metadata: authors: 0: author: Briceno-Garmendia,Cecilia M.
1: author: Shkaratan,Maria
count: World
docna: 0: docna: Power tariffs : caught between cost recovery
            and affordability
docty: Policy Research Working Paper
subtopic: Mining & Extractive Industry (Non-Energy),Power & Energy Conversion,Energy Policies & Economics,Energy Sector Regulation,Energy Demand,Energy and Environment,Energy and Mining
teratopic: Infrastructure Economics and Finance,International Economics and Trade,Energy,Finance and Financial Sector Development
abstracts: cdata!: This is the first paper to build a
            comprehensive empirical picture of power pricing practices
            across Sub-Saharan Africa, based on a new database of tariff
            structures in 27 countries for the years 2004-2008. Using a
            variety of quantitative indicators, the paper evaluates the
            performance of electricity tariffs against four key policy
            objectives: recovery of historic power production costs,
            efficient signaling of future power production costs,
            affordability to low income households, and distributional
            equity. As regards cost recovery, 80 percent of the
            countries in the sample fully recover operating costs, while
            only around 30 percent of the countries are practicing full
            recovery of capital costs. However, due to the fact that
            future power development may be based on a shift toward more
            economic technologies than those available in the past,
            existing tariffs look as though they would be consistent
            with Long Run Marginal Costs in nearly 40 percent of
            countries and hence provide efficient pricing signals. As
            regards affordability, today's average effective
            tariffs are affordable for 90 percent of today's
            customers. However, they would only be affordable for 25
            percent of households that remain unconnected to the grid.
            Tariffs consistent with full recovery of economic costs
            would be affordable for 70 percent of the population. As
            regards equity, the highly regressive patterns of access to
            power services, ensure that subsidies delivered through
            electricity tariffs are without exception also highly
            regressive in distributional incidence. The conclusion is
            that achieving all four of these policy objectives
            simultaneously is almost impossible in the context of the
            high-cost low-income environment that characterizes much of
            SSA today. Hence most countries find themselves caught
            between cost recovery and affordability.
repnme: repnme: Power tariffs : caught between cost
            recovery and affordability
display_title: Power tariffs : caught between cost
            recovery and affordability
listing_relative_url: /research/2011/12/15536521/power-tariffs-caught-between-cost-recovery-affordability
topicv3: Energy and Extractives m1326595 507,Power Systems m1327093 508,Power and Electricity Sector m1327094 515,Mining Sector m1327113 554,Power Tariffs m1503430 3942
publishtoextweb_dt: 2011-12-01T00:00:00Z
historic_topic: Infrastructure Economics and Finance,International Economics and Trade,Energy,Finance and Financial Sector Development
no_of_pages: 64
author_keys: 0: author_key: 420925
1: author_key: 143827
model_extractions: WPS5904 Policy Research Working Paper 5904

# Power Tariffs

Caught between Cost Recovery and Affordability _Cecilia Briceño-Garmendia Maria Shkaratan_ The World Bank Africa Region Sustainable Development Unit December 2011
Policy Research Working Paper 5904

# **Abstract**

This is the first paper to build a comprehensive empirical picture of power pricing practices across Sub-Saharan Africa, based on a new database of tariff structures in 27 countries for the years 2004–2008.

Using a variety of quantitative indicators, the paper evaluates the performance of electricity tariffs against four key policy objectives: recovery of historic power production costs, efficient signaling of future power production costs, affordability to low income households, and distributional equity.

As regards cost recovery, 80 percent of the countries in the sample fully recover operating costs, while only around 30 percent of the countries are practicing full recovery of capital costs. However, due to the fact that future power development may be based on a shift toward more economic technologies than those available in the past, existing tariffs look as though they would be consistent with Long Run Marginal Costs in nearly 40 percent of countries and hence provide efficient pricing signals.

As regards affordability, today’s average effective tariffs are affordable for 90 percent of today’s customers. However, they would only be affordable for 25 percent of households that remain unconnected to the grid. Tariffs consistent with full recovery of economic costs would be affordable for 70 percent of the population. As regards equity, the highly regressive patterns of access to power services, ensure that subsidies delivered through electricity tariffs are without exception also highly regressive in distributional incidence.

The conclusion is that achieving all four of these policy objectives simultaneously is almost impossible in the context of the high-cost low-income environment that characterizes much of SSA today. Hence most countries find themselves caught between cost recovery and affordability.

This paper is a product of the Sustainable Development Unit, the Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at cbricenogarmendi@worldbank.org and mshkaratan@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# POWER TARIFFS: CAUGHT BETWEEN COST RECOVERY AND AFFORDABILITY

CECILIA BRICEÑO-GARMENDIA AND MARIA SHKARATAN
# **Acknowledgments**

This paper draws on contributions from sector specialists from the Africa Infrastructure Country Diagnostic Team; notably, Daniel Camos, Anton Eberhard, Vivien Foster, Sudeshna Ghosh Banerjee, Jean-Noel Gogua, Fatimata Ouedraogo, Orvika Rosnes, Haakon Vennemo, and Quentin Wodon. It is based on data collected by local consultants and benefited greatly from feedback provided by colleagues in Africa Energy Department: Fabrice Bertholet, Luiz Maurer, and Fanny Missfeldt-Ringius.

2
# **Power tariffs: Caught between cost recovery and affordability**<sup>**1**</sup>

The efficient pricing of electricity is central to a well-functioning power sector. Power pricing guides investment decisions and is critical for cost recovery. It also signals to users the cost of marginal consumption and should ideally encourage the optimal utilization of installed capacity. But achieving efficient power pricing is easier said than done. The power sector is characterized by substantive upfront fixed costs, and it takes many years for capacity to be fully utilized. Beyond that, costs vary across times of the day (peak/off-peak), seasons (dry/rainy), users (residential/commercial), and geographic areas (urban/rural), which should be taken into consideration when setting prices that promote efficient use.

As if the technical issues behind setting efficient tariffs were not complex enough, power providers and regulators also face a conflict between promoting economic efficiency and societal well-being. For example, if income-challenged groups are to enjoy the benefits of power provision, policy makers must set affordable tariffs below production costs or introduce an explicit subsidy regime (Borenstein 2008).

In defining tariff structures, policy makers must balance the financial sustainability of the sector on the one hand and the well-being of various segments of society on the other. Given the importance of power, the ramifications of pricing and bill-collection policies are enormous. For example, as imposed transfers from the producer to the consumer, below-cost tariffs can seriously hamper the financial health of the provider.

Another common way of lowering electricity prices is cross-subsidization, which can only be implemented if monopoly rights are granted to the power utility. Cross-subsidization has several undesirable consequences: it discourages use by the overcharged and promotes overconsumption by the subsidized. In some cases, it also opens the door for particular interest groups and communities to influence policy makers, for example, by asking them to reduce tariffs for select customers such as large industrial users. While this may be used as a mechanism to spur the development of select economic sectors, the reduced tariffs, ironically, might not even be made available to the general public with its more limited purchasing power. How to get tariffs right is a critical question for every policy maker, and there is no one answer.

In this paper we aim to better understand how African countries are dealing with these pricing issues in practice. Most African countries have made efforts to organize their tariff structures and levels so as to recover utility costs while also providing affordable electricity to poorer consumers. But this goal is challenging and has not been reached in most of the countries examined. Obstacles include costly operational inefficiencies, lack of economies of scale due to geopolitical fragmentation, large populations too poor to afford tariffs set at cost-recovery levels, and the dauntingly limited coverage of distribution networks.

> 1 Comments should be addressed to Cecilia Briceño-Garmendia (cbricenogarmendi@worldbank.org) and Maria Shkaratan (mshkaratan@worldbank.org).

3
The analysis presented here is based on a database put together as part of the Africa Infrastructure Country Diagnostic (AICD).<sup>2</sup> The database includes the basic institutional characteristics of African power systems as well as standard power sector indicators (performance, capacity, and so on). In addition the database documents the power-tariff regimes of 27 Sub-Saharan African countries in detail.<sup>3</sup> Together, these nations account for over 85 percent of the population and gross domestic product (GDP) of the region. They were carefully selected to represent the economic, geographic, cultural, and political diversity that characterizes Sub-Saharan Africa. They also represent the four Sub-Saharan power pools; include countries with small, medium, and large-scale generation; and constitute a representative mix of predominantly thermal and/or hydro power systems. As such, the sample can be considered a statistically representative basis for inferring tariff-setting trends in Sub-Saharan Africa.

The tariff data set includes one published tariff regime for each country in the sample. Results presented here are based on the latest published tariff regime available for each country during the AICD data collection period (2003–08). For most countries that year was 2006 (see annex 1).

Using the AICD database, we seek to characterize African power tariffs by (i) describing prevalent tariff structures, both residential and nonresidential; (ii) analyzing their ability to recover costs; (iii) assessing their economic efficiency against long-run marginal costs; and (iv) exploring their equitability and affordability vis-à-vis country-specific purchasing power.

# **What power tariff structures are prevalent in Sub-Saharan Africa?**

Most electricity tariffs—and Africa is no exception—are based on block tariff-pricing schemes; that is, the price of power is linked to the level of consumption.

Power tariffs are commonly structured around blocks. A block is a pre-determined range of power consumption; with the unit price of each kWh being fixed within the block. The relation between blocks and prices defines three types of tariff structures:

- Increasing block tariffs (IBTs) is a regime in which the unit price per kWh follows an increasing step-function linked to sequentially defined blocks

- Decreasing block tariffs (DBTs) is a regime in which the unit price per kWh follows a decreasing step-function linked to sequentially defined blocks

- Linear tariffs (LTs) are a regime in which all units of power consumed are charged at exactly the same rate.

> 2 http://www.infrastructureafrica.org .

> 3 Benin, Botswana, Burkina Faso, Cameroon, Cape Verde, Chad, Congo, the Democratic Republic of Congo, Côte d’Ivoire, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, South Africa, Tanzania, Uganda, Zambia, and Zimbabwe.

4
Any of the above tariff structures may be complemented by a fixed monthly charge, and are then described as two-part electricity tariffs. The fixed charge can be used to cover the fixed administrative costs associated with serving a customer, and is sometimes also used to discriminate between customers based on other cost-related characteristics such as load served and network location.

# **a. Residential tariffs**

Two-thirds of the prevailing pricing schemes in Africa are IBTs, and the remaining third are linear (figure 1a). The use of linear rates is more common in countries with prepayment systems (Malawi, Mozambique, and South Africa) (table 1). The prevalence of IBTs is consistent with recent trends in power regulation. IBTs have often been put forward as a good tool for reconciling cost-recovery targets with distributional aims, although their success in doing so is critically dependent of the details of tariff design (Filipovid and Tanid 2009; Borenstein 2008). A more detailed description on residential tariff structures practiced in Africa can be found in Annex 2.

<!-- Start of picture text --> Figure 1. Prevalence of specific tariff schemes<br>a. Block tariff pricing b. Fixed charges<br>70% 60%<br>60% 50%<br>50%<br>40%<br>40%<br>30%<br>30%<br>20%<br>20%<br>10%<br>10%<br>0% 0%<br>Increasing Block Linear Rate Fixed No Fixed Fixed No Fixed<br>Charge Charge Charge Charge<br>(Linear) (Linear) (IBT) (IBT)<br>tariff schemes tariff schemes<br>schemes schemes<br>percentage of prevailing percentage of prevailing<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database.

About half of the sample countries have adopted two-part tariffs (figure 1b), combining fixed charges with block tariff pricing. Among countries practicing linear tariffs, the use of two-part tariffs is more common than where IBTs are applied. For African countries the fixed charge tends to be relatively high: between $1.00 and $3.00 per month (table 1). As a reference, the average fixed charge in Latin American countries is $0.70 (Foster and Yepes 2006).

5
|Table 1. Residen|tial tariff sc<br>|hedules<br>|||<br>||||
|---|---|---|---|---|---|---|---|---|
||Tariff|Fixed chare|Fixed|Number|Size<br>of|Price first|Price|% increase|
||<br>type|g<br>per month?<br>Yes/No|<br>charge<br>($/month)|<br>of blocks|<br> <br>the<br>first<br>block,<br>kWh|,<br>block|,<br>highest<br>block|<br>from first<br>block to<br>highest<br>block|
|Benin|IBT|No|—|3|20|9.6|16.3|70|
|Botswana|Linear|Yes|1.63|1|—|5.9|5.9|—|
|Burkina Faso|IBT|Yes|1.1|3|50|18.4|20.8|13|
|Cameroon|IBT|No|—|3|50|8.6|12|40|
|Cape Verde|IBT|No|—|2|40|22.5|28|24|
|Chad|IBT|No|—|3|30|15.7|38.1|143|
|Congo,<br>Dem.<br>Rep. of|IBT|No||11|100|3.98|8.52|114|
|Congo,Rep. of|Linear|Yes|5.06|1|—|11|11|—|
|Côte d’Ivoire|IBT|Yes|0.64|2|40|6.9|14.2|106|
|Ethiopia|IBT|Yes|0.16–1.58|7|50|3.2|8|150|
|Ghana|IBT|Yes|0.54|3|300|7.6|15.3|101|
|Kenya|IBT|Yes|1.74|4|50|4.9|44|798|
|Lesotho|Linear|No|—|1|—|7.2|7.2|—|
|Madagascar|Linear|Yes|2.98|1|—|7.6|7.6|—|
|Malawi|IBT|Yes|0.92|3|30|2|4.1|105|
|MWI–prepaid|Linear|No|—|1|—|3.1|3.1|—|
|Mali|IBT|No|—|4|200|26.6|31|17|
|Mozambique|IBT|Yes|2.79|4|100|4|12.1|203|
|MOZ–prepaid|Linear|No|—|1|—|11|11|—|
|Namibia|Linear|No|—|1|—|11.7|11.7|—|
|Niger|Linear|Yes|0.43|1|—|13.6|13.6|—|
|Nigeria|IBT|Yes|0.15–2,38|5|20|0.9|6.5|622|
|Rwanda|Linear|No|—|1|—|14.6|14.6||
|Senegal|IBT|No|—|3|—|23.8|26.2|10|
|South Africa|IBT|No|—|2|50|0|7.2|—|
|Tanzania|Linear|Yes|1.93|1|—|10.8|10.8|—|
|TZN–low use|IBT|No|—|2|50|4.1|13|217|
|Uganda|IBT|Yes|1.1|2|15|3.4|23.3|585|
|Zambia|IBT|Yes|1.3|3|300|1.6|3.7|131|
|Zimbabwe|IBT|No|—|3|50|0.60|13.5|2,159|

_Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Note:_ For details see annex 2. MWI = Malawi; MOZ = Mozambique; TZN = Tanzania. — Not available.

The structure of blocks—their number, size, and respective price levels—is what ultimately defines how tariffs reflect costs, affect demand, and address equity issues. The sample countries have adopted widely differing approaches to tariff design, with one third of countries practicing linear tariffs (as in Rwanda and Tanzania) and the remaining two thirds practicing IBTs.

Looking at the two thirds of sample countries that practice IBTs, most have adopted relatively simple structures (figure 2a) with two or three blocks (as in Benin, Burkina Faso, Cameroon, Cape Verde, Côte d’Ivoire, Ghana, Madagascar, Malawi, Senegal, Tanzania, Uganda, Zambia). A minority of countries have opted for more complicated structures that use four and more blocks (the Democratic Republic of 6
Congo, Ethiopia, Kenya, Mali, Mozambique, and Nigeria). The Democratic Republic of Congo is an extreme case, as it uses an 11-block IBT. Interestingly enough, despite the large number of blocks, the price difference across blocks is very small, suggesting that the regime is complicated without really discriminating very much between large and small consumers.

<!-- Start of picture text --> Figure 2. Characterization of residential block pricing<br>a. Number of pricing blocks b. Tariff price differential between first and highest<br>block (%)<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>one (linear two three four or more 0% < 100% 100-200 200% ><br>rate)<br>number of blocks<br>number of countries number of countries<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database The conventional wisdom is that IBTs are designed so that the first and smallest “lifeline” block covering subsistence consumption is subsidized to promote equity, while the subsequent blocks are priced at a higher level that will ultimately enable cost recovery. This of course assumes that poorer customers will have lower consumption. This assumption is more reasonable in the case of power – where usage is driven by ownership of appliances more prevalent among wealthier households and businesses – than it is for example in the water sector – where usage is more correlated with the size of the household.

The first question is whether African countries tend to define the consumption level of the first block at a level low enough to be consistent with subsistence consumption and hence with the “lifeline” principle underlying this tariff design (table 2). The response to this question is quite positive. Two-thirds of the sample countries define the first block at 50 kilowatt-hours (kWh)/month or less; a consumption level that is below the average residential power consumption in Africa (75-100 kWh/month) (Foster and Briceño-Garmendia 2009). For example, Uganda sets the first block at 15 kWh/month; Cape Verde and Côte d’Ivoire at 40 kWh/month; and Burkina Faso, Cameroon, Ethiopia, Kenya, Tanzania at 50 kWh/month. But the sizes of the first blocks in Mozambique and the Democratic Republic of Congo (100 kWh), Mali (200 kWh) and in Zambia and Ghana (300 kWh) seem too high to meet the needs of lowconsumption, low-income residential customers.

7
Table 2. Size of the first pricing block, and differential between that and second

|1st<br>block<br>monthly<br>consumption threshold<br>(kWh)|Countries|Price differential between first and<br>second block (%)|
|---|---|---|
|300|Ghana, Zambia|Between 30 and 100|
|200|Mali|Less than 30|
|100|Mozambique|Over 100|
||Congo,Dem. Repof|Less than 10|
|50 or less|Uganda, Madagascar, Kenya, Nigeria, Tanzania,<br>Chad, Cote d’Ivoire, South Africa, Zimbabwe|Over 100|
||Ethiopia,Benin,Malawi|Between 30 and 100|
||Cameroon,Cape Verde,Burkina Faso|Less than 30|

_Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database The second question is whether tariff levels on successive blocks rise steeply enough to ensure that costs can be fully recovered on higher volumes of consumption. This principle holds in many cases. Most countries with a first-block threshold set at subsistence consumption levels (50 kWh or less), have a price jump of over 100 percent to the second block (Uganda, Madagascar, Kenya, Nigeria, Tanzania, Chad, Côte d’Ivoire, South Africa, Zimbabwe), signaling a clear intention to differentiate among customers so that larger consumers contribute to cost recovery. But the tariff structures of a few countries—for example, the Democratic Republic of Congo, Cameroon, Cape Verde, and Burkina Faso— are not highly differentiated by consumption level. This makes the implementation of targeted subsidies difficult (table 2).

Thereafter, prices among higher consumption blocks do not rise as steeply as they do between the first and second block. In Burkina Faso, the second block is priced only 6 percent higher than the first one and the third block only 7 percent above the second one. In Ethiopia the price increase from the first block to the second is 31 percent and from the second to the third, 40 percent. In Malawi, the increase is 47 percent in the first case and 42 percent in the second one.

Despite the fact that IBTs already incorporate a “lifeline” principle, a number of countries have felt the need to introduce parallel “social tariff” that provide an even larger discount to qualifying customers. For example, the Democratic Republic of Congo, Madagascar, Mali, and Benin (whose block pricing structure does not differentiate low-consumption, low-income customers) provide such a parallel “social tariff” (table 3). Most of these social tariffs are linear in nature and include fixed charges; albeit modest ones. The criteria for determining social tariff eligibility are usually based either on total consumption or technical characteristics of service (voltage, load). The very existence of such parallel “social tariffs” – most of which have eligibility criteria based on low consumption – in itself may be a signal that the main IBT is not managing to perform its intended social function.

8
|Table 3. Tariff sche|dules for low-income, lo<br>Type of tariff|w-consumption cust<br>Fixed charge<br>($)/month|omers<br>Block border|Price per block,<br>cents/kWh|
|---|---|---|---|---|
|Benin<br>|social tranche|n.a.||9.6|
|Botswana|n.a.|n.a.||n.a.|
|Burkina Faso|block 1, residential|0.18||14.3|
|Cameroon*|block 1 residential|12.90||8.6|
|Cape Verde|block 1, residential|—||22.5|
|Chad|block 1 residential|n.a.||15.7|
|Congo, Dem. Rep.<br>of|social tariff|0.01||4.0|
|Congo,Rep. of|n.a.|n.a.||n.a.|
|Côte d’Ivoire|block 1 residential|0.64||6.9|
|Ethiopia|block 1 residential|0.16||3.2|
|Ghana|block 1 residential|0.54||7.6|
|Kenya|block 1 residential|1.74||4.9|
|Lesotho|—|—||—|
|Madagascar|economic tariff|0.30|25|6.0|
||||> 25|27.6|
|Malawi|block 1 residential|0.92||2.0|
|Mali|social tariff|n.a.|50|13.2|
||||100|20.3|
||||200|23.9|
||||>200|27.7|
|Mozambique|block 1 residential|n.a.||4.0|
|Namibia|n.a.|n.a.||n.a.|
|Niger|—|—||—|
|Nigeria|pensioners’ tariff|0.23||3.0|
|Rwanda|—|—||—|
|Senegal|tranche 1 residential|n.a.|150|0.24|
|South Africa|block 1 residential|n.a.||—|
|Sudan|—|—||—|
|Tanzania|n.a.|n.a.||3.0|
|Uganda|block 1 residential|1.09||3.4|
|Zambia|block 1 residential|1.31||1.6|
|Zimbabwe|tranche 1 residential|n.a.||0.6|

_Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Note:_ For details see annex 13. n.a. Not applicable. — Not available.

Another important aspect of social policy in the power sector has been the growing use of prepayment meters. These have dual advantages. On the one hand, they help low income customers to control their expenditures on electricity by spreading them out into small frequent payments that they can more readily afford. On the other hand, they eliminate the commercial risk to the utility of serving low income 9
customers. Although growing in popularity worldwide, providing a prepaid option is a relatively new practice in Africa, and its impact is still not well understood. But for those countries for which data are available, roughly 60 percent have introduced the use of prepayment meters (figure 3). Indeed, Lesotho, Namibia, Mozambique, and South Africa already offer this option to a majority of residential customers.

<!-- Start of picture text --> Figure 3. Prepaid services<br>a. Frequency distribution of countries adopting b. Time trend in percentage of customers using<br>prepaid meters prepaid meters<br>2000 2001 2002 2003 2004 2005<br>10<br>9 Tanzania 4 4<br>8 Benin 0 0 6 6 6 7<br>7<br>6 Burkina Faso 10 10 10 10 10 10<br>5 Malawi 2 4 6 8 10 14<br>4<br>Ghana 5 4 4 10 28<br>3<br>2 Rwanda 48 65<br>10 Mozambique 80 73<br>0 1-20 21-50 51-90 91-100 no data Namibia 93 98<br>Lesotho 100 100 100 100<br>percentage of customers with prepaid<br>meters South Africa 100<br>number of countries<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Indicators Database

# **b. Nonresidential tariffs**

Nonresidential tariffs can be classified in two groups: commercial and industrial (Table 4). Linear tariffs are the most common regime for nonresidential customers in Africa (figure 4). About 60 percent of commercial customers and over 50 percent of industrial customers are billed based on linear tariffs. A more detailed description on commercial and industrial tariff structures practiced in Africa can be found in Annexes 3 and 4.

Pricing for nonresidential customers is typically more complex than for residential customers. It is usually structured as a three-part tariff including a monthly fixed charge (defined by characteristics of the network), a demand charge (defined by the level of peak demand served in kilovolts or kilowatts), and a volume charge (defined by the energy served and reflected in the definition of the blocks). In addition, volume charges may be differentiated by time-of-use (TOU). In fact, only a handful of countries (Benin, Cape Verde, Rwanda, and Mali) apply simple linear tariffs to their nonresidential customers without making use of any of these additional features.

Fixed charges are somewhat more prevalent among non-residential customers, than was the case for residential customers. They are practiced by 18-19 countries in the sample.

10
Demand charges are widespread for non-residential customers, but are almost twice as frequent for industrial customers as for commercial customers (figure 4b). This suggests that most countries find it important to reflect load considerations in designing tariffs for nonresidential customers (table 3). Peak demand is a critical cost driver in the power sector, because it defines the amount of installed capacity needed to provide a given volume of electricity.

<!-- Start of picture text --> Figure 4. Prevalence of specific tariff schemes: Commercial and industrial users<br>a. Tariff type b. Fixed charges<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>FR TOU IBT DBT None Only fixed Only Demand<br>demand and fixed<br>commercial industrial commercial industrial<br>Source: Africa Infrastructure Country Diagnostic Power Tariff Database<br>Figure 5. Characterization of nonresidential block pricing<br>a. Numbers of pricing blocks b. Nonlinear tariff price differential between first and last<br>block (%)<br>20 6<br>5<br>15<br>4<br>10 3<br>2<br>5<br>1<br>0 0<br>One Two Three 4 or more negative no change 0-100% greater<br>than 100%<br>Commercial Industrial Commercial Industrial<br>number of countries number of countries<br>number of countries number of countries<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database Volume charges for nonresidential customers are typically linear for at least 15 of the countries in the sample. In the cases where block tariff structures are used, they are more frequently decreasing blocks (DBT) rather than increasing blocks (IBT) and these are relatively simple with not more than three blocks (figure 5b). The reason for preferring decreasing blocks is to capture the strong scale economies associated with power generation and transmission. The volumetric charge tends to be higher for commercial than for industrial customers. Most commercial tariffs start at over 12 cents/kWh, while most industrial tariffs start at around 8–10 cents/kWh. On average, African commercial tariffs are about 40 percent higher than industrial ones. Only the Democratic Republic of Congo, the Republic of Congo, and Chad have commercial tariffs set at a lower level than residential ones (table 5).

11
|Table 4. Nonresi|dential tariff reg|imes|||||
|---|---|---|---|---|---|---|
||Commercial|||Industrial|||
||Tariff type|Fixed<br>charge/month?<br>yes/no|Demand<br>charge?<br>yes/no|Tariff type|Fixed<br>charge/month?<br>yes/no|Demand<br>charge?<br>yes/no|
|Benin|Linear|No|No|Linear|No|No|
|Botswana|Linear|No|No|Linear|Yes|Yes|
|Burkina Faso|TOU|Yes|Yes|TOU|Yes|Yes|
|Cameroon|DBT|No|Yes|TOU|No|Yes|
|Cape Verde|Linear|No|No|Linear|No|No|
|Chad|IBT|No|Yes|TOU|No|Yes|
|Congo, Dem.<br>Rep. of|DBT|No|No|DBT|No|No|
|Congo,Rep. of|Linear|Yes|No|Linear|Yes|Yes|
|Côte d’Ivoire|DBT|Yes|No|TOU|Yes|No|
|Ethiopia|TOU|Yes|No|TOU|Yes|No|
|Ghana|IBT|Yes|No|Linear|Yes|Yes|
|Kenya|Linear|Yes|No|DBT|Yes|Yes|
|Lesotho|Linear|No|Yes|Linear|No|Yes|
|Madagascar|Linear|Yes|Yes|Linear|Yes|Yes|
|Malawi|Linear|Yes|Yes|Linear|Yes|Yes|
|Mali|Linear|No|No||||
|Mozambique|Linear|Yes|Yes|Linear|Yes|Yes|
|Namibia|Linear|Yes|Yes|Linear|Yes|Yes|
|Niger|Linear|Yes|Yes|Linear|Yes|Yes|
|Nigeria|IBT|Yes|No|IBT|Yes|Yes|
|Rwanda|Linear|No|No|Linear|No|No|
|Senegal|TOU|Yes|No|TOU|Yes|No|
|South Africa|IBT/Linear|Yes|No|TOU|Yes|Yes|
|Tanzania|Linear|Yes|Yes|Linear|Yes|Yes|
|Uganda|TOU|Yes|No|TOU|Yes|Yes|
|Zambia|Linear|Yes|No|DBT|Yes|Yes|
|Zimbabwe|Linear|Yes|Yes|Linear|Yes|Yes|

_Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Note:_ IBT – increasing block tariff; DBT – decreasing block tariffs; TOU – time of use tariff. _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database Time-of-use tariffs (TOUs) are only practiced in a minority of cases, and are twice as prevalent among industrial tariff structures as among commercial tariff structures. TOUs allow power consumption to be associated with hours (peak/off-peak/night) and/or seasons (summer/winter, dry/regular), creating incentives for more efficient use of the power network. One-third of industrial and one-fifth of commercial tariff regimes in Africa incorporate TOUs.

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|Table 5. Nonresi|dential blocks:|Number, size, a<br>Com<br>First bl|nd price le<br>mercial<br>ck|vel||I<br>First|ndustrial<br>block|Price|
|---|---|---|---|---|---|---|---|---|
|||||Price differential<br>||||differential<br>|
||Number<br>of<br>blocks|Size|Price<br>(cents)|between first<br>and last block<br>(%)|Number<br>of<br>blocks|Size|Price<br>(cents)|between first<br>and last block<br>(%)|
|Benin|1|n.a.|15.1|—|1|n.a.|10.7|—|
|Botswana|1|n.a.|6.7|—|1|n.a.|3.1|—|
|Burkina Faso|2|TOU|31.6|–47|2|TOU|22.6|–54|
|Cameroon|2|180kWh/kVA|11.3|–12|2|TOU|8.7|–2|
|Cape Verde|1|n.a.|21.8|—|1|n.a.|17.7|—|
|Chad|3|30 kWh|15.9|152|3|TOU|20.5|85|
|Congo, Dem.<br>Rep. of|5|200 kWh|11.1|–4|5|200|15.2|-4|
|Congo,Rep.|1|n.a.|9.7|—|1|n.a.|11.2|—|
|Côte d’Ivoire|2|18/kVA<br>bimonthly|18.6|–15|3|TOU|10.7|–18|
|Ethiopia|3|TOU|6.7|–6|3|TOU|4.7|26|
|Ghana|3|300|11.1|44|1|n.a.|5.4|—|
|Kenya|1|n.a.|21.4|—|3|20<br>kWA|16.4|–15|
|Lesotho|1|n.a.|1.2|—|1|n.a.|1.1|—|
|Madagascar|1|n.a.|16.9|—|1|n.a.|9.9|—|
|Malawi|1|n.a.|3|—|1|n.a.|2.4|—|
|Mali|1|n.a.|23.2|—|0|n.a.||—|
|Mozambique|1|n.a.|5.4|—|1|n.a.|4.5|—|
|Namibia|1|n.a.|8.4|—|1|n.a.|7.7|—|
|Niger|1|n.a.|12.2|—|1|n.a.|8.8|—|
|Nigeria|4|15 kVA|5|30|5|15<br>kVA|5|30|
|Rwanda|1|n.a.|17.2|—|1|n.a.|17.2|—|
|Senegal|2|TOU|14.4|44|2|TOU|11.8|45|
|South Africa|3/1|25 kVA|4|138|2|TOU|2.6|–31|
|Tanzania|1|n.a.|5.3|—|1|n.a.|4.9|—|
|Uganda|1|n.a.|21.8|—|1|n.a.|16.7|—|
|Zambia|1|n.a.|3.7|—|4|1200|2.2|–45|
|Zimbabwe|1|n.a.||—|1|n.a.||—|

_Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Notes_ : For details see Annexes 3 and 4. n.a. Not applicable; — Not available; kVa = kilovolt-ampere.

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# **Do power tariffs recover costs?**

## **a. What is the level of average effective residential tariffs?**

The effective tariff is the price per kilowatt-hour of electricity consumed at a specific consumption level when all charges—variable and fixed—are taken into account. In a multipart tariff system with a block pricing scheme, the difference between the effective tariff for a kilowatt-hour at a low level of consumption and a kilowatt-hour at a high level of consumption can be significant.

The effective tariff is calculated by dividing the total bill based on the current tariff schedule (in currency) by consumption (in kilowatt-hours). It can also be referred to as the unit price at a particular consumption level. Based on the two-part tariffs described in the preceding section, effective residential _n n_ electricity tariffs can be estimated following the formula: _T_  (  _ai_  1  _xi_  1  _b_ ) / _x_ , where _a_ is the metered consumption unit price (per kilowatt-hour), _x_ is the volume consumed (metered), _i_ is the block number (in the case of block tariffs), and _b_ is the fixed charge.<sup>4</sup> Effective tariffs allow for analyzing pricing patterns at different consumption levels as well as comparing price levels with cost recovery and affordability benchmarks.

For the purpose of this analysis, one residential schedule per country was selected. The selection of a specific tariff schedule for the calculation of the effective tariff was done to capture the largest share of residential consumers based on the most commonly used tariff schedule, or the one that most closely corresponds to the monthly average consumption for that country. For example, in South Africa, the residential schedule selected was the “Home Light 1” prepayment option because this is the one that would be most attractive to a South African household with the average residential power consumption level. While South Africa has an admittedly complicated tariff system, other African countries also offer two or three residential schedules. For effective residential tariff calculations, the lower-usage residential tariff was used and the “social tariff” (where relevant) was analyzed separately. Annex 5 lists the representative tariff schedule used for each country.

Table 6 showcases the calculation of effective residential tariffs for select African countries. The variation in effective tariffs for the first consumption tranche (up to 50 kWh/month) is enormous, going from zero in South Africa to about 24 cents/kWh in Cape Verde (table 6). But over 60 percent of African countries establish prices below 10 cents/kWh for their smallest consumers. For average consumption levels of 100 kWh/month, Africa has effective tariffs ranging from 3 cents to 30 cents, which is undoubtedly a wide range.

> 4 Generally there is a third component in the formula that captures maximum demand level and its price. But the demand part of the formula is not applicable to residential customers

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|Table 6. Effective|residenti|al tariffs b|ylevel of|consumpti<br>Level|on(cents)<br>of consum|ption(kW|h/month)||||
|---|---|---|---|---|---|---|---|---|---|---|
||**50**|**75**|**100**|**150**|**200**|**300**|**400**|**450**|**500**|**900**|
|Benin|12.6|13.3|13.6|14.0|14.1|19.7|22.5|23.5|24.2|27.2|
|Botswana|9.1|8.0|7.5|6.9|6.7|6.4|6.3|6.2|6.2|6.0|
|Burkina Faso|20.6|20.2|20.0|19.9|19.8|20.1|20.3|20.4|20.4|20.6|
|Cameroon|8.6|10.9|10.9|10.9|10.9|12.0|12.0|12.0|12.0|12.0|
|Cape Verde|23.6|25.1|25.8|26.5|26.9|27.3|27.4|27.5|27.5|27.7|
|<br>Chad|22.9|27.3|30.0|32.7|34.1|35.4|36.1|36.3|36.5|37.2|
|Congo, Dem.|4.0|4.0|4.0|4.0|4.0|3.9|3.9|3.9|3.9|5.5|
|Congo, Rep. of|21.1|17.7|16.0|14.3|13.5|12.6|12.2|12.1|12.0|11.5|
|Côte d’Ivoire|9.6|11.1|11.9|12.6|13.0|13.4|13.6|13.6|13.7|13.9|
|Ethiopia|3.9|4.1|4.1|5.3|5.6|6.1|6.2|6.4|6.6|7.2|
|Ghana|8.7|8.4|8.2|8.0|7.9|7.8|9.1|9.6|9.9|11.8|
|Kenya|8.4|12.7|14.8|16.9|18.0|19.1|19.9|20.1|20.4|21.2|
|Lesotho|7.2|7.2|7.2|7.2|7.2|7.2|7.2|7.2|7.2|7.2|
|Madagascar|6.0|4.0|3.0|2.0|1.5|1.0|0.7|0.7|0.6|0.3|
|<br>Malawi|4.8|4.3|4.0|3.8|3.7|3.6|3.5|3.5|3.5|3.4|
|Mali|26.6|26.6|26.6|26.6|26.6|28.1|28.8|29.1|29.3|30.0|
|Mozambique|9.6|7.7|6.8|7.4|7.7|9.0|9.6|9.8|10.0|10.9|
|<br>Namibia|11.7|11.7|11.7|11.7|11.7|11.7|11.7|11.7|11.7|11.7|
|Niger|14.5|14.2|14.1|13.9|13.9|13.8|13.7|13.7|13.7|13.7|
|Nigeria|2.5|3.8|3.4|3.8|4.2|4.9|5.3|5.4|5.6|6.0|
|Rwanda|14.6|14.6|14.6|14.6|14.6|14.6|14.6|14.6|14.6|14.6|
|Senegal|23.8|23.8|23.8|23.8|24.2|24.8|25.1|25.2|25.3|25.7|
|South Africa|—|2.4|3.6|4.8|5.4|6.0|6.3|6.4|6.5|6.8|
|Sudan|—|—|—|—|—|—|—|—|—|—|
|Tanzania|3.2|5.5|6.7|7.9|8.5|9.0|8.8|8.8|8.8|8.6|
|Uganda|19.5|20.7|21.4|22.0|22.3|22.6|22.8|22.8|22.9|23.1|
|Zambia|4.2|3.3|2.9|2.4|2.2|2.0|2.1|2.1|2.1|2.5|

# _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database

_Notes:_ Average residential consumption level is highlighted in bold — Not available.

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<!-- Start of picture text --> Figure 6. Effective residential tariff for 100 kWh (cents/kWh)<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>-<br>Chad Mali Cape Verde Senegal Uganda Burkina Faso Congo, Rep. Kenya Rwanda Niger Benin Côte d'Ivoire Namibia Cameroon Ghana Botswana Lesotho Mozambique Tanzania Zimbabwe Ethiopia Malawi Congo South Africa Nigeria Madagascar Zambia<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database On average, residential electricity in Africa is among the most expensive in the world. For the average consumer, residential tariffs are over 12 cents/kWh in about 60 percent of the sample countries and over 20 cents/kWh in close to 25 percent of the sample (Burkina Faso, Cape Verde, Uganda, Madagascar, Mali, and Chad) (figure 6). To compare it against other world regions, Africa’s residential electricity price averages between 50 and 150 percent higher than the 8 cents/kWh in Latin-America, Eastern Europe, and East Asia, and up to 400 percent higher than average residential tariffs in South Asia.

What is the pattern of average effective residential tariffs across levels of consumption? Tariffs that increase with consumption effectively impose a penalty on higher consumption and vice versa. About half of the sample countries have increasing average effective residential tariffs (for example, Cape Verde, Chad, Côte d’Ivoire, Ethiopia, Ghana, Kenya, Madagascar, Mozambique, Nigeria, Tanzania, and Uganda [figure 7a and b]). One-third of countries have decreasing average effective residential tariffs, not only because they have explicitly adopted DBT (for example, Senegal) but also because the size of their first blocks is large meaning that the weight of the fixed charge is spread across a larger tranche of consumption. Countries with decreasing residential tariffs include Cameroon, Malawi, Zambia, Senegal, Burkina Faso, Cameroon, the Democratic Republic of Congo, Ghana, and Niger (figure 7c and d).

16
<!-- Start of picture text --> Figure 7. Effective tariffs across various consumption levels<br>a. Highly increasing b. Moderately increasing<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>50 75 100 150 200 300 400 450 500 900 50 75 100 150 200 300 400 450 500 900<br>monthly consumption, kWh monthly consumption, kWh<br>Cape Verde Côte d'Ivoire Madagascar<br>Chad Kenya Mali Tanzania Uganda Zimbabwe<br>c. Neutral d. Decreasing<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>50 75 100 150 200 300 400 450 500 900 50 75 100 150 200 300 400 450 500 900<br>monthly consumption, kWh monthly consumption, kWh<br>Cameroon Malawi Senegal<br>Burkina Faso Congo, Dem. Rep. Niger Zambia Congo, Rep. Botswana<br>US cents per kWh US cents per kWh<br>US cents per kWh US cents per kWh<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database

# **b. What is the level of average effective nonresidential tariffs?**

As for residential tariffs, one commercial and one industrial schedule per country were selected for our analysis of nonresidential tariffs. Representative or typical _commercial_ customers are defined as small to medium business users with an average consumption level of 900 kWh/month. Representative or typical _industrial_ customers are medium to large business users usually associated with high-voltage, highusage tariffs. But in order to exclude very large industries with preferential tariffs from our analysis, we did not use the highest voltage bracket included in tariff schedules (box 1). In cases where tariff schedules for commercial and industrial users are not differentiated or where several tariffs apply to commercial users, tariff schedules were selected based on both voltage level and load. For example, commercial customers were associated with a tariff for medium-voltage and medium-consumption users, and industrial customers were associated with a tariff for medium- to high-end users.

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# **Box 1. Special tariffs for large electricity users: The case of Zambia**

The average effective power tariff in Zambia, at 3 cents/kWh, is one of the lowest in Africa. This current level does not even allow for the recovery of operating costs, yet alone total costs—even though Zambia has one of the lowest average costs in the region (due to a combination of hydropower technologies and excess generation capacity).

Such inefficient pricing policies are compounded by the exceptionally favorable prices that the power utility ZESCO gives to mining companies, in particular the Copperbelt Energy Corporation (CEC). A long-term agreement set mining tariffs at 2 cents/kWh, not only below cost recovery but also one-third lower than the effective tariff for an average residential customer (100 kWh/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.

Zambia’s case is not unique in the region. Until 2003 Ghana’s power distribution company, VRA, was engaged in a long-term agreement with Volta Aluminum Company, which was VRA’s most important customer, consuming onethird of its power generation and benefiting from a preferential electricity price estimated to be half of the costrecovery level.

_Source:_ Zambia Electricity Regulator Board 2008; World Bank 2008; Chivakul and York 2006 Table 7 lists the effective nonresidential tariffs for select African countries. Commercial effective tariffs are higher overall than industrial ones at similar levels of consumption. Two-thirds of the sample countries have commercial tariffs that are, on average, 20–30 percent higher than the industrial ones (table 7). These countries include Burkina Faso, Cape Verde, Chad, Madagascar, and Ethiopia. Another handful of countries have even higher price differentials—Benin, Côte d’Ivoire, and South Africa have commercial effective tariffs between 40 and 77 percent higher than industrial tariffs. This pattern is not unusual, as the genuine production costs of the high-voltage power consumed by industrial users are lower and exposed to fewer transmission and distribution losses (figure 8). The Democratic Republic of Congo and Mozambique are notable exceptions to this pattern: commercial customers pay on average 30–40 percent more than industrial customers.

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|Table 7. Effecti|ve nonresi<br>Level o|dential tari<br>Comm<br>f consumpt|ffs bylevel<br>ercial<br>ion(kWh)/|of consu<br>month|mption(cent<br>Indus<br>Level of|s)<br>trial<br>demand|
|---|---|---|---|---|---|---|
|||||||**100**|
||**450**|**900**|**2,500**|**5,000**|**10 kVA **|**kVA **|
|Benin|15.1|15.1|15.1|15.1|10.7|10.7|
|Burkina Faso|27.0|26.7|26.5|26.5|14.1|15.0|
|Cameroon|11.7|11.4|11.3|11.2|8.8|9.2|
|Cape Verde|21.8|21.8|21.8|21.8|17.7|17.7|
|Chad<br>|43.7<br>|44.7<br>|45.3<br>|45.5<br>|45.6<br>|38.8<br>|
|Congo, Dem.<br>Rep. of|11.0|11.0|10.8|10.8|14.6|14.6|
|Côte d’Ivoire|17.8|16.9|16.3|16.1|10.7|10.7|
|Ethiopia|9.8|8.3|7.3|7.0|4.8|4.7|
|<br>Ghana|12.6|13.9|15.2|15.6|5.7|6.4|
|Kenya|22.1|21.7|21.5|21.4|16.6|15.1|
|Lesotho|9.3|9.3|9.3|9.3|1.5|3.3|
|Madagascar|30.9|25.3|21.7|20.7|11.1|10.5|
|<br>Malawi|8.1|6.9|6.1|5.8|2.6|3.1|
|Mozambique|9.0|8.0|7.5|7.3|4.7|5.1|
|Namibia|15.2|14.0|13.2|13.0|12.7|13.6|
|Niger|13.4|13.2|13.0|12.9|9.0|9.3|
|Nigeria|5.1|5.0|5.0|5.5|5.0|5.1|
|<br>Rwanda|17.2|17.2|17.2|17.2|17.2|17.2|
|Senegal|23.8|22.8|26.2|26.0|15.8|15.8|
|<br>South Africa|11.4|7.7|5.3|4.7|2.7|2.7|
|Tanzania|8.6|8.0|7.6|7.5|5.0|5.4|
|Uganda|22.0|21.9|21.8|21.8|16.8|17.0|
|Zambia|5.1|4.4|3.9|3.8|2.3|2.5|
|Congo, Rep.|11.7|10.7|10.1|9.9|11.2|11.2|
|<br>Mali|23.2|23.2|23.2|23.2|||
|Botswana|7.7|7.2|6.9|6.8|3.3|4.0|

_Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Notes:_ Average commercial consumption level is highlighted in bold — Not available.

<!-- Start of picture text --> Figure 8. Increasing, decreasing, and neutral tariff structures<br>20<br>15<br>10<br>5<br>0<br>domestic commercial industrial<br>type of effective tariff structure<br>increasing neutral decreasing<br>number of countries<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database 19
# **c. What have been historic costs of power production?**

As we have discussed, Africa faces effective tariffs that are up to twice as high as in other developing regions. This reflects the use of costly technologies as well as the small scale of most African power generation systems.

But what have been the historic costs across the various power systems? The first step toward answering this question is to attribute to each kilowatt-hour produced both the capital and operating (O&M) costs of the three power production segments: generation, transmission, and distribution.

O&M unit costs are calculated by prorating total operational costs (as reported in utilities’ income statements) by the total generated electricity over a given year. Data are derived from the AICD’s fiscal spending database,<sup>5</sup> and individual results have been verified by country power experts. Operational costs include salaries associated with system operations, fuel charges, the cost of parts needed for daily operations, and so on.

Unit capital costs are calculated using the levelized power methodology (commonly used by the International Energy Agency). This requires allocating the value of an asset over its lifetime capacity. In essence the unit capital cost is a ratio of the net present value of total lifetime investment to the total electricity produced.<sup>6</sup> For this purpose an annualization factor is applied to the value of generation, transmission, and distribution assets. The overnight investment or capital needed to replace existing assets is the proxy for asset value, to allow for cross-country comparison.

The annualization factor is a reversed version of a standard formula for the net present value of a periodic investment of equal amount. It takes into account both depreciation and interest rates: _r_ _ACC_  _OI_  1  1(  _r_ )  _T_ ; where _ACC_ is annualized capital cost, _OI_ is overnight investment, _r_ is annual discount rate, and _T_ is plant life (in years).

In most African countries, the cost of each kilowatt-hour is 10–20 cents (figure 9). In a handful of countries—such as South Africa, Zambia, the Democratic Republic of Congo, Ethiopia, Mozambique, and Malawi (all of them significant hydro-power producers, with the exception of South Africa)—the power cost is below 10 cents/kWh. Niger and Mali, at the upper extreme, have unit costs over 30 cents/kWh. The balance between capital and operating costs also varies from country to country, largely determined by generation technology. By way of example, capital costs as a percentage of total costs range from 14 percent in Botswana to 77 percent in Nigeria. Full details of these calculations are provided in Annexes 6-8.

> 5 <u>http://www.infrastructureafrica.org.</u>

> 6 Lifetime capital costs are estimated using: (i) the discounted net present value of lifelong capital costs (for example, the sum of the annual investment expenditure throughout the life of a plant), (ii) the discounted historical cost of existing assets, and (iii) the relative value of a similar asset (replacement cost). We are using method (i), with the overnight construction costs as a proxy for the net present value of the lifelong investment expenses.

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<!-- Start of picture text --> Figure 9. Historic power costs in select African countries (cents/kWh)<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Operating Costs Capital Costs<br>Source: Africa Infrastructure Country Diagnostic Power Tariff Database<br>Note: For details see annex 8.<br>South Africa Zambia Congo, Dem. Rep. of Ethiopia Mozambique* Malawi Nigeria Uganda Lesotho Cote d'Ivoire Namibia Senegal Ghana Chad Botswana Tanzania Kenya Madagascar Burkina Faso Rwanda Cameroon Cape Verde * Benin * Congo Niger Mali<br><!-- End of picture text --> But what drives costs? In general, hydro-based systems tend to be more skewed towards capital cost and thermal-based systems more skewed toward operating cost In terms of geography. Africa’s landlocked and island nations seem to bear a significant power cost disadvantage vis-à-vis the coastal countries. High power costs are also driven by the size of markets and their associated scales of production (figure 10).

<!-- Start of picture text --> Figure 10. Differences in average costs (US cents per kWh)<br>a. According to type of power system b. According to geography c. According to system size<br>20 20 20<br>15 15<br>10 10 10<br>5 5<br>0 0 0<br>Total Capital Operational Total Capital Operational Total Capital Operational<br>costs costs costs costs costs costs costs costs costs<br>Hydro Thermal Coastal Landlocked Island < 700 MW 700-1,500 MW > 1,500 MW<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Note:_ For details see annex 7.

# **d. Do average effective tariffs cover historic costs?**

Existing effective tariffs allow for the recovery of operational costs for close to 80 percent of African countries (figure 11b). But when capital investments are considered, the picture looks less rosy. On average, sample countries recover only two-thirds of their capital costs. Indeed, barely one-third of the 21
sample countries are practicing full capital cost recovery (figure 11a); Among the countries that should be able to cover total costs under existing tariff regimes (contingent on their ability to collect bills) are Burkina Faso, Côte d’Ivoire, Kenya, Namibia, and Senegal.

<!-- Start of picture text --> Figure 11. Cost-recovery capabilities of effective tariff regimes<br>a. Weighted effective tariffs and total costs b. Weighted effective tariffs and OPEX<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>0 5 10 15 20 25 30 0 5 10 15 20 25 30<br>Historical costs, US cents/kWh Historical opex costs, US cents/kWh<br>(cents/kWh) (cents/kWh)<br>Weighted Effective Tariffs Weighted Effective Tariffs<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Note:_ OPEX = operational expenditure.

Lack of full cost recovery can be partly explained by the large weight of residential customers in utilities’ revenue structure, whose charges – for political and social reasons – often fail to reflect costs. Excluding South Africa, roughly 50 percent of the total power billing in Africa is associated with households, for just over 40 percent of the total power supplied (figure 13). This is a very high share by global standards. In South Africa, by contrast, the total share of residential billing stands much lower at 17 percent versus only 8 percent of total power supplied. Only in 30 percent of the sample countries do residential tariffs allow for 100 percent cost recovery (figure 12a). While residential tariffs do better at covering operating costs (figure 12b), this is not enough to guarantee financial sustainability in the medium to long term.

It is important to underscore that setting tariffs at cost-recovery levels is one thing and actually recovering costs is another. African utilities are characterized by bill-collection rates of well below 100 percent. This translates to financial losses that can amount to more than the entire turnover of a utility (see annex 14 for details).

Have African utilities been improving their cost-recovery rates over time? Power tariffs increased substantially over the period 2001 to 2005, but not fast enough to keep pace with rising costs (figure 14). Indeed, by 2005, average revenues had only just caught-up with the average costs in 2001.

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<!-- Start of picture text --> Figure 12. Cost-recovery capabilities of residential effective tariffs at 100 kWh/month consumption<br>a. Total cost recovery b. Operational cost recovery<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>level of total cost recovery level of operational cost recovery<br>percentage of countries percentage of countries<br>cost cost<br>0%- 30% 30%-50% 50%-80% 80%-99% recovery 0%- 30% 30%-50% 50%-80% 80%-99% recovery<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database <!-- Start of picture text --> Figure 13. Household billing as a share of total power billing<br>a. Composition of annual billing per type of customer b. Residential share of billing and consumption<br>80<br>100%<br>90%<br>80%<br>70%<br>60% 60<br>50%<br>40%<br>30%<br>20% 40<br>10%<br>0%<br>20<br>0<br>0 20 40 60 80<br>High voltage industrial<br>Medium voltage commercial Share of residential bill<br>Low voltage residential<br>% of amount billed<br>Chad Mali Ghana Senegal Niger Tanzania Nigeria Kenya Ethiopia Rwanda<br>Burkina Faso Cameroon Cape Verde Congo, Rep. Congo DRC Cote d'Ivoire Mozambique Zimbabwe South Africa Share of residential supply<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Note:_ See annex 9 for details.

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<!-- Start of picture text --> Figure 14. Costs and revenues over time ($/kWh)<br>a. Unit costs b. Average revenues<br>0.30 0.30<br>Predominantly diesel<br>0.25 0.25<br>Predominantly hydro<br>0.20 0.20 Overall<br>Predominantly diesel<br>Predominantly hydro<br>0.15 Overall 0.15<br>0.10 0.10<br>0.05 0.05<br>0.00 0.00<br>2001 2002 2003 2004 2005<br>2001 2002 2003 2004 2005<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database

# **e. Do IBT structures allow for costs to be recovered?**

As noted above, IBTs are premised on the notion that surcharges on higher volumes of consumption will compensate for discounts on lower volumes of consumption, so that the utility breaks even overall. However, this outcome is contingent on the block sizes and associated price levels being correctly calibrated. It is often the case that the surcharges apply (if at all) to very high levels of consumption that are rarely reached in practice. In the case of Africa, the answer is more promising than might be anticipated.

A minority of countries have residential tariffs that recover costs independent of consumption levels, meaning that even the lowest priced block is priced high enough to recover costs (category I in table 8). (This is ideal from a cost recovery perspective, but raises questions as to whether these IBTs are also performing the intended social function of providing subsidized power to small consumers.) A second batch of countries has IBTs that succeed in recovering operational costs (category II), but will not—or are not likely to—attain total cost recovery based on their historical average household consumption. The third group of countries (category III in table 8) is recovering costs within a consumption range that is close to the historic average. Finally, a fourth group of countries (category IV)—given current tariffs and the historical consumption patterns—will never attain operational (let alone total) cost recovery.

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islands), and diseconomies of scale (due to the prevalence of small national systems). However, in principle, these costs could come down in the future as countries harness more cost-effective sources of energy and exploit regional power trade to expand the scale of production.

LRMCs are calculated using a dynamic model that estimates the needs of African power systems based on economic growth projections and electrification targets. The model simulates optimal (least-cost) strategies for generating, transmitting, and distributing electricity in response to demand increases. It also estimates the cost of meeting power demand under a range of alternative scenarios, including cross-border trade (Vennemo and Rosnes 2008).<sup>7</sup> With few exceptions, a more efficient selection of technologies (with and without greater regional trade) would render LRMCs below 10 cents/kWh (figure 15). Only four countries—Burkina Faso, Mali, Niger, and Senegal—would face LRMCs over 20 cents/kWh.

Developing African power systems with a view toward expanding regional trade clearly reduces LRMCs across most countries. In fact, if regional trade were fully pursued countries would see their LRMCs reduced by about 10 percent on average, and as high as 40 percent in some cases (figure 16a). These would translate into LRMC reductions of as high as 7 cents/kWh, but more typically in the 1–5 cents range (figure 16b).

# Figure 15. Long-run marginal costs (US cents per kWh)

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Current patterns of trade Trade expansion<br>Congo (DRC) Mozambique Malawi Angola Botswana Congo Lesotho South Africa Cameroon Chad Gabon Guinea Equatorial Guinea Gambia Liberia Zambia Zimbabwe Guinea-Bissau Sierra Leone Ghana Tanzania Togo Burundi Central African Rep. Namibia Kenya Rwanda Uganda Nigeria Sudan Cote d'Ivoire Benin Ethiopia Burkina Faso Mali Niger Senegal<br><!-- End of picture text --> _Source:_ Adapted from Vennemo and Rosnes 2008

> 7 For a brief description of the model, see annex 10.

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<!-- Start of picture text --> Figure 16. How trade expansion would effect long-run marginal costs<br>a. Percent reduction on LRMC b. Monetary reduction on LRMC<br>14 14<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>- -<br>number of countries percentage of countries<br>Increase No Change 0-20% 20-40% 40% < Increase No Change 0-3 cents 3-5 cents 5 cents <<br><!-- End of picture text --> _Source:_ Adapted from Vennemo and Rosnes 2008 _Note:_ See details in annex 11.

# **b. Do existing average effective tariffs cover LRMC?**

It is relevant to ask whether existing average effective tariffs are high enough to cover LRMC even if they may not be high enough to cover average historic costs as was demonstrated above. The analysis shows that 38 percent of the sample countries have already achieved average effective tariffs that are high enough for full capital cost recovery (figure 17a). Compare this with only 30 percent of countries that can fully recover historic costs.

<!-- Start of picture text --> Figure 17. Cost-recovery capabilities of residential effective tariffs vis-à-vis LRMCs<br>a. LRMC recovery, trade stagnation b. LRMC recovery, trade expansion<br>30<br>80%<br>70%<br>25<br>60%<br>50% 20<br>40%<br>15<br>30%<br>20% 10<br>10%<br>5<br>0%<br>0<br>0 5 10 15 20 25 30<br>LRMC, trade expansion, US cents/kWh<br>level of LRMC recovery<br>cents/kWh<br>percentage of countries<br>Effective Residential Tariffs US<br>cost<br>0%- 30% 30%-50% 50%-80% 80%-99% recovery<br><!-- End of picture text --> _Source:_ Adapted from Vennemo and Rosnes 2008 and Africa Infrastructure Country Diagnostic Power Tariff Database.

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# **Are power tariffs equitable and affordable?**

## **c. Can African households afford power services?**

Based on information reported in household surveys, on average power bills absorb almost 6 percent of total household budgets. For most countries the share falls below 3 percent; however in a few cases (such as Malawi and Mozambique) it can be as high as 10 or even 20 percent (figure 18). This share is relatively stable across quintiles.

<!-- Start of picture text --> Figure 18. Existing household spending on electricity<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>National Poorest Q2 Q3 Q4 Richest<br>quintile Quintile<br>Below 3% 3-5% 5-10% Over 10%<br><!-- End of picture text --> _Source:_ Adapted from Banerjee and others (2008).

In order to gauge whether power is affordable, two types of evidence can be considered.

One possible measure of affordability is non-payment of services. Based on household surveys, we can compare across quintiles the percentage of households that report _paying_ for power against the percentage of households that report _using_ service. Those using without paying include both clandestine users who steal power from the network and formal customers who fail to pay their bills. Overall, about 40 percent of people connected to electricity do not pay for it (figure 19). Nonpayment rates range from about 20 percent in the richest quintile to about 60 percent in the poorest quintile. A significant nonpayment rate, even among the richest quintiles, suggests that a culture of nonpayment exists in addition to affordability issues.

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<!-- Start of picture text --> Figure 19. Percentage of the population with service connections who do not pay for service<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Poorest quintile 2nd quintile 3rd quintile 4th quintile quintile<br>Source : Banerjee and others 2008.<br><!-- End of picture text --> Another possible measure of affordability is whether the _full economic cost_ of a _subsistence level_ of consumption falls above a _normative affordability threshold_ . Economic cost is defined as the tariff that would fully cover both operating and capital costs and is country specific; reaching an average level of US$0.18/kW for SSA as a whole. Subsistence consumption is defined as 50 kWh per month, which is enough to cover very minimal usage for lighting (roughly one light bulb for two hours per day). The affordability threshold is typically defined as spending on subsistence power needs of between 3 and 5 percent of the total household budget. These values are normative, and are informed by power spending patterns by low income households that have been observed across a wide range of household surveys (recall figure 18 above).

By looking at the distribution of household budgets, one can calculate the percentage of households for which subsistence consumption priced at full economic cost would absorb more than 5 percent of their budgets and thus prove unaffordable. For example, looking across the distribution of household budgets for all of SSA, monthly bills of $2 would be affordable for almost the entire population, whereas monthly bills of $10 would only remain affordable for the entire population of _middle-income_ African countries.

Based on existing average effective tariffs, the bill for subsistence consumption levels looks very affordable _for those that are already connected to the grid_ (Figure 20a). With a 3 percent affordability threshold, the subsistence consumption of 50kWh/month priced at the current average effective tariff is affordable in 60 percent of the sample countries. If the affordability threshold is further raised to 5 percent, the subsistence consumption is affordable in over 90 percent of the sample countries. However, the picture looks very different for those that are _not currently connected to the grid_ . In these cases, the subsistence consumption level priced at the current average effective tariff would only be affordable in about 25 percent of the countries in the sample (Figure 20b). In conclusion, a significant 29
majority of those connected can afford power at existing prices, while a significant majority of those unconnected cannot do so. This raises questions of circularity: either existing tariffs determine who is connected or tariffs are designed to be affordable to those who are connected.

<!-- Start of picture text --> Figure 20. Monthly electricity expenditure as a percentage of total household budget<br>a. Connected households b. Unconnected households<br>70% 60%<br>60%<br>50%<br>50%<br>40%<br>40%<br>30%<br>30%<br>20% 20%<br>10% 10%<br>0%<br>0%<br>Below 3% 3-5% 5-10% Over 10%<br>Below 3% 3-5% 5-10% Over 10%<br>Percentage of Household Budget<br>Percentage of Household Budget<br>50 kWh/mo 75 kWh/mo 100 kWh/mo 50 kWh/mo 75 kWh/mo 100 kWh/mo<br>Percentage of countries (%) Percentage of countries (%)<br><!-- End of picture text --> _Source:_ Adapted from Banerjee and others, 2008 An equally important question is whether tariffs would remain affordable if today’s tariffs were adjusted to allow for the recovery of full economic costs. For this purpose, we use two cost recovery benchmarks: the average historic cost and the Long Run Marginal Cost (table 9).

Under historic cost recovery, a subsistence level of consumption of 50 kWh per month would range in cost from $3 to $16 a month. These monthly bills would on average be affordable for 72 percent of households across the sample. There are only a handful of countries where less than half of the population could afford these bills, notably: Niger (7 percent), Ethiopia (12 percent), Malawi (43 percent).

If instead, a forward-looking Long Run Marginal Cost is used for cost recovery purposes, the results are very similar on average. Monthly bills would on average be affordable for 73 percent of households across the sample. However, the position of individual countries looks quite different. For one group of countries (DRC, Malawi, Tanzania and to a lesser extent Benin and Kenya), LRMC based tariffs are significantly more affordable than historic cost recovery tariffs. For a second group of countries (Ethiopia, Uganda), LRMC based tariffs are significantly less affordable than historic cost recovery tariffs.

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|Table 9. Monthly power b|ill for subsistence|consumption( <br>**Monthly bill**<br>**($)**|50 kWh)|**Share**<br>**can afford t**|**of households**<br>**he monthly bi **<br>Historic|**that**<br>**ll(%) (*)**|
|---|---|---|---|---|---|---|
||Effective tariff|Historic cost|LRMC|Effective tariff|cost|LRMC|
|Benin|6.31|9.92|9.50|95|68|72|
|Burkina Faso|10.29|7.53|12.50|51|69|36|
|Cameroon|4.30|8.56|3.50|100|100|100|
|Cape Verde|11.81|8.95||100|100|—|
|Congo,Dem. Rep. of|1.99|3.38|2.00|100|63|91|
|Côte d’Ivoire|4.81|5.47|7.50|100|99|98|
|Ethiopia|1.97|4.23|9.50|60|12|1|
|Ghana|4.36|6.18|5.00|97|93|96|
|Kenya|4.21|7.10|6.00|99|87|95|
|Madagascar|2.98|7.49||92|55|—|
|Malawi|2.39|4.54|2.50|92|43|91|
|Niger|7.25|16.07|12.50|55|7|19|
|Nigeria|1.25|4.84|6.50|97|84|74|
|Senegal|9.31|5.77|21.50|100|100|—|
|South Africa|—|2.98|3.00|100|100|100|
|Tanzania|1.60|7.04|5.00|99|59|84|
|Uganda|9.74|5.19|6.00|20|66|55|
|Zambia|2.09|3.26|4.00|100|97|96|
|Sub-Saharan African average|4.81|6.58|7.28|86.51|72.39|73.73|

_Source:_ Adapted from Vennemo and Rosnes 2008 and Africa Infrastructure Country Diagnostic Power Tariff Database.

_Notes:_ See Annex 12 for further details

- (*) it is assumed that a bill is affordable if it is below 5 percent of the household budget.

— Not available.

A frequent argument used for not raising tariffs to full cost recovery levels is the potential impact on poverty. However, empirical evidence suggests that the immediate poverty-related effect of raising tariffs to cost-recovery levels is generally quite small, although it may have second-order effects. Detailed analysis of the effect of significant tariff increases of the order of 40 percent for power and water services in Senegal and power services in Mali confirms that the immediate poverty-related effect on consumers is small, essentially because very few poor consumers are connected to the service (Boccanfuso, Estache, and Savard 2008a; 2008b; 2008c). As the consequences of higher power or water prices work their way through the economy, however, broader second-order effects on wages and prices of goods in the economy as a whole can have a more substantial impact on poverty (Boccanfuso, Estache, and Savard 2008a; 2008b; 2008c).

# **d. Are power tariffs equitable?**

Notwithstanding these findings, most African countries subsidize tariffs for power. On average, power tariffs recover only 80 percent of costs. The resulting implicit subsidies amount to as much as $2.3 billion a year on aggregate (or 0.4 percent of Africa’s GDP) (Foster and Briceño-Garmendia 2009). The aggregate burden of underpricing can be as much as 1–1.5 percent of a country’s GDP (as in Cameroon, the Democratic Republic of Congo, Ghana, Mali, Malawi, Nigeria, South Africa, Tanzania, Zambia, 31
Uganda) and even higher (Botswana and Niger). From the utility’s perspective, underpricing can amount to losses valued as much (and even more than) 100 percent of the utility’s turnover (see annex 14 for details).

Because electricity subsidies are typically justified by the need to make services affordable to lowincome households, a key question is whether subsidies reach such households. Results across a wide range of African countries show that the share of subsidies going to the poor is less than half their share of the population, indicating a very pro-rich distribution (figure 21). This result simply reflects the fact that connections to power are already highly skewed toward more affluent households. In SSA as a whole, access to power among the bottom three quintiles of the budget distribution is no more than 12 percent on average compared to 72 percent in the top budget quintile.

To put these results in perspective, one must compare them with the aims achieved by other forms of social policy. Estimates for Cameroon, Gabon, and Guinea indicate that expenditures on primary education and basic health care reach the poor better than do power and water subsidies (Wodon 2007) Figure 21. Extent to which electricity reaches the poor <!-- Start of picture text --> Nigeria<br>Gabon<br>Congo<br>Côte d'ivoire<br>Cape Verde<br>Togo<br>Sao Tome<br>Senegal<br>Cameroon<br>Mozambique<br>Ghana<br>CAR<br>Guinea<br>Burundi<br>Burkina<br>Chad<br>Malawi<br>Uganda<br>Rwanda<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9<br>Measure of distributional incidence<br><!-- End of picture text --> _Sources_ : Banerjee and others 2008; Wodon 2007. _Note_ : A measure of distributional incidence captures the share of subsidies received by the poor, divided by the proportion of the population in poverty. A value greater than 1 implies that the subsidy distribution is progressive (pro-poor), because the share of benefits allocated to the poor is larger than their share of the total population. A value less than 1 implies that the distribution is regressive (pro-rich).

A key message is that power subsidies will always be highly regressive as long as access is highly regressive. The distributional score presented above (figure 21) can be decomposed into access and subsidy design factors (figure 22). The access factor is related to the availability of electricity in the area where the household lives and to the household’s choice to connect to the network if service is available. The subsidy design factor relates to who is targeted to receive the subsidies, rates of subsidization, and consumption levels. As for the overall distributional score, values higher (lower) than one for access and subsidy design factors are indications that those factors are progressive (regressive).

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Figure 22. Access factors and subsidy design factors affecting targeting performance <!-- Start of picture text -->
1.60<br>Congo<br>1.40<br>1.20<br>Gabon<br>1.00 Togo  Nigeria<br>Guinea  Mozambique<br>Cape Verde Côte d'ivoire<br>0.80 Ghana<br>CAR Cameroon<br>Sao Tome<br>Burkina<br>Senegal<br>0.60<br>Chad<br>Rwanda Burundi<br>0.40 Uganda<br>0.20<br>0.00<br>0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90<br>Access Factors<br>Source:  Banerjee and others 2008.<br>Note : Access factors capture the rates of connection among the poor to the network divided<br>by the rates of connection to the population as a whole. Subsidy design factors are the ratio of<br>the average benefit from the subsidy among all poor households connected to the network<br>divided by the average benefit among all households connected to the network. A value<br>greater than 1 implies that the factor distribution is progressive (pro-poor), because the share<br>of benefits allocated to the poor is larger than their share in the total population. A value less<br>than 1 implies that the distribution is regressive (pro-rich).<br>Tariff Structure and Subsidy Design<br><!-- End of picture text -->

In general the findings are that the subsidy design factor exceeds the access factor. As was to be expected, given the pattern of connections to power, the access factor is always less than one meaning highly regressive. The tariff factor, on the other hand, ranges from 0.4 (indicating a highly regressive tariff structure) and 1.6 (indicating a moderately progressive tariff structure). The most progressive tariff structures are found in DRC, Gabon and Togo. However, for the bulk of countries analyzed are marginally below unity, suggesting that the tariff structures are at best distributionally neutral. However, this is not much of an achievement given that the intention behind the predominantly IBT tariff structures is to favor the poor. This finding is explained by the fact that the traditional IBTs that prevail in Africa tend to be poorly targeted because tariff structures subsidize consumption in the first blocks even for customers whose aggregate consumption is high. On top of that, the consumption threshold for the lower blocks tends to be too high to single out the poor, the price difference between blocks is not very large, and fixed charges are too high.<sup>8</sup>

> 8 Also discussed in Wodon (2007) and Banerjee and others (2008).

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How much are poor customers penalized by fixed charges and block structures? As noted above, over 50 percent of the sample countries have incorporated fixed charges in their tariff schemes. Fixed charges in African countries range from US$0.43 cents to US$5.00 per month (figure 23a). Furthermore, these charges constitute a large portion of the aggregate monthly bill particularly at subsistence levels of consumption (figure 23b). At 50 kWh/month—subsistence consumption—the fixed charge represents more than 40 percent of the monthly bill in over 30 percent of the countries. At higher levels of consumption—75kWh/month and 100 kWh/month—the weight of the fixed charge is less prominent. This indicates the disproportionate weight of the fixed charge in the bills of households consuming at the subsistence level.

<!-- Start of picture text --> Figure 23. Fixed portion of residential tariffs<br>a. Residential fixed charge per month ($) b. Fixed charge as a share of monthly bill (%)<br>Niger<br>Nigeria 70%<br>Ghana<br>Côte d'Ivoire 60%<br>Ethiopia 50%<br>Malawi<br>Uganda 40%<br>Burkina Faso<br>30%<br>Zambia<br>Tanzania 20%<br>Botswana<br>Kenya 10%<br>Mozambique 0%<br>Madagascar<br>Cameroon 50 kWh/mo 75 kWh/mo 100 kWh/mo<br>Congo, Rep.<br>1%-20% 21%-40% >40%<br>- 1 2 3 4 5<br>Percentage of countries<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database.

# **e. Are there any other kinds of hidden subsidies?**

Besides tariffs, there are also other less explicit mechanisms by which policy makers subsidize consumption. For instance, when policy makers overlook, tolerate, or even promote certain operational inefficiencies, they are in practice transferring resources from one sector of the economy to another, from the producer to the consumer, from future taxpayers to current customers, and so on.

Tolerance of nonpayment is an implicit tax on utilities (and/or a transfer to consumer). Tolerance of pilferage—one of the main causes of transmission and distribution losses—is an implicit subsidy to customers and an implicit burden on future taxpayers. Acceptance and promotion of over-employment represents an untargeted transfer of resources from the utility to the society. These inefficiencies can be empirically quantified and prove to be substantial relative to GDP (Briceño-Garmendia, Foster, and Smits 2008). For most countries, over-manning as well as collection inefficiencies amount to less than 0.2 percent of GDP, whereas transmission and distribution losses tend to be much larger in value amounting to around 0.2-0.6 percent of GDP (figure 24a). These operational inefficiencies also look very large in 34
comparison with utility revenues; amounting to between 20 and 60 percent of utility revenues in most cases, and exceeding 100 percent of utility revenues in a few cases (figure 24b).

In countries such as the Democratic Republic of Congo, the Republic of Congo, Nigeria, Namibia, and Cameroon—to name only a few—transmission and distribution losses (technical and nontechnical) are the lead cause of hidden losses (figure 24b). In Côte d’Ivoire, Niger, Ghana, Uganda, and Botswana—to cite some examples—under-collection of bills is the main offender, though it is observed that unpaid bills are from the government or other public enterprises. Finally, over-manning seems to be an issue for countries such as Cape Verde and Chad.

<!-- Start of picture text --> Figure 24. Monetary costs of overlooked inefficiencies<br>a. % of GDP b. % of revenue<br>90% Congo DR<br>Cote d'Ivoire<br>80% Nigeria<br>70% Congo, Rep.<br>Niger<br>60% Ghana<br>50% Uganda<br>Mali<br>40% Malawi<br>Botswana<br>30%<br>Cape Verde<br>20% Namibia 2006<br>Cameroon<br>10% Tanzania<br>0% Mozambique<br>Chad<br>Ethiopia<br>Benin<br>Burkina Faso<br>Lesotho<br>Rwanda<br>Senegal<br>Kenya<br>Transmission and distribution Losses Zambia<br>Madagascar<br>South Africa<br>Collection Inefficiencies<br>0 20 40 60 80 100<br>Over-manning<br>Transmission and Distribution Losses<br>Collection Inefficiencies<br>Over-manning<br>Percentage of Countries (%)<br><!-- End of picture text --> _Source_ : Briceño-Garmendia, Foster, and Smits 2008 In fact it is not infrequent that the financial burden of the myriad operational inefficiencies is higher than the cost of subsidizing via under-pricing. In about half of the countries in our sample, the magnitude of these operational inefficiencies is higher than the magnitude of price subsidies (figure 25).

Figure 25. Weight of underpricing vis-à-vis operational inefficiencies 35
<!-- Start of picture text --> 200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>0 50 100 150 200<br>Price subsidy (% Revenues)<br>(% Revenues)<br>Operational Inefficiencies<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database.

# **Overall, how would we rate Sub-Saharan African power tariffs?**

A scorecard combining four of the key goals in the design of power tariffs—cost recovery, efficiency, equity and affordability—illustrates the challenges of simultaneously achieving these sometimes conflicting objectives. For each of these objectives a quantitative indicator is used based on the foregoing analysis. Cost recovery is measured as the ratio of the current average effective tariff to the average historic cost of power production. Efficiency is measured as the ratio of the current average effective tariff to the Long Run Marginal Cost of power production. Affordability is measured as the percentage of households that are able to purchase a subsistence level of consumption of 50 kWh/month at the prevailing average effective tariff without spending more than 5 percent of their household budgets. Equity is measured as the share of the subsidy captured by households living under the poverty line divided by the percentage of households in the population that live under the poverty line.

The analysis shows that the average scores for the sample are 78 percent for cost recovery, 82 percent for efficiency, and 87 percent for affordability. And for equity the average score is 0.29 indicating a highly regressive distributional incidence, relative to a score of 1.00 for a tariff that is neutral in distributional terms (table 10).

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|Table 10: Scorecard for<br>Objective|performance of count<br>Cost Recovery|ry’spower tariffs ag <br>Efficiency|ainst four key policyobjectiv<br>Affordability|es<br>Equity|
|---|---|---|---|---|
|Indicator|Ratio of<br>average effective<br>tariff to average<br>historic cost|Ratio of average<br>effective tariff to<br>LRMC|Share of population that<br>can afford subsistence<br>consumption priced at<br>average effective tariff|Percentage of subsidy<br>captured by poor as a<br>ration of percentage of<br>poor in the population|
|Benin|0.72|0.75|0.95||
|Botswana|0.54|1.00||0.06|
|Burkina Faso|1.00|0.87|0.51||
|Cameroon|0.63|1.00|1.00|0.36|
|Cape Verde|1.00||1.00|0.48|
|Chad|1.00|1.00||0.06|
|Congo,Dem. Rep. of|0.59|1.00|1.00|0.62|
|Congo|0.80|1.00|||
|Cote d'Ivoire|1.00|<br>0.91|1.00|0.51|
|Ethiopia|0.76|0.40|0.60||
|Ghana|0.81|1.00|0.97|0.31|
|Kenya|1.00|1.00|0.99||
|Lesotho|0.79|1.00|||
|Madagascar|0.93||0.92||
|Malawi|0.62|1.00|0.92||
|Mali|0.79|0.95|||
|Mozambique|0.87|1.00||0.31|
|Namibia|1.00|0.97|||
|Niger|0.44|0.47|0.55||
|Nigeria|0.44|0.32|0.97||
|Rwanda|0.88|1.00||0.01|
|Senegal|1.00|0.35|1.00||
|South Africa|0.84|0.72|1.00|0.41|
|Sudan|||||
|Tanzania|0.52|0.91|0.99||
|Uganda|1.00|1.00|0.20|0.02|
|Zambia|0.44|0.36|1.00||
|Zimbabwe||0.47|||
|**Average**|**0.78**|**0.82**|**0.87**|**0.29**|

_Source:_ Derived from Africa Infrastructure Country Diagnostic Power Tariff Database.

What becomes immediately clear is that some countries rank very high for cost recovery but do very poorly for equity and affordability and vice versa (table 11). Countries such as Chad, Mozambique, Rwanda, and Uganda tend to rank well for cost recovery but poorly for affordability and equity. On the other end, countries such as South Africa, the Democratic Republic of Congo, Tanzania, and Zambia fare relatively well in terms of equity and affordability but have not been able to achieve cost recovery. What is striking is that achieving all four objective simultaneously is almost impossible in the context of the high-cost low-income environment that characterizes much of SSA today. Hence most countries are caught between cost recovery and affordability.

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|Table 11. Over|view of scorecard results<br>||
|---|---|---|
||Target fullyachieved|Performs above the median|
|Cost<br>recovery|Burkina Faso, Cape Verde, Chad, Côte<br>d’Ivoire, Kenya, Namibia, Senegal, Uganda|Ghana, Madagascar, Mozambique, Rwanda,<br>South Africa,|
|Efficiency|Chad, Kenya, Uganda, Ghana, Mozambique,<br>Rwanda, Botswana, Cameroon, Lesotho,<br>Malawi,Congo,Rep. of|Namibia, Congo, Dem. Rep. of|
|Affordability|Senegal, Cameroon, Cape Verde, South Africa,<br>Congo, Dem. Rep. of|Kenya, Tanzania, Zambia, Côte d’Ivoire|
|Equity|None|Cameroon, Cape Verde, South Africa, Côte<br>d’Ivoire, Congo, Dem. Rep. of|

_Source:_ Derived from Africa Infrastructure Country Diagnostic Power Tariff Database.

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