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When do rms generate? Evidence on in-house electricity supply in Africa

J. Steinbuks a,, V. Foster b

a Electricity Policy Research Group, University of Cambridge, Austin Robinson Building, Sidgwick Avenue, Cambridge, CB3 9DD, UKb The World Bank, 1818 H Street, Washington, D.C., 20433, USA

a b s t r a c ta 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 2009Available online 31 October 2009

JEL classication:

O13

Q41

Keywords:

Generators

Ownership

Electricity

Reliability

Africa

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 rms 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 rms' 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 benets 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 benets are considered side by side, the balance is not found to be signicantly

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, inefcient users of generating capacity,

decient 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.

Overall, owngeneration byrms which has beenon therise inrecent

years accounts for about 6% of installed generation capacity in Sub-

Saharan Africa (equivalent to at least 4000 MW of installed capacity).

However, this share doubles to around 12% in the low-income countries,

the post-conict 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 than20% 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

Energy Economics 32 (2010) 505514

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

Dveloppement, the European Union, the New Economic Partnership for Africa's

Development, the PublicPrivate 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 notnecessarily correspond to theviewsof theWorldBank.

Corresponding author. Tel.: +44 1223 335283; fax: +44 1223 335475.

E-mail address: [email protected] (J. Steinbuks).

1 These limitations of own-generation do not mean that there are no gains to be had

from thedecentralizationof power generation.Butin noAfrican countryhavethe 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 theuse ofexpensive generators at thermlevel.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 reect a

profoundquestioningof theprinciplesthathavegoverned thesectorsincetheearly1960s.

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. Cte d'Ivoire and Ghana introduced reforms (privatization and restructur-

ing) in the early and mid-1990s, respectively (Karekezi and Mutiso, 1999).

0140-9883/$ see front matter 2009 Elsevier B.V. All rights reserved.

doi:10.1016/j.eneco.2009.10.012

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
mailto:[email protected]://dx.doi.org/10.1016/j.eneco.2009.10.012http://www.sciencedirect.com/science/journal/01409883http://www.sciencedirect.com/science/journal/01409883http://dx.doi.org/10.1016/j.eneco.2009.10.012mailto:[email protected]


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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 rms 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

in

uence, and in particular, the probability owning a generatordoubles in large rms 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.

Thecosts of own-generationare high, drivenmainly by thevariable

cost of diesel fuel. They fall in the range US$0.300.70 per kilowatt-

hour; about three times as high as thepriceof 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 benets of generator

ownership are also substantial. Considering only lost sales resulting

from periods of poweroutages,rmswith their owngenerators 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

benets of own generation is not found signicantly positive. This is

likelybecause theanalysisis only able to capture onedimension of the

benets 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 rm characteristics

on the decision to generate electricity in-house. The fth section

illustrates the results of costbenet analysis of own generation. The

nal 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 rms' 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 signicantcosts on business. Small-scale operators were found to be most

heavily affected by the infrastructure failures.5

Reinikka and Svensson (2002) analyzed the impact of poor provision

of public capital goods on rm performance in Uganda. Using a discrete

choice model on business survey data, the authors predicted that

unreliable powersupplycausesrms to substitute complementary capital

(for backup generators) for decient public services. Estimating invest-

ment equations on the same data, they found that poor complementary

public capital signicantly reduced private investment.

Reconciling the results of the two studies is difcult. Both rely on

limited datasetsfrom businesssurveysdone in a single country.Both useonly a smallnumberof factors amongthe many thatrmsmight consider

in choosingto generatetheir ownpower. Neitheraccounts foreffectsthat

may change the provision of power supply. And the estimated marginal

costs of electricity and effects of unreliable power supply on rms'

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 rms' protability and

access to nance, 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 cross-

country dataset canhelpto identify theeffectsthat affectthe provisionof

power supply, and to some extent, changes in the industrial structure.

Microeconomicdata canbe used to inferrms'and households' response

to provision of electricity supply. Because our study relies on cross-

sectional 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

Ourmaindata source is theWorldBank'sEnterprise 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 rms.6 Our database

comprises 8483 currently operating rms 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 ofthe continent. The surveys are based on a stratied random sampling

methodology. Because in most countries the number of small and

mediumenterprises isfar greater than thenumberof largerms,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 rmlevel by taking the ratio of the deated 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 deator data comes from the World Bank's World

Development Indicators database.

4. What drives rms to generate their own power?

To evaluate the extent to which reliability of power supply and

rm characteristics affect the decision to generate electricity in-house,

2 The operating costs of own-generation are even higher in countries with

unreliable diesel and gasoline supply. During fuel supply shortages rms 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 signicant 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.

6 Detailed information on the World Bank's Enterprise Survey Database can be

found at http://www.enterprisesurveys.org.

506 J. Steinbuks, V. Foster / Energy Economics 32 (2010) 505514
http://www.enterprisesurveys.org/http://www.enterprisesurveys.org/


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we employed two modelling approaches. The rst approach follows

the binary choice model ofReinikka and Svensson (2002) is based on

the following stochastic specication:

PrYi = 1 = 1xi;

2Zi

!; 1

where Yi is the probability that rm i invests in a generator, is the

standard normal distribution function, xi is the frequency of power

outages, Zi is a vector of controls, including country and rm

characteristics, and is a standard deviation of normally distributed

error term. The second approach employs censored regression

approach:

PrYi N 0 = 1 1xi;

2Zi

!; 2

where Yi is the logarithm of the capacity ofrm's i generator (left-

censored 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 rm characteristics

age, size (as measured by number of employees), and export

orientation all have a signicant positive impact on the estimated

probability of investing in generating capacity and the size of the

generator.7

Theprobability that armwill own a generator can beexpressedas a

function of the reliability of power supply and the rm's size. The

probabilityofnding a generator on thepremisesincreasesby nearly 50%

as one moves from small rms (less than 10 employees) to very large

ones (more than 500 employees) (Fig. 1). The probability of having agenerator remains high (about 20%) even where power supply is

completely reliable.8 For largerms theprobability of havinga 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 rm characteristics, such as size, sector,

corporatestructure, andexport orientation. Largermsthatoperate24 h

per day are more likely than smaller rms to install backup generation

capacity compared to smallerrms, which operate only during daylight

hours and therefore are less affected by evening blackouts. Mining rms

tend to require ownpowerto keep elevators, airpumps, andothersafety

devicesfullyoperational regardless of the powersupply frompublic grid.

Petroleum rms 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 rms may be unable to accumulate

signicant 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 signicant across rms, except for small rms and

microenterprises (Table 1). The signicant negative coefcient on the

product of small rms and large power outages indicates that small

rms suffer the most from unreliable power supply, because they lack

the resources to invest in own-generation.9

5. The costs and benets 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, prot-maximizingrmswill insurethemselves againstthe

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 acquirebackup generating power. The

rm'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-neutralrm will maximize expected prots 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 consistsofthe 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 prot-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.

Table 1

Probit and Tobit regression results.

Probit regression

(generator ownership)

Tobit regression

(generator capacity)

Variable Estimated

coefcient

Elasticity P-value Estimated

coefcient

(Elasticity)

P-value

Days of power

outages (log)

0.06 0.02 0.01 0.23 0.05

Age (log) 0.06 0.02 0.03 0.33 0.02

Employment (log) 0.37 0.12 0.00 1.74 0.00

PKM (log) 0.001 0.00 0.31 0.001 0.69

Size1 lost days (log) 0.14 0.04 0.00 0.39 0.03




Exporter 0.29 0.10 0.00 0.56 0.07

statistically signicant at 1 percent level; statistically signicant at 5 percent

level; statistically signicant at 10 percent level.

7 The price-cost margin (PKM) variable was not statistically signicant, possibly

because of simultaneity bias between rm's protability and decision to own a

generator.8 This nding is consistent with rms' insurance behavior. Large and risk-averse

rms 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 rm will own a generator, by number of employees.

9 Further discussion of this subject becomes complicated because access to

electricity and access to nance are frequently simultaneous. For example, in Nigeria

small rms 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.

507J. Steinbuks, V. Foster / Energy Economics 32 (2010) 505514


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The cost to the rm of generating its own power consists of two

elements. The rst 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

thevariable cost perkWh-chiey fuel cost, which is practically constant.11

If thegeneratoris used to capacity during power cuts, thevariable cost per

year will then be v H Kg, wherev isthe fuel costper kWh, and H isthe

expected total duration of outages, measured in hours per year. The total

expected yearly cost per kW of backup generating power is then

CKg = bKg + vHKg: 3

The expected respective marginal cost is

CKg = b

Kg + vH 4

and the expected marginal cost of a kWh generated is simply given by

CKgkWh =

bKg

H+ v: 5

Applying Eq. (5) to the enterprise survey data allows us (using

reasonable assumptions) to estimate the (marginal) cost of own-

generation from observed information about the acquisition and running

costs of in-house generating capacity, and from data on the frequency ofpower 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 efciency (fuel consumption per kWh).

Assuming that most rms in the enterprise survey dataset rely on

thermal generation, theunit cost of fuel is approximated by an average

price per liter of diesel fuel.13 Fuel efciency 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 de

ated the price schedules, applying the corresponding value ofthe country's GDP deator 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, assumingthermal 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 signicantly higher than the cost of electricity

from the public grid (Table 2). This nding reects the differences in

efciency between the small backup generators used by commercial

rms 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.

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 0.04 0.11 0.15 0.03c 0.05

Benin 0.36 0.10 0.46 0.12c 0.27

Burkina Fasoa 0.42 0.32 0.74 0.21c 0.23

Cameroona 0.41 0.04 0.46 0.12c 0.16

Cape Verdea 0.46 0.04 0.50 0.17c 0.26

Egypt, Arab Rep. 0.04 0.26 0.30 0.04c

0.12Eritrea 0.11 0.03 0.13 0.11 0.12

Kenya 0.24 0.06 0.29 0.10 0.14

Madagascar 0.31 0.08 0.39

Malawi 0.46 0.03 0.50 0.05c 0.09

Mali 0.26 0.26 0.52 0.17 0.21

Mauritius 0.26 0.35 0.61 0.14c 0.25

Morocco 0.31 0.32 0.62 0.08c 0.15

Nigera 0.36 0.04 0.41 0.23c 0.26

Senegal 0.25 0.09 0.34 0.16 0.18

Senegalb 0.28 0.40 0.68 0.16 0.30

South Africa 0.18 0.36 0.54 0.04 0.05

Tanzania 0.25 0.04 0.29 0.09 0.13

Uganda 0.35 0.09 0.44 0.09 0.14

Zambia 0.27 0.18 0.45 0.04 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).

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

signicantly bias our estimates, because they are likely to be substantial for rms with

huge back-up capacity. In our dataset among 1352 rms that owned electric

generators, only 2 rms 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 rm's backup equipment.

13 The fuel prices came from GTZ International Fuel Prices 2005.14 These manufacturers included Wrtsil 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 rms in Africa rely on

internal rather than external nancing. Therefore, given limited access to nance, 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%

condence interval.



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The second column of Table 2 reports the rst 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 inefcient low-capacity

generators (Senegal).

The third column of Table 2 shows the average total cost of self-

generated electricity, calculatedas a sum of the average capital cost and

the average variable costs. Overall, the average total cost of self-

generatedelectricity is aboutve times thepriceof 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 theaverage 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. Benets

Our analysis of the economic benets of own-generation takes two

paths. The rst follows the literature on the reliability of electricity

supply (see, for example, Kariuki and Allan, 1995) and computes the

value oflostload, denedas thevaluean average consumer puts on an

unsupplied kWh of energy. Thevalue of lost load is represented by the

customer damage function

y = fX: 6

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 rms

with and without their own generators. For rms owning a generator

the lost load value was calculated as

y = vKg 7

where v is the generator's operational cost (in dollars per kWh), as

described earlier, and Kg is generator's capacity in kW. For rms

without a generator the lost load value was calculated as

y =Z

t8

where Z is reported sales lost from power outages and t is reported

frequency of power outages, multiplied by their average duration19

The economic benet of owning a generator is thus expressed as the

reduced loss per interrupted kW. The average values of lost load for

rms with and without generators are reported in Table 3. In all

countries, except Uganda, thelost load is considerablyhigher forrms

without a generator, especially in countries with infrequent (Maur-

itius, South Africa) or costly (Malawi) power outages.The second way to determine the benet of own-generation is to

estimate the marginal benet 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

countriesandrms20Selectedestimated parametersof sucha regression

are reported in Table 4; the complete set of parameters appears in

Appendix A, Table 3. The estimated parameters are jointly statistically

signicant (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 coefcient for duration of power outages is

negative and signicant, and the sign of the coefcient for generator

ownership is positive and signicant. The size of the estimated

coefcient for generator ownership suggests that, when controlling for

other factors, owning a generator decreases losses from power outagesby approximately 1% of a rm's sales.

5.3. Costs vs. benets

5.3.1. With no improvement in quality of public power supply

Here we summarize the costs and benets of own-generation by

integrating them at the rm level, assuming no improvement in

quality of public power supply. The costs are computed as the sum of

the annualized xed costs of acquiring a generator and the net annual

Table 3

Losses due to outages (lost load) for rms with and without their own generator.

Country Lost load (no generator, $/h) Lost load (with generator, $/h)

Algeria 155.8 52.2

Benin 38.4 23.1

Burkina Fasoa 114.1 13.0

Cameroona 403.6 12.3

Cape Verdea 177.7 36.4

Egypt, Arab Rep. 201.5 30.4

Eritrea 31.9 10.2Kenya 113.1 37.1

Madagascar 434.5 153.0

Malawi 917.3 401.4

Mali 390.3 9.5

Mauritius 468.6 13.9

Morocco 377.5 22.9

Nigera 81.3 22.6

Senegal 166.0 19.2

Senegalb 12.9 1.9

South Africa 1140.1 66.1

Tanzania 444.3

Uganda 27.6 191.4

Zambia 286.6 39.2

= data not available.a Survey of tourism sector.b Survey of informal sector.

18 This indicator is based on the assumption that the electricity tariffs charged by the

utilities are set according to marginal-cost pricing schedules.

19 Power outages were assumed to last 8 h on average.20 An attempt to estimate the marginal benet of owning a generator with respect to

physical capital losses on a smaller sample ofrms did not yield signicant results. The

details are available from authors upon request.

Table 4

Marginal benet of owning a generator.

Reduction in lost sales

Variable Estimated coef cient P-value

Days of power outages (lo g) 1.94 0.00

Generator ownership 1.16 0.01

Constant 1.71 0.11

F-statistic 26.25 0.00

R2 0.21

N 4254

statistically signicant at 1 percent level.



6/10

operating costs of generation. The xed costs of own-generation were

annualized assuming linear depreciation. The net annual operating

costs were calculated as the product of the rm's consumption of self-

generated 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 benets of own-generation.

In the rst, moreconservative approach we assume that rms that own

a generator may incur lost sales from power outages, for example,

because of disruptions in generator's fuel supply. Themarginalbenet of

own generation is calculated as the reduced sales loss from the

regression analysis in the previous section, adjusted fora corresponding

xed-effect.22 In the second approach we assume thatrms that own a

generator do not lose sales from power outages. The marginal benet of

own generation is computed as an opportunity cost of not having agenerator, e.g. the lost sales from power outages by rms without

generator.23 Both costs and benets of owning a generator are

expressed as percentages of sales. The resulting difference between

the benets from the rst approach and the costs of own-generation

(the benet-cost margin) was tested for statistical signicance from

zero using the Student's t test.24 The results are summarized in

Tables 5a5c below.

The results from the rst approach indicate that in most countries,

the costs of own-generation outweigh the benets (Table 5a).

However, only in Egypt and Zambia this difference is statistically

signicant. In four countries (Mauritius, Senegal, South Africa, and

Uganda) the benets outweigh the costs, and the difference between

the benets and the costs is not statistically signicant from zero.

These results imply that investment in in-house generation at leastallows rms to break even. The results from the second approach

indicate that benets of own generation outweigh the costs for all

countries. The results from two approaches can be reconciled under

less restrictive assumptions. First, the xed costs of own-generation

can be sunk or depreciated nonlinearly. In Egypt and Zambia the

negative and statistically signicant difference between benets and

costs of own generation is driven by xed costs. Second, the analysis

presented above does not account for other potential gains from own-

generation, 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 benets of own-generation differ according to rm

size (Table 5b). The total costs of own-generation vary nonlinearly byrm size, being most efcient for medium-sized rms. For small rms

own-generation imposes relatively lowxed costs but higher variable

costs. Larger rms have relatively high xed costs, and increasing

variable costs. The total benets of own-generation calculated as

reduced sales losses increase non-linearly with rm size, being the

highest for medium-size rms. Larger rms without generator are

found to incur smaller losses from the power outages. The difference

between the costs and benets of own-generation calculated as

reduced sales losses is negative across all size categories, except for

the medium size. However, it is statistically signicant only for large

rms. The difference is insignicant or marginally signicant for

microenterprises, small medium, and very large rms. For all size

categories the average sales losses of rms without generator are

higher than total costs of own generation.The costs and benets of own-generation vary signicantly 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 xed costs of own-generation, whereas

nonmetaland plastic materialsand mininghave thehighest operational

costs. The highest gains from own-generation calculated as reduced

sales losses areobservedin mining, chemicals, andconstruction. In most

industries, the difference between the costs and the benets of own

generation is not statistically signicant. The costs of own-generation

signicantly outweigh the benets 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 forrms

withoutgenerator.Therefore,the differencebetween two approaches is

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 benets,

because rms 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 benets

given that some generator owners reported lost sales from power outages, or because

rms 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 benets from the second approach and the

costs of own generation, because these estimates are based on different set of rms.

Table 5a

Costbenet analysis of own-generation, by country.

Percent

Country Investment

costs

Own-generation

costs

Total costs Reduced sales

losses

Benetcost

margin

Lost sales without

generator

Algeria 3.12 0.61 5.44

Benin 0.43 1.62 2.05 1.79 0.99 7.70

Egypt 2.34 0.86 3.20 1.33 2.25 6.47

Eritrea 1.54 0.41 1.95 1.28 1.43 6.55

Kenya 0.14 0.49 10.07


Malawi 0.09 0.78 0.87 0.23 0.23 24.10

Mali 0.31 0.17 0.48 0.51 0.04 2.77

Mauritius 0.17 0.01 0.18 0.84 1.10 3.93

Morocco 0.06 0.39 0.45 0.08 0.12 0.87

Senegal 0.32 0.50 0.82 1.23 0.18 6.96

Senegala 0.98 0.41 5.66

South Africa 0.10 0.09 0.19 1.19 0.90 0.84

Tanzania 0.43 1.01 0.94

Uganda 0.45 0.49 0.94 1.35 0.62 5.41

Zambia 3.02 0.95 3.96 0.07 3.90 4.35

= data not available.a Survey of informal sector. Statistically signicant at 1 percent level. Statistically signicant at 5 percent level.



7/10

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 ofpower supply will affect generator ownership. The marginal effects

of the probit model (Eq. (1)) suggest that the probability of a rm'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

effect would be greater. First, the regression analysis does not account

for unobserved explanatory variables, such as rms' access to nance

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 con-

ducted in this study does not capture these dynamics. Third, the gainsto 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 rms, 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 behavioralinformation about decisions relating to own power generation at the

rm level; at least for the case of larger formal sector manufacturing

enterprises.

The decision of a rm 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 inuence. In

particular, the probability owning a generator doubles in large rms

relative to small ones. Moreover, the behavioral model predicts that

the percentage ofrms 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

Table 6

Simulated change in generator ownership.

Change in probability that rm will own a

generator

Variable Mean MinNMax mean change std change

Days of power outages 37.9 0.12 0.02 0.03

Age 18.3 0.16 0.02 0.02

Employment 111.7 0.87 0.12 0.16

Exporter 0.19 0.10 n.a. n.a.

Source: World Bank, Enterprise Survey Database.

Note.Exporter is a binary variable, thereforemean changeand std changestatistics

are not reported.

n.a. = not applicable.

Table 5b

Costbenet analysis of own generation, by size ofrm.

Percent

Size Investment

costs

Own-generation

costs

Total costs Reduced sales

losses

Benet-cost

margin

Lost sales without

generator

Micro 0.49 0.67 1.16 0.45 0.72 5.97

Small 1.19 0.51 1.71 0.82 0.67 6.09

Medium 0.43 0.36 0.78 1.48 0.66 4.93

Large 1.64 0.77 2.41 0.05 1.56 3.80

Very large 1.20 1.22 2.42 0.54 1.97 3.27

Statistically signicant at 5 percent level. Statistically signicant at 10 percent level.

Table 5c

Costbenet analysis of own-generation, by sector.

World Bank, Enterprise Survey Database.

Percent

Industry Investment

costs

Own-generation

costs

T ota l c osts Redu ced sales

losses

Benetcost

margin

Lost sales without

generator

Textiles 0.42 0.29 0.71 0.84 0.10 4.81

Food and beverages 0.79 0.77 1.56 0.28 1.41 6.12

Metals and machinery 1.08 0.13 1.21 0.41 1.72 7.00

Chemicals 2.92 0.14 3.06 1.98 0.44 4.84Construction 2.08 0.46 2.54 1.37 0.22 3.57

Wood and furniture 0.42 0.37 0.79 1.33 0.53 5.30

Nonmetallic and plastic materials 0.91 2.30 3.20 0.99 2.21 6.79

Mining and quarrying 0.11 3.10 3.22 2.41 2.40 3.28

Statistically signicant at 1 percent level. Statistically signicant at 5 percent level.



8/10

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, whichisoften threetimesas high asthepriceof 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 rms 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 benets of

generator ownership are also substantial. Considering only lost sales

resulting from periods of power outages, rms with their own

generators report a value of lost load of typically less than US$50 per

hour, which isonlya fractionof the value oflostloadin excessof US$150

per hour that is reported by rms in the same country that do not have

their own generators.

Nevertheless, when costs and benets are considered side by side, the

balance is not found to be signicantly 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

benets of generatorownership namelyreductionin lost sales butfails

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 benet of own generation, show

that the benets of generator ownership outweigh the costs.

The following policy implications emerge from our ndings.

Though improvements in the reliability of public power supplies

would reduce generator use, many rms would still maintain their

own back-up generators in order to meet international quality

standards for participation in export markets. Through own genera-

tion, the majority of large formal sector enterprises are able to

effectively insulate themselves from the impact of unreliable power

supplies.Despitethe highcost of runningsuch generators (typicallyUS

$0.2545 per kilowatt-hour), given that outages are only intermittent,

the overall impact on the weighted average cost of power supply to

these rms 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

prevalentthanin theformalsector. Thecostimposedon thesermsfor

unreliable power is very high, andrms demonstrate high willingness

to pay for reliable power through their investments in auto-generation. There is thereforea marketfor more expensive and higher

quality power supply. It shouldbe possible to strikea bargain between

government andprivate sector to the effectthatrmspay 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 aretherefore subsidizing thepublic sectorto thetune

of easily $0.10 per kWh to receive power that they would have been

willing to supply themselves at fullcost 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,althoughit would likelybe tooexpensive to be

of interest for the residential sector.

Table 1

Summary statistics from the enterprise survey data, by country.

Country Electricity cited

as business

constraint

(% rms)

Power outages

(days)

Power outages

(% sales)

Equipment destroyed

by outages (% sales)

Generator owners

(% rms)

Power from own

generator (%)

Generator

capacity (KW)

Cost of KWH from

public grid (US$)

Algeria 11.47 12.32 5.28 29.49 6.22 322

Benin 69.23 56.12 7.79 1.54 26.90 32.80 35

Botswana 9.65 22.29 1.54 14.91 17.57

Burkina Faso 68.97 7.82 3.87 29.82 6.52 31

Burundi 79.56 143.76 11.75 39.22 25.28

Cameroon 64.94 15.80 4.92 57.79 7.62 25

Cape Verde 70.69 15.18 6.87 43.10 13.53 23

Egypt 26.46 10.40 6.12 19.26 5.87 353

Eritrea 37.66 74.61 5.95 43.04 9.31 103 0.11

Ethiopia 42.45 44.16 5.44

17.14 1.58

0.06Kenya 48.15 53.40 9.35 0.34 73.40 15.16 78 0.10

Madagascar 41.30 54.31 7.92 0.91 21.50 2.23 190

Malawi 60.38 63.21 22.64 49.06 4.44 50

Mali 24.18 5.97 2.67 1.36 45.33 5.09 43 0.17

Mauritania 29.66 37.97 2.06 26.25 11.75

Mauritius 12.68 5.36 4.01 0.42 39.51 2.87 37

Morocco 8.94 3.85 0.82 13.81 11.16 58

Namibia 15.09 0.00 1.20 13.21 13.33

Niger 26.09 3.93 2.72 27.54 14.74 73

Senegal 30.65 25.64 5.12 0.62 62.45 6.71 31 0.16

South Africa 8.96 5.45 0.92 9.45 0.17 64 0.04

Swaziland 21.43 32.38 1.98 35.71 10.33

Tanzania 60.24 63.09 0.81 59.05 12.28 70 0.09

Uganda 43.85 45.50 6.06 0.74 38.34 6.56 31 0.09

Zambia 39.61 25.87 4.54 0.28 38.16 5.13 51 0.04


Note:

= data not available.

Appendix A



9/10

Table 2

Probit regression results (generator ownership).

Variable Estimated coef cient Elasticity P-value

Days of power outages (log) 0.06 0.02 0.01

Age (log) 0.06 0.02 0.03

Employment (log) 0.37 0.12 0.00

PKM (log) 0.001 0.0002 0.31

Micro Lost Days (log) 0.14 0.04 0.00

Small Lost Days (log) 0.04 0.01 0.14

Large

Lost Days (log) 0.00 0.00 0.99Very large Lost Days (log) 0.06 0.02 0.05

Exporter 0.29 0.10 0.00

Algeria 1.53 0.55 0.00

Benin 1.57 0.57 0.00

Botswana 1.09 0.41 0.00

Burkina Faso 1.43 0.53 0.00

Burundi 2.37 0.71 0.00

Cameroon 2.17 0.69 0.00

Cape Verde 2.11 0.68 0.00

Egypt 0.98 0.35 0.00

Eritrea 1.62 0.58 0.00

Ethiopia 1.20 0.45 0.00

Kenya 2.47 0.73 0.00


Malawi 1.49 0.54 0.00

Mali 2.16 0.69 0.00

Mauritania 1.50 0.55 0.00Mauritius 1.53 0.56 0.00

Namibia 0.89 0.34 0.01

Niger 1.69 0.60 0.00

Senegal 2.42 0.73 0.00

Swaziland 1.37 0.51 0.00

Tanzania 2.24 0.70 0.00

Uganda 1.80 0.63 0.00

Zambia 1.14 0.43 0.00

Food and beverages 0.80 0.28 0.00

Metals and machinery 0.32 0.11 0.00

Chemicals and pharmaceutics 0.65 0.24 0.00

Construction 0.68 0.25 0.00

Wood and furniture 0.19 0.06 0.03

Nonmetallic and plastic materials 0.61 0.22 0.00

Other manufacturing 0.52 0.19 0.00

Other services 0.18 0.06 0.65

Hotels and restaurants 1.11 0.42 0.00

Mining and quarrying 0.53 0.19 0.11Constant 4.00 0.00

2 Statistic 1360.04 0.00

Pseudo R2 0.26

N 4246

Note: Base country: South Africa; base industry: textiles; base size category: medium

size (50100 employees), Source: World Bank, Enterprise Survey Database.

Table 3

Tobit regression results (generator capacity).

Variable Estimated coef cient/Elasticity P-value

Days of power outages (log) 0.23 0.05

Age (log) 0.33 0.02

Employment (log) 1.74 0.00

PKM (log) 0.001 0.69

Micro Lost Days (log) 0.39 0.03

Small Lost Days (log) 0.14 0.24

Large Lost Days (log) 0.01 0.96

Very large Lost Days (log) 0.22 0.15

Exporter 0.56 0.07

Algeria 7.74 0.00

Benin 7.82 0.00

Burkina Faso 2.09 0.49

Cameroon 1.87 0.46

Cape Verde 0.44 0.87

Egypt 5.43 0.00

(continued on next page)

Table 3 (continued)

Variable Estimated coef cient/Elasticity P-value

Eritrea 7.94 0.00

Kenya 10.86 0.00


Malawi 6.06 0.00

Mali 9.75 0.00

Mauritius 7.35 0.00

Niger 2.24 0.44

Senegal 10.31 0.00

Tanzania 10.47 0.00

Uganda 8.06 0.00

Zambia 3.94 0.00

Food and beverages 3.01 0.00

Metals and machinery 1.18 0.00

Chemicals and pharmaceutics 2.46 0.00

Construction 1.91 0.00

Wood and furniture 0.21 0.64

Nonmetallic and plastic materials 2.11 0.00

Other manufacturing 2.73 0.00

Other services 1.05 0.56

Hotels and restaurants 14.02 0.00

Mining and quarrying 0.42 0.83

Constant 18.61 0.00

2-Statistic 1269.15 0.00

Pseudo R2 0.17

N 3756

Note: Base country: South Africa; base industry: textiles; base size category: medium

size (50100 employees), Source: World Bank, Enterprise Survey Database.

Table 4

Marginal benet of generator ownership (lost sales).

Variable Estimated Coeff. P-value

Days of power outages (log) 1.94 0.00

Generator ownership 1.16 0.01

Algeria 2.20 0.04

Benin 0.74 0.56

Botswana 3.31 0.09

Burkina Faso 0.56 0.79

Burundi 3.24 0.03Cameroon 1.21 0.39

Cape Verde 1.85 0.36

Egypt 2.12 0.03

Eritrea 1.21 0.45

Ethiopia 0.70 0.52

Kenya 3.62 0.00

Kenya (informal) 28.68 0.00


Malawi 15.76 0.00

Mali 0.60 0.68

Mauritania 3.39 0.03

Mauritius 1.78 0.19

Morocco 0.95 0.41

Namibia 3.24 0.18

Niger 0.64 0.75

Senegal 0.84 0.48

Senegal (informal) 3.75 0.01South Africa 1.25 0.25

Swaziland 3.01 0.09

Uganda 0.54 0.63

Uganda (informal) 10.63 0.00

Agroindustry 0.84 0.13

Metals and machinery 2.20 0.00

Chemicals and pharmaceutics 0.18 0.81

Construction 0.08 0.94

Wood and furniture 0.04 0.94

Non-metallic and plastic materials 1.02 0.15

Other manufacturing 0.06 0.95

Other services 1.80 0.34

Hotels and restaurants 1.69 0.39

Mining and quarrying 0.77 0.79

Micro 1.40 0.05

Small 0.85 0.12

(continued on next page)



10/10

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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 13.96 0.28 0.21 0.25

Benin 74.73 0.20 0.11 0.14

Botswana 24.86 0.19 0.11 0.13

Burkina Faso 20.42 0.26 0.17 0.20

Burundi 130.09 0.44 0.32 0.36

Cameroon 30.36 0.59 0.55 0.59

Cape Verde 27.30 0.46 0.39 0.43

Egypt 17.23 0.18 0.12 0.14

Eritrea 110.15 0.46 0.35 0.39

Ethiopia 60.63 0.19 0.07 0.09

Kenya 82.03 0.69 0.65 0.69

Madagascar 74.15 0.19 0.11 0.13

Malawi 76.88 0.46 0.36 0.40

Mali 14.28 0.47 0.42 0.47

Mauritania 65.65 0.27 0.16 0.19

Mauritius 7.49 0.41 0.37 0.41

Namibia 15.00 0.20 0.11 0.14

Niger 38.35 0.41 0.34 0.38

Senegal 29.15 0.61 0.56 0.60

South Africa 5.95 0.09 0.05 0.09

Swaziland 34.87 0.38 0.27 0.30

Tanzania 70.15 0.57 0.49 0.53Uganda 61.38 0.34 0.24 0.27

Zambia 36.34 0.38 0.30 0.33

Table 4 (continued)

Variable Estimated Coeff. P-value

Large 0.96 0.17

Very Large 0.16 0.82

Constant 1.71 0.11

F-Statistics 26.25 0.00

R2 0.21

N 4254


Note: Base country: Zambia; base industry: textiles; base size category: medium size(50100 employees).


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