16
0.87
0.10
0.02
0.02
0.12
n.a.
0.03
0.02
0.16
n.a.
Source: World Bank, Enterprise Survey Database.
Note. Exporter is a binary variable, therefore ½ mean change and ½ std change statistics
are not reported.
n.a. = not applicable.
effect would be greater. First, the regression analysis does not account
for unobserved explanatory variables, such as firms' access to finance
and productivity, which may bias the regression results downward.
Second, because investment in in-house generation is irreversible,
reductions in generator ownership will occur with a lag as public
power supply is improved. The cross-sectional data analysis conducted in this study does not capture these dynamics. Third, the gains
to be had by improving the reliability of power supply may be greater
if the effects of unreliable power supply on observed industrial
structure and external competitiveness are taken into account.
Because energy-intensive industries require more stable power
supplies, improving reliability will diversify country's production
base and result in additional economic gains.
6. Conclusions and policy recommendations
This paper aims to deepen our understanding of the widespread
phenomenon of own generation of electric power by firms, as well as
its relationship to unreliable public power supplies in the African
context. It does so by analyzing the World Bank's Enterprise Survey
Database, which provides a detailed set of attitudinal and behavioral
information about decisions relating to own power generation at the
firm level; at least for the case of larger formal sector manufacturing
enterprises.
The decision of a firm to maintain its own-generation capability is
driven by a variety of factors. Our empirical analysis shows that
unreliable public power supplies, though an important constraint to
business operations, is far from being the only or the largest factor
driving generator ownership. Firm characteristics such as size, age,
industrial sector and export orientation all have a major influence. In
particular, the probability owning a generator doubles in large firms
relative to small ones. Moreover, the behavioral model predicts that
the percentage of firms owning their own generators would remain
high (at around 20%) even if power supplies were perfectly reliable,
suggesting that other factors such as emergency driven back-up
512
J. Steinbuks, V. Foster / Energy Economics 32 (2010) 505–514
requirements or export driven quality regulations play a critical role
in the decision to own a generator.
The costs of own-generation are high, driven mainly by the variable
cost of diesel fuel. In most cases they fall in the range US$0.30–0.70 per
kilowatt-hour, which is often three times as high as the price of purchasing
electricity from the public grid; although the latter is typically subsidized.
Nevertheless, in most cases, this does not hugely affect the overall
weighted average cost of power to firms given that own-generation is
only used during a relatively small percentage of the working year.
At the same time, the survey evidence shows that the benefits of
generator ownership are also substantial. Considering only lost sales
resulting from periods of power outages, firms with their own
generators report a value of lost load of typically less than US$50 per
hour, which is only a fraction of the value of lost load in excess of US$150
per hour that is reported by firms in the same country that do not have
their own generators.
Nevertheless, when costs and benefits are considered side by side, the
balance is not found to be significantly positive; a pattern which holds
across countries, industrial sectors, and business scales. This may simply
be because the analysis is only able to capture one dimension of the
benefits of generator ownership – namely reduction in lost sales – but fails
to capture many other important aspects — such as reduced damage to
equipment, higher quality of production, and meeting reliability criterion
for access to export markets. Less conservative estimates using the
opportunity cost as the measure of the benefit of own generation, show
that the benefits of generator ownership outweigh the costs.
The following policy implications emerge from our findings.
Though improvements in the reliability of public power supplies
would reduce generator use, many firms would still maintain their
own back-up generators in order to meet international quality
standards for participation in export markets. Through own generation, the majority of large formal sector enterprises are able to
effectively insulate themselves from the impact of unreliable power
supplies. Despite the high cost of running such generators (typically US
$0.25–45 per kilowatt-hour), given that outages are only intermittent,
the overall impact on the weighted average cost of power supply to
these firms is relatively small. The major victims of unreliable power
supply are in the informal sector, where the limited survey evidence
suggests that generator ownership is an order of magnitude less
prevalent than in the formal sector. The cost imposed on these firms for
unreliable power is very high, and firms demonstrate high willingness
to pay for reliable power through their investments in autogeneration. There is therefore a market for more expensive and higher
quality power supply. It should be possible to strike a bargain between
government and private sector to the effect that firms pay higher price
for grid power to fund investments that will make their power supply
more reliable.
Many African governments are today contracting emergency
generation capacity to avoid further utility outages. This power is
very expensive (at least $0.25 per kWh), and thus not far short of the
cost that private sector pays for own generation. By the time you add
transmission and distribution costs including losses, the costs of
emergency generation can easily top $0.30 per kWh. However,
governments continue to sell this power to industrial users at regular
grid prices. They are therefore subsidizing the public sector to the tune
of easily $0.10 per kWh to receive power that they would have been
willing to supply themselves at full cost had the government not leased
the emergency capacity. Therefore, power from emergency capacity
should be sold to the industrial sector at full cost. In countries where
own-generation capacity is substantial relative to total national
installed capacity, the question is whether this capacity could play a
role in improving national power supplies. This would require
regulation to allow large consumers to sell excess power into the
grid at a price related to cost. There might be a market for such power
among industrial users, although it would likely be too expensive to be
of interest for the residential sector.
Appendix A
Table 1
Summary statistics from the enterprise survey data, by country.
Country
Electricity cited
as business
constraint
(% firms)
Power outages
(days)
Power outages
(% sales)
Equipment destroyed
by outages (% sales)
Generator owners
(% firms)
Power from own
generator (%)
Generator
capacity (KW)
Cost of KWH from
public grid (US$)
Algeria
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Egypt
Eritrea
Ethiopia
Kenya
Madagascar
Malawi
Mali
Mauritania
Mauritius
Morocco
Namibia
Niger
Senegal
South Africa
Swaziland
Tanzania
Uganda
Zambia
11.47
69.23
9.65
68.97
79.56
64.94
70.69
26.46
37.66
42.45
48.15
41.30
60.38
24.18
29.66
12.68
8.94
15.09
26.09
30.65
8.96
21.43
60.24
43.85
39.61
12.32
56.12
22.29
7.82
143.76
15.80
15.18
10.40
74.61
44.16
53.40
54.31
63.21
5.97
37.97
5.36
3.85
0.00
3.93
25.64
5.45
32.38
63.09
45.50
25.87
5.28
7.79
1.54
3.87
11.75
4.92
6.87
6.12
5.95
5.44
9.35
7.92
22.64
2.67
2.06
4.01
0.82
1.20
2.72
5.12
0.92
1.98
–
6.06
4.54
–
1.54
–
–
–
–
–
–
–
–
0.34
0.91
–
1.36
–
0.42
–
–
–
0.62
–
–
0.81
0.74
0.28
29.49
26.90
14.91
29.82
39.22
57.79
43.10
19.26
43.04
17.14
73.40
21.50
49.06
45.33
26.25
39.51
13.81
13.21
27.54
62.45
9.45
35.71
59.05
38.34
38.16
6.22
32.80
17.57
6.52
25.28
7.62
13.53
5.87
9.31
1.58
15.16
2.23
4.44
5.09
11.75
2.87
11.16
13.33
14.74
6.71
0.17
10.33
12.28
6.56
5.13
322
35
–
31
–
25
23
353
103
–
78
190
50
43
–
37
58
–
73
31
64
–
70
31
51
–
–
–
–
–
–
–
–
0.11
0.06
0.10
–
–
0.17
–
–
–
–
–
0.16
0.04
–
0.09
0.09
0.04
Source: World Bank, Enterprise Survey Database.
Note: – = data not available.
J. Steinbuks, V. Foster / Energy Economics 32 (2010) 505–514
Table 3 (continued)
Table 2
Probit regression results (generator ownership).
Variable
Estimated coefficient
Elasticity
P-value
Days of power outages (log)
Age (log)
Employment (log)
PKM (log)
Micro ⁎ Lost Days (log)
Small ⁎ Lost Days (log)
Large ⁎ Lost Days (log)
Very large ⁎ Lost Days (log)
Exporter
Algeria
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Egypt
Eritrea
Ethiopia
Kenya
Madagascar
Malawi
Mali
Mauritania
Mauritius
Namibia
Niger
Senegal
Swaziland
Tanzania
Uganda
Zambia
Food and beverages
Metals and machinery
Chemicals and pharmaceutics
Construction
Wood and furniture
Nonmetallic and plastic materials
Other manufacturing
Other services
Hotels and restaurants
Mining and quarrying
Constant
Χ2 Statistic
Pseudo R2
N
0.06
0.06
0.37
0.001
0.14
0.04
0.00
0.06
0.29
1.53
1.57
1.09
1.43
2.37
2.17
2.11
0.98
1.62
1.20
2.47
0.91
1.49
2.16
1.50
1.53
0.89
1.69
2.42
1.37
2.24
1.80
1.14
0.80
0.32
0.65
0.68
0.19
0.61
0.52
0.18
1.11
0.53
4.00
1360.04
0.26
4246
0.02
0.02
0.12
0.0002
0.04
0.01
0.00
0.02
0.10
0.55
0.57
0.41
0.53
0.71
0.69
0.68
0.35
0.58
0.45
0.73
0.34
0.54
0.69
0.55
0.56
0.34
0.60
0.73
0.51
0.70
0.63
0.43
0.28
0.11
0.24
0.25
0.06
0.22
0.19
0.06
0.42
0.19
0.00
0.00
0.01
0.03
0.00
0.31
0.00
0.14
0.99
0.05
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.03
0.00
0.00
0.65
0.00
0.11
Note: Base country: South Africa; base industry: textiles; base size category: medium
size (50–100 employees), Source: World Bank, Enterprise Survey Database.
Table 3
Tobit regression results (generator capacity).
Variable
Days of power outages (log)
Age (log)
Employment (log)
PKM (log)
Micro ⁎ Lost Days (log)
Small ⁎ Lost Days (log)
Large ⁎ Lost Days (log)
Very large ⁎ Lost Days (log)
Exporter
Algeria
Benin
Burkina Faso
Cameroon
Cape Verde
Egypt
Estimated coefficient/Elasticity
0.23
0.33
1.74
0.001
0.39
0.14
0.01
0.22
0.56
7.74
7.82
2.09
1.87
0.44
5.43
513
P-value
0.05
0.02
0.00
0.69
0.03
0.24
0.96
0.15
0.07
0.00
0.00
0.49
0.46
0.87
0.00
(continued on next page)
Variable
Estimated coefficient/Elasticity
P-value
Eritrea
Kenya
Madagascar
Malawi
Mali
Mauritius
Niger
Senegal
Tanzania
Uganda
Zambia
Food and beverages
Metals and machinery
Chemicals and pharmaceutics
Construction
Wood and furniture
Nonmetallic and plastic materials
Other manufacturing
Other services
Hotels and restaurants
Mining and quarrying
Constant
χ2-Statistic
Pseudo R2
N
7.94
10.86
4.15
6.06
9.75
7.35
2.24
10.31
10.47
8.06
3.94
3.01
1.18
2.46
1.91
0.21
2.11
2.73
1.05
14.02
0.42
18.61
1269.15
0.17
3756
0.00
0.00
0.00
0.00
0.00
0.00
0.44
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.64
0.00
0.00
0.56
0.00
0.83
0.00
0.00
Note: Base country: South Africa; base industry: textiles; base size category: medium
size (50–100 employees), Source: World Bank, Enterprise Survey Database.
Table 4
Marginal benefit of generator ownership (lost sales).
Variable
Days of power outages (log)
Generator ownership
Algeria
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Egypt
Eritrea
Ethiopia
Kenya
Kenya (informal)
Madagascar
Malawi
Mali
Mauritania
Mauritius
Morocco
Namibia
Niger
Senegal
Senegal (informal)
South Africa
Swaziland
Uganda
Uganda (informal)
Agroindustry
Metals and machinery
Chemicals and pharmaceutics
Construction
Wood and furniture
Non-metallic and plastic materials
Other manufacturing
Other services
Hotels and restaurants
Mining and quarrying
Micro
Small
Estimated Coeff.
1.94
1.16
2.20
0.74
3.31
0.56
3.24
1.21
1.85
2.12
1.21
0.70
3.62
28.68
1.70
15.76
0.60
3.39
1.78
0.95
3.24
0.64
0.84
3.75
1.25
3.01
0.54
10.63
0.84
2.20
0.18
0.08
0.04
1.02
0.06
1.80
1.69
0.77
1.40
0.85
P-value
0.00
0.01
0.04
0.56
0.09
0.79
0.03
0.39
0.36
0.03
0.45
0.52
0.00
0.00
0.14
0.00
0.68
0.03
0.19
0.41
0.18
0.75
0.48
0.01
0.25
0.09
0.63
0.00
0.13
0.00
0.81
0.94
0.94
0.15
0.95
0.34
0.39
0.79
0.05
0.12
(continued on next page)
514
J. Steinbuks, V. Foster / Energy Economics 32 (2010) 505–514
Table 4 (continued)
Variable
Estimated Coeff.
P-value
Large
Very Large
Constant
F-Statistics
R2
N
0.96
0.16
1.71
26.25
0.21
4254
0.17
0.82
0.11
0.00
Source: World Bank, Enterprise Survey Database.
Note: Base country: Zambia; base industry: textiles; base size category: medium size
(50–100 employees).
Table 5
Simulated change in generator ownership from improved reliability of power supply.
Country
Power
outages
Predicted
generator
ownership
Simulated
generator
ownership
(no power outages)
Simulated
generator
ownership
(outages at South
African average)
Algeria
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Egypt
Eritrea
Ethiopia
Kenya
Madagascar
Malawi
Mali
Mauritania
Mauritius
Namibia
Niger
Senegal
South Africa
Swaziland
Tanzania
Uganda
Zambia
13.96
74.73
24.86
20.42
130.09
30.36
27.30
17.23
110.15
60.63
82.03
74.15
76.88
14.28
65.65
7.49
15.00
38.35
29.15
5.95
34.87
70.15
61.38
36.34
0.28
0.20
0.19
0.26
0.44
0.59
0.46
0.18
0.46
0.19
0.69
0.19
0.46
0.47
0.27
0.41
0.20
0.41
0.61
0.09
0.38
0.57
0.34
0.38
0.21
0.11
0.11
0.17
0.32
0.55
0.39
0.12
0.35
0.07
0.65
0.11
0.36
0.42
0.16
0.37
0.11
0.34
0.56
0.05
0.27
0.49
0.24
0.30
0.25
0.14
0.13
0.20
0.36
0.59
0.43
0.14
0.39
0.09
0.69
0.13
0.40
0.47
0.19
0.41
0.14
0.38
0.60
0.09
0.30
0.53
0.27
0.33
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