Sub-Saharan Africa’s electricity challenges and opportunities
Financing Public Infrastructure in Sub-Saharan Africa ... · recognized the importance of scaling...
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BACKGROUND PAPER 15 (PHASE I)
Financing Public Infrastructure in Sub-Saharan Africa:
Patterns and Emerging Issues
Cecilia Briceño-Garmendia, Karlis Smits, and Vivien Foster
JUNE 2008
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© 2009 The International Bank for Reconstruction and Development / The World Bank
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About AICD
This study is a product of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the
world’s knowledge of physical infrastructure in Africa.
AICD will provide a baseline against which future
improvements in infrastructure services can be measured, making it possible to monitor the results achieved from
donor support. It should also provide a better empirical
foundation for prioritizing investments and designing policy reforms in Africa’s infrastructure sectors.
AICD is based on an unprecedented effort to collect
detailed economic and technical data on African infrastructure. The project has produced a series of reports
(such as this one) on public expenditure, spending needs,
and sector performance in each of the main infrastructure
sectors—energy, information and communication technologies, irrigation, transport, and water and sanitation.
Africa’s Infrastructure—A Time for Transformation,
published by the World Bank in November 2009, synthesizes the most significant findings of those reports.
AICD was commissioned by the Infrastructure Consortium
for Africa after the 2005 G-8 summit at Gleneagles, which recognized the importance of scaling up donor finance for
infrastructure in support of Africa’s development.
The first phase of AICD focused on 24 countries that
together account for 85 percent of the gross domestic product, population, and infrastructure aid flows of Sub-
Saharan Africa. The countries are: Benin, Burkina Faso,
Cape Verde, Cameroon, Chad, Côte d'Ivoire, the Democratic Republic of Congo, Ethiopia, Ghana, Kenya,
Lesotho, Madagascar, Malawi, Mozambique, Namibia,
Niger, Nigeria, Rwanda, Senegal, South Africa, Sudan,
Tanzania, Uganda, and Zambia. Under a second phase of the project, coverage is expanding to include as many other
African countries as possible.
Consistent with the genesis of the project, the main focus is on the 48 countries south of the Sahara that face the most
severe infrastructure challenges. Some components of the
study also cover North African countries so as to provide a broader point of reference. Unless otherwise stated,
therefore, the term “Africa” will be used throughout this
report as a shorthand for “Sub-Saharan Africa.”
The World Bank is implementing AICD with the guidance
of a steering committee that represents the African Union,
the New Partnership for Africa’s Development (NEPAD),
Africa’s regional economic communities, the African Development Bank, the Development Bank of Southern
Africa, and major infrastructure donors.
Financing for AICD is provided by a multidonor trust fund to which the main contributors are the U.K.’s Department
for International Development, the Public Private
Infrastructure Advisory Facility, Agence Française de Développement, the European Commission, and Germany’s
KfW Entwicklungsbank. The Sub-Saharan Africa Transport
Policy Program and the Water and Sanitation Program
provided technical support on data collection and analysis pertaining to their respective sectors. A group of
distinguished peer reviewers from policy-making and
academic circles in Africa and beyond reviewed all of the major outputs of the study to ensure the technical quality of
the work.
The data underlying AICD’s reports, as well as the reports themselves, are available to the public through an
interactive Web site, www.infrastructureafrica.org, that
allows users to download customized data reports and
perform various simulations. Inquiries concerning the availability of data sets should be directed to the editors at
the World Bank in Washington, DC.
iii
Contents
Summary iv
Public infrastructure spending: the headlines v
The anatomy of public spending v
General government expenditure vii
Budget efficiency viii
The hidden cost of utilities’ inefficiencies ix
Emerging messages x
1 Motivation for this study 1
2 Description of tools: data and methodology, key definitions 5
General government expenditures sourced from budget documents 7
Expenditures of public, nonfinancial corporations, sourced from financial accounts 8
Introducing a country typology 10
3 Public infrastructure spending: headlines 11
Country efforts and purchasing possibilities 11
Anatomy of public infrastructure spending 13
4 General government expenditures 23
The macro outlook and its fiscal implications for infrastructure 23
Prioritizing within the infrastructure budget envelope 28
Budget efficiency: execution and maintenance 31
5 The hidden costs of utilities’ inefficiencies 39
6 Conclusions and policy implications 52
References 54
Appendixes 57
Appendix 1. Sector scope, functional classification 58
Appendix 2. Sources of data on infrastructure expenditures 59
Appendix 3. Country groups 59
Appendix 4. Primary fiscal balances 60
Appendix 5. Net change in central government budget: breakdown by source 61
Appendix 6. Net change in central government budget: breakdown by use 62
Appendix 7. Expenditure on main road network 63
Appendix 8. Variance around the trend line of road expenditure 64
Appendix 9. Contributions to QFCs (country aggregates)—water sector 65
Appendix 10. Contributions to QFCs (country aggregates)—power sector 66
Appendix 11. Water: efficiency and production indicators, 2006 67
Appendix 12. Electricity: efficiency and production indicators, 2006 69
Appendix 13. Annual maintenance and preservation expenditures, 2001–05 70
Appendix 14. Average annual maintenance expenditures on main road network, 2001–05 71
Summary
o be credible, any plan for scaling up infrastructure in Africa must rest on a thorough evaluation
of how fiscal resources are allocated and financed. Because in every plausible scenario the public
sector retains the lion’s share of infrastructure financing, with private participation remaining
limited, a central purpose of such an evaluation is to identify where and how fiscal resources can be better
used—if not increased—without jeopardizing macroeconomic and fiscal stability. The stakes are high,
because the magnitude of Africa’s infrastructure needs carries a commensurate potential for misuse of
scarce fiscal resources.
We analyze recent public expenditure patterns to identify ways to make more fiscal resources
available for infrastructure. We do this in three ways. First, we quantify the level and composition of
public spending on infrastructure so as to match fiscal allocations to the particular characteristics of
individual subsectors and to countries’ macroeconomic type (low-income fragile, low-income nonfragile,
oil-exporting, and middle-income). Second, we evaluate public budgetary spending for infrastructure
against macroeconomic conditions to get a sense of the scope for making additional fiscal resources
available based on actual allocation decisions in recent years. And, third, we look for ways to make public
spending for infrastructure more efficient, so as to better use existing resources.
The Government Finance Statistics of the International Monetary Fund are neither comprehensive nor
disaggregated enough to support an analysis of the fiscal costs of infrastructure for the period 2001–06.
For that reason, our analysis is based on a new, standardized cross-country dataset of fiscal indicators for
infrastructure that covers, but also extends beyond, spending from central government budgets. State-
owned enterprises (SOEs) and extrabudgetary financing vehicles are also covered, as are private
operators, as long as the assets they operate belong to the state or the operator continues to rely on public
subsidies. Expenditure by subnational jurisdictions is only partially covered, however. Data are collected
in such a way as to permit cross-classification by economic categories (including capital and current
spending) as well as functional categories—information and communication technologies (ICT), power,
roads, water, and sanitation. As far as possible, both budgeted and actual expenditures are recorded.
Any exercise of this kind encounters data limitations. First, because it was not feasible to visit all
subnational entities, some decentralized infrastructure expenditures probably have been underrepresented,
with particular implications for the water sector. Second, it was not always possible to fully identify
which items of the budget are financed by donors, and contributions by nongovernmental organizations
(NGOs) to rural infrastructure projects are likely to have been missed completely. Third, it was not
always possible to obtain full financial statements for all of the infrastructure special funds that we
identified. Fourth, accurate recording of annual changes in fixed capital formation (capital expenditure) of
SOEs remains a methodological challenge. Fifth, accurate measurement of existing public infrastructure
stock will require further methodological development.
T
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
v
Public infrastructure spending: the headlines
Most governments in Sub-Saharan Africa spend about 6–12 percent of their gross domestic product
(GDP) each year on infrastructure, understood as comprising ICT, power, roads, water, and sanitation
(figure A). Roughly half spend more than 8 percent of GDP, while only a quarter of countries spend less
than 5 percent, the level commonly encountered among the countries of the Organisation for Economic
Co-operation and Development. Cape Verde, Ethiopia, and Namibia spend well above 10 percent of their
GDP on infrastructure. In the few middle-income countries of the region for which comparative
information is available the level of public spending is known to be between 6 and 8 percent of GDP.
Expressed as shares of GDP,
these fiscal efforts seem larger than
when put in dollar terms. Most
countries of the region spend less
than $600 million a year on
infrastructure services—less than
$50 per person. Among landlocked
countries, whose infrastructure
needs tend to be particularly high,
the annual total is less than $30 per
capita. These annual expenditures
pale in comparison with the
amounts needed. An investment
budget of US$100 million
purchases no more than about
100 MW of electricity generation,
or 100,000 new household
connections to water and sewerage,
or 300 kilometers of two-lane
paved road.
The anatomy of public spending
Most public spending on infrastructure in Sub-Saharan Africa passes through SOEs. SOEs have a
particularly large role in the middle-income countries, where they account for over 70 percent of all
public infrastructure spending. In Namibia, for example, 90 percent of expenditures on infrastructure are
made by SOEs. In non-oil-exporting low-income countries, the share of expenditures realized by SOEs is
close to 60 percent, or just below two-thirds of total infrastructure spending.
The bulk of the fiscal resources that pass through SOEs go for current spending. Current spending
includes spending on operations and maintenance, which is essential to harness the economic returns of
capital. However, most of recorded current spending relates to so-called nonproductive expenses, namely
wages and salaries. High levels of recurrent spending may indicate that operational inefficiencies are
diverting resources away from investment.
Figure A. Fiscal flows devoted to infrastructure
-
2
4
6
8
10
12
14
16
18
20
Cote
d'Iv
oir
e
Rw
anda
Nig
eri
a
Cam
ero
on
Nig
er
Chad
Tanzania
Uganda
Benin
Madagasc
ar
Senegal
Mala
wi
Mozam
biq
ue
Zam
bia
Ghana
Kenya
Eth
iopia
Leso
tho
South
Afr
ica
Nam
ibia
Cape V
erd
e
Spen
din
g %
GD
P
-
100
200
300
400
500
600
Spen
din
g U
SD
per
cap
ita
GDP Share (%) Spending per capita
Source: Africa Infrastructure Country Diagnostic, Fiscal Baseline (2008).
Note: Based on annual averages for the period 2001–05.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
vi
Governments are the
most prominent financiers
of infrastructure
investment in Sub-Saharan
Africa. Except in the
middle-income countries,
governments are
responsible for between
80–90 percent of total
capital investment,
consistently allocating at
least 80 percent of their
infrastructure budgets to
investment. In low-income
countries that are aid-
dependent or that export
oil, the prevalence of
governments as investors
is driven by their role in
channeling external funds
and/or natural resource
royalties. Most external
development funds are
earmarked by donors for
investment. The dominant
role of the central
government as an investor
is consistently found in
most subsectors:
accounting for 80 percent
of total public investment
in transport and water
supply, and about 40 percent in energy (figure B). The noticeable exceptions to this pattern are the ICT
sector and, as noted, the middle-income countries.
Even though capital budgets may fall far short of actual needs, on average, most countries are not able
to spend more than one-third of the budgeted amounts. For a number of countries we were able to
compare actual capital spending with the amounts originally budgeted. The budget execution ratios that
emerged ranged from 28 percent (Benin) to 89 percent (Madagascar), with the average being 66 percent.
This means that capital spending in the region might be 50 percent higher if only government agencies
had the capability to spend all of the resources allocated to them. The problems behind the low execution
rates include poor planning, deficiencies in project preparation, and delays in procurement. Budget
execution ratios for current spending are, on average, a little higher.
Figure B Public infrastructure spending by sector and institution
Investment
-
0.5
1.0
1.5
2.0
2.5
3.0
MIC
Oil
Ex
po
rtin
g
LIC
-No
Fra
gil
e
LIC
-Fra
gil
e
MIC
Oil
Ex
po
rtin
g
LIC
-No
Fra
gil
e
LIC
-Fra
gil
e
MIC
Oil
Ex
po
rtin
g
LIC
-No
Fra
gil
e
LIC
-Fra
gil
e
MIC
Oil
Ex
po
rtin
g
LIC
-No
Fra
gil
e
LIC
-Fra
gil
e
GD
P S
har
es
SOEs
Gral Govern't
Water TransportICTPower
Current Spending
-
0.5
1.0
1.5
2.0
2.5
3.0
MIC
Oil
Ex
po
rtin
g
LIC
-No
Fra
gil
e
LIC
-Fra
gil
e
MIC
Oil
Ex
po
rtin
g
LIC
-NoF
ragil
e
LIC
-Fra
gil
e
MIC
Oil
Ex
po
rtin
g
LIC
-NoF
ragil
e
LIC
-Fra
gil
e
MIC
Oil
Ex
po
rtin
g
LIC
-NoF
ragil
e
LIC
-Fra
gil
e
GD
P S
har
es
SOEs
Gral Govern't
Energy ICT TransportWater
Source: AICD, Fiscal Baseline (2008).
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
vii
Transport and energy sectors together absorb the lion’s share of infrastructure spending—about 80
percent in low-income countries. The heavy spending on power is a response to the widely recognized
power crisis on the continent. The efforts of the middle-income countries to support energy development
contrast starkly in absolute spending terms with those of the poorer countries. Middle-income countries
spend almost 5 times more on power than do aid-dependent low-income countries. Actual spending for
water may be higher than shown here, because of difficulties in capturing spending data from municipal
water utilities.
Sectoral allocations differ markedly across different groups of countries. Aid-dependent countries
tend to show relatively high levels of investment in roads and water, which together account for 80–95
percent of donors’ allocations to infrastructure in the region. Funds from donors make up about 50
percent of water spending and 25 percent of roads spending. By contrast, donors’ commitments to the
energy sector have been low or inexistent in sharp opposition to the efforts of low-income countries that
by themselves have been allocating close to 25 percent of their public infrastructure budgets to power to
redress chronic underinvestment in that sector.
General government expenditure
For several years running, a favorable external environment (notably high commodity prices) and
sustained domestic economic growth averaging at least 4.5 percent annually have expanded the resources
available to the governments of Sub-Saharan Africa. The economies of oil-producing countries have
grown at the fastest pace (up to 15 percent a year), for obvious reasons. Non-resource-intensive countries
benefited from debt relief and successful policy reforms that offset the negative impacts of higher oil
prices. Even heavily indebted poor countries (HIPC) grew at an annual average rate of 5.5 percent.
Domestic revenues have been the largest source of additional funds for resource-intensive countries,
whereas external grants played the most significant role for the poorest countries in the region.
The favorable external environment helped many countries expand their budgets. In the period 2001–
05, Sub-Saharan governments’ budgets grew by almost 1.9 percent of GDP, with the regional average
driven largely by increases in middle-income countries (table A). Not all countries benefited, however.
Zambia’s budget contracted by more than 8 percent, while that of the Democratic Republic of Congo
chalked up a 9 percent increase.
The additional budgetary resources helped low-income aid-dependent countries to bolster capital
investments, including infrastructure. As a share of GDP, capital investment increased in the low-income
countries by more than 1 percent in 2002–05. About 40 percent of the additional resources were allocated
to clearly favored infrastructure sectors.
It is striking that the oil-exporters and middle-income countries decreased their investment despite
having more fiscal resources available. The oil-exporting countries lowered their capital expenditures on
average by 3.3 percent of GDP. In oil-exporting countries, the decrease in budgetary expenditure was
largely absorbed by a significant reduction in infrastructure expenditures. To a large extent this reflects
developments in Nigeria, where infrastructure expenditures decreased by 2.2 percentage points of GDP
during the study period. The middle-income countries appear to have chosen to devote more resources to
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
viii
maintenance. Most of their additional capital budget was allocated outside infrastructure, but not to health
and education, as the table shows.
Table A Net change in central government budgets by country group, financing source, and destination, 2001–06
% GDP
Financing sources Spending allocations
Country group
Net central government expenditure
budget
Of which domestic revenues
Of which donor grants
Of which infrastructure
Of which health and education
Middle-income 4.08 3.40 (0.03) 0.02 0.13
Oil-exporting (3.73) 5.25 (0.07) (1.43) (0.34)
Low-income, nonfragile 1.69 0.83 1.98 0.54 0.93
Low-income fragile 3.85 0.79 1.90 0831 0.43
Africa average 1.89 3.04 0.57 (0.14) 0.24
Source: AICD, Fiscal Database, 2008; IMF Statistical Appendixes, WB DDP.
Note: Averages weighted by national GDP. Totals may not add up.
— = data not available.
Budget efficiency
Infrastructure stocks in many of the region’s countries are sorely in need of rehabilitation after years
of poor maintenance. The percentage requiring rehabilitation ranges from 12 percent (Burkina Faso) to 48
percent (Democratic Republic of Congo)—the average for the survey group is 30 percent. Rehabilitation
needs are significantly higher for rural infrastructure (35 percent) than for other types (25 percent),
reflecting the difficulty of maintaining assets in isolated rural areas. Because rehabilitating assets is much
more costly (in present-value terms) than maintaining them well, the magnitude of the rehabilitation
backlogs indicates substantial inefficiency in lifecycle spending on infrastructure.
Maintenance is the most challenging aspect of road spending. In environments characterized by weak
fiscal management (nontransparent and politically dominated budget processes), assets often are
neglected. Because maintenance yields little observable immediate benefit and is easily deferred, its
budgetary allocations often are not protected by the executive or parliament. Furthermore, in Africa,
donors have a dominant role in channeling funds to the sector. They earmark much of their funding,
extended on concessional terms, for investment, which has the effect of making maintenance more costly
than investment, because most maintenance funds must be raised domestically. Although the share of
external financing that is allocated to road rehabilitation has increased in recent years, road spending in
Sub-Saharan Africa is dominated by new construction, leaving maintenance a secondary priority.
Roughly half of the countries in the sample have shortfalls of 40 percent or more in annual
maintenance. Expenditure shortfalls are greater than 60 percent in Chad, Uganda, and Niger. Countries
that have established well-functioning road funds tend to be more successful at maintaining their road
networks and reducing the volatility of spending.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
ix
The hidden cost of utilities’ inefficiencies
Reducing inefficiencies in infrastructure operations is perhaps the most practical and realistic way of
making more resources available for infrastructure in the region. While most countries are devoting
considerable effort to improving infrastructure, they are severely constrained in what they can spend.
They have trouble raising domestic revenue and in reallocating revenue from other uses, which often
requires structural reforms. By contrast, efficiency improvements can quickly enlarge governments’
availability of funds, allowing them to provide new services. Because spending on infrastructure
consumes a significant share of GDP, even small efficiency gains can contribute large savings.
For electricity, water supply, and, to some extent, telecommunications, we measure inefficiencies by
quantifying their hidden costs. For the water and power sectors, hidden costs are estimated by using the
end-product approach. The methodology identifies three relevant quasi-fiscal activities in utilities:
underpricing (charging less than the economic cost of the good), undercollection (where bills are never
sent or allowed to go unpaid), and excessive unaccounted losses (to leaks or theft, for example). Hidden
costs are then estimated by
comparing actual indicators
of a functioning SOE
against ideal norms of cost-
recovery, collection ratios,
and distribution losses.
For telecommunications
utilities, we quantify the
hidden cost of labor
redundancies by comparing
partial labor-productivity
ratios of existing telecom
incumbents against world-
class fixed-line providers in
OECD countries.
Quasi-fiscal activities in
Africa represent average
annual hidden costs of the
following (minimum)
magnitudes: 0.5 percent of
GDP in the water sector
(figure C), 0.8 percent in
the power sector, and 0.3 in
the telecom sector. The
smaller economic size of
water utilities, together with
skewed coverage in the
Figure C Hidden costs for water and power utilities as share of GDP
Water
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
Tan
zan
ia
Nig
eria
Ben
in
Cap
e V
erde
Eth
iop
ia
Ugan
da
Nam
ibia
Ken
ya
Burk
ina
Fas
o
Rw
anda
Sudan
Nig
er
South
Afr
ica
Les
oth
o
Mo
zam
biq
ue
Cote
d'I
voir
e
Sen
egal
Mad
agas
car
Zam
bia
Mal
awi
Ghan
a
DR
CMispricing Unaccounted Losses Collection Inefficiencies
Power
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
So
uth
Afr
ica
Ben
in
Ken
ya
Mo
zam
biq
ue
Ch
ad
Cap
e V
erd
e
Mad
agas
car
Les
oth
o
Nig
eria
Bu
rkin
a F
aso
Rw
and
a
Eth
iop
ia
Ug
and
a
Cam
ero
on
Zam
bia
Tan
zan
ia
Sen
egal
Gh
ana
Nig
er
Mal
awi
DR
C C
on
go
Under-Pricing Unaccounted Losses Collection Inefficiencies
Source: Authors’ own calculations using data from the AICD Database.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
x
sample because of decentralization and fragmentation, partially explains their lower hidden costs.
Underpricing is the main source of hidden costs in both power and water utilities. Not only is
underpricing inefficient, but the associated capital subsidies are hugely inequitable because access to
these services is skewed toward the better off, with substantial shares of the poor remaining unconnected
to the electrical grid and water supply network.
In middle-income countries, unaccounted losses stand out as the greatest source of inefficiency for
power utilities, particularly maintenance-deprived distribution networks. Aid-dependent countries show
slightly higher levels of hidden costs relative to their peers, largely because of mispricing, and, in the
water sector, poor collection practices. In the telecom sector, countries that have maintained state
ownership of telecommunications incumbents, thereby deterring competition, not only are forgoing future
tax revenues from expanded business activity but also are creating an additional burden of hidden costs
from inefficiency (usually a bloated workforce). Such costs can exceed 0.3 percent of GDP.
Emerging messages
The countries of the region are devoting substantial shares of their GDP to infrastructure (6–12
percent when all sources are taken into account), but that does not amount to much in absolute terms,
because the economies in question are small. On average, low-income countries are spending less than
$50 per capita per year, with public investment being only a fraction of this.
There is a marked division of labor between SOEs and central governments. While SOEs account for
the bulk of infrastructure spending in most countries, they undertake very little capital spending. Most
public investments for infrastructure continue to be made through central government budgets, with the
resulting assets often transferred to SOEs for subsequent operation and maintenance.
Despite a favorable budget environment, only aid-dependent countries seem to be allocating
additional resources to infrastructure. The combination of a commodity boom and widespread debt relief
has created substantial buoyancy in government budgets. In the case of aid-dependent countries, about 30
percent of the additional funds have been allocated to infrastructure. However, in middle-income
countries almost none of the additional resources gleaned from the recent good years have gone for
infrastructure. In oil-exporting countries infrastructure investment has actually fallen even as resource
revenues have surged.
Regardless of how windfall revenues are spent, governments in the region could substantially enlarge
their fiscal space by redressing inefficiencies in infrastructure psending. Three major sources of
inefficiency have been identified here: inattention to maintenance, failures to spend budgeted funds, and
hidden costs.
There is substantial direct and indirect evidence of undermaintenance, which leads to higher costs
over the infrastructure lifecycle. On average, almost a third of the infrastructure assets of the countries of
the region are in need of rehabilitation. With the present value of rehabilitating infrastructure exceeding
the cost of preventive maintenance, it is easy to see that, over time, countries are spending more than they
need to spend to preserve a fixed amount of infrastructure stock.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
xi
Second, very low ratios of execution of capital budgets point the way to an easy and budget-neutral
increase in public investment—if only execution ratios can be raised. Addressing the causes of low
budget execution deserves very serious attention, as solving the problem could increase public investment
by 50 percent without any increase in budgeted resources. Moreover, until such deficiencies are addressed
it will remain difficult to achieve higher levels of investment, even if more external resources are injected.
Third, the hidden costs of utilities absorb some 1.8 percent of GDP, indicating a major potential
dividend in return for the right set of actions. Underpricing is by far the largest contributor to hidden costs
in power and water utilities, although, as noted, unzealous bill collection and distribution losses are also
important.
1 Motivation for this study
The coordinated efforts of African countries and the international community toward achieving the
Millennium Development Goals (MDGs) has drawn attention to an enormous funding challenge. The cost
of achieving the MDGs is estimated at 13 percent of average GDP in Sub-Saharan Africa (Sachs and
others, 2004). Looking beyond the MDGs, recent research1 estimates Sub-Saharan Africa’s aggregate
infrastructure needs—both new investment and operations and maintenance (O&M)—at $75 billion a
year for 2006–15, or 11.7 percent of average GDP (table 1.1). Estimates for power alone are about $43
billion a year, or 7 percent of GDP, half for the investments needed to overcome chronic shortages and to
propel trade in power. For low-income countries, infrastructure needs quickly add up to 20 percent of
GDP (in fragile states, this reaches an impossible 70 percent). For middle-income countries, investment
needs are two-thirds of O&M, while most low-income countries have investment needs 70–80 percent
higher than O&M costs.
Table 1.1 Sub-Saharan Africa infrastructure needs 2006–15, by sector
Water supply and sanitation Energy ICT Transport Total
US$ billion a year GDP share (%) Shares
Middle income 17.92 6.62 4.89 80.93 0.95 13.23 100.00
Oil exporting 18.73 8.97 16.84 41.97 3.14 38.05 100.00
LIC-nonfragile 24.15 21.40 16.87 48.42 3.54 31.17 100.00
LIC-fragile 16.38 42.92 10.96 56.99 2.34 29.71 100.00
Africa 74.90 11.69 13.39 56.90 2.57 27.14 100.00
Source: Africa Infrastructure Country Diagnostic, 2008
Note: Averages weighted by country GDPs. Totals may not add up because of rounding.
The private sector’s historically limited contribution to infrastructure provision and financing
underscores the importance of the public sector in meeting infrastructure needs, at least for the
foreseeable future. Despite efforts and good intentions, until recently the scale of private finance was not
as great as anticipated in the 1990s and early 2000s (up to 2006). Nor has it extended beyond the more
lucrative areas of infrastructure, such as telecommunications, power generation, railways, and ports—or
the larger and wealthier economies, such as South Africa, Nigeria, and Kenya. Since the late 1990s, a
number of Sub-Saharan African countries have raised private finance for traditionally state-funded
infrastructure. But this has amounted to roughly 0.8 percent of GDP per year—minuscule when compared
with the approximately 4 percent needed to fill the “infrastructure financing gap,” or, in other words, the
funds still needed after factoring in known financing sources (cost recovery, public expenditure, official
development assistance, and financing from the private sector and countries not in the Organisation for
1 Infrastructure needs estimates per sector within the Africa Infrastructure Country Diagnostic (2008).
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
2
Economic Co-operation and Development, OECD). Private sector contributions are also hindered by
stagnation in domestic capital accumulation, traceable to aggregate savings rates that are many times
lower than in other developing regions. Average saving rates from national income accounts are 11
percent in Sub-Saharan Africa, compared with 20 percent in Latin America and the Caribbean, 18 percent
in South Asia, 19 percent in the Middle East and North Africa, and 34 percent in East Asia and the
Pacific.
The characteristics that make infrastructure industries prone to government intervention—in the form
of public ownership, regulation, or both—are also widely accepted. From the supply viewpoint,
infrastructure service provision is characterized by large, fixed investments (usually site- and industry-
specific) and sharply increasing returns to scale. From the demand viewpoint, infrastructure services
range from those with the characteristics of private goods, such as telecommunications (for which prices
could be set efficiently by markets), to those that are closer to public goods, such as rural roads.
Shifting fiscal resources to and from infrastructure is not free of controversy. Easterly and Servén
(2003) point out the growing evidence that, in developing countries, fiscal stabilization has been achieved
by compressing productive public investment—notably in infrastructure—thus sapping the potential for
long-run economic growth. The authors propose a shift away from the short-term preoccupation with
fiscal deficits, toward a longer-term focus on fiscal solvency.2 But the International Monetary Fund (IMF,
2004) questions the assumption that public infrastructure spending in developing countries is necessarily
growth-enhancing, and suggests that efforts to make more resources available for infrastructure should
focus on reallocating spending within the current fiscal envelope. Recent empirical work finds that the
growth-enhancing effects of public investment in infrastructure are not always greater than those of public
investment in health and education (Estache and Muñoz, 2007).
Such controversy is attributable to the very nature of infrastructure, whose characteristics complicate
its treatment in public finances.
• Infrastructure investments are large, lumpy, and infrequent; they often take more than one budget
cycle to complete. They are therefore difficult to accommodate within a single budgetary cycle and
much better suited to a medium-term expenditure framework. In addition, budget allocations for
multiyear investment projects may not be sustained over time, thus delaying implementation and
reducing projects’ eventual rate of return. Even worse, interruptions in funding may leave a country
with a graveyard of incomplete public works that never materialize into a productive asset.
• Infrastructure assets require sustained preventive maintenance. Failure to maintain such assets
eventually leads to deterioration and the need for major rehabilitation, which costs considerably more
in present-value terms than does preventative maintenance. Nevertheless, deterioration is a gradual
process, and maintenance has low visibility, creating a permanent temptation to defer such spending
to accommodate more politically rewarding expenditures.
• Some efficiency gains from long-lived assets materialize only with time, as higher marginal returns
begin to appear. This applies to assets constructed according to 20–30 year demand projections there
2 At around the same time, Blanchard and Giavazzi (2003) advanced a similar argument with respect to the
European Union’s Stability and Growth Pact, which limited budget deficits to 3 percent of GDP and formed the
macro-economic underpinning of European Monetary Union.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
3
delivered low returns early in their life cycle. Other efficiency gains depend on the successful
implementation of policy decisions aimed at a more efficient execution of multiyear projects (to
ensure that costs are kept to a minimum and that countries begin to reap their benefits as soon as
possible), and at making sure that periodic maintenance of relatively lightly used assets (such as new
roads) is not postponed.
The debate over the availability and allocation of fiscal resources has particular relevance for Sub-
Saharan Africa, which is faced with burgeoning infrastructure needs, limited private participation, and
meager public budgets. It is in this context that fiscal space becomes relevant. The IMF has defined
“fiscal space” as the budgetary room that allows a government to provide resources for a desired purpose
without imperiling its financial position.3 The concept focuses on the linkages between fiscal policy trade-
offs and the availability of resources in the medium-term to ensure fiscal sustainability.
There are several ways to make more fiscal resources available for development needs (including
infrastructure), utilizing both domestic and external sources. The Development Committee of the World
Bank Group (World Bank, 2006) has proposed a framework that defines four options for central
governments: (i) raise additional tax revenues, (ii) increase public sector borrowing, (iii) obtain more
international aid, and (iv) improve the efficiency of current expenditures. Making fiscal resources
available has an important intertemporal component, since effective use of resources today leads to
increased productivity, thus generating more resources to fund tomorrow’s policy choices. Countries that
make more fiscal resources available by cutting development expenditures may undermine long-term
growth, thereby restricting their fiscal space in the future. Aid-dependent countries face the challenge of
using donor financing to remove bottlenecks to growth and, in so doing, to expand their future fiscal
space.
This paper analyzes recent spending patterns to identify ways to increase the availability of fiscal
resources. We do this in three steps.
First, we quantify the level and composition of public infrastructure expenditures. By examining how
infrastructure spending is allocated across subsectors, institutions, and expense categories, we
characterize past patterns and levels of fiscal allocations in terms of subsector specificities and country
types (such as resource-rich, aid-dependent). This helps identify forward-looking options for fiscal
expansion and other financing alternatives
Second, we assess public spending on infrastructure against the background of overall fiscal resource
availability. The initial fiscal conditions for infrastructure spending are framed in a macroeconomic
context so as to give the reader a realistic sense of the scope for creating additional fiscal space by
increasing budgetary allocation. Fiscal resources are broken down into three observable aspects: revenue
effort, access to aid, and access to financial markets.
Third, we look for ways to increase the efficiency of public infrastructure spending. Changing the
composition and increasing the efficiency of public spending for infrastructure are two of the most
practical ways of increasing governments’ resource envelope. This study provides a cross-country
comparison of the efficiency of expenditures in the water, electricity, and communications sectors. In the
3 For details, see Heller and others (2006).
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
4
water and electricity sectors, potential efficiency gains are quantified by measuring the hidden costs of
state-owned service providers.
To achieve our purpose, we assembled a detailed database of public spending flows, the absence of
which had prevented systematic evaluation of the quality and impact of public spending in infrastructure.
2 Description of tools: data and methodology, key
definitions
The Government Finance Statistics (GFS) compiled by the International Monetary Fund (IMF) do not
provide a detailed or comprehensive picture of infrastructure spending. At present, the GFS constitutes
the main source of cross-country data on public finance. But the information on infrastructure presents a
number of problems, particularly for Africa. First, the GFS focuses on tracking general government
expenditure, whereas a large share of infrastructure spending passes through nonfinancial public
corporations (parastatals). Second, even within the category of general government spending, the GFS is
limited in practice to central government spending, with little reporting of subnational and special
funds—two other important channels of infrastructure spending.4 Lastly, the GFS does not break down
infrastructure spending by subsector or expense category.
In response to these limitations, we built a new database of standardized cross-country data that seeks
to give a detailed yet comprehensive picture of infrastructure spending.5 Our analysis is based on a
systematic, cross-country study of public spending on infrastructure both within and beyond the bounds
of central government budgets. The data-collection process is based on a standardized methodology
developed and explained in detail by Briceño-Garmendia (2007). To ensure the cross-country
comparability of the data, detailed templates guided the data-collection process in the field.
The methodology is designed to be comprehensive in the sense of covering all relevant budgetary and
nonbudgetary areas of infrastructure spending. The collection of data on fiscal spending was grounded in
an overview of the institutional framework for delivering infrastructure services in each of the countries
while aiming at identifying all of the channels through which public expenditure on infrastructure flows.
The work began with a detailed review of the central government budget. Thereafter, financial statements
were collected from all the parastatals and special funds that had been identified in the institutional
review. In countries where infrastructure service providers are highly decentralized (as in the case of
municipal water utilities), it proved possible to collect financial statements only from the three largest
service providers. Privatized infrastructure service providers were included if a majority of their shares
remained government owned, or if they continued to depend on the state for capital or operating
subsidies. Thus, telecommunications incumbents are typically included, whereas mobile operators are not.
In some countries, local governments have begun to play an increasing role in infrastructure service
provision. It was not possible to collect comprehensive expenditure data at the local government level.
However, in some cases the central government produces consolidated local government accounts. Where
these do not exist, an alternative source of information is the fiscal transfers from central to local
governments, which are reported in the budget and on which local governments rely heavily, given
4 Based on IMF (2001), the public sector can be roughly divided into general government and public corporations.
General government comprises central, state, and local governments. Public corporations can be grouped, according
to the nature of their activities, into financial corporations (engaged in providing financial services for the market)
and nonfinancial corporations (engaged in producing goods and nonfinancial services). 5 Soon available online at www.AfricaInfrastructure.org.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
6
limited alternative sources of revenue. In some cases, transfers are earmarked for infrastructure-related
spending, whereas in others the share allocated to infrastructure could only be estimated.6
Data were collected in such a way as to permit both classification and cross-classification by
economic and functional categories. That is, a matrix was established so that spending on each functional
category could be decomposed according to the economic nature of the expense, and vice versa.
Functional classification followed as closely as possible the 4-digit category or class level of the
functional classification (COFOG) proposed in the IMF’s Government Financial Statistics Manual 2001
(GFSM, 2001),7 which allowed us to identify all the major infrastructure subsectors.8 The economic
classification of expenses also followed the IMF framework, permitting us to distinguish to some extent
between current expenditures, capital expenditures, and various subcategories thereof.9
The institutional scope of the study builds on the definition of the public sector spelled out in the
system of national accounts described by the United Nations (1993), that is, all units of general
government and all public corporations. The system of national accounts defines public corporations as
those in which the public authorities are considered the owners by virtue of owning all, or a majority of,
shares, equity, or other form of capital (table 2.1).10
Table 2.1 Institutional distribution of gross national expenditure
General government Financial corporations
Nonfinancial corporations
Nonprofit institutions serving household sector
Household sector
B. Expenditures of public financial corporations
D. Expenditures of public nonfinancial corporations
of which expenditures on infrastructure
A. Public expenditures
of which public expenditures on infrastructure
C. Expenditures of private financial corporations
E. Expenditures of private nonfinancial corporations
F. Expenditures of nonprofit institutions serving households sector
G. Household current and capital expenditures
Source: United Nations, 1993.
Note: Shaded area reflects expenditures of public sector.
6 For a quick summary of sources of data and information, see table 2.1. 7 Definitions and explanations of the infrastructure cost elements figuring in the database can be found in Briceño-
Garmendia (2007). 8 The main categories covered in the study are electricity (0435), road transport (0451), water transport (0452),
railway transport (0453), air transport (0454), pipeline and other transport (0455), communication (0460), waste
water management (0520), and water supply (0630). Irrigation spending is estimated as a share of agriculture
(0421). 9 Current expenditures are broken down into compensation of employees, use of goods and services, consumption of
fixed capital, interest, subsidies, grants and transfers, social benefits, and other current expenditure. Capital expenditures are broken down into buildings, structures, machinery, and equipment; other fixed assets; other capital
expenditure: and transfers of capital expenditures to lower levels of government. 10 See United Nations (1993) for details. The system of national accounts also states that enterprises in which the
government holds less than half of the shares may still be classified as public corporations if the government
controls the business by influencing all principal aspects of management.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
7
General government expenditures sourced from budget documents
As far as possible, both budget estimates and actual expenditures were recorded for the period 2001–
05. There are usually three stages in the public expenditure process. First, resources are budgeted for
particular purposes. Second, funds are released from the Ministry of Finance (MoF) to the responsible
institutions. Finally, resources are spent by the recipient institutions. Expenditure patterns can differ
substantially across these three different stages. While actual spending is the variable of greatest interest,
and the main focus of the results presented in this report, it is also important to understand how
infrastructure spending is affected by the budget execution process. To this end, both budgeted and actual
expenditures are recorded wherever possible. Attempts were also made to capture release figures—but
with very limited success.
For countries that have not yet fully implemented GFSM 2001 standards, it was necessary to perform
a line-by-line recoding of the budget in consultation with the corresponding line ministries. In several
countries, national budget expenditures are grouped according to programs for each line ministry (such as
in Zambia). In these cases, expenditures had to be disaggregated according to infrastructure subsectors.
The advantage of using GFSM 2001 functional codes is that they reflect the purpose of the expenditure in
a consistent pattern that is not distorted, for example, by changes in institutional responsibilities over
time. Functional areas can be spread across many institutions and are subject to reallocation from one
institution to another, contingent on changes over time in institutional responsibilities and institutional
frameworks.
It is difficult to ensure that expenses are analyzed and classified according to their economic use. On
the one hand, there is no clear-cut separation between what should be considered a capital expense, a
rehabilitation expense, or even a maintenance expense. So, capital and current expenditures can be
mistakenly accounted under inappropriate budget categories by the lack of clear definition of the
expenditure’s nature. There may also be deliberate misclassification done in order to increase the chances
of budgetary approval.11 As much as possible, the economic classifications used in each country were
remapped in accord with the GFSM 2001 framework to develop a common understanding across
countries.
Efforts were made to avoid double-counting of transfers across levels of government. There is a
danger that transfers from the central government to parastatals, special funds, and subnational
governments may be reported twice: once as a central government transfer and once as an expenditure by
the recipient institution. Great care was taken to match up these line items across institutions and ensure
that they were counted only once—as an expenditure made by the recipient institution. Similar care was
taken to eliminate double-counting subsidies that originated as central government transfers to state-
owned enterprises (SOEs).
11 Unfortunately, the GFMS 2001 leaves many economic categorizations open to interpretation, particularly with
regard to the uses of the expenses that are most relevant to infrastructure services (for example, rehabilitation,
operations, and maintenance). In this first report, the analysis will be limited to establishing the broad distinctions
between current and capital expenses.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
8
Expenditures of public, nonfinancial corporations, sourced from financial
accounts
Creating standardized expenditure data for public nonfinancial corporations requires using principles
established in the system of national accounts. Data on the expenditures of public nonfinancial
corporations are very heterogeneous, so this component of the data set was essentially built from scratch.
The challenge is to measure the sources and uses of parastatals’ income and changes in fixed assets
(focusing on current and cumulative accounts) in a way that permits both classification and cross-
classification by economic and functional category. According to the system of national accounts,
corporations’ transactions can be grouped into current accounts (production, distribution of income, use
of income), accumulation accounts (changes in assets and liabilities, changes in net worth), and balance
sheets (stocks of assets and liabilities, and net worth). Recurrent expenditure flows can be determined by
a relatively straightforward classification of entries in the annual financial statement accounts.
Standardizing capital expenditures is not a straightforward exercise. According to the system of
national accounts, investments can be classified as changes in financial or nonfinancial assets. In this
report, however, we focus exclusively on changes in nonfinancial assets. In particular, we seek to measure
tangible nonfinancial assets, excluding inventories, consumption of own capital, and production of other
intermediary goods (table 2.2).
Notwithstanding these efforts, it is important to recognize the inevitable data limitations in any
exercise of this kind. These limitations should be borne in mind when interpreting the results of the
analysis.
First, it is likely that decentralized infrastructure expenditures may be improperly covered, with
particular implications for the water sector. The study does not adequately capture spending by small,
decentralized service providers (for example, municipal water utilities), or spending by local governments
that is not funded through fiscal transfers. This
concern does not affect countries with highly
centralized administrations or national water
utilities (such as Benin, Cape Verde, Madagascar,
Rwanda, and Uganda). However, in some
countries, local utilities and subnational
governments have begun to play an increased role
in infrastructure services.
Second, it proved difficult to fully identify
externally financed public infrastructure
expenditures, since not all donor aid is reported in
national budget accounts. Aid disbursements are
most often channeled through the central government (via government agencies or line ministries) but in
several cases might bypass government agencies, leaving no accounting record for the central
government. An example of a direct expenditure is funds given directly to nongovernmental organizations
to support small-scale infrastructure projects in rural communities. Official government budget accounts
would accurately measure only funds that are channeled through the central government, leaving a
Table 2.2 Classification of assets in the system of national accounts
Gross fixed capital formation
Consumption of fixed capital
Changes in inventories
Acquisitions less disposals of valuables
Nonfinancial assets
Acquisitions less disposals of nonproduced, nonfinancial assets
Financial assets
Source: United Nations (1993).
Note: Shaded area includes a subset of assets that will be used to measure capital expenditure flows.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
9
significant proportion of other external funds uncounted. For example, in aid-dependent Rwanda only 50
percent of donor aid is reflected in the national budget.
Data on the execution of the budget—money actually spent out of or over budget estimates—are not
available in all countries.12 In some countries, we had problems accounting for the execution of foreign-
financed expenditures.13
Third, it was not always possible to obtain full financial statements for infrastructure special funds
(for example, rural electrification funds or privatization funds, which have often been used for public
investment). We found that the level of transparency and accountability in the spending of infrastructure
special funds was highly variable across countries and sectors. In a significant number of cases, it was not
possible to obtain financial statements for these funds. However, it should be noted that this problem
generally did not apply to road funds. Only in Côte d’Ivoire was it impossible to obtain a financial
statement on the road fund.
Fourth, capturing annual changes in fixed capital formation (capital expenditure) of SOEs using
financial statements represents a methodological challenge. For one thing, financial statements often do
not distinguish clearly between expenditure on financial and nonfinancial assets, making it very hard to
measure actual capital expenditures. In some cases, financial statements lacked information on levels or
changes in spending on assets. Review of investment plans might provide a better understanding of
capital expenditures, but that would involve considerably more effort, and it would be very difficult to
make a standardized comparison across countries, since these investment plans do not always accurately
measure actual expenditure levels. Secondly, while SOEs are “off budget,” important transactions
between public budgets and these entities nevertheless occur with regularity, particularly with regard to
on-lending, capitalization, debt swaps, transfers of already built assets, and various kinds of operating
subsidies. Examples exist of public subsidies made to utilities to finance capital spending but recorded as
current spending, as well as examples of the opposite: capital infusions that are used to pay wages or
other current costs.
Fifth, there are serious constraints in supplementing expenditure flows with statistics on asset stocks.
For the purpose of this study, expenditure flows may be sufficient to give us a basic understanding of
short-term fiscal sustainability and efficiency issues. However, for a more thorough analysis of
infrastructure expenditures and growth, or to control for initial conditions in efficiency analyses, it is of
the utmost importance to be able to account for public infrastructure stocks. Accurate measurement of
existing public infrastructure stocks remains a very challenging task and a natural extension of the
exercise presented here.
12 Proxies for actuals are used in Zambia (estimates), Côte d’Ivoire (releases), Senegal (releases), Mozambique
(estimates), and Nigeria (estimates). 13 Proxies for externally financed actual expenditures are used in Ghana (estimates), Lesotho (estimates), Tanzania
(estimates), and Benin (releases).
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
10
Introducing a country typology
This report organizes the Sub-Saharan country sample into four nonoverlapping groups, closely
following IMF (2007): oil-exporting, low-income nonfragile, low-income fragile, and (non-oil-exporting)
middle-income countries (table 2.3).14
Table 2.3 Typology of countries
Alternative classification for low-income
Oil-exporting countries
Low-income
nonfragile
Low-income
fragile
Middle- income countries
Low-income
coastal
Low-income
landlocked
Angola
Cameroon
Chad
Congo
Equatorial Guinea
Gabon
Nigeria
Sudan*
Benin
Burkina Faso Ethiopia Ghana
Kenya
Madagascar
Malawi
Mali
Mozambique Niger
Rwanda Senegal Tanzania Uganda Zambia
Burundi
Central Africa Rep.
Comoros
Congo, Dem. Rep. of
Côte d’Ivoire
Eritrea
Gambia,The
Guinea
Guinea-Bissau
Liberia
Sao Tome and Principe
Sierra Leone
Togo
Zimbabwe
Botswana
Cape Verde
Lesotho
Mauritius
Namibia,
Seychelles
South Africa
Swaziland
Benin
Comoros
Côte d’Ivoire
Eritrea
Gambia, The
Ghana
Guinea
Guinea-Bissau
Kenya
Liberia
Madagascar
Mozambique
Sao Tome and Principe
Senegal
Sierra Leone
Tanzania
Togo
Burkina Faso,
Burundi
Central Africa Rep.
Congo, Dem. Rep. of
Ethiopia
Malawi
Mali
Niger
Rwanda
Uganda
Zambia
Zimbabwe
Source: Classification as proposed in IMF (2007), except for Sudan, which was added for completeness.
We also explored an alternative classification as proposed by Collier and O’Connell (2006), who
argue that countries’ economic behaviors are strongly affected by their endowment of natural resources
and geography. Endowment of natural resources can so define a country ‘s potential that we classify oil-
exporting countries as such even if they would otherwise qualify for another group according to their
income or geography. Geography is reflected in the landlocked/coastal split. The landlocked condition
increases the demand for—and importance of—transport services,15 as well as their costs. Higher
transport costs have a usually unaccounted negative effect on private stock creation, growth outcomes,
and returns of capital.16
14 See annex 3 for definitions. 15 See Ndulu (2004). 16 See Agénor and Moreno (2006), Limão and Venables (2001), and Collier and Gunning (1999).
3 Public infrastructure spending: headlines
Country efforts and purchasing possibilities
The vast majority of African countries have annual public expenditures on infrastructure17 in the order
of 6–12 percent of GDP. Roughly half of the countries spend the equivalent of more than 8 percent of
their GDP, while in three out of four African countries infrastructure public spending accounts for more
than 5 percent of GDP.18 Only a quarter of countries spend less than 5 percent of their GDP on
infrastructure (figure 3.1). By comparison, in most OECD countries, annual public expenditure on
infrastructure is less than 5 percent of GDP. The level is 6.4 percent in Chile, 5.7 percent in Turkey, and
7.6 percent in Indonesia.
Large fiscal efforts, as expressed in terms of share of GDP, are incommensurate with the meager
amount of infrastructure services they can provide. In dollar terms, the majority of countries spend less
than $600 million on infrastructure services—less than $50 per person per year—or less than $10 per
person per year in investment. Annual expenditures pale when compared to the amounts needed. An
investment budget of $100 million could purchase around 100 MW of electricity generation, or 100,000
new household connections to water and sewerage, or 300 kilometers of a two-lane paved road.
Figure 3.1 Public spending on infrastructure across Africa (frequency for sample)
0
10
20
30
40
50
60
below 5 % 5 - 8 % over 8%
As a % of GDP
0
10
20
30
40
50
60
70
80
below 600 m$ 600-2000 m$ over 2000 m$
In m$ per year
0
10
20
30
40
50
60
70
80
below 50 $ 50-400 $ over 400 $
In $ per capita
Source: Africa Infrastructure Country Diagnostic, Fiscal Database (2008). Based on data presented in annex 1, table 1 (separately bound).
African governments differ greatly in their ability to afford the sums they spend for infrastructure
(figure 3.2). The countries of Sub-Saharan Africa are faced with a dilemma. The tax base in most
countries is too narrow to finance the most obvious infrastructure needs and too narrow to support
subsidized access for poor consumers (Estache, 2007). That base cannot be increased, however, without
economic growth, which in turn depends on the elimination of bottlenecks caused by inadequate
infrastructure.
17 Consistently with the outlined methodology, infrastructure is understood as these 4 sectors: water and sanitation,
power, transport (including roads) and ICT. 18 Excluding expenditures by local governments.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
12
Figure 3.2 Fiscal flows devoted to infrastructure
-
2
4
6
8
10
12
14
16
18
20
Cote
d'Iv
oir
e
Rw
anda
Nig
eri
a
Cam
ero
on
Nig
er
Chad
Tanzania
Uganda
Benin
Madagasc
ar
Senegal
Mala
wi
Mozam
biq
ue
Zam
bia
Ghana
Kenya
Eth
iopia
Leso
tho
South
Afr
ica
Nam
ibia
Cape V
erd
e
Spen
din
g %
GD
P
-
100
200
300
400
500
600
Spen
din
g U
SD
per
cap
ita
GDP Share (%) Spending per capita
Total Fiscal Spending
GDP share ( percent)
Spending per capita
($)
MIC 8.86 550.51
Oil-exporting 5.44 76.15
LIC–nonfragile 8.74 34.95
LIC–fragile 3.44- 46.56
LIC–coastal 8.06 46.92
LIC–landlocked 8.39 27.36
Totals 24–country sample 7.74 231.74
Totals, excluding South Africa 6.74 39.70
Totals excluding South Africa and
Nigeria 7.93 43.11
Source: Africa Infrastructure Country Diagnostic, Fiscal Baseline (2008).
Note: Based on annual averages for the period 2001–05. Detailed data presented in annex 1, tables 1 and 2 (separately bound).
In low-income countries, fiscal efforts ranging from 4 percent of GDP in Rwanda and Côte d’Ivoire
to more than 10 percent of GDP in Ethiopia translate into annual spending of less than $45 per capita.
These averages mask enormous differences between countries and country groups. The lowest level of
per capita spending on infrastructure is found in Rwanda (about $8 per capita per year). The lowest per
capita expenditures on infrastructure are found in landlocked countries, which spend, on average, $28.
Low-income countries, of course, have narrow tax bases and are generally unable to attract significant
private investment to crowd out the lack of public resources.
In middle-income countries (including South Africa), public infrastructure spending is about ten times
higher in per capita terms than in low-income countries. Middle-income countries spend roughly $550 per
year per person, while for low-income countries (including both oil-exporting and nonfragile states) this
is roughly $45 per year. The disparities are more shocking at the country level. Rwanda’s per capita
spending is 3 and 7 percent of total spending in Namibia and Cape Verde, respectively, while Niger and
Malawi only managed to spend 2.5 percent of what South Africa does.19
A country’s income level is highly correlated with public infrastructure investment (figure 3.3). The
emerging results for Africa simply confirm assumptions. As a ratio, public infrastructure investment is
negatively associated with GDP per capita, while per capita infrastructure spending is positively related
19 Differences in per capita spending levels shrink when expenditures are adjusted for differences in purchasing
power parity. (Poor countries tend to have higher purchasing power.) Expenditure levels measured in purchasing
power parity (PPP) dollars per capita are more or less the same across different country groupings: resource-
intensive countries, non-resource-intensive countries, and aid-dependent countries.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
13
with GDP per capita. These relations have been documented in the literature and essentially underscore
the endogenous nature of investment and wealth.
Figure 3.3 Infrastructure investment and country income
Zambia
Uganda
Tanzania
Senegal
Rwanda
Nigeria
Niger
Namibia
Mozambique
MalawiLesotho
KenyaGhana
EthiopiaCote d'Ivoire
ChadCameroon
Benin
-
10.0
20.0
30.0
40.0
50.0
60.0
- 1,000 2,000 3,000 4,000 5,000 6,000
GDP per Capita ($)
An
nu
al
Pu
bli
c I
nfr
ast
ructu
re I
nv
est
men
t
--U
S$
per c
ap
ita
--
ZambiaUganda
Tanzania
South Africa
Senegal
Rwanda
NigeriaNiger
Namibia
Mozambique
Malawi
Madagascar
Lesotho
Kenya
Ghana
Ethiopia
Cote d'Ivoire
Chad
Cameroon
Benin
-
1.0
2.0
3.0
4.0
5.0
6.0
- 1,000 2,000 3,000 4,000 5,000 6,000
GDP per Capita ($)
An
nu
al
Pu
bli
c I
nfr
ast
ructu
re I
nv
est
men
t
--G
DP
Sh
ares--
Source: Africa Infrastructure Country Diagnostic, Fiscal Baseline (2008).
Low-income countries, so to speak, are doubly penalized in their possibilities for buying
infrastructure services. On the one hand, the association between infrastructure spending and national
income does not imply causality, but it does confirm that poor countries are severely limited by their own
purchasing power. On the other hand, low-income countries are more ill-equipped than middle-income
countries in existent, functioning infrastructure assets. This suggests that even if low-income countries
counted with the quantity and quality of infrastructure that middle-income countries have, they would be
at a disadvantage when trying to move forward. But they are not even there. That is why the fiscal efforts
of individual countries will not go far unless other players such as traditional donors, non-OECD
financiers (such as India, China and Gulf States) and the private sector act coordinately.
Anatomy of public infrastructure spending
Institutional background
On aggregate, state-owned enterprises (SOEs) channel the largest share of public infrastructure
expenditures in Sub-Saharan Africa. SOEs have a particularly large role in the middle-income countries,
where they account for over 70 percent of all public spending on infrastructure. In Namibia, which has the
highest absolute and relative expenditure level, 90 percent of expenditures on infrastructure are made by
SOEs. In non-oil-exporting, low-income countries, the share of expenditures realized by SOEs is close to
60 percent or just below two-thirds of total infrastructure spending (figure 3.4).
Large differences between expenditure levels are driven, to a large extent, by differences in the
spending levels of SOEs. This is a very important finding from the fiscal viewpoint for their implications
for institutional development, fiscal balances, and infrastructure planning. First, SOEs’ main stakeholder
and lender of last resort is the central government. Thus, while SOEs are expected to partially or totally
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
14
recover costs, they don’t, and their net operating positions affect the country’s fiscal balances when
accumulated debts trigger bail-outs, capitalizations, or simply debt-swaps between the government and
the SOEs. Secondly, SOEs are expected to function on a quasi-commercial basis, playing a key role in
matching infrastructure supply with country needs as dictated by the nature of business, carrying out most
of the investment, particularly if they handle the highest shares of public infrastructure spending and
because they are charging user fees that should cover the enterprise’s capital and operations and
maintenance (O&M) needs.
Figure 3.4 Public infrastructure spending by institution
-
2
4
6
8
10
12
14
16
18
20
Cote
d'Iv
oir
e
Rw
anda
Nig
eri
a
Cam
ero
on
Nig
er
Chad
Tanzania
Uganda
Benin
Madagasc
ar
Senegal
Mala
wi
Mozam
biq
ue
Zam
bia
Ghana
Kenya
Eth
iopia
Leso
tho
South
Afr
ica
Nam
ibia
Cape V
erd
e
GD
P S
ha
res (
%)
Nonfinancial Public Institutions
On-Budget
Composition of fiscal spending
Shares on total ( percent) On-budget
Nonfinancial public
institutions
MIC 19.3 80.7
Oil exporting 52.5 47.5
LIC–nonfragile 36.9 63.1
LIC–fragile 25.4 74.6
LIC–coastal 32.6 67.4
LIC–landlocked 47.2 52.8
Totals 24–country sample 29.2 70.8
Totals excluding South Africa 45.1 52.7
Totals excluding South Africa and Nigeria 35.8 61.1
Source: Africa Infrastructure Country Diagnostic, Fiscal Baseline (2008).
Note: Based on annual averages for the period 2001–05. Detailed data presented in annex 1, table 2.
However, the bulk of SOE resources goes to current spending; between 70–85 percent of total current
in public infrastructure goes through SOEs. Current spending includes O&M spending. Though the latter
is essential to harness the economic returns of capital, it is often recorded as a nonproductive expense
because it does not directly translate into asset creation. In fact, the crudest proxy equates public
investment with productive expenditure even though, admittedly, not all investment is productive and not
all current expenditure is waste. Overall, however, very high levels of recurrent spending may indicate
that operational inefficiencies are diverting resources away from investment.
Oil-exporting countries are an exception for institutional spending patterns as the central government
accounts for the most significant portion of infrastructure expenditures. Boosted by record-high oil
revenues, central governments’ expenditures are covering 70 percent of total public spending. This figure
is driven by developments in Chad and Nigeria, where central governments account for close to two-
thirds of all infrastructure expenditures. The relative high importance (though not dominant role) of
central governments’ expenditures in low-income countries—accounting for about 40 percent of public
infrastructure spending—partly derives from their in channeling external development funds. In aid-
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
15
dependent countries, the share of externally funded expenditures absorbs more than half of all central
government spending on infrastructure.
Governments are the
most prominent financiers
of infrastructure investment
in Sub-Saharan Africa.
Excluding middle-income
countries, governments
carry out between 80 and 90
percent of total capital
investment, consistently
allocating 80 percent of
their infrastructure budgets
to investment. This pattern
can be driven in low-
income and oil-exporting
countries by the
government’s role in
channeling external
development funds and
commodities’ royalties,
respectively. The majority
of external development
funds are earmarked for
investment. The central
government is a dominant
investor in most
infrastructure subsectors: 80
percent of total public
investment in transport and
water supply, and about 40
percent in energy despite
the recent retrenchment of
public sector donors (figure
3.5). A noticeable exception
to this pattern is information and communication technology (ICT) and, as alluded, middle-income
countries.
Half of the countries invest between 3 and 4 percent of GDP; Mozambique, Uganda, Ethiopia, and
Cape Verde invest over 4 percent of their GDP. These numbers are spearheaded by investments in
transport and to some extent energy, certainly led by the central government itself. Cape Verde is the
most salient case of high investment effort essentially doubling regional averages, with reliance on SOE
investment, including the notorious publicly owned TACV, the Cape Verdian airline.
Figure 3.5 Public infrastructure spending, functional and economic breakdown
Investment
-
0.5
1.0
1.5
2.0
2.5
3.0
MIC
Oil
Ex
po
rtin
g
LIC
-No
Fra
gil
e
LIC
-Fra
gil
e
MIC
Oil
Ex
po
rtin
g
LIC
-No
Fra
gil
e
LIC
-Fra
gil
e
MIC
Oil
Ex
po
rtin
g
LIC
-No
Fra
gil
e
LIC
-Fra
gil
e
MIC
Oil
Ex
po
rtin
g
LIC
-No
Fra
gil
e
LIC
-Fra
gil
e
GD
P S
har
es
SOEs
Gral Govern't
Water TransportICTPower
Current Spending
-
0.5
1.0
1.5
2.0
2.5
3.0
MIC
Oil
Ex
po
rtin
g
LIC
-No
Fra
gil
e
LIC
-Fra
gil
e
MIC
Oil
Ex
po
rtin
g
LIC
-No
Fra
gil
e
LIC
-Fra
gil
e
MIC
Oil
Ex
po
rtin
g
LIC
-No
Fra
gil
e
LIC
-Fra
gil
e
MIC
Oil
Ex
po
rtin
g
LIC
-No
Fra
gil
e
LIC
-Fra
gil
e
GD
P S
har
es
SOEs
Gral Govern't
Energy ICT TransportWater
Source: AICD, Fiscal Baseline (2008).
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
16
Figure 3.6 Public infrastructure capital investment by sector and institution
-
1
2
3
4
5
6
7
Cote
d'I
voir
e
Tan
zan
ia
Cam
ero
on
Rw
anda
Ken
ya
Mal
awi
Ghan
a
Mad
agas
car
Ben
in
Nig
eria
Nig
er
Ch
ad
Zam
bia
Sen
egal
Mo
zam
biq
ue
Ugan
da
Eth
iop
ia
Les
oth
o
South
Afr
ica
Nam
ibia
Cap
e V
erde
GD
P s
har
es (
%)
WSS Power ICT Transport
-
1
2
3
4
5
6
7
Cote
d'Iv
oir
e
Tanzania
Cam
ero
on
Rw
anda
Kenya
Mala
wi
Ghana
Madagasc
ar
Benin
Nig
eri
a
Nig
er
Chad
Zam
bia
Senegal
Mozam
biq
ue
Uganda
Eth
iopia
Leso
tho
South
Afr
ica
Nam
ibia
Cape V
erd
e
GD
P s
har
es (
%)
General Government SOEs
Source: AICD, Fiscal Baseline (2008)
Note: No data were available for SOE investment in Côte d’Ivoire, Madagascar, Chad, Senegal, and Mozambique.
Sectoral background
The transport and energy sectors account for the lion’s share of infrastructure spending. For low-
income countries these two sectors combined absorb about 80 percent of their infrastructure expenditures
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
17
(figure 3.7). The heavy spending on power is a response to the widely recognized power crisis on the
continent. The efforts of middle-income countries to support energy development contrast starkly, in
fiscal terms, with those of the poorer countries. Middle-income countries spend almost 5 times more than
what aid-dependent and heavily indebted poor countries (HIPC) spend on power. Low shares for water
can be partly attributed to difficulties in capturing expenditure data from municipal water utilities.
Figure 3.7 Public infrastructure spending by sector
-
2
4
6
8
10
12
14
16
18
20
Cote
d'I
voir
e
Nig
eria
Nig
er
Tan
zan
ia
Ben
in
Sen
egal
Mo
zam
biq
ue
Ghan
a
Eth
iop
ia
Les
oth
o
Nam
ibia
GD
P S
har
es (
%)
WSS
ICT
Power
Transport
Composition of fiscal spending
Shares on total
( percent) WSS Power ICT Transport
MIC 12.9 21.3 24.0 41.9
Oil-exporting 14.1 44.6 7.6 33.6
LIC–nonfragile 11.5 41.1 14.9 32.4
LIC–fragile 12.6 61.8 – 25.6
LIC–coastal 8.4 39.8 21.3 30.5
LIC–landlocked 18.3 45.8 6.1 29.8
Average, Africa 12.5 30.7 19.1 37.7
Average, excluding South Africa 12.6 43.1 12.0 32.4
Average, excluding South Africa and Nigeria 12.3 38.5 17.3 31.9
Source: Africa Infrastructure Country Diagnostic, Fiscal Baseline (2008).
Note: Based on annual averages for the period 2001–05. Detailed data presented in annex 1, table 2.
Most infrastructure expenditures on energy are made by SOEs and used for current expenditure. Only
in countries that have large shares of foreign-donor-financed expenditures on electricity (Senegal and
Tanzania) and in resource-intensive countries (Chad and Nigeria) do direct expenditures by central
governments remain large (figure 3.7). The minor role of SOEs in capital investment may be partially
explained by ambiguities in asset ownership. But inefficient pricing policies are probably the most
important cause of the shortage of investment funds.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
18
Figure 3.8 Aggregate expenditures on energy: sources and uses
-
1
2
3
4
5
6
7
Cam
ero
on
Rw
and
a
Ch
ad
Nig
er
Mo
zam
biq
ue
Co
te d
'Iv
oir
e
Nig
eria
Mad
agas
car
Gh
ana
Ben
in
Tan
zan
ia
Mal
awi
Ug
anda
Zam
bia
Sen
egal
Eth
iop
ia
Ken
ya
So
uth
Afr
ica
Les
oth
o
Nam
ibia
Cap
e V
erd
e
GD
P S
har
es (
%)
Budget Nonfinancial Enterprises
-
1
2
3
4
5
6
7
Cam
ero
on
Rw
anda
Ch
ad
Nig
er
Mo
zam
biq
ue
Cote
d'I
voir
e
Nig
eria
Mad
agas
car
Ghan
a
Ben
in
Tan
zan
ia
Mal
awi
Ugan
da
Zam
bia
Sen
egal
Eth
iop
ia
Ken
ya
South
Afr
ica
Les
oth
o
Nam
ibia
Cap
e V
erde
GD
P S
har
es (
%)
Investment Current
Source: Africa Infrastructure Country Diagnostic, Fiscal Baseline (2008).
Note: Based on annual averages for the period 2001–05. Detailed data presented in annex 1, table 4.
Data limitations prevent us from fully capturing aggregate expenditures on water and sanitation, but a
rough estimate is slightly more than 1 percent of GDP. Subnational entities account for a large share of
overall spending on water and sanitation, which complicates cross-country comparisons of spending
levels. The data provided in figure 3.9 represent the lower bound of spending on water supply and
sanitation services. The largest expenditures for water supply and sanitation are recorded in Niger,
Namibia, and Ghana.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
19
Figure 3.9 Aggregate expenditures on water and sanitation: sources and uses
-
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Mad
agas
car
Ken
ya
Ch
ad
Co
te d
'Iv
oir
e
Tan
zan
ia
Ben
in
Nig
eria
Ug
and
a
Cam
ero
on
Rw
and
a
Mal
awi
Mo
zam
biq
ue
Gh
ana
Sen
egal
Zam
bia
Eth
iop
ia
Nig
er
Les
oth
o
Cap
e V
erd
e
So
uth
Afr
ica
Nam
ibia
GD
P S
har
es (
%)
Budget Nonfinancial Enterprises
-
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Mad
agas
car
Ken
ya
Ch
ad
Cote
d'I
voir
e
Tan
zan
ia
Ben
in
Nig
eria
Ugan
da
Cam
ero
on
Rw
anda
Mal
awi
Mo
zam
biq
ue
Ghan
a
Sen
egal
Zam
bia
Eth
iop
ia
Nig
er
Les
oth
o
Cap
e V
erde
South
Afr
ica
Nam
ibia
GD
P S
har
es (
%)
Investment Current
Source: Africa Infrastructure Country Diagnostic, Fiscal Baseline (2008).
Note: Based on annual averages for the period 2001–05. Detailed data presented in annex 1, table 3.
While the public sector retains an active role in providing communication services in several cases,
that role has been taken over by the private sector in most countries. In recent decades, many countries
have privatized publicly owned telecommunications, but communication services are still provided by
SOEs in Benin, Cameroon, Chad, Ethiopia, Ghana, Kenya, Mozambique, Namibia, and Zambia. For the
African countries with public incumbents, cost-recovery and successful productive spending mostly rely
on SOE governance and operational efficiency. Over 90 percent of public expenditure is channeled
through SOEs, and from that amount only 25 percent goes to capital. Ethiopia and Namibia are the only
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
20
countries where central government expenditures on communication amount to a significant share of
GDP (0.33 percent and 0.47 percent, respectively) (figure 3.10).
Figure 3.10 Aggregate expenditures on communications: sources and uses
-
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Sudan
Zam
bia
Ugan
da
Nig
er
Nig
eria
Rw
anda
Mad
agas
car
Mal
awi
Sen
egal
Ch
ad
Tan
zan
ia
Cam
ero
on
Eth
iop
ia
Mo
zam
biq
ue
Ken
ya
Ben
in
Ghan
a
Cap
e V
erde
Les
oth
o
South
Afr
ica
Nam
ibia
GD
P S
har
es (
%)
Budget Nonfinancial Enterprises
-
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Sudan
Zam
bia
Ugan
da
Nig
er
Nig
eria
Rw
anda
Mad
agas
car
Mal
awi
Sen
egal
Ch
ad
Tan
zan
ia
Cam
ero
on
Eth
iop
ia
Mo
zam
biq
ue
Ken
ya
Ben
in
Ghan
a
Cap
e V
erde
Les
oth
o
South
Afr
ica
Nam
ibia
GD
P S
har
es (
%)
Investment Current
Source: Africa Infrastructure Country Diagnostic, Fiscal Baseline (2008).
Note: Based on annual averages for the period 2001–05. Detailed data presented in annex 1, table 5.
Countries with publicly-owned telecom utilities are Benin, Cameroon, Chad, Ethiopia, Ghana, Kenya, Mozambique, Namibia, South Africa, and Tanzania.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
21
Figure 3.11 Aggregate expenditures on transport: sources and uses
-
2
4
6
8
10
12
Cote
d'I
voir
e
Nig
er
Rw
anda
Nig
eria
Ben
in
Cam
ero
on
Sen
egal
Ghan
a
Ken
ya
Ugan
da
Tan
zan
ia
Zam
bia
Ch
ad
Eth
iop
ia
Mo
zam
biq
ue
Mal
awi
Mad
agas
car
Les
oth
o
Nam
ibia
South
Afr
ica
Cap
e V
erde
GD
P S
har
es (
%)
Budget Nonfinancial Enterprises
-
2
4
6
8
10
12
Cote
d'I
voir
e
Nig
er
Rw
anda
Nig
eria
Ben
in
Cam
ero
on
Sen
egal
Ghan
a
Ken
ya
Ugan
da
Tan
zan
ia
Zam
bia
Chad
Eth
iop
ia
Mo
zam
biq
ue
Mal
awi
Mad
agas
car
Les
oth
o
Nam
ibia
South
Afr
ica
Cap
e V
erde
GD
P S
har
es (
%)
Investment Current
Source: Africa Infrastructure Country Diagnostic, Fiscal Baseline (2008).
Note: Based on annual averages for the period 2001–05. Detailed data presented in annex 1, table 6.
Central government spending on the transport sector differs from other sectors. Funds are primarily
focused on investment. The exception to this pattern is Cape Verde, where there is considerable spending
on the air transport SOE. For the rest of the countries, the profile of transport spending is dominated by
roads. Overall, roads absorb between 60-80 percent of total transport budgets. That might be behind the
higher ratios of current-to-investment spending found in middle-income countries such as Tanzania and
Kenya, all with better records in road maintenance. Also, the very high importance of roads within the
transport sector underscores the impact of donors’ agendas. The limited evidence available indicates the
heavy dependence of roads investment on foreign funding, which ranges from just over 50 percent in a
country such as Senegal to almost 90 percent in a country such as Rwanda. The volatility of official
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
22
development assistance (ODA) flows contributes to the volatility of public investment in the sector. Thus,
the very high ratio of road investment to GDP in Chad in 2003–05, in Tanzania in 2000, and in
Madagascar in 2004–05, are all associated with short-lived surges in ODA.
4 General government expenditures
The macro outlook and its fiscal implications for infrastructure
African countries have seen their economies grow at a solid 4 percent annual average in recent years,
with half of the countries in the sample achieving growth rates in excess of 4.5 percent for the period
2001–05. The highest growth rates are observed in oil-exporting countries, which have benefited from
rising oil prices. The completion of the oil pipeline in Chad pushed the average annual growth rate in that
country over 15 percent. Côte d'Ivoire, in a postconflict situation, is the only country that has experienced
a growth slowdown. Low-income countries that benefited from debt relief and successful policy reforms
managed to offset the negative effects of higher oil prices; heavily indebted poor countries (HIPC) saw
annual average growth rates in excess of 5.5 percent. Particular robust growth rates were observed in
Tanzania and Mozambique. The middle-income countries grew more slowly than the other groups, at an
annual average growth rate below 4.3 percent.
But growth rates in Sub-Saharan Africa trail those in other developing regions with higher income
levels. Over the last five years, economic growth in Africa has lagged considerably behind growth in
other developing regions, notably East Asia, Europe, and Central Asia. Average annual growth rates in
per capita GDP were only 2.5 percent in Sub-Saharan Africa, compared with 7.3 percent in East Asia and
5.24 percent in Europe and Central Asia. Furthermore, with the notable exception of South Africa,
average per capita income remains below $600, which is about seven times lower than in Latin America
and the Caribbean and four times lower than in Europe and Central Asia. These disparities underscore the
size of the development challenge facing Sub-Saharan African countries. Furthermore, most of these
countries are likely to fall short of the Millennium Development Goals (MDGs). The World Bank’s
Global Monitoring Report for 2005 notes that in order to meet key MDGs by 2015, the region needs
average annual growth rates of more than 7 percent over the next decade. But beyond the MDGs, the
region faces important hurdles to achieve sustained growth.
Inadequate public infrastructure (notably energy and transport) has been identified as a critical
bottleneck to sustained growth in Sub-Saharan countries. Empirical work on African countries that
explicitly models infrastructure as a growth variable finds that infrastructure strongly supports economic
growth.20 Studies that address the issue of reverse causation (higher output creates higher demand for
infrastructure) are scarce, but a few examine African countries. Using a sample of 52 countries and
addressing the issue of reverse causation, Canning and Bennathan (2000) find rates of return to
investment in electrical generating capacity of around 40 percent. For most countries, this rate was
roughly the same as the return on noninfrastructure capital. But for the 11 African countries in the sample,
the average rate of return to generating capacity was 53 percent—1.3 times higher than the African rate of
return to noninfrastructure capital. Even higher rates of return were found for investment in paved roads,
the African average being 69 percent, about 1.7 times higher than returns from noninfrastructure capital.
20 For details see Estache and Wodon (2006) and references therein.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
24
Furthermore, Sachs (2004) has identified high transport costs as one of the five structural reasons for
persistent poverty traps in Sub-Saharan Africa.
Efforts to provide basic infrastructure and meet development needs are frustrated by weak public
finances. Compared with other developing regions, public financing capabilities in Sub-Saharan Africa
are characterized by weak domestic revenue collection, limited borrowing capacity, and a high reliance on
external grants. Countries are prone to negative fiscal balances, especially in low-income countries.
Domestic revenue generation remains weak in comparison to other developing countries; nonetheless,
domestic revenues constitute the most important source of fiscal resources, particularly for oil-exporting
and middle-income countries (table 4.1).
Table 4.1 Primary fiscal balance (share of GDP)
Revenues
Domestic revenues Grants
Budgetary expenditures, excl.
debt service Primary balance Primary balance,
excl. grants
MIC 23.62 0.06 21.21 2.46 2.41
Oil–exporting 35.38 0.19 30.55 5.02 4.83
LIC–nonfragile 15.22 4.84 21.27 (1.21) (6.04)
LIC–fragile 12.22 1.01 12.26 0.98 (0.03)
LIC–coastal 15.50 3.25 19.23 (0.48) (3.74)
LIC–landlocked 13.24 5.79 20.45 (1.41) (7.20)
Africa average 23.29 1.33 22.57 2.06 0.72
Excl. South Africa 23.34 2.55 24.31 1.59 (0.97)
Excl. South Africa and Nigeria 15.72 3.62 19.75 (0.41) (4.03)
Source: AICD, Fiscal Database, 2008; IMF Statistical Appendixes, WB DDP.
Note: Net change over the period 2001–06. Averages weighted by countries’ GDP. Totals may not add up.
Yet, external grants play a significant role as a budgetary financing source in the poorest countries of
Sub-Saharan Africa. External grants account for about one-third of the fiscal resources available in low-
income countries. The share of foreign grants in the overall fiscal resource envelope ranges from more
than 40 percent in Rwanda to less than 5 percent in the middle-income countries of the sample. Foreign
grants exceed 30 percent of fiscal resources in eight countries of the sample. Excluding South Africa and
Nigeria, foreign grants account, on average, for more than 3.5 percent of GDP in the study countries. The
highest levels of foreign grants are found in low-income countries recovering from conflict and in
landlocked countries.
The favorable external environment has helped countries expand their available budgets. In the period
2001–05, central government budgets grew by almost 1.9 percent of GDP, driven largely by increases in
middle-income countries (table 4.2). Paired with continued progress in economic reforms in some
countries (such as Ghana and Tanzania), the positive international environment strengthened domestic
investment and increased capital inflows, easing pressure on fiscal policy, fostering economic growth, and
increasing fiscal space in most countries of the region. Changes in available budgets differ significantly
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
25
across nations, ranging from a 9 percent increase in the Democratic Republic of Congo to a contraction of
more than 8 percent in Zambia.21
Table 4.2 Net change in central government budget: breakdown by financing sources (share of GDP)
Financing sources
Net expenditure
budget Domestic revenues Oil revenues Grants Net borrowing
MIC 4.08 3.40 – (0.03) 0.71
Oil–exporting (3.73) 5.25 7.74 (0.07) (8.92)
LIC–nonfragile 1.69 0.83 – 1.98 (1.12)
LIC–fragile 3.85 0.79 0.61 1.90 1.16
LIC–coastal 2.81 0.87 0.17 1.06 0.88
LIC-landlocked 0.75 0.73 – 3.59 (3.56)
Africa average 1.89 3.04 1.67 0.57 (1.71)
Excl. South Africa (0.33) 2.66 3.20 1.09 (4.07)
Excl. South Africa and Nigeria 1.49 0.93 0.19 1.54 (0.99)
Source: AICD, Fiscal Database, 2008; IMF Statistical Appendixes, WB DDP.
Note: Net change over the period 2001–06. Averages weighted by countries’ GDP. Totals may not add up.
In oil-exporting countries, additional resources from record-high oil prices supported debt-reduction
efforts. A significant portion of oil-price-triggered increases in domestic revenues has been used to pay
back debt, narrowing in tandem the net budget available for nondebt expenditures. This arrangement
clearly makes remaining debt more sustainable in the long run, as reflected in the decrease in net
borrowing, perhaps at the risk of postponing productive investment in infrastructure.
In non-oil-exportion, low-income countries, domestic revenues have increased only marginally, but
the slow pace has been compensated by increases in foreign grant inflows. For the other low-income
countries, and to some extent middle-income countries, further increases in domestic revenue increases
are contingent to institutional reforms to make revenue collection more effective and widen the tax base.
Sub-Saharan Africa can only somewhat rely on favorable external conditions, as those seen in recent
years.
Marginal propensity to invest in infrastructure
In Sub-Saharan Africa, fiscal adjustments might be achieved though capital investments, and to some
extent, cuts in infrastructure investments. While more research is needed to prove this as a pattern, the
infrastructure investment driving fiscal balance behaviors are not a new phenomenon (figure 4.1). The
macroeconomic literature has long acknowledged that fiscal adjustment episodes tend to include
disproportionate public investment cuts.22 For example, Calderón and Servén (2004), based on averages
21 See annex 11 for details. 22 Serven (2006) and Hicks (1991) summarizes the facts on Latin American and other developing countries. For
industrialized counties, see also Roubini and Sachs (1989); De Haan, Sturn, and Sikken (1996) document the
experience of industrialized countries.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
26
for eight Latin American countries, show that cuts in infrastructure investment amounted to about 40
percent of the observed fiscal adjustment between the early 1980s and late 1990s. This was remarkable
because public infrastructure investment represented less than 25 percent of overall public expenditure in
Latin American countries. The other side of this adjustment pattern is the volatility in annual capital
expenditures.
Figure 4.1 Changes in infrastructure investment, primary surplus, and government budgets
Benin
Cameroon
Cape Verde
ChadCote d'Ivoire
Ethiopia
Ghana
Kenya
Lesotho
Madagascar
MalawiMozambique
NamibiaNiger
Nigeria
Rwanda
Senegal
South Africa
Tanzania
Uganda
Zambia
(2.5)
(2.0)
(1.5)
(1.0)
(0.5)
-
0.5
1.0
1.5
2.0
2.5
(10) (5) - 5 10 15
Changes in Primary Surplus (GDP Shares)
Chan
ges
in I
nfr
astr
uct
ure
Inves
tmen
t (G
DP
Shar
es)
Zambia
Uganda
Tanzania
South Africa
Senegal
Rwanda
Nigeria
NigerNamibia
MozambiqueMalawi
Lesotho
Kenya
Ghana
Ethiopia
Cote d'IvoireChad
Cape Verde
Cameroon
Benin
(2.5)
(2.0)
(1.5)
(1.0)
(0.5)
-
0.5
1.0
1.5
2.0
2.5
(8) (6) (4) (2) - 2 4 6 8 10
Changes in Government Budget (GDP Shares)
Ch
ang
es i
n I
nfr
astr
uct
ure
Inves
tmen
t (G
DP
Shar
es)
Source: AICD, Fiscal Baseline (2008), processed.
A vast majority of African countries show strong volatility of infrastructure investment despite the
multiyear nature of infrastructure projects and the potential effect of time lags. Changes in the overall
budget envelope are correlated with changes in infrastructure investment, consistent with the well-
documented procyclical behavior of infrastructure investment (figure 4.2). In this context, half of the
countries in the sample decreased their capital expenditures,23 and with the exception of Chad, Namibia,
and Uganda, cuts in capital spending were accompanied by cuts in capital spending on infrastructure.24
Thus, the additional budgetary resources helped low-income countries to bolster capital investments.
Capital investment as a share of GDP increased, in the low-income countries in 2002–05, by more than 1
percent of GDP, about 40 percent of which went toward addressing infrastructure bottlenecks (table 4.4).
It is striking that the oil-exporting countries and middle-income countries decreased investment
despite having more fiscal resources available. Middle-income countries seem to have chosen to
strengthen maintenance. Capital expenditures in oil-exporting countries—led by Nigeria—decreased on
23 For details see annex 11. 24 In Chad, a decrease in budgetary resources and a decrease in capital expenditures as shares of GDP were
accompanied by an increase in nominal GDP owing to the significant increase in oil revenues after completion of
the oil pipeline. When measured as a share of non-oil GDP, Chad’s fiscal resources and capital expenditures
increased.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
27
average by 3.3 percent as a share of GDP. In other words, the favorable external environment did not
cause sharp or even proportional increases in expenditures in oil-exporting countries.
Table 4.3 Net change in central government budgets: breakdown by economic use (share of GDP)
Economic uses
Net expenditure
budget Wages Other
current
o/w
Infrastructure Capital
expenditure o/w
Infrastructure
MIC 4.08 (0.11) 4.95 (0.02) (0.75) 0.04
Oil–exporting (3.73) (1.82) (0.23) 0.03 (1.69) (1.46)
LIC–nonfragile 1.69 0.05 0.49 (0.00) 1.15 0.54
LIC–fragile 3.85 0.79 2.49 0.09 0.58 0.22
LIC–coastal 2.81 0.16 1.00 0.04 1.65 0.45
LIC–landlocked 0.75 0.21 0.58 (0.03) (0.04) 0.55
Africa average 1.89 (0.39) 2.68 0.00 (0.39) (0.14)
Excl. South Africa (0.33) (0.63) 0.41 0.02 (0.10) (0.31)
Excl. South Africa and Nigeria 1.49 0.09 0.69 0.01 0.71 0.47
Source: AICD, Fiscal Database, 2008; IMF Statistical Appendixes, WB DDP.
Note: Net change over the period 2001–06.
Averages weighted by countries’ GDP. Totals may not add up.
Infrastructure allocation across social sectors
Only low-income countries—particularly aid-dependent countries—have allocated additional fiscal
resources to clearly favored infrastructure sectors. Roughly one-third of low-income additional resources
were allocated to infrastructure. The education and health sectors were benefited (table 4.5). In middle-
income countries, most of the additional budget was allocated to sectors other than infrastructure and
social. In oil-exporting countries, the decrease in budgetary expenditure was largely absorbed by a
significant reduction in infrastructure expenditures. To a large extent this reflects developments in
Nigeria, where infrastructure expenditures decreased by 2.2 percent of GDP during the study period. As
previously mentioned, this might be largely explained by debt-reduction efforts.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
28
Table 4.4 Net change in central government budget: breakdown by functional category (share of GDP)
Functional categories
Net expenditure budget Infrastructure Education Health Other sectors
MIC 4.08 0.02 0.12 0.01 3.93
Oil–exporting (3.73) (1.43) (0.17) (0.17) (1.96)
LIC–nonfragile 1.52 0.54 0.69 0.24 0.07
LIC–fragile 3.85 0.32 – 0.43 3.11
LIC–coastal 2.60 0.48 0.57 (0.03) 1.57
LIC–landlocked 0.75 0.52 0.56 0.80 (1.13)
Africa average 1.85 (0.14) 0.19 0.05 1.75
Excl. South Africa (0.41) (0.29) 0.28 0.08 (0.48)
Excl. South Africa and Nigeria 1.37 0.49 0.40 0.23 0.26
Source: AICD, Fiscal Database, 2008.
Note: Net change over the period 2001–06. Averages weighted by countries’ GDP. Totals may not add up. Data on education are not available for Burkina Faso, Democratic Republic of Congo, Nigeria, or Tanzania.
In conclusion, for most countries in Sub-Saharan Africa, the infrastructure sectors have benefited
from additional allocations following the expansion of countries’ fiscal budgets. This might be the result
of explicit policy making. It remains to be seen whether these gains in resources for infrastructure are
adequate to remove the bottlenecks to growth—and whether, ultimately, they are affordable. It may well
be that the tax base of most African countries is simply too narrow to permit them to cure their
infrastructure problems by investing incrementally greater shares of resources from their modest fiscal
space. Long-term reliance on a favorable external environment is probably not an option.
Prioritizing within the infrastructure budget envelope
About half of the infrastructure budget goes to the transport sector, particularly roads, which account
for the second-highest share of aggregate infrastructure spending (including that provided by the
nonfinancial sector) and the single largest item of central government spending. In low-income countries,
power captures close to one-fourth of the infrastructure budget. This is somewhat higher than the 20
percent allocated to water and sanitation. In middle-income countries, water is undoubtedly the second-
most-important focus of government; most, if not all, spending on energy is let out of the budget to
nonfinancial public enterprises.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
29
Figure 4.2 Central government budget by sector and use
Budget Infrastructure Spending
-
0.5
1.0
1.5
2.0
2.5
3.0
3.5
MIC
Oil
Ex
po
rtin
g
LIC
-NoF
ragil
e
LIC
-Fra
gil
e
MIC
Oil
Ex
po
rtin
g
LIC
-NoF
ragil
e
LIC
-Fra
gil
e
MIC
Oil
Ex
po
rtin
g
LIC
-No
Fra
gil
e
LIC
-Fra
gil
e
GD
P S
har
esICT
Power
WSS
Transport
Investment Current SpendingTotal Government Budget
Source: Africa Infrastructure Country Diagnostic, Fiscal Baseline (2008).
Note: Based on annual averages for the period 2001–05. Detailed data presented in annex 1, tables 3–6.
Budget prioritization is strongly influenced by donors’ agendas, particularly for roads and water.
Donors tend to have a bias toward investment over maintenance and significantly favor spending on water
and roads, particularly for rural areas. On average, roads and water together make up between 80 and 95
percent of donors’ allocation to the region (table 4.5). In most non-oil exporting, low-income countries,
donors provide 50–60 percent of what is spent on water and sanitation. In the case of roads, donor
funding accounts, on average, for one-fourth of total spending (table 4.6). In terms of shares of GDP, road
spending is highest in Ethiopia, Lesotho, Mozambique, and Chad, all countries with significant donor
inflows for road rehabilitation.
Donors have not focused on the energy sector, while governments are allocating increasing amounts
of domestically raised resources to the sector. The divergence between donors’ agendas and countries’
priorities is manifested in the spending profiles of the energy sector. Donors have kept a very low profile
in the sector in an environment of low cost-recovery, low affordability, and very high risks (table 4.5). On
the contrary, the governments of low-income countries have allocated close to 25 percent of their budgets
to tackle the ongoing crisis of chronic underinvestment.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
30
Table 4.5 Donors’ intervention in Africa: budget relevance and composition
Share of gov’t spending financed by donors Composition of donor allocations
Ratios in percent Overall Budget Energy WSS Roads Total Energy Water Roads
MIC 2.0 0.2 2.3 4.4 100 5 21 74
Oil–exporting 5.9 1.1 2.2 11.0 100 7 7 87
LIC–nonfragile 35.9 38.1 50.6 31.5 100 18 24 57
LIC–fragile – – – – – – – –
LIC–coastal 39.6 39.4 62.2 33.4 100 22 26 52
LIC–landlocked 26.2 32.1 29.6 24.9 100 9 20 71
Africa average 18.8 10.6 24.0 21.8 100 17 23 61
Source: Africa Infrastructure Country Diagnostic, Fiscal Baseline (2008).
Note: Based on annual averages for the period 2001–05.
Sub-Saharan countries might be paying a high price for relative overspending on investment in roads
and water to the detriment of other priority sectors and overall maintenance. While budget allocation
across sectors is as much an economic decision as a political one, undoubtedly the cost of funds is one of
the most important considerations for governments when allocating funds, and donor money is, by design,
concessional. The rational decision is to take advantage of cheap money.
Furthermore, Sub-Saharan countries are paying a higher price for funds to the energy sector—
admittedly in crisis—due to the retrenchment of donors. Each dollar raised and spent by a Sub-Saharan
government has a social value premium of almost 20 percent (Warlters and Auriol, 2005). This captures
the incidence of that tax on the society’s welfare (due to changes in consumption patterns) and the
administrative costs of raising the money, among other things. 25 In comparison, official development
assistance (ODA) and funding from the World Bank’s International Development Association (IDA) are
subsidized—with respect to domestically raised funds—26 at about 70 and 55 percent, respectively.27
Funds from countries outside the Organisation for Economic Co-operation and Development (OECD),
such as India and China, are approaching some African countries with quasi-concessional lending. The
subsidy ratio for India and China funds is estimated at around 25 percent, while for Arab funding it is 50
percent.28
25 The marginal cost of public funds measures the “change in welfare associated with raising an additional unit of
tax revenue” (Warlters and Auriol, 2005). 26 Note that, except for grants, ODA and IDA loans have to be repaid by governments using domestically raised
resource. That established the basic price tag for money. 27 This is based in the standard concessional terms For instance, IDA loans charge zero interest (though 0.75 percent
service charge) with 10 years of grace (Foster and others, 2008) 28 India, China and the Gulf States on average charge 4 percent, 3.6 percent, and 1.5 percent of interest and grant a
4–year grace period (Foster and others, 2008).
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
31
Figure 4.3 Costs of capital by funding source
Cost of Capital relative to the Marginal Cost of Public Funds
0.0
0.3
0.5
0.6
0.9
0.9
1.1
1.2
0.0 0.2 0.4 0.6 0.8 1.0 1.2
Grants
IDA
ODA
Arabs
China
India
Private
Public
Source: Average marginal cost of public funds as estimated by Warlters and Auriol (2005). Cost of Equity for Private Sector as in Sirtaine and other (2005) and Estache and Pinglo (2004). For the rest, author’s calculations.
Budget efficiency: execution and maintenance
Budget execution
Infrastructure services provided directly through government budgets (or special extrabudgetary
funds) face institutional challenges that can often prevent spending money even if available.
Administrative and procedural difficulties can often prevent budget allocations from being ultimately
released and realized. The ratios of released to budgeted expenditure and of realized to budgeted
expenditure are usually know as budget-variation or budget-execution ratios.
Central governments’ budget-variation ratios help measure the efficiency of public administration.
Execution ratios of less than 100 percent indicate that not all of the budgeted funds were spent, either
because of inefficiencies in implementation or competing pressures for the original allocation that arose
during the fiscal year. Execution ratios of more than 100 percent indicate that the amount spent was
greater than that budgeted, which most likely indicates a cost overrun or policy realignment.
In the aggregate, African countries are not able to spend one-third of their capital budgets and one-
fourth of their recurrent budgets (figure 4.4). Poor timing of project appraisals and late releases of
budgeted funds because of procurement problems often prevent resources from being used in the budget
year. Delays in in-year fund releases are also associated with poor project preparation, leading to changes
in the original terms agreed on with contractors (such as changes in deadlines, technical specifications,
budget, costs, and so on). In other cases capital budgets are reallocated to current expenditures because of
political or social pressures.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
32
Figure 4.4 Budget-variation ratios for capital and recurrent spending
Budget Execution Ratios for Capital
82.7
89.0
73.8
73.3
69.4
65.5
63.8
60.8
60.7
53.3
27.8
- 20.0 40.0 60.0 80.0 100.0
Benin
Ghana
Chad
Niger
Kenya
Average
Malawi
Uganda
Cameroon
Ethiopia
Madagascar
Budget Execution Ratios for Current Spending
115.92
137.62
98.17
82.47
82.34
65.5
72.32
69.79
67.16
64.74
33.96
- 20.00 40.0060.00 80.00 100.0
0
120.0
0
140.0
0
160.0
0
Benin
Uganda
Malawi
Niger
Average
Kenya
Ghana
Ethiopia
Chad
Madagascar
Cameroon
Source: Africa Infrastructure Country Diagnostic, Fiscal Baseline (2008).
Note: Based on annual averages for the period 2001–05.
Maintenance: the case of roads
The roads sector is the most salient example of a service provided by the government. Roads are
typically provided without user fees, since tolling is only economic above a relatively high minimum
traffic threshold, making pricing issues less relevant. Whether due to political appeal or donor influence,
there is now a generalized sense that investment in the road network in Africa might be overextended.
The issue is that high levels of investment can be a problem if they displace essential maintenance of
existing assets, since it is much more costly over time to rehabilitate or replace poorly maintained assets
than to keep up a sound preventive maintenance regime.
In Sub-Saharan Africa, the share of assets in need of rehabilitation is a striking 30 percent overall,
reaching more than 40 percent for rural roads. Uganda, Nigeria, Rwanda, and the Democratic Republic of
Congo have the highest shares of rehabilitation backlogs (figure 4.5). Close to half of the assets of
railways and rural roads are in urgent need of rehabilitation. But roads deserve special attention. From the
financial viewpoint, roads are primarily a responsibility of governments and in most cases road budgets
are the most prominent single item in national budgets in Sub-Saharan Africa. Transportation is a proven
key ingredient of sustainable development, and roads, for the foreseeable future, are the main transport
mode in the region.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
33
Figure 4.5 Infrastructure assets in need of rehabilitation (percent of total)
0%
10%
20%
30%
40%
50%G
ener
atio
n
Non-r
ura
l av
erag
e
Mai
n R
oad
s
Aver
age
Irri
gat
ion
Urb
an W
ater
Ru
ral
Wat
er
Ru
ral
aver
age
Rai
lway
s
Ru
ral
Ro
ads
Av
erag
e re
hab
ilit
atio
n i
nd
ex
0%
10%
20%
30%
40%
50%
60%
Burk
ina F
aso
So
uth
Afr
ica
Cape V
erd
e
Ghana
Cote
d'Iv
oire
Chad
Nam
ibia
Tanzania
Cam
ero
on
Eth
iopia
Kenya
Nig
er
Madagascar
Benin
Mala
wi
Zam
bia
Mozam
biq
ue
Sudan
Senegal
Lesoth
o
Uganda
Nig
eria
Rw
an
da
DR
C
Avera
ge r
ehabili
tation index
Source: AICD.
Rehabilitation ratios only underscore the need for paying increased attention to road maintenance
deficiencies, and the most challenging aspect of investing in roads is providing for their maintenance. In
environments characterized by weak fiscal management practices (nontransparent and politically
dominated budgeting processes), assets are rarely maintained. This is particularly true when, as in the case
of long-life road networks, maintenance has little observable benefits in the short term, and therefore its
budgetary allocations are not naturally protected by the executive level and the parliament.29 Furthermore,
in the context of Africa, donors have a dominant role in channeling funds to the sector. Most of this
funding, earmarked to investment, is concessional by nature, making the cost of additional investments
cheaper than the cost of maintenance, usually raised domestically.
Not surprisingly, there is a bias toward capital spending at the expense of maintenance. Except in the
middle-income countries, more than two-thirds of spending on roads goes to capital expenditures;
maintenance remains a secondary spending priority.30 Oil-exporting countries, especially Nigeria and
Chad, spend around 80 percent of all related expenditures on capital (table 4.6). In other low-income
countries, such as Ethiopia, Senegal, and Zambia, the spending on capital is 12, 24, and 36 thousand
dollars per kilometer respectively—well above the continent’s average, 10 thousand dollars per kilometer.
These amounts are also more than 15 times higher than the average capital spending in middle-income
countries. These startling differences between middle-income countries and low-income countries can be
attributed to differences in initial conditions—middle-income countries already have an established road
network. However, from the perspective of fiscal policy, how each country allocates resources toward
maintenance and capital expenditures has important intertemporal implications. Countries with a high
29 Gwilliam and Shalizi (1996) suggest another method might be to reduce the expected rate of return on projects
resulting in fewer projects crossing the hurdle rate, since inadequate maintenance leads to lower levels of return. 30 All expenditures from road funds and current expenditures by central and local governments are assumed to be for
maintenance (current expenditure), and all foreign-financed expenditures and capital expenditures by central and
local governments are assumed to be for capital rehabilitation. Furthermore, we assume that all foreign-funded and
central government capital expenditures are used on the primary road network. Central governments’ current
expenditures (adjusted for overhead) are assumed to be transfers to local governments for maintenance of the
secondary network (local roads). The distribution of road fund spending between primary and secondary networks is
based on an RMF matrix.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
34
share of capital spending on roads will have to allocate greater resources for maintenance in the future,
thus straining public finances. An increase in capital expenditures undoubtedly puts additional fiscal
pressure to provide larger resources on road maintenance to preserve rehabilitated road networks.
Table 4.6 Economic classification of expenditures on road transport
Shares on total ( %) Overhead Capital expenditure (incl. rehabiliation)
Current spending
(incl. maintenance)
MIC 0.54 4.96 93.95
Oil–exporting 5.77 81.03 13.20
LIC–nonfragile 2.95 56.64 35.72
LIC–fragile 2.94 65.95 0.37
LIC–coastal 4.03 59.90 36.07
LIC–landlocked 1.01 55.39 17.58
Africa average 2.35 37.37 57.17
Excl. South Africa 4.12 67.33 22.70
Excl. South Africa and Nigeria 2.77 56.87 32.06
Source: AICD, Fiscal Database, 2008.
Note: Annual averages over the period 2001–06. Averages weighted by countries’ GDP. Totals may not add up.
High capital expenditure on roads may be justified by large rehabilitation backlogs. Using the
RONET model, it is possible to produce detailed estimates of the rehabilitation requirements for each
country’s road network, taking into account the current distribution of network condition, and working
toward a target of clearing the current rehabilitation backlog within a five-year period. On that basis, the
rehabilitation requirements can be compared with the current levels of capital expenditure to determine
whether these are high enough to eliminate the rehabilitation backlog within a reasonable period of time.
It is important to note that this calculation is only illustrative, and is based on the assumption that the
entire capital budget is devoted to network rehabilitation. While rehabilitation does tend to dominate
capital spending, other types of investment do take place, including upgrading road categories or adding
new roads; available data do not make it possible to know the exact split. However, the calculation is
helpful in illustrating whether current levels of capital expenditure would be high enough to address the
rehabilitation problem if they were fully allocated to rehabilitation works.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
35
Figure 4.6 Capital expenditure as a percentage of rehabilitation requirements
-100%
-50%
0%
50%
100%
150%
200%
250%
300%
350%
400%
Ke
nya
Benin
Nig
er
Nig
eria
Mala
wi
Cam
ero
on
Uganda
Zam
bia
Lesoth
o
Rw
anda
Madagascar
Mozam
biq
ue
Senegal
Cote
d'Iv
oire
Ghana
Tanzania
Chad
Eth
iopia
Perc
enta
ge d
evia
tion fro
m r
equirem
ent
Rehabilitation Rehabilitation adjusted for capital execution
Source: Africa Infrastructure Country Diagnostic, Fiscal Baseline (2008), AICD RONET Analysis, 2008.
Note: Based on annual averages for the period 2001–05.
When compared to norms, roughly half of the countries have shortfalls of 40 percent or more in
annual maintenance (figure 4.7). It is relevant to compare current maintenance expenditures with the
norm for Africa. Using the RONET model, it is possible to produce detailed estimates of the routine and
periodic maintenance requirements for each country’s road network, taking into account the current
distribution of network condition. On that basis, the maintenance requirements can be compared with the
current levels of maintenance expenditure to determine whether these are high enough to preserve the
network in good condition. It is important to note that this calculation is only illustrative, and is based on
the assumption that the entire maintenance budget is spent on maintenance works at efficient unit costs.
However, the calculation is helpful in illustrating whether current levels of maintenance would be high
enough to preserve the network if they were efficiently spent.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
36
Figure 4.7 Maintenance expenditure as a percentage of requirements
-100%
0%
100%
200%
300%
400%
500%
600%
Chad
Nig
eria
Ugan
da
Nig
er
Sen
egal
Mal
awi
Mad
agas
car
Eth
iop
ia
Leso
tho
Rw
anda
Gh
ana
Ken
ya
Tan
zan
ia
Mo
zam
biq
ue
Ben
in
Nam
ibia
Cam
ero
on
Zam
bia
So
uth
Afr
icaM
ain
tenance e
xpenditure
as %
requirem
ents
Routine plus periodic maintenance Routine maintenance
Source: Africa Infrastructure Country Diagnostic, Fiscal Baseline (2008), AICD RONET Analysis, 2008.
Note: Based on annual averages for the period 2001–05.
Maintenance shortfalls are more pronounced in countries without road funds and fuel levies. In cases
such as Chad, Nigeria, Uganda, Niger, and Senegal, maintenance spending is less than half the normative
requirements. Underspending on maintenance is evident in low-income countries (particularly the
resource-rich) and in countries with difficult geographical environments and terrain. Middle-income
countries tend to spend substantially above the maintenance norm. However, the problem of
underspending on maintenance is by far the most pronounced in countries that lack a road fund and hence
a fuel levy. Among countries with fuel levies, those with high levies do substantially better than those
with low ones.
If the definition of maintenance is narrowed to consider only the routine aspects of maintenance, the
costs are reduced by about one half. Yet around a quarter of the countries in the sample are not devoting
adequate resources to cover even routine maintenance activity.
The most important vehicle for domestic funds are the so-called road funds, which are designed to
sequester funds for use on maintenance. Domestic financing for roads is channeled mainly through a mix
of central government transfers to local governments and spending from road funds. Road fund
expenditures vary significantly in proportion and absolute amount from country to country, but, on
average, less than one-fifth of total spending for roads is channeled through road funds. Most funding
passes through central governments’ main budgets, either as transfers to regional governments or as
capital expenditure.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
37
Table 4.7 Average expenditures for roads
GDP share ( %) Total spending of which external
of which road fund
MIC 0.31 0.01 0.06
Oil–exporting 1.31 0.14 0.21
LIC–nonfragile 2.36 0.74 0.51
LIC–fragile 0.66 – –
LIC–coastal 1.81 0.61 0.53
LIC–landlocked 2.51 0.62 0.22
Africa average 1.03 0.22 0.17
Excl. South Africa 1.76 0.43 0.33
Excl. South Africa and Nigeria 1.97 0.61 0.47
Source: AICD, Fiscal Database, 2008.
Note: Annual averages over the period 2001–06.
Averages weighted by countries’ GDP. Totals may not add up. Implementation of second-generation road funds seems to help protect resources for road
maintenance. Although it is possible to compute an optimal maintenance requirement (various
maintenance norms for any type of road), implementing a mechanism that ensures the availability of
sufficient funding to meet those requirements over the life cycle of the road network is a major challenge,
not only in developing countries but in many developed countries as well. Finding the right balance
between new investment and maintenance has been also the focus of extensive debate.31 In fact, during
the last decade, the majority of the countries of Sub-Saharan Africa made efforts to establish so-called
second-generation road funds, 32 based on relaying charges from users of roads33 (rather than earmarking a
proportion of general tax revenues) while incepting a separate administrative agency built upon principles
of commercializing road maintenance by collecting user charges. Countries such as Ghana, Namibia, and
Zambia that have made genuine efforts toward implementing second-generation road funds spend on
average well above $2,000 per kilometer per year on maintenance of major networks.34 Road maintenance
in these countries does not differ significantly from estimated maintenance expenditures in South Africa.
Expenditures per kilometer of primary network are below $500 in Niger and Rwanda.
With the notable exception of Malawi, maintenance expenditures via road funds have increased in the
majority of countries, but at a lower pace than overall road expenditure. These increases on maintenance
expenditures are significantly smaller than increases in capital expenditures financed by external donors
31 Gwilliam and Shalizi (1996), Benmaamar (2006), and references cited therein. 32 The predecessor to this mechanism were “first generation road funds,” which were characterized by the
earmarking of certain road-related taxes and charges to a special off-budget account. Technically, however, these
funds are nothing more than a separate budget line item with no separate oversight. 33 In most cases, these are indirect charges for access (various vehicle registration fees) and use (fuel levies), but there are a few examples of direct charges (e.g., toll roads). 34 Because of data limitations, we were able to clearly identify maintenance expenditures only for the main network.
Estimating expenditure on maintenance on the secondary network is difficult, because it is not possible to obtain a
precise estimate of the maintenance expenditures of local governments. Different degrees of data accuracy prevent
reliable cross-country comparisons of maintenance expenditures on secondary roads.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
38
or central government. The share of external financing, most of which is allocated to road rehabilitation,
has increased in every country that receives external financing. Central government allocations to roads
have increased in the majority of countries, but have decreased in Malawi, Madagascar, and Nigeria.
Another important achievement of road funds has been to avoid the volatility of maintenance
expenditures. In addition to supporting higher levels of road maintenance expenditure, there is some
evidence that road funds have also helped to improve the predictability of road maintenance expenditure.
Expenditure data for 2001–05 show that volatility of road fund expenditures (measured by calculating the
standard deviation around the trend line) was only half that of expenditures arising from external funding
and one-third that of central government allocations.35 Moreover, the volatility of road fund expenditures
appears to be lower in countries that have made efforts to ensure the independence of their road funds and
increased the proportion of revenues channeled directly into the road funds.
In conclusion, countries in Sub-Saharan Africa are allocating a significant share of GDP to road
sector expenditures, which have only increased in most countries. This resource allocation to roads is
driven in part by large donor funding inflows in aid-dependent countries and robust domestic revenues in
oil-exporting countries. The vast majority of this expenditure is allocated to capital expenditures, leaving
maintenance a secondary priority. Countries that have made efforts at establishing well-functioning road
funds tend to be more successful at ensuring optimal provision of maintenance expenditures and reducing
the volatility of expenditure flows.
35 For details see annex 14.
5 The hidden costs of utilities’ inefficiencies
There are myriad approaches to the measurement of efficiency. The most widely accepted are based
on the idea that rational economic agents will pursue maximum output from a given set of inputs.36 In his
seminal work, Farrell (1957) introduced the concept of production frontiers—or a possible set of optimal
outputs—that provide a basis for measuring efficiency.37 His approach to efficiency measurement relies
on the estimation of a production frontier for which inputs and outputs can be correctly measured.
Technical efficiency refers to the inputs needed to produce a given level of output using a specific
technology. Allocative efficiency measures the extent to which factors of production are used in optimal
proportion, given their prices or economic returns.
Accurate measurement of efficiency is extremely data-intensive and requires detailed disaggregation
and precise measurement of both inputs and associated outputs. The efficiency of public spending for
infrastructure services involves the simultaneous production of multiple outputs. For instance, electricity
generation will have no benefit if access to the network is not created in parallel. Similarly, delivering an
improved water supply implies a multioutput production function. On the input side, expenditures on
infrastructure services are natural proxies for the inputs of the production functions. In the case of
services provided by public enterprises, however, money flows do not capture the costs of inefficient
administration or poor policy decisions, which are frequently hidden from central government accounting.
These so-called hidden costs have fiscal implications and reveal the implicit use of resources across
sectors and, to some extent, across economic agents. Thus, public expenditures are not always what they
seem from the input or cost perspective and should be reassessed for efficiency.
Hidden costs are estimated by monetizing the costs of four quasi-fiscal activities (QFAs): mispricing,
unaccounted-for losses, undercollection of invoiced amounts, and labor redundancies. Hidden costs are
important to gauge. Not only do they give a sense of the scope, scale, and opportunity cost of inefficient
operations, but they also help to pinpoint the sources of inefficiency, which may be policy or operational
in nature. From a macro perspective, estimating hidden costs is essential for any accurate assessment of a
country’s budget. The majority of utilities’ hidden costs are ultimately financed by subsidies, direct or
indirect. In efficiency analysis, adding hidden costs to the level of public spending provides a more
realistic proxy of public resource utilization for infrastructure provision, both within and across countries.
QFAs disguise the size and scope of the public sector in general and have sizeable macroeconomic
effects beyond the cash-flow shocks they produce in the public sector. A permanent source of concern in
public finance management, QFAs can: (i) increase financial instability, (ii) be a highly distorting factor
in resource allocation, and (iii) potentially create substantial contingent liabilities (Mackenzie and Stella,
1996). They also create distorted incentives in the economy, leading to overconsumption and waste (Petri
and Taube, 2003). From a macro perspective, QFAs affect the overall public sector balance without
explicitly affecting the budget. More often than not, QFAs escape parliamentary scrutiny, distort the size
36 Or the converse, to produce a specific level of output at the least cost. 37 For a survey of the literature on production frontiers, see Forsund and others (1980) and references cited therein.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
40
of the public sector, and derail economic policy making. To allocate scarce resources properly, it is
critical to know how they are actually allocated, and this is masked by QFAs.
When utilities engage in QFAs, conventional measures of borrowing requirements based on financial
activities are misleading. In terms of fiscal discipline, it is critical to have an accurate estimate of the
magnitude of the budget and its financing. In the presence of QFAs, the overall budget deficit (or
budgetary spending in general) is understated. Cumulative debts and triggered contingent liabilities lead
to ad hoc periodic capitalization and debt swaps that usually bypass parliamentary resource-allocation
approvals. This implies that, in a very inefficient and nontransparent manner, fiscal resources are
allocated without reference to development plans and without any economic or strategic rationale. These
lumpy and ad hoc fiscal interventions divert funds from priority projects, funneling scarce resources into
uses that bring zero or negative return.
The hidden costs of QFAs partially quantify the often unintended and usually nontransparent transfers
from the producer to the consumer, while also yielding a sense of the opportunity cost of inefficient
operations at the sector and enterprise level. Hidden costs include all utility operations that could in
principle be duplicated by specific budgetary measures in the form of an explicit tax, subsidy, or other
direct expenditure (Mackenzie and Stella, 1996). Such costs are self-perpetuating: the absence of financial
sustainability and inappropriate expansion of maintenance, rehabilitation, and service lead to further
waste and create more incentives for utilities to hide their poor financial situation and seek more bailouts.
Identifying and quantifying the sources of hidden costs in utilities is prerequisite to getting them on the
policy agenda and eventually eliminating them. Special attention has been paid to this issue in the energy
sector and in transition economies (Petri and Taube, 2003; Saavalainen and ten Berge, 2006; and
Chivakul and York, 2006), motivated by the heavy burden imposed on these economies by policies that
pursued universal access to electric power regardless of cost.38 For infrastructure sectors the most
comprehensive attempt to estimate hidden costs outside the energy sector has been made by Ebinger
(2006), who estimates the hidden costs for power, natural gas, water supply, and, to some extent,
railways. She finds that for a sample of around 15 transition countries in Europe and Central Asia, QFCs
averaged 4.4 percent of GDP for power, 1 percent GDP for gas, and 1.2 percent for water.
Identifying and reducing inefficiencies in infrastructure operations is perhaps the most practical and
realistic way to free up resources for infrastructure in Africa. Most countries are making huge efforts to
improve their infrastructure. However, they are severely constrained by their ability to afford the
quantities of infrastructure needed to stimulate economic growth. This first attempt to estimate hidden
costs in Africa’s utilities uses, for the water and power sectors, the most common approach for
quantifying hidden costs—the so-called end-product approach. Described in detailed by Ebinger (2006),39
the methodology identifies three relevant QFAs in utilities: mispricing, undercollection, and excessive
unaccounted-for losses. It estimates hidden costs by comparing actual indicators of a functioning state-
owned enterprise (SOE) against ideal norms of cost-recovery, collection ratios, and distribution losses.
For the telecom utilities we quantify the hidden cost of labor redundancies by comparing partial labor
38 In transition economies, QFCs in the power and gas sector combined have been estimated, as a share of GDO, at
between 10 percent (Kyrgyz Republic, Turkmenistan) and 20 percent (Tajikistan, Uzbekistan, Azerbajan). For
details, see Petri and Taube, 2003; and Saavalainen and ten Berge, 2006. 39 See also, Petri, Gunter, and Tsyninki, 2002.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
41
productivity ratios of existing African telecommunications incumbents against world-class fixed-line
providers in the member countries of the Organisation for Economic Co-operation and Development.
Hidden costs in water and power utilities
Quasi-fiscal
activities in Africa entail
average annual hidden
costs of 0.6 percent of
GDP in the water sector,
and 1.9 percent in the
power sector. These
overall aggregates mask
differences across
sectors and among
countries (figure 5.1).
Relative to GDP, hidden
costs for power utilities
are more than double
those for water utilities.
The smaller economic
size of water utilities,
together with their
miscoverage in the
sample (due to
decentralization and
fragmentation), partially
explains the lower
apparent losses. In the
water sector, hidden
costs amount to no more
than 1.5 percent of GDP
except in the
Democratic Republic of
Congo (2.6 percent
GDP), while in the power sector hidden costs are close to zero in South Africa, about 0.2 percent of GDP
in Benin, and more than 4 percent of the GDP in Malawi.
Mispricing is the main source of hidden costs in both power and water supply utilities (table 5.1). In
other words, the main source of hidden costs for African utilities is linked to policy decisions. The
implication is not that subsidies for the poor should be eliminated across the board, but rather that
subsidies and fiscal transfers must be made explicit and accounted for in budgetary appropriations. The
second main source of hidden costs is collection inefficiencies—or failure to collect bills—a cost that is
Figure 5.1 Hidden costs of water and power utilities (share of GDP)
Water
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
Tan
zan
ia
Nig
eria
Ben
in
Cap
e V
erde
Eth
iop
ia
Ugan
da
Nam
ibia
Ken
ya
Burk
ina
Fas
o
Rw
anda
Sudan
Nig
er
South
Afr
ica
Les
oth
o
Mo
zam
biq
ue
Cote
d'I
voir
e
Sen
egal
Mad
agas
car
Zam
bia
Mal
awi
Ghan
a
DR
C
Mispricing Unaccounted Losses Collection Inefficiencies
Power
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
So
uth
Afr
ica
Ben
in
Ken
ya
Mo
zam
biq
ue
Ch
ad
Cap
e V
erd
e
Mad
agas
car
Les
oth
o
Nig
eria
Bu
rkin
a F
aso
Rw
and
a
Eth
iop
ia
Ug
and
a
Cam
ero
on
Zam
bia
Tan
zan
ia
Sen
egal
Gh
ana
Nig
er
Mal
awi
DR
C C
on
go
Under-Pricing Unaccounted Losses Collection Inefficiencies
Source: Authors’ own calculations using data from the AICD Database.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
42
inextricably tied up with related to managerial and administrative practices. For that reason, political will
and capacity building must play a key role in any approach to the problem.
Aid-dependent countries show slightly higher levels of hidden costs relative to their African peers.
The chief causes are mispricing and, in the water sector, collection inefficiencies. Both sources of
inefficiency are distortionary and nontransparent mechanisms of transferring resources to users—and are
very costly from the fiscal viewpoint. In resource-rich countries, mispricing is also the predominant
source of hidden costs, whereas in middle-income countries unaccounted-for losses are the salient factor
for power utilities. The latter are quite worrisome because they are associated with maintenance-deprived
distribution networks.
What these numbers suggest is that, consciously or unconsciously, African governments are using
public utilities for quasi-fiscal purposes. The composition of hidden costs implicitly reveals rational
policy decisions that are somehow related to the overall characteristics of the economy. Tariff regimes
that admittedly do not allow for cost recovery (and that thus provide an implicit subsidy to consumers)
have existed alongside official tolerance of arrearages in customer payments or low collection rates (an
implicit tax on utilities), particularly in oil-exporting countries. Furthermore, tolerance of pilferage (an
implicit subsidy of customers) adds to deferred maintenance and underinvestment (implicit borrowing
from future taxpayers) as the main causes of excessive losses in distribution utilities.
Table 5.1 Typology of quasi-fiscal activities and hidden costs
Water sector Power sector
Mispricing Collection inefficiencies
Unaccounted for losses
Kenya
Mozambique
Nigeria
Malawi
Tanzania
Rwanda
Sudan
Zambia
Rwanda
Malawi
Nigeria
Mozambique
Ghana
Tanzania
Mor
e th
an 3
3% o
f hid
den
cost
s fr
om
Mor
e th
an 6
6 %
of h
idde
n co
sts
from
Senegal
Côte d'Ivoire
Niger
Lesotho
Madagascar
DRC
South Africa
Namibia
Benin
Ethiopia
Cape Verde
Burkina Faso
Uganda
Mispricing Collection inefficiencies
Unaccounted for losses
Lesotho
Senegal
Niger
Cape Verde
Nigeria
Uganda
Mozambique
Nigeria
Benin
Mozambique
Uganda
Mor
e th
an 3
3% o
f hid
den
cost
s fr
om
Mor
e th
an 6
6% o
f hid
den
cost
s fr
om
Zambia
Rwanda
Malawi
Tanzania
Cameroon
Chad
Madagascar
Kenya
Burkina Faso
DRC
Ghana
South Africa
Benin
Kenya
Source: AICD Database.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
43
Table 5.2 Average quasi-fiscal hidden costs and their drivers according to different typologies
Total hidden cost
Unaccounted losses Underpricing
Collection inefficiencies
GDP share (Share of total hidden costs)
WATER
MIC 0.47 12.26 76.65 11.09
Oil–exporting 0.13 14.27 37.72 26.70
LIC–nonfragile 0.50 25.92 51.26 22.83
LIC–fragile 0.95 14.14 76.01 9.85
LIC–coastal 0.56 23.46 64.82 11.72
LIC–landlocked 0.60 24.49 39.23 36.28
Africa average 0.43 16.37 62.04 17.07
Excl. South Africa 0.39 19.86 48.34 23.15
Excl. South Africa and Nigeria 0.49 19.94 49.42 18.40
POWER
MIC 0.02 96.42 0.67 0.24
Oil–exporting 1.46 24.94 39.48 29.35
LIC–nonfragile 1.78 34.77 43.54 21.69
LIC–fragile 1.43 8.45 - 22.29
LIC–coastal 1.26 32.13 33.01 15.70
LIC–landlocked 2.52 26.44 40.84 32.72
Africa Average Ctry 0.84 62.33 19.62 12.99
Excl. South Africa 1.60 27.93 37.53 24.84
Excl. South Africa and Nigeria 1.68 28.69 39.26 18.32
Source: Authors’ calculations using data from the AICD, Fiscal Database, 2008.
Note: Annual averages over the period 2001–06. Averages weighted by countries’ GDPs. Totals may not add up.
Collection rates: tolerance of end-users’ arrears?
Utilities seem to be tolerant of nonpayment even though they cannot fully recover those losses via
cross-subsidies. Utilities in Africa do not “officially” report significant inefficiencies due to collection
problems. About 60 percent of African power and water utilities report collection ratios higher than 90
percent of total bills, with only 10 percent of the utilities reporting collection rates below 60 percent.
These official figures are seemingly contradicted by household data, based on which up to 40 percent
(power) and 60 percent (water) of households do not pay their utility bills (figure 5.2). Regardless,
whether the latter figures compound nonpayment by illegal connections and therefore overstate the
problem, the two sides of the story are difficult to reconcile. On the one hand, the data underscore failures
to enforce payments. On the other hand, collection ratios inconsistent with nonpayments might just reflect
cross-subsidization. Thus, cross-subsidies and relaxed collection practices seem to be joined (“If large
users pay their bills and small users leave unpaid bills, what is the problem?”). By design or by default,
neither collection index is a good proxy for miscollection or aggregate transfers from the producer to the
consumer.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
44
Figure 5.2 Collection and payment rates
Water Power
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
<60 60<>90 <90
Ranges (%)
Reported by Utility Implicit Reported by HHs
0.0
10.0
20.0
30.0
40.0
50.0
60.0
<60 60<>90 <90
Ranges (%)
Reported by Utility Implicit Reported by HHs
Source: Authors’ own calculation using data from the AICD Database.
A more useful assessment of collection inefficiencies compares collected bills against tariff regimes
in place. A quick and dirty implicit collection measure is calculated as the ratio between collected
revenues and effective tariffs. The implicit collection brings a more solid ground to the story of hidden
cost by miscollection when allowing benchmarking of this type of inefficiencies against the aim of policy
makers (decision variable tariff regime and level). Estimates show that about 35 percent (water) and 45
percent (power) of African utilities have implicit collection ratios higher than 90 percent, while 75 percent
(water) and 90 percent(power) have implicit collection rates lower 60 percent of their potential bills.
Frequency charts of implicit ratios show a less extreme distribution of payers and no payers.
Hidden costs associated with collection inefficiencies are relatively small, averaging 0.1 percent of
GDP for water utilities and 0.4 percent GDP for power. In water, only Zambia and Malawi have hidden
collection costs of around 0.4 percent of GDP, perhaps the highest in region. In power, Burkina Faso,
Uganda, Ghana, and Niger have hidden collection costs of more than 1 percent of GDP.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
45
Box 5.1 Subsidies for large electricity users: the case of Zambia
The average effective power tariff in Zambia, at 3 cents per kWh, is one of the lowest in Africa. This current level
does not even allow for recovering operating costs, needless to say total costs. Zambia has one of the lowest average
costs of the region (due to the combination of hydropower technologies with excess generation capacity).
This situation of inefficient pricing policies gets compounded by exceptionally favorable prices that the power utility ZESCO gives to mining companies, particularly Copperbelt Energy Corporation (CEC). A signed long-term
agreement set mining tariffs at 2 cents per kWh, not only below cost-recovery but even one-third lower than the
effective tariff for an average residential customer (100 kWh per month).
As the mining sector is the recipient of 50 percent of total ZESCO sales, this translates into a conservative estimate
of $30 million in annual subsidies, with a projected cumulative deficit of $926 million over the next 10 years.
The Zambia case is not unique to the region. Until the year 2003, Ghana’s power distribution company VRA, was
engaged in a long-term agreement with Volta Aluminum Company. VALCO was for a while VRA’s most important
customer, consuming one-third of its power generation and benefiting from an electricity preferential price estimated
to be half of the cost-recovery level.
Source: (i) Zambia Electricity Regulator Board, 2008 Press Statement on the ERB Decision on ZESCO Application to Revise Electricity Tariffs, other Charges, Fees and Penalties (ii) World Bank (2008), Zambia Growth Infrastructure and Investments: A Role for Public Private Partnership (iii) Chivakul, M. and York, R,M 2006 Implications of Quasi-Fiscal Activities in Ghana, International Monetary Fund Working Paper, Washington D.C.
Unaccounted losses: paying today for yesterday’s neglect?
Two-thirds of African power utilities have transmission and distribution losses more than twice
higher than the acceptable norm for the sector.40 The water sector has a U-shape frequency chart. About
one quarter of the utilities achieves losses within the international norm in clear contrast to a 60 percent of
the sector suffering of losses more than twice higher than the acceptable norm of the sector (figure 5.3).
Excessive power transmission and distribution losses and/or excessive nonrevenue water levels are a
predictable consequence of poor maintenance and rehabilitation as much as inadequate billing and
metering practices. They are also a very crude evidence of the consequence of poor project appraisal
(preference of investment projects over maintenance). Deferring spending on maintenance or/and
misinvesting is perhaps the most perverse and difficult to track financing mechanism of utilities’ cash
deficits. Mismaintenance and investment can disguise today’s poor financial positions but not the
permanent decay of the service—in quantity and quality—for future customers. In fact, provisioning for
subsequent maintenance and replacement investment is the most efficient way of fully grabbing the
economic returns of existing assets.
40 Norms of good performing networks: 10 percent transmission and distribution for power, 20 percent of
nonrevenue water for water.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
46
Figure 5.3 Excessive distribution losses
0%
10%
20%
30%
40%
50%
60%
70%
+/- 10
%
10%
-30%
Hig
her
30%
-60%
Hig
her
60%
-100
% H
ighe
r
100%
+ Hig
her
Deviation from the Norm in Percent
Water Power
On average, hidden costs associated with unaccounted losses amount are a low 0.1 percent of GDP in
the water sector, contributing by less than 20 percent to total cost inefficiencies. Only the Democratic
Republic of Congo sticks out with 0.5 percent of GDP in hidden costs. For Mozambique unaccounted
losses are the main source of inefficiencies, accounting for close to 50 percent of its hidden costs. In
power, however, hidden costs average 0.5 percent of GDP, reaching their highest level in Niger, Ghana,
and Uganda.
Tariff regimes: aiming at cost-recovery?
With the notable exception of Cape Verde and Uganda, tariff regimes in the water sector do not allow
for cost recovery. Clearly, tariffs can be—and actually are—used as a conduit for subsidies by policy
makers. From a fiscal perspective, the budget deficit (or spending) is underestimated. From a sector
perspective, about 80 percent of AFrican countries (19 out of a 24 country sample) have in place cost-
recovery policies to recoup overall operations and maintenance (O&M) and some investment.
Nonetheless, for the large majority of water utilities, tariffs did not meet various investment-cost
threshold levels. At the highest block prices, where cost-recovery is more feasible, only one-third of water
utilities meet capital costs41 while if focusing in average (across user groups) effective tariffs only four
water utilities (out of 40 in the sample) meet simultaneously operation, maintenance and capital costs
41 Seven utilities in South Africa, and Namibia, together with utilities in Cape Verde, Benin , Niger, Rwanda,
Senegal, Burkina, Lesotho and Côte d’Ivoire.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
47
(figure 5.4). This suggests that investment costs are either completely assumed or subsidized by the
governments (central and/or local), communities, and/or NGOs.42
Figure 5.4 Average effective tariffs against historical average costs
Water sector Power sector
Source: Banerjee and others, AICD 2008. Source: Briceño-Garmendia and Shkratan, AICD 2008.
Artificially low tariffs not only create additional economic costs but also induce over-consumption
and waste of already insufficient water supply resources. Altogether, in the water sector, hidden costs due
to underpricing (or mispricing) amount to an average of 0.4 percent for Africa in GDP terms and about 55
percent of total hidden costs in the sector. The hidden costs due to mispricing top 2 percent of GDP in the
Democratic Republic of Congo but also reach over 0.5 percent of GDP for Malawi, Madagascar, Senegal,
and Côte d’Ivoire.43 Admitting the existence of affordability issues that will continue if not addressed,44
the emerging policy issue is that these hidden costs accumulate from the fiscal viewpoint, eventually
inducing instability and ad-hoc spending.
The African power sector is characterized by higher prices and even higher costs. In the power sector,
6 out of 20 countries in the sample have effective tariffs aligned with average historic costs (figure 5.5).
Power mispricing entails hidden costs equivalent to about 1 percent of GDP or 60 percent of total hidden
costs in the sector. This is more than twice the cumulative mispricing hidden costs from the water sector
(0.4 percent of GDP). Malawi (3.3 percent of GDP), Zambia (2.3 percent), Niger (1.7 percent) and
Cameroon (1.6 percent) stick out as the countries creating the highest hidden costs to their economies due
to mispricing.45 Nonetheless, the main concern in power is that historic costs are extremely high by
international standards, being mostly driven by extremely operational costs (Foster and others, 2008).
Given that investment levels carried out by utilities are known to be low, cost-recover tariffs for
42 For details Banerjee, S., Foster V, at al., 2008. 43 For detailed calculations see annex 15. 44 Thoroughly covered in AICD sister piece by Banerjee, S, Wodon, Q., at al., 2008 45 For detailed calculations see annex 16.
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
0.00 1.00 2.00 3.00 4.00
Historical Average Costs ($/m3)
Av
erag
e E
ffec
tiv
e T
arif
f ($
/m3
)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35
Historical Average Costs ($/kWh)
Av
erag
e E
ffec
tiv
e T
arif
f ($
/kW
h)
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
48
admittedly high operational inefficiencies implicitly assume governments would/should finance capital
requirements (further tariff increases might just essentially be unaffordable or infeasible).
Box 5.2 Distributional incidence of capital subsidies to power and water
Widespread underpricing of power and water by utilities in Sub-Saharan Africa prevents the utilities from
recovering their capital costs, or even their full operating costs, from their operating revenues. To make up the
deficits, governments routinely provide substantial capital subsidies to the utilities.
Subsidies for basic services such as water and electricity would be socially benign if access to those services were
more evenly distributed. In fact, however, access is low and sharply skewed to wealthier households. This means
that the capital subsidies are highly regressive—they benefit the rich disproportionately.
Access to power and water provided by utilities is almost nonexistent across the bottom three-fifths of the household
income distribution in Sub-Saharan Africa—with the result that about 80 percent of the utilities’ customers are
drawn from the wealthiest 40 percent of households. Although the precise rate of subsidy distribution does not
depend on access alone but also on levels of consumption and tariff structures, the highly inequitable distribution of
subsidy suggested by the patterns of access is not substantially modified by tariff structures, often because the subsidized tariff bands are so wide that many well-off households fall within them.
0%
20%
40%
60%
80%
100%
Q1 Q2 Q3 Q4 Q5
Piped Water
Electricity
An AICD team studied subsidy incidence for power utilities in 18 Sub-Saharan countries and for water utilities in 15
countries. They devised a parameter ( ) to designate the share of the utility subsidy captured by the poor relative to
their share in the population. Thus, if the poor represent half of the population and capture half of the subsidy, the
parameter has a value of one. Values lower than one indicate a regressive distribution of the subsidy with the poor
capturing less than their share of the population, and vice versa.
The findings are sobering. In all cases studied the value of lies well below one; ranging from as low as 0.01 to a
high of 0.78. In fact, the average value of across all countries and for both services turns out to be only 0.33 for
both power and water. This means that the poor capture a share of the subsidy that is only a third of their share in the
population.
Sources: Banerjee and others, 2008; Wodon and others, 2008.
Investment plans based on more efficient existing operational technologies might lead to lower costs,
and lower cost-recovery power tariffs. A recent costing exercise of investment requirements for the East
Africa and the South Africa power pools (EAPP and SAPP) provides a reference for what incremental
costs could be were investment plans carried out under reasonable (benchmark) unit cost assumptions
(Econ Analysis, 2008). One would expect incremental costs under investment expansions to be higher
than historical costs because capital premiums are essentially nonexistent for historical costs. However,
with few exceptions, in both trade and nontrade scenarios, incremental costs are significantly lower than
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
49
historical costs. In the trade scenario all countries but Madagascar and Ethiopia would face lower average
costs, reduced by about 40 percent on average (figure 5.5). Salient beneficiaries of trade would be
Zambia, Uganda, and Malawi, which while facing increased average costs if carrying power investment
in isolation, would reduce about 25 percent of their current average costs if expanding with trade.
Figure 5.5 Percent improvement of “reasonable” cost investment plans over historic average costs
-100
-80
-60
-40
-20
0
20
40
60
80
100R
wanda
South
Afr
ica
Mozam
biq
ue
Tanzania
Zam
bia
Kenya
Lesoth
o
Uganda
Mala
wi
Eth
iopia
Madagascar
No-trade Trade
Source: Authors’ own calculation using data from the AICD database.
Hidden costs can be reduced by one-fourth just by improving power technologies and therefore
reducing production costs. These results suggest that current mispricing hidden costs are not a disguise
for postponed investment plans but rather a cover up for very high operational inefficiencies. In the
hopeful scenario of (good) scaling-up the sector, reduced “mispricing hidden costs” can potentially be a
significant source of fiscal space or resources in the power sector.
In summary, mispricing hidden costs in power (0.8 percent of GDP) are almost double the 0.5 percent
GDP hidden costs in water. While these aggregate levels might not seen outrageously high from a macro
perspective, they certainly are a substantial source of fiscal space. From a sectoral viewpoint, they hint at
very important issues that claim immediate attention. For instance, long-term sector sustainability would
like require revising tariff schedules and reducing cost inefficiencies by improving the quality of
investments and technologies.
In conclusion, tapping inefficiencies created by quasi-fiscal activities in power and water utilities
could increase the value for money and availability of fiscal resources by about 1.5 percent of GDP in an
average country. This translates into an estimate of over $9.5 billion across Sub-Saharan Africa. The
feasibility of raising tariffs toward cost-recovery levels is contingent on political and social
considerations. However, prices should start reflecting costs in order not to induce waste and
overconsumption, and if subsidies are needed they should be explicit, either in transfers to the utility or,
even better, subsidies targeted to those who actually need them. In the case of collection inefficiencies,
policy and administrative measures can bring short-term results and immediate cash-in benefits.
Obviously, this requires political commitment, first, in netting out payment arrears between government
agencies and utilities, and then creating without demagogy the culture of paying for services. Reducing
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
50
distribution inefficiencies passes through a period where more spending is inevitable before more
resources are made available. Distribution losses are a consequence of neglect because of poor
maintenance, lack of rehabilitation, and/or poor metering and billing, all of which require timely and
targeted spending to overcome.
Telecommunications utilities as social buffers
It is not uncommon that public utilities are used as social buffers redistributing wealth via excessive
employment. This is an evident sign of labor indiscipline. The dollar value of labor redundancies—or
hidden costs of excessive employment—is estimated to amount to up to 0.4 percent of GDP (Benin,
Mozambique and South Africa) or cost in excess that can reach $400 per subscriber (Chad) (table 5.4).
These estimates take as a norm the number of subscribers per employees seen in OECD fixed-line
operators.
Table 5.4 Monetary value of labor redundancies (annual average, 2001–06)
OECD benchmark 700 main lines per employee
Main lines per employee % of GDP $ per subscriber
Benin 20 0.40 91.0
Cameroon 15 0.15 47.9
Chad 63 0.14 488.9
Ethiopia 90 0.07 4.7
Kenya 19 0.00 0.4
Mozambique 138 0.41 69.6
Namibia 22 0.38 10.5
South Africa 17 0.43 0.3
Tanzania 30 0.31 123.2
Average 46 0.26 92.9
Source: Authors’ own calculation using data from the AICD Database.
Estimates only include countries with state-owned telecom utilities.
These striking results for labor inefficiencies underscore the importance of strengthening external
governance mechanisms that can impose discipline on SOEs behavior. The relation between increased
governance and lower labor costs attributed to inefficiencies is direct (figure 5.6).
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
51
Figure 5.6 Link between governance and cost of labor redundancy
y = -5.513x + 248.26
0
100
200
300
400
500
600
0 20 40 60 80 100
Governance Score
Cost
of
Lab
or
Red
undan
cy
(US
$/s
ubsc
riber
)
Source: AICD.
In conclusion, telecommunications labor inefficiencies are almost 0.3 percent of GDP on average,
equivalent to $2.1 billion. While public incumbents do not have to play an important role in providing
telephony services, the hidden costs of labor redundancies would be borne by governments if and when
the explosion of small private cellular providers continues and prices continue their downward trend. If
the regulatory framework is such to deter competitors and induce discriminatory practices, the hidden
labor costs would very likely then be transferred to users via higher tariffs.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
52
6 Conclusions and policy implications
Sub-Saharan African countries are devoting substantial shares of GDP to infrastructure, but that does
not amount to much in absolute terms. Countries typically devote 6-12 percent of their GDP to
infrastructure, when all sources are taken into account. While these shares look relatively large compared
to middle- and high-income countries, due to very low levels of GDP, they amount to very little in
absolute terms. On average, low-income countries are spending less than $50 per capita per year, with
public investment being only a fraction of this.
There is a marked division of labor between state-owned enterprises (SOEs) and central government.
While SOEs account for the bulk of infrastructure spending in most countries, they undertake very little
capital spending. Most public investment on infrastructure continues to be undertaken through the central
government budget, with assets often transferred to SOEs for subsequent operation and maintenance.
Despite a favorable budget environment, only aid-dependent countries seem to allocate additional
resources to infrastructure. The combination of a commodity boom and widespread debt relief has created
substantial buoyancy in government budgets. In the case of aid-dependent countries, about 30 percent of
the additional envelope has been allocated to infrastructure. However, in middle-income countries almost
none of the additional budget has gone to infrastructure. Moreover, in oil-dependent countries,
infrastructure investment has actually fallen substantially even as resource revenues have surged.
Budget envelopes could be substantially increased by addressing substantial inefficiencies. Three
major sources of inefficiency have been identified in infrastructure spending: undermaintenance, budget-
execution failures, and hidden costs.
There is both direct and indirect evidence of undermaintenance leading to higher lifecycle costs of
infrastructure. On average, around 30 percent of the region’s infrastructure assets are in need of
rehabilitation. Given that the present value of rehabilitating infrastructure is substantially higher than that
associated with a sound preventive maintenance regime, this finding already indicates that, over time,
countries are spending more than they should to preserve a given infrastructure stock. In the case of the
road sector, it is possible to isolate maintenance expenditure with greater precision than elsewhere and to
compare it to rigorously determined country-specific benchmarks. Here it becomes apparent that roughly
half of the countries show shortfalls of 40 percent or more in maintenance spending.
Capital budget-execution ratios of only 66 percent on average indicate potential for a budget-neutral
50 percent increase in public investment. Deficiencies in planning, project preparation, and public
procurement conspire to create delays that prevent countries from spending more than two-thirds of the
public investment allocated to infrastructure in the budget. Addressing the causes of low budget execution
deserves very serious attention, since this alone could increase public investment by 50 percent without
any increase in budgeted resources. Moreover, until such deficiencies are addressed it will remain
difficult to achieve higher levels of investment even if more external resources are injected.
The hidden costs of power, water, and telecommunications utilities absorb around 1.8 percent of
GDP, presenting a major incentive to address inefficiency. A combination of underpricing,
undercollection of revenues, and very high distribution losses relative to technical norms lead to high
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
53
hidden costs of power and water. Underpricing is by far the largest contributor in both sectors, although
inefficiencies are also important. The dividend attached to solving these problems is very high—1.5
percent of GDP, and even more in some cases. Increasing efficiency would free up substantial resources
for public investment without increasing budget allocations.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
54
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Appendixes
Appendix 1. Sector scope, functional classification
Appendix 2. Sources of data for data for expenditures of infrastructure
Appendix 3. Country groups
Appendix 41. Primary fiscal balances
Appendix 5. Net change in central government budget: breakdown by source
Appendix 6. Net change in central government budget: breakdown by use
Appendix 7. Expenditure on main road network
Appendix 8. Variance around the trend line of road expenditure
Appendix 9. Contributions to QFCs (country aggregates)—water sector
Appendix 10. Contributions to QFCs (country aggregates)—power sector
Appendix 11. Water: efficiency and production indicators, 2006
Appendix 12. Electricity: efficiency and production indicators, 2006
Appendix 13. Annual maintenance and preservation expenditures, 2001–05
Appendix 14. Average annual maintenance expenditures on main road network, 2001–05
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
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Appendix 1. Sector scope, functional classification
Code Category Included in cross-country comparison
704 Economic affairs
7042 Agriculture, Forestry, Fishing and Hunting
Irrigation Systems (a share of 70421 Agriculture) no
7047 Other Industries
70474 Multipurpose development projects no
7043 Fuel and energy
70431 Coal and other solid mineral fuels yes
70432 Petroleum and natural gas yes
70433 Nuclear fuel yes
70434 Other fuels yes
70435 Electricity yes
70436 Nonelectric energy yes
7045 Transport
70451 Road transport yes
70452 Water transport no
70453 Railway transport no
70454 Air transport no
70455 Pipeline and other transport no
7046 Communication
70460 Communication yes
705 Environmental protection
7052 Waste water management yes
70520 Waste water management yes
706 Housing and community amenities
7063 Water supply
70630 Water supply yes
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
59
Appendix 2. Sources of data on infrastructure expenditures
General government sector Nonfinancial corporations sector
Source of documents Consolidated budget of general government (or separate budget books of each government unit):
Recurrent budget
Development budget
of which external development budget
Special funds
Financial statements of public nonfinancial corporations:
Balance sheet
Income statement
Cash flow statement
Stages Estimates, releases, actuals Actuals
Institutional categories Central government, local government
Economic classification GFMS2001
Source Ministry of Finance SOE
Sources of expenditures Externally funded (donor) expenditures are identified separately
Not available (e.g., what share of expenditures by borrowing)
Appendix 3. Country groups46
Oil-exporting countries
Countries where net oil exports make up 30 percent or more of total exports. Oil exporters are
classified as such even if they would otherwise qualify for another group
Middle-income countries
Countries that are not oil exporters and have per capita income higher than $905, according to 2006
gross national income per capita as calculated by the World Bank.
Non-fragile low-income countries
Countries that are not oil exporters and have per capita income equal or lower than $905 and a score
higher than 3.2 on the Country Policy and Institutional Performance Assessment of the World Bank,
following the classification in the 2007 Global Monitoring Report.
Non-fragile low-income countries
The remaining countries of the sample.
46 Adapted from IMF (2007) p.4.
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Appendix 4. Primary fiscal balances
GDP share Domestic revenues Grants
Budgetary expenditures
excl. debt service Primary balance
Primary balance excl.
grants
Benin 16.26 2.05 19.09 (0.79) (2.84)
Burkina Faso 11.86 5.28 20.50 (3.37) (8.65)
Cameroon 16.63 0.41 13.40 3.64 3.24
Cape Verde 22.36 7.48 31.42 (1.57) (9.06)
Chad 8.94 3.18 16.88 (4.77) (7.94)
Congo, Dem. Rep. 8.64 1.92 10.46 0.10 (1.82)
Côte d'Ivoire 13.81 0.61 13.05 1.37 0.76
Ethiopia 16.95 4.99 25.27 (3.32) (8.32)
Ghana 20.90 5.27 24.69 1.48 (3.79)
Kenya 18.48 1.42 18.82 1.08 (0.34)
Lesotho 45.59 2.59 43.75 4.43 1.85
Madagascar 10.29 5.03 25.10 (9.79) (14.81)
Malawi 18.92 8.66 28.08 (0.50) (9.16)
Mozambique 12.79 9.49 24.46 (2.18) (11.67)
Namibia 31.28 0.12 32.76 (1.36) (1.48)
Niger 10.79 5.76 18.39 (1.84) (7.60)
Nigeria 41.63 - 35.24 6.39 6.39
Rwanda 12.77 9.48 22.31 (0.05) (9.54)
Senegal 18.09 1.82 20.88 (0.97) (2.79)
South Africa 23.24 - 20.67 2.57 2.57
Tanzania 11.42 5.07 17.43 (0.94) (6.01)
Uganda 9.33 5.97 16.85 (1.55) (7.52)
Zambia 17.93 9.65 25.62 1.96 (7.70)
Source: AICD, Fiscal Database, 2008; IMF Statistical Appendixes, WB DDP
Note: Annual averages over the period 2001–06
Averages weighted by countries’ GDP. Totals may not add up.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
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Appendix 5. Net change in central government budget: breakdown by source
GDP share CG net expenditure
budget Domestic revenues of which oil revenues Grants Net borrowing
Benin (1.07) 0.42 - 1.06 (2.54)
Burkina Faso (0.10) 0.63 - (0.89) 0.16
Cameroon 0.05 1.11 0.15 0.22 (1.28)
Cape Verde 0.73 (0.08) - (1.91) 2.72
Chad (6.59) 1.27 4.22 (2.75) (5.11)
Congo, Dem. Rep. 9.06 3.63 - 4.84 0.59
Côte d'Ivoire 1.54 (0.47) 0.88 0.59 1.42
Ethiopia (0.23) (0.21) - 0.93 (0.95)
Ghana 7.14 5.80 - 2.11 (0.77)
Kenya 1.12 (1.00) - 1.21 0.90
Lesotho (3.26) 6.57 - (3.06) (6.77)
Madagascar 2.87 0.77 - 1.85 0.25
Malawi 7.53 7.71 - 5.28 (5.46)
Mozambique (3.87) 1.15 - (4.01) (1.01)
Namibia (1.89) 1.27 - (0.10) (3.06)
Niger 1.83 (0.57) - 2.38 0.03
Nigeria (4.68) 6.80 10.44 - (11.48)
Rwanda 3.37 0.59 - 5.15 (2.38)
Senegal 4.73 1.42 - (0.00) 3.32
South Africa 4.32 3.45 - - 0.87
Tanzania 6.64 1.43 - 3.10 2.11
Uganda (2.79) (0.66) - (0.32) (1.82)
Zambia (6.38) (1.05) - 17.43 (22.76)
Source: AICD, Fiscal Database, 2008; IMF Statistical Appendixes, WB DDP.
Note: Net change, 2001–06. Averages weighted by countries’ GDP. Totals may not add up.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
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Appendix 6. Net change in central government budget: breakdown by use
GDP share
CG net expenditure
budget Wages Other current of which
Infrastructure Capital
expenditure of which
infrastructure
Benin (1.07) 1.06 (2.24) 0.04 0.11 0.20
Burkina Faso (0.10) 0.13 (0.74) - 0.51 -
Cameroon 0.05 (0.58) 0.63 (0.01) 0.01 0.71
Cape Verde 0.73 1.89 (0.87) (0.04) (0.29) (0.50)
Chad (6.59) (1.21) (2.31) 0.21 (3.08) 0.18
Congo, Dem. Rep. 9.06 2.32 3.90 - 2.84 -
Côte d'Ivoire 1.54 0.11 1.86 0.13 (0.43) 0.32
Ethiopia (0.23) 0.22 (2.25) (0.04) 1.80 2.16
Ghana 7.14 (0.00) 1.26 (0.09) 5.88 0.81
Kenya 1.12 (0.03) (0.17) (0.00) 1.31 (0.30)
Lesotho (3.26) (1.17) 0.98 0.10 (3.08) 0.01
Madagascar 2.87 0.04 (0.14) (0.02) 2.98 2.04
Malawi 7.53 0.74 3.25 (2.11) 3.54 0.30
Mozambique (3.87) 0.38 0.06 0.01 (4.31) 0.44
Namibia (1.89) 0.60 (0.98) (0.04) (1.51) (0.01)
Niger 1.83 (0.31) (0.66) 0.26 2.80 0.18
Nigeria (4.68) (2.36) (0.27) 0.04 (2.05) (2.20)
Rwanda 3.37 (1.35) 0.81 0.09 3.91 0.73
Senegal 4.73 0.23 1.95 (1.61) 2.55 (0.20)
South Africa 4.32 (0.13) 5.17 (0.02) (0.71) 0.04
Tanzania 6.64 0.25 2.80 1.00 3.59 1.13
Uganda (2.79) (0.30) 0.98 (0.08) (3.47) 0.87
Zambia (6.38) (0.79) 2.33 0.73 (7.91) (1.97)
Source: AICD, Fiscal Database, 2008; IMF Statistical Appendixes, WB DDP.
Note: Net change, 2001–06. Averages weighted by countries’ GDP. Totals may not add up.
FINANCING PUBLIC INFRASTRUCTURE IN SUB-SAHARAN AFRICA
63
Appendix 7. Expenditure on main road network
Length of main network Maintenance expenditures (**)
Rehabilitation/capital expenditures (***)
(km) Annual Averages in $ per km
Benin 4,735 $ 3,015.81 $ 4,306.67
Burkina Faso 10,231 n.a. n.a.
Cameroon 11,008 $ 2,608.66 $ 5,823.50
Chad n.a. n.a.
Côte d'Ivoire 13,291 n.a. $ 9,016.45
Ethiopia 9,721 $ 2,599.32 $ 19,524.64
Ghana 11,139 $ 2,339.67 $ 11,557.48
Kenya 22,341 $ 2,872.55 $ 1,176.95
Lesotho 3,005 $ 1,255.09 $ 8,353.15
Madagascar 9,599 $ 1,450.28 $ 11,851.26
Malawi 3,444 $ 1,960.15 $ 6,952.26
Mozambique 9,808 $ 2,371.65 $ 12,209.19
Namibia 16,362 $ 3,462.30 $ 166.70
Niger 6,055 $ 493.59 $ 4,137.90
Nigeria $ 16,963.74
Rwanda 2,836 $ 491.75 $ 6,466.14
Senegal 4,780 n/a $ 24,937.63
South Africa (*) 76,288 $ 2,074.64 $ 708.23
Tanzania 28,730 $ 1,433.37 $ 4,162.02
Uganda 9,171 n/a $ 14,367.39
Zambia 3,290 $ 3,029.45 $ 36,348.70
Averages (weighted according to the size of network)
Resource-intensive countries $ 2,852.39 $ 13,243.52
Non-resource-intensive (excluding South Africa) $ 2,001.27 $ 9,973.38
Aid dependent $ 2,020.88 $ 11,342.76
HIPC countries (end of 2005) $ 1,985.69 $ 11,470.62
Middle-income countries $ 2,134.58 $ 684.84
Average $ 2,076.51 $ 8,384.69
Average excluding South Africa $ 2,077.23 $ 10,769.11
Source: AICD Fiscal Baselines, 2007. Primary network length data from RONET. South Africa, expenditures of SANRAL.
Note: Based on annual averages for the period 2001–05
n.a. - time series were not long enough to calculate the standard errors.
(*) Excludes local governments )
(**) Includes expenditures of roads fund plus current expenditures of central government (current expenditures excluding transfers to local municipalities)
(***) Capital expenditures of central government and externally funded expenditures
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Appendix 8. Variance around the trend line of road expenditure
Current expenditures (*) Capital expenditures (**)
Road funds Central government External Central government
Benin 16.50 44.01 36.51 62.39
Cameroon 23.91 9.87 23.17 19.17
Chad 2.06 21.84 33.42 79.40
Côte D'Ivoire n.a. 57.44 n.a. 12.86
Ethiopia 3.59 n.a. n.a. 13.48
Ghana 10.89 36.77 19.51 20.08
Kenya 4.14 3.61 n.a. 12.85
Madagascar 27.12 43.12 24.65 16.92
Malawi 10.02 78.18 n.a. 45.17
Namibia 4.00 n.a. n.a. n.a.
Niger 19.59 n.a. n.a. n.a.
Rwanda 21.79 16.16 38.18 50.64
Senegal n.a. 102.67 18.74 57.93
Tanzania n.a. 4.54 28.83 8.09
Uganda n.a. n.a. n.a. n.a.
Mozambique 7.92 13.02 32.32 23.25
Nigeria n/a 47.36 n.a. 15.10
Lesotho 11.18 9.42 9.25 35.66
Zambia n.a. 12.05 25.65
Weighted averages (according to size of network)
Resource-intensive countries 13.55 38.29 27.18 23.05
Non-resource-intensive (excluding South Africa)
10.22 22.02 29.59 27.59
Aid dependent 11.13 22.07 31.80 28.06
Weighted average (size of network)
10.59 26.61 29.30 26.36
Simple average 12.52 33.34 26.46 31.16
Source: AICD Fiscal Baselines, 2007
Note: Based on annual averages for the period 2001–05.
n.a. - time series were not long enough to calculate the standard errors.
(*) Includes expenditures of roads fund plus current expenditures of central government (current expenditures excluding transfers to local municipalities)
(**) Capital expenditures of central government and externally funded expenditures
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Appendix 9. Contributions to QFCs (country aggregates)—water sector
Total hidden
cost Unaccounted
losses 2/ Underpricing 3/
Collection inefficiencies
4/ Unaccounted
losses Underpricing Collection
inefficiencies
Country Share of GDP (%) Share of total QFC (%) Share of GDP (%)
Tanzania 0.1 33.1 35.8 31.1 0.0 0.0 0.0
Nigeria 0.2 19.7 45.7 34.6 0.0 0.1 0.1
Benin 0.2 15.1 73.0 11.9 0.0 0.1 0.0
Cape Verde 0.3 15.3 0.0 84.7 0.0 0.0 0.2
Ethiopia 0.3 26.8 70.8 2.4 0.1 0.2 0.0
Uganda 0.3 27.7 0.0 72.3 0.1 0.0 0.2
Namibia 0.3 0.1 73.5 26.4 0.0 0.2 0.1
Kenya 0.3 28.2 60.0 11.8 0.1 0.2 0.0
Burkina Faso 0.3 0.0 22.6 77.4 0.0 0.1 0.2
Rwanda 0.3 29.7 34.4 35.9 0.1 0.1 0.1
Sudan 0.4 26.1 27.3 46.5 0.1 0.1 0.2
Niger 0.4 0.0 95.2 4.8 0.0 0.4 0.0
South Africa 0.5 12.5 77.0 10.4 0.1 0.4 0.0
Lesotho 0.5 17.0 83.0 0.0 0.1 0.4 0.0
Mozambique 0.5 49.2 50.4 0.4 0.3 0.3 0.0
Côte d'Ivoire 0.8 2.3 97.7 0.0 0.0 0.8 0.0
Senegal 0.9 0.2 99.8 0.0 0.0 0.9 0.0
Madagascar 0.9 20.3 79.7 0.0 0.2 0.7 0.0
Zambia 1.1 28.1 32.7 39.1 0.3 0.3 0.4
Malawi 1.2 23.6 43.0 33.4 0.3 0.5 0.4
Ghana 1.2 42.7 30.6 26.7 0.5 0.4 0.3
Congo, D.R. 1.3 40.8 27.2 32.0 0.5 0.4 0.4
Source: Authors’ own calculations using data from the AICD Database.
2/ Unaccounted Losess = (End-User Consumption) * (Average Cost Recovery Price)* (Total Loss Rate - Normative Loss Rate)/(1-Normative Loss Rate)
3/ Underpricing = End-User Consumption * (Average Cost Recovery Price - Average Actual Tariff).
4/ Collection Inefficiencies = End-user Comsumption* Average Actual Tariff* (1 - Collection rate)
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Appendix 10. Contributions to QFCs (country aggregates)—power sector
Total Hidden Cost Unaccounted
Losses 2/ Underpricing
3/
Collection Inefficiencies
4/ Unaccounted
Losses Underpricing Collection
Inefficiencies
Country Share of GDP (%) Share of total QFC (%) Share of GDP (%)
South Africa 0.00 100.0 0.0 0.00 0.00 0.00 0.0
Benin 0.24 61.7 0.0 38.26 0.15 0.00 0.1
Kenya 0.47 60.4 39.6 0.00 0.28 0.19 0.0
Mozambique 0.67 45.1 14.0 40.91 0.30 0.09 0.3
Chad 1.00 28.5 71.5 0.00 0.29 0.72 0.0
Cape Verde 1.18 26.1 45.3 28.56 0.31 0.53 0.3
Madagascar 1.31 28.9 71.1 0.00 0.38 0.93 0.0
Lesotho 1.37 22.7 61.9 15.48 0.31 0.85 0.2
Nigeria 1.42 26.1 33.4 40.51 0.37 0.47 0.6
Burkina Faso 1.47 24.6 0.0 75.38 0.36 0.00 1.1
Rwanda 1.74 23.2 76.8 0.00 0.40 1.33 0.0
Ethiopia 1.78 30.0 70.0 0.00 0.54 1.25 0.0
Uganda 1.85 40.8 0.0 59.17 0.75 0.00 1.1
Cameroon 2.22 28.2 71.8 0.00 0.62 1.59 0.0
Zambia 2.33 3.5 96.5 0.00 0.08 2.25 0.0
Tanzania 2.41 24.7 75.3 0.00 0.59 1.82 0.0
Senegal 2.47 24.9 50.4 24.73 0.62 1.24 0.6
Ghana 3.14 25.7 0.0 74.32 0.81 0.00 2.3
Niger 3.51 22.8 49.3 27.96 0.80 1.73 1.0
Malawi 4.36 17.4 76.6 6.0 0.8 3.3 0.3
DRC 4.66 27.5 0.0 72.51 1.28 0.0 3.4
Source: Authors’ own calculations using data from the AICD Database.
2/ Unaccounted Losses = (End-User Consumption) * (Average Cost Recovery Price) * (Total Loss Rate - Normative Loss Rate) / (1-Normative Loss Rate)
3/ Underpricing = End-User Consumption * (Average Cost Recovery Price - Average Actual Tariff)
4/ Collection Inefficiencies = End-user Comsumption* Average Actual Tariff* (1 - Collection rate)
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Appendix 11. Water: efficiency and production indicators, 2006
Country Utility
End user consumption
(M3/year)
Average cost
recovery tariff
(USD/m3)
Average effective tariff (USD/m3) 5/
NRW (%)
Implicit collection ratio (%) 6/
Benin SONEB 22,977,000 1.10 0.85 24 95
Burkina Faso ONEA 33,817,240 1.15 1.03 18 60
Cameroon SNEC — 1.06 0.67 35 —
Cape Verde ELECTRA 2,931,781 3.24 4.20 31 81
Chad STEE — 1.06 0.29 35 33
Côte d'Ivoire SODECI 128,853,000 1.03 0.09 22 100
DRC REGIDESO 137,052,336 1.10 0.91 41 75
Ethiopia ADAMA 2,660,048 0.72 0.39 43 89
Ethiopia AWSA 52,395,813 0.72 0.33 37 96
Ethiopia DIRE DAWA 2,565,138 0.58 0.24 22 96
Ghana GWC 130,463,393 1.06 0.71 53 63
Kenya KIWASCO 1,800,000 1.53 0.81 71 79
Kenya MWSC 12,516,164 0.89 0.67 38 21
Kenya NWASCO 97,869,588 0.56 0.24 38 100
Lesotho WASA 11,213,684 1.10 0.58 28 100
Madagascar JIRAMA 63,756,027 0.87 0.29 34 100
Malawi BWB 14,185,900 0.97 0.40 51 100
Malawi CRWB 4,722,000 0.81 0.32 17 57
Malawi LWB 23,600,000 0.63 0.74 22 57
Mozambique AdeM Beira 3,771,240 0.91 0.52 60 100
Mozambique AdeM Maputo 25,432,162 1.13 0.52 62 100
Mozambique AdeM Nampula 2,447,001 0.75 0.52 44 98
Mozambique AdeM Pemba 1,214,471 0.93 0.52 45 82
Mozambique AdeM Quilimane 978,095 0.82 0.52 35 100
Namibia Oshakati Municipality 1,031,884 1.84 1.98 21 81
Namibia Walvis Bay Municipality 3,626,811 1.06 0.97 16 100
Namibia Windhoek Municipality 17,500,000 2.48 1.73 14 85
Niger SPEN 33,791,483 0.86 0.49 19 96
Nigeria Borno — 1.06 0.67 35 31
Nigeria FCT 17,300,000 1.06 0.53 80 31
Nigeria Kaduna 63,690,000 1.06 0.22 21 31
Nigeria Katsina 31,000,000 0.46 0.26 14 24
Nigeria Lagos 50,405,903 0.66 0.67 57 31
Nigeria Plateau 14,660,000 1.06 0.67 23 31
Rwanda ELECTROGAZ 9,780,000 0.91 0.67 38 63
Senegal ONAS nap 1.06 0.67 35 87
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Senegal SDE 99,640,165 1.25 0.50 20 100
South Africa Cape town metro 215,660,000 1.61 0.47 18 100
South Africa Drakenstein Municipality 13,464,003 1.10 0.35 12 100
South Africa eThekwini Metro (Durban) 200,000,000 1.96 1.42 32 79
South Africa Joburg 328,246,350 1.90 0.32 31 45
Sudan Khartoum Water Corporation 150,000,000 0.68 0.50 40 38
Sudan South Darfur Water Corporation 3,700,000 0.89 0.86 49 59
Sudan Upper Nile Water Corporation 1,788,500 1.13 0.80 29 48
Tanzania DAWASCO — 1.06 0.61 35 30
Tanzania DUWS 5,110,000 0.82 0.54 30 63
Tanzania MWSA 7,432,028 0.59 0.47 49 73
Uganda NWSC 38,000,000 1.00 1.01 34 53
Zambia LWSC 35,346,257 0.67 0.53 54 43
Zambia NWSC 73,799,000 0.60 0.35 37 33
Zambia SWSC 8,136,000 0.70 0.40 55 33
— = data not available.
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Appendix 12. Electricity: efficiency and production indicators, 2006
Volume electricity billed
(GWh/year)
Historical normative cost recovery tariff ($) – average
total cost
Average effective tariff (USD/kwH) 1/
System losses
(% electricity produced)
Implicit collection ratio
(%) 2/
Benin SBEE 506.6 0.14 0.15 18.25 95
Burkina Faso SONABEL 537.5 0.24 0.31 24.68 63
Cameroon AES SONEL 3263.7 0.14 0.06 30.79 100
Cape Verde Electra 161.7 0.23 0.20 17.32 90
Chad STEE 78.0 0.84 0.30 33.20 100
Côte d'Ivoire CIE 5741.9 0.34 — — 77
DRC Congo SNEL 4091.8 0.07 0.10 40.00 42
Ethiopia EEPCO 2069.2 0.09 0.02 40.00 100
Ghana VRA 3762.0 0.13 0.15 25.44 55
Kenya KPLC 4379.0 0.14 0.13 18.10 53
Lesotho LEC 353.6 0.11 0.08 20.00 89
Madagascar JIRAMA 753.7 0.17 0.10 23.74 100
Malawi Escom 1055.0 0.10 0.04 23.00 86
Mozambique EDM 1306.7 0.09 0.09 25.00 84
Namibia NamPower 3363.0 0.00 — 12.00 —
Namibia NORED 131.4 — — 18.00 —
Niger NIGELEC 333.1 0.43 0.26 27.00 61
Nigeria PHNC 21401.9 0.08 0.05 30.00 52
Rwanda ELECTROGAZ 157.4 0.38 0.20 23.00 100
Senegal SENELEC 1710.0 0.24 0.18 21.19 84
South Africa ESKOM 256959.0 0.06 0.08 10.03 100
Sudan NEC — — — — —
Tanzania TANESCO 2628.0 0.16 0.07 26.00 100
Uganda UEDCL 1806.4 0.12 0.14 36.00 63
Zambia ZESCO 3516.0 0.08 0.03 12.00 100
Source: AICD Database.
— = data not available.
1/ Average Effective Tariff = (Effective Residential Tariff at 100 kwh)*{Residential Share on Consumption / Residential Share on Revenue} 2/ Implicit Collection Ratio = (Average Revenue/Average Effective Tariff) or (Reported Non Payment)
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Appendix 13. Annual maintenance and preservation expenditures, 2001–05
Km of primary in fair and good condition
Maintenance expenditure (**) (US$ millions)
Preservation norms (***)
(US$ millions)
Benin 911.32 $ 16.43 $ 27.20
Cameroon 2,372.09 $ 43.05 $ 57.82
Chad 862.42 $ 3.88 $ 23.49
Côte D'Ivoire 4,545.94 $ 0.68 $ 61.28
Ethiopia 4,122.02 $ 43.82 $ 57.11
Ghana 6,284.60 $ 71.73 $ 103.03
Kenya 6,657.12 $ 110.36 $ 122.96
Lesotho 676.72 $ 9.61 $ 22.62
Madagascar 3,804.92 $ 19.15 $ 47.53
Malawi 2,567.75 $ 15.67 $ 35.42
Mozambique 4,819.70 $ 67.73 $ 75.94
Namibia 5,589.24 $ 101.81 $ 85.45
Niger 3,250.09 $ 8.24 $ 29.86
Rwanda 945.43 $ 5.79 $ 8.57
Senegal 2,141.99 $ 20.00 $ 47.53
South Africa 59,665.02 $ 390.54 $ 537.79
Tanzania 4,100.02 $ 128.48 $ 123.11
Uganda 2,119.06 $ 24.87 $ 91.10
Zambia 3,989.00 $ 86.06 $ 65.31
Notes: Primary network length and road quality data from RONET. South Africa, expenditures of SANRAL
Source: AICD Fiscal Baselines, 2007
(**) Includes expenditures of roads fund plus current expenditures of central government (current expenditures excluding transfers to local municipalities)
(***) RONET data
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Appendix 14. Average annual maintenance expenditures on main road
network, 2001–05
Length of main
network (km)
Road fund expenditure
on main roads (*) ($)
Maintenance expenditures
on main roads (**) ($ per km per year)
Rehabilitation
expenditures (***) ($)
Benin 4,735 $ 14,279,046 $ 3,015.81 $ 20,390,875
Burkina Faso 10,231 n.a. n.a. n.a.
Cameroon 11,008 $ 28,714,978 $ 2,608.66 $ 64,102,491
Chad n.a. n.a. n.a. $ 100,431,165
Côte d'Ivoire 13,291 n.a. n.a. $ 119,835,431
Ethiopia 9,721 $ 25,267,135 $ 2,599.32 $ 189,792,767
Ghana 11,139 $ 26,061,377 $ 2,339.67 $ 128,737,666
Kenya 22,341 $ 64,176,053 $ 2,872.55 $ 26,294,362
Lesotho 3,005 $ 3,770,923 $ 1,255.09 $ 25,097,111
Madagascar 9,599 $ 13,921,492 $ 1,450.28 $ 113,762,596
Malawi 3,444 $ 6,750,000 $ 1,960.15 $ 23,940,957
Mozambique 9,808 $ 23,262,139 $ 2,371.65 $ 119,752,630
Namibia 16,362 $ 56,650,000 $ 3,462.30 $ 2,727,596
Niger 6,055 $ 2,988,755 $ 493.59 $ 25,055,751
Rwanda 2,836 $ 1,394,509 $ 491.75 $ 18,336,585
Senegal 4,780 n.a. n/a $ 119,199,012
Tanzania 28,730 $ 41,180,461 $ 1,433.37 $ 119,573,801
Uganda 9,171 n/a n/a $ 131,767,055
Zambia 3,290 $ 9,966,882. $ 3,029.45 $ 119,587,211
Arithmetic average $ 2,097.22 $81,576,948
South Africa (*) 76,288 $ 158,270,950.42 $ 2,074.64 $ 119,199,012.08
Notes: Primary network length data from RONET. South Africa, expenditures of SANRAL
Source: AICD Fiscal Baselines, 2007
Notes: Primary network length data from RONET. South Africa, expenditures of SANRAL
(*) Excludes local governments)
(**) Includes expenditures of roads fund plus current expenditures of central government (current expenditures excluding transfers to local municipalities)
(***) Capital expenditures of central government and externally funded expenditures