metadata: authors: 0: author: Abramovsky,Laura Fernanda
1: author: Andres,Luis Alberto
2: author: Joseph,George
3: author: Rud,Juan Pablo
4: author: Sember,German Eduardo
5: author: Thibert,Michael David
count: World
docna: 0: docna: Study of the Distributional Performance of
            Piped Water Consumption Subsidies in 10 Developing Countries
docty: Policy Research Working Paper
subtopic: Hydrology,Labor & Employment Law,Small Private Water Supply Providers,Sanitary Environmental Engineering,Health and Sanitation,Water Supply and Sanitation Economics,Town Water Supply and Sanitation,Engineering,Water and Human Health,Environmental Engineering,Sanitation and Sewerage
teratopic: Law and Development,Health, Nutrition and Population,Environment,Water Supply and Sanitation,Science and Technology Development,Water Resources
abstracts: cdata!: This paper provides new evidence on how
            effectively piped water consumption subsidies are targeting
            poor households in 10 low- and middle-income countries
            around the world. The results suggest that, in these
            countries, existing tariff structures fall short of
            recovering the costs of service provision, and the resulting
            subsidies largely fail to achieve their goal of improving
            the accessibility and affordability of piped water for poor
            households. Instead, the majority of subsidies in all 10
            countries are captured by the richest households. This is in
            part because the most vulnerable population segments
            typically face challenges in accessing and connecting to
            piped water services. The paper also reveals shortcomings in
            the design of the subsidies, which are conditional on poor
            households being connected to a piped network.
repnme: repnme: Study of the Distributional Performance of
            Piped Water Consumption Subsidies in 10 Developing Countries
display_title: Study of the Distributional
            Performance of Piped Water Consumption Subsidies in 10
            Developing Countries
listing_relative_url: /research/2020/05/32029704/study-distributional-performance-piped-water-consumption-subsidies-10-developing-countries
topicv3: Sanitation m1326453 2000,Water m1326522 137,Water Supply m1326961 138,Wastewater Collection m1327591 148,Household Surveys m1504071 2761
publishtoextweb_dt: 2020-05-12T00:00:00Z
historic_topic: Law and Development,Health, Nutrition and Population,Environment,Water Supply and Sanitation,Science and Technology Development,Water Resources
origu: Water Africa 3 (SAEW2)
no_of_pages: 28
author_keys: 0: author_key: 1233336
1: author_key: 676776
2: author_key: 912994
3: author_key: 1132319
4: author_key: 793562
5: author_key: 1542605
model_extractions: Policy Research Working Paper 9245 Study of the Distributional Performance of Piped Water Consumption Subsidies in 10 Developing Countries _Laura Abramovsky Luis Andrés George Joseph Juan Pablo Rud Germán Sember Michael Thibert_ Water Global Practice May 2020
Policy Research Working Paper 9245

# **Abstract**

This paper provides new evidence on how effectively piped water consumption subsidies are targeting poor households in 10 low- and middle-income countries around the world. The results suggest that, in these countries, existing tariff structures fall short of recovering the costs of service provision, and the resulting subsidies largely fail to achieve their goal of improving the accessibility and affordability of piped water for poor households. Instead, the majority of subsidies in all 10 countries are captured by the richest households. This is in part because the most vulnerable population segments typically face challenges in accessing and connecting to piped water services. The paper also reveals shortcomings in the design of the subsidies, which are conditional on poor households being connected to a piped network.

This paper is a product of the Water Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at Landres@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
# **Study of the Distributional Performance of Piped Water Consumption Subsidies in 10 Developing Countries**

**Laura Abramovsky, Luis Andrés, George Joseph, Juan Pablo Rud,**

**Germán Sember, and Michael Thibert**<sup>*****</sup>

**JEL Classification:** D3, H220, L95, O13, O2

**Keywords:** Distributional performance of subsidies, consumption of piped water, developing country.

> * Laura Abramovsky: Institute for Fiscal Studies (IFS) and MacroConsulting (labramovsky@gmail.com, corresponding author), postal address: 7 Ridgmount Street

> London WC1E 7AE; Luis Andrés: World Bank (WB) (landres@worldbank.com), postal address: World Bank, 1818 H Street, NW, Washington, DC 20433 USA; George Joseph: WB  (gjoseph@worldbank.org), postal address: World Bank, 1818 H Street, NW, Washington, DC 20433 USA; Juan Pablo Rud: Royal Holloway University of London and IFS (Juan.Rud@rhul.ac.uk), postal address: Royal Holloway University of London Egham Hill, Egham TW20 0EX, UK; Germán Sember: MacroConsulting (gsember@macroconsulting.net), postal address: Lavalle 190, Piso 5° Oficina I, Buenos Aires, Argentina; and Michael Thibert: WB (mthibert@worldbank.org), postal address: World Bank, 1818 H Street, NW, Washington, DC 20433 USA.
# **1. Introduction**

Universal access to water and sanitation is a stated international development goal (UN 2015).<sup>2</sup> Well-designed subsidies—along with investment, technological innovation, and better governance and planning—stand out as a key instrument to reach this objective. In the context of high levels of poverty, where markets alone do not result in the desired levels of service provision and consumption, targeted subsidies can help address affordability and equity issues without necessarily jeopardizing the objectives of cost recovery or economic efficiency (WWAP 2019). Additionally, water and sanitation subsidies are advocated because greater access to and consumption of water and sanitation services are associated with rising productivity and living standards, and positive externalities related to public health.

The design and performance of subsidies hinges on the structure and technology of piped water production and delivery. But it is difficult to design subsidies. The water and sanitation sector involves large up-front (fixed) costs, long-lived assets, and network characteristics that make it difficult to estimate marginal costs, and thus to recover costs through efficient pricing. Instead, services often receive a blanket subsidization that may not increase either accessibility or affordability, and is likely to undermine the financial sustainability of the system. It is recommended that tariffs at least cover the operating expenditures incurred by suppliers, but even this minimal objective is rarely achieved. In this paper, we consider the extent to which subsidies meet equity objectives and affect providers’ ability to recover service costs. In the process, we provide new evidence of the distributional performance—and, specifically, the propoor targeting—of subsidies for piped water consumption in 10 developing countries, and document the gap between estimates of actual tariffs and cost-recovery tariffs.

We consider 10 low- and middle-income countries around the world: Ethiopia, Mali, Niger, Nigeria, Uganda, El Salvador, Jamaica, Panama, Bangladesh, and Vietnam. This sample is relatively heterogeneous in terms of gross domestic product per capita, connection rates, tariff structures, and cost-recovery tariffs. Our analysis is founded upon data from household surveys that capture water expenditure, utility providers’ administrative reports, and a novel estimation of country-specific costs of service.<sup>3</sup> Our results suggest that, in all the countries analyzed, the mean unit prices charged are, on average, lower than the overall cost of producing and distributing piped water (that is, lower than would be needed to cover both capital and operating expenditures), resulting in substantial subsidies. Having said this, in some countries the average prices charged are higher than the tariffs that would be needed to cover operating expenditure. In addition, subsidies are regressive, with the amount of resources allocated to water consumption subsidies increasing over the expenditure distribution, and richer households in the top five deciles usually capturing the lion’s share. The results also suggest that, in most countries, subsidies are regressive due to a combination of factors that are not related to their design: (i) poor households are located in areas not covered by piped water networks, (ii) poor households are in areas that are covered,

> 2 The United Nations’ Sustainable Development Goals (SDGs), adopted in 2015, include the aims to “achieve universal and equitable access to safe and affordable drinking water” and to “achieve access to adequate and equitable sanitation and hygiene for all” by 2030.

> 3 These estimates are calculated using an improved methodology and new data presented in Andres et al. (2019).

2
but are not connected to the network themselves, and (iii) poor households that are connected to the network appear to be consuming smaller quantities of water than the general population. This means that there are high errors of inclusion (rich households receiving subsidies) and even higher errors of exclusion (poor households not receiving subsidies). This issue is particularly pronounced in the African countries considered, where errors of exclusion fall between 90 and 100 percent.

Our findings are consistent with a small number of previous studies that also find that water consumption subsidies in developing countries are not reaching the poor as intended. Key examples of this literature—such as Komives et al. (2005) and Angel-Urdinola and Wodon (2007b)—have shown that quantity-based, targeted subsidies in Nicaragua, Cabo Verde, Sri Lanka, and the cities of Kathmandu (Nepal) and Bangalore (India) are usually regressive, with a smaller share of benefits accruing to the poor than the general population. These studies indicate that this problem is mostly associated with low rates of access to water networks in poor neighborhoods, as well as low connection rates among poor households in neighborhoods with access. More recently, the World Bank (2017) found that Tunisian households in the bottom quintile of the income distribution receive 11 percent of water subsidies, while those in the top quintile receive 27 percent.

Our findings may be used to inform a long-standing policy debate that is particularly heated where resources to fund water infrastructure are scarce. Despite evidence that poor households’ need for affordable, high-quality services remains unmet, and that wealthier segments of the population benefit most from subsidies, countries find it challenging to introduce policy reforms. This can be due to political economy constraints but also due to lack of technical and institutional capacity.

The rest of the paper is structured as follows: section 2 presents the data used, related statistics, and our methodology; section 3 presents our findings; and the final section summarizes these findings and discusses their policy implications.

# **2. Data and methodology**

A subsidy represents the difference between the cost of providing a service and the actual price, or tariff, paid by a user. In practice, water consumption subsidies can take different forms. They may be untargeted (e.g., due to the general underpricing of water supply); implicit (e.g., because of inadequate metering or low rates of bill collection); or explicit (e.g., the setting of higher tariffs for larger consumers, using increasing block tariffs). The funding for these subsidies comes from one or a combination of the following sources: government transfers, cross-subsidies (i.e., money taken from other consumer segments of the same or another sector), or simply the money freed up in the short term by, say, a lack of maintenance or service interruptions.

Estimating subsidies for piped water at the household level requires calculating the efficient economic cost of delivering a unit (cubic meter, m<sup>3</sup> ) of piped water and the unit price paid. In order to do this, we use a methodology that closely follows Komives et al. (2005) and AngelUrdinola and Wodon (2007a) and that we describe below. It is worth noting that the estimates 3
of subsidies’ distributional and targeting performance are arithmetic, nonbehavioral, and express a partial equilibrium.<sup>4</sup>

# **2.1 Data sources**

As no single data source provides all the information needed to compute the efficient economic cost of service, which is needed to estimate efficient subsidies at the household level, we use several data sources combined with a series of assumptions to construct the variables for our analysis.

## **Household surveys**

We estimate piped water access, connections, and expenditure using information self-reported during national household-level surveys (see the appendix for details). These surveys also provide, with varying degrees of quality across countries, the information necessary to construct total expenditure (and/or income) variables for each household. The quantities of water consumed, and the prices or tariffs paid by each household, are usually not reported in household surveys. We therefore estimate these by complementing the household-level surveys with administrative data from service providers and government programs detailing tariff structures.

## **Administrative data on tariff structures**

We gathered information about tariff structures from the International Benchmarking Network for Water and Sanitation Utilities (IBNET) database. Some countries list most of their water providers in IBNET, while others list only a few. For example, IBNET includes data on the tariff structures of all water providers in Mali and Niger, each of which has only one piped water provider. Meanwhile, for countries such as Ethiopia, Nigeria, Uganda, and Bangladesh—where the provision of services is decentralized and a multitude of utilities provide piped water—IBNET covers only a sample of providers.

## **Data on cost-reflective tariffs**

Cost-reflective tariffs cover the costs of providing service, including not only the efficient economic cost but also the costs arising from the inefficiencies of the service provider. The _efficient economic cost_ , meanwhile, covers all the economic resources required for efficient service delivery. These include operation and maintenance expenses and also all capital costs such as depreciation and return on capital. Estimating cost-reflective tariffs is a data-intensive endeavor.

For our purposes, cost-reflective tariffs are taken from a novel data set provided by Andres et al. (2019). The alternative approach followed by Andres et al., despite its assumptions and simplifications, improves on the existing estimates. In a nutshell, this approach entails answering the following question: “What is the long-run incremental cost of providing water and sanitation services for a given company?” It is based on a simple, utility-wide, bottom-up model, drawing

> 4 See, for instance, Abramovsky and Phillips (2015) for a description of different microsimulation models for taxes and benefits using household survey data.

4
from estimates of the capital stock per customer for an efficient firm plus information on operation and maintenance costs and efficiency levels (in terms of losses and employees) for the period 2010–15, derived from the IBNET data set. A total cost-reflective tariff that covers both operating expenditure (OPEX) and capital expenditure (CAPEX) (defined as CRT) and a costreflective tariff that covers only OPEX (defined as CRTopex) are estimated.<sup>5</sup>

# **2.2 Descriptive statistics**

Table 1 presents some descriptive statistics of the rates of households connected to piped water in each country analyzed; countries are listed in descending order by gross domestic product (GDP) per capita. These statistics are country-wide, and outline the areas covered by each household survey and not only those served by utility companies.<sup>6</sup> Column 1 shows the proportion of all households (regardless of whether they have access or not) that report using piped water as their main source of drinking water.<sup>7</sup> Column 2 shows the same figure for rural households, column 3 for urban households, and column 4 for poor households. Despite wide variations in coverage rates across countries, connection levels are consistently higher among urban households than both rural and poor ones.<sup>8</sup> The African countries in our sample with the lowest GDP per capita (Ethiopia, Mali, Niger, and Uganda; column 5) also have the lowest connection rate, at below 5 percent among rural and poor households. Bangladesh, a relatively poor country, presents a similar pattern. Nigeria’s connection rates are quite low given the country’s income per capita, but disparities among different population segments are smaller. The three Latin American countries have substantially higher connection rates, between 70 and 95 percent for the general population, and around 50 percent or over for rural and poor households. Vietnam falls in the middle, with a connection rate of 53 percent for the whole population, and reaching 70 percent for urban households.

**Table 1. Households connected to piped water**

|Country|Househol|ds connecte<br>(piped wat|d to network<br>er) (%)|water|<br>GDP per capita in<br>USD PPP 2016|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||Total|Rural|Urban|Poor||
|Panama|93.2|80.4|98.7|84.6|21,335|
|Jamaica|69.7|49.0|88.1|56.2|8,180|
|El Salvador|79.3|64.1|88.0|67.9|7,990|
|Vietnam<sup>a</sup>|53.0|28.8|70.2|50.4|5,838|
|Nigeria|13.6|9.9|19.0|10.5|5,435|

5 Estimated cost-reflective tariffs exclude environmental costs due to data constraints. For Ethiopia, we use averages across the 24 Sub-Saharan African countries considered in Andres et al. (2019), which excludes Ethiopia, since otherwise the values for cost-reflective tariffs in this particular country are unrealistically high.

6 For most countries, the surveys are nationally representative. In Vietnam, the survey includes only five regions: Hanoi City, Da Nang, Dak Nong, Thu Dau Mot City and Bình Dương Province, and Ho Chi Min City.

7 Country estimates might differ slightly from those of the Joint Monitoring Programme (JMP) of the World Health Organization and United Nations Children’s Fund (UNICEF), due to some minor differences in variable definitions and sources. One common difference between our computation and JMP’s is that we define a household as connected to piped water if it uses piped water as its main source of drinking water either in the dry or wet season. The JMP also includes public standpipes as part of its piped water definition in some countries such as Ethiopia and Mali, whereas we include only household connections. Thus, our figures may be easily compared against the JMP’s for household connections only. For example, figures for Ethiopia and Nigeria are slightly higher in table 1 (19.8 percent and 13.6 percent, respectively, in column 1) than those presented by the JMP (15.2 percent and 12.2 percent, respectively). 8 As one might expect, there are also wide variations across the administrative geographic areas covered by the household surveys in each country, variations that are not shown in this paper.

5
|Bangladesh<sup>b</sup>|13.6|2.8|41.3|4.7|3,319|
|---|---|---|---|---|---|
|Mali|11.1|2.2|29.6|0.9|1,971|
|Uganda|8.7|1.8|29.1|0.9|1,687|
|Ethiopia|19.8|3.5|63.9|2.4|1,608|
|Niger|9.1|0.8|49.7|0.1|914|

_Source:_ Original calculations using data from several household surveys. _Note:_ These are countrywide figures. Poor households are defined as belonging to the first four deciles of the expenditure (or income) distribution in each country. All figures are calculated using sample weights. Water connection is derived from questions about main source of drinking water—but some countries have information about sources of water for other uses (like Bangladesh), which are then also used to construct water connection. More information can be found in the appendix. Column 5 refers to GDP per capita, PPP (constant 2011 international U.S. dollars). GDP = gross domestic product; PPP = purchasing power parity.

a The figures for rural Vietnam, using the 2015 Vietnam Household Registration Survey (VHRS), are much higher than the figures presented in JMP, using the Household Living Standards Survey (HLSS) 2012 that shows a rural rate of 13 percent. This is likely due to differences in the regional coverage of each survey. The VHRS 2015 has a sample that is representative of the population in five provinces—Ho Chi Minh City, Ha Noi, Da Nang, Binh Duong, and Dak Nong— while the HLSS 2012 is supposed to be nationally representative.

bThe figures for urban and total are higher than the figures published in JMP for the latest year. The data for Bangladesh used in this analysis differ from the data used in JMP. We use the Household Income and Expenditure Survey 2016, and the figures presented here are consistent with the official figures published in the Preliminary Report on Household Income and Expenditure Survey 2016, page xiii (BBS 2016).

Table 2 summarizes the number of providers (as listed in the IBNET dataset) whose service areas are also covered in the household surveys, the average cost-reflective tariffs, and imputed unit prices (conditional on the reporting of positive expenditure on water). (More information about IBNET data for each country can be found in the appendix.) Most countries in the sample try to target subsidies using increasing block tariff structures, though two African countries, Nigeria and Uganda, have fixed rates. In Bangladesh, some service providers charge a flat consumption rate while others use increasing block tariffs. El Salvador is the only country that has a volumedifferentiated tariff structure. Some countries or providers also place a value-added tax on tariffs. For the analysis carried out in the following section, we use all the households surveyed in each country, and information from the utilities serving them. As explained earlier in this section, when a household is located in an area or region with no available tariff structure, we use information from other households in that country to impute quantities and unit prices paid.

Remarkably, all countries show higher cost-reflective tariffs than the average unit price paid by all households that report paying a positive amount for piped water. This already shows that all countries operate in a way that on average subsidizes residential consumers. However, six of the ten countries show a unit price higher than the estimated CRTOpex, suggesting that they can cover at least operating expenditures.

**Table 2. Tariff data and estimated unit costs and prices**

|Country|Number of<br>providers<br>covered in|Type of tariff<br>structure|Estimated cost-<br>reflective tariff<br>(unit cost/m<sup>3</sup>)|Estimated OPEX<br>cost-reflective<br>tariff (unit|Estimated<br>effective<br>average|
|---|---|---|---|---|---|
||the||CRT|cost/m<sup>3</sup>) CRTopex|price/m<sup>3</sup>|
||analysis<sup>a</sup>||($2017)|($2017)|($2017)|

6
|Ethiopia<sup>b</sup>|9|IBT|3.68|0.67|0.23|
|---|---|---|---|---|---|
|Mali|1|IBT with fixed<br>charge + VAT|2.29|0.37|1.04|
|Niger|1|IBT|3.97|0.48|0.37|
|Nigeria<sup>c</sup>|10|Fixed rate/m<sup>3</sup>,<br>some with a<br>fixed rate|2.05|0.40|0.80|
|Uganda|1|Fixed rate/m<sup>3</sup>|1.89|0.37|0.86|
|El Salvador<sup>d</sup>|1|VDT, with fixed<br>charge for 10 m<sup>3</sup><br>or less|0.85|0.19|0.32|
|Jamaica<sup>e</sup>|1|IBT|2.51|0.55|1.69|
|Panama<sup>f</sup>|1|IBT|1.18|0.26|0.20|
|Bangladesh|23|Depends on<br>utility (IBT, 16<br>with flat rate)|0.49|0.12|0.26|
|Vietnam|3|IBT (+ VAT for<br>some)|2.70|0.62|0.38|

_Source:_ IBNET database, World Bank, and other resources.

_Note:_ Since, there are no available data on OPEX cost-reflective tariffs for El Salvador and Jamaica, we use the value for the total cost-reflective tariff, multiplying it by 0.22, which is the ratio of the average OPEX cost-reflective tariff to the average total cost-reflective tariff from Andres et al. (2019). The far-right column shows the unit price conditional on paying for water services (i.e., self-reported expenditure on water greater than zero). The figure is calculated using sampling weights. CRT = cost-reflective tariff; CRTopex = OPEX cost-reflective tariff; IBT = increasing block tariff; m<sup>3</sup> = cubic meter; OPEX = operating expenditure; VAT = value added tax; VDT = volume-differentiated tariff. a These are providers that cover areas included in the household surveys and for which there are available data online. Most of the data come from IBNET.

b Data are from 2007 for Dire Dawa, and 2014 for Addis Ababa. The cost-reflective tariff includes both OPEX and CAPEX and is estimated using averages across 24 Sub-Saharan African countries in Andres et al. (2019), since the values otherwise presented for Ethiopia seem unrealistically high. c Data come from Abubakar (2016).

d Data come from the state-owned piped-water provider, Administración de Acueductos y Alcantarillados (ANDA). e Data are from the National Water Commission.

f Data from the Autoridad Nacional de Servicios Públicos are used for OPEX cost-reflective tariffs for the year 2015 in U.S. dollars and divided by 0.22, which is the relationship between the average of the OPEX cost-reflective tariffs across countries from Andres et al. (2019), and the average of total cost-reflective tariffs across countries from the same paper.

# **2.3 Definition of variables of interest**

In the following subsections we describe how we classify households by economic status (i.e., poor or not), identify whether they benefit from water consumption subsidies, and estimate the magnitude of the subsidies they receive. (Further information on how each variable is constructed for each country is provided in the appendix.)

## **Poor households (P) and all households (H)**

Although we conduct our analysis at the household level, we construct deciles of expenditure (or income) on the basis of per capita expenditure (or income). Poor households are defined as belonging to the first four deciles of the national distribution of expenditure (or income) per capita in each country, and denoted by the subscript _P._ All households in each country are denoted by the subscript _H._ Sampling weights were used to correct for the bias inherent in representative household surveys, which tend to underrepresent poorer households in low-income countries. Because we 7
conducted our analysis of expenditure at the household level—and average household size may vary across deciles, particularly in Africa—average total expenditure at the household level may not increase as expected when moving across the household per capita expenditure distribution.

# **Service area (SA)**

Following Angel-Urdinola and Wodon (2007a), households are considered to have potential access to a network if they are located in a service area ( _SAh_ ),<sup>9</sup> which takes the value of 1 if other households in their neighborhood reported having water connections in a household survey. We assume that households in a _SAh_ have the option of connecting to the piped water network present in their neighborhood. The neighborhood is proxied by the enumeration area where the household is located, according to household surveys. However, this assumption may not always be correct. If a neighborhood covers a large geographical area, or if the presence of an adjacent water main varies by household, the survey figures may overestimate the actual number of households with potential access to the piped water network.

We can define the ratio of all households that have potential access ( S h=1 1(S h<sup>= 1)</sup> ]/ ) and the equivalent ratio for the poor, which is defined as the number of poor households HH HH<sup>= ∑</sup> that have potential access divided by the total number of poor households ( S HH [∑h=1 1(S h<sup>= 1)</sup> ). PP<sup>=</sup> PP ]/PP

# **Connected (C)**

We set this variable to 1 if a household is connected to and drawing from the water network, and to zero otherwise. This is determined by the main source of drinking water, as self-reported in household surveys. We then define , as the share of households that are connected to the network among those with potential access (i.e., located in a service area), which is a subset of all households _H._ HH|SSAA CC Hence, S<sup>xCC</sup> is the proportion of poor households that are connected, conditional on their having HH HH|SSAA<sup>is equal to the actual connection rate among all households. Analogously,</sup> potential access (or being located in a service area) or, equivalently, the number of poor PP|SSAA CC households that are connected divided by the number of poor households that have potential access to the network.

# **Quantity of water consumed among those connected (Q)**

We construct this variable by imputing water consumption volumes from self-reported household water expenditure, _EH|T_ , conditional on being connected to the network. The conversion is undertaken using the corresponding tariff structure from the IBNET data.<sup>10</sup>

In the first round of calculations, we impute quantities for households for which (i) the total bill for water consumption depends on the quantity consumed, (ii) the value of self-reported

> 9 The variable _service area_ ( _SA_ ) is equivalent to the variable _access_ ( _A_ ) in Komives et al. (2005) and Angel-Urdinola and Wodon (2007a). In this paper we prefer the phrase _service area_ because it better reflects the variable we are measuring.

> 10 Information on how this match is made for each country is provided in the appendix.

8
expenditure on water is greater than zero, and (iii) there is a specific provider for which we have tariff structure information. For households that pay a fixed rate per cubic meter, the monthly quantity of water consumed equals the total expenditure on water divided by the rate. For households facing unit prices that vary by quantity consumed (such as increasing block or volume-differentiated tariffs), each block within the tariff structure is assigned a maximum expenditure level and associated quantity of water consumed. Then quantities are assigned to the household sample by matching self-reported household water expenditure with the corresponding level of the tariff structure. For the remainder of households, for which (i) the bill is a flat rate unrelated to water quantities consumed or (ii) self-reported expenditure on water equals zero,<sup>11</sup> we assume consumption quantities at the median of those households in the same country for which we could assign quantities. For those households that report a positive expenditure on piped water but are located in areas where they cannot be mapped to a specific provider, we use a median unit price derived from households with data, and recover the quantity of water consumed.

We then define _QP|T_ as the average quantity consumed by the poor, conditional on receiving a subsidy and being connected, and _QH|T_ as the analogous measure for the population as a whole. This variable is related to the subsidy design because the quantity of water consumed by subsidy recipients determines the total value of the subsidy each recipient will receive.

# **Average unit price of water paid among those connected (UP)**

We define this variable as the ratio of (i) water expenditure (as self-reported in household surveys) to (ii) assigned quantities, as described above. For a household with a water expenditure of zero, the unit price would also be zero.

# **Subsidy beneficiaries among those connected (B), and targeting measures (T)**

We define variable _B_ as equal to 1 if a household is connected and pays a unit price ( _UP_ ) for water that is lower than the cost-reflective tariff ( _CRT_ , as defined in above).

We then define _BH_ as the total number of households that receive water consumption subsidies, _BP_ as the total number of poor households that receive water consumption subsidies, and _BNP_ as the total number of non-poor households that receive water consumption subsidies. We then define as the share of households that receive a subsidy (or are targeted) conditional on _C_ (that is, being connected to the network within a service area). Analogously, HH|CC TT represents the share of poor households that receive a subsidy conditional on _C_ . Therefore, the beneficiary incidence of the consumption subsidy is determined by _SA, C,_ and _T._ The share PP|CC TT of all households receiving the subsidy is equal to S HH<sup>xCC</sup> HH|S<sup>xTT</sup> HH|CC<sup>.</sup>

> 11 In many countries, and particularly Ethiopia and Nigeria, many households appear to be connected to the network and use piped water as their main source of drinking water but report paying zero for their water. Anecdotal evidence indicates that many households are connected illegally to the network or that utilities do not invoice and collect payments as they should.

9
## **Value of the subsidy (S)**

This number is constructed using the variables described above; for each household, this is equal to _S = QP|T x (CRT - UP),_ conditional on _UP<CRT_ (i.e., _B_ = 1). Then, is the value of the subsidies accruing to the poor, and is the total value of the subsidy accruing to the population as a whole. PP HH SS SS

## **Rate of subsidization (R)**

The average rate of subsidization is _RH|T =_ 1 _- EH|T/(QH|T x CRT) =_ 1 - _UP/CRT._

# **2.4 Distributional performance of subsidies**

Having identified beneficiary households ( _B_ ) and the size of subsidies ( _S_ ) they receive, we estimate the share of subsidies accrued to each decile of the national expenditure (or income) distribution, and consider whether subsidies are progressive or not.

- i. _Progressive subsidies_ : Subsidies are considered progressive when the share of subsidies accrued to each decile decreases over the expenditure (or income) per capita distribution. This means that poor households capture a higher portion of the subsidy pie.

- ii. _Regressive subsidies_ : Conversely, subsidies are considered regressive when the share of subsidies accrued to each decile increases over the expenditure (or income) distribution.

We also estimate the share of overall countrywide household-level expenditure captured by each decile, and contrast this with the share of subsidy captured by each decile. This helps understand whether the subsidy in effect reduces inequality—that is, whether a greater percentage of subsidies is allocated to households in poorer deciles than is the case for countrywide expenditure. When the subsidy distribution is more skewed toward richer deciles than is the distribution of expenditure or income, the subsidy is regressive and increases inequality. However, if subsidies are less regressive than the distribution of expenditure or income, they can serve to reduce inequality.<sup>12</sup>

# **2.5 Performance of subsidies’ targeting and underlying mechanisms**

Following Angel-Urdinola and Wodon (2007a), we define the targeting performance indicator (Ω), which relates to a subsidy’s distributional performance, as the share of subsidy benefits received by the poor ( _SP/SH_ ), divided by the proportion of poor households in the total population ( _P/H_ ).

The targeting performance indicator (Ω) can be split into factors related to water network access and factors related to subsidy design, using the variables defined in the previous section:

> 12 These measures are simple statistics that can be easily displayed in bar charts, and are conceptually similar to the quasi-Gini coefficients described, for example, in Komives et al. (2005: 140–41).

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**(Equation 1)**

Where: _SAP/SAH_ is the ratio of (i) the share of poor households located in a service area ( _SAP_ ) to (ii) the share of all households located in a service area ( _SAH_ ) (access related); _CP|SA/CH|SA_ is the ratio of (i) the proportion of poor households located in any given service area that are connected (CP|SA) to (ii) the proportion of _all_ households located in any given service area that are connected ( _CH|SA_ ) (access related);

_TP|C/TH|C_ is the relative proportion of (i) poor households with a subsidy, conditional on connection (subsidy design related) to (ii) all households with a subsidy, conditional on connection (subsidy design related);

_RP|T/RH|T_ is the relative rate of subsidization of poor households or all households, respectively, conditional on receiving a subsidy (subsidy design related); and

_QP|T/QH|T_ are relative quantities consumed by poor households or all households, respectively, conditional on being subsidized (subsidy design related).

Access factors are fixed in the short term, since they are determined by network expansion and households’ decisions, and will affect the distributional performance of a consumption subsidy, regardless of its type or structure.

Two additional measures used to assess subsidies’ targeting performance are errors of inclusion and exclusion.

The error of exclusion (EE) is the share of households in poverty that are not benefiting from the subsidy: EE = 1 - ( _BP/P) = AP x UP|A_ x _TP|U = (P - BP)/P._

The error of inclusion (EI) is the share of beneficiary households that are not in poverty: _EI = BNP/BH = BNP/(BP + BNP)._ Figure 1 shows graphically how these errors are defined.

**Figure 1. Illustration of the errors of inclusion and exclusion**

<!-- Start of picture text --> Beneficiaries<br>BP BNP<br>P NP<br><!-- End of picture text --> 11
_Note:_ BP = share of households in poverty; BNP = share of households not in poverty; P = poor; NP = not poor.

# **3 Findings**

## **3.1 Distributional performance of existing subsidies**

In this section we show the overall distributional incidence of water consumption subsidies across households by expenditure decile (or income decile for El Salvador and Panama).

As explained in section 2, each figure shows the percentage of subsidy captured by each decile (i.e., percentage of money spent on subsidies accruing to all households in the given decile). In addition, we present the share of total expenditure or income in the economy accruing to each decile, to show if the subsidy reduces inequality (i.e., is less concentrated in top deciles than is the overall expenditure or income distribution) or not.

As presented in figure 2, the results for the five African countries demonstrate a common trend: richer households enjoy a greater share of subsidies since the poor have less access to piped water and, even when connected, consume less water generally. In all countries, richer households receive the lion’s share of subsidies. In Niger and Uganda, households in the top decile receive over 60 percent of all water consumption subsidies. In Mali, Niger, and Uganda, the poorest do not benefit from the subsidy at all. Nigeria is the only country where subsidies reduce rather than increase inequality.

Figure 3 shows the distribution of beneficiaries (households that receive the subsidy) across expenditure deciles for the same five countries. It complements the picture painted by the distributional incidence of the amount of subsidies. Wealthier deciles included the largest share of total subsidy beneficiaries (blue bars) and the largest share of all households within each decile that are beneficiaries (red bars). The differences between the top three deciles and the remaining ones are remarkable in all countries, although less pronounced in Nigeria. As noted above, in Niger, the poor (i.e., the first four deciles) do not benefit from the subsidy at all; Uganda shows a similar pattern, and in Mali only the fourth decile shows a very small amount of beneficiaries. We conclude that in the five African countries analyzed, and using the household data, tariff data, and the methodology described, consumption subsidies are regressive (albeit relatively less so in Nigeria).

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**Figure 2. Distributional incidence of subsidies (by decile) in five African countries**

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail. _Note:_ All figures are calculated using sample weights. Total expenditure is total household expenditure in all categories. The distribution of expenditure refers to the countrywide distribution of expenditure per capita, i.e., households are ranked according to their expenditure per capita.

**Figure 3. Distributional incidence of subsidy beneficiaries (by decile) in five African countries**

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail.

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_Note:_ Subsidy beneficiaries are households. All figures are calculated using sample weights. Total expenditure is total household expenditure in all categories. The distribution of expenditure refers to the countrywide distribution of expenditure per capita, i.e., households are ranked according to their expenditure per capita.

Presented in figure 4, the results for Panama, Jamaica, and El Salvador have a similar pattern, demonstrating subsidies’ regressivity. However, the degree of regressivity in Latin American countries is lower than in African countries. As shown in the next subsection, this is partly because both a greater number of poor households reside in service areas, and, conditional on this potential access, a greater percentage of poor households are connected to the network. The figures suggest that subsidy schemes in all three Latin American countries reduce inequality.

**Figure 4. Distributional incidence of subsidies (by decile) in three Latin American countries**

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail. _Note:_ All figures are calculated using sample weights. Total expenditure is total household expenditure in all categories. The distribution of expenditure (Jamaica) or income (El Salvador and Panama) refers to the countrywide distribution of expenditure/income per capita, i.e., households are ranked according to their expenditure/income per capita.

Figure 5 shows the distribution of beneficiaries (households that receive the subsidy) across expenditure or income deciles for the three countries. As with the African countries, it complements the picture painted by the distributional incidence of the amount of subsidies. Richer deciles feature the largest share of total subsidy beneficiaries (blue bars) and the largest share of all households within each decile that are beneficiaries (red bars), although in Panama the pattern is less pronounced.

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**Figure 5. Distributional incidence of subsidy beneficiaries (by decile) in three Latin American countries**

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail. _Note:_ Subsidy beneficiaries are households. All figures are calculated using sample weights. Total expenditure is total household expenditure in all categories. The distribution of expenditure (Jamaica) or income (El Salvador and Panama) refers to the countrywide distribution of expenditure/income per capita, i.e., households are ranked according to their expenditure/income per capita.

Figure 6 shows the results for two Asian countries, Bangladesh and Vietnam. In Bangladesh, water consumption subsidies are strongly regressive and increase inequality. While subsidies in Vietnam are still regressive, albeit less so, they are actually reducing inequality, suggesting slightly better targeting than in Bangladesh. Figure 7, which depicts the distribution of beneficiary households across deciles, shows strong regressivity in Bangladesh but a more even distribution across expenditure deciles in Vietnam, once again suggesting better targeting.

In the next subsection, we look at a more synthetic measure of targeting performance that complements this analysis and consider both access and subsidy design factors driving this performance.

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**Figure 6. Distributional incidence of subsidies (incidence by deciles) in two Asian countries**

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail. _Note:_ All figures are calculated using sample weights. Total expenditure is total household expenditure in all categories. The distribution of expenditure refers to the countrywide distribution of expenditure per capita, i.e., households are ranked according to their expenditure per capita.

**Figure 7. Distributional incidence of subsidy beneficiaries (incidence by deciles) in two Asian countries**

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail. _Note:_ Subsidy beneficiaries are households. All figures are calculated using sample weights. Total expenditure is total household expenditure in all categories. The distribution of expenditure (Jamaica) or income (El Salvador and Panama) refers to the countrywide distribution of expenditure/income per capita, i.e., households are ranked according to their expenditure/income per capita.

# **3.2 How well are existing subsidies targeting the poor?**

Table 3 and figure 8 show the targeting performance of water consumption subsidies in the 10 countries under analysis. Figure 8 shows how access factors ( _SA*C_ ) and subsidy design factors ( _T*R_ ) drive the omega (Ω) value across countries. Table 3 presents the values of each factor for each country. A value below 1 indicates that the factor lowers performance, whereas a value above 1 indicates that the factor boosts performance.

As shown in figure 8, all African, Latin American, and Asian countries present Ω values below 1, demonstrating that water consumption subsidies are poorly targeted to the most vulnerable populations. Access factors (blue dots) are the driving force explaining low Ω values in most 16
countries. Subsidy design factors (red dots) are in general around or above 1. These indicators suggest that policy makers should mainly focus on increasing access to services, although enhancing subsidy design could also be beneficial.

**Figure 8. Factors driving the targeting performance of subsidies (Ω) across countries**

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail. _Note:_ Of access factors, _SA = SAP/SAH_ is the share of households located in a service area for the poor relative to the population as a whole; _C = CP|SA/CH|SA_ is the relative share of households that are connected to the service conditional on potential access (being located in a service area). Of subsidy design factors, _T = TP|C/TH|C_ is the relative proportion of households with a subsidy conditional on usage; _R = RP|T/RH|T_ is the relative rate of subsidization. Factor values below 1 indicate that the factor lowers targeting performance, whereas factor values above 1 indicate that the factor boosts targeting performance. BGD = Bangladesh; ETH = Ethiopia; JAM = Jamaica; MLI = Mali; NER = Niger; NGA = Nigeria; UGA = Uganda; PAN = Panama; SLV = El Salvador; VNM = Vietnam.

Various factors influence the degree to which targeting performance is inadequate across countries. In table 3 we explore relevant access and subsidy design factors in more detail.

Four African countries—Ethiopia, Mali, Niger, and Uganda—show an estimated value of Ω lower than 0.1. In all four the main factors that decrease the efficiency with which subsidies target poor households relative to all households include (i) poor households’ lower probability of being located in a service area and (ii) their lower probability of being connected to a water network, conditional on being located in a service area. Additionally, in Ethiopia and Niger, the relatively low water quantities consumed by the poor significantly decrease targeting performance.

Nigeria shows an Ω above 0.5, revealing a significant problem, although it is less acute than in Ethiopia, Mali, Niger, and Uganda. _SA, C,_ and _Q_ are closer to 1, and _T_ = 1 and _R_ = 1.03. This is consistent with the evidence in the previous subsection, which shows that Nigeria’s distributional incidence of subsidies by decile is regressive, although to a less pronounced degree than in the other four African countries.

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In order to improve the targeting performance of water consumption subsidies, these governments should expand the service area (i.e., _SA_ ) of their utilities and connect more households within this service area (i.e., _C_ ) to the water network. Additionally, these countries should improve the design of their water consumption subsidies to better target poor households (increasing _T_ to values over 1).

In El Salvador, Jamaica, and Panama, the problem is less acute, with an Ω of 0.84, 0.69, and 0.87, respectively. In these countries the factors lowering Ω to below 1 are different. In all three, the probability of living in a service area is high for both types of households, poor and non-poor ( _SA_ is close to 1). However, connection rates conditional on location in a service area and water quantities consumed are both significantly lower for poor households, driving Ω to below 1. These countries would benefit from increasing service connection rates, as well as improving subsidy design to better target the poor (resulting in the increase of variables _T_ and _R_ further above 1).

Bangladesh falls in between African and Latin American countries, with an Ω of 0.35. Both the variables _SA_ and _C_ are low in general, and particularly low for poor households. Water quantities consumed by the poor are similar to those of other households, but this is potentially driven by the lack of information on households’ expenditures and the flat rates being charged, which complicates imputing Bangladesh’s household-specific water consumption (therefore, a median consumption quantity is assumed for most households). A parameter _Q_ close to 1 is potentially reasonable given that any connected household, regardless of its consumption level, pays a flat rate. Nonetheless, potentially all factors ( _SA, C, T, R_ ), in addition to the tariff structure, could be improved. In Vietnam, the value for Ω is much higher, though still below 1, and a focus on subsidy design ( _T_ and _R_ ) could increase targeting performance.

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**Table 3. Decomposing the targeting** **<u>performance of water consumption subsidies</u>**

||**Ω**|**SA**|**C**|**T**|**R**|**Q**|
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
||**Ω>1**<br>**pro-poor**|Potential access<br>to water<br>connection<br>(service area)|Connection rate (for<br>those with access)|Receipt of<br>subsidy (for those<br>connected)|Rate of subsidization<br>(for those with a<br>subsidy)|Quantities (for those<br>connected)|
|**Ethiopia**|0.047||||||
|Poor||0.255|0.095|1.000|0.957|3.275|
|All||0.446|0.444|0.996|0.964|8.567|
|Poor/All||0.571|0.214|1.004|0.993|0.382|
|**Mali**|0.085||||||
|Poor||0.036|0.263|0.907|0.858|14.491|
|All||0.239|0.467|0.958|0.793|14.751|
|Poor/All||0.150|0.563|0.948|1.081|0.982|
|**Niger**|0.006||||||
|Poor||0.030|0.040|1.000|0.921|10.453|
|All||0.209|0.433|1.000|0.909|23.651|
|Poor/All||0.141|0.093|1.000|1.014|0.442|
|**Nigeria**|0.661||||||
|<br>Poor||0.507|0.208|0.997|0.929|3.270|
|All<br>||0.585|0.233|0.993|0.900|3.972|
|Poor/All||0.867|0.894|1.004|1.032|0.823|
|**Uganda**<br>|0.079||||||
|Poor||0.204|0.045|1.000|0.544|5.173|
|All||0.332|0.261|1.000|0.549|6.965|
|Poor/All||0.610|0.17|1.00|0.99|0.74|
|**El Salvador**|0.839||||||
|Poor||0.945|0.719|0.996|0.679|19.230|
|All||0.966|0.821|0.983|0.647|20.863|
|Poor/All||0.977|0.876|1.013|1.050|0.922|
|**Jamaica**|0.690||||||
|Poor||0.998|0.563|0.969|0.773|10.318|
|All||0.998|0.698|0.955|0.653|14.456|
|Poor/All||1.000|0.806|1.015|1.183|0.714|
|**Panama**|0.867||||||
|Poor||1.000|0.846|1.000|0.872|40.453|
|All||1.000|0.932|1.000|0.829|44.484|
|Poor/All||1.000|0.907|1.000|1.052|0.909|
|**Bangladesh**|0.345||||||
|Poor||0.286|0.164|0.998|0.999|36.555|
|All||0.384|0.355|0.998|0.995|36.579|
|Poor/All||0.745|0.462|1.000|1.003|0.999|
|**Vietnam**|0.858||||||
|<br>Poor||0.804|0.627|1.000|0.861|13.863|
|All||0.840|0.631|1.000|0.858|15.430|
|Poor/All||0.958|0.993|1.000|1.004|0.898|

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail.

_Note:_ Access factors: _SA = SAP/SAH_ is the share of households located in a service area for the poor relative to the population as a whole; _C = CP|SA/CH|SA_ is the relative share of households that are connected to the service conditional on potential access (being located in a service area). Subsidy design factors: _T = TP|C/TH|C_ is the relative proportion of households with a subsidy conditional on usage; _R = RP|T/RH|T_ is the relative rate of subsidization; _Q = QP|T/QH|T_ are relative quantities consumed. A factor value below 1 indicates that the factor contributes to reduced targeting performance, whereas a value above 1 indicates that the factor contributes to increased targeting performance. All figures are calculated using sample weights. Poor households are defined as belonging to the first four deciles of the expenditure (or income) distribution in each country.

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Table 4 shows the errors of inclusion (the proportion of beneficiary households that are in the top six deciles, or relatively rich) and the error of exclusion (the proportion of poor households that do not receive a water consumption subsidy). These errors are not conditional on location in a service area or connection, so they differ from those in table 3. The estimates show that in most countries covered here, the errors of exclusion range from 78 percent to nearly 100 percent, except for Jamaica, Panama, and Vietnam (column 2). This is consistent with the very low levels of Ω presented in table 3. The countries on the lower end of this spectrum exhibit errors of exclusion around 50 percent, suggesting that their level of outreach to the poor could still be improved.

Inclusion errors (column 1 of table 4 ) are extremely high in all countries, suggesting a wide margin for the improvement of targeting, consistent with the analysis presented so far. The inclusion errors are particularly high in African countries, with figures close to 100 percent in all countries except Nigeria. Bangladesh also stands out with figures close to 90 percent.

**Table 4. Water consumption subsidies, and errors of inclusion and exclusion**

||Error of inclusion (%)|Error of exclusion (%)|
|---|---|---|
||(1)|(2)|
|Ethiopia|96.01|97.57|
|Mali|97.82|99.15|
|Niger|99.73|99.93|
|Nigeria|74.35|89.59|
|Uganda|98.28|99.60|
|El Salvador|82.01|78.27|
|Jamaica|51.78|51.78|
|Panama|71.14|38.56|
|Bangladesh|87.67|95.45|
|Vietnam|66.51|50.28|

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail. _Note:_ Poor households are defined as belonging to the first four deciles of the expenditure (or income) distribution in each country. Error of inclusion is measured by the percentage of all beneficiary households that are rich; error of exclusion is measured by the percentage of poor households that do not get a subsidy. All figures are calculated using sample weights.

# **4 Conclusion**

This paper offers a methodology to both estimate the amount of subsidy resulting from piped water consumption tariffs and assess the resulting subsidy’s efficacy in improving the accessibility and affordability of service for the poor. In an analysis of 10 low- and middle-income countries, we find that piped water is substantially subsidized, as existing tariffs do not fully recover the costs of service provision. Moreover, these consumption subsidies fail to efficiently address service gaps among the poor due to ineffective targeting. Our analysis reveals remarkably consistent findings across countries, despite their great diversity in terms of economic development, national income levels, piped water coverage, and the costs of producing and distributing piped water.

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Overall, we find that water consumption subsidies in all 10 countries are regressive and therefore do not adequately target the poor (Ω has a value under 1 in all cases, with an average value of 0.45 across all 10 countries). We also find that the severity of regressivity varies significantly (from 0.006 in Niger to 0.87 in Panama).

Only a very small number of poor households benefit from subsidies, particularly among the African countries in our sample. This is because poor households are less likely to be located in areas serviced by utility providers (the variable _SA_ in the analysis), and even where they are, they are less likely to be connected and consuming piped water (variable _C_ ) than the general population.

This suggests that subsidies insufficiently target the poor primarily because of access factors ( _SA_ takes a value of 0.7 and _C_ takes a value of 0.6 on average across all 10 countries). This problem is particularly pressing in the African countries (with an average _SA_ of 0.47 and average _C_ of 0.39 across the sample) and Bangladesh (which has an _SA_ of 0.75 but a _C_ of 0.46).

Aside from improving access for the poor, subsidy design could also be improved to better target the poor. As shown, the share of poor households that receive a subsidy conditional on being connected to the network—relative to the equivalent share for the whole population (T)— averages 0.99. The rate of subsidization conditional on receiving a subsidy for the poor—relative to the equivalent rate for the whole population ( _R_ )—averages 1.02 across all 10 countries. This signals a need for improved subsidy design that reflects current network coverage and connection rates. This trend is observed in all countries.

All in all, this means that in the 10 countries considered here—and, perhaps, in most developing countries—improving the targeting of current subsidy schemes to the poorest households will primarily require improving these households’ access to service. Therefore, network expansion into poorer neighborhoods and policies that facilitate household connections should be pursued. In countries where water access is no longer a significant issue, modifying a subsidy’s design could itself improve how well the subsidy targets the poor.

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