ASSESSING THE EFFECTS OF COVID-19
ON ACCESS TO WATER, SANITATION,
AND HYGIENE IN USAID HIGH PRIORITY
AND STRATEGY-ALIGNED COUNTRIES
Synthesis Report

Photo credit: Bashiru Yachori, 2020.

DECEMBER 2020
This publication was produced for review by the United States Agency forInternational Development. It was prepared by Tetra Tech.

ACKNOWLEDGEMENTS
Jeff Albert (Aquaya) managed this task, with additional conception and design input by Ranjiv Khush (Aquaya), Rishi
Agarwal (FSG), Aditi Krishna (Iris Group), and Morris Israel (Tetra Tech). Deep dives, including identification ofand interviews with key informants, were led by Miriam Otoo (Tetra Tech), Maneshka Eliatamby and Prakash Luitel(Iris Group), Subhash Chennuri, Harshika Gupta, Akshay Kohli, and Mahesh Nayak (FSG), Brian Mwangi, Chloé
Poulin, and Edinah Samuel (Aquaya).
David Levine (Haas School of Business, University of California Berkeley) and Xinyu Ren conducted theeconometric analysis, building on research by Marc Jeuland (Duke University), David Fuente (University of South
Carolina), Dale Whittington (UNC Chapel Hill), Maura Allaire (UC Irvine), and Semra Ozdemir (Duke University).
GeoPoll executed the SMS surveys across six countries, and Rick Johnston of the World Health Organization (andUNICEF/WHO’s Joint Monitoring Program) provided invaluable feedback on our survey instrument.
USAID Mission staff in seven countries agreed to participate in and support this work via informed guidance andintroductions to key informants. We extend our appreciation to Abdoulaye Boly (Dakar), Bindi Borg (Kathmandu),
Pape Dieye (Dakar), Clara Dimene (Maputo), Leonor Domingos (Maputo), Arvil Gonzalez (Dakar), Emmanuel
Odotei (Accra), Nicole Mukunzi (Kigali), Sandy Ngilambi (Kinshasa), Carrie Rasmussen (Kathmandu), Amanda
Robertson (Nairobi), Deepana Shanthan (Kathmandu), Pragya Shrestha (Kathmandu), and Erica Trauba (Dakar).
This research would not have been possible without the generous time and insights of over 300 government
officials, service providers, service provider associations, and private sector actors across DRC, Ghana, Kenya,
Mozambique, Nepal, Rwanda, and Senegal.
A number of USAID WASH programs provided key information and analysis. We highlight in particular the Water,
Sanitation, and Hygiene Finance (WASH-FIN) centrally-funded program as well as the WASH for Health (W4H)
program in Ghana. Other programs include: ACCES (Senegal), Gikuriru (Rwanda), Isuku Iwacu (Rwanda), KIWASH(Kenya), Safaa Paani (Nepal), and TAYAR (Nepal).
UNICEF has also been a source of important information and perspective, and we wish to thank Carrie Kramer inMozambique, the UNICEF WASH team in Ghana, and Rebecca Rydberg at the Global WASH Cluster (GWC) for
their invaluable time and expertise.
Greg Allgood of World Vision and Antoinette Kome of SNV provided key introductions – we thank you.
Finally, we thank Elizabeth Jordan, Alison Macalady, Jesse Shapiro, Brian Banks, and Ryan Mahoney of USAID forthe close and enriching attention they have afforded the work.
Prepared for the United States Agency for International Development by the Water, Sanitation, and Hygiene
Partnerships and Learning for Sustainability (WASHPaLS) project under Task Order number AID-OAA-TO-1600016 of the Water and Development Indefinite Delivery Indefinite Quantity Contract (WADI), contract number
AID-OAA-I-14-00068.
Tetra Tech Contacts:

Morris Israel, Project Director
morris.israel@washpals.org
Jeff Albert, Deputy Project Director
jeff.albert@washpals.org
Lucia Henry, Project Manager
lucia.henry@tetratech.com
Tetra Tech
1320 N. Courthouse Road, Suite 600, Arlington VA 22201
Tel: 703-387-2100 Fax: 703-414-5593
www.tetratech.com/intdev

Assessing the Effects of COVID-19 on
Access to Water, Sanitation, andHygiene in USAID High Priority andStrategy-Aligned Countries
Synthesis Report
DECEMBER 2020

DISCLAIMER
The author’s views expressed in this publication do not necessarily reflect the views of theUnited States Agency for International Development or the United States Government.

TABLE OF CONTENTS
LIST OF FIGURES ........................................................................................................................ ii
LIST OF TABLES ......................................................................................................................... iii
ACRONYMS AND ABBREVIATIONS .......................................................................................iv
EXECUTIVE SUMMARY..............................................................................................................vi
1.0 INTRODUCTION .................................................................................................................. 1
2.0 METHODS .............................................................................................................................. 3
2.1 DEEP DIVES ......................................................................................................................................................... 3
2.2 ECONOMETRIC MODEL FOR FORECASTING CHANGES IN ACCESS TO WASH SERVICES ........................ 5
3.0 GOVERNMENT RESPONSE IN DEEP-DIVE COUNTRIES AND REPORTEDECONOMIC DISTRESS ........................................................................................................ 6
4.0 DEEP DIVE RESULTS: DRINKING WATER ...................................................................... 9
4.1 THE CONSUMER SIDE........................................................................................................................................ 9
4.2 THE SUPPLIER SIDE – CURRENT STATUS ...................................................................................................... 11
4.2.1 Large providers .................................................................................................................................. 12
4.2.2 Small providers ................................................................................................................................... 12
4.2.3 Supply chain issues............................................................................................................................. 13
4.2.4 Donor response ................................................................................................................................. 14
5.0 DEEP DIVE RESULTS: SANITATION .............................................................................. 16
5.1 THE CONSUMER SIDE...................................................................................................................................... 16
5.2 THE SUPPLIER SIDE – CURRENT STATUS ...................................................................................................... 17
5.2.1 Fecal Sludge Management ................................................................................................................ 17
5.2.2 Household Sanitation ........................................................................................................................ 18
6.0 DEEP DIVE RESULTS: HANDWASHING ....................................................................... 21
6.1 THE CONSUMER SIDE...................................................................................................................................... 21
6.2 THE SUPPLIER SIDE – CURRENT STATUS ...................................................................................................... 22
6.3 VARIATIONS IN REPORTED ACCESS AND PRICE TRENDS ............................................................................ 23
7.0 SUMMARY OF ECONOMETRIC MODEL RESULTS ...................................................... 25
8.0 FUTURE WASH ACCESS TRENDS IN HIGH-PRIORITY AND STRATEGY ALIGNEDCOUNTRIES ........................................................................................................................ 27
8.1 WATER SUPPLY ................................................................................................................................................ 27
8.2 SANITATION ..................................................................................................................................................... 28
8.3 HANDWASHING .............................................................................................................................................. 29
REFERENCES .............................................................................................................................. 30
APPENDIX 1. KEY INFORMANT LISTS ................................................................................. 32
APPENDIX 2. SMS SURVEY INSTRUMENT .......................................................................... 37
APPENDIX 3. ECONOMETRIC MODEL ................................................................................. 42
APPENDIX 4: SANKEY DIAGRAMS FROM SMS SURVEYS ................................................ 54

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORTiLIST OF FIGURES
Figure 1. Highlighted countries are USAID high priority and strategy-aligned countries, with the deep-divecountries in green. .....................................................................................................................................................................3
Figure 2. Percent departure from baseline mobile phone mobility, residential category, February to early October
2020. Periods of the SMS surveys and deep-dive interviews are noted. There are no mobility data available for
DRC. Source: Google COVID-19 Community Mobility Reports (Google, 2020). .....................................................6
Figure 3. Percentage of respondents answering, “I lost my job” or “I earn less money” to the question, “How has
COVID-19 changed your employment?” ..............................................................................................................................7
Figure 4. Percentage of respondents answering “Yes” to the question, “Has COVID-19 made it more difficult to
get your drinking water?” .........................................................................................................................................................9
Figure 5. Heat map depicting percentage point changes in reported water service modality, pre-COVID-19 vs. atpresent, among those responding “Yes” to the question, “Has COVID-19 made it more difficult to get yourdrinking water?”....................................................................................................................................................................... 10
Figure 6. At left, percentages of respondents (N=500+ respondents); at right, estimated populations in millions ofeach country falling below Basic service levels on the JMP Water Ladder. ............................................................... 11
Figure 7. Indicators of sanitation service difficulties reported by respondents to the team’s SMS surveys. N = 500+
participants per country. ....................................................................................................................................................... 16
Figure 8. Heat map depicting percentage point changes in reported sanitation level, pre-COVID-19 vs. at present.
Magnitude of change is captured both by the color and the number in a particular cell. For example, theproportion of urban Rwandan respondents to who reported reliance on a public community toilet dropped
by 5 percentage points, and those reporting reliance on a shared household toilet increased by 7 percentage
points.......................................................................................................................................................................................... 17
Figure 9. SMS survey response to the question, “Do you notice your neighbors and friends washing their handswith soap more often than before COVID-19?” ............................................................................................................. 21
Figure 10. Percentage of the respondents listing “washing hands” in response to the question, “What measures
have you taken to prevent infection from COVID-19 in the past week?” in the MIT COVID-19 Beliefs,
Behaviors & Norms Survey. X-axis labels correspond to two-week long survey waves for 11 countries.
Source: MIT Initiative on the Digital Economy and Facebook, 2020........................................................................... 22
Figure 11. SMS survey response to the question, “Since COVID arrived, has it become easier or more difficult foryour family to obtain any kind of soap to wash hands?” ................................................................................................ 23
Figure 12. Prices of selected non-food items, reported by households contacted as part of the Kenya COVID-19
Economic Tracker. .................................................................................................................................................................. 24
Figure 13. Results of Model 1 for sanitation – the effect size per country examining wealth effects only................... 26
Figure 14. Distribution of 30 Mozambican small private water providers interviewed in August 2020. ...................... 34
Figure 15. The UNICEF/WHO Joint Monitoring Program (JMP) water service ladder. ................................................... 42
Figure 16. Water supply numeric score as a function of wealth quintile for the seven deep-dive countries. ............. 48
Figure 17. Kenya Sankey diagram. ................................................................................................................................................. 54
Figure 18. Rwanda Sankey diagram. .............................................................................................................................................. 55
Figure 19. Ghana Sankey diagram. ................................................................................................................................................. 56
Figure 20. DRC Sankey diagram. ................................................................................................................................................... 57
Figure 21. Mozambique Sankey diagram. ..................................................................................................................................... 58
Figure 22. Senegal Sankey diagram. ............................................................................................................................................... 59

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LIST OF TABLES
Table 1. Pre-Specified Hypotheses ...................................................................................................................................................2
Table 2. JMP Level and model numeric score associated with a MICS/DHS water supply category. ............................ 43
Table 3. JMP Level and model numeric score associated with a MICS/DHS sanitation category................................... 43
Table 4. Model datasets employed for high priority and strategy-aligned countries. ........................................................ 44
Table 5. Model coefficients and effect sizes for all USAID high priority and strategy-aligned countries. ..................... 46
Table 6. Model coefficients from the analyzed countries (27 for water and 28 for sanitation). ..................................... 47
Table 7. World Bank forecast GDP growth for USAID high-priority and strategy-aligned countries (as of 8 June2020) .......................................................................................................................................................................................... 49
Table 8. Estimated decline in water and sanitation access (based on estimates from DHS or MICS cross section
multiplied by World Bank estimates of income changes). ............................................................................................. 52
Table 9. Estimates of population dropping below JMP “Basic” service levels for water and sanitation. Deep-divecountries are highlighted in gray. ......................................................................................................................................... 53

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ACRONYMS AND ABBREVIATIONS
AFDAgence Française de Développement (French Development Agency)

COVID-19

Coronavirus Disease 2019

CRS

Catholic Relief Services

DHS

Demographic and Health Survey

DRC

Democratic Republic of the Congo

FEPEAR

Forum des Exploitants Privés des Systemes d’Eau er Assainissement (Association of Private
Water and Sanitation System Operators [Rwanda])

FIPAG

Fundo de Investimento e Património do Abastecimento de Água (Water Supply Assets andInvestment Fund [Mozambique])

FPA

Fornecedor Privado da Água (Private Water Provider [Mozambique])GDP

Gross Domestic Product

GWC

Global WASH Cluster

HDPE

High Density Polyethylene

HH

Household

ICRC

International Committee of the Red Cross

IMF

International Monetary Fund

JICA

Japan International Cooperation Agency

JMP

Joint Monitoring Programme of WHO and UNICEF

KfW

Kreditanstalt für Wiederaufbau (German state-owned development bank)

LIA

Low-Income Area

MAWASCOMathira Water and Sanitation Company (Kenya)

MICS

Multiple Indicator Cluster Survey

MIS

Malaria Indicator Survey

NGO

Nongovernmental Organization

NYEWASO

Nyeri Water Services Company (Kenya)

OD

Open Defecation

REGIDESO

Régie de distribution d’eau (Water Distribution Board [DRC])

SINAS

Sistema de Informação Nacional de Água e Saneamento (Mozambique)

SME

Small-to-Medium-Sized Enterprise

SMS

Short Message Service (Text Message)

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT

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SWA

Sanitation and Water for All

THIWASCO

Thika Water and Sewerage Company (Kenya)

USAID

United States Agency for International Development

WASAC

Water and Sanitation Corporation (Rwanda)

WASH

Water, Sanitation, and Hygiene

WASH-FIN

Water, Sanitation, and Hygiene Finance program of USAIDWASHPaLS

Water, Sanitation, and Hygiene Partnerships and Learning for Sustainability Project

WASREB

Water Services Regulatory Board (Kenya)

WHO

World Health Organization

WSP

Water Service Provider

WSPA

Water Service Provider Association (Kenya)

WSTF

Water Sector Trust Fund

WSUP

Water and Sanitation for the Urban Poor

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORTv

EXECUTIVE SUMMARYIn May 2020, the United States Agency for International Development (USAID) tasked the Water,
Sanitation, and Hygiene Partnerships and Learning for Sustainability (WASHPaLS) project with assessing
the effects of the novel Coronavirus Disease 2019 (COVID-19) on access to water, sanitation, andhygiene (WASH) services and products in USAID high-priority and strategy-aligned countries. 1 The
assignment sought to characterize the current state of affairs and to forecast near-term (6–18 month)
trends that could assist governments, donors, and implementers prepare an informed response to the
WASH-related impacts of the pandemic.
The assessment team pursued two lines of inquiry. The first is a set of “deep dives” in seven countries(the Democratic Republic of the Congo [DRC], Ghana, Kenya, Mozambique, Nepal, Rwanda, andSenegal) selected to reflect a spectrum of geographic, cultural, and vulnerability characteristics. These
deep dives consisted of interviews with key informants (WASH product and service providers,
government officials, donors, and WASH program implementers) as well as SMS-based surveys of over
3,000 randomly selected individuals in all countries save Nepal. The second line of inquiry isdevelopment of an econometric model linking income changes to WASH outcomes, relying on
Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey (MICS) information,
constructed using data from the 28 USAID high-priority and strategy-aligned countries, to generate
WASH outcome forecasts for those same countries.
This document represents the synthesis of that work, carried out by the WASHPaLS team between Juneand October 2020. Detailed findings for each of the seven deep-dive countries are presented in separate
reports. The team’s topline findings, by subsector, are as follows:
WATER SUPPLY – CURRENT STATUS
1) COVID-19 has made water access more difficult for consumers—significantly moredifficult than would be expected based only on cross-sectional correlations between
income and water service level. Declines in water access are linked not only to economic shocks
borne by consumers but also to financial pressures endured by water service providers (WSPs) as they
suffer revenue shortfalls, which are a function of both government directives for free water provisionand tariff collection difficulties. These consumer reports of pandemic-related water access difficulties
exceed what is predicted based on the statistical relationship the assessment team measured between
income and water supply outcomes using DHS and MICS data.
2) COVID-19-driven water access difficulties are reported across the urban-to-rural
spectrum. Cumulatively across six countries in which the team conducted SMS surveys, roughly onethird of both urban and rural respondents reported that the pandemic had made accessing drinking
water more difficult. There was no consistency across countries as to whether urban or rural dwellers
were impacted more. For example, in DRC, Ghana, Mozambique, and Senegal, the proportion of urban
respondents reporting difficulties was 7 percentage points lower than that for rural respondents (36percent vs. 29 percent). In Kenya and Rwanda, the proportions of urban respondents reporting COVIDlinked water access difficulties were 5 and 15 percentage points higher than that of rural respondents,
respectively.
3) Water suppliers face major financial challenges, and (to a lesser extent) operational
obstacles related to supply chain disruptions and price increases for chemicals and
1

The team’s analysis proceeded on the hypothesis that COVID-19’s direct health consequences in terms of morbidity and mortality would
ultimately be far outweighed by the pandemic’s economic shock, based in part on predictions of an epidemiological model for the World
Health Organization’s African region published in May (Cabore et al., 2020).

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vi

hardware. International donor and lender assistance has been necessary to bridge service providers
through to a period of economic recovery; in some countries, that assistance has been significant andsufficient for the immediate term. The situation for smaller providers is likely much more dire. In
selected countries, the team learned of service disruptions already having begun as smaller operators
had exhausted cash reserves and were unable to cover electricity costs to operate pumps.
SANITATION – CURRENT STATUS
4) Consumer-reported pandemic-driven declines in sanitation access—latrine components
and installation services as well as desludging services—are, so far, much less pronounced
than those for water supply, and more in line with the sanitation services’ directconnection to income. Very few SMS survey respondents reported changes in sanitation service
modality. This is understandable, given that sanitation is not sensitive to sudden operational disruptions
as can happen with water supply systems (with the exception of piped sewer service, whose profile
matches that of piped water supply, but which is enjoyed by an exceedingly small fraction of the
populations under study). The consumer-reported changes in sanitation service modality are comparable
to what the statistical relationship between income and sanitation outcomes would predict.
5) Providers of sanitation services are suffering because of declines in demand. The fecal
sludge management sector is particularly at risk, although a number of key informants also
reported some recovery as movement restrictions had been lifted and economic activity returned indifferent countries. In particular, fecal sludge treatment plant operators face a steep path to recovery,
given their dependency on government and donor funding to close financial gaps.
6) Supply chain problems are making sanitation product and service provision lessprofitable. The team’s interviews indicated significant margin contraction among both suppliers of
onsite containment inputs and fecal sludge value chain actors resulting from increasing costs of hardwareand chemicals.
HANDWASHING – CURRENT STATUS
7) Consistent with a number of studies, self-reported handwashing behavior is very highduring the pandemic period, with self-reports reinforced by consultations with value chain actors
who confirm that sales of soap have risen dramatically since the onset of the pandemic.
8) The team found no evidence of persistent or widespread shortages of soap and noted
multiple examples of both soap prices and availability remaining constant. At the same time,
the affordability of soap does appear to have declined, insofar as the ratio of spending power to prices
dropped as household income losses have mounted. Two of the six deep-dive countries in which theteam conducted SMS surveys, Kenya and Rwanda, had much higher fractions of respondents reporting
soap access becoming relatively more difficult post-COVID-19 (35 percentage points higher in Kenya
and 33 percentage points in Rwanda). This is in contrast to the other four countries where soap access
was reported to become either relatively easier (Ghana and Senegal) or remaining constant (DRC andMozambique). The team suspects that these difference are likely linked to the relatively higher income
shocks that Kenyans and Rwandans reported, as the evidence the team found of soap price trends
indicated no increase.
9) Manufacturers and distributors of hygiene products report margin losses despite
increased demand. These losses are driven by: 1) a shift in preference away from luxury brands
toward lower margin economy brands, 2) increases in raw material costs, and 3) the inability to raise
prices because of increased competition and a recognition of the weakened purchasing power ofconsumers.

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORTvii

NEAR-TERM FUTURE TRENDS
10) The assessment team anticipates that the largest WSPs will mostly avoid major
disruptions in the next 6–12 months as their financial positions are secured with assistancefrom the development banks and bilateral and multilateral aid institutions. In a number ofthe investigated countries, major aid packages have been finalized and appear sufficient to bridge theutilities through periods of acute difficulty, especially as revenue collections have resumed following
limited tariff suspensions.
11) Smaller piped water supply systems are susceptible to system disruptions, particularly
in contexts where they have had to suspend tariff collection for extended periods in
response to government policy/mandates. Without external assistance, smaller providers will feelpressure to re-initiate billing of economically burdened customers. Even in the absence of full suspensionof services, such measures of supplier performance as frequency of running water and duration of downtimes are likely to suffer from operator inability to cover electricity maintenance costs.
13) In the rural context, deferred maintenance on handpumps from economic burdens
may cause system failures over the next 6–18 months if recovery from COVID restrictions
lag. That said, the assessment team does not expect disruptions as immediate or severe as
those anticipated for schemes relying on electricity or diesel for production andconveyance. The down time of deep lift handpumps may well increase if the declining revenues limit
the resources available to operators for regular maintenance. It is worth noting that even in prepandemic circumstances, handpumps recover only on the order of 10 percent of the operating costs(McNicholl et al., 2019).
13) The team expects demand for sanitation products and services to track economic
conditions. Unlike water supply, for which extended financial difficulties can result in both sudden andextended performance declines by providers, consumer demand for sanitation commodities, installation
services, and tank and pit desludging should recover if and when economic activity rebounds (as theassessment team did not uncover evidence of enterprises shutting down completely). If the economic
recovery is more prolonged and there are delays in consumer ability to service, replace, and/or repair
their latrines, reversion to open defecation is possible. The assessment team does not envision this as awidespread problem.
14) The team is cautiously optimistic that the pandemic may have brought about a social
norms shift with respect to handwashing, and foresees few immediate crises with respect
to hygiene product supplies and general availability. Although value chain actors did reportmargin contractions, none interviewed reported even considering withdrawing from the market.

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1.0

INTRODUCTION

Between June and October 2020, the United States Agency for International Development (USAID)
Water, Sanitation, and Hygiene Partnerships and Learning for Sustainability (WASHPaLS) project
conducted a rapid assessment and forecasting analysis of the effects of the novel Coronavirus Disease2019 (COVID-19) pandemic on access to WASH services and products in USAID high priority andstrategy-aligned countries. The central question the team sought to answer was:How and for whom will the COVID-19 pandemic (and resulting economic crisis) affectaccess to water supply services, sanitation services and products, and hygiene products
across the WASH high priority and aligned countries, and how will these access effects
vary by subsector, geography, and provider type?
The assessment team proceeded on the assumption that direct health effects of the pandemic in USAID
high-priority and strategy-aligned countries would be exceeded by the economic shock of measurestaken to contain the pandemic (restrictions of movement, closures of business, disruptions of supply
chains, and so forth). 2
On 4 May 2020, the Global WASH Cluster (GWC) and Sanitation and Water for All (SWA) released an
advocacy document entitled “Mitigating the Socio-Economic Impacts [of COVID-19] on the Water,
Sanitation, and Hygiene (WASH) Sector,” which predicted the following:
•

•
•
•
•

Decline in access to and increase in prices for WASH commodities and services due to rupture inglobal supply chains caused by restrictions or no movements of goods and essential consumables(e.g. fuel, chemicals), affecting continuity of services. …
Decline in the financial viability of WASH services due to loss of revenue and subsidies, and income
loss by households, limiting ability to pay for WASH commodities and services.
Decline in national government’s ability to deliver WASH services, affecting social cohesion, leading
to tension and instability.
Diversion and deprioritization of domestic funding away from the WASH sector, due to inability to
pay for or suspension of loans.
Shift in donor funding from the existing commitments and priorities…resulting in a significant
reduction in the overall funding of [the WASH sector].” (GWC et al., 2020, p.3).

The assessment was intended to provide both a snapshot of current WASH access conditions andforecasting of near-term trends, but the team also found it useful to investigate the degree to which the
GWC/SWA predictions play out in practice. The predictions also served to help the team formulate a
set of hypotheses prior to commencing activities (Table 1).

2

COVID-19 is likely to cause the first increase in global poverty in two decades, pushing some 100 million people into poverty and 50
million into extreme poverty in 2020, with an estimated 23 million going into extreme poverty in sub-Saharan Africa (Mahler et al., 2020).
As described herein, the economic shocks of COVID-19 were experienced immediately and profoundly by high-priority and strategyaligned countries, and persist even as some of these countries inch back toward pre-pandemic conditions of economic activity.

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT1

Table 1. Pre-Specified Hypotheses
Water Supply
Service provider revenues will
decline because of 1) government
policies regarding tariff collection,2) consumer interpretation of
those policies, and 3) reduced
ability-to-pay by consumers.
Supply chains for key commodities
will be disrupted.

Sanitation
Fragile sanitation value chains (withrespect to both excreta
containment and management) in
urban and peri-urban areas will bemost highly impacted. There will be
increased stress on working capitaland cash flows, profitability, andinvestment capacity.
Consumer spending could shift
away from sanitation leading to: 1)
slower rate of improved toilet
adoption and 2) reversion to OD
in case of unaffordability of pit
emptying services.

The degree of operational andfinancial challenges faced by water
service providers will varyconsiderably by modality and target
population. The “in-betweeners”
will be the most heavily affected;
larger utilities will gain donor
attention, and rural self-supply will
be largely unaffected. Smaller
providers, informal sector actors,
and centralized community systems
will have less “safety net.”
Rural populations who rely on selfsupply will see far less dramaticaccess effects. Supply chains forpump parts and maintenance will
be affected, but given the alreadyhigh failure rates of rural water
infrastructure, rural populations
generally rely on multiple water
sources.

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT

HygieneWholesale costs of soap will rise, afunction of the reduced buying
power of local currency as well as
disrupted supply chains.

Consumer spending on theseproducts may decline as assets arediminished, with priority spending
directed at food and otherimmediate family needs, but thatthese spending declines may bepartially offset by widespread
campaigns to wash hands toprevent COVID infection.
There have been supply chain
disruptions in most countries,
particularly those which are net
importers of hygiene products or
product components.
Compounded by limited mobility
due to lockdowns or curfews andpanic buying from wealthy
consumers, supply chain
disruptions could lead to productshortages.

2

2.0

METHODS

The assessment team sought to test the hypotheses and predictions noted above through an assessmentof COVID-19-linked WASH access effects consisting of two elements:
1. A “deep dive” into seven countries, consisting of interviews with hundreds of key
informants and SMS-based consumer surveys of 500–750 respondents per country(with the exception of Nepal); and2. Construction of an econometric model to forecast changes in access to water andsanitation access from losses in income, using Demographic and Health Survey(DHS) and Multiple Indicator Cluster Survey (MICS) data.
2.1

DEEP DIVES

Given the time frame for this analysis, the assessment team elected to conduct a detailed investigation
into a subset of the high-priority and strategy-aligned countries. The team selected seven countries fordeep-dive analysis based on their representation of a spectrum of geographic, cultural, and vulnerability
characteristics, as well as the confidence in the team’s ability to secure interviews with key informants
identified via snowball sampling. The seven countries are the Democratic Republic of the Congo (DRC),
Ghana, Kenya, Mozambique, Nepal, Rwanda, and Senegal (Figure 1).

Figure 1. Highlighted countries are USAID high priority and strategy-aligned countries, with the deep-divecountries in green.

Key Informant Interviews. The team conducted semi-structured interviews for 277 key informants
via phone or videoconference across the seven countries, ranging from 15 in Rwanda to 73 in Ghana,following pre-set interview guides for service providers, government regulatory and oversight officials,
private sector actors, and program implementers. The purpose of these interviews was twofold: first, tosecure macro-level insights from well-positioned observers (essential during a period in which incountry visits were rendered impossible) and second, to hear directly from suppliers of WASH products
and services of their present and anticipated financial and operational challenges. Ultimately, theinterviews served to complement the consumer surveys, providing a depth of observation that is notpossible to gain from a short SMS questionnaire. The assessment team also hoped that the interviews

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would contribute to predictions of future trends and aid in clarifying differences observed among
countries. Key informant interviewees included (see Appendix 1):
•
•
•
•
•
•

National and local government officials, both policymakers and regulators;
Operators of water supply systems;
Providers of sanitation products (latrine inputs) and services (pit/tank emptying, fecal sludge
transport, and waste management);
Producers and distributors of hygiene products (mainly soap);
Implementers of donor-funded WASH programs; andMultilateral and bilateral donors and implementers (including the World Bank, UNICEF, United
Kingdom Department for International Development [now the Foreign, Commonwealth &Development Office], Japan International Cooperation Agency [JICA], and others).

SMS Consumer Surveys. The assessment team complemented the investigation of supplierperspectives on the WASH access effects from COVID-19 with those of consumers by way of crosssectional SMS surveys of at least 500 respondents per deep-dive country (with the exception of Nepal,
where SMS surveying is not yet routinely executed). The team contracted the mobile-based researchfirm GeoPoll to conduct the survey using an instrument of the team’s design (see Appendix 2).
SMS surveying is an extraordinarily efficient means of collecting consumer information. With formal
access to mobile subscriber databases consisting of millions of people in each of the African deep-divecountries analyzed, GeoPoll was able to secure SMS survey responses from a sample with geographic
and age distributions representative of the broader population of each country. This survey could be
easily read and filled out with a basic feature phone (non-smartphone), and was offered to potential
respondents incentivized by a modest offer of top-up credit. The instrument consisted of 33 questions,with skip patterns that meant that a respondent typically saw on the order 20–25 questions. The teamoffered the surveys in the following languages for each country:
•
•
•
•
•
•

DRC: French and Swahili;
Ghana: English;
Kenya: English and Swahili;
Mozambique: Portuguese;
Rwanda: English and Kinyarwanda; andSenegal: French and Wolof.

The survey contained modules on employment and migration, water supply, sanitation, and handwashing.
Completion rates of the survey ranged from 19 percent in Rwanda to 55 percent in Senegal (meaning,for example, that roughly 2,800 opt-ins were required to achieve 500 completed surveys in Rwanda, but
only 860 opt-ins were required to achieve 500 completed surveys in Senegal). The surveys for DRC,
Ghana, Kenya, Mozambique, and Senegal were completed during the latter half of August 2020. The
survey for Rwanda was executed during the first two weeks of October 2020.
The team notes that SMS survey respondents, by virtue of their possession of a charged cell phone andthe technical ability to fill out a survey, are likely a biased sample of the broader populations of the deepdive countries. Cell phone ownership is estimated to be 10 percent lower among women than among
men in low-to-middle-income countries (GSMA Connected Women, 2019). The team attempted toaddress this by setting a 50-50 gender split quota for survey results. The team considers it likely for
respondents to have an elevated wealth and educational status than those who do not own a functional
phone. Nonetheless, the team considers these biases to be small enough to make using the SMS surveys
extremely useful, given the relative ease of deploying them.

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2.2

ECONOMETRIC MODEL FOR FORECASTING CHANGES IN ACCESS TO
WASH SERVICES

A central objective of this assignment was to forecast future trends in access to WASH services andproducts. Consumer surveys were expected to reflect current WASH access conditions, while the key
informant interviews would provide essential insights on the intensity and possible duration of service
disruptions and performance challenges.
In the interest of offering quantitative estimates of future access trends to the degree possible, theassessment team considered how economic indicators relate to WASH outcomes and then to explore
numerical forecasts of WASH access changes resulting from an economic shock. There is somepublished literature to support this endeavor: Jeuland et al. (2013) developed long range (1975–2050)
projections for Joint Monitoring Programme of the World Health Organization (WHO) and UNICEF(JMP) -defined improved water and improved sanitation and piped water and sewer coverage, based oncross-country associations between income and water/sanitation access, and urbanization. Their analysis
employs projections of urbanization, population growth, and economic growth to simulate trends inwater and sanitation coverage levels. Building on this analysis, Fuente et al. (2020) focused on subSaharan Africa to estimate WASH-related mortality and economic losses associated with poor access towater and sanitation infrastructure. The assessment team extended this approach by focusing exclusively
on the income effect, dividing it into two sub-questions:
1. How responsive is WASH to falling income; and2. How much has the COVID-19 crisis affected incomes?
Summary of the approach. The team first estimated how wealth predicts WASH access by runningcross-sectional models in each high-priority and strategy-aligned country. Then, in a univariate model,
the team regressed water or sanitation access level on an index measure of wealth. Subsequently, tocontrol for rural vs. urban effects, education, and neighborhood/village effects, the team ran three
additional multivariate models. The team then multiplied the estimate of the responsiveness of WASH
access to wealth, education, and neighborhood/village effects by forecasts of the size of Gross Domestic
Product (GDP) shock (from World Bank estimates). This allowed the team to estimate the water andsanitation effects from the pandemic’s economic shock scaled by an economic decline of a user’s choice.
This was done for all high-priority and strategy-aligned countries. For a detailed description of the model
approach and results, see Appendix 3.
Datasets. The team used the most recent, pre-pandemic DHS (including the Malaria Indicator Surveys,
or MIS) or MICS in each high-priority or strategy-aligned country (see Appendix 3), analyzing watersupply effects in 27 countries and sanitation in 28 countries.
Dependent variable. The response variable in the model is WASH access. DHS and MICS record themeans by which respondents report accessing drinking water (“piped into dwelling,” “piped into
yard/plot,” “public tap/standpipe”, etc.), as well as sanitation service level (“piped sewer system,” “septic
tank,” “pit latrine,” etc.). The team’s model focuses on changes in rungs of the JMP service ladder, withthe objective of estimating the percentage of the population falling below the “Basic” level of water or
sanitation service (see Appendix 3).
Explanatory variables. The team sought to explain changes in WASH access as a function of changes
in wealth. The wealth measure is based on asset ownership, housing quality, land and home ownership,and livestock ownership, following the DHS procedure for creating a wealth index and examining
quintiles, 3 but diverging by removing the water and sanitation access measures from the list of assets.

3“Steps to constructing the new DHS Wealth Index.” Shea O. Rutstein, DHS Program, no date.
https://dhsprogram.com/programming/wealth%20index/Steps_to_constructing_the_new_DHS_Wealth_Index.pdf

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3.0

GOVERNMENT RESPONSE IN DEEPDIVE COUNTRIES AND REPORTEDECONOMIC DISTRESSIn addition to differing pre-existing and contextual vulnerabilities to the pandemic, deep-dive countries
saw differing degrees and durations of government response, enforcement, and population adherence torestrictions, which in turn drove impacts on economic activity. One way to visualize these differences is
to consider the trends in restricted movement indicated by cellular phone mobility, with the caveat thatmobile phone penetration varies from country to country and is complex to estimate with precision. 4
Figure 2 displays the trend in percentage departure from baseline levels of time spent in residential
settings (as distinct from commercial/industrial settings or other location categories). The higher thevalue, the more time the cell phone user spends at home, and less time at commercial, industrial, orother non-residential locations, relative to baseline.

Figure 2. Percent departure from baseline mobile phone mobility, residential category, February to early
October 2020. Periods of the SMS surveys and deep-dive interviews are noted. There are no mobility data
available for DRC. Source: Google COVID-19 Community Mobility Reports (Google, 2020).

For the deep-dive countries in the analysis, there is a notable range in both the intensity and duration ofeffects on movement. Rwanda’s lockdown resulted in the most extreme movement restrictions. The
4

According to a 2017 survey by the Pew Research Center, reported ownership of mobile phones by adults was roughly 80% in Ghana,
Kenya, and Senegal (Pew Research Center, 2018). Meanwhile, the 43 million mobile connections in Nepal correspond to 148% of its
population, and the 9.4 million mobile connections in Rwanda in January 2020 correspond to 73% of its total population (Kemp, 2020).
Because multiple SIM card use per person is common in the Global South, it can be difficult to estimate mobile phone penetration using
accounts alone; a survey by Research ICT Africa estimates the 2017 penetration in Rwanda at 50% and Mozambique at 40% (Research ICT
Africa, 2019). With these cautions in mind, cellular mobility data still provide the best possible proxy for movement effects resulting fromCOVID-19 restrictions.

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easing of those restrictions in May brought the country in line with those in place for Ghana and Nepal;
for about four weeks, the country witnessed even less disruption than in Senegal. Ghana’s lockdown was
intense but very short, while the effects of Mozambique’s movement restrictions were consistently theleast extreme of the deep-dive countries. Kenya’s restrictions were sustained for a longer period thanother countries; from mid-June until the first week of August, its restrictions resulted in the greatest
limits on movement. Some countries re-introduced restrictions at different times: the government of
Nepal issued a strict nationwide lockdown from 24 March to 21 July 2020, prohibiting domestic andinternational travel, instituting border closures, and suspending non-essential services, but then reimposed restrictions in Kathmandu Valley on 19 August following an increase in local transmission
attributed in part to large-scale entry of travelers from India when the border re-opened. These
measures are reflected in Nepal’s second peak in residential mobility in early August corresponding to
the re-imposition of lockdown measures through the end of the month.
While there are other important drivers of COVID-19 economic burden (particularly the loss of the
tourism revenue and remittances), these movement indicators are a useful indicator of the magnitude ofpressure endured by different countries.
Additionally, the team’s SMS surveys asked respondents how their employment had changed due to thepandemic (Figure 3). In addition to how widespread the pandemic’s economic pain is being felt, withgreater than half the respondents reporting job or income loss stemming from COVID-19, the numbers
for Kenya and Rwanda show pronounced effects to consider in the analysis of WASH access effects.

Figure 3. Percentage of respondents answering, “I lost my job” or “I earn less money” to the question,“How has COVID-19 changed your employment?”
Source: The team’s SMS surveys, conducted in August 2020 (except for Rwanda, which was conducted in October 2020).

In most of the deep-dive countries, radical emergency travel restrictions were put in place from lateMarch through June or July, although the duration of (and continued adherence to) these measuresvaried. The Government of Nepal issued a strict nationwide lockdown from 24 March to 21 July 2020,
prohibiting domestic and international travel, instituting border closures, and suspending non-essential
services. Following a relaxation of restrictions, it reimposed measures in Kathmandu Valley on 19
August following an increase in local transmission attributed in part to large-scale entry of travelers from
India when the border re-opened (Nepali Times, 18 August 2020). In Ghana, following initial borderclosings; travel restrictions; partial lockdowns in the major hubs of Greater Accra and Kumasi
metropolitan regions; closings of bars, restaurants, schools, and universities; and restrictions on the sizes

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of public gatherings (conferences, workshops, funerals, festivals, political rallies, church activities andother related events), relaxation began in late April. This restriction relaxation is reflected in a rapid
return toward baseline levels of mobility (Figure 2), and then a further steady decline in time spent inresidential settings from May through September. Taken together, this suggests a shorter period of
economic shock in Ghana than other countries analyzed.
Rwanda implemented the most comprehensive nationwide lockdown, from late March to early May,
resulting in the highest movement disruption of any of the deep-dive countries, with renewed
restrictions in September (Figure 2). Rwanda has resumed most business operations as of November
2020, including restaurants, hotels, shops, and tourism operations, although bars remain closed. Schools
are in the process of a phased reopening.
In Kenya, by contrast, curfews from 19h00 to 05h00 were instituted from 27 March to 7 July, adjusted
to 21h00 to 04h00 through September. Schools were closed 15 March and remain so, but are due toreopen in January 2021. These restrictions correspond to restrictions on mobility (and consequent
increases time spent at home) that were sustained for longer periods than other deep-dive country.
In terms of WASH sector support, most governments instituted free water directives, although thesevaried in scope and duration. The Ghanaian government initially allocated US$205 million to pay water
bills for all customers for three months (April to June) and to provide water tanker services tovulnerable communities, as well as investing in hand-washing facilities in public places. The directive wassubsequently extended through August, and then again through December 2020. In Kenya andMozambique, free water initiatives explicitly prohibited disconnection of customers with outstanding
water and sewer bills and required the reconnection of previously disconnected customers. Tariff
suspensions in these countries were targeted to Low Income Areas (LIAs, defined specifically as informal
settlements) in Kenya, and to customers using less than 5m3/month and those relying on water frompublic and private standpipes in Mozambique.
In DRC and Senegal, the free water mandate covered the entire country, but was limited to twomonths. In DRC, however, the tariff holiday was followed by a rate increase of 13–15 percent,depending upon the monthly consumption volume.
Neither the Nepalese nor Rwandan governments issued formal water tariff relaxation policies. Rwanda
launched a fund to support affected businesses through subsidized loans from commercial banks andmicrofinance institutions and credit guarantees, targeting small-to-medium-sized enterprises (SMEs) andhard-hit sectors such as the hospitality industry (International Monetary Fund [IMF], 2020). The
Government of Rwanda also extensively promoted handwashing, including making handwashing stations
and sanitizers compulsory in all public places, such as churches, bus stations, and market places, andissuing decrees to keep the consumer cost of hand hygiene products reasonable.

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4.0

DEEP DIVE RESULTS: DRINKING WATER

This section summarizes the results of the team’s analysis of COVID-related drinking water accesschanges in the seven deep-dive countries, based on key informant interviews and the SMS consumer
surveys conducted in the six African countries.
4.1

THE CONSUMER SIDE

Consumers widely report that COVID-19 has made getting drinking water more difficult, with thosedifficulties reflected in how respondents to the SMS surveys reported changes in how they accessdrinking water before and after the onset of the pandemic. The most notable reported challenges were
shifts away from piped water among both urban and rural respondents in Kenya (and toward surface
water among rural respondents) as well as declines in bottled/sachet water and cart vendor use in
several countries.

Figure 4. Percentage of respondents answering “Yes” to the question, “Has COVID-19 made it moredifficult to get your drinking water?”
Source: SMS surveys conducted in August 2020 (except for Rwanda, which was conducted in October 2020). Sample sizes
for each country segment shown at the base of the columns.

Figure 4 offers the results of the SMS survey question on pandemic-related drinking water difficulties.
Although there is some important variation among countries, of those surveyed a significant number
reported that COVID-19 has made accessing drinking water more difficult. In all but Kenya and Rwanda,
more self-identified rural respondents reported pandemic-linked water access problems than urban

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9respondents. The slightly higher urban numbers in Kenya co-occur with its urban respondents reporting
considerably more economic hardship than the other countries’ urban respondents (with 75 percent ofurban Kenyans reporting either losing jobs or suffering income losses, vs. a mean of 52 percent of urban
respondents in the other countries). For Rwanda, where the urban vs. rural divide on WASH difficulties
is by far the most pronounced (with a difference of 15 percentage points), the team observed nocomparable income link; the 57 percent of urban Rwandans who report losing jobs or suffering an
income loss is almost exactly the mean value (and near the median value) of the deep-dive countries.
Through the SMS surveys, the team also sought to learn how those reporting water access problems
were experiencing them. Those who responded in the affirmative to the question, “Has COVID-19 madeit more difficult to get your drinking water?” were then asked a series of questions of how they accesseddrinking water before and after the onset of the pandemic. The results of those questions appear in
Figure 5.

Figure 5. Heat map depicting percentage point changes in reported water service modality,
pre-COVID-19 vs. at present, among those responding “Yes” to the question, “Has COVID19 made it more difficult to get your drinking water?”

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Those survey respondents reporting difficulties with drinking water access frequently reported changes
in the means by which they acquired drinking water. The largest percentage point changes are apparent
in Kenya and Rwanda. Rural Ghanaians reported major drops in bottled water/sachet usage, possibly
related to Ghana’s ambitious free water directive, which required water service providers (WSPs) toprovide water to all domestic customers at no charge.
Across all the deep-dive countries, an average of 3.7 percent of SMS survey respondents reported falling
below the “Basic” water service level defined by the JMP. 5 (This even as others moved up the ladder,including a progression from either surface water or unimproved services to Basic, where they may havebeen served by emergency distribution locations. What will happen when emergency distribution ofdrinking water stops is not clear.) If the country-level numbers are assumed to be representative of thebroader population of each of these countries, the totals are reflected in Figure 6 (1.25 million in
Mozambique, 2.1 million in DRC, and nearly 3.2 million in Kenya). Appendix 4 presents, by country, thepathways for water supply modality changes among the combined rural and urban sets of respondents
who report pandemic-driven water access difficulties.

Figure 6. At left, percentages of respondents (N=500+ respondents); at right, estimated populations inmillions of each country falling below Basic service levels on the JMP Water Ladder.

4.2

THE SUPPLIER SIDE – CURRENT STATUS

Interviews with regulators and operators of water supply systems in all of the deep-dive countries
confirmed expectations of significant declines in revenue resulting from both financial distress ofcustomers (leading to non-payment) and tariff holidays issued by governments (as discussed in
Section 3 above). The following sections present the impacts on large and small WSPs.

5

The highest JMP drinking water ladder level is “Safely Managed” (from an improved water source on premises, available when needed andfree from fecal and priority chemical contamination), followed by “Basic” (an improved source, provided collection time is not more than
30 minutes for a round trip including queuing). Levels below “Basic” are: “Limited” (from an improved source for which collection timeexceeds 30 minutes for a round trip including queuing), “Unimproved” (from an unprotected dug well or an unprotected spring), and“Surface Water” (drinking water sourced directly from a river, dam, lake, pond, stream, canal, or irrigation canal).

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4.2.1LARGE PROVIDERS

Revenue losses have already been documented among larger water utilities in USAID high-priority andstrategy-aligned countries. The USAID-supported Water, Sanitation, and Hygiene Finance (WASH-FIN)
project has been conducting financial stress tests of utilities in a number of countries employing a tool
developed by the World Bank, some of which are now complete and have been publicly released. In
Kenya, WASH-FIN conducted the stress test in June 2020 on three WSPs: Nyeri Water Services
Company (NYEWASCO), Mathira Water and Sanitation Company (MAWASCO), and Thika Water andSewerage Company (THIWASCO) (USAID, 2020). WASH-FIN forecast that MAWASCO and
THIWASCO alone could require US$1.5 million to sustain operations through October in the absenceof taking mitigating actions (maximizing revenue collections, extreme internal cash preservation, asset
liquidation, debt restructuring, long-standing debt collection, and delaying electricity bill payments).
WASH-FIN’s survey of utilities in Senegal found that 8 of 13 respondents reported 50 percent
reductions in revenue compared to comparable periods in 2019, and 10 expected annual reductions of
between 10 percent and 50 percent.
In an interview the assessment team conducted with the Water and Sanitation Corporation (WASAC),
Rwanda’s national urban asset owner and operator, WASAC reported considerable losses (even as it
did not execute an official tariff holiday), with consumers reeling from income losses due to thecountry’s strict lockdown order, although collections are climbing again with relaxation of movement
restrictions. The team also heard reports of major revenue declines experienced by Mozambique’s parastatal owner of urban water supply assets, Fundo de Investimento e Património do Abastecimento de Água(FIPAG).
DRC’s national utility, Régie de Distribution d’Eau (REGIDESO), reported to the assessment team a loss ofnearly US$15 million during the two months of that country’s free water directive—a particularly
difficult stretch for a supplier that already had been suffering financially for years prior to COVID-19.
4.2.2

SMALL PROVIDERS

Smaller WSPs are significantly more vulnerable to revenue losses than larger ones, given their oftenprecarious financial picture even outside of periods of economic turmoil 6 of the magnitude inflicted by
COVID-19. Key informant interviews across multiple countries indicated that these smaller operators,
particularly smaller piped providers, are now under acute financial pressure.
In Nepal, WASH-FIN’s rapid assessment of small-to-mid-sized utilities (up to 30,000 customers)
indicated widespread losses as of July, with 4 of 10 reporting no revenues whatsoever since the onset ofthe pandemic, and another five reporting declines of 25–50 percent.
In Mozambique, the assessment team conducted phone interviews with 30 randomly selected small
private water system operators (Fornecedores Privados da Água, or FPAs), drawn from a public dataset of
1,876 WSPs. Of these 30, 25 reported revenue declines linked to the pandemic, even as none
interviewed were complying with the national free water directive (although 8 of the 30 had decreased
their prices, and 10 of 30 reported selling more water than prior to the pandemic). However, nonereported an imminent shutdown of operations, which would be consistent with the pattern observed in
Figure 5 (where reported piped connections actually increased).
The situation the team observed in Mozambique stands in stark contrast to reports regarding smalltown and rural piped system providers in Ghana, where multiple operators reported dire financial
6

A 2019 assessment of data supplied from five high-performing service providers across Burkina Faso, the Central African Republic, Kenya,
and Uganda found that among 800 piped schemes, the median annual operational working ratio was about 70% (McNicholl et al., 2019).
Even well-run small systems have difficulty covering their operating costs.

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conditions from having supplied water at no cost for seven months at volumes higher than before thepandemic, with reimbursements publicly committed by the government not keeping pace with the losses(with reimbursements averaging just over 7 percent of the amount for which providers applied, basedon the team’s interviews). Approximately 2.3 million Ghanaians rely on water from small town and small
community piped systems and are vulnerable to widespread disruptions in service that will occur rapidly
if operational costs cannot be met by providers. While not as dramatic (and functioning outside of an
extended water tariff holiday as in Ghana), Rwanda’s rural water system operators’ association (Forumdes Exploitants Privés des Systemes d’Eau er Assainissement [FEPEAR]) also reported major losses from its
members and stated difficulties collecting revenues.
Rural handpumps are an important means of water service delivery; but as very few are monitored
regularly, assessing their current status was difficult. The SMS surveys did not reveal any reported shifts
away from “wells” (although the team did not distinguish between tube wells with deep lift pumps andshallow wells). The interviews with implementers of rural water supply programs did not point towidespread performance issues; indeed, in Ghana and Senegal, key informants indicated not only that
production was steady, but that water payments were continuing despite economic hardship.
The best objective performance information the assessment team located came from thenongovernmental organization (NGO) charity: water from Ethiopia (outside of the deep-dive sample),where a sample of 142 of its network of remote sensors indicated a 25 percent decrease in handpump
production, when comparing April 2019 and April 2020. The assessment team cannot conclude whether
this decline was attributed to performance issues. Given how little time had passed between theimposition of movement restrictions and the collection of these data, the assessment team hypothesizes
that the decline was likely due to reluctance of populations to congregate, out of fear of contracting
COVID-19. More broadly, the assessment team found no evidence of widespread rural
handpump performance issues to date, but cautions that it may be a lagging indicator in
response to economic hardship: deferred maintenance from significantly reduced operating reserves
will likely require more time to manifest themselves. The next three to six months will clarify the
picture. Despite the team’s (anecdotal) interview findings, a delayed onset of pump maintenancechallenges would not be unexpected.
Another water supply modality that appears to have been strongly affected is worth mentioning. First,
the SMS survey in Kenya reveals a dramatic shift away from cart vendors of water in jerrycans (known
locally as Mkokoteni). Although the assessment team did not have occasion to interview Mkokoteni
directly, these survey responses suggest that the vendors have seen their business decline significantly.
Second, key informant interviews indicate that “jar” water suppliers of 20-liter plastic jugs of treatedwater in Kathmandu, Nepal, where they are a central source of drinking water, appear to be heavily
impacted, with 50 percent declines in sales resulting from major out-migration from the capital at theonset of the pandemic.
4.2.3

SUPPLY CHAIN ISSUES

Although interviews did not yield widespread evidence of supply chain issues affecting watersuppliers, the team learned of some specific cases in which it had been a problem. In July, theKenyan Water Services Provider Association (WSPA) cited chemical shortages in chlorine pellets as well
as flocculants (specifically polyacrylamides). In DRC, REGIDESO reported diminishing stocks of chlorine
for which it maintained only a two-month reserve, a shortage which the World Bank and later
Kreditanstalt für Wiederaufbau (KfW, the German state-owned development bank) stepped in to rectify
with financial assistance.
Hardware supply chain issues were reported in Nepal and Rwanda. The advocacy group of private
Rwanda providers (FEPEAR) reported delays in procurement of hardware coming from Dubai and

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China, and multiple key informants in Nepal reported that both maintenance and system expansion ofrural systems had been hampered by plastic (high-density polyethylene [HDPE]) pipe procurement
difficulties as well as disruptions of cement production.
Only 4 of 30 private operators in Mozambique mentioned supply chain issues as operational challenges.
If mobility restrictions remain in place in neighboring South Africa, Mozambique’s main source ofchlorine and imported hardware, shortages could become a factor.
4.2.4

DONOR RESPONSE

The assessment team initially hypothesized that while large utilities would experience large declines inrevenue during the pandemic and its aftermath, they would also be able to garner attention of bilateraland multilateral donors to keep them afloat. In a blog post on 30 July entitled “Non-payment is COVID’sbiggest threat to water,” Christopher Gasson (the publisher of Global Water Intelligence), wrote:
My feeling is that Africa’s big city utilities will probably get bailed out. Their size makes themdifficult to ignore. Small island utilities will take a battering, but they are usually close enough togovernment to get by. The biggest problem is therefore likely to be small systems serving lowincome communities. Typically these utilities are a long way away from politics and money. The
smart ones will look for ways of clubbing together to make themselves matter. There may never
be a cure for the rest (Gasson, 2020).
Indeed, via interviews with donors as well as with recipient institutions, the assessment team
documented donor rescue packages of varying magnitudes, some quite significant, aimed principally atlarge urban utilities:
•
•
•

•

•

DRC: The IMF approved US$30 million to REGIDESO under its COVID-19 Rapid Credit
Facility. KfW committed on the order US$4.2 million to cover the cost of water treatment
chemicals for the entire country.
Ghana: The World Bank is reportedly adding US$25 million to its US$125 investment toexpand water services in Greater Accra.
Kenya: The World Bank will support the water sector via US$75 million to be spent over six
months, US$60 million of which are grants and the remaining US$15 million in performancebased loans, administered by Water Services Regulatory Board (WASREB) and the Water
Services Trust Fund (WSTF). JICA contributed over US$400,000 in chlorine and coagulant tocover three months of need for nine WSPs (Embu, Meru, Mavoko, Kilifi Mariakani, Nakuru,
Ruiru Juja, Eldoret, Nyahururu, and Kisumu).
Mozambique: US$5 million remaining from the World Bank’s Water Services and Institutional
Support program’s typhoon response was redeployed to support FIPAG, along with another
US$5.4 million from the World Bank’s general budget support, to cover FIPAG’s supply of freewater at urban standpipes (up to the first 5m3).
Senegal: The World Bank’s US$100 million aid package to Senegal is divided equally between
grants and credit, of which water/sanitation services are a part. Concurrently, the Agence
Française de Développement (AFD) committed US$23.6 million to help cover the country’s freewater directive for April and May.

Large urban utilities in Nepal and Rwanda do not appear to have received major bilateral or multilateralbudget support during the crisis. In Nepal, World Bank focus is on rural development, and its COVID19 support has been directed through the Rural Water Supply and Sanitation Fund Development Board.
In Rwanda, no World Bank urban infrastructure support has been directed to WASAC; only social
protection funds have served that purpose in the form of US$10–12 million in payment of public works
employee salaries while they were sequestered at home during the lockdown (although this includes

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more than WASAC staff). JICA’s support of WASAC has been in the form of flocculant/coagulantpurchases as well as procurement of 10m3 storage tanks for emergency distribution points andcontracting of privately operated tanker trucks.

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5.0

DEEP DIVE RESULTS: SANITATIONThis section summarizes results of the team’s analysis of COVID-related sanitation access changes in theseven deep-dive countries based on key informant interviews and SMS consumer surveys conducted in
the six African countries.
5.1

THE CONSUMER SIDE

In comparison to their responses with respect to water supply, participants in the SMS surveys reported
modest pandemic-related difficulties around sanitation (both onsite containment and desludging).
Overall, between 8 and 19 percent of respondents reported trouble buying, installing or upgrading
latrines, and between 6 and 11 percent reported difficulties with desludging (see Figure 7).

Figure 7. Indicators of sanitation service difficulties reported by respondents to the team’s SMS surveys. N= 500+ participants per country.

Among those reporting difficulty with purchase, installation, or upgrades of latrines, “I cannot afford it”
was the leading reason given in five of the six countries surveyed (ranging from 54 percent ofrespondents in Rwanda to 87 percent in Mozambique). In DRC, the leading explanation of difficultyoffered by respondents was “I cannot find anyone who was selling what I need,” (53 percent ofrespondents), compared to 33 percent who cited affordability.
The surveys also do not indicate major changes in sanitation service level resulting from COVID-19 (seeFigure 8). This is to be expected, given that declines in sanitation service would occur more graduallythan those for water supply, which can cease when, for example, a provider cannot afford to operate apump. Drops in the sanitation service ladder can occur, for example, as a result of migration from a
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home with piped sewer to one with onsite containment, inability to repair a latrine following failure,inability to access a desludging service, or due to a shift in preference due to perceived risk (e.g. urban
Rwandans moving from public facilities to sharing with a neighbor).

Figure 8. Heat map depicting percentage point changes in reported sanitation level, pre-COVID-19 vs. atpresent. Magnitude of change is captured both by the color and the number in a particular cell. For
example, the proportion of urban Rwandan respondents to who reported reliance on a public community
toilet dropped by 5 percentage points, and those reporting reliance on a shared household toilet increasedby 7 percentage points.

5.2

THE SUPPLIER SIDE – CURRENT STATUS

Though consumers do not report significant sanitation service changes in our SMS surveys, our key
informant interviews across the deep-dive countries do indicate financial pressure on both players in the
fecal sludge value chain and providers of onsite containment inputs. In addition, supply chain
challenges appear to be bearing on sanitation product and service provision more acutely thanthey have for water service provision.
5.2.1

FECAL SLUDGE MANAGEMENT

Although expected to increase again in parallel with economic recovery from the COVID-19 economic
shock, deep declines in demand for pit and septic tank-emptying services were reported inseveral of the deep-dive countries (Ghana, Kenya, and Nepal), reflecting the drop in income acrossall customer segments, including households and corporate clients and commercial/industrial institutions(with the significant exception of public toilets as reported by pit emptying businesses in Ghana). In
some instances, the assessment team learned of shifts away from mechanized desludging to
manual emptying, in a sign of consumers searching for lower-cost means for maintaining their pits andseptic tanks.

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Vacuum truck operators in western Kenya reported a veritable collapse of demand initially, with sales
dropping sevenfold, even as movement restrictions meant that household tanks and pits filled more
quickly than usual. Reported declines in household demand for desludging there ranged from 30–100
percent (partially offset by increases in desludging demand at public toilets). More modest reductions inpit emptying businesses were reported in Kathmandu, but still exceeded 50 percent declines from preCOVID-19 levels due to both household income losses and out-migration from the city during the
lockdown period.
While the effect of the economic shock on suppliers of emptying and transport services was a consistentfinding of the team’s interviews, the assessment team also documented multiple instances in which
decreased household incomes did not significantly impact demand for desludging services, resulting
instead in a shift in emptying service preferences. In Tatale District Assembly (Northern Ghana), wheremanual emptying is readily available, the team learned of significant decreases in demand for mechanizedemptying services in parallel with increases in demand for manual emptying as households opt for alower-cost consumption basket (with manual emptying fees only on the order of 20 percent of the priceof mechanized emptying).
Key informant interviews in DRC and Senegal, two countries with desludging sectors at very different
levels of maturity, indicated few significant pandemic effects. The sector in DRC is extremely small andlimited to Kinshasa, and the team learned of no significant changes in demand. In Senegal, where fecal
sludge management is much more developed, no major changes in demand for pit emptying werereported by Delvic, a major private sector provider of onsite sanitation services in Dakar, Thies, andDiourbel. Delvic did not observe any decrease in revenues at its sludge treatment plant, which isindicative of unchanged household demand.
Delvic did, however, report that supply chains for spare parts and chemicals such as alum, imported
from South Africa, were disrupted from border closures; having to pay a premium for supplies
purchased in local markets did drive down profit margins. This was emblematic of broader reports ofdesludging margins under pressure, with increasing operational costs (largely fuel, repairs, andmaintenance). Similarly, in Ghana, the lockdown in primary centers of Accra and Kumasi restrictedtransport of supplies across the country, resulting in increased transaction costs. All pit emptying
businesses interviewed in Ghana noted the disruption in key supply chains (e.g., spare parts, tires) andthe negative impact on their businesses. The lifting of the Ghanaian pandemic lockdown has eased thesourcing of spare parts; but prices remain elevated. Interviewees noted that even with the recent border
opening, in the medium term, they do not expect a decrease in the price of inputs.
5.2.2

HOUSEHOLD SANITATIONHouseholds appear to be have de-prioritized spending on sanitation facilities, particularly during
the initial, more stringent phases of pandemic response, although there are encouraging signs ofrecovery in selected settings.
In Nepal, rural sanitation entrepreneurs, particularly cement ring manufacturers and latrine component
suppliers, have been finding it difficult to remain viable and continue operations since the onset of thepandemic, due to a lack of availability of inputs, restrictions on construction, and scarce demand. Several
key informants suggested that due to reduced incomes, Nepali households would likely install lowercost, lower-quality toilets (e.g., unlined pits) when their current facilities fill up.
All the organizations interviewed in Senegal reported declines in latrine sales, more than 60 percent
reductions in the northern regions and 30–40 percent in the south (although with recovery in thesouthern regions in June and July). In Kenya, Sanergy reported a complete cessation of latrine installation

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activities from mid-March to June, compared to a monthly average of 100 toilets delivered prior to thepandemic, and have since recovered to about 50 percent of pre-pandemic levels.
In Ghana, urban sanitation enterprises affiliated with Water and Sanitation for the Urban Poor (WSUP)
that averaged 10 sales per month (unsubsidized) pre-lockdown had not received orders for any new
toilets in the months since the national lockdown was lifted in April 2020.
By contrast, suppliers in DRC have largely maintained revenue levels and steady cash-flows, as thedemand for toilet construction materials has remained relatively unchanged, even with price increases.
This trend has played out in the provinces of Kinshasa, Katanga, and Nord-Kivu, and runs counter to the
hypothesis that the decline in household income levels would further deprioritize the purchase of new
toilet facilities. Suppliers, however, note that it might be too early to confirm these demand levels, whichmight be short-lived, as they had only been back in business for a couple of weeks at the time of theinterviews. Suppliers worried that economic burdens would persist, precluding investments in household
sanitation.
Independent of affordability, interviewees in DRC also suggested that COVID-19 is less likely to have anegative impact on rural household construction of new latrines than in urban areas. Improved toilets
are relatively more affordable in the countryside, where they are built using local materials andcommunities contribute to the costs of latrines. Mercy Corps reported an increase in household toilet
construction and handwashing facilities, although the team cautions that that this may not be
representative across all Congolese regions.
The relatively encouraging findings from DRC are echoed by reports from rural Ghana, where the
USAID-funded WASH for Health program reported that sales did not decline at all, with a weekly
average of 150–200. The harvest season (July–August), when households typically finance large
purchases, is a plausible explanation for the sustained sales. In Rwanda, the USAID-funded Isuku Iwacu
program (a focus of which has been establishment of district-level sanitation centers that act as market
facilitators) reported that “sanitation product demand did not significantly decline during the lockdown
period.” At the same time, though, USAID’s companion sanitation program, Gikuriru (which employs
the Community Health Club model to promote latrine adoption, cessation of open defecation, andother hygienic behaviors), reported a shift in emphasis toward local soap manufacture.
As with fecal sludge management, supply chain disruptions were widely reported in the context
of the market for onsite containment products and services. In Nepal, supply of most inputs (e.g.,cement, HDPE pipes) ceased completely during the lockdown period. Most NGO implementers have
had to delay the timelines for their ongoing projects (e.g., toilet upgrade program or community toilet
construction) by 6–9 months. In Ghana, the lockdown in primary centers of Accra and Kumasi had
restricted transport of supplies across the country, but existing inventories with wholesalers andretailers were adequate to meet demand, which had been reduced due to a slowdown in construction
activity. Ghanaian building materials retailers have cut back on inventory levels, however, given theslowdown in sales.
In DRC, NGOs and sanitation commodity suppliers confirm that the pandemic has significantly affectedsupply chains. For cities like Bukavu that heavily depend on imports from Rwanda, border closures haverestricted movement of all goods with the exception of food products. Lockdown measures in high
COVID-19 transmission areas (largely urban and semi-urban zones) also created challenges with internal
movement of goods. KfW reported difficulties in sourcing building materials for toilet construction in
Kikwit (Kwilu province) and Mbuji-Mayi (Kasaï province).
Finally, working capital is in a critical state as trade credit has dried up for all but large businesses andtransactions have shifted strictly to cash-and-carry. In Ghana, building material retailers and sanitationenterprises interviewed reported suspension of credit period or line from their suppliers, and they, in

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turn, have stopped giving credit to customers. Nationally, 76 percent of manufacturing businesses and 83
percent of wholesale and retail businesses reported cash flow problems. Micro, small, and medium
enterprises businesses are disproportionately affected with more than 70 percent citing cash flow issues
compared with 47 percent of large enterprises (Ghana Statistical Service, 2020).

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6.0

DEEP DIVE RESULTS: HANDWASHING

This section summarizes results of the analysis of COVID-related hygiene product access changes in theseven deep-dive countries based on key informant interviews as well as SMS consumer surveys
conducted in the six African countries.
6.1

THE CONSUMER SIDE

The assessment team found reported
handwashing to be extremely high in theaftermath of the onset of the pandemic.
Among over 3,000 respondents to the
SMS surveys, 84 percent reported that
handwashing with soap by neighbors andfriends had increased due to thepandemic, with country-level numbers
ranging from 79 percent in Ghana to 88
percent in Mozambique (Figure 9). The
urban vs. rural split for both of thesenumbers is minimal, though rural
respondents did report slightly lower
increases in behavior change and slightly
higher levels of soap access difficulty.
The team’s findings are consistent withthose reported elsewhere. A WaterAid
assessment in Nepal (where we wereunable to conduct an SMS survey)
Figure 9. SMS survey response to the question, “Do you
included phone surveys of 380 households
notice your neighbors and friends washing their hands withacross the country conducted in July. Of
soap more often than before COVID-19?”
those interviewed, 97 percent reportedchanging their handwashing behavior postCOVID commencement (MITRA Samaj, 2020). That assessment also found consensus amonginterviewed key informants that the change in behavior was real, and not strictly courtesy bias.
In a recent survey conducted among over 4,800 Rwandans in June 2020, 87 percent reported washing
their hands more often (Finmark Trust, 2020). Key informants attributed this positive shift in behavior
to the government’s extensive promotion of handwashing in the fight against COVID-19 and its decision
to make handwashing stations and sanitizers compulsory in all public places, such as churches, bus
stations, and market places.
Dramatic changes in behavior in response to crises can be transitory; increased handwashing in the timeof COVID-19 may not be sustained. Indeed, there is suggestive evidence that initial increases in selfreported handwashing may be declining.
In collaboration with Facebook, Johns Hopkins University, and the World Health Organization, the MIT
Initiative on the Digital Economy has been executing an online survey of 24,000 individuals across 20
countries repeated bi-monthly since July 2020. Responses from selected countries are presented in
Figure 10. Over the course of about 10 weeks, there appear to be modest declines in reported
handwashing as a COVID-19 preventative measure, with a 3.9 percentage point drop across the 11
countries represented in Figure 10, from 85.3 percent to 81.4 percent. The overall numbers for

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handwashing for this online sample remain extremely high (and consistent with this assessment team’s
SMS surveys). The declining numbers suggest that some meaningful fraction of the survey participants
are responding truthfully (with courtesy bias also minimized by virtue of the survey being anonymous).

Figure 10. Percentage of the respondents listing “washing hands” in response to the question, “Whatmeasures have you taken to prevent infection from COVID-19 in the past week?” in the MIT COVID-19
Beliefs, Behaviors & Norms Survey. X-axis labels correspond to two-week long survey waves for 11
countries. Source: MIT Initiative on the Digital Economy and Facebook, 2020.

6.2

THE SUPPLIER SIDE – CURRENT STATUS

In both Ghana and Kenya, soap manufacturers and distributors interviewed report increases in demandof up to triple the pre-COVID levels. Higher sales were also reported by producers in Mozambique,
Nepal, and Rwanda. These increases in sales are not exclusively linked to increased consumer demand;
governments and donors have also purchased both soap and sanitizer in bulk to enable changes in
handwashing behavior. Still, increased sales overall is notable, given the backdrop of major reductions in
consumer spending power because of income losses. Multiple market players expressed optimism thatthe positive shift will continue over the longer term, driven by both retail and institutional channels.
Manufacturers and distributors did report reductions in their profit margins from supply chain issues aswell as changes in consumer brand preference. Although the assessment team’s interviews yielded noreports of widespread or extended shortages of handwashing products (and none were revealed byresponses to the SMS consumer survey), the team learned of country-specific supply chain issues, bothwith respect to imports of soap and its raw materials as well as local transport disruptions frommovement restrictions (particularly in Nepal and Rwanda). The assessment team received reports ofprice increases in raw materials (e.g., tallow, palm oil, stearin, and caustic soda) as high as 30–40 percent,but these were widely alleged to have been absorbed by manufacturers rather than passed on to
consumers (either due to competition or out of recognition of reduced consumer purchasing power).
In Mozambique and Nepal, manufacturers and distributors noted that the increases in soap sales havebeen accompanied by shifts in preference away from premium brands to lower-priced or more hygieneoriented brands, further squeezing margins, in some markets as high as 35 percent as compared tocomparable periods in 2019.
COVID-driven behavior change represents an opportunity for new entrants into local soap markets.
Interviews in Ghana revealed that the increase in demand for soap has attracted many new market
entrants, primarily SMEs who are convinced that the newly adopted handwashing behaviors will besustained over time, creating a viable longer-term business opportunity. In Rwanda, two USAIDsupported programs have intensified activities centered around fostering local commercial soap
production.

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6.3

VARIATIONS IN REPORTED ACCESS AND PRICE TRENDS

Although they report more handwashing due to the pandemic, significant fractions of consumers
surveyed via SMS also report that access to hygiene products has gotten more difficult because of
reduced affordability, despite the fact that significant and widespread consumer price increases have notmaterialized. That said, the assessment team saw variation across deep-dive countries. Overall, 36
percent of surveyed respondents reported that soap access had gotten more difficult as a result of
COVID-19, ranging from 16 percent of respondents in Senegal to 58 percent in Rwanda (Figure 11).
Those reporting access difficulties said they were due to price increases as opposed to lack of availability
in local markets. Conversely, 28 percent of respondents (on average) reported easier access, ranging
from 15 percent in Kenya to 39 percent in Ghana, attributed to either lower prices or free distribution.
Across the deep-dive countries, 37 percent of respondents reported no change in soap access due to
COVID-19.

Figure 11. SMS survey response to the question, “Since COVID arrived, has it become easier or moredifficult for your family to obtain any kind of soap to wash hands?”
Fractions reporting “no change” are not displayed, but in each case equal the remainder to reach 100 percent.

Among the surveyed countries, there appears to be three distinct groups: first, those for which
reported soap access improvements outpace reported difficulties by 2:1 (Ghana and Senegal); second,
those for which access difficulties are far more widely reported than improvements (over 3:1 in Kenya
and more than 2:1 in Rwanda); and third, those for which reported improvements and difficulties are
similar (DRC and Mozambique).
The concerning patterns regarding the difficulty to obtain soap in Kenya and Rwanda were not clearly
reinforced by key informant interviews, but the team can put forth some possible explanations. First,
these countries reported the highest level of employment-related distress in the SMS surveys (see Figure3), and thus affordability might be a much more pronounced problem there.
Second, notably for Rwanda, lockdown measures may have some lingering effects. The country
implemented some of the strictest lockdown measures in Africa, decreasing the mobility of its citizens
by about twice the average for the continent (see Figure 2). While the lockdown was progressively lifted
after May 2020, temporary lockdowns in urban hotspots (e.g., Kigali city market, Nyabugogo market)
continue to be re-introduced in response to episodic COVID-19 outbreaks. Retailers in rural areas
depend on markets in Kigali, which is the central trade district, for a regular supply of consumer goods.
The lockdown of urban markets, therefore, may also have had an adverse impact on the distribution ofsoap to rural areas. Based on anecdotal observations, key informants cited an increase in home-based
production of liquid soap in rural areas for self-consumption and sale within the community/village since
the start of the pandemic. Localized supply could be alleviating distribution challenges to an extent, buteven those faced challenges early on during the strict lockdown period. One small-scale manufacturer in

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Kigali reported that “for two months (March and April), we stayed at home; we did not venture out at all, noteven to sell our soaps.”
The assessment team found no evidence of sustained, widespread increases in consumer prices for handhygiene products. Even as production and distribution costs rose, supply chain actors across the deepdive countries generally reported avoiding raising consumer prices in response. In some cases, theyreported competition from new local manufacturers; in others (such as Rwanda), the government
actually intervened with decrees to keep consumer prices low.
The best available consumer survey information that the assessment team located during the deep-diveresearch is in line with the key informant interviews conducted. The Kenya COVID-19 Economic
Tracker is analyzing ongoing phone survey data suggesting that bar soap prices have not risen in Kenya(perhaps even dropping slightly), based on over 17,000 household phone interviews in Siaya county,
Nyanza province, in a series of 21 weekly surveys between the beginning of April and the end of August(Figure 12).

Figure 12. Prices of selected non-food items, reported by households contacted as part of the Kenya
COVID-19 Economic Tracker.
Prices are expressed as a percentage of the value reported in the survey’s first round in April.

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7.0

SUMMARY OF ECONOMETRIC MODELRESULTS

The assessment team’s key informant interviews in deep-dive countries sought to examine supply-side
disruption and how reduced revenue has affected service delivery, as well as how COVID-19 publichealth messaging has increased demand for handwashing inputs. The SMS surveys complement theseinterviews by collecting information directly from consumers as to how their service levels may havechanged as a result of the pandemic. Both of these study elements allow some basic, forward-looking
analysis of how WASH access could evolve in the near-term, presented in Section 8.
This section provides a summary of trend predictions in the context of COVID-19 based on an
econometric model of wealth effects on WASH (described in Section 2.2). Appendix 3 provides detailsof model assumptions and results. In brief: the model examines the relationship between wealth in theform a wealth index measure following DHS procedure as the explanatory variable, and either water
service level or sanitation service level, respectively, as dependent variables, as captured in DHS andMICS datasets. 7
Having developed four different cross-sectional models, this section focuses on two of them. The firstexamines the simple relationship between wealth index and water (or sanitation) service level, exploring
how a difference in wealth quintile predicts a difference in service level. The second model examines thesame relationship but controls for neighborhood, education, and other household factors, asking thequestion: “If a household is wealthier than its neighbors by a given amount, how likely is it to have superiorwater or sanitation service, and by how much?”
Establishing the degree to which wealth controls water and sanitation access, albeit at a single point in
time, allows a prediction of how that access might change in response to a decline in wealth, as mightoccur following an economic downturn. Relying on World Bank estimates of country-specific GDP
growth declines for 2020, the assessment team ran the models for water supply in the 27 high priority
and strategy-aligned countries for which there are water supply data available, and for sanitation in all 28of the countries (see Table 5 and Table 9 in Appendix 3).
The model results suggest vastly lower service level declines for water supply than those indicated by
the SMS survey results in the deep-dive countries, although they are more in line with the SMS surveys’
sanitation results. Cumulatively across all these countries, the relationship between wealth and WASH
indicators predicts that the pandemic’s economic shock will cause nearly 29 million people to drop
below JMP Basic sanitation levels and 1.9 million to drop below JMP Basic water service levels (with India
constituting the majority in each case), relying on the model that does not control for neighborhood,education, and other household factors (Model 1). The map in Figure 13 displays these results, showing
the effect size of the estimated pandemic income shock by country expressed as the proportion of the
population falling below the JMP Basic for sanitation. The totals for sanitation drop to roughly 7.1 million
people falling below JMP Basic for sanitation – with no effective change in JMP Basic for water – whencontrolling for neighborhood and the other factors (Model 2). For the country breakdown of theseestimates, see Table 9 in Appendix 3.

7

Although the income-WASH correlations presented here are not from an experiment, they do have the benefit of using nationally
representative data from each high-priority or strategy-aligned country. Thus, although the team acknowledges the likely biases that arise in
the estimates, results may be less biased than if the team had relied on careful experimental data from different settings. In a study of classsize and private school effects on educational outcomes, Pritchett and Sandefur (2014) found that non-experimental estimates from therelevant context can be a better guide to policy than an experimental estimate from a different context.

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Figure 13. Results of Model 1 for sanitation – the effect size per country examining wealth effects only.
Values correspond to the proportion of a country’s population falling below the JMP’s Basic level for sanitation in response to
the pandemic-induced economic shock, for all USAID WASH high-priority and strategy-aligned countries.

These results offer an approximation of how COVID-19 economic shocks could play out in terms ofwater and sanitation access in USAID’s high priority and strategy-aligned countries, albeit with important
limitations. First, there is reason to believe that the World Bank’s estimates of GDP growth declines (amedian of 6.2 percentage points across the 28 countries under study) may be an underestimate, given
the results of the SMS surveys across six countries as well as an array of other individual data points
suggesting much larger economic effects (see Appendix 3).
Second, declines in water access are driven not only by income shocks borne by consumers but also by
financial pressures endured by WSPs as they suffer revenue shortfalls (a function of both government
directives for free water provision and tariff collection difficulties). These financial pressures can result in
immediate disruptions in water supply service, and could explain why consumer-reported water accessdifficulties so vastly exceeded those predicted by the models. By contrast, sanitation is not sensitive tosudden operational disruptions as can happen with water supply systems (with the exception of piped
sewer service, whose profile matches that of piped water supply, but which is enjoyed by an exceedingly
small fraction of the populations under study). The consumer-reported changes in sanitation service
modality are much more comparable to what the statistical relationship between income and sanitation
outcomes would predict.
Third, like any model, predictive power is constrained by the reliability of the model inputs andassumptions. While the results may be underestimates if the assessment’s assumed GDP changes are
too low, results could also overestimate effects because:••••

The model predicts effect of a long-term income change, not of an episodic shock;
Many utilities have not cut off service; indeed, many have offered varying periods of tariff suspension;
People dislike giving up facilities they are used to: people may pay more to keep services than theywould have initially paid to gain services (Kahneman et al., 1991); andBecause improved water and sanitation often requires sunk costs, the estimates are especially likely
to overstate the short-term water and sanitation effects of a reduction in household income. In
other words, major investments in water and sanitation (e.g., the acquisition of a household water
connection, the purchase of a pump, or the installation of an improved latrine) that occurred prior
to the shock do not suddenly vanish when it occurs.

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8.0

FUTURE WASH ACCESS TRENDS IN
HIGH-PRIORITY AND STRATEGYALIGNED COUNTRIESThe following is a synthesis of the team’s findings across subsectors, integrating the findings of the key
informant interviews, SMS consumer surveys, and econometric analysis of current conditions in an effort
to make measured forecasts of future trends in access to WASH services and products in the period of
COVID-19. Clearly, macro-level economic shocks bear upon water access, which is reflected in theeconometric model results in Section 7. Notably, these modeled water supply effects are orders ofmagnitude lower than the losses reflected in the SMS surveys (see Figure 6), but the modeled sanitation
effects may well be much more in line with survey results for sanitation.
A robust estimation of future trends must take into account both reasonable predictions ofchanges in wealth and factors related to the provision of services. Such factors are apparent in the
team’s deep-dive investigations, which, when combined with careful analyses conducted by WASH-FIN
and others, reveal acute near-term issues, particularly with respect to water supply service. One such
issue is that WSPs are in discouraging financial shape due to the inability to collect revenues for varying
periods since the onset of the COVID-19 pandemic. As discussed, water supplier losses are driven byboth consumer income shocks and government policies on tariff collection, both of which vary acrossthe deep-dive countries (and certainly across the USAID high-priority and strategy-aligned countries aswell). It is especially important here to recognize the very different ways in which water, sanitation, andhandwashing products are delivered, as they bear upon how future trends should be considered.
8.1

WATER SUPPLY

For those populations whose water supply service relies on mechanized pumps, whether via pipedconveyance or at a fixed point (e.g., a standpipe fed by an elevated tank), maintaining the financial
resources to operate pumps (via grid electricity or diesel) is essential to keep water flowing and, in thecase of sewered sanitation, to prevent wastewater from backing up into connected households. It alsomeans that the potential catastrophic loss of service to large numbers of consumers can be sudden
when power for running for pumps cannot be acquired if 1) a system operator runs out of cash and 2) is
not either immediately financially rescued or provided electricity/fuel free of charge on credit. Thisquality is not shared by onsite sanitation, for which sudden losses of service across a geographic area do
not occur.
The assessment team’s interviews with water suppliers of varying sizes across seven countries, taken
together with focused analyses of larger utilities conducted by others (particularly WASH-FIN), paint a
picture of a fragile situation whereby speedy financial rescue has become essential to maintaining
continuity of service. Some large utilities have received financial rescue in certain countries (DRC,
Ghana, Kenya, Mozambique, and Senegal); whether the committed packages are sufficient to forestallliquidity crises by the large national asset owners and operators remains to be seen. This area should bemonitored closely.
For smaller service providers, whether they be operators of piped systems, mechanized boreholes,
kiosks, or providers of last-mile connections to households from larger systems (like small private
network concessionaires or cart vendors of jerrycans), there is no possibility of direct rescue by
bilateral or multilateral donors, given the sheer number of actors involved and the associatedadministrative and logistical challenges. In Kenya, WASREB is slated to administer World Bank support
to 87 regulated water utilities across the country, 30 of which have fewer than 5,000 connections, which

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at least affords the opportunity for a national-level aid recipient to channel support to individual small
providers. In several countries, associations of WSPs are in place, but none appear to have the capacity
currently to act as a conduit for significant financial support to smaller providers.
The assessment team’s deep-dive key informant interviews, along with responses to the SMS surveys,
confirm the initial hypotheses that small town and village-level piped providers and last-mile providers
occupy a vulnerable middle between the largest utilities and the non-piped village-level sources(handpumps, shallow wells, and springs, which include self-supply). The former is most likely to receive
financial bailout, and the latter is the least-sensitive to COVID-19 economic shocks (with the exceptionof handpumps, for which increased failure rate from deferred maintenance, although not yet widely
observed, could emerge as a performance issue a bit further down the road). Depending upon the
duration of both economic contraction and government directives for free water provision, thesesystems are the most at risk of service disruption, raising the essential question of how to keep them
functioning. This is a matter of paramount importance given the size of the population these systems
serve (often exceeding the populations served by large, national, and often-urban focused utilities).
The assessment team anticipates that the largest WSPs will largely avoid large-scale disruptionsin the next 12–18 months as their financial positions are secured with assistance from thedevelopment banks and bilateral and multilateral aid institutions, continuing a pattern of major (and
often hidden) subsidies that disproportionately benefit wealthier segments of urban populations. Thereare exceptions, such as the situation in DRC, where REGIDESO followed the country’s two-month freewater policy with a three- to four-fold increase water tariffs, prompting action by KfW to mitigate thisincrease in two Congolese cities.
Effects on non-piped water access in rural settings are also likely to be less pronounced, with the
exception of deep lift handpumps, whose down time may well increase if the declining revenues limit theresources available for regular maintenance. Even in pre-pandemic circumstances, handpumps recovered
only on the order of 10 percent of the operating costs (McNicholl et al., 2019).
The assessment team cautions that the vulnerable middle is currently at the greatest risk. Interviews
with smaller system operators indicate that financial stress is widespread, although the severity of thatstress varies. The associations of private water providers in Rwanda reported deep concern about theirability to sustain operations. In Mozambique, direct interviews with 30 small private operators yieldedanxiety, although without many explicit expressions of concern about imminent shutdowns of theirsystems. In Ghana, however, the financial toll of the most widespread and longest-duration free water
directive on the continent has resulted in reports of service disruptions already occurring among small
town and rural piped providers as they had exhausted cash reserves to cover basic operating costs (suchas for electricity), with substantially more system disruptions on the way if the pace of promised
reimbursements by the central government is not expedited.
As economic conditions recover with the easing of movement restrictions, the team expects WSP
revenue collection to rebound as well (as was reported in selected countries), although the speed withwhich revenues rebound is uncertain. The team is not in a position to forecast the rate of economic
recovery, even as the comparatively low direct health burden of the pandemic throughout much of
Africa along with a significant rollback of restrictions and restoration of movement and commerce as of
the beginning of October 2020 do signal that recovery is underway.
8.2

SANITATION

The assessment team’s SMS surveys of sanitation revealed minimal changes in sanitation service
modality, changes much more in line with the findings of the economic model. Very small fractions of
the population of the countries investigated are served by piped sewer—only 1.9 percent in Ghana, for

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28

example, according to the 2017–2018 MICS—so the focus here is on onsite sanitation. Financial
hardship suffered by consumers was universally reported to have dampened demand for the inputs for
new latrine installation or upgrade, and businesses selling such inputs reported reduced sales. The
limitations of the econometric model for water supply (specifically, accounting for the challenges ofutility performance and the real risks of system-wide disruptions if basic operational functions couldn’t
be paid for) do not apply for sanitation. The team has more confidence in the ability of the model results
to offer a reasonable estimate of how changes in sanitation service level will respond directly to income
shocks.
Key informant interviews revealed that although sales of sanitation-related products as well as services(both installation and desludging) did drop in parallel with consumer spending in response to income
losses, a recovery was also apparent. The sanitation sector’s near term trajectory in high-priority andstrategy-aligned countries is tied much more directly to economic conditions than the water sector,
whose performance risks are compounded by extended periods of utility revenue declines. As
economic activity restarts, the assessment team believes that rebounds in income will bringabout a return to the markets for both sanitation inputs and desludging.
8.3

HANDWASHING

This subsector is probably the most difficult to forecast, largely because the changes in handwashing
behavior that are indicated by the SMS surveys and echoed by interviews with value chain actors are, tothe team’s knowledge, unprecedented. Although there are indications of a modest decline in selfreported handwashing, the team has no historical precedent on which to base an assumption that the
decline will continue rather than the change in behavior becoming entrenched as a durable social norms
shift.
What the team deems likely is that soap will become more affordable to consumers in response
to income recovery from the COVID-19 shock. Markets have so far adapted well to maintainsoap availability. Whether increased consumer spending power will result in sustained increases insoap sales (or returns to luxury brands at rates that restore the margins of manufacturers) is uncertain;
indeed, it is possible that handwashing behaviors will decline again as the pandemic recedes. This is notsupported by any strong evidence that the assessment team could find of how handwashing has either
increased in response to a crisis (e.g., Ebola) or faded as the crisis diminishes. The team does not see
declines in soap access outside of the affordability challenges of reduced incomes—indeed, soap prices
remain stable across the deep-dive countries.

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29

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Cabore, J. W., Karamagi, H. C., Kipruto, H., Asamani, J. A., Droti, B., Seydi, A. B. W., Titi-Ofei, R.,
Impouma, B., Yao, M., Yoti, Z., Zawaira, F. Tumusiime, P., Talisuna, A., Kasolo, F. C., & Moeti, M. R.
(2020). The potential effects of widespread community transmission of SARS-CoV-2 infection in theWorld Health Organization African Region: a predictive model. BMJ Global Health, 5(5).
https://doi.org/10.1136/bmjgh-2020-002647
COVID-19 Economic Tracker. Kenya. (2020). https://www.kenyacovidtracker.org/
Finmark Trust. (2020). COVID-19 tracking survey status in Rwanda.
https://covid19tracker.africa/country/RWA/summary
Fuente, D., Allaire, M., Jeuland, M., & Whittington, D. (2020). Forecasts of mortality and economic losses
from poor water and sanitation in sub-Saharan Africa. PLoS One, 15(3).
https://doi.org/10.1371/journal.pone.0227611
Gasson, C. (2020, July 30). Non-payment is COVID’s biggest threat to water. Global Water Intelligence.
https://www.globalwaterintel.com/news/2020/31/non-payment-is-covid-s-biggest-threat-to-water.
Ghana Statistical Service. (2020). “Prime Building Costs Index (PBCI).” Accra.
Global Wash Cluster (GWC), Sanitation and Water for All (SWA), UNICEF, & International Committeeof the Red Cross (ICRC). (2020). COVID-19 and WASH: Mitigating the socio-economic impacts on
the Water, Sanitation and Hygiene (WASH) Sector. Global Wash Cluster (GWC), Sanitation andWater for All (SWA), UNICEF, ICRC.
GMSA Connected Women. (2019). The Mobile Gender Gap Report. GSMA.
Google. (2020). COVID-19 Community Mobility Reports. https://www.google.com/covid19/mobility/
IMF. (2020). Policy Responses to COVID19. IMF. https://www.imf.org/en/Topics/imf-and-covid19/PolicyResponses-to-COVID-19
Jeuland, M. A., Fuente, D. E., Ozdemir, S., Allaire, M. C., & Whittington, D. (2013). The Long-Term
Dynamics of Mortality Benefits from Improved Water and Sanitation in Less Developed Countries.
PLoS ONE, 8(10). https://doi.org/10.1371/journal.pone.0074804
Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies: The Endowment Effect, Loss Aversion,and Status Quo Bias. Journal of Economic Perspectives, 5(1), 193–206.
https://doi.org/10.1257/jep.5.1.193
Kemp, S. (2020, February 18). Digital 2020: Rwanda - DataReportal – Global Digital Insights.
https://datareportal.com/reports/digital-2020-rwanda
Mahler, D. G., Lackner, C., Aguilar, R. A. C., & Wu, H. (2020, April 20). The impact of COVID-19(Coronavirus) on global poverty: Why Sub-Saharan Africa might be the region hardest hit. WorldBank Blogs. https://blogs.worldbank.org/opendata/impact-covid-19-coronavirus-global-poverty-whysub-saharan-africa-might-be-region-hardest.
McNicholl, D., Hope, R., Money, A., Lane, A., Armstrong, A., van der Wilk, N., Dupuis, M., Harvey, A.,
Nyaga, C., Womble, S., Favre, D., Allen, J., Katuva., J., Barbotte, T., Buhungiro, E., Thomson, P., andKoehler, J. (2019). Performance-based funding for reliable rural water services in Africa. Uptime
consortium, Working Paper 1. https://www.smithschool.ox.ac.uk/research/water/report-

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performance-based-funding.html
MIT Initiative on the Digital Economy, & Facebook. (2020). COVID-19 Beliefs, Behaviors & Norms Survey.
MIT COVID-19 Survey. https://covidsurvey.mit.edu/
MITRA Samaj. (2020). “Rapid Research on Handwashing Promotion during COVID - 19 Pandemic in
Nepal.” Kathmandu.
Nepali Times (2020). “Kathmandu Valley back to lockdown Surge in Covid-19 cases forces authorities todeclare a week-long curfew till 26 August.” 18 August 2020.
https://www.nepalitimes.com/banner/kathmandu-valley-back-to-lockdown/
Pew Research Center. (October, 2018). Internet Connectivity Seen as Having Positive Impact on Life in
Sub-Saharan Africa. Pew Research Center.
https://www.pewresearch.org/global/2018/10/09/majorities-in-sub-saharan-africa-own-mobilephones-but-smartphone-adoption-is-modest/
Pritchett, L., & Sandefur, J. (2014). Context Matters for Size: Why External Validity Claims andDevelopment Practice do not Mix. Journal of Globalization and Development, 4(2).
https://doi.org/10.1515/jgd-2014-0004
Research ICT Africa. (2019). The State of ICT in Mozambique 2018. RIA Policy Paper No. 6(5, After
Access). https://researchictafrica.net/2019_after-access_the-state-of-ict-in-mozambique/
Sanitation and Water for All, and Global WASH Cluster. (2020). “Mitigating the Socio-Economic
Impacts on the Water, Sanitation, and Hygiene Sector.”
USAID (2020). WSP COVID Financial Stress Testing and Mitigation. Water, Sanitation and Hygiene
Finance (WASH-FIN) Project.

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT31

APPENDIX 1. KEY INFORMANT LISTS
The tables below include the organizations with which the assessment’s key informants are affiliated. To
protect confidentiality, the tables do not include individual names. In numerous cases, the assessment
team interviewed several key informants from the same organization. Additionally, the assessment teaminterviewed value chain actors who are sole proprietors of businesses who could be identified only as“anonymous vacuum truck operator” or the similar titles; these also are not included in the tables.
DRC
Category
Central Government
Central Government
Central Government
Central Government
Donor
Donor
Multilateral Institution
NGO
NGO
NGO
NGO
NGO
Service Provider (Association)
Service Provider (Association)
Value Chain Actor
Value Chain Actor

Organization
National Water and Sanitation Committee (Comité National d’Action de l’Eau etl’Assainissement)
Department of Hygiene and Public Sanitation
Directorate of Sanitation and Health
Ministry of Water Resources and Electricity
KfW/Deutsche Gesellschaft für Internationale Zusammenarbeit
World Bank
UNICEF
Food for the Hungry
ICRC
Mercy CorpsOXFAMWorld Vision
Association of Pit Emptiers, Kinshasa
Association of Private Operators of Latrine Construction
Ela Chemicals and Engineering;
Établissement Shalom, distributor of toilet construction materials

GHANA
Category
Central Government
Financial Institution
Local Government
Local Government
Local Government
Local Government
Local Government
Local Government
Local Government
Local Government
Local Government
Local Government
Local Government
Multilateral Institution
NGO
NGO
NGO
NGO
NGO

Organization
Community Water and Sanitation Agency
Fidelity Bank
Accra Metropolitan AssemblyCape Coast Metropolitan Assembly
Effutu Municipal Assembly
Ho Municipal Assembly
Kumasi Metropolitan Assembly
Offinso North District Assembly
Prestea Huni Valley Municipal Assembly
Saboba Municipal Assembly
Tamale Metropolitan Assembly
Tatale District AssemblyTema Metropolitan Assembly
UNICEFCONIWAS
Global Communities Ghana
iDE Ghana (Sama Sama), Tamale
IRC WASH
WaterAid Ghana

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT

32

CategoryNGO
Researcher
Researcher
Sanitation Enterprise
Service Provider
Service Provider
Service Provider
Service Provider
Service Provider
Service Provider
Service Provider
Service Provider
Service Provider
Service Provider
Service Provider
Service Provider (Association)
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor

Organization
WSUP Ghana
Presbyterian University College
Kwama Nkrumah University of Science and Technology
Greater Accra
Asutifi North district, Brong-Ahafo Region
Fecal Sludge Truck Operator – Framcy Services
Fecal Sludge Truck Operator – J. Okoe Sanitation Company, Kumasi
Fecal Sludge Truck Operator, Achimota, Accra
Fecal Sludge Truck Operator, Kasoa – God First Septage Services
Fecal Sludge Truck Operator, Korle-Gonno, Accra
Fecal Sludge Truck Operator, Sekondi-Takoradi
iCESSPOOL
Samalex, Greater Accra
Sewerage Systems Ghana Limited FSTP
Zoomlion Ghana Limited
Environmental Service Providers Association
Animbile Kayi Hardware, Northern Region
Berrak Manufacturing Company Limited
Clean Smell Limited
Coppel Plumbing, Greater Accra
Geosfier Hardware, Cape Coast
Homesense Ghana
Jekora Ventures Limited
Morning Dew
Proprietorship in Madina market, a peri-urban area in the Greater Accra
regionSam Bennet Hardware, Brong-Ahafo Region
Soap Proprietorship in Brong Ahafo, Western North and Bono regions
Stonches Enterprises, small-scale soap manufacturer and grocery store in
Sunyani city, Bono
Tiwajo Industry Limited
Wilmar International, Ghana

KENYA
Category
Central Government
Local Government
Multilateral Institution
NGO
NGO
NGO
NGO
NGO
NGO
NGO
NGO
NGO
Service Provider
Service Provider
Service Provider
Service Provider

Organization
WASREB
Water, Environment, Energy & Natural Resources, County of Nakuru
UNICEF
Afya Uzazi - Nakuru
Dandorra Transform League
Kenya Water and Health Organization
RHYCO
Sanergy
Sanivation
Umande Trust
World Vision
WSUP“Master operators” - via KIWASCO
Busy Bee – Manual Pit Emptiers Kisumu
Gasia Poa – Manual Pit Emptying
Kalamaki Pit Emptiers Nakuru

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT

33

Category
Service Provider
Service Provider
Service Provider (Association)
USAID Program
USAID Program
Value Chain Actor

Organization
Kisumu Waste Water Association
Obunga Stars – Mechanized Pit Emptiers Kisumu
WSPA
KIWASH (DAI)WASH-FIN (Tetra Tech)
Anserer Limited Nakuru Sanitizers

MOZAMBIQUE
Donor

Category

DonorMultilateral Institution
NGO
USAID Program
Value Chain Actor

Organization
Department for International Development (now the Foreign, Commonwealth& Development Office)World Bank
UNICEF
SNV
WASH-FIN (Tetra Tech)
Maeva GroupIn Mozambique, the assessment team also interviewed 30 private water system operators randomly
selected from the SINAS dataset. Their locations are mapped in Figure 14 below.

Figure 14. Distribution of 30 Mozambican small private water providers interviewed in August 2020.
Data source: the SINAS dataset at www.sinasmz.com.

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT34

NEPAL
Category
Central Government
Central Government
Central Government
Central Government
Central Government
Donor
Donor
Donor
Multilateral Institution
Multilateral Institution
Multilateral Institution
NGO
NGO
NGO
NGO
NGO
NGO
NGO
NGO
NGO
NGO
NGO
NGO
NGO
Researcher
Service Provider
Service Provider
Service Provider
Service Provider
Service Provider
Service Provider
Service Provider
Service Provider (Association)
USAID Program
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor
Value Chain Actor

Organization
Department of Water Supply and Sewerage Management
Ministry of Health
Ministry of Physical Infrastructure Development
Ministry of Water Supply
National WASH Cluster Secretariat
Asian Development Bank
Embassy of Finland
Islamic Development Bank
UNICEF
World BankWorld Health Organization
Environment and Public Health OrganizationiDE Nepal
KIRDARC (Karnali Integrated Rural Development and Research Center)
MHM Alliance
MITRA Samaj
Nepal Water for HealthOXFAM
Plan International
SAFAA PAANI
Save the Children
SNV
WaterAid
Women for Human Rights
Tribhuwan University
Fecal Sludge Treatment Plant: Lubhu (Mahalaxmi)
Fecal Sludge Truck Operator
Janakpur Zonal Hospital, Janakpur, Province 2
Koshi Zonal Hospital, Biratnagar Province 1
Nepal CRS Company
Nilakantha Natural Spring Water
Rural Village Water Resources Management Project
Nepal Water Tanker Transportation Association & Siddhartha Khanepani
Transportation Services
TAYAR (DAI)
Asian Pharmaceutical Pvt. Ltd & Universal Formulation Pvt. Ltd
Cement Distributor, Pokhara
Gangajal Minerals Pvt. Ltd.
Greenberry Minerals Industries
Jasmine Hygiene
Karmacharya Group
Mahashakti Soap and Chemicals
Mott McDonald
Radha Paudel Foundation
Sonapur Cement
Unilever Nepal

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT35

RWANDA
Category
Donor
Donor
NGO
NGO
NGO
NGO / USAID Program
NGO / USAID Program
Service Provider
Service Provider (Association)
Value Chain Actor
Value Chain Actor
Value Chain Actor

JICA
World Bank
Catholic Relief Services (CRS)
SNV
WaterAid
Gikuriru (CRS)
Isuku Iwacu (SNV)WASAC
FEPEAR
Iwacu Soap
Mount Meru Soyco
Sulfo Industries

Organization

SENEGAL
Category
Central Government
Central Government
Central Government
Donor
Multilateral Institution
NGO
NGO
Service Provider
Service Provider
Service Provider
Service Provider
USAID Program

Organization
National Sanitation Office of Senegal
National Water Company of Senegal
Office of Rural Drilling
AFD
UNICEFOXFAMWorld Vision
DelvicFLEX’EAUNational Water Partnership Senegal - Global Water partnership West Africa
SEOH
ACCES (NRCE)

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT36

APPENDIX 2. SMS SURVEY INSTRUMENTENGLISH VERSION (USING THE GHANA SURVEY’S OPTION FOR LOCATION OF RESIDENCE)Q#

Q NameNA

Opt-In-Incentive

NA

Help

NA

RefusalNA

Ineligible

EnglishGeoPoll: Reply 1 to answer questions on Coronavirus and earn #TOPUP# ! No cost to reply. For
help reply HELP
GeoPoll is a global network of people shaping their community by answering short surveys. Free
to respond. Reply STOP to Opt-Out. Visit GeoPoll.com for info
Thank you for your time, you will be removed from today’s survey. For more information or toregister for future surveys please visit GeoPoll.com
You are ineligible for this survey. For more information on Coronavirus prevention visit who.int

1BirthYearIn what year were you born? Reply with a four-digit number like 1980.

2

Gender

3

ADM-1

4

Urban/Rural

5

MigrateAre you male or female? Reply with 1 or 2.
1)Male2)Female
What Region do you currently live in?
1)Ashanti2)Brong-Ahafo
3)Greater Accra
4)Central
5)Eastern6)Northern7)Western8)Upper East
9)Upper West
10)Volta
Do you live in a urban or rural area? Reply with 1 or 2.
1)Urban area2)Rural area
Has COVID-19 and the lockdown led you to move to a new home?
1)Yes - Within my city/town2)Yes - Outside of my city/town3)No - I still live in the same place

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORTSkip Pattern
1 = BirthYear
HELP = Help
1 = BirthYear
STOP = Refusal
End poll declined
End poll ineligible
1900-1919 = Ineligible
1920-2005 = Gender
2006-2020 = Ineligible
1-2 = ADM-1

1-10 = Urban/Rural

1 = Migrate
2 = Employment
1-3 = Employment

37

Q#

Q Name6

Employment

7

Business1

8

Business2

9WaterChange

10

WaterChangeHow

11

PreWaterSupply

12

PipeDetails

13

WellDetails

EnglishHas COVID-19 changed your employment?
1)No - It is the same2)Yes - I earn less money3)Yes - I lost my job
4)Yes - I got a new job
5)Yes - I earn more money
Before COVID-19, did you run a business (not a farm)? Reply with 1 or 2.
1)Yes
2)No
How has COVID19 affected your business?
1)More income2)No change
3)Income dropped a little
4)Income dropped a lot5)I closed my business
Has COVID-19 made it more difficult to get your drinking water? Reply with 1 or 2.
1)Yes
2)No
How is it more difficult to get your drinking water?
1)I have less money to pay for it2)Prices are up3)It is harder to find4)I must travel further to get it
Before COVID-19, how did you get your drinking water?
1)Piped connection2)Well3)Bottled water/sachet
4)Tanker5)Cart vendor
6)Rainwater7)Spring
8)River/pond
Where is the pipe that you use? Reply with 1 or 2.
1)In my home or compound2)I must walk to it
Where is the well that you use? Reply with 1 or 2.
1)In my home or compound2)I must walk to it

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORTSkip Pattern

1-5 = Business1

1 = Business2
2 = WaterChange

1-5 = WaterChange

1 = WaterChangeHow
2 = Toilet

1-4 = PreWaterSupply

1 = PipeDetails
2 = WellDetails
3 = BottlewaterDetails
4=
CurrentWaterSupply
5 = VendorDetails
6-8=
CurrentWaterSupply
1-2 =CurrentWaterSupply
1-2 = WellDetails2

38

Q#

Q Name14

WellDetails2

15

BottlewaterDetails

16

VendorDetails

17

CurrentWaterSupply

18

WaterService

19

WaterShort20

ToiletEnglishHow do you get your water from the well?
1)With a handpump2)With a diesel pump3)With a rope and bucket
4)Not sure/otherHas getting bottled or sachet water changed since COVID arrived?
1)More expensive2)Less expensive3)Harder to find4)Easier to find5)No change
Has buying water from vendors changed since COVID arrived?
1)More expensive2)Less expensive3)Harder to find4)Easier to find5)No change
How do you get your drinking water now?
1)Piped connection2)A well3)Bottled water/sachet
4)Tanker truck5)Vendor6)Rainwater7)Spring
8)River
What else makes getting water difficult now?
1)Fewer hours per day of service2)Problems take longer to be fixed3)I am afraid of waiting in a queue4)No change
In the past week, was there a day when you couldn’t get enough water to meet your household’s
needs? Reply with 1 or 2.
1)Yes
2)No
Before COVID arrived, what kind of toilet did you use?
1)A private one at home2)One I share with a few other households

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORTSkip Pattern
1-4 =CurrentWaterSupply

1-5 =CurrentWaterSupply

1-5 =CurrentWaterSupply

1-8 = WaterService

1-4 = WaterShort

1-2 = Toilet

1-4 = Toilet2

39

Q#

Q Name21

Toilet2

22

Toilet3

23

PitEmptying1

24

PitEmptying2

25

PitEmptying3

26

Upgrade27

Upgrade2

28

Handwashing

29

Handwashing2

English3)A public community toilet
4)NoneWhat kind of toilet do you currently use?
1)A private one at home2)One I share with a few other households3)A public community toilet
4)None
Does the toilet you use most of the time include a septic tank or pit?
1)Yes
2)No
3)Not sure
Do you pay someone to empty your latrine pit or septic tank when it is full? Reply with 1 or 2.
1)Yes
2)No
Since COVID arrived, have you had trouble emptying your full latrine pit or septic tank?
1)Yes2)No - I haven’t tried to empty it3)Pit/tank not yet full
How has emptying your latrine pit or septic tank changed since COVID arrived?
1)I cannot afford it2)The service is no longer available in my area3)Other
Since COVID arrived, have you had trouble buying, installing, or upgrading a latrine?
1)Yes
2)No
3)Did not try to buy/install/upgrade since COVID arrived
How has buying, installing, or upgrading a latrine changed since COVID arrived?
1)I cannot afford it2)I cannot find anyone who is selling what I need3)Other
Do you notice your neighbors and friends washing their hands with soap more often than before
COVID-19?
1)Much more
2)A bit more
3)The same amount4)LessHow do you usually wash your hands?
1)With water

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORTSkip Pattern

1-3 = Toilet3
4 = Handwashing

1 = PitEmptying1
2-3 = Handwashing
1 = PitEmptying2
2 = Upgrade
1 = PitEmptying3
2-3 = Upgrade

1-3 = Upgrade

1 = Upgrade2
2-3 = Handwashing

1-3 = Handwashing

1-4 = Handwashing2

1-3 = Handwashing3

40

Q#

Q Name30

Handwashing3

31

Handwashing4

32

Handwashing5

NA

Close-out-Incentive

English2)With water and soap3)With water and sand/ash/other
Since COVID arrived, has it become easier or more difficult for your family to obtain any kind ofsoap to wash hands?
1)Easier2)Harder3)About the same
What has made it easier to obtain soap for handwashing?
1)Lowered prices2)Free give-aways
3)Other
What has made it harder to obtain soap for handwashing?
1)Higher prices2)Shops ran out of it3)Shops don’t sell it4)Shops selling it have closed
GeoPoll: Thank you! You will receive #TOPUP# airtime credit within 2 days. For more
information on Coronavirus prevention visit who.int

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORTSkip Pattern

1 = Handwashing4
2 = Handwashing5
3 = Close-out-Incentive
1-3 = Close-outIncentive

1-4 = Close-outIncentive

41

APPENDIX 3. ECONOMETRIC MODEL
By estimating income effects on WASH access, the assessment team offers forecasts of changes inaccess to WASH services that result from COVID-19 economic shocks.
Historical correlations are not always helpful for predicting causal effects immediately after a decline inincome. For example, wealthy, educated, and urban people tend to have better water and sanitation
services; even after an income shock, these groups may well continue to enjoy favorable WASH
outcomes. Similarly, when a poor rural migrant moves to the city, they may move to a slum with waterand sanitation services below the urban average. In addition, piped water and sanitation to a home
usually require a fixed cost, so access will not necessarily decline right away following a drop in income.
As discussed in this report, utilities in many countries offered tariff holidays and/or are not shutting off
connections for unpaid bills during the crisis. Finally, people develop habits and norms; many will work
hard not to lose piped water and sanitation. They may pay more to keep services than they would haveinitially paid to gain services.
On the economic side, since the assessment team does not have recent income and WASH microdata
from the high-priority and strategy-aligned countries, the team looked at wealth (asset ownership) to
define an upper bound on income effects. On the WASH side, while many households in low-income
countries may employ complex strategies to meet their water and sanitation needs, the assessment
must largely rely on datasets that survey only a single drinking water source and a single sanitation
source.

Figure 15. The UNICEF/WHO Joint Monitoring Program (JMP) water service ladder.
The arrow corresponds to the shift from above to below the threshold between “basic” and “limited” levels.

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT

42

The assessment team assigned DHS and MICS water and sanitation modalities to JMP ladder rungs(Figure 15) as well as a numeric score, as indicated in Table 2 and Table 3.
Table 2. JMP Level and model numeric score associated with a MICS/DHS water supply category.
Modality
Piped into dwelling
Piped to yard/plot, tube well, rain
Piped to neighbor, bottled water
Public tap/standpipe
Water kiosk, sachet water
Protected well or spring
Tanker truck
Cart with small tank
Unprotected well
Unprotected spring
Surface water, other

JMP Level
Safely managed
Basic or Limited
Basic or Limited
Basic or Limited
Basic or Limited
Basic or Limited
Basic or Limited
Basic or Limited
Unimproved
Unimproved
Surface water

Numeric score
1
0.9
0.8
0.6
0.6
0.5
0.5
0.4
0.3
0
0

Table 3. JMP Level and model numeric score associated with a MICS/DHS sanitation category.
Sanitation access
Piped sewer
Septic tank
Ventilated improved pit latrine, Composting toilet
Pit latrine with slab
Flush to open drain or don’t know where
Pit Latrine (slab not mentioned)
Pit latrine without slab/open pit
Bucket, hanging toilet, don’t know
Open defecation, other

JMP Level
Safely managed
Safely managed
Basic or Limited
Basic or Limited
LimitedUnimproved
Unimproved
Unimproved
Open defecation

Numeric score
1
0.9
0.8
0.6
0.5
0.4
0.3
0.1
0

To estimate how COVID-19 has affected incomes, the assessment began with the World Bank’s June
2020 forecasts of GDP. The income shock is expressed in percent changes. The wealth quintiles are incentiles of the wealth distribution. The assessment uses World Bank estimates of Gini coefficients 8 tocapture the distribution of income, 9 simulating what percent change in income led to a quintile change inincome matching each Gini coefficient. For example, Kenya’s Gini coefficient of 40.9 is consistent with adecline in income of 8 percentage points, corresponding to roughly a 5 percent lower position in theincome distribution, equal to about one-fourth of a quintile. (The team’s results were not very sensitive
to measures of inequality.) The team assumes that wealth quintile reflects that of the permanent income
distribution; that is, long-run average income.

8

The Gini coefficient was developed as a measure of inequality in an income distribution, with values theoretically ranging from 0 (where allincomes are equal) to 1 (where all income is collected by a single actor, and everyone else earns nothing). Gini coefficients are sometimes
expressed in a range from 0 to 100. Corrado Gini, 1912. “Concentration and dependency ratios” (in Italian). English translation in Rivista di
Politica Economica, 87 (1997), 769–789.

9Gini coefficients are drawn from: https://www.indexmundi.com/facts/indicators/si.pov.gini/map/africa,
https://www.indexmundi.com/facts/nepal/indicator/SI.POV.GINICongo https://www.indexmundi.com/facts/congo/indicator/SI.POV.GINI
Kenya: https://wid.world/country/kenya/

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT

43

The team specifies four models. The simplest, Model 1, captures the relationship between wealth andWASH:
numeric score (0 to 1) = β wealth quintiles(1)

The team also conjectured that WASH access score rises with wealth quintile in part because urban
location correlates with both. Thus, the team employs Model 2:
numeric score (0 to 1) = β wealth + γ rural(2)

The team further conjectured that WASH access score rises with wealth quintile partly because higheducation correlates with both. Thus, the team employs Model 3, which controls for education andother household factors:
numeric score (0 to 1) = β wealth + γ rural + δ1 wife & partner
education + δ2 HH size + δ3 # children + δ4 male head ofhousehold + δ5 age of head of household(3)

Finally, the team examines the role of neighborhood or village. Knowing someone’s village orneighborhood allows reasonable predictions of their water or sanitation service level. For example, if aparticular neighborhood or village is not served by piped water service, an change in wealth will rarely
result in securing a piped connection.
The DHS and MICS collect multiple observations in a neighborhood or village (that is, they cluster
observations in enumeration areas). In this model, Model 4, the team estimates the effect of wealthwithin a neighborhood or village, n, by permitting each neighborhood or village to have its own
intercept, ϕn.
Numeric score = β wealth + δ1 wife & partner education + δ2 HH
size + δ3 # children + δ4 male head of household + δ5 age of headof household + Σn ϕn(4)This last model answers the question, “when a household is wealthier than its neighbors, how does its
predicted water or sanitation access score change?” Finally: the team decides on an income shock, runs
each model, and converts the predicted water and sanitation access scores changes into JMP ladderchanges. The datasets used to establish the relationships are listed in Table 4.
Table 4. Model datasets employed for high priority and strategy-aligned countries.
Afghanistan
Bangladesh
Cambodia
DRC
Ethiopia
Ghana
Guatemala
Haiti
India
Indonesia
JordanKenya
Lebanon
Liberia
Madagascar

Country

DatasetDHS 2015
DHS 2014
DHS 2014
MICS 2017-18
DHS 2016
DHS 2019
DHS 2014-15
DHS 2016-17
DHS 2015-16
DHS 2017
DHS 2017-18
DHS 2015
MICS 2011
DHS (MIS) 2016
DHS (MIS) 2016

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT# Households
24,39517,30015,667
20,81016,6505,799
21,383
13,405
601,50947,96318,802
6,481
5,190
4,218
11,28444

Country
Malawi
Mali
Mozambique
Nepal
Nigeria
Philippines
RwandaSenegalSouth Sudan
UgandaUnited Republic of Tanzania
Yemen
ZambiaDataset
DGS (MIS) 2017
DHS 2018
DHS 2018
DHS 2016
DHS 2018
DHS 2017
DHS 2017
DHS 2018
MICS 2010
DHS (MIS) 2018-19
DHS (MIS) 2017
DHS 2013
DHS 2018# Households
3,729
9,510
6,196
11,04040,42727,496
5,041
4,592
9,950
8,957
9,330
17,351
12,831

MODEL COEFFICIENTSTable 5 presents all the USAID high priority and strategy-aligned country-specific coefficients from
Model 1 and Model 4; Table 6 summarizes all models.

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT45

Table 5. Model coefficients and effect sizes for all USAID high priority and strategy-aligned countries.
Country
Afghanistan
Bangladesh
Cambodia
DRC
Ethiopia
Ghana
Guatemala
Haiti
India
Indonesia
JordanKenya
Lebanon
Liberia
Madagascar
Malawi
Mali
Mozambique
Nepal
Nigeria
Philippines
RwandaSenegalSouth Sudan
UgandaTanzania
Yemen
ZambiaMean
S.D.
Minimum
Maximum
MedianPop.-weighted mean

Coefficient (p.p. change in the top 2 JMP ladder rungs
from a 1 quintile decline in wealth)
Water, no
Water, within
Sanitation,
Sanitation, within
controls
neighborhood no controls
neighborhood
-0.03
-0.03
0.1
0.06
-0.02
-0.05
0.13
0.04
0.18
0.03
0.04
0.02
0.04
0.03
-0.02
0.02
0.08
0.02
-0.03
0.01
0.12
0.05
-0.006-0.019
0.09
0.03
0.14
0.04
0.13
0.02
0.02
-0.02
0.18
0.04
-0.04
-0.03
0.14
0.03
-0.02
-0.05
0.02
0.02
0.04
-0.01
0.11
0.04
0.01
-0.06
0.05
0.09
0.04
0.05
0.16
0.02
0.06
0.01
0.04
0.02
-0.01
-0.02
0.11
0.05
0.06
0.03
0.1
0.04
-0.02
0.01
0.15
0.07
-0.02
-0.03
0.06
0.03
0.02
-0.02
0.13
0.05
-0.04
-0.04
0.1
0.03
0.004
0.01
0.003
0.03
0.03
0.007
0.18
0.04
0.01
0.03
0.04
0.05
0.04
0.02
0.09
0.03
0.05
0.03
0.15
0.03
-0.0001
-0.04
0.17
0.07
0.03
-0.01
0.11
0.03
0.013
-0.005
0.106
0.039
0.040
0.030
0.050
0.017
-0.040-0.060
0.003
0.020
0.140
0.050
0.180
0.090
0.010
-0.010
0.110
0.030
0.008
-0.0170.146
0.038

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT

Effect Size (p.p. change in the top 2 JMP ladder rungs
using GDP decline estimate)
Water, no
Water, within Sanitation, no Sanitation, within
controls
neighborhood
controls
neighborhood-0.17%-0.17%
0.58%
0.35%-0.08%-0.21%
0.54%
0.17%
1.01%
0.17%
0.13%
0.07%
0.13%
0.10%
-0.09%
0.09%
0.36%
0.09%
-0.08%
0.03%
0.33%
0.14%
-0.02%-0.08%
0.37%
0.12%
0.15%
0.04%
0.14%
0.02%
0.18%
-0.18%
1.63%
0.36%-0.17%-0.13%
0.59%
0.13%-0.10%-0.25%
0.10%
0.10%
0.10%
-0.02%
0.26%
0.10%
0.09%
-0.55%
0.46%
0.82%
0.02%
0.02%
0.06%
0.01%
0.22%
0.04%
0.15%
0.07%
-0.02%-0.04%
0.20%
0.09%
0.29%
0.14%
0.48%
0.19%
-0.03%
0.01%
0.20%
0.09%
-0.09%-0.14%
0.28%
0.14%
0.08%
-0.08%
0.49%
0.19%-0.30%-0.30%
0.76%
0.23%
0.02%
0.04%
0.01%
0.13%
0.11%
0.03%
0.67%
0.15%
0.04%
0.11%
0.14%
0.18%
0.09%
0.04%
0.20%
0.07%
0.11%
0.06%
0.32%
0.06%
0.00%
-0.16%
0.68%
0.28%
0.09%
-0.03%
0.32%
0.09%
0.02%
-0.06%
0.41%
0.17%
0.13%
0.15%
0.34%
0.15%
-0.30%-0.55%
0.01%
0.01%
0.29%
0.14%
1.63%
0.82%
0.02%
-0.02%
0.33%
0.13%
0.07%
-0.13%
1.03%
0.25%

46

Table 6. Model coefficients from the analyzed countries (27 for water and 28 for sanitation).
Values are medians, except where the team notes means for Models 1 and 4 and confidence intervals calculated via arandom effects meta-analysis. 10A) WaterModel 1: wealth only
Mean coefficient and
95% confidence interval
Model 2: wealth plus rural
location• wealth quintile• ruralModel 3: wealth plus
education & householdcomposition controls•
wealth quintile
Model 4: wealth effects
within neighborhood only•
wealth quintile
Mean coefficient and
95% confidence interval# countriesB) Sanitation
Model 1: wealth only
Mean coefficient and
95% confidence interval
Model 2: wealth plus rural
location• wealth quintile• ruralModel 3: wealth plus
education & householdcomposition controls• wealth quintile
Model 4: wealth effects
within neighborhood only
• wealth quintile
Mean coefficient and
95% confidence interval# countries

10

Decline in top 2 JMP ladderrungs (p.p. change due to 1
quintile loss in wealth)Effect sizes for water score (0to 1, for a 1 quintile wealth loss

0.01

0.07

0.013
[-0.002 - 0.028]

0.062[0.046, 0.078]

0.01-0.008

0.06
-0.07

0.01

0.06

-0.01
-0.006[-0.017 - 0.006]
27

0.02
0.02
[0.012 – 0.021]
27

0.11

0.105

0.106
[0.088 - 0.125]

0.105[0.086 - 0.124]

0.11
-0.02

0.09
-0.08

0.08

0.08

0.03
0.038
[0.033 - 0.043]
28

0.05
0.54
[0.042 - 0.067]
28

The team applied meta-analysis to identify the common effects across the 27 nations for the water analysis and the 28 nations for thesanitation analysis. Rather than compute a simple mean of the effect sizes, the team computed a weighted mean: With a random effects
model, the team assumed that the true effect could vary from nation to nation. Thus the team assigned weight as 1/(𝑠𝑠𝑠𝑠�𝑗𝑗 2 + 𝜏𝜏̂ 2 ), where 𝑠𝑠𝑒𝑒�𝚥𝚥is the estimated standard error of each nation, 𝜏𝜏̂ is the estimated true variance across nations. The mean effects from random effects metaanalysis are similar to the median effects across the nations. Results are robust to assuming a common effect and weighting by inverse
standard errors.

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT

47

Figure 16. Water supply numeric score as a function of wealth quintile for the seven deep-dive countries.

As expected, both water and sanitation service levels tend to rise with wealth (see Figure 16 for the
relationship for water supply score). These wealth effects are governed neither by residence in urban
areas nor by education or other household effects, but they do appear to be strongly linked toneighborhood or village (although more so for water supply than for sanitation).
Across 27 countries, the median coefficient for water access score in Model 1, provided in both andTable 6, is 0.07, meaning that moving down from the bottom wealth quintile (quintile 1) up to quintile 2
increases a household’s water access numeric score (see Table 2 and Table 3) by 0.07 (and moving fromquintile 1 to quintile 5 predicts that a water score will rise by 0.28). In almost all the high-priority andstrategy-aligned countries, this coefficient is positive.
For water supply, this access score response to wealth translates into JMP ladder change asfollows: gaining one quintile in wealth predicts a one percentage point increase in the likelihoodof water service level being Safely Managed or Basic (the top two rungs of the JMP ladder). For
sanitation, the model finds that gaining one quintile in wealth predicts an 11 percentage point
increase in the likelihood of sanitation service level being Safety Managed or Basic.
The effects of rural location on both water are very modest; the wealth effect on water access is slightly
smaller in rural than urban areas (with the coefficient dropping from 0.07 to 0.06 for water and from
0.105 to 0.09 for sanitation). Importantly, Model 2 reinforces the point that the improvement in wateror sanitation service level that comes with increased wealth is not due to the benefits a household might
enjoy from urban residence vs. rural residence.
Model 3’s results indicate that the wealth effect on service level is not a proxy for education.
Critically, the wealth effect nearly disappears within neighborhoods. Model 4 reinforces that if ahousehold suffers a critical job loss but does not move out of their village or neighborhood, its water
service level likely did not change. There is a stronger wealth effect within neighborhoods for sanitation thanfor water, however. This result is consistent with the likelihood of more homogeneity of water access within aneighborhood than sanitation access within a neighborhood. That is, if some people in a neighborhood have piped

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT48

water, very often they all do—a pattern that is likely less common for higher-quality sanitation, in particular when
the service modality is onsite containment rather than piped sewer.
The team notes that estimates for JMP water ladder shifts are near zero (and often of an
unexpected sign) because the assessment’s specification registers no change for moving from pipedwater to a borehole (for example), making no distinction in terms of service quality. For example,
Bangladesh has more piped water and less use of surface water for the top two quintiles than thebottom three, as expected; nevertheless, access to the top two JMP water ladder rungs is higher for thebottom three quintiles than the top two. That unexpected result holds because 94 percent of bottom
three quintiles in Bangladesh rely on boreholes. Because of this pattern, especially for water, it isimportant to consult the coefficient for the assessment’s numerical access score in addition tothat for access to the top two JMP ladder rungs. (For sanitation, the coefficients for the two
measures are very much in line).
SIZE OF THE INCOME SHOCK
Although the exact size of the income shock from COVID-19 is unknown, one approach for estimating
it is to compare the World Bank’s estimate of GDP growth in 2019 versus their forecast (as of 8 June2020) for growth in 2020. Table 7 presents those comparisons. The median 2019 estimated growth was
5.0 percent, while the median forecast for 2020 was -1.7 percent. The median country-specific year-onyear decline was 6.2 percentage points (slightly larger than the decline in the median).
Table 7. World Bank forecast GDP growth for USAID high-priority and strategy-aligned countries (as of 8June 2020) 11
Country
Afghanistan
Bangladesh
Cambodia
DRC
Ethiopia
Ghana
Guatemala
Haiti
India
Indonesia
JordanKenya
Lebanon
Liberia
Madagascar
Malawi
Mali
Mozambique
Nepal
Nigeria
Philippines
RwandaSenegalSouth Sudan
112019 GDP
Estimate
3.9
8.1
7.1
4.4
9
5.8
3.8
-1.4
4.2
5
2
5.4
-6
-2.3
4.9
4.4
5.1
2.2
7
2.2
6
9.4
5.3

2020 GDP
Forecast
-5.5
2
-2
-1.7
2.3
1.1
-3.5
-3.1
-9.6
-1.6
-5.5
1.5
-19.2
-2.9
-1.2
1.3
-2
-0.4
0.2
-4.1
-6.9
2
-0.7

Estimated
GDP Decline
9.4
6.1
9.1
6.1
6.7
4.7
7.3
1.7
13.8
6.6
7.5
3.9
13.2
0.6
6.1
3.1
7.1
2.6
6.8
6.3
12.9
7.4
6
6.2a

Gini
Coefficient40.95 b32.4
40.95 b
48.9
35
43.5
48.3
41.1
35.7
38.1
33.7
40.8
31.8
35.3
42.6
44.7
33
54
32.8
43
44.4
43.7
40.3
46.3

Population
32,890,171
169,435,234
15,288,489
91,994,000
100,829,000
30,955,202
16,858,333
11,743,017
1,368,145,959
269,603,400
10,786,232
47,564,296
6,825,442
6,825,442
26,251,309
19,129,952
20,250,833
30,066,648
29,996,478
206,139,587
109,266,718
12,663,116
16,705,608
13,249,924

https://www.worldbank.org/en/publication/macro-poverty-outlook/mpo_ssa

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT

49

Country

2019 GDP
Estimate
6.8
5.8

2020 GDP
Forecast
3.1
2.5

Estimated
GDP Decline
3.7
3.3
6.2a
5.9

Gini
Coefficient
42.8
37.8
36.7
57.1

Population

Uganda41,583,600
Tanzania
57,637,628
Yemen
29,825,968
Zambia1.4-4.5
17,885,422
Notes:a
The team imputed Gini coefficients for nations lacking an estimate (Afghanistan and Cambodia) as the median of theother nations.
b
The team imputed GDP decline for nations lacking a GDP forecast (Yemen and South Sudan) as the median of othernations. Results were robust to dropping nations with missing data.

The decline in growth forecast by the World Bank is similar to the IMF change in forecasts from January
2020 to June 2020 for all of sub-Saharan Africa. 12 This consistency provides some confidence in theWorld Bank forecasts, but economic forecasts are often inaccurate, even without a global pandemic.
Forecasting in a time of COVID-19 is particularly challenging. 13
The assessment team’s position is that it is likely the economic shock (and resulting GDP decline) was
larger than the World Bank June 2020 forecast. For example, the International Labour Organization
estimates that earnings of informal sector workers in Africa would decline by 81 percent14 in the firstmonth of the crisis. Similarly, in April 2020 (after a month of lockdown) a telephone survey in Senegal
found 88 percent of respondents reported lower incomes due to COVID-19. 15
The assessment team’s SMS phone surveys included a question on employment effects (see Figure 3).
While phone and SMS surveys tend to oversample the literate and (by definition) those with telephones
or charged cellular phones, they tend to miss the poorest. With that caution in mind, the median value
for those reporting they had lost their employment entirely due to COVID as of August 2020 was 29
percent.
Unless the COVID-19 crisis is very brief, the large income declines found in these surveys suggests alarger decline in GDP than the World Bank June 2020 forecasts. Consistent with this hypothesis of aworse shock, the IMF in June 2020 forecast GDP growth in India for 2020 at -4.5 percent. 16 Since then,India reported a “23.9% decline in gross domestic product in the June quarter from a year ago… [which]prompted banks like Nomura Holdings Inc. to downgrade their full-year forecasts for this year to 10.8%.” 17South Africa also had a terrible second quarter of 2020: “Compared with the second quarter last year,
South Africa’s gross domestic product plummeted by 17.6%.” 18 Consistent with what the assessment

12

https://www.imf.org/en/Publications/WEO/Issues/2020/06/24/WEOUpdateJune2020

13

https://www.imf.org/en/Publications/WEO/Issues/2020/06/24/WEOUpdateJune2020

14

https://www.ilo.org/wcmsp5/groups/public/@dgreports/@dcomm/documents/briefingnote/wcms_743146.pdf
https://www.cgdev.org/blog/five-findings-new-phone-survey-senegal

15
16

https://www.imf.org/en/Publications/WEO/Issues/2020/06/24/WEOUpdateJune2020

17

Anirban Nag, “India faces dwindling policy options after record GDP slump,” Sept. 1, 2020.
https://economictimes.indiatimes.com/news/economy/indicators/india-faces-dwindling-policy-options-after-record-gdpslump/articleshow/77875349.cms

18

Aaisha Dadi Patel and Gabriele Steinhauser, “South Africa’s Economy Shrinks 51% as Lockdown Restrictions Hurt Businesses.” Wall St.
Journal. Sept. 8, 2020. https://www.wsj.com/articles/south-africas-economy-shrinks-51-as-lockdown-restrictions-hurt-businesses11599563965?mod=searchresults&page=1&pos=2 Last accessed Sept. 8, 2020.

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT

50

team found in the deep-dive countries, a nationally representative survey in South Africa “found that
27% of workers lost their income in April, while 47% of households ran out of money to buy food.” 19More generally, the October 2020 IMF World Economic Outlook forecasts worse growth of GDP in2020 in India than it did in July (by a very large -6.6 percentage points), but slightly better growth in subSaharan Africa (by +0.2 percentage points). 20
If the actual income declines in high priority and strategy-aligned countries is twice the World Bank
estimates in Table 7, then the team’s prediction about how WASH access will decline will also be twiceas large. Given the especially high decline in earnings reported in the SMS surveys in Figure 3, the GDP
declines could easily be even larger.
The assessment team also cautions that GDP does not include remittances from abroad. Before
COVID-19 struck, Nepal received about 25 percent of GDP as remittances. 21 Although India hosted the
majority of Nepalese workers, migrant workers in Malaysia and the Middle East sent back the bulk ofremittances, and Nepali press reports suggest very large declines in remittances. 22 If so, householdincome will fall faster than GDP in Nepal, perhaps almost doubling the size of the shock to incomescompared to the forecasted 6.8 percentage point decline in GDP reflected in Table 7.
In Africa, the World Bank expects the COVID shock to reduce remittances from abroad by less than 1
percent of GDP. 23 If so, the fall in remittances will not be as large as in the other high-priority andstrategy-aligned countries as in Nepal. Ghana has the largest share of remittances among the African
high-priority and strategy-aligned countries—about 5 percent of GDP in 2019. If remittances decline by
23 percent (as the World Bank forecasts for sub-Saharan Africa), the COVID-19 shock will lower
household incomes in Ghana by an additional 1.2 percent of GDP (in addition to the -6.8 percentage
point forecast decline in GDP reported in Table 7). In short, it is quite plausible that the decline in GDP
in many nations is 50–100 percent larger than the estimates that the team employed in model
calculations.
PREDICTED DECLINES IN WASH ACCESS DUE TO LOWER GDP
Table 8 highlights the median and population-weighted means of COVID-19 economic shock changes inwater and sanitation access (which also appear in the bottom of Table 5). These are computed bymultiplying the DHS or MICS cross-sectional estimates of how wealth predicts water and sanitation
access (from Table 5) by the World Bank estimates of GDP changes (from Table 7). The team thenconverts percent changes in income into quintile changes in wealth. Model 1 estimates have nocovariates, capturing how changes in permanent income predict water or sanitation. Model 4 estimates
adjust for education and consider within-neighborhood effects only.

19

Aaisha Dadi Patel and Gabriele Steinhauser, “South Africa’s Economy Shrinks 51% as Lockdown Restrictions Hurt Businesses.” Wall St.
Journal. Sept. 8, 2020. https://www.wsj.com/articles/south-africas-economy-shrinks-51-as-lockdown-restrictions-hurt-businesses11599563965?mod=searchresults&page=1&pos=2 Last accessed Sept. 8, 2020.

20

IMF, World Economic Outlook, Oct. 2020, p. 9. https://www.imf.org/~/media/Files/Publications/WEO/2020/October/English/text.ashx?la=en.

21“COVID-19 and Reverse Migration in Nepal” Upasana Khadka June 10th, 2020 https://blogs.eui.eu/migrationpolicycentre/covid-19reverse-migration-nepal/

22

For example, Gill and Sapkota suggest up to a million Nepalese are trying to repatriate. Peter Gill and Janak Raj Sapkota. “COVID-19:
Nepal in Crisis.” The Diplomat. June 29, 2020 https://thediplomat.com/2020/06/covid-19-nepal-in-crisis/ See also Meenakshi Ganguly.
“Nepal Abandons Migrant Workers in Fight Against COVID-19” Human Rights Watch. March 31, 2020.
https://www.hrw.org/news/2020/03/31/nepal-abandons-migrant-workers-fight-against-covid-19

23

Kalantaryan, S. and S. McMahon, “Covid-19 and Remittances in Africa.” European Commission Joint Research Center Technical report,
2020.
https://publications.jrc.ec.europa.eu/repository/bitstream/JRC121055/remittances_in_the_context_of_covid_19_africa_12062020_final_onl
ine_clean.pdf

EFFECTS OF COVID-19 ON ACCESS TO WASH | SYNTHESIS REPORT51

Table 8. Estimated decline in water and sanitation access (based on estimates from DHS or MICS crosssection multiplied by World Bank estimates of income changes).
Change in the top 2 JMP ladder rungs
Model 1: no controls
Model 4: effects w/in neighborhood only
Water Effects
MedianPop.-weighted mean
Sanitation effects
MedianPop.-weighted mean

0.02%
0.07%

-0.02%-0.13%

0.33%
1.03%

0.13%
0.25%

These results provide a feel for the effect of a permanent loss in income equal to that forecast by theWorld Bank. Looking at Model 1 estimates with no controls (ignoring omitted variables, the time ittakes to move to a new location, etc.), across nations, the median estimate of the COVID-19 shock is achange of 0.33 percent increase in the population falling below JMP basic sanitation levels (with apopulation weighted mean of just over 1 percent), and little to no change in access to the top two JMP
water access ladder rungs. When results across the 27 nations for which the assessment team has watersupply data and the 28 for which the team has sanitation data in Table 7, Model 1 estimates predict that28.8 million people across the high-priority and strategy-aligned countries would drop below JMP Basicsanitation levels and 1.9 million would drop below JMP Basic water service levels (with India constituting
the majority).
A perhaps more realistic short-run estimate, assuming no relocation and controlling for education andhousehold size, is the Model 4 result of about 7.1 million people dropping below JMP Basic for sanitation
and a no effective change in JMP Basic for water.

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Table 9. Estimates of population dropping below JMP “Basic” service levels for water and sanitation.
Deep-dive countries are highlighted in gray.

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APPENDIX 4: SANKEY DIAGRAMS FROM SMS SURVEYS
The Sankey figures presented below represent the change in water service type resulting from the COVID-19 pandemic, as reported byrespondents of the SMS surveys. At left of each figure is the reported breakdown of supply modalities pre-COVID, and at right is the reported
breakdown at the time the survey was administered. Modalities are arrayed vertically in decreasing levels of water service. Upward sloping
curves from left to right indicate an increase in service level, and downward sloping curves indicate a decrease in service level. The steeper the
curve, the more dramatic the service level change. Numbers within the columns refer to the total number of respondents reporting a particularservice modality either pre-COVID (at left) or at present (at right).

Figure 17. Kenya Sankey diagram.
Water modality changes reported by respondents to the SMS survey in Kenya among the subset of respondents who reported pandemic-driven water supply difficulties. The
team notes the comparatively large number of respondents who reported moving from piped service, either to their home or nearby, to “river,” in addition to a significant
decline in reported use of cart vendors, split fairly evenly between those moving up on the ladder (to nearby standpipes – possibly those operated by water service providers
on an emergency basis) and those moving down to surface waters.

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Figure 18. Rwanda Sankey diagram.
Water modality changes reported by respondents to the SMS survey in Rwanda among the subset of respondents who reported pandemic-driven water supply difficulties.
The team notes, in addition to those reporting movement away from piped systems, the comparatively large number of respondents who reported corresponding increases inservice level, particularly to piped connections within walking distance (possibly due to increased provision at emergency distribution sites).

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Figure 19. Ghana Sankey diagram.
Water modality changes reported by respondents to the SMS survey in Ghana among the subset of respondents who reported pandemic-driven water supply difficulties.
Ghanaians reported the lowest amount of pandemic-linked water access difficulty of any of the deep-dive countries. What is notable here is the frequency of upward sloping
curves in this subset – those who report water access difficulty but who have experienced an improvement in service level. The movement away from the bottled water
category (more often “sachet” water in Ghana) is also notable, largely in the direction of standpipes or wells.

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Figure 20. DRC Sankey diagram.
Water modality changes reported by respondents to the SMS survey in DRC among the subset of respondents who reported pandemic-driven water supply difficulties. The
team notes how comparatively little change Congolese report compared to the patterns in the other countries.

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Figure 21. Mozambique Sankey diagram.
Water modality changes reported by respondents to the SMS survey in Mozambique among the subset of respondents who reported pandemic-driven water supply
difficulties. The pattern in Mozambique is largely driven by progress up the ladder (away from springs in rural Mozambique and in the direction of standpipes), but alsomodest amounts of decrease in service level below the JMP “Basic” level (the Spring and River categories at left).

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Figure 22. Senegal Sankey diagram.
Water modality changes reported by respondents to the SMS survey in Senegal among the subset of respondents who reported pandemic-driven water supply difficulties.

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