WPS7789
Policy Research Working Paper 7789
Who Uses Electricity in Sub-Saharan Africa?
Findings from Household Surveys
Masami Kojima
Xin Zhou
Jace Jeesun Han
Joeri de Wit
Robert Bacon
Chris Trimble
Energy and Extractives Global Practice Group
August 2016
Policy Research Working Paper 7789
Abstract
Analysis of household expenditure surveys since 2008 in the subsistence and other levels. Affordability varied across
22 Sub-Saharan African countries shows that one-third countries, with grid electricity even at the subsistence level
of all people use electricity. As expected, users are dispro- being out of reach for the poor in half the countries and even
portionately urban and rich. In communities with access more so once connection charges are considered. Exami-
to electricity, lack of affordability is the greatest barrier to nation of the gender of the head of household shows that
household connection. Lifeline rates enabling the poor to female-headed households are not disadvantaged in elec-
use grid electricity vary in availability, with six countries tricity use once income and the place of residence (urban
allowing 30 kilowatt-hours or less of electricity usage a or rural) are taken into account. However, female-headed
month at low prices. Affordability challenges are aggravated households tend to be poorer, making it all the more impor-
by sharing of meters by several households—denying them tant to focus on helping the poor for the goal of achieving
access to lifeline rates—and high connection costs in many universal access. Installing individual meters and subsidiz-
countries, made worse by demands from utility staff for ing installation, encouraging prepaid metering so as to avoid
bribes in some countries. Collection of detailed information disconnection and reconnection charges, reformulating
on residential schedules enabled calculation of the percent- lifeline blocks and rates as appropriate, and stamping out
age of total household expenditures needed for electricity at corruption to eliminate bribe-taking can all help the poor.
This paper is a product of the Energy and Extractives Global Practice Group. It is part of a larger effort by the World
Bank to provide open access to its research and make a contribution to development policy discussions around the world.
Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted
at mkojima@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
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Produced by the Research Support Team
Who Uses Electricity in Sub‐Saharan Africa?
Findings from Household Surveys
Masami Kojima, Xin Zhou, Jace Jeesun Han, Joeri de Wit, Robert Bacon, and Chris Trimble1
Key words: access, electricity, affordability, subsidies, poverty gap, households, gender, expenditure surveys
JEL codes: Q41, Q48, D19, H29
1
Masami Kojima, Joeri de Wit, and Chris Trimble are at the World Bank, and Xin Zhou, Jace Jeesan Hun, and
Robert Bacon are consultants. The paper benefited from useful comments provided by Pedro Antman,
Sudeshna Banerjee, Malcolm Cosgrove‐Davies, Vivien Foster, and Wendy Hughes, all of the World Bank. The
authors owe special thanks to Rose Mungai of the World Bank for providing survey data and information for
many countries, and to Xavier Daudey for helping with tariff information. The authors are grateful to
Prospere Backiny‐Yetna, Yele Batana, Nadia Belghith, Tom Bundervoet, Andrew Dabalen, Kristen Himelein,
Dean Jolliffe, Roy Katayama, Talip Kilic, Vasco Molini, Djibril Ndoye, Clarence Nkengne, Aly Sanoh, Victor
Sulla, Ayago Esmubancha Wambile, Quentin Wodon, and Precious Zikhali, all of the World Bank, and Harold
Coulombe, Zakaria Koncobo, Paul Saumik, Ilana Selff, and Xiao Ye for various data files and answers to
questions. The financial support from the Africa Renewable Energy and Access Program (AFREA), part of the
Energy Sector Management Assistance Program (ESMAP), is gratefully acknowledged.
2
Contents
Abbreviations ................................................................................................................................................ 2
Introduction .................................................................................................................................................. 3
Scope and Methodology ............................................................................................................................... 5
Cross‐Regional Perspective ......................................................................................................................... 11
Descriptive Statistics ................................................................................................................................... 12
Measuring Access in Household Surveys .................................................................................................... 12
Spending on Electricity ................................................................................................................................ 15
Charges for Grid Electricity Use and Connection ........................................................................................ 17
Affordability of Electricity ........................................................................................................................... 21
Grid‐electricity affordability at the subsistence level of consumption ........................................................... 21
Affordability at higher levels of consumption ................................................................................................ 25
Grid‐electricity poverty measures .................................................................................................................. 25
Subsidy to make the subsistence level of electricity affordable to all ............................................................ 27
Relationships between access, poverty, and quasi‐fiscal deficits................................................................... 28
Affordability of connection costs .................................................................................................................... 30
Community versus Household Access ........................................................................................................ 33
No spending on grid electricity ................................................................................................................... 35
Multiple Connections .................................................................................................................................. 37
Outages ....................................................................................................................................................... 38
Gender of Head of Household .................................................................................................................... 39
Conclusions ................................................................................................................................................. 42
Annex .......................................................................................................................................................... 46
References .................................................................................................................................................. 68
Abbreviations
GDP gross domestic product
kWh kilowatt‐hour(s)
VAT value‐added tax
3
Introduction
Sub‐Saharan Africa (Africa hereafter) lags behind all other regions of the world in household access2 to
electricity, per capita power consumption, and installed generation capacity. Africa accounted for 13 out of
the top 20 countries in the world with the largest numbers of people without access to electricity identified
in the Global Tracking Framework report published in 2015 (World Bank and IEA 2015). The report found that
the percentage of people living in households with access to electricity in 2012 was about 35 percent.
However, disparity between urban and rural areas was stark—69 percent of urban residents but only 15
percent of rural residents were estimated to have had access to electricity. The region with the second
lowest access rate, South Asia, had more than double the percentage of people living with electricity (79
percent), and nearly five times the percentage share of rural residents with electricity (70 percent).
The global initiative, Sustainable Energy for All, brings diverse partners together to work toward universal
access to electricity by 2030. Africa is likely to be the last region of the world to reach this target. Years of
underspending, serious shortcomings in operational efficiency, underpricing, high costs of small‐scale
operation, over‐reliance on expensive oil‐based power generation, and the inability of many customers to
pay for electricity services have contributed to much lower generation capacity per capita than in other
regions. Existing customers face frequent power outages, and new connections in some countries have
barely kept with population growth.
Benefits of electrifying households are many. Electric lighting is safer, more convenient, and far more
efficient than kerosene lamps. Electricity enables households to use electronic forms of communication, and
refrigeration makes food safer as well as reduces the frequency of grocery shopping and cooking. Studies
analyzing data from household surveys have found positive effects of electrification on income (Kumar and
Rauniyar 2011; Khandker et al. 2012; Khandker, Barnes, and Samead 2013; Van de Walle et al. 2013),
employment (Dinkelman 2011; Khandker et al. 2012; Grogan and Sadanad 2013; Van de Walle et al. 2013),
and education (Kumar and Rauniyar 2011; Khandker et al. 2012; Khandker, Barnes, and Samead 2013; Van de
Walle et al. 2013).
In order to understand better why so many households in Africa do not have access to electricity despite its
many benefits, some have studied possible constraints facing households. Komives et al. (2005) analyzed
electricity access by quintile and by poverty status. The study covered about 40 countries, and predictably
found that access was much lower for poor households. However, only three countries in Africa were
included in the study, and most other countries had much higher rates of access. The study assessed the
effectiveness of subsidies embedded within the tariff structure and the potential improvements that could
be made by redesign of the tariff structure.
Komives et al. broke down access into two factors in assessing whether subsidies could benefit low‐income
households: the “community access rate” and the “connection rate” for those living in communities with
2
In international initiatives to achieve universal access, access has come to mean having electricity at home, and this
paper uses the word in that sense. However, some literature has defined access to mean availability of electricity in the
community and uses the term “coverage” to mean having electricity at home. The difference between percent coverage
and percent access is the percentage of people or households who live in communities with electricity but have chosen
not to have it at home (for example, have chosen not to connect to the grid), most usually for financial reasons. To the
extent that anyone can have a generator at home, private generators are excluded from access in the latter sense.
4
electricity. Together these factors defined the “access” rate.3 For example, 88 percent of all households in
Cabo Verde lived in communities that had connection to the grid, while 63 percent of these households were
actually connected, leading to an overall access rate of 55 percent. The difference between community
access and household access was much greater for poor households—72 percent of the poor had community
access in Cabo Verde, but only 34 percent was actually connected, giving an access rate of 25 percent. The
situations in São Tomé and Príncipe and Rwanda were similar—community access was only slightly lower for
the poor, but household connection of those with community access was much lower. The distinction
between “community access” and “household access” was shown to be important in the study by Van de
Walle et al. (2013). This showed that non‐connected households, when their neighbors were connected, may
have also received benefits from the electrification. This conclusion suggested that the overall benefits from
providing access could be greater than those accruing just to the households connected.
Briceño‐Garmendia and Shkaratan (2011) analyzed spending on electricity by quintile in Africa. They found
that the share of total household expenditure allocated to electricity was below 3 percent in most countries,
and that the share was relatively stable across expenditure quintiles. The authors used this information to
check whether households could afford to pay the full economic cost of consumption at the subsistence
level. Affordability was defined as spending being in the range of 3–5 percent of total household expenditure,
while consumption at the subsistence level was defined as 50 kilowatt‐hours (kWh) per month. The study
found that on existing tariffs consumption at the subsistence level is affordable for the large majority of
those already connected. However, for those not already connected consumption at the subsistence level
would be affordable at existing tariffs for only one‐quarter of such households. Were tariffs to be raised to
cost recovery levels, far fewer of the unconnected would be able to afford to purchase even consumption at
the subsistence level. An even larger barrier to access is the existence of a connection charge. Golumbeanu
and Barnes (2013) described the way in which connection charges can be so large that they are unaffordable
for many low‐income households. Evidence from a number of countries indicated that countries with higher
connection charges, controlling for the level of gross domestic product (GDP) per capita, tended to have
lower access.
Surveys of household access to electricity and its use have highlighted the issue that some households pay
nothing or only part of the appropriate charge for the use of electricity. Smith (2004) pointed out that
electricity theft could be in the form of fraud (meter tampering), stealing (illegal connections), billing
irregularities, and unpaid bills. Calculations based on data from 102 countries showed that average
transmission and distribution losses, which include losses from theft, had increased between 1980 and 2000
for all regions except Western Europe and North America. Antmann (2009) provided an analysis of methods
of reducing technical and non‐technical losses in the power sector drawn from experience in several
countries. The use of pre‐payment meters has been identified as one way of dealing with delinquent bill
payment by households. Tewari and Shah (2003) describe the South African experience, starting in 1989, of
the introduction of pre‐paid meters and their benefits, costs, and problems. A study by Jack and Smith (2015)
of purchasing patterns of electricity in South Africa through pre‐payment meters found that the bottom‐
quintile households tended to buy electricity three times as often but in amounts that are only one‐fifth of
those households in the top quintile. The pattern of small frequent transactions is consistent with the
existence of liquidity constraints and difficulties in smoothing income, suggesting that the pay‐as‐you‐go
approach of pre‐paid meters is preferable for low‐income households to a monthly billing cycle. In Uganda
3
Komives et al. used the term “access” to denote the fact that households in the neighborhood had an electricity
connection and “connection” to indicate that the household was itself connected to the grid. Angel‐Urdinola and
Wodon (2007) called the former “community access” and the latter “users”.
5
bulk metering was introduced for micro and small enterprises in 2009 and pre‐payment meters for
households in 2012. An analysis of the bulk metering program by Never (2015) indicated that although this
approach improved bill collection, there were still problems of theft and inability to pay.
This paper is part of a broader study examining the financial viability and related aspects of the power sector
in Africa. The broader study focuses primarily on grid electricity for reasons of data availability at the regional
level. The study examines quasi‐fiscal deficits—difference between the total cost of providing electricity and
total cash collected by utilities—in 39 countries (Trimble et al. 2016), tariff structures in 39, and household
use of electricity in 22.4 The component on household use of electricity, which is the subject of this paper,
takes household expenditure surveys to gain a better understanding of who uses electricity, what type of
electricity, and how much. It asks the following questions:
How widely is electricity used?
What are the barriers to making electricity the main source of energy in Africa for lighting and
powering appliances?
Is electricity affordable?
Is there potential evidence suggesting that female‐headed households are disadvantaged in some
ways with respect to electricity use?
Although household expenditure surveys capture spending on all forms of electricity, as with the broader
study, detailed analysis is carried out primarily on issues associated with grid connection.
Scope and Methodology
The household expenditure surveys analyzed in this paper were carried out in 2008 or later. The survey
period and the sample size are summarized in Table A.1 in the annex. This paper reports results calculated
using household weights or population weights, depending on whether percentages of households or people
are being examined. The only exception is when the sample size is discussed, primarily to point out that the
number of households being interviewed was too small for meaningful results (for example, the number of
rural households in the bottom 40 percent connected to the grid). In each country, people are divided into
quintiles based on per capita expenditures, with quintile 1 being the poorest and quintile 5 being the richest.
Per capita expenditures are derived from consumption aggregates used to construct official poverty
measures.5 Poverty measurements are based on real expenditures, either per capita or per adult equivalent,
except in Botswana where nominal values are used. Each quintile has the same number of people, not
households. Because household size typically declines with increasing wealth, and because the rich tend to
be concentrated in urban areas, upper quintiles have more households than lower quintiles and are more
urban than rural. The results are analyzed by consumption quintile, households’ poverty status (poor or non‐
poor based on the official poverty measures), location (urban or rural), and gender of the head of household.
Where official poverty is based on per capita expenditure, the bottom quintile is always poor, but where
poverty is based on expenditure per adult equivalent, the overlap between the poor and the bottom quintile
4
The 39 countries are not identical. Non‐overlapping countries between the components are Angola, Chad, and
Namibia in the tariff analysis and the Central Africa Republic, the Republic of Congo, and Sudan in the analysis of quasi‐
fiscal deficits. Of the 22 countries with household surveys, all but Angola were analyzed for quasi‐fiscal deficits.
5
The methodology for consumption aggregation for the purpose of defining poverty varies from government to
government, and therefore quintiles are not strictly comparable across countries because what is included in
consumption aggregates is not the same.
6
is not complete. Households that had spent more than 30 percent of their total expenditures on electricity
are considered outliners and dropped.
All questionnaires except the one in South Africa asked for the primary sources of energy for lighting and
cooking; the South African survey asked if the household was connected to the grid, and the response to that
question substitutes the question about the primary energy source for lighting in the rest of this paper.
Some surveys listed off‐grid electricity separately (typically solar and backup generators), while some had
only one category called electricity. “Solar energy”—the term used in Botswana, Burkina Faso, Ethiopia,
Ghana, Senegal, Swaziland, and Uganda—could mean solar home systems only or could include solar
lanterns. This lack of distinction is unfortunate because solar lamps would not fall under the category of
electricity access, whereas solar home systems would. Mali, Mozambique, Rwanda, Sierra Leone, Tanzania,
and Zambia asked specifically about solar panels. Seventeen surveys asked specifically about grid electricity,
of which sixteen asked if the household was connected to the grid, and Togo asked about paying electricity
bills over the previous two months (thereby excluding those connected to the grid but had not made
payments during the recall period); several asked respondents if they had their own meters or were sharing
meters with others.
Spending on electricity in this paper excludes expenditures on items not considered as part of access to
electricity: batteries, candles, kerosene, and liquefied petroleum gas. While fuels for generators would be
part of spending on electricity, only Rwanda and Zambia enabled isolation of spending on fuel for generators;
all other countries bundled spending on gasoline and diesel for vehicles or lawn mowers with spending on
either fuel for generators. For consistency across countries, spending on diesel used in generators in these
countries was excluded from expenditure on electricity.
The surveys did not enable systematic analysis of access at different levels according to the recently
formulated multi‐tier access framework for the Sustainable Energy for All initiative. The framework defines
energy access as the ability to obtain energy that is adequate, available when needed, reliable, affordable,
legal, convenient, healthy, and safe for all required energy applications (World Bank 2015). For household
connection to electricity, there are five tiers in the framework, with grid electricity corresponding to tiers 3–5
depending on appliances being used and kWh consumed per year and per day. Information on appliance use
was not available in any survey, and plausible data on kWh consumed were available in only two to three
surveys. In the face of these data limitations, this paper instead examined access at five different levels:
1. People living in households that reported connection to the grid
2. Adding to 1 people living in households that reported using electricity—excluding generators and
solar energy if they are separately counted—as the primary source of energy for lighting, cooking, or
both
3. Adding to 2 people living in households that reported using generators or diesel as the primary
source of energy for lighting, cooking, or both, or reported owning generators
4. Adding to 3 people living households that reported using solar panels as the primary source of
energy for lighting, cooking, or both, or reported owning solar panels; or solar energy in the place of
solar panels if only information on solar energy was available
5. Adding to 4 people who reported non‐zero expenditures on electricity.
Definition 2 is the same as definitions 3 and 4 in Malawi and São Tomé and Príncipe where the survey did not
specifically asked about use of generators and solar panels or solar energy. Access to solar panels is preferred
7
whenever information is available so as to avoid including people living in households using one or two solar
lanterns. In countries where survey questionnaires asked only about solar energy (Botswana, Burkina Faso,
Ethiopia, Ghana, Senegal, South Africa, Swaziland, Togo, and Uganda), definition 4 could well over‐estimate
the number of people with access, if households with solar lanterns had replied that they were using solar
energy for lighting.
All surveys asked about spending on electricity, most commonly in the last 30 days. There are limitations with
this approach. Payment arrears may be common, but no household surveys in this paper probed this point.
By contrast, the 2012 National Survey of Household Income and Expenditures in Mexico asked a series of
questions to understand arrears, including when the last payment for electricity had been made, while the
2005 Integrated Sample Household Budget and Labor Survey in the Kyrgyz Republic asked for kWh of
electricity consumed, the amount billed, and the amount paid for three successive months as well as the
amount of subsidy received. Most household surveys, however, simply ask how much the household paid last
month for electricity. Asking just one question about how much the household paid over a fixed period of
time could under‐ or over‐estimate (the latter if past debts are being repaid to utilities) monthly expenditures
on electricity. Cross‐checking household survey data against data from the utilities could indicate the
magnitude of these problems and ways of adjusting data for a more accurate picture. Lampietti and Junge
(2006) combined billing and payment records from the utility and merged them with household survey data
to address recall errors, under‐ and over‐reporting, and the presence of arrears, which enabled more
accurate estimation of current and historical electricity consumption as a function of household income and
other characteristics. However, obtaining utility data for such cross‐checking was outside the scope of the
study.
The survey in Senegal asked if the household had spent anything on electricity over the last 12 months, which
would more likely capture whether the household was paying something for electricity. If the question is
about spending only in the last month only, no payment might have been made for a variety of reasons and
the household would be registered as having zero expenditure. Similarly, some households did not answer
the question (they might have found it difficult to recall the amount in the short time given to answer this
question during the interview), and all missing responses were recorded as zero in the analysis. In all 22
countries, not all who cited electricity as the primary source of energy for lighting reported positive
expenditures in electricity. Legitimate reasons for having no expenditures for grid electricity, the focus of this
study, include electricity included as part of the rent, bundling of utility services (such as combined water and
electricity bills), or free electricity being part of the compensation package for employment. It is also possible
that the household happened not to have made payments during the recall period—for example, the
household could have missed one bill payment, making up for it later.
Examination of spending on electricity was necessarily confined to those who reported positive expenditures
on electricity. Among those who did, spending on electricity was computed as a share of total household
expenditures as one measure of affordability. Because power tariffs in many countries are pan‐territorial and
do not have large regional differences that are observed with food and other items, nominal expenditures on
electricity and nominal total household expenditures were used to compute expenditure shares, except in
Angola and Mozambique where nominal expenditures were not available and where regionally adjusted
expenditures were used.
Where enough information was available on how consumption aggregates were derived for official poverty
statistics, expenditures on food were disaggregated into those paid for by cash and freely acquired food that
had been assigned imputed values. The purpose of doing so was to estimate the total amount of cash
8
available to pay for electricity. Imputed values are also assigned to items other than food, such as collected
firewood. However, separating cash expenditure on food from imputed values was considered adequate for
the purpose of assessing affordability of electricity among low‐income households, because by far the
greatest share of non‐cash expenditures is food consumption, the share of which increases with decreasing
income.
To examine the affordability of grid electricity, monthly consumption corresponding to tiers 3–5 in the multi‐
tier matrix for access to household electricity supply was taken—30 kWh, 100 kWh, and 250 kWh,
respectively—and corresponding electricity bills were computed. The bills are inclusive of energy charges,
fixed charges, other charges (such as a rural electrification fund fee), and applicable value‐added tax (VAT)
and any other tax. They do not include charges unrelated to electricity use (specifically public television and
radio license fee) even if they are always added to every residential electricity bill. In addition, 50 kWh a
month was considered for comparison with the AICD and because several countries set 50 kWh as the cap on
highly subsidized lifeline rates.
The monthly electricity bills for these amounts were then expressed as shares of total household
expenditures for all households, whether or not they were connected to the grid. The multi‐tier access
framework defines electricity as being affordable if households spend less than 5 percent of total
expenditures on monthly consumption of 30 kWh. This paper similarly considers grid electricity to be
unaffordable if a household has to spend more than 5 percent of its total expenditures on electricity. As part
of the broader study, detailed information on the tariff schedule in effect in July 2014 was collected in 39
countries, including all the 22 countries with household survey data. In many countries, more than one
schedule existed, and the least‐cost option was taken for the purpose of examining affordability. If the survey
was undertaken when a different tariff schedule was in effect, then monthly nominal per capita expenditures
were increased at the same rate as nominal per capita GDP in local currency to the year when the tariffs
prevailing in July 2014 first came into effect and total household expenditures were then computed from the
adjusted per capita expenditure. If the tariff schedule in effect in July 2014 had been first introduced before
the survey date, no adjustment of expenditure was necessary—Burkina Faso, Ethiopia, Mali, Mozambique,
Senegal, Sierra Leone, and Togo fell under this category and did not require any adjustment to total
household expenditures. Connection charges were available for all but Mali and São Tomé and Príncipe. They
were computed as multiples of total monthly household expenditures. If information on when the
connection charges came into effect was not available, the dates of effectiveness were assumed to be the
same as those for tariff schedules (see Table A.8 in the annex for more detail).
This paper computes the poverty gap for grid electricity in the same way the poverty gap is defined in
economics as follows:
%
∑ / ,
where the required monthly payment is the monthly bill inclusive of taxes and other charges that a
household has to pay to consume the corresponding amount of electricity, P is the total population living in
households for whom the monthly payment exceeds 5 percent of their total monthly household
expenditures (inclusive of freely acquired food and other items), and N is the total population of the country.
Where the monthly electricity bill exceeds the 5‐percent share, electricity is deemed unaffordable, and the
degree of unaffordability for a household is the size of the gap between the bill and the 5‐percent share
when this is positive, zero otherwise. Although monthly consumption of 30 kWh is the basis for defining
affordability, the poverty gap is also computed for 50, 100 (multi‐tier framework tier 4), and 250 kWh (multi‐
tier framework tier 5) a month to see how many people can afford higher consumption. For these
9
calculations, the lowest‐cost tariff schedule for each consumption level is taken where there are two or more
possible schedules. This paper also takes the numerator in the above equation and aggregates the
affordability gap (where it is positive) across all households using household weights. The sum is the amount
of subsidy needed to enable every household to keep spending on electricity at or below 5 percent of its total
household expenditure.
In addition to the poverty gap, this paper also computes the poverty headcount for grid electricity. The
poverty gap takes account of the depth of poverty and is 100 percent only if every person has zero total
household expenditure. The poverty headcount, by contrast, is simply the percentage of people who find
consumption of a certain amount of electricity unaffordable according to the household‐expenditure‐share
threshold of 5 percent. If every person has to spend 5.05 rather than 5.00 percent of total household
expenditures to purchase electricity, the poverty headcount would be 100 percent but the poverty gap would
be 1 percent, which is the difference between 5.05 and 5.00 divided by 5.00.
Lastly, in additional to analysis of supplementary questions related to grid electricity, the study also
investigated differences in electricity use between female‐ and male‐headed households. Simplified
regression analysis was carried out country by country to see if, after accounting for total expenditures and
location (urban or rural), female‐headed households were any more likely to use electricity than male‐
headed households, and whether spending on electricity showed differences. Probit regressions (for the first
three below) and ordinary least squares (for the last) were carried out on the following dependent variables:
1/0 “expenditure dummy” for having positive expenditures on electricity (1 if positive, 0 if zero or
missing)
1/0 “electricity dummy” for citing electricity of all forms, including generators and solar, as the
primary source of energy for lighting or cooking (1 if electricity was used for lighting or cooking, 0
otherwise)
1/0 “grid dummy” for connection to the grid, or if that information was not available, for citing
electricity excluding generators and solar as the primary source of energy for lighting or cooking
where generators, solar, or both were separately listed
logarithm of expenditures (“log expenditure”) on electricity for those households that reported
positive expenditure, and in addition repeating the regression confining the sample only to those
connected to the grid
The following explanatory variables were tried and their statistical significance was tested using at 5‐percent
significance test6 (that is, the probability that the coefficient for the independent variable is actually zero
when the regression shows a non‐zero value is less than 5 percent):
logarithm of total household expenditures
logarithm of per capita expenditure and logarithm of household size as an alternative to the above
1/0 dummy for female‐ and male‐headed households (1 for female, 0 for male)
1/0 dummy for urban and rural (1 for urban, 0 for rural)
The dummy for urban/rural may be viewed as a proxy for the level of infrastructure development, and more
specifically a crude proxy for the availability of grid electricity. This variable was always statistically
6
This paper uses a 5‐percent significance test, or a 1‐percent test when results are highly implausible were the true
coefficient zero.
10
significant, raising the question of whether the independent variable of interest, female/male dummy, may
be correlated with it. To examine this question, another probit was tested with the urban/rural dummy as
the dependent variable and the logarithms of per capita expenditure and household size and the
female/male dummy as explanatory variables. With the exception of Angola, the female/male dummy was
always statistically significant. Therefore, the sample was split into urban and rural in every country, and
regressions were run separately, eliminating the urban/rural dummy as an explanatory variable.7
In every case where the coefficient for female/male dummy was statistically significant, the probability of
increasing the dependent variable by switching from male‐ to female‐headed households was computed. For
that purpose, the values of the remaining two variables assumed the weighted averages in urban and rural
areas, respectively. For ordinary least squares, which drops all households with zero expenditure on
electricity and regresses the logarithm of spending on electricity on the three explanatory variables, the
percent increase in spending on electricity (not logarithm) was computed, again setting the values of the two
remaining variables at average values of the households in the sample.
For cross‐regional comparison of household access to electricity, presented first, access rates in 1990 and
2012 in different regions from the 2015 Global Tracking Framework report (World Bank and IEA 2015) were
compared as a function of two measures of the poverty gap ($3.10 and $1.90 per day per person at
purchasing power parity in 2011 international dollars) and of logarithms of per capita GDP (at the market
exchange rate in 2005 U.S. dollars and at purchasing power parity in 2011 international dollars). 2012 was
selected because it is the last year for which access rates are available in the Global Tracking Framework
report, three other years being 1990, 2000, and 2010. 1990 was selected as an alternative because the
poverty gap in South Asia, the region with the second lowest rate of access to electricity, was close to that in
Africa in 2012. Unlike GDP, the availability of the poverty gap data is sporadic, reducing the sample size by
more than half. For access in 2012, corresponding poverty gap data were taken from 2012 whenever they
were available, and otherwise from 2011 or 2013, and if the data were not available from any of these years,
they were taken from 2010 (the poverty gap in 2014 was not available for any country). For 1990, in order to
capture India, which had the poverty gap data in 1987 and 1993 but no other year in between, the range of
years was extended to 1987–1992. For regression equations, in addition to access, per capita GDP, and the
poverty gap, 1/0 regional dummies for Africa, South Asia, East Asia and Pacific, Latin American and the
Caribbean, and “other” for the countries in the remaining regions were also used.
The next section entitled “Cross‐Regional Perspective” is the only one that does not use data from recent
household surveys in the 22 countries covered in the rest of the paper. It is also the only section that defines
the poverty gap according to the poverty lines of $3.10 and $1.90 per person per day. Elsewhere, the poverty
gap refers to the grid‐electricity poverty gap defined in the equation above. To distinguish different measures
of the poverty gap, “poverty gap” is followed by threshold values: $3.10, $1.90, 30 kWh, 50 kWh, 100 kWh,
and 250 kWh.
7
For the probit for the dependent variable that excludes generators and solar, ideally the sample should be restricted to
those who could potentially have connected to the grid. Only Malawi and Nigeria asked that question of each household
and the main reason for choosing not to connect. One approach is to assume that if there was at least one household
citing electricity use in a sampling unit, the entire sampling unit was electrified. In the context of Africa, such an
assumption may be too sweeping, and was not examined in this study.
11
Cross‐Regional Perspective
Broadly, access expansion mirrors economic development. Does access in Africa as a function of income or
depth of poverty fall in line with the rest of the world? To answer this question, access in different countries
was compared as a function of the poverty gap at $3.10 and $1.90 and per capita GDP. Correlation
coefficients between access and the two measures of the poverty gap and per capita GDP are statistically
significant at 1 percent, with correlation coefficients ranging from ‐0.72 for the logarithm of GDP per capita
at the market exchange rate to ‐0.89 for the poverty gap at $3.10.
Figure 1 plots the relationship between access and the poverty headcount at $3.10 for 2012. There is a
downward sloping line on which most countries in regions other than Africa lie, whereas several countries in
Africa lie markedly below, as do Cambodia and Vanuatu. When access is regressed on any one of the
measures of the poverty gap or per capita GDP together with 1/0 regional dummies, Africa is the only region
for which the coefficient for the regional dummy is consistently negative and statistically significant at 1
percent. The predictive power of the poverty gap at $3.10 was the highest, followed by the poverty gap at
$1.90, the logarithm of per capita GDP at purchasing power parity, and finally the logarithm of per capita
GDP at the market exchange rate.
Figure 1: Relationship between poverty gap and electricity access in 2012
Access to electricity, % of population
100
80
60
40
20
0
0 10 20 30 40 50 60
Poverty gap in % at US$3.10/person/day
Africa Latin America & Caribbean South Asia East Asia & Pacific
Source: World Bank staff analysis using data from World Bank 2016 and World Bank and IEA 2015.
Note: Bubbles are in proportion to population.
Figure 2 compares access and the poverty gap in Africa in 2012 with corresponding values in three other
regions in 1990. China stands out as a (positive) outliner, with the access rate far above that of other
countries at similar levels of the poverty gap. Several countries in Africa again lie below the global trend line.
When access (in 2012 in Africa and in 1990 elsewhere) is regressed on the poverty gap at $3.10 (in 2012 in
Africa and in 1990 elsewhere) together with regional dummies as dependent variables, the Africa dummy is
again negative and statistically significant at 1 percent. While these are simplified analyses, these findings
point to the special challenges facing Africa in expanding household access.
12
Figure 2: Comparison of Africa in 2012 with other regions in 1990
Access to electricity, % of population
100
80
60
40
20
0
0 10 20 30 40 50 60
Poverty gap in % at US$3.10/person/day
Africa 2012 Latin America & Caribbean 1990
South Asia 1990 East Asia & Pacific 1990
Source: World Bank staff analysis using data from World Bank 2016 and World Bank and IEA 2015.
Note: Bubbles are in proportion to population.
Descriptive Statistics
Household surveys in the 22 countries show that, on average, two‐thirds of all people lived in rural areas (see
Table A.2 in the annex). In terms of numbers of households, one‐third of households lived in rural areas in
South Africa, but in all other countries the percentage was 42 or higher. In 16 countries, the average per
capita expenditure in every rural quintile (calculated using population weights) was lower than in the
corresponding urban quintile, and in seven countries household expenditure in every rural quintile
(calculated using household weights) was similarly lower. The median for the percentage of people who were
officially classified poor across the 22 countries was 46 percent. In rural areas, the poor constituted a
majority, with a median of 56 percent of all people and a weighted average of 50 percent. About one‐fifth of
all people lived in households headed by a woman. The median of the percentage of people living in female‐
headed households was 23 in urban areas, 20 in rural areas, and 21 percent of the total population. At the
highest end of the spectrum, Botswana stands out with half of all people living in female‐headed households;
at the opposite end is Mali with only 4 percent reporting living in female‐headed households. Across the 22
countries studied, the median monthly per capita expenditure in 2014 U.S. dollars8 was $73 in urban areas
and $37 in rural areas, giving a national median of $49 per capita per month.
Measuring Access in Household Surveys
What information about access do household surveys provide? How many surveys provide enough
information about kWh of electricity consumed by each household—essential information for designing
targeted subsidies? How do access rates differ by income, between urban and rural, between the poor and
8
Expenditures were adjusted for inflation to 2014 and converted to U.S. dollars using the exchange rate in 2014.
13
non‐poor? As explained on page 6, this paper uses five measures to calculate access. Access statistics by
location, quintile, and poverty status using the most expansive definition (definition 5) are shown in Table 1;
the statistics using the first definition (grid connection) are reported in Table A.3 in the annex. The results are
consistent with the general observations made elsewhere that access rates in rural Africa are significantly
lower than in urban areas, the poor are far less likely to have access to electricity than the rich, and in some
countries access of the rural poor to electricity is essentially non‐existent. Overall, the poor in nine out of 22
countries had an access rate of less than 5 percent.
Table 1: Percentage of people with access to electricity according to definition 5
All people People classified poor
Country Urban Rural Total Q1 Q5 Urban Rural Total
Angola 75 14 47 8 85 40 6 16
Botswana 63 23 46 15 79 42 10 25
Burkina Faso 47 3 13 2 38 14 2 3
Côte d'Ivoire 88 31 57 41 38 14 2 3
Ethiopia 96 12 23 7 45 86 6 10
Ghana 89 47 68 37 91 74 32 41
Madagascar 38 6 12 1 44 12 3 4
Malawi 38 4 9 1 31 8 0 1
Mali 92 57 65 49 80 86 50 52
Mozambique 47 2 16 1 51 11 1 2
Niger 61 6 15 2 47 20 3 4
Nigeria 93 48 64 33 88 88 38 48
Rwanda 48 6 12 1 46 6 1 1
São Tomé and Príncipe 69 48 59 49 72 62 44 53
Senegal 93 32 59 36 84 84 26 44
Sierra Leone 42 2 17 3 43 25 1 8
South Africa 94 81 89 78 99 87 77 81
Swaziland 70 30 40 4 83 45 16 19
Tanzania 52 9 20 4 58 7 4 4
Togoa 80 10 37 6 76 65 6 19
Uganda 39 7 15 3 42 6 4 4
Zambia 59 16 31 8 78 19 11 12
Median 66 13 34 7 65 33 6 11
Source: World Bank staff analysis of household surveys
a. The statistics for Togo are not for those who are connected to the grid because that question was not asked, but for
those who reported positive expenditures on electricity utility bills.
Differences in access rates according to the metrics as defined on page 6 are shown in Figure 3. The
difference in the rate of access between definition 1 and definition 5 represents largely people who use
electricity other than grid electricity and people who failed to report grid connection (possibly those
connected to neighbors’ grid and did not consider themselves connected to the grid). The difference was
striking in Mali, especially in rural areas and among the poor where the differences reached 50 percentage
points. Two factors accounted for this difference. For rural residents, the most important cause of the
difference was extensive use of solar panels. For the urban poor, it was non‐zero expenditures on electricity
by those who did not cite electricity as the main source of lighting or cooking. These expenditures were small
in urban areas. However, in rural areas, spending on electricity by those who did not cite electricity as the
main source of lighting or cooking, did not indicate connection to the grid, or did not own a generator or a
solar panel was much higher when expressed as a share of total household expenditures, and significantly
14
higher among the rural poor. It was not possible to infer from the questionnaire what explains this odd
result. The second largest difference was found in Zambia, where the difference was due largely to use of
generators in rural areas. Lastly, in Niger the difference in access between definitions 1 and 5 was larger than
10 percentage points for urban households as well as for the urban poor. The greatest contributing factor
was use of electricity as the primary source of energy by those who did not report connection to the grid,
followed by use of generators (even among the urban poor); the survey did not ask questions about solar
energy. The difference also exceeded 10 percentage points in rural Nigeria and rural Ghana. In rural Nigeria,
it was due to generator ownership: nearly one‐fifth of all rural households own generators; the questionnaire
did not ask about solar energy. In rural Zambia, the difference was due three‐fifths to generator use and two‐
fifths to solar panels.
Figure 3: Percentage of people using electricity according to different definitions of access
100
90
Percentage of population
80
70
60
50
40
30
20
10
0
Definition 1 Definition 2 Definition 3 Definition 4 Definition 5
Source: World Bank staff calculations using household survey data.
Note: For a descriptions of the five definitions of access, see page 6.
Only four surveys—Madagascar, Mozambique, São Tomé and Príncipe, and Togo—asked about quantities of
electricity consumed. Recalling quantities of electricity consumed is even more challenging than recalling the
amount spent, and the evidence of this challenge was clear in São Tomé and Príncipe, where 15 surveyed
households reported electricity consumption of 1 kWh a year, 11 of them in the top quintile. The results
were even more striking in Togo, where average monthly consumption was only 5 kWh, reaching only 6 kWh
even in the top urban quintile. Such a consumption pattern seems more indicative of a lack of understanding
of kWh as a unit of electricity than actual consumption. In addition, there was one country—Rwanda—that
enabled back‐calculation of kWh consumed from spending on electricity because the tariff schedule
consisted of a single block with a unit charge applicable to all residential consumers and no fixed charges,
and the survey asked about the last electricity bill. Taking the tariff schedule in effect at the time of the
survey, kWh consumed was calculated. The results for those reporting non‐zero quantities (and non‐zero
spending on grid electricity in the case of Rwanda) are presented in Figure 4 for all these countries other than
Togo.
15
Figure 4: kWh of electricity consumed per month by expenditure quintile and location
120
Bottom quintile Quintile 2 Quintile 3 Quintile 4 Top quintile
100
kWh per month
80
60
40
20
0
Madagascar Mozambique Rwanda São Tomé and Príncipe
Source: World Bank staff calculations using household survey data.
Note: The survey in São Tomé and Príncipe had many households reporting exceptionally low monthly consumption,
including those in the top quintile. They are reported to illustrate difficulties encountered in obtaining information on
electricity consumption. Rwandan consumption is calculated from expenditures and the unit price in effect at the time
of the survey.
The results show the predictable pattern of increasing consumption with increasing quintile, and significantly
higher consumption by the top quintile in the first three countries where the results seem more plausible
than in São Tomé and Príncipe or Togo. Rwanda, the only country in which kWh consumed was calculated
from reported billed amounts, shows low consumption compared to Madagascar or Mozambique. In addition
to seeming confusion about units for electricity consumption, another reason for caution in interpreting the
results is the very small sample size in lower quintiles, especially in rural areas, leading to results that are not
meaningful. For example, the bottom three quintiles in rural Mozambique had a total of only 11 households
reporting kWh consumed. The bottom two quintiles in Rwanda similarly had only 7 and 12 households,
respectively, connected to the grid with positive expenditures.
Spending on Electricity
The share of household expenditures spent on electricity among those with non‐zero spending on electricity
provides an indication of how much households are willing to spend, balancing affordability with the
perceived value of electricity. The results are summarized in Table 2 for the share of household expenditures
that include imputed values of freely acquired items, largely home‐grown food. The corresponding results by
quintile are given in Table A.4 in the annex. The median expenditure share is about 3 percent, and not
markedly higher among the poor. The findings are consistent with households not spending much more than
5 percent of their total expenditures on electricity. However, there was considerable variation across the
countries, ranging from a mere 0.3 percent in Malawi to 9.3 percent in Swaziland. Notable exceptions include
the poor in Botswana and Swaziland. Urban households spent a larger share in 13 out of 22 countries.
16
However, Sierra Leone stands out with the average rural expenditure share being twice as high. Among the
poor, urban expenditure shares were higher in 15 countries, although the numbers of households in the
sample are too small for meaningful results in many countries. Expenditure shares were higher for female‐
headed households in 19 out of 22 countries. Uganda was exceptional in that there were no households
among the urban poor who had purchased electricity; 0.8 percent of the urban poor reported receiving free
electricity.
Table 2: Share of total household expenditures spent on electricity
All households Poor Household head
Country Urban Rural Total Urban Rural Total Female Male
Angola 4.0 2.9 4.0 5.6 4.2 5.5 3.1 4.2
Botswana 6.2 7.6 6.5 9.5 11.1 9.8 6.8 6.2
Burkina Faso 4.6 4.1 4.5 6.2 3.3 5.2 4.6 4.5
Cote d'Ivoire 2.5 2.4 2.5 3.1 3.0 3.1 2.5 2.5
Ethiopia 2.8 1.3 2.2 4.6 2.4 3.7 2.8 2.0
Ghana 2.6 2.0 2.4 3.1 2.4 2.7 2.5 2.4
Madagascar 4.4 4.1 4.3 4.1 3.7 3.9 4.6 4.3
Malawi 0.3 0.3 0.3 0.4 0.4 0.4 0.3 0.3
Mali 2.9 1.4 2.2 2.4 1.2 1.4 2.6 2.2
Mozambique 3.7 4.0 3.7 6.1 7.5 6.4 4.2 3.6
Niger 3.4 2.4 3.2 2.8 1.9 2.2 3.6 3.1
Nigeria 2.6 2.3 2.5 3.1 2.7 2.9 2.8 2.4
Rwanda 1.4 1.6 1.5 2.3 3.3 2.9 1.6 1.4
São Tomé and Príncipe 2.0 1.9 2.0 2.3 2.1 2.2 1.9 2.1
Senegal 3.7 3.6 3.7 3.7 3.5 3.6 3.7 3.7
Sierra Leone 4.5 9.1 4.6 4.9 13.2 5.2 4.5 4.6
South Africa 5.3 5.2 5.3 7.3 6.0 6.6 5.5 5.1
Swaziland 9.5 9.1 9.3 13.9 10.9 11.9 9.7 9.0
Tanzania 3.0 3.3 3.1 1.9 5.8 3.1 3.4 2.9
Togo 3.1 3.1 3.1 3.6 3.3 3.5 3.1 3.1
Uganda 2.4 1.9 2.4 — 3.6 3.6 2.6 2.3
Zambia 5.4 6.3 5.5 8.3 5.4 7.8 5.7 5.4
Median 3.3 3.0 3.1 3.7 3.4 3.6 3.3 3.1
Source: World Bank staff calculations using household survey data.
Note: Only households with positive expenditures on electricity are analyzed. In Ghana, South Africa, Uganda, and
Zambia, freely provided electricity is excluded.
— = No households with positive cash expenditures on electricity.
Because the amount of cash available is an important determinant of the household’s ability to pay for
electricity, Table 3 re‐computes the shares by excluding these imputed values from household expenditures
in the 18 countries where cash expenditures were computed. The corresponding results by quintile are given
in Table A.5 in the annex. As an indication of how much rural households rely on freely acquired food, the
urban expenditure share was larger in only 5 out of 13 countries (38 percent), in contrast to 59 percent found
in Table 2. Sierra Leone and Uganda stand out with rural expenditure shares being about two‐and‐a‐half
times higher. Among the poor, in addition to these two countries, the rural expenditure share is three‐and‐a‐
half times higher in Tanzania. In absolute terms, the expenditure share appears exceptionally high in rural
Sierra Leone, Botswana, and Swaziland, but this is in part because of the small sample sizes reducing
representativeness of the households reporting positive expenditures. The pattern with respect to the
17
gender of the head of household remains the same, with female‐headed households showing higher
expenditure shares.
Table 3: Share of total household cash expenditures spent on electricity
All households Poor Household head
Country Urban Rural Total Urban Rural Total Female Male
Botswana 6.3 7.9 6.6 9.6 13.3 10.4 7.0 6.3
Burkina Faso 5.4 5.0 5.3 6.8 5.1 6.2 5.4 5.3
Cote d'Ivoire 2.6 2.9 2.7 3.3 3.8 3.5 2.7 2.6
Ethiopia 3.1 2.1 2.7 4.9 3.2 4.2 3.4 2.4
Ghana 2.6 2.3 2.5 3.3 2.9 3.1 2.6 2.5
Malawi 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3
Mali 2.9 1.7 2.4 2.4 1.6 1.8 2.7 2.4
Niger 3.4 2.8 3.3 3.1 2.2 2.5 3.7 3.2
Nigeria 3.7 3.9 3.8 4.7 4.6 4.7 4.4 3.7
Rwanda 1.4 1.8 1.6 2.4 4.3 3.5 1.7 1.5
São Tomé and Príncipe 2.0 2.0 2.0 2.3 2.2 2.3 1.9 2.1
Senegal 3.8 3.8 3.8 3.7 3.9 3.7 3.8 3.8
Sierra Leone 4.5 10.7 4.6 4.9 15.5 5.3 4.5 4.6
Swaziland 9.8 9.9 9.8 14.3 12.1 12.8 10.4 9.4
Tanzania 3.4 4.3 3.5 2.0 7.2 3.7 4.0 3.4
Togo 3.2 3.5 3.2 3.7 4.0 3.8 3.1 3.2
Uganda 2.6 2.2 2.5 — 4.8 4.8 2.8 2.4
Zambia 5.7 7.0 5.8 8.8 7.9 8.7 6.1 5.7
Median 3.3 3.2 3.3 3.7 4.2 3.8 3.6 3.2
Source: World Bank staff calculations using household survey data.
Note: Only households with positive expenditures on electricity are analyzed. Total household expenditures exclude
imputed values of freely acquired items, largely food.
— = no households with positive cash expenditures on electricity.
When the shares in Table 2 and Table 3 are compared, they increase more in rural areas than in urban areas
when imputed values are excluded from household expenditures. The differences also predictably decrease
with increasing quintile. Both demonstrate greater reliance on home‐grown food by the poor and rural
households. Nationally, the largest increase was found in Nigeria (53 percent increase), followed by Ethiopia
(21 percent), and Burkina Faso (18 percent). There was little difference between female‐ and male‐headed
households, indicating comparable dependence on home‐grown food.
Charges for Grid Electricity Use and Connection
How expensive is it to buy electricity? Are tariffs designed to make electricity affordable to the poor? Do
connection charges take the ability to pay into account, or is every residential customer charged the same?
This study collected information on residential tariffs for grid electricity in effect in July 2014 in 39 countries,
including the 22 countries with household survey data. Table 4 summarizes tariff concessions available to
those consuming little electricity in the 39 countries. The column labeled kWh shows the size of the lifeline
block (the first block in a tariff schedule with a low price per kWh, intended to help the poor consume
electricity) or, in the absence of an explicit lifeline block, the first block if there are two or more blocks. The
column labeled “ratio” calculates the ratio of the unit energy charge (price per kWh) in the second block to
that in the first block, or if there are two or more schedules of a single block each with increasing levels of
18
installed capacity, the ratio of the second level of service to the schedule corresponding to the lowest unit
energy charge. The unit energy charges used in computing the ratio include ad valorem taxes but exclude
fixed charges, which are spread over the entire consumption and the unit values of which vary with
consumption. If the first block is exempt from fixed charges and higher blocks are not, the ratio will be larger
what the table shows, but if all blocks are subject to the same fixed charge, the ratio will be smaller. The
comment column describes concessions offered to the lifeline rate or the first block, such as exemption from
tax and fixed charges. The comment column also indicates whether increasing block tariffs apply in going
from the first to the second block (different unit energy charges are applied to corresponding segments of
consumption, with the unit price for the first block applying to kWh consumed up to the limit of the first
block, the unit price for the second block applying to kWh above the limit of the first block up to the limit of
the second block, and so on) or if volume‐differentiated tariffs apply (a single unit charge, determined only
by the total consumption, is applied to the entire consumption).
Table 4: Summary of residential tariff information related to lifeline rates
Country kWha Ratiob Comments
Angola 50 3.19 Increasing block tariff to 200 kWh in the social tariff schedule
Benin 20 1.65 Tax exempt but cannot exceed 20 kWh a month
Botswana 200 1.31 Information not available on whether increasing or volume‐differentiated
Burkina Faso 75 1.71 75 is for 1–3 amperes and has no fixed premium; increasing block tariff
Burundi 50 2.03 Increasing block tariff; no fixed charge up to 150 kWh
Cabo Verde 60 1.23 Cannot exceed 60 kWh a month
Cameroon 110 1.72 Cannot exceed 110 kWh a month, but 110 kWh is exempt from VAT for all
consumption levels; only first block is not explicitly subsidized
Chad 150 1.47 Information not available on whether increasing or volume‐ differentiated
Comoros n.a. n.a. Single block
Côte d'Ivoire 40 2.05 First 40 kWh is VAT‐exempt; increasing block tariff
Ethiopia 25 1.31 Same low energy charge for the first 50 kWh but the first 25 kWh has a
lower service charge; increasing block tariff
Gabon 120 1.62 There are two schedules for social tariff 1 (capped at 120 kWh) but one
(prepaid) is exempt from the fee for contribution to special electricity; tax
is halved for social tariffs 1 and 2 (240 kWh)
Gambia, The 300 1.04 Increasing block tariff; prepaid energy charge is the same as that for the 1st
block but with no kWh limit
Ghana 50 2.01 Lower monthly service charge, but cannot exceed 50 kWh a month
Guinea 60 2.58 Increasing block tariff
Kenya 50 5.47 Increasing block tariff
Lesotho n.a. n.a. Single schedule, single block
Liberia n.a. n.a. Single schedule, single block
Madagascar 25 4.40 Tariffs change by region; increasing block tariff; first 25 kWh exempt from
the National Electricity Fund fee; 1st 100 kWh is exempt from VAT
Malawi n.a. n.a. 1 block each for prepaid (much cheaper for low consumption) and
postpaid
Mali 50 1.58 Increasing block tariff; 1st two blocks exempt from VAT; access to the low
rates for the first 2 blocks (50 and 100 kWh) is retained for prepaid
customers if monthly consumption exceeds 100 kWh, but if post‐paid
exceedance automatically switches the customer to the normal tariff
schedule, with a higher unit price subject to 18% VAT
Mauritania n.a. 1.92 There are 8 schedules of single block each, and low tariffs are charged for
low subscribed kVA. The monthly fixed charge increases nearly six‐fold
between the lowest and the second‐lowest subscribed kVA.
19
Country kWha Ratiob Comments
Mauritius 25 1.39 Increasing block tariff
Mozambique 100 2.34 Cannot exceed 100 kWh a month; no fixed charge
Namibia n.a. n.a. There are 5 schedules of a single block each with no limits on
consumption.
Niger 50 1.33 Increasing block tariff
Nigeria 50 3.68 Cannot exceed 50 kWh a month
Rwanda n.a. n.a. Single schedule, single block
São Tomé and Príncipe 100 1.47 volume‐differentiated tariff
Senegal 75 1.07 Increasing block tariff
Seychelles 200 1.19 Increasing block tariff
Sierra Leone 30 1.43 Increasing block tariff
South Africac 500 1.14 Increasing block tariff
Swaziland n.a. n.a. 2 schedules of single block each
Tanzania 75 3.50 Increasing block tariff
Togo 40 1.57 Exceeding 40 kWh a month moves the consumer to another schedule
subject to 18% VAT
Uganda 15 3.45 Increasing block tariff
Zambia 100 2.07 Increasing block tariff
Zimbabwe 50 5.50 Increasing block tariff
Source: Utility and regulator websites and reports, and World Bank staff calculations.
Note: Names of countries with household survey data are shown in bold. “Cannot exceed so many kWh a month” means
that the lifeline rate does not apply if monthly consumption exceeds the limit, and the entire consumption is charged a
higher tariff, instead of increasing block tariffs. n.a. = not applicable.
a. The monthly size of a tariff subject to a lifeline rate, or the size of the first block when there are two or more blocks.
Some countries have multiple schedules, each of which has a single block. Senegal defines the size of the consumption
blocks over two months. The first block is 150 kWh over two months, shown here as 75 kWh a month.
b. The ratio of the effective energy charge, inclusive of ad valorem tax such as VAT but exclusive of fixed charges and
taxes applied to fixed charges, between the second block (or the second level of service if there are several schedules of
a single block each with increasing installed capacity).
c. These numbers are for Johannesburg. Depending on eligibility, poor households are provided with 50, 100, or 150
kWh of free electricity a month in Johannesburg.
To the extent that subsidies for the poor are offered, all countries use some measure of consumption (kWh a
month, amperage, or kilovolt‐amperes) as a proxy for income. One exception is Johannesburg in South Africa,
where the city calculates a poverty index for each household based on income and other indicators and
offers three different levels of free electricity—currently 50, 100, and 150 kWh a month—depending on the
poverty index.
The most common lifeline block size is 50 kWh (8 countries), followed by 25, 75, and 100 kWh (3 countries
each). Eight countries have lifeline blocks up to 40 kWh, and five are 25 kWh or smaller. In these five
countries, the lifeline block is not sufficient to meet the daily electricity need in tier 3 of the multi‐tier access
framework; all five except Benin have increasing block tariffs. Increasing block tariffs at least allow poor
households to enjoy a large price subsidy for the first block. South Africa, represented by the tariff schedule
in Johannesburg, may appear as having an exceptionally large first block, followed by The Gambia. However,
many municipalities in South Africa offer free electricity to the poor—such as 25, 50, 60, 100, and 150 kWh a
month—depending on eligibility criteria (for example, prepaid lifeline customers consuming less than 250
kWh a month based on a 12‐month average can receive 60 kWh of free electricity a month in Cape Town,
falling to 25 kWh for consumption of up to 450 kWh a month on average), which differ by municipality. An
interesting case is Cameroon, which has increasing block tariffs but where the unit energy charge in the
20
upper four of the five blocks are explicitly subsidized, the largest unit subsidy reserved for the block covering
801 to 2,000 kWh a month.
Subsidized lifeline rates are limited only to those consuming less than the cap in nine countries (Benin,
Cameroon, Cabo Verde, Gabon, Ghana, Mozambique, Nigeria, São Tomé and Príncipe, and Togo), shifting
households to tariffs for the next tier for the entire consumption if the cap is exceeded. This has the same
effect as volume‐differentiated tariffs in Gabon, Mozambique, and Nigeria. Not having access to the subsidy
by exceeding the limit by even 1 kWh makes it more difficult for the poor when the size of the block size is
relatively small (say less than 50 kWh). The median increase in the effective unit energy charge for
consuming more than the limit on the first block is 68 percent. There is large variation across countries,
however, ranging from 4 percent in The Gambia and 7 percent in Senegal to 340 percent in Madagascar to
450 percent in Kenya and Zimbabwe. In Mali, pre‐paid customers do not lose access to the low rates for the
first two blocks and tax exemption even if they consume more than 100 kWh, but post‐paid customers lose
both benefits if 100 kWh is exceeded.
Table A.6 in the annex provides additional information, including the number of schedules and blocks and the
type of tariffs (increasing block or volume‐differentiated) in each of the 39 countries. Among the 22 countries
with household survey data, only Rwanda had a single tariff schedule with one block and no fixed charges. All
other countries had more than one schedule, more than one block, or both, making back‐calculation of kWh
consumed from spending on electricity not possible.9 Increasing block tariffs were the most common tariff
type, but there are several with volume‐differentiated tariffs, of which three have volume‐differentiated
tariffs only between the first and second blocks, with increasing block tariffs above the second block. In
countries with volume‐differentiated tariffs, exceeding the lifeline block increases the unit cost of the lifeline
volume by at least 47 percent (São Tomé and Príncipe), and as much as 268 percent in Nigeria, although
many customers falling in the lifeline category are not being metered there. Eleven countries list separate
schedules for prepaid customers.
Table A.7 compares effective unit charges (US$/kWh) for consuming 30, 50, 100, and 250 kWh a month,
inclusive of taxes and all fixed charges. Fixed charges punish low‐consumption households. For example, unit
tariffs (price per kWh) are higher for monthly consumption of 30 kWh than 50 kWh in 14 countries due solely
to fixed charges. The median rises from US$0.12/kWh for 30 and 50 kWh to US$0.14 for 100 kWh and
US$0.17 for 250 kWh, reflecting generally progressive tariff schedules. The table also shows the payment
that needs to be made to connect to grid electricity for the first time. Some countries do not charge for
connection or charge very little (US$2 in Cabo Verde, US$13 in Swaziland, and US$31 in Mauritius), but
others have high fees, the highest of which is $680 in Botswana. Botswana gives an option of paying over 18,
60, or 180 months, but the first payment is the same irrespective of the payment period and is high at
US$140. The household survey data show that about two‐fifths of customers select 18 months (no interest
charged), one‐quarter settle the connection charge in a single payment, one‐fifth pay over 60 months (prime
interest rate minus 0.5 percent), and one‐tenth pay over 180 months (prime interest rate). The percentage of
households in each quintile selecting a particular payment plan is surprisingly independent of quintile for the
one‐off payment. The share is twice in the bottom two quintiles as in the top quintile for the 18‐month
option and about the same difference for the 60‐month option. The largest difference is seen with the 180‐
month option, selected by 13 percent of the households in the bottom quintile but only 5 percent of those in
9
If there is only one schedule with multiple blocks, as in Benin, Botswana, Burundi, Cameroon, Chad, Ghana, Kenya,
Niger, Sierra Leone, and Uganda, back‐calculation from monthly spending on electricity is possible if there is no sharing
of meters. However, as will be seen later, meter‐sharing is widespread in Africa.
21
the top quintile. In Angola and Cabo Verde, the cost of reconnection after disconnection following non‐
payment is much higher than the initial connection cost.
To the extent that subsidized connection is offered to the poor, consumption is used as a proxy for income.
An example is South Africa, where connection is free for 20 amperage but there is a fee for 60 amperage. In
Mauritania, there are six connection fees for residential customers depending on kilo‐volt amperes
subscribed, ranging from US$128 to US$950. There is one country that takes the ability to pay into account:
The Gambia, where the connection fee is twice as high in the Greater Banjul area because of higher income
in the capital than in the rest of the country. However, this segmentation does not target the poor
specifically.
Affordability of Electricity
How affordable is electricity to households? More specifically, what proportion of households would not be
able to afford the subsistence level of electricity, having to spend more than 5 percent of total household
expenditures? Among those who cannot afford 30 kWh, what is the gap between the percentage of
household expenditures and the affordability threshold of 5 percent? How big a subsidy would it take to
make monthly consumption of 30 kWh affordable to every household in each country? How do the
calculated subsidies compare to utilities’ quasi‐fiscal deficits? What if, instead of 30 kWh (tier 3), monthly
consumption is 50, or 100 (tier 4), or 250 (tier 5) kWh? How well are the measures of “affordability” at
different consumption levels correlated with access? This section probes these questions. Because it is much
more costly to connect rural households to the grid, it is most unlikely that current tariffs can be maintained
for large‐scale grid electrification in rural areas without adversely affecting quasi‐fiscal deficits. The results
related to rural areas should therefore be treated with caution.
Grid‐electricity affordability at the subsistence level of consumption
Using tariff schedules from the utilities, this paper calculated hypothetical expenditure shares of grid
electricity when consuming varying amounts, starting at 30 kWh a month, for all households, whether or not
households were connected to the grid. Table 5 presents the results. On average, this share exceeds 5
percent in five out of 22 countries, but varies markedly both within and across countries. Electricity
consumption at this level is least affordable in Madagascar, followed by Rwanda, Burkina Faso, Togo, and
Sierra Leone. Barring provision of free electricity in South Africa, the amount of which varies by municipality
and total monthly consumption, grid electricity is most affordable in Angola, with an exceptionally low lifeline
rate, followed by Nigeria.10 Electricity consumption at the subsistence level is not affordable to the poor on
average in seven countries, nor to the bottom 20 percent in ten countries. Burkina Faso and Madagascar
stand out for grid electricity being marginally unaffordable even to the top quintile. The result from
Madagascar may at first seem inconsistent with Figure 4. However, large bill collection losses may explain in
part why households in the top quintile reported monthly consumption of 70 kWh: the utility fails to collect
40 percent of the billed amounts (Trimble et al. 2016). Senegal is the only country where the electricity share
10
A customer is placed in R1, the social tariff category capped at 50 kWh a month, by the distribution company based
on its assessment of the customer’s likely demand, which in turn is based on the company’s estimation of the
customer’s income, likely ownership of electric appliances, and the neighborhood. Most customers in R1 are not
metered and are billed based on the distribution company’s estimation. The customer may be moved to R2 if the
distribution company concludes that the consumption in the area has increased significantly; metered customers may
be moved to R2 sooner if consumption exceeds 50 kWh. In practice, there are few R1 customers in the entire network
and they are mostly in rural areas where consumption is minimal.
22
of household expenditures is smaller for female‐headed households; in all other countries, the share is the
same or greater than for male‐headed households. Lastly, the expenditure share is greater in every country
in rural areas than in urban areas.
Table 5: Expenditure share of monthly consumption of 30 kWh by location, quintile, poverty status, and
gender of household head
All households Poor households Household head
Country Urban Rural Total Q1 Q5 Urban Rural Total Female Male
Angola 0.1 0.2 0.1 0.2 0.1 0.1 0.2 0.2 0.1 0.1
Botswana 2.4 5.5 3.7 15.6 1.0 6.7 9.0 8.0 4.3 3.2
Burkina Faso 6.8 9.6 8.8 15.7 5.4 11.8 12.6 12.5 13.4 8.1
Côte d'Ivoire 1.4 2.2 1.8 4.6 0.9 2.8 3.3 3.2 2.4 1.7
Ethiopia 1.1 1.3 1.3 2.7 0.6 3.3 2.2 2.3 1.8 1.1
Ghana 1.1 1.8 1.4 3.1 0.8 2.6 3.0 2.9 1.6 1.3
Madagascar 6.8 10.4 9.6 18.8 5.3 9.9 11.8 11.6 13.8 8.6
Malawi 1.3 2.3 2.2 3.8 1.3 2.3 3.4 3.2 2.7 2.0
Mali 1.0 1.5 1.4 2.5 0.7 1.8 2.2 2.2 1.4 1.4
Mozambique 1.5 2.6 2.3 5.5 0.8 3.1 3.5 3.5 3.0 2.0
Niger 2.2 3.9 3.6 5.2 2.4 4.1 4.4 4.4 5.3 3.3
Nigeria 0.3 0.4 0.3 0.6 0.2 0.4 0.4 0.4 0.5 0.3
Rwanda 5.3 9.6 8.9 16.8 3.7 11.7 13.3 13.2 11.4 8.0
São Tomé and Príncipe 1.7 1.8 1.7 2.7 1.2 2.1 2.2 2.1 1.9 1.6
Senegal 2.1 3.7 2.9 6.5 1.9 3.7 5.0 4.6 2.6 3.0
Sierra Leone 3.8 5.9 5.1 8.7 3.1 5.6 7.1 6.8 5.1 5.1
South Africaa 1.0 1.6 1.2 2.8 0.3 2.1 2.0 2.1 1.3 1.1
Swaziland 1.7 2.0 1.9 4.2 1.0 3.1 2.6 2.7 1.9 1.9
Tanzania 1.2 2.2 1.9 3.7 1.0 3.4 3.6 3.6 2.3 1.7
Togo 4.1 9.0 6.9 16.4 3.5 6.6 11.1 10.0 9.3 6.3
Uganda 3.1 4.9 4.4 8.6 2.3 8.6 8.8 8.7 5.3 4.0
Zambia 2.1 5.2 4.1 8.6 1.4 4.1 6.2 5.9 4.9 3.8
Median 1.7 2.5 2.2 4.9 1.1 3.3 3.6 3.5 2.7 2.0
Source: World Bank staff calculations based on utility information and household survey data.
a These numbers are for Johannesburg. Depending on eligibility, poor households are provided with 50, 100, or 150
kWh of free electricity a month in Johannesburg.
Table 6 presents results that are more easily compared to those of Briceño‐Garmendia and Shkaratan (2011),
except that, thanks to significant progress in energy efficiency improvement, power consumption at the
subsistence level has declined from 50 kWh a month to 30 kWh in the intervening years. The table shows the
hypothetical expenditure shares for those with and without actual expenditures on electricity as well as for
those with and without access to the grid (definition 1 on page 6). It also calculates the percentage of
households for which the expenditure share lies below 5 percent. The expenditure shares for those who
reported non‐zero expenditures on electricity lie close to the expenditure shares of those with access to the
grid due to a considerable overlap between these groups. As expected, with the exception of Angola and
Nigeria, a greater percentage of those not connected to the grid found electricity unaffordable than grid‐
connected households.
23
Table 6: Expenditure share of monthly electricity consumption of 30 kWh by grid connection status and
current spending
Share of household expenditure % of households for which share 5%
Expenditure on Grid Expenditure on Grid
All electricity connection All electricity connection
Country Yes No Yes No Yes No Yes No
Angola 0.1 0.0 0.1 0.1 0.1 100 100 100 100 100
Botswana 3.7 1.3 4.9 1.7 5.4 86 98 80 96 77
Burkina Faso 8.8 4.2 9.5 4.5 9.5 32 72 26 71 26
Côte d'Ivoire 1.8 1.2 2.4 — — 95 99 92 — —
Ethiopia 1.3 0.9 1.4 1.1 1.3 98 99 98 99 98
Ghana 1.4 1.1 1.9 1.2 2.0 98 99 96 99 95
Madagascar 9.6 4.0 10.4 — — 25 75 18 — —
Malawi 2.2 0.7 2.3 0.8 2.3 96 100 95 100 95
Mali 1.4 1.1 1.5 0.8 1.6 99 99 99 100 99
Mozambique 2.3 0.6 2.5 0.0 0.0 93 100 92 — —
Niger 3.6 1.7 3.8 1.6 3.8 82 98 80 98 81
Nigeria 0.3 0.3 0.4 0.3 0.4 100 100 100 100 100
Rwanda 8.9 2.2 9.7 2.4 9.7 31 92 25 90 24
São Tomé and Príncipe 1.7 1.3 1.9 — — 97 99 96 — —
Senegal 2.9 2.1 4.1 1.9 4.1 88 94 79 95 79
Sierra Leone 5.1 2.9 5.5 2.9 5.5 61 90 56 90 56
South Africaa 1.2 1.0 1.5 1.0 2.2 98 99 97 99 95
Swaziland 1.9 0.9 2.4 — — 94 100 92 — —
Tanzania 1.9 0.9 2.1 0.9 2.1 95 100 94 100 94
Togo 6.9 3.6 9.0 3.6 9.0 54 81 37 81 37
Uganda 4.4 1.7 4.7 2.0 4.7 71 97 69 96 68
Zambia 4.1 1.0 4.6 1.2 4.9 71 100 66 99 63
Median 2.2 1.1 2.4 1.2 3.0 93 99 92 99 81
Source: World Bank staff calculations based on utility information and household survey data.
Note: — = Grid connection status not available in the survey.
a. These numbers are for Johannesburg. Depending on eligibility, poor households are provided with 50, 100, or 150
kWh of free electricity a month in Johannesburg.
In Angola (effective unit charge of US$0.012/kWh), Ethiopia (US$0.022), Ghana (US$0.069), Mali (US$0.12),
Nigeria (US$0.026), and South Africa (US$0.10 used in the calculation, although it could be zero in some
municipalities), virtually the entire population should find electricity consumption at the subsistence level
affordable. At the opposite end of the spectrum are Burkina Faso (US$0.28/kWh), Madagascar (US$0.12),
and Rwanda (US$0.23), where only one‐quarter or less of those not yet connected to the grid can afford
electricity.
The definition of affordability in the multi‐tier framework is based on the share of household income,
arguably making cash expenditures more suitable as the basis for computing the expenditure share. Data on
cash expenditures excluding home‐grown food were available in 18 countries. The results are shown in Table
7.
24
Table 7: Expenditure share of monthly consumption of 30 kWh by location, quintile, poverty status, and
gender of household head, based on total household cash expenditures
All households Poor households Household head
Country Urban Rural Total Q1 Q5 Urban Rural Total Female Male
Botswana 2.4 6.8 4.2 16.7 1.4 6.9 10.7 9.0 4.8 3.8
Burkina Faso 7.8 15.0 13.0 22.3 7.8 13.1 18.9 18.1 19.1 12.0
Côte d'Ivoire 1.6 3.2 2.4 6.5 1.1 3.2 5.1 4.5 3.1 2.2
Ethiopia 1.3 3.7 3.3 7.2 1.4 3.7 6.2 6.0 4.7 2.9
Ghana 1.2 2.4 1.8 4.4 0.9 2.9 4.3 4.0 1.9 1.7
Malawi 1.6 4.0 3.6 6.3 1.9 3.0 5.8 5.5 4.8 3.2
Mali 1.0 2.2 1.8 3.5 0.9 1.9 3.1 3.0 1.7 1.8
Niger 2.6 5.3 4.8 7.7 3.0 5.9 6.4 6.3 7.9 4.4
Nigeria 0.4 0.7 0.6 1.2 0.3 0.7 0.9 0.8 0.8 0.5
Rwanda 6.8 15.3 14.1 26.9 5.1 16.1 21.7 21.3 18.4 12.4
São Tomé and Príncipe 1.7 1.9 1.8 2.8 1.2 2.1 2.2 2.2 1.9 1.7
Senegal 2.2 4.4 3.3 7.8 2.0 3.8 6.1 5.4 2.8 3.5
Sierra Leone 3.9 7.6 6.2 10.6 3.7 5.8 8.8 8.2 6.2 6.2
Swaziland 1.8 2.8 2.4 5.3 1.1 3.4 3.6 3.5 2.5 2.3
Tanzania 1.6 5.8 4.5 10.0 1.5 5.3 10.1 9.7 6.0 3.9
Togo 4.3 12.3 8.8 22.2 3.9 7.0 15.4 13.4 11.0 8.2
Uganda 4.3 9.2 7.9 15.9 3.3 15.0 16.3 16.2 10.6 6.7
Zambia 2.4 11.1 8.0 17.4 1.9 4.9 13.3 12.0 10.4 7.3
Median 2.0 4.9 3.9 7.8 1.7 4.4 6.3 6.2 4.8 3.6
Source: World Bank staff calculations based on utility information and household survey data.
Predictably, electricity becomes markedly less affordable. The 5‐percent threshold is exceeded for the poor
in two‐thirds of the countries (against one‐third previously), and for four‐fifths of the bottom quintile (against
half previously). These results are consistent with much greater reliance on home‐grown food by the poor
than the rich.
Table 8 reproduces Table 6 based on total household cash expenditures. In Burkina Faso and Rwanda, only
about one out of every eight households not connected to the grid would find 30 kWh of electricity a month
affordable, compared to one out of every four in Table 6. Out of the 15 countries for which full information
was available, more than half of non‐connected households would not find electricity affordable in six
countries.
Table 8: Expenditure share of monthly electricity consumption of 30 kWh by grid connection status and
current spending, based on total household cash expenditures
Share of household expenditure of households for which share 5
Expenditure on Grid Expenditure on Grid
All electricity connection All electricity connection
Country Yes No Yes No Yes No Yes No
Botswana 4.2 1.4 5.6 1.8 6.3 83 98 76 96 72
Burkina Faso 13.0 5.0 14.2 5.2 14.2 21 64 14 63 14
Côte d'Ivoire 2.4 1.3 3.4 — — 92 98 85 — —
Ethiopia 3.3 1.2 3.7 1.4 3.9 86 98 83 96 83
Ghana 1.8 1.2 2.5 1.4 2.7 95 99 91 98 90
Malawi 3.6 0.7 3.8 0.9 3.8 80 100 78 100 78
Mali 1.8 1.3 2.2 0.8 2.2 96 98 94 100 94
25
Share of household expenditure of households for which share 5
Expenditure on Grid Expenditure on Grid
All electricity connection All electricity connection
Country Yes No Yes No Yes No Yes No
Niger 4.8 1.8 5.2 1.6 5.2 68 97 64 98 64
Nigeria 0.6 0.4 0.8 0.4 0.8 100 100 99 100 99
Rwanda 14.1 2.4 15.3 2.6 15.4 21 90 14 88 13
São Tomé and Príncipe 1.8 1.3 2.0 — — 97 99 96 — —
Senegal 3.3 2.1 5.0 2.0 4.9 85 94 72 95 73
Sierra Leone 6.2 2.9 6.7 2.9 6.7 50 90 43 90 43
Swaziland 2.4 0.9 3.1 — — 89 100 84 — —
Tanzania 4.5 1.1 5.2 1.1 5.3 78 100 73 100 73
Togo 8.8 3.7 12.0 3.8 12.0 46 79 25 79 26
Uganda 7.9 1.9 8.5 2.2 8.6 51 97 47 94 46
Zambia 8.0 1.0 9.1 1.3 9.9 52 99 44 99 39
Median 3.9 1.3 5.1 1.6 5.3 81 98 74 96 72
Source: World Bank staff calculations based on household survey data.
Note: — = Grid connection status not available in the survey.
Affordability at higher levels of consumption
Corresponding results for 50, 100, and 250 kWh are shown in Table A.9–Table A.12, Table A.13–Table A.14,
and Table A.15–Table A.16, respectively, in the annex. Although this paper uses monthly consumption of 30
kWh to define affordability, higher consumption levels were also tested to see how many households could
“afford” (that is, pay no more than 5 percent of total household expenditures to purchase) more electricity.
The number of countries in which electricity consumption becomes unaffordable (exceeding the 5‐percent
threshold) to the poor, based on total household expenditures (including imputed values), nearly doubles
from 7 to 13 at 50 kWh. At 100 and 250 kWh a month, electricity is unaffordable to the poor in all but Angola
and South Africa (where the poor are entitled to varying amounts of free electricity, depending on the
municipality). The number of countries in which electricity becomes unaffordable to urban households
doubles from three at 30 kWh to six at 50 kWh, and more than doubles to 14 at 100 kWh a month.
Table A.10 in the annex enables a limited degree of comparison with the findings of Briceño‐Garmendia and
Shkaratan (2011). That study covered 18 countries, 14 of which are also in this study. Briceño‐Garmendia and
Shkaratan found that monthly consumption of 50 kWh would be affordable to about one‐third of
unconnected households on average, whereas this study found that the median for 50 kWh being affordable
would be two‐thirds of unconnected households and the average would be three‐fifths. These results suggest
that electricity tariffs became more affordable in the intervening years.
Grid‐electricity poverty measures
The electricity poverty gaps for different levels of consumption are shown in Figure 5. In the figure, each
rectangle for different monthly consumption is incremental. For example, the poverty gaps in Rwanda for 50,
100, and 250 kWh are 46, 67, and 85 percent, respectively. Table A.17 in the annex provides the results by
location (urban and rural). Figure 6 contrasts the poverty gap and headcount. The highest poverty gap for 30
kWh is less than 30 percent, but the corresponding poverty headcount is in excess of 70 percent.
26
Figure 5: Grid‐electricity poverty gap for monthly consumption of 30, 50, 100, and 250 kWh
100
30 kWh 50 kWh 100 kWh 250 kWh
90
80
70
% poverty gap
60
50
40
30
20
10
0
Source: World Bank staff calculations based on utility information and household survey data.
Note: Municipalities in South Africa have different eligibility criteria for households entitled to receive free electricity.
For simplicity, the lowest tariffs in Johannesburg without entitlement to free electricity are used for these calculations.
Figure 6: Comparison of grid‐electricity poverty gap and poverty headcount for monthly consumption of 30,
50, 100, and 250 kWh
100
90 30 kWh
50 kWh
80
Grid electricity poverty gap, %
100 kWh
70
250 kWh
60
50
40
30
20
10
0
0 10 20 30 40 50 60 70 80 90 100
Grid electricity poverty headcount, % of total population
Source: World Bank staff calculations based on utility information and household survey data.
One way of gauging the effects of tariff structures on grid electricity users is to look at the incremental
impact of increasing consumption on the grid‐electricity poverty gap. The cost increase from 30 to 50 kWh is
27
substantial relative to household expenditures, more than doubling the poverty gap in 17 out of 22 countries
including, predictably, every country where the lifeline block is smaller than 50 kWh a month. Going from 50
kWh to 100 kWh and from 100 kWh to 250 kWh a month more than double the poverty gap in 16 countries
each. A rapid rise in the poverty gap between 30 and 50 kWh raises concerns. At the opposite end of the
spectrum, there is no compelling reason to make 250 kWh of monthly consumption so affordable in Angola—
which unfortunately could not be covered in the analysis of quasi‐fiscal deficits for lack of data.
Subsidy to make the subsistence level of electricity affordable to all
Given limited resources, and in line with the affordability criterion for the multi‐tier access framework,
enabling households to consume 30 kWh a month would arguably be an appropriate goal for the foreseeable
future in Africa. Table 9 lists the grid‐electricity poverty gap and headcount for 30 kWh, the access rate
(according to definition 1, or definition 2 in the absence of data for grid connection), the percentage of the
total household expenditures needed to pay for 30 kWh of grid electricity a month, and the subsidies needed
to make up the difference between the amount in excess of 5 percent of household expenditures and the
monthly electricity bill, expressed as percentage of the quasi‐fiscal deficit, total revenue collected by the
utility, and GDP. These subsidy calculations assume a perfect world where precise targeting of individual
households based on income is possible. The countries are listed in order of decreasing grid‐electricity
poverty gap. The calculation procedures and detailed results for quasi‐fiscal deficits are available in Trimble
et al. (2016).
Table 9: Electricity poverty, access, and subsidy statistics for monthly consumption of 30 kWh
% Poverty Access 30 kWh as % of Subsidies as % of
Country Gap Headcount % HH expenditures QFD Cash collected GDP
Madagascar 30 71 11 9.6 2.6 6.4 0.06
Rwanda 27 63 11 8.9 7.3 6.3 0.08
Burkina Faso 24 60 11 8.8 7.2 3.5 0.07
Togo 15 39 34 6.9 2.6 0.9 0.04
Sierra Leone 9 33 13 5.1 2.7 3.4 0.02
Zambia 7 24 22 4.1 0.3 0.2 0.00
Uganda 7 22 9 4.4 ‐2.1 0.1 0.02
Botswana 5 12 43 3.7 0.0 0.0 0.00
Senegal 2 8 53 2.9 0.1 0.1 0.00
Niger 2 10 10 3.6 1.8 0.7 0.01
Swaziland 2 3.2 38 1.9 0.0 0.0 0.00
Mozambique 1.4 4.6 15 2.3 0.1 0.1 0.00
Côte d’Ivoire 0.9 2.5 57 1.8 0.0 0.0 0.00
Malawi 0.6 2.9 9 2.2 0.1 0.1 0.00
Tanzania 0.6 2.5 16 1.9 0.2 0.0 0.00
Ghana 0.3 1.1 66 1.4 0.1 0.0 0.00
South Africa 0.2 0.9 87 1.2 0.0 0.0 0.00
Ethiopia 0.2 0.8 19 1.3 0.0 0.0 0.00
São Tomé and Príncipe 0.2 0.9 56 1.7 0.0 0.0 0.00
Mali 0.1 0.4 22 1.4 0.0 0.0 0.00
Nigeria 0.0 0.0 56 0.3 0.0 0.0 0.00
Angola 0.0 0.0 41 0.1 — — —
Source: World Bank staff calculations based on utility information and household survey data.
Note: For South Africa, see the note for Figure 5. Quasi‐fiscal deficits are current deficits. HH = household; QFD = quasi‐
fiscal deficit; — = not available.
28
The results show that the subsidies needed to enable all households to consume 30 kWh a month are
generally not large. In seven countries, the total (perfectly targeted) annual subsidy is less than US$1 million.
As a percentage of the cash collected by utilities, the highest is 6 percent in Madagascar and Rwanda.
However, it should be noted that such assistance is unlikely to make sense for rural households—there are
many rural households for whom connection to the grid network would be too costly for the utility, and they
would be better served by off‐grid electricity. A lower bound would be the total subsidy required to make
grid electricity affordable to every urban household. Table A.19 in the annex provides additional results for
annual subsidies for monthly consumption of 30, 50, and 100 kWh, splitting subsidies into urban and rural
areas. The highest annual subsidy to make grid electricity affordable to all urban households is US$14 million
in Burkina Faso, followed by US$10 million in Madagascar. The annual subsidy for this purpose is less than
US$1 million in 15 countries. Given the relatively small levels of subsidies required, cross‐subsidies—by
better‐off residential customers as well as commercial and industrial customers—are likely to be a
reasonable source of funding.
Further analysis of the top ten countries in Table 9 is informative. Among them, only Togo, Uganda, and Niger
do not suffer from underpricing, which is a measure of how much lower tariffs are compared to the tariffs
required to eliminate quasi‐fiscal deficits if utilities improve operational efficiency by reducing network
losses, bill collection losses, and overstaffing (Trimble et al. 2016)—in other words, they have the “fiscal
space,” as it were, to cross‐subsidize needy households. All other countries will continue to have
underpricing as a problem even after attaining benchmark performance in operational efficiency. Aside from
Sierra Leone, where underpricing is less than 0.1 percent of GDP, other countries have underpricing of at
least 0.4 percent, and Zambia, Botswana, and Senegal have underpricing in excess of 1 percent of GDP, with
3.3 percent in Botswana even at benchmark performance being the highest. The current quasi‐fiscal deficits
are not small in the top three countries: 2.2 percent of GDP in Madagascar, 1.0 percent in Rwanda, and 1.1
percent in Burkina Faso. The presence of underpricing does not mean there is no scope for making the
subsistence level of electricity consumption more affordable. Two examples of mitigation steps are to
introduce a lifeline rate (Rwanda, Botswana) and consider volume‐differentiated in the place of increasing
block tariffs (Madagascar, Sierra Leone, Zambia, Senegal). But these observations point to the challenges
facing the countries with high electricity poverty gaps and low access rates.
Relationships between access, poverty, and quasi‐fiscal deficits
Which is the best predictor of access, grid‐electricity poverty at 30 kWh? Do quasi‐fiscal deficits tend to rise
with increasing affordability of electricity? Or are deficits high precisely in countries where people are too
poor to pay for electricity? To answer these questions, relationships between access, poverty gap, poverty
headcount, and quasi‐fiscal deficits at current performance as well as benchmark performance (eliminating
operational inefficiencies) were examined. Poverty measures at 30, 100, and 250 kWh, and nine measures of
quasi‐fiscal deficits—deficit at current utility performance, that at benchmark performance (which is identical
to underpricing, the degree to which tariffs fall short of cost recovery at efficient operation), and deficit due
to operational inefficiencies, each as a percentage of GDP, bills sent out by the utilities, and cash collected by
the utilities—were examined for this purpose.
Correlation coefficients between access (using definition 1 where the information is available, and definition
2 otherwise) and the two measures of grid‐electricity poverty at different electricity consumption levels are
all negative and statistically significant using a 5‐percent significance test, with the exception of the poverty
headcount at 250 kWh. The poverty gap at 250 kWh and the poverty headcount at 100 kWh have the highest
correlation coefficients and remain statistically significant at 1 percent. Correspondingly, when access is
29
regressed on different measures of grid‐electricity poverty, the poverty gap at 250 kWh and headcount at
100 kWh, shown in Figure 7, are the two best predictors of access. However, although countries do not
overlap, the correlation coefficients are not as high as between access and the economic poverty gap at
$3.10 or $1.90. The share of total household expenditures required to consume different amounts of
electricity is another predictor of access, but is not as good as the two measures of grid‐electricity poverty.
Figure 7: Comparison of access with grid‐electricity poverty gap and poverty headcount
100
90 Electricity poverty gap for 250 kWh/month
Electricity poverty headcount for 100 kWh/month
% of people connected to grid
80
70
60
electricity
50
40
30
20
10
0
0 20 40 60 80 100
Grid‐electricity poverty gap / poverty headcount in %
Source: World Bank staff calculations based on utility information and household survey data.
As mentioned above, because extending grid electricity to rural households is costly and off‐grid electricity
will be cheaper for low‐density, low‐consumption areas, and because the grid network is nonexistent in many
parts of rural areas, these grid‐electricity poverty statistics are arguably more relevant for urban households.
Repeating the above exercise for urban households only delivers similar results, with the urban poverty gap
at 250 kWh and urban poverty headcount at 100 kWh having the highest correlation coefficients with urban
access rates. The highest correlation coefficients between urban access and the measures of grid‐electricity
poverty are obtained when the latter are calculated nationally, and different measures of national grid‐
electricity poverty are better correlated with urban access than with national access. There is no obvious
explanation for these results.
The poverty gap and headcount at 30 kWh have the smallest correlation coefficients and have the weakest
predictive power for access for both national and urban statistics. This may seem counterintuitive at first
glance—should the affordability to the poor not determine the rate of access? Several reasons may be
offered for this finding. First, the total household expenditures used include freely acquired food and other
items, making cash available much smaller for some households, especially in rural areas. Yet excluding freely
acquired goods and services is not necessarily a more compelling approach, because two households with
the same cash income but one of which does not have to pay for food are clearly not in the same financial
situation. Second, a household that can barely pay for 30 kWh a month of electricity may have other pressing
demands on its budget. Third, connection costs are high in many countries (see the next paragraph), and a
household that can just afford 30 kWh a month may have no financial resources to pay for the initial
connection. Lastly, depending on how many higher‐consumption, better‐off consumers are in the same area,
it may not be financially viable for the utility to extend the grid to poor households for whom even 30 kWh a
month poses an affordability challenge.
30
By contrast, correlation coefficients between access with different measures of quasi‐fiscal deficits are not
statistically significant at 5 percent as long as South Africa (which has the highest rate of access and high
quasi‐fiscal deficits) is omitted. This finding would suggest that access expansion is not an important driver of
quasi‐fiscal deficits. The poverty gap and headcount at 30 kWh are not correlated with any of the nine
measures of quasi‐fiscal deficits using a 5‐percent significance test, and quasi‐fiscal deficits due to
operational inefficiencies are not correlated with any measure of poverty at any monthly consumption level.
The highest correlation coefficients are for the poverty headcount at 250 kWh for quasi‐fiscal deficits at
benchmark performance. Using a 1‐percent significance test, the correlations that remain are those between
the three measures of quasi‐fiscal deficits at benchmark with the poverty headcount at 250 kWh and the
poverty gap at 250 kWh. All coefficients are negative. This finding seems reasonable: the larger the
underpricing—that is, the more subsidized the tariffs compared to cost‐recovery levels at efficient operation,
the lower the grid‐electricity poverty.
Affordability of connection costs
For the poor, compounding the challenge of paying for minimal monthly consumption of electricity is the
upfront cost of getting connected to the grid. Mozambique and South Africa offer free connection to those
who consume little electricity, and Senegal offers free connection to those living within 40 meters of the grid.
Nigeria has prohibited connection fees, although customers pay for the materials needed for the connection.
Table 10 summarizes the lowest costs charged for connecting to the grid as multiples of monthly household
expenditures. The costs shown typically do not apply to cases where the distribution line needs to be
extended, requiring the household to be located close to the distribution network. Table 11 recalculates the
same costs as multiples of monthly household cash expenditures.
Table 10: Initial connection payment as multiples of monthly household expenditures
All households Poor Household head
Country Urban Rural Total Q1 Q5 households Female Male
Angola 0.1 0.2 0.1 0.3 0.1 0.2 0.2 0.1
Botswanaa 1.1 2.5 1.7 7.1 0.4 3.6 2.0 1.4
Burkina Faso 1.0 0.6 0.7 1.1 0.5 0.9 1.0 0.6
Côte d'Ivoire 0.9 1.3 1.1 2.8 0.6 2.0 1.5 1.0
Ethiopia 1.1 1.3 1.3 2.8 0.7 2.4 1.9 1.1
Ghana 0.5 0.8 0.6 1.3 0.3 1.2 0.7 0.6
Madagascar 2.8 4.2 3.9 7.7 2.2 4.7 5.6 3.5
Malawi 0.6 1.0 1.0 1.7 0.6 1.4 1.2 0.9
Mali 0.5 0.8 0.7 1.3 0.4 1.2 0.7 0.7
Mozambique 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Niger 0.1 0.1 0.1 0.2 0.1 0.1 0.2 0.1
Nigeriab n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
Rwanda 0.5 0.9 0.9 1.6 0.4 1.3 1.1 0.8
São Tomé and Príncipe 0.3 0.3 0.3 0.5 0.2 0.4 0.3 0.3
Senegal 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Sierra Leonec 0.8 1.3 1.1 1.8 0.6 1.4 1.1 1.1
South Africa 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Swaziland 0.1 0.1 0.1 0.2 0.0 0.1 0.1 0.1
Tanzania 1.2 1.2 1.2 2.2 0.8 2.1 1.5 1.1
Togo 1.2 2.7 2.0 4.9 1.0 3.0 2.7 1.8
Uganda 0.5 0.9 0.8 1.5 0.4 1.5 0.9 0.7
Zambia 0.6 1.5 1.2 2.5 0.4 1.7 1.4 1.1
31
All households Poor Household head
Country Urban Rural Total Q1 Q5 households Female Male
Median 0.5 0.9 0.8 1.5 0.4 1.3 1.0 0.7
Source: World Bank staff calculations based on household survey data.
Note: n.a. = information on connection costs not available.
a. The connection payment is the first payment in an installment plan (same first payment irrespective of the number of
months over which payments are spread). A one‐off payment is 4.8 times higher.
b There is no connection fee but customers buy the materials needed and present them to the utility. There is supposed
to be no exchange of money between the customer and the utility.
c. The connection payment is the first payment in an 18‐month installment plan. A one‐off payment is twice as high.
Table 11: Initial connection payment as multiples of monthly cash household expenditures
All households Poor Household head
Country Urban Rural Total Q1 Q5 households Female Male
Botswanaa 1.1 3.1 1.9 7.5 0.6 4.1 2.2 1.7
Burkina Faso 1.1 0.9 1.0 1.5 0.7 1.3 1.4 0.9
Côte d'Ivoire 1.0 2.0 1.5 4.1 0.7 2.8 1.9 1.4
Ethiopia 1.4 3.8 3.4 7.4 1.4 6.2 4.8 2.9
Ghana 0.5 1.0 0.8 1.9 0.4 1.7 0.8 0.7
Malawi 0.7 1.8 1.6 2.8 0.8 2.4 2.1 1.4
Mali n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
Mozambique 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Niger 0.1 0.2 0.1 0.2 0.1 0.2 0.2 0.1
Nigeriab n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
Rwanda 0.7 1.5 1.4 2.6 0.5 2.1 1.8 1.2
São Tomé and Príncipe 0.3 0.3 0.3 0.5 0.2 0.4 0.3 0.3
Senegal 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Sierra Leonec 0.8 1.6 1.3 2.2 0.8 1.7 1.3 1.3
South Africa 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Swaziland 0.1 0.1 0.1 0.3 0.1 0.2 0.1 0.1
Tanzania 1.6 3.2 2.7 5.8 1.1 5.6 3.7 2.4
Togo 1.3 3.6 2.6 6.6 1.1 4.0 3.3 2.4
Uganda 0.8 1.6 1.4 2.8 0.6 2.8 1.9 1.2
Zambia 0.7 3.2 2.3 5.0 0.5 3.4 3.0 2.1
Median 0.7 1.6 1.3 2.4 0.6 1.9 1.6 1.2
Source: World Bank staff calculations based on household survey data.
Note: For footnotes a–c, see the previous table. n.a. = information on connection costs not available.
The countries in which monthly consumption of 30 kWh is already unaffordable to the poor tend to have high
connection charges. The correlation coefficient between the expenditure share of monthly electricity
consumption and multiples of monthly household expenditures for the lowest connection costs is 0.58,
statistically significant at 5 percent. Botswana and Sierra Leone offer installment plans to make it easier to
pay for the upfront cost of connection. However, the costs remain relatively high, and the subsequent
monthly payments make electricity even at the subsistence level too expensive for the poor. In Botswana,
the total payment even when payments are spread over 180 months make the monthly bill for 30 kWh 28
percent of monthly cash expenditures and the corresponding percentage is 19 in Sierra Leone. These would
be unaffordable by any measure.
It is informative to compare the share of total expenditures required for the initial connection between those
with and without positive expenditures on electricity or grid connection. The results are tabulated in Table
32
12. It is clear that those not connected to the grid are much more income‐constrained than those connected
to the grid.
Table 12: Initial connection payment as multiples of monthly expenditures by electricity spending and grid
connection status
Total household expenditures Total household cash expenditures
Electricity spending Grid connection Electricity spending Grid connection
Country Yes No Yes No Yes No Yes No
Angola 0.1 0.2 0.1 0.2 n.a. n.a. n.a. n.a.
Botswanaa 0.6 2.2 0.8 2.4 0.6 2.5 0.8 2.8
Burkina Faso 0.6 0.7 0.6 0.7 0.7 1.0 0.7 1.0
Côte d'Ivoire 0.7 1.5 n.a. n.a. 0.8 2.1 n.a. n.a.
Ethiopia 0.9 1.4 1.1 1.4 1.3 3.8 1.5 4.0
Ghana 0.5 0.8 0.5 0.8 0.5 1.1 0.6 1.2
Madagascar 1.6 4.2 n.a. n.a. n.a. n.a. n.a. n.a.
Malawi 0.3 1.0 0.3 1.0 0.3 1.7 0.4 1.7
Mali 0.6 0.8 0.4 0.8 0.7 1.2 0.4 1.2
Mozambique 0.0 0.0 n.a. n.a. n.a. n.a. n.a. n.a.
Niger 0.1 0.1 0.0 0.1 0.1 0.2 0.0 0.2
Nigeriab n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
Rwanda 0.2 0.9 0.2 0.9 0.2 1.5 0.3 1.5
São Tomé and Príncipe 0.2 0.3 n.a. n.a. 0.2 0.3 n.a. n.a.
Senegal 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Sierra Leonec 0.6 1.2 0.6 1.2 0.6 1.4 0.6 1.4
South Africa 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Swaziland 0.0 0.1 n.a. n.a. 0.0 0.2 n.a. n.a.
Tanzania 0.8 1.3 0.9 1.3 1.0 3.1 1.0 3.2
Togo 1.1 2.7 1.1 2.6 1.1 3.5 1.1 3.5
Uganda 0.3 0.8 0.3 0.8 0.3 1.5 0.4 1.5
Zambia 0.3 1.3 0.3 1.4 0.3 2.6 0.4 2.8
Median 0.3 0.8 0.4 0.9 0.3 1.4 0.4 1.5
Source: World Bank staff calculations based on household survey data.
Note: For footnotes a–c, see the previous table. n.a. = not available.
Corruption in the form of bribes extracted add to the cost of using electricity. Surveys in Malawi and Nigeria
asked households connected to the grid if they had to pay an “informal fee” over and above the official
connection charge to get the connection (free since August 2012 in Nigeria), while the survey in Senegal
asked about the general perception of corruption in the government and specifically about the power sector.
The results for Malawi and Nigeria are shown in Figure 8. Bribery was more prevalent in Nigeria. In Malawi,
there were too few households in the bottom quintiles for meaningful statistics, but in Nigeria every quintile
had enough households sampled. The percentage of households reporting informal payments was lowest in
the bottom quintile and highest for the top quintile—that is, those who are most able to pay bribes are also
most likely to be asked and pay—although there was no consistent pattern with increasing quintile.
Recognizing the pervasive nature of this problem, the Nigerian Electricity Regulatory Commission in August
2012 prohibited connection fees and required that new customers be given a list of materials to purchase
and provide the utilities with the materials, so as to avoid a “direct exchange” of money between the
customer and the utility. The only variable is the length of the connection wire. After the materials are
presented and checked by the utility, the utility has 48 hours in which to connect the new customer (NERC
33
2012). The regulator announced that this new regulation was intended to “rid the power sector of corruption
and protect electricity consumers from fraud and extortion often committed against them by electricity
companies.” As one potential indication of progress in this area, the percentage of households reporting
informal payments declined from those in the 2010 survey by 3.8 percentage points nationally, 2.0
percentage points in urban areas, and 5.8 percentage points in rural areas. The decline was primarily among
lower‐decile households, while the top decile statistics remained largely the same.
Figure 8: Percentage of households who paid a bribe to get grid connection
60
40
20
0
Malawi urban Malawi rural Malawi total Nigeria urban Nigeria rural Nigeria total
Source: World Bank staff calculations using household survey data.
In Senegal, 86 percent of households said that corruption had increased over time. However, only 1 percent
of those connected to the grid said that they had been asked by utility officials to pay bribes. The question
asked in Senegal is not comparable to that in Malawi and Nigeria. It is possible that this more positive
outcome in Senegal was due in part to the fact that the question concerned only the past 12 months, and if
bribes are associated primarily with the initial connection, those households that had obtained grid
connection more than 12 months earlier would not have reported bribe payments.
Community versus Household Access
In Malawi, Niger, and Nigeria, the survey asked whether their communities had electricity.11 The data can be
used to examine electrified communities and compute the percentage of households that have chosen to
connect to the grid. In urban Nigeria, 92 percent of all households living in electrified communities had
connected to the grid, whereas less than half of such urban households in Niger and Malawi had grid
electricity. In rural areas, the percentages were much lower in the latter two countries, as shown in Figure 9.
Predictably, the percentage increased with quintile, but remained one‐third or lower in rural Malawi and
Niger.
Figure 10 examines households without grid connection. Most urban households without grid connection in
Malawi and Niger lived in electrified neighborhoods, whereas a lower percentage in urban Nigeria lived in
neighborhoods connected to the grid, pointing to lack of community electrification as an important barrier.
Most rural households without grid connection lived in communities without grid electricity.
11
In Malawi and Nigeria, the survey asked each household, whereas in Niger the survey asked this question in the
community questionnaire. In Ghana the survey also asked in the community questionnaire, but the data were not
available to the World Bank. In Senegal the survey asked if the community had been newly electrified over the previous
five years, but that would not identify all households living in communities with electricity.
34
Figure 9: Households living in electrified neighborhoods that have household connection
100
Bottom quintile Quintile 2
90
% of households in community
Quintile 3 Quintile 4
80
Top quintile
70
60
50
40
30
20
10
0
Urban Rural National Urban Rural National Urban Rural National
40% 13% 26% 49% 19% 39% 92% 80% 87%
Malawi Niger Nigeria
Source: World Bank staff calculations using household survey data.
Figure 10: Households without grid connection living in electrified neighborhoods
100
% of households without connection
Bottom quintile Quintile 2 Quintile 3
90
Quintile 4 Top quintile
80
70
60
50
40
30
20
10
0
Urban Rural National Urban Rural National Urban Rural National
85% 19% 27% 94% 8% 17% 62% 17% 22%
Malawi Niger Nigeria
Source: World Bank staff calculations using household survey data.
For households living in communities connected to the grid, in Malawi and Nigeria the survey asked
households without grid electricity why they had not connected (Table 13). The reasons were specified in the
questionnaire and households selected from a range of options. Not surprisingly, unaffordability of the
connection charge was the most common reason. In Malawi, the payment for the initial connection to the
grid is large, costing about US$160 even when no grid extension is required and about US$450 if one‐pole
extension is required. The second most common reason in urban Nigeria was poor reliability of grid
electricity; all but one of those respondents who cited poor reliability had a private generator.
35
Table 13: Percentage of households citing reasons for not connecting to the grid
Too Dwelling Discon‐ Unreliable No
Location costly No need unsuited Pending nected service Other reply
Malawi urban 37 2 4 6 0.0 — 0.1 51
Malawi rural 56 3 8 2 0.1 — 4 27
Malawi total 49 2 7 4 0.1 — 2 36
Nigeria urban 59 6 2 1 — 15 16 —
Nigeria rural 63 8 8 5 — 8 9 —
Nigeria total 62 7 6 4 — 10 11 —
Source: World Bank staff calculations using household survey data.
Note: No need = no need for electricity; dwelling unsuited = dwelling unsuited for connection to the grid; pending =
application for connection pending; disconnected = disconnected for non‐payment; — = reason not among the options
cited in the questionnaire.
No spending on grid electricity
Survey questionnaires are generally not sufficiently detailed to quantify how many households do not pay for
electricity. The data collected should ideally be able to separate the absence of reported household spending
on electricity for legitimate reasons—such as inclusion of electricity in the rent, or in the compensation
package for employment—from evasion of payment, such as theft of electricity or non‐payment without
disconnection. No household survey enabled probing of this question at that level of detail.
In Mali, Niger, and Zambia, the survey asked if the rent included electricity. In South Africa, the
survey asked if rent or free rental included “electricity, water, etc.” without isolating electricity. In
South Africa, the survey also asked how much the household had spent on utilities if water and
electricity bills were bundled.
In Mali and Niger, the survey asked if the household received free “water‐electricity” as part of
employment compensation, without isolating electricity.
In South Africa, the survey asked if the household received free electricity and its value. Low‐
consumption or poor households (depending on the municipality) can obtain certain amounts free of
charge and pay for the rest. Therefore, answering yes to the question does not mean no payment to
the utility was required. It is also possible that some households would report stolen electricity as
free electricity. In Uganda and Zambia, the survey did not ask about free electricity, but the
household was asked to value electricity received without making payment. Neither survey asked
why free electricity was available.
In Sierra Leone, the survey asked if the household had paid electricity bills (either bills addressed to
the household or shared with others) over the past 12 months.
Asking if the household had made payment at any point over a long time period, such as 12 months in Sierra
Leone, is far more likely to catch payments than asking about the past 30 days, when the household may
have missed a payment, pre‐paid enough in advance, or pay less frequently than once a month. Households
with missing values (missing answers) may not have answered the question despite having paid (for example,
the respondent may have had trouble recalling the amount paid), but all missing values were assigned zero
for the purpose of this study.
It is important to note that national household expenditure survey data cannot be used to estimate bill
collection losses (percent of the total billed amount not collected by the utility) from residential consumers
because no survey asks what should have been paid. It is not possible to tell from non‐zero expenditure
36
whether the payment was made in full, or in part, or whether it was to pay for arrears. Nevertheless, a large
percentage of grid‐connected households reporting zero expenditures could signal many households not
paying fully, if at all, for electricity.
The results, summarized in Figure 11, show that Angola, Botswana, Ethiopia, Malawi, South Africa, Uganda,
and Zambia had one‐fifth or more of households connected to the grid reporting zero expenditures on
electricity, with Angola (46 percent), Zambia (38 percent), and Botswana (31 percent) leading. By contrast,
Nigeria—where bill collection losses have been documented to be very high—significantly under‐reported
non‐payment, showing a non‐payment rate of only 4 percent. The survey in Senegal, which asked for
expenditures over the last 12 months, found no households connected to the grid not having made
payments. The results by quintile are shown in Table A.20 in the annex. Generally, the share of grid‐
connected households reporting zero expenditures declined with quintile, but Ethiopia and Uganda showed
the opposite trend, with the top quintile having the highest proportion of grid‐connected households
reporting zero expenditures. There are several potential explanations. The rich may be more likely to get jobs
with generous benefits, including free electricity. They may be more likely to rent houses with electricity
included in the rent. Or they are able to manipulate meters or bribe utility employees to avoid bill payment,
in which case they should be targeted for reducing commercial and bill collection losses.
Figure 11: Share of households connected to the grid with zero expenditures on electricity
80
Urban Rural Total
70
60
% of households
50
40
30
20
10
0
Source: World Bank staff calculations using household survey data.
Among the countries with one‐fifth or more of grid‐connected households reporting zero expenditures, only
South Africa and Zambia enable further analysis. Of the two, only Zambia isolates electricity for inclusion in
rent, whereas South Africa includes water in addition to electricity, making disaggregation of electricity from
other items not possible. If every appearance of combined electricity and water bills paid is assumed always
to include electricity, and if everyone receiving free electricity is considered to rely only on free electricity,
which is unlikely—the percentage of households receiving free electricity increases from 2 percent in the
bottom two quintiles to 3 percent in the top three quintiles—the share of households not paying for grid
electricity without justification declines from 29, 11, and 24 percent for urban, rural, and national to 2
percent each in South Africa. The reduction is primarily from inclusion of electricity in rent or electricity and
water bills being bundled.
In Zambia, the number of grid‐connected households who had zero expenditures on electricity but whose
rent included electricity was significant in urban areas. The share of non‐paying households decreases from
37
33, 65, and 38 percent for urban, rural, and national to 17, 59, and 23 percent, respectively, once inclusion of
electricity in rent is taken into account.
Multiple Connections
Progressive tariffs to protect the poor would be effective only if each household is individually metered.
Unfortunately, shared metering is all too common in Africa. Multiply connected households cannot take full
advantage of lifeline rates, and end up paying higher effective unit prices charged to the bulk of the
consumption (see Table 4 for more detail). Four surveys asked questions from which the prevalence of
multiple connections can be estimated:
In Côte d’Ivoire, the survey asked if the household had its own meter, or shared a meter with others,
or had no meter.
In Ethiopia, the survey asked if the household had its own meter or shared a meter.
In Sierra Leone, the survey asked if the household shared an electricity bill with others.
In Senegal, the survey asked the household to report the amount paid to the neighbor over the last
12 months if the household did not have a contract with the utility and was using its neighbor’s
electricity.
The results show that multiple connections are widespread in the first three countries. The share of
households connected to the grid that had their own meters in Côte d’Ivoire and Ethiopia or did not share a
utility bill with others in Sierra Leone was less than half, 42 percent, 40 percent, and 44 percent, respectively.
In Côte d’Ivoire, this percentage did not vary much by quintile, but in Sierra Leone the share increased from
the bottom quintile (26 percent) to the top quintile (46 percent), signaling that shared metering was
dominant among low‐income households. By contrast, in Ethiopia, the share fell markedly in the top quintile,
meaning shared meters were, unexpectedly, most common among the top quintile. To the extent that
progressive tariffs are intended to help the poor, shared meters among the richest 20 percent do not defeat
the purpose of progressive tariff design.
Perhaps not surprisingly, in Senegal—which does not charge the connection fee if a household is within 40
meters of the nearest power line—a much lower percentage reported not having a contract with the utility
and paying their neighbors instead. The actual percentage of households with shared meters depends on the
assumption about how many households are connected to a single meter on average, with the highest
percentage of shared meters corresponding to only two households per meter. If only two households are
assumed to be connected to the same meter on average, then 84 percent of households would have had
their own meters. This percentage was lowest in the bottom quintile, 53 percent, and highest in the top two
quintiles, 90–91 percent. If three households are connected to a meter on average, the percentage increases
to 88 percent of all households having their own individual meters.
If there is only one distribution company and the number of residential consumers is separately reported by
that company, comparison of the number of households connected to the grid found in the survey and the
number of residential customers reported by the utility gives another indication of the prevalence of multiple
connections. Such comparison is possible only in Malawi, where the utility data are available for the survey
year. If every shared meter is assumed to be connected to two households, the percentage of grid‐connected
households with their own unshared meters is 57 percent. If more than two households are connected, this
percentage increases, for example to 68 percent if three households on average are connected to a single
shared meter.
38
In Mali, the monopoly distribution company reports the total number of low‐voltage (but not residential)
customers in the survey year. The number of households reporting connection to the grid exceeds the
number of low‐voltage customers by 30 percent. If all voltage customers are assumed to be residential—
which they are not—and an average of two households are assumed to be connected to a shared meter, the
percentage of households with their own unshared meter is calculated to be 54 percent. The percentage
increases to 65 percent if the assumption is changed to three households for every shared meter on average.
Lastly, in Ethiopia, the monopoly distribution company reports the total number of residential customers in
the year before the survey year. The number is 1.65 million customers in 2012, against 1.63 million
households that reported having their own private meters out of a total of 4.2 million households reporting
connection to the grid in 2013. These numbers strongly suggest that many households that replied that they
had private meters were actually sharing their meters with their neighbors, with the distinction understood
by survey respondents to be between having meters installed on their premises (private meters) versus
connected to neighbors’ meters (shared meters). This in turn would mean that the percentage of households
sharing meters was much greater in Ethiopia than the survey finding of 60 percent.
Outages
Seven surveys—Ethiopia, Madagascar, Malawi, Mali, Niger, Nigeria, and Senegal—asked questions about
power outages. Because questions asked varied from country to country, it is not possible to compare them
on a common basis. The results are summarized in Table 14 for households connected to the grid who
replied to the questions on the frequency, duration, or both of blackouts. Power outages were common in all
the countries, and serious in Malawi (six out of every seven households reporting daily blackouts over the
last 12 months), Niger, Nigeria, and Senegal.
Table 14: Summary of power blackout information
Country Findings Quintile dependence Urban vs. rural
Ethiopia 27% of households replying Both measures (no blackouts, 20% of urban and 38% of rural
reported no blackouts during more than 3 blackouts) households reported no
the last 7 days, but 24% decreased with increasing blackouts, while 22% of urban
reported more than 3 quintile. The numbers of and 28% of rural households
blackouts. The number of hours without electricity did reported more than 3
hours without electricity over not vary much with quintile. blackouts. The number of
the last 7 days translated to hours per day without
1.8 hours a day. electricity translated to 1.6
hours in urban and 2.3 hours
in rural areas.
Madagascar The number of hours per day Excluding the bottom two The number of hours
electricity was available quintiles, which had too few electricity was available per
averaged 19. 68% of samples, the number of hours day averaged 20 in urban and
households replying reported electricity was available per 18 in rural areas. 70% of
24‐hour availability. day increased with quintile urban and 64% of rural
nationally. households reported 24‐hour
availability of electricity.
Malawi 86% of households said they The bottom 2 quintiles in 89% of urban and 80% of rural
had experienced blackouts urban and the bottom 4 households reported daily
every day over the last 12 quintiles in rural areas had blackouts.
months. too few sampled households
for meaningful statistics.
There was no consistent
39
Country Findings Quintile dependence Urban vs. rural
pattern as a function of
quintile.
Mali 49% of households reported The bottom 2 quintiles had Rural households reported
at least one blackout over the too few sampled households more frequent blackouts. The
last 7 days. The number of for meaningful statistics. largest difference was in
days with blackouts averaged There was no consistent answer to the question on
2.1 days, the number of times pattern by quintile. whether the household had
per day averaged 1.5, 33% experienced at least one
reported that blackouts lasted blackout over the last 7 days:
15 to 30 minutes at a time, 46% of urban and 60% of rural
21% reported 30 to 60 households replied “yes.”
minutes, and 20% reported
less than 15 minutes, while
11% reported longer than 3
hours.
Niger Questions were identical to The bottom 2 in urban and Meaningful comparison was
those in Mali. 91% of the bottom 4 quintiles in rural not possible because even the
households reported at least areas had too few sampled top quintile in rural areas had
one blackout over the last 7 households for meaningful a total of only 31 samples.
days. The average number of statistics. The frequency of
days with blackouts was 4.4, blackouts increased with
the number of blackouts per quintile in urban areas.
day was 2.2, 34% reported
that blackouts lasted 30 to 60
minutes, followed by 25%
reporting 15 to 30 minutes,
17% reporting 1 to 3 hours,
14% reporting less than 15
minutes, and 11% reporting
more than 3 hours.
Nigeria 57% of households reported The percentage of households 55% of urban and 59% of rural
daily blackouts in their areas, reporting daily blackout households reported daily
and 31% reported several declined from 62 in the blackouts.
blackouts over the last 7 days. second to 54 in the top
quintile.
Senegal Over the last 4 weeks, The number of days with The average number of days
households connected to the outages decreased slightly with blackouts over the last 4
grid reported blackouts 15 from the second to the top weeks was 14 in urban and 18
days on average, or 53% of quintile. in rural areas.
the time.
Source: World Bank staff analysis of household surveys
Note: All percentages are percentages of households connected to the grid who replied to the questions on blackouts.
Gender of Head of Household
Is there evidence that female‐headed households tend to be poorer? If they are, examining the relationship
between the gender of the head of household and access could be misleading, confusing poverty with
gender. Are female‐headed households less likely to have access to electricity, or buy less electricity, than
their male‐counterparts, after accounting for per capita and household expenditures?
40
Female‐headed households are over‐represented in lower quintiles in Botswana, Malawi, Rwanda, South
Africa, and Swaziland. The worst is South Africa, where only 10 percent of all people living in female‐headed
households is in the top quintile. Conversely, female‐headed households are over‐represented in upper
quintiles in Burkina Faso, Ethiopia, Mali, Niger, and Senegal. In Mali, only 3 percent of all people living in
female‐headed households was in the bottom quintile and 40 percent in the top quintile. Generally, per
capita expenditures in each quintile were comparable between female‐ and male‐headed households.
However, with the exception of South Africa and Swaziland, female‐headed households were considerably
smaller, making their household expenditures smaller than their male counterparts.
In the regression analysis, the logarithms of per capita expenditure and household size as independent
variables consistently gave a better fit than the logarithm of total household expenditure. Probit regression
of the urban/rural dummy on the female/male dummy and the above two logarithms found that the gender
of the head of household was insignificant only in Angola, and was negative only in Malawi and Swaziland.
Being female‐headed increased the probability of residing in urban areas by as much as 23 percent in Senegal
(see Table A.21 in the annex); the median increase was 7 percent. This may be in part because rural areas
tend to be more traditional, making it more difficult for women to head households.
Probit regression of the female/male dummy on three dependent variables—urban/rural dummy, logarithm
of per capita household expenditure, and logarithm of household size—showed that the coefficients for both
per capita expenditure and household size were significant and negative, except in Senegal where female‐
headed households had a positive significant coefficient for per capita expenditure and Botswana where the
coefficient for household size was positive. (The coefficient for expenditure was insignificant in Mali and
Sierra Leonne and that for household size was insignificant in South Africa and Swaziland.) The coefficient for
the urban/rural dummy was usually statistically significant and positive. While the equation specification
tested was overly simplified, these results suggest that, after accounting for the location (urban/rural),
female‐headed households may have tended to be poorer on average in most countries.
Statistically significant results found in the regression analysis relating the gender of the head of household
to electricity use are shown in Table 15. For probit equations, the results show the difference in the
probability between female‐ and male‐headed households that the dependent variable (for example,
electrification) would be 1 at mean values of per capita expenditure and household size. For ordinary least
squares, the table shows the percentage by which spending on electricity increases, again at mean values of
per capita expenditure and household size. In total, the gender dummy was statistically significant in at least
one equation in 15 countries. The coefficients for probit equations were negative in Ghana (all urban), Sierra
Leone (rural expenditure and rural grid dummies), Togo (all urban), and Zambia (rural grid), and positive
elsewhere. While the gender dummy was negative for rural households in Sierra Leone, these were the only
equations in which the logarithm of per capita expenditure was not statistically significant, casting doubt on
the results. Among those who reported positive expenditures on electricity, the gender of the head of
household affected the amount spent only in four countries, with negative coefficients in Ghana and positive
elsewhere. On balance, female‐headed households did not differ from male‐headed households once per
capita expenditure and household size were taken into account, and if anything evidence pointed to female‐
headed households being more likely to use electricity, with urban Ghana and urban Togo being notable
exceptions. However, when the logarithm of expenditure on electricity is examined among grid‐connected
households only, female‐headed households had spent considerably less in rural São Tomé and Príncipe and
Sierra Leone, as shown in Table A.22 in the annex, although the opposite was observed in rural Angola.
41
Table 15: Impact of gender of household head on electricity‐related variables
Expenditure
Electricity dummyb Grid dummyc Log expenditured
dummya
Urban Rural Urban Rural Urban Rural Urban Rural
Angola — — — — — — — 24
Botswana — 0.07 — 0.09 — 0.08 — —
Ethiopia 0.10 — — — — — — —
Ghana ‐0.04 0.11 ‐0.02 0.11 ‐0.02 0.11 ‐10 ‐10
Madagascar 0.06 — 0.09 — 0.08 — — —
Mali — — — — 0.09 0.10 — —
Niger — — — — 0.12 — — —
Nigeria — 0.14 — 0.10 — 0.17 — —
Rwanda — — — — — — 14 —
São Tomé and Príncipe 0.08 — — — n.d. n.d. — —
Senegal — 0.24 — 0.23 — 0.23 — 35
Sierra Leone — ‐0.003 — — — ‐0.003 — —
South Africa 0.10 — n.a. n.a. 0.02 — — —
Togo ‐0.06 — ‐0.07 — ‐0.06 — — —
Zambia 0.04 — — — — ‐0.01 — —
Median 0.06 0.09 ‐0.02 0.11 0.05 0.10 NA 24
Source: World Bank staff calculations.
a. 1 if spending on electricity is positive, zero otherwise.
b. 1 for electricity (grid as well as off‐grid, such as solar and diesel generators) as the primary source of energy for
lighting or cooking, 0 otherwise.
c. 1 for households connected to the grid or electricity (excluding off‐grid, such as solar and diesel generators) as the
primary source of energy for lighting or cooking, 0 otherwise; see footnotes d and e and associated results in Table A.21
for more detail.
d. Logarithm of spending on electricity retaining all households with positive expenditure.
The results for the dummy represent the increase in the probability of the dependent variable being 1 as result of
changing the gender dummy from 0 (male) to 1 (female). The results for log expenditure on electricity represent the
percent increase in expenditure on electricity (not logarithm) as a result of changing the gender dummy from 0 to 1. —
= statistically insignificant at 5; n.d. = no distinction made for different sources of electricity; n.a. = not available (data
not collected); NA = not applicable.
The surveys in Malawi, Niger, and Nigeria enabled isolation of households who lived in communities
connected to the grid. The probit regression for grid connection for households that could have connected
was performed by taking these households as a single group as well as by separating urban from rural
households. Statistically significant results were obtained when urban and rural households were combined
only in Niger—female‐headed households were more likely to connect—and separating urban and rural
households similarly returned statistically significant results only in urban Niger—again female‐headed
households were more likely to connect.
Given the data limitations, these findings should be viewed only as preliminary, calling for more detailed
investigation. A recent detailed analysis of the gender of the head of household in Africa since the 1990s
suggests that the share of female‐headed households has been rising, while that poverty has been declining
faster among female‐headed households than among their male counterparts (Milazzo and van de Walle
2015). These changing dynamics can inform future research and affect policy direction.
42
Conclusions
It is worth highlighting the limitations of this study—in scope and data availability—before summarizing the
key findings and possible policy implications:
With respect to household access, there is much less information on the supply side compared to the
demand side. Connection charges tend to increase rapidly with increasing distance from the power
line, and yet information on the distance between each household and the nearest distribution line is
not available. Grid connection may not be a practical option even for many living in electrified
communities.
Information on income, let alone disposable cash income, is not available and is substituted by
household expenditures used to calculate official poverty statistics. Those using kerosene for lighting
have some cash‐based expenditures to redirect to electricity purchase, but no survey separates
spending on kerosene for lighting from that for cooking. Trying to pinpoint determinants of access
beyond affordability based on national household expenditure surveys requires more data collection
and analysis in individual countries.
It would not be appropriate to extrapolate the conclusions and recommendations in this paper to off‐
grid electricity where different factors will be at play. There are many areas in Africa—areas with low
population density and low household expenditures, and far from cities and towns—where it would
be too costly to supply grid electricity. Where the unit cost of grid electricity supply markedly
exceeds what would be considered reasonable to pay for grid electricity, off‐grid solutions are likely
to offer more practical solutions.
This paper confirms many widely accepted observations, such as that the percentage of population using
electricity remains low in Africa, that electricity use is far more prevalent in urban than in rural areas, and
that adoption of electricity and quantities consumed rise sharply with income. There are also relatively new
findings. The rate of household access to grid electricity is only weakly correlated with affordability of tariffs
applicable to the subsistence level of electricity consumption. There is little evidence of a trade‐off between
utilities’ deficits and household access, but there is evidence of trade‐offs between affordability at relatively
high residential consumption levels and underpricing. There is no marked difference between female‐ and
male‐headed households with respect to access to electricity and spending on electricity, once income (per
capita as well as household, captured through the combination of per capita expenditures and household
size) and the place of residence are accounted for. The remainder of this section is largely about grid
electricity.
The findings of this study suggest several measures that can enable use of electricity by more households:
Avoid multiple connections. They substantially reduce, if not eliminate altogether, the intended
benefits of the subsidies provided by progressive tariffs. Shared connections also degrade the quality
of service to those so connected because the combined demand of the households can easily exceed
the installed capacity.
Making connection charges affordable is one of the top priorities in a number of countries. The
difference in the share of household expenditures needed to pay for the initial connection between
those with and without grid connection is stark, with medians differing by a factor of two when total
household expenditures are considered and a factor of three when only cash expenditures are
considered. In some countries, connection charges are beyond the reach of low‐income households,
arguably an important reason for multiple connections. Easing payment terms for new connection
43
and building connection charges in general tariffs of all—and especially high‐consumption consumers
in all sectors—would help. In easing payment terms, attention should be paid to balancing tariffs and
monthly installments for connection charges, because the combined effect could make monthly
payments unaffordable.
Utilities can do more to stamp out corruption and eliminate bribe payments demanded by their
employees. In the two surveys where grid‐connected households were asked if they had paid a bribe
to get connection, many replied “yes.” Bribes for connection further encourage multiple connections.
Lifeline rates for blocks in the vicinity of 30–50 kWh a month may help strike a balance between
affordability on the one hand and utility financial viability on the other. The first block of 25 kWh or
less per month in some countries seems too small. If the country has a single schedule of a single
block, progressive tariffs may be considered. For full benefits to be reaped, however, households
need to be individually and accurately metered.
Prepaid meters help low‐income households. The ability to pay in small increments when cash is
earned, and not having to face the prospect of disconnection and a high reconnection charge, help
especially the poor. However, electricity services are unreliable—in every one of the seven countries
in which households were asked about service quality, blackouts were common—and this could
mean that prepaid customers pay in advance for electricity they cannot get when needed. For this
reason, it may be unfair to mandate prepaid metering on all customers.
Consideration may be given to basing lifeline rates on a moving average of consumption over several
months. Volume‐differentiated tariffs with a large increase in unit price from the first to the second
block in particular may pose financial hardships for the poor if they are applied strictly on monthly
consumption. To address this concern, in Cape Town, South Africa, free electricity is based on
monthly consumption averaged over the past 12 months. Using moving averages and issuing warning
messages in advance to customers as they approach the limit are two ways of alleviating such
hardships.
High appliance efficiency further helps reduce monthly bills. Recent advances in energy efficiency
improvement are enabling households to meet basic electricity needs for as little as 15 kWh a month.
For example, using four light‐emitting diode light bulbs of 800 lumens each for four hours a day,
charging a cell phone, running a large fan for five hours a day, and watching one small efficient
television for three hours a day consume about 15 kWh over a month. That said, light‐emitting diode
light bulbs and other efficient appliances are relatively expensive, making it difficult for the poor to
pay the upfront costs—one of the main reasons energy is more expensive for the poor than for the
rich, exacerbating energy poverty. Financial assistance may be needed.
For gender equality, helping the poor is likely to go a long way in helping female‐headed households.
Once both per capita and total household expenditures are accounted for, there is little evidence
that female‐headed households are disadvantaged with respect to electricity use, except in urban
Ghana and Togo. That said, these are preliminary findings, and the gender dimension should be
investigated further to see how robust this finding is.
Utilities can take steps to provide more targeted assistance to low‐income households without increasing
their deficits markedly:
Rolling out prepaid meters helps utilities. Utilities are assured of prompt payment for every kWh
consumed.
44
Tariffs do not have to be exceptionally low. Where consumption at the subsistence level is a very
small share of monthly household cash expenditures, consideration may be given to raising tariffs. In
the sampled countries, Angola and Nigeria have exceptionally low unit tariffs in absolute terms and
relative even to the income of the poor.
Volume‐differentiated tariffs at the high end may be considered. Several countries have volume‐
differentiated tariffs between the first and the second block, followed by increasing block tariffs
thereafter. But if households are individually metered so that high monthly consumption signals a
household with many appliances, their ability to pay can be exploited more to cross‐subsidize the low
end of monthly consumption by switching the order: increasing block tariffs between the first and
the second block, followed by volume‐differentiated tariffs.
Reallocation of cross‐subsidies can help make electricity affordable in most countries. Targeted
subsidies needed to make 30 kWh a month of grid electricity affordable to all urban residents, are
not large in most countries. While recognizing that perfect targeting is not possible, the subsidies
calculated in this study point to the potential for achieving universal access in urban and peri‐urban
areas.
Eliminating operational inefficiencies may not eliminate underpricing, but can make subsidies to the
poor more financially viable to utilities.
Many Latin American countries treat assets associated with new connection no differently from all
other assets—high‐ and medium‐voltage transmission lines, for example—and capture them in the
regulatory assets for tariff setting. Such an approach spreads the costs of new connection and helps
reduce multiple connections by making the initial connection much more affordable in countries
where connection fees are currently high.
Lastly, this paper found areas where questions in household surveys delivered ambiguous or questionable
answers. A standardized approach to data analysis would also be helpful. Some examples follow.
Quantitative information on kWh consumed seems difficult to obtain. Enumerators should be better
trained to spot implausible answers if this question is asked. Without information on kWh consumed,
it is not possible carry out the distributional analysis of subsidies outlined by Komives et al. (2005). In
this paper, multiple schedules in many countries and the widespread practice of sharing meters
meant that kWh consumed could not be back‐calculated (except in Rwanda), because there is no way
of telling which tariff schedule was applicable to each household.
Given the policy implications of pervasive use of shared meters, the wording of questions concerning
shared meters can be tightened, for example by distinguishing between “private” meters on one’s
premises to which other households connect and unshared meters on one’s premises.
It would be useful to distinguish between solar lanterns and solar panels, and avoid using the term
“solar energy.”
In countries where generator ownership is prevalent, it would be useful to separate spending on
automotive fuels from spending on fuels for generators.
In countries with large commercial and bill collection losses, it might be worth asking questions about
nonpayment in a way that would enable more robust conclusions. Separating electricity from water
where the questionnaire currently reads “free electricity/water” as employment benefits and
inclusion in the rent are two examples.
45
National household expenditure surveys are useful because they enable identification of the poor.
They are less useful for calculating access to electricity. Agreeing how to standardize analysis across
divergent household surveys among energy practitioners would help compare computation of access
rates across studies.
More systematic collection of freely acquired basic goods and services to separate cash expenditures
from own consumption would help estimate disposable cash income, which is needed to assess the
ability to pay for electricity.
A national household expenditure survey cannot substitute a dedicated energy survey. One problem
with energy surveys is that information on total household expenditures is not collected because that
requires a long list of questions. One option is to launch the energy survey in parallel with the
national household expenditure survey, visiting the same households. Further, examination of
household survey data side by side with data on residential customers from utilities would enrich
analysis considerably.
This paper has examined access largely from the point of view of demand. But supply constraints are equally
important. Indeed, where data on community access and household connection to the grid were available,
most unconnected households did not live in electrified communities, a result dominated by lack of
community access in rural areas. This points to the importance of developing a financially viable strategy for
rural electrification, while making grid electricity affordable in urban areas.
46
Annex
The surveys used in this study are listed in Table A.1. Total expenditures used to measure poverty are in real
terms (accounting for regional and sometimes temporal differences in prices), except in Botswana, where the
government’s decision to use nominal values was followed. Poverty is based on per capita or per adult‐
equivalent expenditures, as indicated in the last column.
Table A.1: Household survey description
Country Name Survey period Sample size Poverty
Angola Inquérito Integrado sobre o Bem Estar da May 2008–Jul 2009 9,002 Adult
População (IBEP)
Botswana Botswana Core Welfare Indicator Survey Apr 2009–Mar 2010 7,644 Adulta
Burkina Faso Enquête Intégrale sur les Conditions de Vie Jul–Sep 2009 4,801 Adult
des Ménages Burkinabè (EICVM) 2009–2010
Côte d'Ivoire Enquête Niveau de Vie des Ménages (ENM) Jun‐Oct 2008 12,600 p.c.
Ethiopia Ethiopia Socioeconomic Survey May–Dec 2013 5,257 n.d.b
Ghana Ghana Living Standards Survey 6 Oct 2012–Sep 2013 16,702 Adult
Madagascar Enquête Périodique Auprès des Ménages Jun–Oct 2010 12,460 p.c.
(EPM) 2010
Malawi Integrated Panel Household Survey 2013 Apr–Dec 2013 4,000 p.c.
Mali Living Standards Measurement Study ‐ 2014 p.c.
Integrated Surveys on Agriculture (LSMS‐ISA)
Mozambique Inquérito sobre Orçamento Familiar (IOF) Sep 2008–Aug 2009 10,832 p.c.c
2008/9
Niger Enquête Nationale sur les Conditions de Vie Jul 2011–Jan 2012 3,859 p.c.
des Ménages (ECVM) 2011
Nigeria General Household Survey Panel Wave 2 Feb–Nov 2013 4,568 p.c.
(2012/13) Post‐Harvest Visit
Rwanda Enquête Intégrale sur les Conditions de Vie Mar 2010–Mar 14,308 Adult
des Ménages (EICV3) 2011
São Tomé and Inquérito aos Orçamento Familiar (IOF) 2010 3,128 p.c.
Príncipe 2009/2010
Senegal Enquête de Suivi de la Pauvreté au Sénégal Aug 2011–Feb 2012 5,944 Adult
(ESPS‐II)
Sierra Leone Sierra Leone Integrated Household Survey Jan–Dec 2011 6,727 Adult
(SLIHS)
South Africa Income and Expenditure Survey 2010/11 Jul 2010–Sep 2011 25,251 p.c.
Swaziland 2009/10 Swaziland Household Income and Apr 2009–Mar 2010 3,177 Adult
Expenditure Survey
Tanzania National Panel Survey (NPS) 2012/2013 Oct 2012–Nov 2013 4,883 Adult
Togo Questionnaire des Indicateurs de Base du Jul–Aug 2011 5,491 Adult
Bien‐être (QUIBB) 2011
Uganda Uganda National Household Survey (UNHS) Jun 2012–Jun 2013 6,888 Adult
2012/13
Zambia Living Conditions Monitoring Survey (LCMS) Jan–Apr 2010 19,368 Adult
VI – 2010
Sources: Household surveys named in the table.
a. Households’ poverty status as determined by official poverty measurements is used in this work, but the official
measurements included rare large expenditures in consumption aggregates. For the purpose of defining total household
47
expenditures to define quintiles and expenditure share on electricity, expenditures were reconstructed by omitting
large purchases in the official consumption aggregates and adding back imputed values of non‐purchased food and fuels
(dung and wood) that had been excluded in the consumption aggregates constructed by the government.
b. This survey is not used to measure poverty. Based on the poverty rate of 30 in 2011, this paper assigned the poverty
status to the bottom 30.
c. Poverty is based on regional poverty lines.
p.c. = per capita; n.d. = not defined.
Some descriptive statistics are given in Table A.2. Aggregated across the sample countries, 67 percent of all
people and 63 percent of all households were in rural areas. The officially poor consisted of 51 percent of
rural residents and 31 of urban residents, or 42 percent of all people. Where poverty is based on per capita
expenditures, the bottom two quintiles were poor in all countries except Ethiopia where the bottom 30
percent were defined to be poor in the absence of poverty measurement using the 2013 Ethiopia
Socioeconomic Survey. There tended to be a higher percentage of female‐headed households in urban areas
than in rural areas. The table also shows per capita real expenditures (except in Botswana, where real
expenditures are not available and nominal expenditures are used) expressed in 2014 U.S. dollars.
48
Table A.2: Descriptive statistics
of people officially of people living in female‐ Total real monthly expenditure
Rural
classified poor headed households per capita in 2014 US$
Country People Households Urban Rural Total Urban Rural Total Urban Rural Total
Angola 45 48 19 58 37 19 17 18 152 73 117
Botswana 43 42 16 24 19 49 51 50 199 115 163
Burkina Faso 78 72 25 53 47 13 7 8 53 29 34
Côte d'Ivoire 54 50 29 54 43 20 10 15 91 56 72
Ethiopia 87 82 11 33 30a 35 15 17 35 21 23
Ghana 50 45 11 38 24 30 20 25 92 50 71
Madagascar 80 78 51 81 75 18 14 15 23 13 15
Malawi 84 83 25 40 38 16 22 21 54 31 35
Mali 78 69 9 44 36 9 2 4 68 48 52
Mozambique 70 71 29 62 52 28 23 24 54 23 32
Niger 83 82 18 55 48 14 6 7 65 33 39
Nigeria 64 60 28 64 51 14 8 10 79 45 58
Rwanda 86 85 22 49 45 22 23 22 72 24 31
São Tomé and Príncipe 49 50 64 59 62 39 34 37 73 77 75
Senegal 57 51 33 57 47 36 15 24 78 51 63
Sierra Leone 62 61 31 66 53 30 24 26 42 26 32
South Africa 39 33 31 69 45 36 54 43 269 83 197
Swaziland 76 63 31 73 63 38 53 50 124 51 69
Tanzania 74 69 6 26 21 24 21 22 62 29 38
Togo 62 56 35 73 58 20 15 17 73 36 50
Uganda 77 73 10 22 19 32 26 27 76 38 47
Zambia 65 64 28 77 60 20 19 20 69 22 39
Median 67 63 26 56 46 23 20 21 73 37 49
Sources: Household expenditure surveys and World Bank staff calculations.
Note: Per capita expenditures in 2014 U.S. dollars take real expenditures in the survey (except Botswana where only nominal expenditures are available) and
inflate them to 2014 in local currency using the country’s consumer price index, and convert them to U.S. dollars using the official exchange rate in 2014.
a. This survey is not used to measure poverty. Based on the poverty rate of 30 in 2011, this paper assigned the poverty status to the bottom 30.
49
Access to grid electricity expressed as the percentage of all people who live in households reporting
connection to the grid is shown in Table A.3. The statistics in the table correspond to the lowest
measure of access rate.
Table A.3: Percentage of all people who have grid connection
All people People classified poor
Country Urban Rural Total Q1 Q5 Urban Rural Total
Angola 67 10 41 6 73 38 4 14
Botswana 60 21 43 15 74 41 10 24
Burkina Faso 46 2 11 1 35 13 1 2
Côte d'Ivoire — — — — — — — —
Ethiopia 94 8 19 5 38 83 4 8
Ghana 88 44 66 35 89 73 30 40
Madagascar — — — — — — — —
Malawi 38 3 9 0 30 8 0 1
Mali 75 7 22 2 54 36 1 3
Mozambique — — — — — — — —
Niger 48 2 10 1 37 9 1 1
Nigeria 89 37 56 26 82 84 29 40
Rwanda 46 5 11 1 43 5 0 1
São Tomé and Príncipe — — — — — — — —
Senegal 90 25 53 26 83 79 18 36
Sierra Leone 35 0 13 2 37 20 0 6
South Africa 93 78 87 76 99 86 75 80
Swaziland — — — — — — — —
Tanzania 50 4 16 1 53 7 0 1
Togoa 77 9 34 5 73 60 5 18
Uganda 33 2 9 1 31 3 1 1
Zambia 55 4 22 1 71 16 1 4
Median 60 7 22 2 54 36 1 6
Source: World Bank staff analysis of household surveys.
a. In Togo, households were not asked directly about connection to the grid, but were asked about expenditures
during the last two months on grid electricity, and the response is used in this table.
— = Grid connection status not available in the survey; Q1 = expenditure quintile 1 (bottom 20); Q5 = expenditure
quintile 5 (top 20).
50
Table A.4 and Table A.5 show electricity expenditure shares by quintile for total expenditures with and without imputed values, respectively.
Table A.4: Share of total household expenditures spent on electricity by quintile
Urban Rural National
Country 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
Angola 4.7 5.3 3.3 7.2 2.3 — 4.0 3.1 2.7 2.2 4.7 5.2 3.3 7.0 2.3
Botswana 10.2 9.3 7.7 7.5 4.9 11.4 11.5 9.2 9.0 4.8 10.5 10.1 8.1 7.8 4.9
Burkina Faso 5.2 6.4 4.7 4.8 4.5 0.7 5.0 3.5 5.0 4.0 1.4 6.0 4.4 4.9 4.4
Cote d'Ivoire 3.5 3.0 2.7 2.5 2.2 3.7 2.5 2.5 1.9 2.0 3.6 2.8 2.7 2.4 2.2
Ethiopia 4.8 4.0 3.7 2.7 2.2 3.4 1.4 1.1 1.2 1.0 4.2 2.8 2.8 2.1 1.8
Ghana 3.2 2.9 2.8 2.7 2.3 2.5 1.9 1.9 2.0 1.7 2.8 2.4 2.5 2.6 2.2
Madagascar 7.2 4.9 3.7 4.2 4.5 9.5 2.6 3.3 4.0 4.1 8.8 4.2 3.4 4.1 4.4
Malawi 0.5 0.4 0.3 0.2 0.3 — 0.4 0.3 0.3 0.3 0.5 0.4 0.3 0.3 0.3
Mali 2.1 2.9 2.6 2.8 3.0 1.1 1.1 0.9 1.0 2.2 1.3 1.5 1.8 2.2 2.8
Mozambique 9.2 7.0 5.1 3.5 3.6 9.4 4.3 5.1 3.7 2.8 9.4 6.7 5.1 3.5 3.5
Niger 3.6 3.3 2.6 2.6 3.5 1.4 2.2 1.6 1.7 3.1 2.6 2.5 2.1 2.3 3.5
Nigeria 4.3 2.8 2.5 2.1 2.7 3.5 2.5 2.1 2.1 2.0 3.8 2.6 2.3 2.1 2.5
Rwanda 1.8 3.1 2.2 2.1 1.3 4.7 3.2 2.2 2.4 1.2 3.6 3.1 2.2 2.2 1.3
São Tomé and Príncipe 2.8 2.2 2.2 1.9 1.8 2.9 3.1 1.7 1.9 1.7 2.8 2.3 2.0 1.9 1.8
Senegal 4.6 3.4 3.0 3.5 4.0 4.2 3.3 2.6 3.7 3.9 4.5 3.4 2.9 3.6 4.0
Sierra Leone 7.5 5.1 4.3 4.8 4.3 11.6 14.3 11.4 5.0 5.0 8.0 5.3 4.5 4.8 4.3
South Africa 8.8 6.9 6.1 5.2 3.9 6.8 5.4 4.8 4.3 3.6 7.6 6.1 5.6 5.0 3.8
Swaziland 8.7 9.2 17.2 11.3 8.3 19.6 11.7 10.2 8.9 8.0 17.2 11.3 12.3 9.8 8.2
Tanzania 1.9 3.0 3.7 3.3 2.9 5.9 5.0 5.1 3.8 2.8 3.1 3.5 4.2 3.4 2.9
Togo 4.1 4.1 3.3 3.1 3.0 3.2 4.3 2.6 3.3 2.9 3.8 4.1 3.2 3.1 3.0
Uganda 1.8 2.6 2.7 2.1 2.5 5.3 0.3 1.3 1.8 1.9 3.7 2.1 2.5 2.0 2.4
Zambia 4.4 8.0 7.9 7.4 4.9 1.1 5.9 7.6 10.2 5.1 1.7 7.5 7.8 7.6 4.9
Median 4.4 3.7 3.3 3.2 3.0 4.0 3.2 2.6 3.0 2.8 3.7 3.5 3.0 3.3 2.9
Source: World Bank staff calculations using household survey data.
Note: — = No households with non‐zero expenditures on electricity
51
Table A.5: Share of total household cash expenditures spent on electricity by quintile
Urban Rural National
Country 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
Botswana 10.9 9.4 7.7 7.6 4.9 11.4 12.2 9.7 9.4 5.0 11.0 10.4 8.3 7.9 4.9
Burkina Faso 5.4 7.1 5.2 5.6 5.3 1.4 7.5 4.9 6.1 4.5 2.0 7.2 5.2 5.6 5.2
Cote d'Ivoire 3.7 3.1 2.8 2.6 2.3 4.8 3.1 3.1 2.3 2.3 4.2 3.1 2.9 2.5 2.3
Ethiopia 5.0 4.4 4.4 2.9 2.2 4.4 2.2 1.7 2.4 1.7 4.7 3.4 3.4 2.7 2.1
Ghana 3.4 3.0 2.9 2.8 2.3 3.1 2.2 2.1 2.3 1.9 3.2 2.7 2.6 2.7 2.3
Malawi 0.5 0.4 0.4 0.3 0.3 — 0.4 0.3 0.4 0.3 0.5 0.4 0.3 0.3 0.3
Mali 2.2 3.0 2.6 2.8 3.0 1.5 1.6 1.2 1.3 2.5 1.6 1.9 1.9 2.3 2.9
Niger 4.0 3.6 2.8 2.7 3.6 2.0 2.5 2.1 1.9 3.5 3.1 2.8 2.4 2.4 3.6
Nigeria 6.2 4.3 3.8 3.2 3.7 6.0 4.5 3.4 3.5 3.2 6.1 4.4 3.6 3.3 3.6
Rwanda 1.8 3.4 2.3 2.2 1.3 5.3 4.9 2.7 2.8 1.3 4.0 4.3 2.5 2.5 1.3
São Tomé and Príncipe 2.8 2.2 2.2 1.9 1.8 3.1 3.2 1.7 1.9 1.7 2.9 2.4 2.0 1.9 1.8
Senegal 4.6 3.4 3.0 3.6 4.0 4.8 3.5 2.7 3.8 4.0 4.7 3.5 3.0 3.6 4.0
Sierra Leone 7.5 5.1 4.3 4.8 4.3 13.5 14.3 14.9 5.0 6.8 8.2 5.4 4.6 4.8 4.3
Swaziland 8.8 10.0 17.5 11.8 8.5 20.8 13.2 11.4 9.8 8.6 18.2 12.6 13.2 10.6 8.5
Tanzania 2.0 3.6 4.2 3.7 3.2 7.4 6.9 7.0 4.9 3.6 3.7 4.5 5.2 3.9 3.3
Togo 4.4 4.2 3.3 3.2 3.0 3.9 5.4 2.9 3.5 3.0 4.2 4.6 3.3 3.2 3.0
Uganda 1.8 2.7 3.1 2.2 2.6 6.8 1.0 1.5 2.3 2.1 4.5 2.3 2.9 2.2 2.6
Zambia 4.5 8.5 8.5 7.7 5.1 4.0 10.6 8.8 11.2 5.4 4.1 9.0 8.5 8.1 5.1
Median 4.2 3.6 3.2 3.0 3.1 4.8 4.0 2.8 3.2 3.1 4.1 3.9 3.1 3.0 3.2
Source: World Bank staff calculations using household survey data.
Note: — = No households with non‐zero expenditures on electricity
52
Residential tariff structures in 39 countries in Africa are provided in Table A.6. A schedule has its own unique energy and fixed charges, and
therefore two schedules with identical energy charges but different fixed charges are considered distinct and counted as two. The table shows
the number of schedules for single‐phase and three‐phase electricity connections. Some countries do not make this distinction, but where the
schedule depends on installed capacity (kilowatts or kilovolt‐amperes), the inferred number of phases is used to construct the entries in Table
A.6. If no distinction is made and the schedule is independent of installed capacity, the number of schedules appears under single phase only in
the table below, with no entries under three phases.
Table A.6: Residential tariff structure characteristics
# of single phase # of 3‐phase Prepaid
Country schedules # of blocks Type of tariff schedule schedules # of blocks Type of schedule schedule
Angola 2 2/1 increasing block tariff/— 2 1 — N
Benin 1 3 volume differentiated for — — — N
block 2, increasing thereafter
Botswana 1 2 n.a. — — — N
Burkina Faso 7 3 increasing 5 3 increasing N
Burundi 1 3 Increasing — — — N
Cabo Verde 2 2 Volume differentiated 2 2 volume N
differentiated
Cameroon 1 5 volume differentiated, but — — — N
first block is exempt from tax
for all consumption levels
Chad 1 2 n.a. — — — N
Comoros 2 1 — — — — Y
Côte d'Ivoire 3 2/1/2 increasing/—/decreasing — — — N
Ethiopia 1 9 increasing 1 7 increasing N
Gabon 9 1 — 7 1 — Y
Gambia, The 2 4/1 increasing /— — — — Y
Ghana 1 4 volume differentiated for — — — N
block 2, increasing thereafter
Guinea 1 3 increasing 1 3 increasing N
Kenya 1 3 increasing — — — N
Lesotho 1 1 — — — — N
Liberia 1 1 — — — — N
Madagascar 2 2 increasing block tariff; each — — — N
of the four regions has two
different schedules
Malawi 2 1 — 2 1 — Y
53
# of single phase # of 3‐phase Prepaid
Country schedules # of blocks Type of tariff schedule schedules # of blocks Type of schedule schedule
Mali 17 4 for prepaid increasing 10 2 increasing Y
social, 2
otherwise
Mauritania 8 1 — — — — N
Mauritius 1 8 increasing 1 8 increasing N
Mozambique 3 1/3/1 —/increasing/— — — — Y
Namibia 4 1 — 1 1 — Y
Niger 1 2 increasing — — — N
Nigeria 2 1 volume differentiated in that 3 1 — N
exceeding 50 kWh moves the
consumer to another
schedule
Rwanda 1 1 — — — — N
São Tomé and 2 3 volume differentiated 2 3 volume N
Príncipe differentiated
Senegal 2 3/1 increasing/— 3 3/1/2 increasing/—/TOU Y
Seychelles 2 5 increasing 1 5 increasing N
Sierra Leone 1 3 increasing — — — N
South Africa 7 5 or TOU increasing 6 5/TOU increasing Y
(Johannesburg) seasonal seasonal
Swaziland 2 1 — — — — N
Tanzania 2 2/1 increasing/– 1 1 — N
Togo 1 4 volume differentiated for 2 3 increasing N
block 2, increasing thereafter
Uganda 1 2 increasing — — — N
Zambia 2 3/1 increasing /— — — — Y
Zimbabwe 3 3/1/3 increasing/—/increasing — — — Y
Sources: Utility and regulatory agency websites.
Note: “Volume differentiated for block 2, increasing thereafter” means the tariff schedule is volume‐differentiated between block 1 and block 2, but assumes a
increasing block tariff structure from block 2 to block 3 and higher blocks.
# of single‐phase schedules = number of schedules for single‐phase connection or all connections in countries with no distinction between single and three
phases; # of blocks = number of blocks in the schedules preceding the column; # of 3‐phase schedules = number of schedules explicitly associated with three‐
phase connection, or with power capacity that is associated with three phases; prepaid schedule = Y (yes) if a schedule for pre‐paid is separately listed, N (no)
otherwise; n.a. = not available (information could not be obtained); TOU = time of use.
54
Table A.7 shows examples of effective unit tariffs for monthly consumption of 30, 50, 100, and 250 kWh, as well as examples of upfront
payments needed to connect to grid electricity. For effective tariffs, the lowest‐cost option is presented in the table. Unless indicated otherwise,
connection charges also represent lowest‐cost options (such as single phase and not requiring a grid line extension). All the charges were in
effect in July 2014 and converted to U.S. dollars using the official exchange rate in July 2014.
Table A.7: Examples of effective unit tariffs and upfront cost of grid connectionfor households in July 2014
Effective tariff charge in US$/kWh for
monthly consumption in kWh below Upfront connection cost in US$
Country 30 50 100 250 Rate 1 Description Rate 2 Description
Angola 0.012 0.012 0.023 0.038 52a — — —
Benin 0.32 0.30 0.29 0.28 278 — — —
Botswana 0.16 0.13 0.10 0.090 679 One‐off payment 141 1st of 180 payments over 180
months; $5 thereafterb
Burkina Faso 0.28 0.25 0.25 0.28 270 Ouagadougou and Bobo‐Dioulasso 70 Provinces
Burundi 0.044 0.044 0.066 0.128 155 Beginning Sep 2014, households also — —
purchase materials
Cabo Verde 0.46 0.45 0.54 0.54 2c — — —
Cameroon 0.10 0.10 0.10 0.16 50 — — —
Chad 0.18 0.18 0.18 0.21 n.a. — — —
Comoros 0.36 0.36 0.36 0.36 181 — — —
Côte d'Ivoire 0.10d 0.11d 0.14d 0.17d 212 Lowest of four social connection costs, the — —
other three being $308, $370, and $411
Ethiopia 0.022 0.019 0.022 0.027 76 — — —
Gabon 0.11 0.11 0.11 0.25 114 — — —
Gambia, The 0.23 0.23 0.23 0.23 169 Greater Banjul Area 81 Provinces
Ghana 0.069 0.064 0.12 0.12 87e No extension 436e Extension with 1 pole
Guinea 0.040 0.029 0.029 0.031 n.a.
Kenya 0.20 0.18 0.23 0.27 171 If next to a clinic and other facilities with 370 If not next to facilities
electricity
Lesotho 0.11 0.11 0.11 0.11 187 — — —
Liberia 0.51 0.51 0.51 0.51 54 — — —
Madagascar 0.12 0.20 0.17 0.17 165 No extension 453 Extension with 1 pole
Malawi 0.076 0.076 0.076 0.076 101 — — —
Mali 0.12 0.12 0.16 0.24 196 — — —
Mauritania 0.20 0.16 0.13 0.12 128 For 2 kilo‐volt amperes 168 6 kilo‐volt amperes
Mauritius 0.12 0.13 0.15 0.19 31 — — —
Mozambique 0.034 0.034 0.034 0.091 0 Social tariff customers 4 Others, single phase
Namibia 0.22 0.18 0.16 0.14 113 Urbanf — —
Niger 0.18 0.16 0.17 0.18 19 — — —
55
Effective tariff charge in US$/kWh for
monthly consumption in kWh below Upfront connection cost in US$
Country 30 50 100 250 Rate 1 Description Rate 2 Description
Nigeria 0.026 0.026 0.14 0.11 n.a. There is no connection fee but customers — —
are charged for costs of materials
Rwanda 0.23 0.23 0.23 0.23 82 Within 35 meters of a pole
São Tomé and 0.105 0.100 0.096 0.14 52 Single phase 108 Three phase
Príncipe
Senegal 0.25 0.24 0.24 0.27 0 Free if within 40 meters. — —
Seychelles 0.11 0.11 0.11 0.11 Based on actual investment cost
Sierra Leone 0.19 0.19 0.19 0.21 233 One‐off paymentg 118 First of 18 payments over 18
months, $7/month
thereafterg
South Africa 0.10h 0.10h 0.10h 0.10h 0 Homelight 20A (20 amperes) — —
Swaziland 0.088 0.088 0.088 0.088 13 — — —
Tanzania 0.073 0.073 0.12 0.20 197 Urban 110 Rural
Togo 0.22 0.28 0.24 0.24 244 — — —
Uganda 0.20 0.21 0.22 0.23 101 No extension 244 Extension with 1 pole
Zambia 0.15 0.10 0.064 0.062 125 Low‐cost areas 277 Medium‐cost areasi
Zimbabwe 0.021 0.021 0.069 0.095 95 Above ground, high‐density areasj 185 Above ground, low density
areasj
Median 0.12 0.12 0.14 0.17 103 — 141 —
Sources: Utility and regulatory agency websites.
Notes: Lowest costs (usually corresponding to the lowest installed capacity) are shown if a customer has more than one option. Effective tariff charges are
inclusive of fixed charges and taxes. Upfront costs include deposits that are not refunded until the end of the contract with the utility. — = not applicable; n.a. =
not available (information could not be obtained).
a. The reconnection charge after disconnection is more than double this charge.
b. New customers can spread the payment over 18, 60, or 180 months. The first payment is identical for all three options, but subsequent monthly payments
are US$30 and US$11 over 18 and 60 months, respectively.
c. The reconnection charge after disconnection is much higher at US$15.
d. These are outside of Abidjan, where power tariffs are slightly lower than in Abidjan.
e. Assumes no VAT on connection (information on VAT not available).
f. Rural connection charges not available.
g. Costs of materials are charged to all customers and included in the figures, taking US$70 as a typical average cost. These are both for prepaid customers,
who do not have to pay an initial deposit of US$31.
h. There are three categories of consumers entitled to free electricity for the first 50, 100, and 150 kWh a month. For those entitled to 150 kWh of free
electricity, the effective unit tariff for monthly consumption of 250 kWh is US$0.04/kWh.
i. The connection cost in high‐cost areas is US$466.
j. These are for single‐phase connections. Single‐phase underground cable service costs are US$155 and US$224 for high‐density and low‐density areas,
respectively. Three‐phase above‐ground and underground costs in low‐density areas are US$333 and US$362, respectively.
56
Table A.8 shows adjustments made to per capita expenditures to compute hypothetical grid electricity expenditures shares, and monthly
spending on electricity for varying consumption levels and connection fees expressed in 2014 US$ for cross‐country comparison. The tariff years
are the closest years to the survey years when the tariffs used came into effect. The timing of when the connection fees came into effect was not
necessarily the same, in which case different adjustment factors for total household expenditures were used.
Table A.8: Costs of monthly consumption of 30–250 kWh and initial connection charges, and expenditure adjustment factors
Adjustment Monthly spending on electricity, 2014 US$ Connection fee Adjustment
Country factor for 2014 US$ factor for
Survey Tariff tariff 30 kWh 50 kWh 100 kWh 250 kWh connection
Angola 2008 2012 1.84 0.36 0.59 2.32 9.48 52 2.02
Botswana a a a a a
2009 2014 1.52 4.76 6.28 10.07 22.53 679/141 1.00
Burkina Faso 2009 2008 1.00 8.46 12.26 25.47 70.71 270 capital city/70 provinces 1.37
Cote d'Ivoire 2008 2012 1.12 3.15 5.57 13.80 42.25 212 1.38
Ethiopia 2013 2006 1.00 0.66 0.94 2.19 6.39 76 1.13
Ghana 2013 2014 1.23 2.07 3.19 12.31 29.20 87 1.21
Madagascar 2010 2012 1.13 3.67 9.84 17.18 42.06 165 1.25
Malawi 2013 2014 1.30 2.28 3.81 7.61 19.04 101 1.30
Mali 2014 2014 1.00 3.65 6.09 15.79 59.47 196 1.00
Mozambique 2009 2009 1.00 1.03 1.72 3.44 22.83 0 1.50
Niger 2011 2012 1.03 5.54 8.20 16.89 46.09 19 1.19
Nigeria 2013 2014 1.09 0.78 1.30 14.06 28.34 0 1.09
Rwanda 2010 2012 1.21 6.93 11.55 23.11 57.77 82 1.46
São Tomé and Príncipe 2010 2012 1.32 3.15 4.99 9.60 34.26 52 1.30
Senegal 2011 2009 1.00 7.52 12.03 23.69 66.45 0 1.05
Sierra Leone 2011 2008 1.00 5.62b 9.33b 18.59b 52.47b 233/118b 1.00
South Africac 2011 2014 1.23 3.02 5.03 10.07 25.17 0 1.00
Swaziland 2009 2014 1.26 2.63 4.38 8.75 21.88 13 1.63
Tanzania 2013 2014 1.12 2.20 3.67 11.93 50.46 197 urban/110 rural 1.00
Togo 2011 2011 1.00 6.54 13.99 24.32 58.97 244 1.26
Uganda 2012 2014 1.27 6.00 10.64 22.26 57.10 101 1.22
Zambia 2010 2014 1.66 4.41 4.99 6.44 15.45 125 1.66
Source: World Bank staff calculations based on utility data.
a. US$141 is the first payment in an installment plan of 18, 60, and 180 months. For the 180‐month plan, the monthly spending increases by US$4.98 in the
remaining 179 months if the customer chooses to pay over 180 months. US$141 is used to examine the connection‐charge share of expenditures.
b. US$118 is the first payment in an installment plan of 18 months. The monthly spending increases by US$6.72 in the remaining 17 months if the customer
chooses this payment plan. US$118 is used to examine the connection‐charge share of expenditures.
c These numbers are for Johannesburg. Depending on eligibility, poor households are provided with 50, 100, or 150 kWh of free electricity a month. Because
free electricity cannot be generalized to the rest of the country, monthly spending without free allocation of electricity is shown.
57
Table A.9–Table A.12 reproduce Table 5–Table 8 for monthly consumption of 50 kWh. Table A.13 and Table
A.14 present results for monthly consumption of 100 kWh, and Table A.15 and Table A.16 do the same for
250 kWh. Table A.17 shows poverty‐gap calculations for grid electricity using the lowest‐cost tariff schedule
for each consumption amount. Table A.18 shows the corresponding poverty headcount, which is the
percentage of people (rather than households) who have to spend more than 5 percent of their total
household expenditures (inclusive of freely acquired food and other items) on grid electricity. For South
Africa, where different municipalities with their own eligibility criteria provide varying amounts of free
electricity, the tariff schedule in Johannesburg without free electricity was used for this purpose. Table A.19
summarizes the levels of subsidies needed to enable every household to consume 30, 50, and 150 kWh of
electricity a month, expressed in millions of U.S. dollars as well as a proportion of the total revenue collected
by the utilities.
Table A.9: Expenditure share of monthly consumption of 50 kWh by location, quintile, poverty status, and
gender of household head
All households Poor households Household head
Country Urban Rural Total Q1 Q5 Urban Rural Total Female Male
Angola 0.1 0.3 0.2 0.4 0.1 0.2 0.3 0.3 0.2 0.2
Botswana 3.1 7.3 4.8 20.4 1.3 8.8 11.8 10.5 5.7 4.1
Burkina Faso 9.8 13.9 12.8 22.7 7.8 17.1 18.2 18.1 19.5 11.8
Côte d'Ivoire 2.5 3.8 3.2 8.1 1.7 5.0 5.9 5.6 4.2 3.0
Ethiopia 1.6 1.8 1.8 3.8 0.9 4.5 3.1 3.2 2.5 1.5
Ghana 1.8 2.8 2.2 4.8 1.3 4.1 4.6 4.5 2.6 2.1
Madagascar 18.2 27.9 25.8 50.4 14.3 26.5 31.7 31.0 36.9 23.2
Malawi 2.2 3.9 3.6 6.3 2.1 3.8 5.6 5.4 4.5 3.3
Mali 1.7 2.5 2.3 4.2 1.2 3.0 3.7 3.6 2.3 2.3
Mozambique 2.5 4.4 3.8 9.2 1.3 5.3 6.0 5.9 5.0 3.3
Niger 3.3 5.7 5.3 7.7 3.6 6.1 6.6 6.6 7.8 5.0
Nigeria 0.4 0.6 0.5 1.0 0.4 0.7 0.7 0.7 0.8 0.5
Rwanda 8.9 15.9 14.9 27.9 6.2 19.6 22.1 21.9 19.0 13.3
São Tomé and Príncipe 2.6 2.8 2.7 4.4 1.9 3.3 3.4 3.3 3.0 2.6
Senegal 3.4 5.9 4.7 10.3 3.0 5.9 7.9 7.3 4.2 4.9
Sierra Leone 6.3 9.9 8.5 14.5 5.1 9.3 11.8 11.2 8.5 8.4
South Africaa 1.6 2.7 2.0 4.7 0.5 3.5 3.4 3.5 2.1 1.9
Swaziland 2.8 3.4 3.2 7.0 1.6 5.2 4.4 4.6 3.2 3.2
Tanzania 2.0 3.6 3.1 6.1 1.7 5.6 6.0 6.0 3.9 2.9
Togo 8.9 19.3 14.8 35.1 7.5 14.0 23.8 21.4 19.8 13.4
Uganda 5.5 8.7 7.8 15.2 4.1 15.3 15.5 15.5 9.4 7.1
Zambia 2.4 6.0 4.7 9.9 1.6 4.7 7.2 6.8 5.7 4.4
Median 2.6 4.2 3.7 7.9 1.7 5.3 6.0 5.9 4.4 3.3
Source: World Bank staff calculations based on utility information and household survey data.
a. 50 kWh a month of free electricity may be provided to the poor in some municipalities, including Johannesburg.
58
Table A.10: Expenditure share of monthly electricity consumption of 50 kWh by grid connection status and
current spending
Share of household expenditure of households for which share 5
Expenditure on Grid Expenditure on Grid
All electricity connection All electricity connection
Country Yes No Yes No Yes No Yes No
Angola 0.2 0.1 0.2 0.1 0.2 100 100 100 100 100
Botswana 4.8 1.7 6.4 2.2 7.0 79 96 70 93 67
Burkina Faso 12.8 6.1 13.8 6.5 13.8 16 51 11 50 11
Côte d'Ivoire 3.2 2.1 4.2 — — 86 94 78 — —
Ethiopia 1.8 1.2 1.9 1.5 1.9 96 98 96 97 96
Ghana 2.2 1.7 2.9 1.9 3.1 93 97 88 95 87
Madagascar 25.8 10.7 27.8 — — 3 15 1 — —
Malawi 3.6 1.1 3.8 1.3 3.8 80 100 78 100 78
Mali 2.3 1.8 2.6 1.3 2.6 94 97 91 99 92
Mozambique 3.8 1.0 4.2 0.0 0.0 79 100 77 — —
Niger 5.3 2.5 5.7 2.3 5.6 58 93 53 96 53
Nigeria 0.5 0.4 0.7 0.4 0.7 100 100 99 100 99
Rwanda 14.9 3.7 16.1 4.0 16.1 15 75 8 73 8
São Tomé and Príncipe 2.7 2.0 3.1 — — 91 98 88 — —
Senegal 4.7 3.3 6.6 3.1 6.6 72 85 55 87 54
Sierra Leone 8.5 4.8 9.1 4.8 9.1 28 66 21 66 21
South Africaa 2.0 1.7 2.4 1.7 3.6 93 95 89 95 81
Swaziland 3.2 1.5 4.0 — — 83 98 76 — —
Tanzania 3.1 1.5 3.5 1.6 3.5 85 98 82 98 82
Togo 14.8 7.7 19.2 7.7 19.1 19 39 7 40 7
Uganda 7.8 3.1 8.3 3.5 8.4 41 85 37 80 36
Zambia 4.7 1.1 5.3 1.4 5.6 65 99 59 99 55
Median 3.7 1.8 4.2 1.8 4.7 80 96 76 95 67
Source: World Bank staff calculations based on utility information and household survey data.
Note: — = Grid connection status not available in the survey.
a. 50 kWh a month of free electricity may be provided to the poor in some municipalities, including Johannesburg.
Table A.11: Expenditure share of monthly consumption of 50 kWh by location, quintile, poverty status, and
gender of household head, based on total household cash expenditures
All households Poor households Household head
Country Urban Rural Total Q1 Q5 Urban Rural Total Female Male
Botswana 3.2 8.8 5.5 21.8 1.8 9.0 14.0 11.8 6.3 4.9
Burkina Faso 11.3 21.7 18.8 32.3 11.3 19.0 27.4 26.2 27.6 17.4
Côte d'Ivoire 2.8 5.7 4.2 11.6 1.9 5.7 9.1 7.9 5.5 3.9
Ethiopia 1.9 5.2 4.6 10.0 1.9 5.2 8.6 8.4 6.4 4.0
Ghana 1.9 3.8 2.8 6.9 1.4 4.6 6.8 6.3 3.0 2.7
Malawi 2.6 6.7 6.0 10.5 3.2 4.9 9.6 9.1 8.0 5.3
Mali 1.7 3.6 3.0 5.8 1.4 3.2 5.2 5.1 2.8 3.0
Niger 3.8 7.8 7.1 11.4 4.4 8.8 9.4 9.4 11.8 6.5
Nigeria 0.7 1.2 1.0 2.0 0.5 1.1 1.5 1.4 1.4 0.9
Rwanda 11.4 25.6 23.5 44.8 8.5 26.8 36.2 35.5 30.6 20.7
São Tomé and Príncipe 2.7 3.0 2.8 4.5 1.9 3.3 3.5 3.4 3.1 2.7
Senegal 3.5 7.1 5.3 12.5 3.2 6.1 9.8 8.6 4.4 5.6
59
All households Poor households Household head
Country Urban Rural Total Q1 Q5 Urban Rural Total Female Male
Sierra Leone 6.5 12.6 10.2 17.6 6.1 9.6 14.7 13.6 10.2 10.2
Swaziland 3.0 4.6 4.0 8.9 1.8 5.7 6.0 6.0 4.2 3.8
Tanzania 2.7 9.6 7.5 16.7 2.5 8.8 16.8 16.1 9.9 6.5
Togo 9.3 26.3 18.8 47.5 8.3 15.1 32.9 28.6 23.5 17.5
Uganda 7.7 16.4 14.0 28.1 5.8 26.5 29.0 28.7 18.8 11.9
Zambia 2.7 12.8 9.2 20.0 2.2 5.6 15.2 13.7 11.9 8.3
Median 2.9 7.5 5.8 12.0 2.3 5.9 9.7 9.2 7.2 5.5
Source: World Bank staff calculations based on utility information and household survey data.
Table A.12: Expenditure share of monthly electricity consumption of 50 kWh by grid connection status and
current spending, based on total household cash expenditures
Share of household expenditure of households for which share 5
Expenditure on Grid Expenditure on Grid
All electricity connection All electricity connection
Country Yes No Yes No Yes No Yes No
Botswana 5.5 1.8 7.4 2.4 8.2 75 95 65 93 61
Burkina Faso 18.8 7.3 20.6 7.6 20.6 10 39 5 39 5
Côte d'Ivoire 4.2 2.3 6.0 — — 80 92 69 — —
Ethiopia 4.6 1.7 5.2 2.0 5.4 76 96 72 94 71
Ghana 2.8 1.8 3.9 2.1 4.2 89 96 81 94 77
Malawi 6.0 1.2 6.3 1.5 6.4 55 99 52 98 51
Mali 3.0 2.2 3.6 1.4 3.6 84 93 77 99 78
Niger 7.1 2.7 7.7 2.4 7.6 42 91 35 95 36
Nigeria 1.0 0.7 1.4 0.7 1.4 99 100 97 100 97
Rwanda 23.5 4.1 25.5 4.3 25.7 11 73 5 72 5
São Tomé and Príncipe 2.8 2.0 3.2 — — 90 98 87 — —
Senegal 5.3 3.4 7.9 3.1 7.8 69 84 47 87 48
Sierra Leone 10.2 4.8 11.2 4.9 11.2 23 66 15 65 15
Swaziland 4.0 1.6 5.2 — — 75 98 65 — —
Tanzania 7.5 1.8 8.7 1.8 8.8 62 97 54 97 53
Togo 18.8 8.0 25.7 8.0 25.6 17 39 4 39 4
Uganda 14.0 3.3 15.1 4.0 15.3 28 84 22 78 21
Zambia 9.2 1.2 10.5 1.5 11.3 48 99 40 98 34
Median 5.8 2.1 7.5 2.4 8.2 65 94 53 94 48
Source: World Bank staff calculations based on utility information and household survey data.
Note: — = Grid connection status not available in the survey.
60
Table A.13: Expenditure share of monthly consumption of 100 kWh by location, quintile, poverty status, and
gender of household head
All households Poor households Household head
Country Urban Rural Total Q1 Q5 Urban Rural Total Female Male
Angola 0.5 1.0 0.7 1.4 0.3 1.0 1.3 1.2 0.9 0.7
Botswana 5.0 11.8 7.8 33.0 2.1 14.2 19.0 16.9 9.2 6.7
Burkina Faso 20.4 28.9 26.5 47.2 16.2 35.5 37.9 37.5 40.4 24.4
Côte d'Ivoire 6.3 9.5 7.9 20.0 4.2 12.4 14.6 13.9 10.4 7.3
Ethiopia 3.7 4.3 4.2 9.0 2.1 10.8 7.4 7.6 6.0 3.7
Ghana 6.8 10.7 8.6 18.4 4.8 15.7 17.7 17.3 9.8 8.0
Madagascar 31.8 48.7 45.0 87.9 25.0 46.3 55.4 54.1 64.4 40.4
Malawi 4.4 7.8 7.2 12.6 4.2 7.7 11.2 10.8 9.1 6.6
Mali 4.4 6.5 5.9 10.8 3.0 7.9 9.5 9.4 5.9 5.9
Mozambique 4.9 8.7 7.6 18.2 2.6 10.5 11.9 11.6 9.9 6.6
Niger 6.8 11.7 10.9 15.8 7.4 12.6 13.6 13.5 16.1 10.2
Nigeria 4.9 6.5 5.8 10.9 3.8 8.0 7.9 7.9 8.7 5.3
Rwanda 17.8 31.8 29.8 55.9 12.5 39.1 44.2 43.8 37.9 26.7
São Tomé and Príncipe 5.0 5.5 5.3 8.4 3.6 6.3 6.6 6.4 5.7 5.0
Senegal 6.7 11.7 9.2 20.4 6.0 11.7 15.6 14.4 8.3 9.6
Sierra Leone 12.5 19.7 16.9 28.9 10.2 18.4 23.4 22.4 16.9 16.8
South Africaa 3.2 5.3 3.9 9.3 1.0 7.0 6.8 6.9 4.3 3.7
Swaziland 5.5 6.8 6.3 13.8 3.2 10.4 8.7 9.0 6.4 6.2
Tanzania 6.6 11.8 10.2 19.9 5.6 18.2 19.6 19.5 12.5 9.3
Togo 15.4 33.6 25.7 61.1 13.1 24.4 41.4 37.3 34.5 23.2
Uganda 11.5 18.2 16.4 31.7 8.6 31.9 32.5 32.4 19.7 14.9
Zambia 3.1 7.7 6.1 12.8 2.1 6.0 9.2 8.7 7.3 5.7
Median 5.9 10.1 7.8 18.3 4.2 12.0 14.1 13.7 9.5 7.0
Source: World Bank staff calculations based on utility information and household survey data.
a. 100 kWh a month of free electricity may be provided to the poor in some municipalities, such as Johannesburg.
Table A.14: Expenditure share of monthly electricity consumption of 100 kWh by grid connection status and
current spending
Share of household expenditure of households for which share 5
Expenditure on Grid Expenditure on Grid
All electricity connection All electricity connection
Country Yes No Yes No Yes No Yes No
Angola 0.7 0.3 0.8 0.3 0.9 100 100 100 100 100
Botswana 7.8 2.8 10.3 3.6 11.4 62 87 50 83 45
Burkina Faso 26.5 12.7 28.7 13.4 28.7 3 15 2 14 2
Côte d'Ivoire 7.9 5.2 10.5 — — 50 62 37 — —
Ethiopia 4.2 2.9 4.5 3.5 4.5 76 89 73 84 74
Ghana 8.6 6.5 11.2 7.2 11.9 39 50 26 46 22
Madagascar 45.0 18.6 48.6 — — 1 4 0 — —
Malawi 7.2 2.2 7.6 2.5 7.7 37 95 33 93 32
Mali 5.9 4.7 6.7 3.5 6.8 52 66 42 82 40
Mozambique 7.6 1.9 8.3 0.0 0.0 49 96 43 — —
Niger 10.9 5.2 11.6 4.8 11.5 15 60 9 66 10
Nigeria 5.8 4.6 7.4 4.6 7.6 60 73 44 73 43
Rwanda 29.8 7.5 32.2 8.0 32.3 6 46 1 43 1
São Tomé and Príncipe 5.3 3.8 5.9 — — 61 78 53 — —
61
Share of household expenditure of households for which share 5
Expenditure on Grid Expenditure on Grid
All electricity connection All electricity connection
Country Yes No Yes No Yes No Yes No
Senegal 9.2 6.5 13.0 6.1 12.9 36 53 12 56 13
Sierra Leone 16.9 9.6 18.1 9.6 18.1 5 17 3 17 3
South Africaa 3.9 3.4 4.9 3.4 7.2 75 79 68 79 47
Swaziland 6.3 3.0 8.0 — — 54 84 39 — —
Tanzania 10.2 5.0 11.4 5.1 11.4 27 61 19 59 19
Togo 25.7 13.4 33.4 13.4 33.3 6 14 1 14 1
Uganda 16.4 6.4 17.4 7.3 17.6 12 47 9 40 8
Zambia 6.1 1.4 6.8 1.8 7.3 53 99 46 97 41
Median 7.8 4.8 10.4 4.7 11.4 44 64 35 66 22
Source: World Bank staff calculations based on utility information and household survey data.
Note: — = Grid connection status not available in the survey.
a. 100 kWh a month of free electricity may be provided to the poor in some municipalities, such as Johannesburg.
Table A.15: Expenditure share of monthly consumption of 250 kWh by location, quintile, poverty status, and
gender of household head
All households Poor households Household head
Country Urban Rural Total Q1 Q5 Urban Rural Total Female Male
Angola 1.9 4.1 3.0 5.9 1.3 3.9 5.3 5.0 3.8 2.7
Botswana 11.2 26.3 17.5 73.9 4.7 31.8 42.6 37.9 20.5 14.9
Burkina Faso 56.7 80.3 73.7 131.2 44.8 98.6 105.1 104.2 112.2 67.8
Côte d'Ivoire 19.2 29.0 24.1 61.2 12.7 38.0 44.8 42.5 31.7 22.5
Ethiopia 10.9 12.7 12.4 26.4 6.3 31.7 21.7 22.4 17.6 10.7
Ghana 16.2 25.6 20.4 43.7 11.5 37.4 42.2 41.1 23.2 19.1
Madagascar 77.9 119.3 110.3 215.2 61.3 113.3 135.6 132.5 157.6 99.0
Malawi 11.0 19.5 18.1 31.4 10.5 19.2 27.9 27.0 22.7 16.6
Mali 16.4 24.6 22.1 40.8 11.4 29.6 36.0 35.5 22.4 22.1
Mozambique 32.2 57.8 50.4 121.0 17.5 69.6 78.7 77.2 65.5 44.0
Niger 18.5 32.0 29.6 43.1 20.3 34.4 37.0 36.8 44.0 27.9
Nigeria 9.8 13.1 11.7 22.1 7.7 16.1 15.9 15.9 17.6 10.6
Rwanda 44.5 79.6 74.5 139.7 31.2 97.8 110.5 109.6 94.8 66.7
São Tomé and Príncipe 18.0 19.5 18.8 29.9 12.8 22.5 23.4 22.9 20.5 17.7
Senegal 18.9 32.7 25.9 57.1 16.7 32.8 43.9 40.3 23.2 26.9
Sierra Leone 35.3 55.5 47.6 81.6 28.7 52.1 66.2 63.2 47.8 47.5
South Africaa 8.1 13.3 9.8 23.4 2.6 17.5 17.1 17.3 10.7 9.2
Swaziland 13.8 17.1 15.8 34.6 8.0 26.0 21.9 22.7 16.1 15.6
Tanzania 28.1 49.9 43.1 84.1 23.5 77.0 83.1 82.6 53.1 39.5
Togo 37.4 81.5 62.3 148.2 31.8 59.1 100.4 90.4 83.6 56.3
Uganda 29.4 46.6 42.0 81.4 22.0 81.8 83.3 83.1 50.6 38.2
Zambia 7.3 18.4 14.4 30.4 4.9 14.3 21.9 20.7 17.4 13.5
Median 18.2 27.7 23.1 50.4 12.8 33.6 42.4 39.1 23.2 22.3
Source: World Bank staff calculations based on utility information and household survey data.
a. Where the poor are entitled to varying amounts of free electricity (as much as 150 kWh a month for some poor
families in Johannesburg), the expenditure share would be lower.
62
Table A.16: Expenditure share of monthly electricity consumption of 250 kWh by grid connection status and
current spending
Share of household expenditure of households for which share 5
Expenditure on Grid Expenditure on Grid
All electricity connection All electricity connection
Country Yes No Yes No Yes No Yes No
Angola 3.0 1.2 3.4 1.4 3.8 84 100 80 99 75
Botswana 17.5 6.2 23.0 7.9 25.5 31 58 18 51 15
Burkina Faso 73.7 35.2 79.8 37.3 79.7 0 1 0 1 0
Côte d'Ivoire 24.1 16.0 32.1 — — 8 12 4 — —
Ethiopia 12.4 8.5 13.2 10.3 13.1 20 38 17 31 17
Ghana 20.4 15.4 26.6 17.1 28.3 7 11 3 9 2
Madagascar 110.3 45.5 118.9 — — 0 0 0 — —
Malawi 18.1 5.5 19.0 6.4 19.2 6 54 3 45 3
Mali 22.1 17.7 25.1 13.0 25.5 6 9 5 12 4
Mozambique 50.4 12.5 55.3 0.0 0.0 3 17 1 — —
Niger 29.6 14.2 31.8 13.0 31.5 1 8 0 10 0
Nigeria 11.7 9.3 15.0 9.2 15.2 19 28 8 28 7
Rwanda 74.5 18.6 80.4 19.9 80.7 1 14 0 13 0
São Tomé and Príncipe 18.8 13.7 21.0 — — 4 10 2 — —
Senegal 25.9 18.3 36.6 17.0 36.3 2 3 0 3 0
Sierra Leone 47.6 27.1 51.2 27.2 51.2 0 0 0 0 0
South Africaa 9.8 8.6 12.2 8.6 18.1 39 42 33 44 8
Swaziland 15.8 7.4 20.0 — — 19 43 7 — —
Tanzania 43.1 21.1 48.1 21.7 48.3 1 5 0 5 0
Togo 62.3 32.4 81.0 32.5 80.7 1 2 0 2 0
Uganda 42.0 16.5 44.5 18.8 45.1 1 11 1 9 1
Zambia 14.4 3.4 16.2 4.2 17.3 22 81 12 70 8
Median 23.1 14.8 29.2 13.0 26.9 5 11 2 12 2
Source: World Bank staff calculations based on utility information and household survey data.
Note: — = Grid connection status not available in the survey.
a. Where the poor are entitled to varying amounts of free electricity (as much as 150 kWh a month for some poor
families in Johannesburg), the expenditure share would be lower.
Table A.17: Grid‐electricity poverty gap, percent
Urban Rural National
kWh/month 30 50 100 250 30 50 100 250 30 50 100 250
Angola 0 0 0 2 0 0 0 4 0 0 0 3
Botswana 3 4 9 27 8 12 22 47 5 7 15 36
Burkina Faso 15 26 54 81 27 42 68 88 24 38 65 86
Côte d'Ivoire 0 2 13 48 1 5 22 61 1 4 18 55
Ethiopia 0 0 3 22 0 1 5 36 0 1 5 34
Ghana 0 0 13 44 0 2 27 60 0 1 20 52
Madagascar 18 56 81 89 32 71 88 93 30 68 87 92
Malawi 0 1 5 36 1 4 17 61 1 4 14 57
Mali 0 0 6 48 0 1 17 64 0 1 15 60
Mozambique 1 2 8 59 2 6 21 81 1 4 17 74
Niger 0 2 7 53 3 9 25 76 2 8 22 72
Nigeria 0 0 5 23 0 0 12 39 0 0 10 33
63
Urban Rural National
kWh/month 30 50 100 250 30 50 100 250 30 50 100 250
Rwanda 13 25 44 67 29 49 71 88 27 46 67 85
São Tomé and Príncipe 0 1 7 54 0 1 9 58 0 1 8 56
Senegal 1 3 11 50 4 10 32 73 2 7 23 63
Sierra Leone 3 13 40 77 12 34 63 87 9 26 55 83
South Africaa 0 1 4 21 0 2 9 39 0 1 6 28
Swaziland 1 3 15 40 2 6 22 53 2 6 20 50
Tanzania 0 1 15 65 1 4 34 80 1 3 29 76
Togo 5 22 42 72 20 49 68 87 15 39 59 81
Uganda 3 11 33 65 8 23 52 79 7 20 47 76
Zambia 1 2 4 20 11 14 22 55 7 10 16 43
Median 1 2 10 49 2 6 22 63 2 5 19 59
Source: World Bank staff calculations based on utility information and household survey data.
a. Tariffs in Johannesburg are used. No allowance for free electricity is assumed for the purpose of these calculations.
Table A.18: Grid‐electricity poverty headcount, percent of total population
Urban Rural National
kWh/month 30 50 100 250 30 50 100 250 30 50 100 250
Angola 0 0 0 4 0 0 0 21 0 0 0 11
Botswana 7 11 24 57 19 28 49 80 12 18 34 67
Burkina Faso 41 61 89 99 65 82 97 100 60 77 95 100
Côte d'Ivoire 1 5 29 80 4 12 46 92 2 8 38 87
Ethiopia 1 1 10 54 1 2 18 77 1 2 17 74
Ghana 0 2 38 85 2 6 65 95 1 4 52 90
Madagascar 50 92 98 100 77 98 100 100 71 97 99 100
Malawi 0 4 28 72 3 18 64 97 3 16 58 93
Mali 0 1 19 84 1 6 48 94 0 5 42 92
Mozambique 2 6 21 90 6 19 52 99 5 15 43 96
Niger 2 9 44 90 12 35 85 100 10 31 78 98
Nigeria 0 0 18 62 0 0 39 83 0 0 31 76
Rwanda 34 53 71 90 68 87 97 100 63 82 93 98
São Tomé and Príncipe 1 4 28 93 1 5 33 96 1 4 30 95
Senegal 3 8 33 94 11 29 75 100 8 20 57 98
Sierra Leone 13 43 85 100 45 82 98 100 33 67 93 100
South Africaa 1 3 14 47 1 6 29 79 1 4 20 59
Swaziland 2 7 28 62 4 12 40 80 3 11 37 76
Tanzania 1 2 42 95 3 13 75 99 3 10 66 98
Togo 16 55 81 97 53 88 97 100 39 76 91 99
Uganda 11 33 69 94 25 58 90 99 22 52 85 98
Zambia 5 8 14 48 34 42 57 90 24 30 42 75
Median 2 7 29 87 5 19 60 96 4 16 47 94
Source: World Bank staff calculations based on utility information and household survey data.
a. Tariffs in Johannesburg are used. No allowance for free electricity is assumed for the purpose of these calculations.
64
Table A.19: Annual subsidies needed for monthly consumption of 30, 50, and 100 kWh
Subsidies US$ million % of cash collected
Location Urban Rural National Urban Rural Total
kWh/month 30 50 100 30 50 100 30 50 100 30 30 30
Angola 0 0 0 0 0 0 0 0 0 — — —
Botswana 0 1 4 1 2 7 2 3 11 0.01 0.04 0.05
Burkina Faso 14 32 119 58 122 375 72 153 494 0.67 2.83 3.50
Côte d'Ivoire 1 4 55 2 10 88 3 14 143 0.01 0.03 0.04
Ethiopia 0 0 6 1 2 33 1 3 38 0.01 0.03 0.04
Ghana 0 2 118 1 4 166 1 6 284 0.00 0.01 0.01
Madagascar 10 70 256 60 316 987 69 386 1,243 0.88 5.54 6.42
Malawi 0 0 10 1 7 54 1 7 64 0.00 0.05 0.05
Mali 0 0 9 0 1 47 0 2 56 0.00 0.00 0.00
Mozambique 0 1 7 1 6 39 2 7 46 0.01 0.05 0.06
Niger 0 2 7 7 28 96 7 30 103 0.04 0.63 0.66
Nigeria 0 0 194 0 0 527 0 0 721 0.00 0.00 0.00
Rwanda 5 15 50 61 161 442 66 176 492 0.46 5.87 6.33
São Tomé and Príncipe 0 0 0 0 0 0 0 0 0 0.01 0.01 0.01
Senegal 1 6 37 4 16 87 6 22 124 0.02 0.07 0.09
Sierra Leone 2 12 62 10 40 139 12 52 201 0.59 2.76 3.35
South Africaa 1 7 67 1 7 67 2 14 135 0.00 0.00 0.00
Swaziland 0 0 2 0 1 4 0 1 6 0.00 0.01 0.01
Tanzania 0 2 82 3 16 374 3 18 456 0.00 0.03 0.03
Togo 4 27 81 15 68 153 19 94 234 0.18 0.77 0.95
Uganda 6 35 197 44 195 806 50 230 1,003 0.13 0.96 1.09
Zambia 1 1 3 12 17 32 13 18 36 0.01 0.14 0.15
Median 0 2 44 2 9 87 2 14 0 0.01 0.05 0.05
Source: World Bank staff calculations based on utility information and household survey data.
Note: Cash collected is the amount of revenue collected by the utility. — = data not available.
a. Tariffs in Johannesburg are used. No allowance for free electricity is assumed for the purpose of these calculations.
65
Table A.20 shows the percentage of grid‐connected households reporting no expenditures on electricity.
Table A.20: Percentage of households connected to the grid with zero expenditures on electricity by quintile
Urban Rural National
Country 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
Angola 59 54 51 43 37 100 81 63 81 71 73 60 53 47 39
Botswana 45 32 29 35 28 59 23 23 41 28 50 29 28 36 28
Burkina Faso 76 19 17 12 6 100 23 16 21 85 15 17 12 7
Ethiopia 21 22 31 35 46 19 9 11 5 10 20 16 26 25 38
Ghana 26 29 26 20 16 37 32 32 25 22 33 30 28 21 17
Malawi 5 38 47 23 19 — 19 34 19 5 35 42 25 19
Mali 0 23 16 8 10 86 34 53 15 5 55 26 21 9 9
Niger 0 0 14 4 6 0 0 0 0 0 0 0 7 3 6
Nigeria 2 3 4 1 2 5 5 9 9 9 4 4 6 4 4
Rwanda 46 22 25 6 10 23 12 20 17 10 29 17 22 12 10
Senegal 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sierra Leone 0 0 4 0 1 37 0 0 0 0 6 0 4 0 1
South Africa 18 15 23 30 38 8 10 10 17 20 12 12 18 27 36
Tanzania 0 12 27 3 5 38 21 20 18 9 16 14 25 6 6
Uganda 82 74 30 23 24 100 100 81 25 19 88 92 40 24 23
Zambia 67 63 62 42 30 100 91 87 81 49 93 74 69 50 32
Total 13 14 18 17 19 11 11 13 14 12 12 12 16 16 17
Source: World Bank staff calculations using household survey data.
Note: Imputed values of free electricity in Ghana, South Africa, Uganda, and Zambia are excluded from expenditure on electricity. — = No households with
non‐zero expenditures on electricity; total = weighted average across all countries in the table.
66
The last two tables show the results of probit and least‐squares regression analysis of the gender of the
head of household.
Table A.21: Increase in the probability of the dummy dependent variable being 1 and percent increase
(for log expenditure) for switching from male‐ to female‐headed households
Dependent variable All households Urban households only
Urban Expenditure Electricity Log
Country dummya dummyb dummyc Grid dummyd,e expendituref,g
Angola — — — — —
Botswana 0.04 — — — —
Burkina Faso 0.06 — n.a. — —
Côte d'Ivoire 0.14 — — — —
Ethiopia 0.12 0.10 — — 16g
Ghana 0.13 ‐0.04 ‐0.02 ‐0.02e ‐10f,g
Madagascar 0.10 0.06 0.09 0.08d —
Malawi ‐0.06 — — n.d. —
Mali 0.07 — — 0.09e —
Mozambique 0.07 — — — —
Niger 0.15 — — d,h
0.10 , 0.12e —
Nigeria 0.08 — — — —
Rwanda 0.02 — — — 14f
São Tomé and Príncipe 0.11 0.08 — n.d. —
Senegal 0.23 — — — —
Sierra Leone 0.04 — — — —
South Africa 0.10 0.10 n.a. 0.02e —
Swaziland ‐0.10 — — n.d. —
Tanzania 0.06 — — — —
Togo 0.05 ‐0.06 ‐0.07 ‐0.05d,e —
Uganda 0.07 — — — —
Zambia 0.05 0.04 — — —
Median 0.07 0.06 ‐0.02 NA NA
Source: World Bank staff calculations.
Note: — = statistically insignificant at 5%; n.d. = no distinction made for different sources of electricity; n.a. = not
available (data not collected); NA = not applicable.
a. 1 for urban, 0 for rural.
b. 1 if spending on electricity is positive, zero otherwise (spending is zero or missing).
c. 1 for electricity (grid as well as off‐grid, such as solar and diesel generators) as the primary source of energy for
lighting or cooking, 0 otherwise.
d. 1 for electricity (excluding off‐grid, such as solar and diesel generators) as the primary source of energy for
lighting or cooking, 0 otherwise.
e. 1 for grid connection, 0 otherwise, for the 17 countries where the survey asked about the status of grid
connection.
f. Logarithm of spending on electricity retaining all households with positive expenditure.
g. Logarithm of spending on electricity retaining only grid‐connected households with positive expenditure.
67
Table A.22: Probability of increase and percent increase (for log spending) for rural female‐headed
households
Country Expenditure dummy Electricity dummy Grid dummya,b Log spendingc,d
Angola — — — 24a,b
Botswana 0.07 0.09 a
0.09 , 0.08b —
Burkina Faso — — — —
Côte d'Ivoire — n.a. — —
Ethiopia — — — —
Ghana 0.11 0.11 0.11a,b a
‐10 , ‐9b
Madagascar — — — —
Malawi — — n.d. —
Mali — — 0.10b —
Mozambique — — 0.01a —
Niger — — — —
Nigeria 0.14 0.10 0.08a, 0.17b —
Rwanda — — — —
São Tomé and Príncipe — — n.d. ‐25d
Senegal 0.24 0.23 0.23 a,b 35c
Sierra Leone ‐0.003 — ‐0.003 a,b ‐49d
South Africa — n.a. — —
Swaziland — — n.d. —
Tanzania — — — —
Togo — — — —
Uganda — — — —
Zambia — — ‐0.01b —
Median 0.09 0.11 NA NA
Source: World Bank staff calculations.
Note: For table headers, abbreviations, and footnotes, see Table A.21. Footnotes a–d correspond to d–g in Table
A.21.
68
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