Who Walks for Water?

Water Consumption and Labor Supply Response to

Rainfall Scarcity in Uganda

Akito Kamei∗†

University of Illinoisat Urbana-Champaign

November 4, 2020

Abstract

Although the World Health Organization suggests that twenty liters of water per person per day isessential to satisfy basic daily activity needs, obtaining water is not costless. Moreover, the burdenof obtaining water is expected to rise in response to climate change-induced water shortages in-curred by different household members. This paper provides one of the first empirical evidence onhow rainfall scarcity affects water consumption and the cost of obtaining water using a nationallyrepresentative panel and satellite precipitation data from Uganda. I document a decrease in the useof surface water and an increase in distance and waiting time at the water source for drought-hithouseholds. Reflecting water demand’s inelastic nature, households do not reduce water consump-tion due to water supply shocks. Instead, households hit by droughts are 2.6 percentage points morelikely to pay user fees for water and increase time spent fetching water by 1.9 hours per week (a 13percent increase), compared to years without a drought. Results suggest that women and girls arethe ones who spend more time fetching water in order to meet the additional burden. This studyhighlights the undocumented cost of weather shocks on vulnerable households and inequitable costsfor members within a household.

JEL Classification: J22, Q54

Keywords: Climate, Water, Time Allocation and Labor Supply, Gender, Domestic Labor, Uganda

∗Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, Ph.D. Studentin Agricultural and Applied Economics, 326 Mumford Hall, MC-710, 1301 West Gregory Drive, Urbana, IL 61801-3605, E-mail: kamei.akito@gmail.com†I would like to thank Mary Arends-Kuenning, Rebecca Thornton, Kath Baylis, Hope Michelson, Benjamin Crost,

Deborah Levison, and James Manley, for comments.

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1 Introduction

Expanding access to clean and sufficient water is a global priority. In 2010, the United Nations

General Assembly recognized access to sufficient, safe water and sanitation as a basic human right

(United Nations, n.d.). Since then, the importance of the water, sanitation, and hygiene (WASH)

program is even more emphasized, and the Sustainable Development Goal (Goal 6) targets to

achieve universal access to safe water and sanitation by 2030 (Weststrate, Dijkstra, Eshuis, Gianoli,

& Rusca, 2019; WHO, 2017).

Lack of access to clean and enough amount of water leads to lower productivity, deterioration

of health, and public health concern including the spread of the infectious disease (Cheuvront and

Kenefick (2011); Howard and Bartram (2003); Wegman et al. (2018)).

However, people in low- and middle-income countries (LMICs) still rely primarily on manual

water collection and traveling long distances to obtain their household’s water. For 24 sub-Saharan

African countries, 3.36 million children and 13.5 million adult females spend more than 30 minutes

per day collecting water (Graham, Hirai, & Kim, 2016). In total, sub-Saharan Africans spend

about 200 million hours each day collecting water (Farley, 2018).

The burden of obtaining water is expected to rise in response to climate change-induced water

shortages. Reports from UNICEF (2015) points out that children affected by droughts walk long

distances to fetch water because local water sources dry up. However, despite increasing interest

and concern about the effect of climate change-induced water shortages on water access in LMICs

(National Geographic, 2019; UNESCO, 2019; WHO, 2019), to date there is no empirical evidence

of this relationship.1 Does drought affect the choice of water source in LMICs? Do people reduce

water consumption during droughts? Do households walk longer distances? If so, who walks for

water?

In this paper, I apply an economic lens to examine recent arguments regarding climate induced

water scarcity and water consumption-related activities. My findings contribute to the literature

on water security, labor, and gender inequality in the following ways:

1A number of studies reveal the effects of climate shocks on social and behavioral changes. See review fromCarleton and Hsiang (2016); Dell, Jones, and Olken (2014); Maccini and Yang (2009). In addition, many in thelabor economics literature focus on the impact of weather on agriculture and adult wage labor (Bandara, Dehejia, &Lavie-Rouse, 2015; Dimova, Gangopadhyay, Michaelowa, & Weber, 2014; Ito & Kurosaki, 2009; Jayachandran, 2006;Rose, 2001; Shah & Steinberg, 2017).

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First, this study is the first to establish a causal link between climate change-induced water

shortages and household’s choice of water source, walking distance to obtain water, and waiting time

for fetching water. Rainfall is the main factor affecting hydrological systems overland, and studies

in geography and environmental science have documented the effect of drought on environmental

degradation (Kull, 2006; Nkhonjera, 2017; Nsubuga & Rautenbach, 2018). My paper investigates

similar questions, but I focus on behaviors related to household water consumption using household

surveys.

Second, this paper studies the elasticity of water consumption using drought as incidence.

Several studies about water consumption and pricing have documented the inelastic nature of

water from industrialized countries (Nauges & Whittington, 2009). Although more study is needed

to support the evidence from LMICs, previous study in Sudan documents the inelastic nature of

water by comparing water price and consumption for two communities (Cairncross & Kinnear,

1992). However, estimating water price elasticity in LMICs is difficult. Unlike higher income

countries, objectively-measured water-use data (i.e., from water meters) is not available in areas

where households are not connected to the piped water system. Most previous studies of water

consumption in LMICs have relied on cross-sectional household surveys based on self-reported

consumption information, which may entail reporting bias.2 Using panel household survey, the

estimations in this paper present the change in water consumption for the same household over

time, which is less subject to household-specific reporting errors.

Third, I investigate who in the household incurs the additional cost (labor burden) of obtaining

the household’s water supply during water shortages. In many developed countries, the water bill is

paid at the household level, not the individual level (Jack, Jayachandran, & Rao, 2018). However,

in settings where water is mostly transported by hand, the cost of obtaining water is paid as a

labor cost born by individual household members. In sub-Saharan Africa, the burden of collecting

water is not gender neutral: Seventy-one percent of water collection is carried out by women and

girls (UN, 2012).

Prior literature on the gendered division of labor in response to water infrastructure has shown

2Diakite, Semenov, and Thomas (2009) study the optimal tariffs for water using water bill data for approximately150 towns in Cote d’Ivoire from 1998 to 2002. Cheesman, Bennett, and Son (2008) estimates water demand using“artificial panel” data set pooled by household records of metered municipal water consumption and their statedpreferences for water consumption contingent on hypothetical prices.

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that installation of a public pump reduces male labor involved in fetching water in Benin (Gross,

Gunther, & Schipper, 2018). However, it is not clear how households allocate additional labor time

needed to fetch water in response to water scarcity. This study investigates whether an increase

in household labor burden increases the time spent obtaining water for girls and women — who

already face a high burden — or if it is buffered by boys or adult men, who typically spend less

time fetching water.

A growing number of evidence suggests that climate shocks are likely to exacerbate gender

inequality in domains such as food consumption and food insecurity (Flatø, Muttarak, & Pelser,

2017), child education and long-term health outcomes (Bjorkman-Nyqvist, 2013; Burke, Gong, &

Jones, 2015; Maccini & Yang, 2009), and society’s adoption of women’s rights and norms (Eastin,

2018). This paper provides the first evidence of the gendered effects of droughts on water consump-

tion behaviors.

Using satellite data on precipitation, matched to information on a nationally representative

household panel survey in Uganda, I find that rainfall scarcity adds stress on water consumption

related activities, especially in communities where water infrastructure has not been developed.

Households living in communities without private tap water infrastructure (i.e. communities that

no household reported the use of private tap water) on average walk 0.61km more to their primary

water source.3 The estimation results suggest that droughts, defined as more than a 15 percent

reduction in total rainfall during the past 12 months relative to the local average, increase the

distance to the primary water source by 9.3 percent, and waiting time at the water source by 5.7

minutes (from 12.9 minutes in the non-drought years).4 However, I find that poor households living

with a low level of water consumption do not reduce consumption even when they face a higher

cost of water. Average daily water consumption per person in Uganda is 13.4 liters, less than the

twenty liters needed to meet daily needs, as suggested by WHO, UNICEF, et al. (2000). Because of

the inelastic nature of water, households are not able to reduce consumption in response to higher

costs.5

Instead, in response to water scarcity, I find that households are 2.6 percentage points more

3The number is much shorter for communities with private water connection and it is 0.21 km.4Drought has no impact on water consumption related activities in the areas with private tap water connection.5The reduction of water consumption is only observed for households that were consuming a lager amount of

water in non-drought years (Appendix I).

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likely to pay user fees (payment to access to private or community managed water source) when

they are hit by droughts. Similarly, drought-hit households spend 1.9 additional hours fetching

water per week, a 12.6 percent increase from the average. The impact of drought (rainfall scarcity

in the last 12 months) is especially large for households surveyed in dry seasons, and exposure

to drought increases the weekly labor supply of girls aged 13-18 and adult women aged 19-59 by

2.4 hours and 1.5 hours, respectively. Although adult women and girls typically spend more time

fetching water, the change in labor supply due to rainfall scarcity puts an additional burden on

them.

This paper is among the first to provide empirical evidence of the cost of droughts to households

that rely primarily on manual water collection. My results suggest that the domestic labor of

households without access to private tap water are significantly affected by external factors —

namely, climate change. Results in this paper highlight the importance of WASH projects especially

the importance of improving equitable access to clean water among vulnerable populations without

access to private water taps.

The remainder of the paper is organized as follows. I begin by motivating this study by providing

a framework of water demand and supply in communities without private water infrastructure. Sec-

tions 3 and 4 present the data and descriptive statistics. Section 5 presents the empirical strategy,

identification assumptions, and the construction of the drought variable and its validity. Section

6 first presents the effects of drought on choice of water source, distance traveled to collect water,

and waiting time. Then, I present estimation results on the effect of drought on household’s water

consumption, whether households pay tariffs to access water sources, and household/individual

labor supply. Section 7 concludes.

2 Framework: Water Demand and Supply in Rural Community

Water is essential. Water is necessary not only for drinking, but also for daily activities such as

cooking, washing hands, and bathing. The WHO and UNICEF suggest that twenty liters of water

per day, per person, is necessary to meet basic daily activities (WHO et al., 2000). This number

is important: Howard and Bartram (2003) further argues that less than twenty liters of water is

a possible threat to public health and hygiene including increased case of diarrhea or spread of

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infectious disease. However, the average water consumption in many LMICs is much lower than

twenty liters (Rosen, Vincent, et al., 1999).6

Water consumption in LMICs is likely to become a pressing challenge in the coming years, as

extreme weather such as draughts are expected to increase in incidence and severity (IPCC, 2007).

Indeed, studies have already documented the effects of climate shocks on water availability and

shortage. Using remote sensing data, Pekel, Cottam, Gorelick, and Belward (2016) shows that,

around the globe, surface water (e.g. small lakes, ponds, water banks) disappears due to rainfall

scarcity. In Uganda, the site of this study, Kull (2006) found that the water levels of Lake Victoria

were one meter below the ten-year average during a large drought in 2004 and 2005. Dey et al.

(2011) is one of the few quantitative studies to investigate the link between droughts and water

consumption related activities using a household survey.7 The mean comparison of household water

use from two different years shows that households in Northern Bangladesh collect drinking and

domestic water from a more distant source in drought years than in non-drought years.8

The effect of drought on household water consumption depends on the nature of household

water demand. Figure 1 shows hypothetical demand for water. At the minimum amount of water

consumption needed for survival (Qmin), water is expected to be price inelastic. In other words,

people are willing to pay a high price to secure the minimum water consumption.

Given the inelastic nature of water demand at low consumption levels, a water shortage (a supply

shift from S to S’) affects price significantly but has only a small change on quantity consumed.9

Therefore, if households are not able to reduce water consumption when facing a higher cost, a

water shortage will entail an increased cost to secure water. In communities where water is fetched,

water is paid for in three ways: cash payments to purchase water, cash payments to access a water

6Gleick (1998) shows that average water consumption per capita in Zimbabwe was 48.2 liters in 1990, while thesame number was just eight liters in Mali.

7Buechler (2009); Tichagwa (1994) argue that water sources changes during droughts in their qualitative study.8Changes in water source is associated with changes in waiting time at the water source. Although time spent for

queuing is found to be a large part of water collection (Gross et al., 2018; Thompson et al., 2000), there is no empiricalstudy investigating the waiting time and incidence of droughts. If droughts decrease the number of functional watersource in the community, congestion to functional water source increase waiting time for every household includingthe ones without change in water source.

9Depending on the demand curve, the water consumption may increase at the time of drought, rather thandecrease. Recommended water consumption for drinking is highly subject of temperature of the environment. Peopleworking in a tropical climate are expected to take 4.5 liters for survival compared to 2 liters for normal temperature.If the weather shock increase demand for water, it is possible that households consume more water during droughts.The minimum consumption level is considered to varies by many factors. Appendix A presents a review of minimumwater consumption for different household characteristics.

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source, and the time cost spent obtaining water.

Following this framework, I investigate the effect of drought on i) the fetching water environment

(walking distance a household travels to a water source, the time they wait at the water source), ii)

household water consumption, and iii) the monetary and time costs the household pays for water.

In line with evidence suggesting that climate change will have a stronger effect on vulnerable

populations (Flatø et al., 2017), I examine heterogeneous effects of draught on the following sub-

groups: First, I conduct my analysis separately by whether a household resides in a community

with private tap water infrastructure or not. Second, I investigate who within households incurs

the costs of water shortage.

3 Setting and Data

Uganda is one of the country experiencing the climate change (Nsubuga & Rautenbach, 2018).

Ssentongo et al. (2018) shows that average rainfall in Uganda has decreased by twelve percent from

1983 to 2016.

To measure the rainfall scarcity, this paper uses satellite collected data from Climate Hazards

Group InfraRed Precipitation with Station (CHIRPS).10 A nice feature of the dataset is the avail-

ability of rainfall information for high grid granularity by daily frequency; 0.05◦ spatial resolution,

which is approximately 5.3 km by 5.3 km.

The household data used in this study are three rounds of the Ugandan Living Standard Mea-

surement Panel Survey (LSMS) conducted between 2009 and 2012. The data collection had been

conducted throughout the year in each region.11 The survey collects information about the main

water source including distance from the house, waiting time, user fees to access, as well as daily

household water consumption.12 In labor module, time spent fetching water seven days prior to the

interview date for all household members above age five is recorded.13 The final analytical sample

10A database developed by the U.S. Geological Survey (USGS) and the Climate Hazards Group at the Universityof California, Santa Barbara.

11Because the data collection has been conducted throughout the year, the effects presented in this paper are theannual average effect unless specified (see Appendix B for seasonality).

12The amount of water recorded in this section excludes water used for household based enterprises. I calculateper capita water consumption by dividing household water consumption by the number of household members.

13Although the survey covers rich subjects, it does not collect information on number of trips taken to the source,nor quality of water collected. Appendix C presents the effect of drought on water-born illness as a proxy for waterquality.

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is 17,683 individuals from 3,195 households.14

Following Boone, Glick, and Sahn (2011), I classified water sources into 1) private tap 2) public

tap 3) ground water source, and 4) surface water (river, dam, pond, lake). To focus on the vulnerable

populations to weather shocks, analysis in this paper is conducted separately whether households

reside in communities with private tap water infrastructure or not. I define communities without

private tap infrastructure if no household in the survey community reported the use of private tap

water (connected to residents) as the main water source. I define communities with at least one

household reported the use of private tap as communities with private tap infrastructure.

4 Descriptive Statistics

Table 1 presents household level descriptive statistics. The descriptive statistics highlights higher

burden of water consumption related activities for households living in the communities without pri-

vate tap water infrastructure. In these communities, unimproved water source such as well/borehole

and surface water are common (Panel A, Column 3).

Panel B shows daily water consumption per capita. The water consumption is low for commu-

nities with no private tap infrastructure (13.0 liters). However, even in the areas with private tap

water access, the consumption is 15.1 liters and lower than what has been set as minimum by the

WHO.

Due to a higher use of private and public water in the communities, almost half of the households

in communities with private tap water infrastructure pays for user fees (Panel C).15 Only seven

percent of the households pay user fees in communities with no private tap water infrastructure.

Among households who pay fees, average households pay approximately 5.0-6.7 USD (2010 constant

price) per month.

In Uganda, fetching water takes large part of domestics labor. Households in total spend 13

hours for fetching water over the last seven days (Panel D). However, not all household members

participate in fetching water activity. For the average household size of 5.9 persons, the number

of participation for the water collection is 2.49 persons. Therefore, labor burden is skewed toward

14Approximately two to three percent of the households are dropped from the sample as the labor module was notrecorded. Another round of LSMS was collected in 2014. However, this paper does not use the LSMS 2014 becausethe minimum age for interview in the module of domestic work was raised to ten years and above.

15Appendix D provides descriptive statistics by water source.

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household members who participate. People who participate spend 5.45 hours per week on average

for fetching water. Households living in communities without private tap water infrastructure

spend almost twice as long time for fetching water than populations living in communities with

water infrastructure. This attributes to longer distance (0.61 km) and waiting time at the water

source (14 minutes).

Table 2 shows statistics by individual level.16 The labor participation of fetching water is high

for girls aged 5-18 with 64 percent spend at least one hour fetching water. Among those who

participate, females aged 19-59 spend 6.2 hours on fetching water per week.

5 Estimation Strategy

To measure the impact of rainfall scarcity on the water consumption and its cost, I estimate the

equation (1). The estimation utilizes panel data structure and rainfall exogeneity as the identifica-

tion strategies.

(1)yht = α+ β0Droughtht + ξh +Xht + θyear + θmonth + εht

where yht is the outcomes of the household “h” from the survey collected at time “t”. The main

outcomes are choice of water source and its traits (distance and waiting time), daily water con-

sumption (per capita), payment of user fee to access water, and weekly time spent for fetching

water.

Household fixed effects (ξh) are included to control for time-invariant characteristics such as

household/community/regional environments.17 Other time-variant factors (Xht) are included to

control for the number of household members. The estimation includes interview year and month

fixed effects. For the individual labor supply, the same equation is estimated with individual sample

by controlling for demographic characteristics such as age and gender.

16Boys and girls are defined as children under the age of 18. Adult men and women are defined as individual aged19-59, and seniors are defined as aged 60-80. Although school repetition is common, I divide the child sample by age:5-12 as primary education age, 13-18 as secondary school age.

17Since the analysis in this paper includes household fixed effect, a sample that only appears once in the data isdropped from the analysis (see Appendix E). Sixty-four percent of the individuals appear all three rounds, and therest appears twice in the data.

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5.1 Construction of Drought Variable

Following Abiona, Koppensteiner, et al. (2016); Bjorkman-Nyqvist (2013); Rocha and Soares (2015),

I first construct the rainfall log deviation variable from the local average.

Deviation from local averageht = ln(Rht) − ln(Rh10years)

where Rht is the rainfall of the past 12 months from the interview month “t”. For example, if the

interview was conducted on June 2010, the past 12 month rainfall is the total rainfall from June

2009 to May 2010. Focusing on the past 12 month rainfall from the interview months reduces the

concern for rainfall seasonality of the interview months affect the results (Carpena, 2019).

Rh10years is the annual local average rainfall of the past ten years. The deviation is defined as

the log of the historical annual local average rainfall (Rh10years) subtracted from the log of total

rainfall for the last 12 months. If the deviation is zero, the rainfall of the past 12 months is the

same as the local historical average. Since the deviation is described as differences in log form, the

value represents approximate percent change from the local mean (Maccini & Yang, 2009).18

Figure 2 (a) shows the density distribution of the log deviation. To define drought without

assuming linear specification, I group the distribution into five by percentile (Label 1 - 5 ).

The LSMS collects data on households drought experiences or irregular rainfall in the past 12

months. Although I do not use household-reported drought incidence in the analysis, to check the

consistency of the satellite collected precipitation data and household-reported droughts, I estimate

the equation (1) with household reported drought as an outcome.

The results show that the reported incidence of droughts are especially high for label 1 ; the

bottom tenth percentile of the distribution (Figure 2 (b)). Compared to the label 3 (30-70% of the

distribution), the households categorized in label 1 reports 25 percentage points higher probability

of drought experience in the last 12 months.

Therefore, in this paper, the label 1 area is referred as Drought. It corresponds to the last

18This study investigates the effect of the past 12 months rainfall as the household survey is collected annually.Because the rainfall scarcity of the past 12 months is considered, the main results do not differ whether the interviewmonths fixed effects are included or not (results upon request). Together with the long-term rainfall scarcity (drought),Appendix F provides the effect of concurrent precipitation. The results show that the rainfall of the past few weeksfrom the interview date affects time for fetching water as well. Even thought the coefficient is small, the result assuresthat the rainfall affect the daily activity in rural community.

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12 months rainfall reduction of more than 15 percent from its local mean. The drought coefficient

(β0) represents change in outcomes when the annual rainfall reduce more than 15 percent. The

other areas are referred as “moderate scarcity” ( 2 bottom 10th-30th percentile: annual rainfall

reduction of 7-15 percent), “normal” ( 3 bottom 30th-70th percentile), “moderate abundance” ( 4

bottom 70th-90th percentile), and “abundance” ( 5 top 10th percentile: annual rainfall increase

of more than 13 percent).

5.2 Validity of the Drought Variable and Robustness Check

Table 3 presents descriptive statistics of the rainfall variables. For households defined as drought

affected, 62 percent of the households reported that they experienced drought. The reported

incidence of drought decrease as the rainfall deviation moves to the right side of the distribution

(more rainfall in the past 12 months). Figure 3 shows a map of Uganda with the location of

the households by drought exposure. The households that experienced droughts during the study

period are dispersed across the country.

This paper presents several robustness tests to check the validity of the drought variable con-

struction. Appendix G.1 considers the rainfall deviation calculated by Z-scores: deviation from the

local mean divided by the standard deviation. Alternative rainfall variable construction provides

an evidence that the main results are not driven by a specific construction of drought variable. To

check the sensitivity of the cut-off values, the estimates in Appendix G.2 provides results using the

fifth and the 15th percentile for the definition of drought and rainfall abundance (the tenth per-

centile is used for main analysis). Appendix G.3 provides the supportive evidence that the results

are not driven by self-reporting bias. Furthermore, using the distance to school as false outcome,

I show that the main results are not driven by change in distance perception due to drought or

severe weather (as we do not expect distance to school changes due to drought).

6 Empirical Results

Figure 4 shows the choice of water source category as a function of rainfall deviation in the com-

munities without private tap water infrastructure.19 The outcomes are binary variables of one if

19There is no effect for households from communities with private tap water infrastructure (Appendix H).

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the household chooses a given water category as their main water source, zero otherwise.

Although statistical significance varies, the dependency on surface water (river, dam, pond, lake)

decreases by 0.8-1.8 percentage points at the time of rainfall scarcity. Contrary, the dependency

on public taps increases by 1.7 percentage points during droughts.

However, these results do not promise that households switch from surface water to public

tap directly. It is possible that the households who were usually using surface water change to

well/borehole, and people using well/borehole switch to public water.

Another issue for the analysis of source choice is the lack of detail in the survey design. Changes

within a category - such as a household switching from borehole A to borehole B - are not enu-

merated in this dataset. The survey only collects data about the category of main water source

used by a household.20 Therefore, rather than the water source choice itself, the rest of the paper

focuses on attributes of the main water sources such as distance or waiting time.

6.1 Distance to Water Source, Waiting Time

Table 4 shows the estimation results on the distance to the main water source and waiting time at

the water source. Columns (1) - (3) shows that no change is observed for outcomes from house-

holds living in communities with private water infrastructure. Column (4) shows the estimation of

distance with IHS transformation for households living in communities without private water in-

frastructure. In these communities, drought hit households see 9.3 percent increase in the distance

to their main water source. A similar increase is observed for the distance without using the IHS

transformation, but extreme values trimmed at the top one percent (Columns 5).

Given that drought-hit households walk a longer distance, households may walk to a water

source with a shorter waiting time. However, the estimation results suggest that waiting time

increases by 5.7 minutes as well (Column 6). The increase in waiting time can be explained by an

increase in households who switch to groundwater from surface water. Another mechanism that

increase waiting time is congestion; that is the people who were using surface water start using

public/ground water. Reduction in the number of functional water sources causes an increase in

20Ideally, this survey would also include an inventory of geographic information about all water sources around thehousehold. The choice of water source is determined by geographical location of the household and surrounding watersources. Together with the fact that people use multiple water source (and people may fetch water from differentsource for different usage), the detailed questionnaire design is necessary for future study.

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waiting time for both households who switched the water source and who did not.

6.2 Water Consumption

Then the question is how people change water consumption as droughts deteriorate the environment

for fetching water. One coping strategy is to simply reduce the water consumption.

Figure 5 (top) shows the change in per capita daily water consumption by deviation of rainfall

from its local mean. No statistical change in water consumption is observed throughout the distri-

bution. This result is consistent to the inelastic nature of water that is documented in the previous

studies. Households are not able to reduce water consumption especially if their consumption is

already around the minimum.

To further investigate the water demand, Appendix I seeks heterogeneous effect. The results

suggest that, households with higher water consumption in the non-drought years actually reduce

water consumption when they are hit by a drought. However, households with low water consump-

tion in the non-drought years show no change in the consumption level.

6.3 Water Payment: User Fees for Water Source Access

To secure water during its scarcity, households can take multiple strategies. Another coping strategy

to high cost to fetch water is to pay user fees for the better access to water source. Table 5 shows the

estimation results for user fees to access water source. Although only seven percent of households

living in communities without private water infrastructure pay user fees in non-drought years,

households affected by droughts are 2.6 percentage more likely to pay user fees (Column 4).

For the amount of payment, droughts add the burden of 9.5 cents to access water source

(t=1.46). When the sample is restricted to the households with some payment, the estimate shows

non-significant coefficient (Column 6: The sample size decrease to N=416).

6.4 Labor Supply in Fetching Water: Who Pay the Cost of Water?

If households are not willing to reduce the water consumption, nor pay user fees, the households

have to keep fetching water under the worse condition. Table 6 shows effect of drought on weekly

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time spent for fetching water for sample without private tap water infrastructure.21 Consistent

with the increase in distance and waiting time, households in total spend 1.9 hours more when they

experience a drought. Given that the total household labor hours of fetching water for non-drought

years is 14.2 hours, this is an increase of 13 percent from the mean.

Column (2) shows the estimation results for the number of household members who participated

in fetching water (number with positive labor hours). There is no change in the number of people

who participated in the activity. Therefore, the average labor supply per participated members

increases (0.81 hours increase shown in Column 3).

Then, the question is who in the household is affected by drought. Table 7 shows the im-

pact of drought on time for fetching water estimated with individual samples with household fixed

effects (sample restricted to communities without private tap infrastructure). The average im-

pact of drought on weekly individual labor supply is 0.36 hours. The inclusion of demographic

characteristics in the estimation does not change the coefficient of droughts, which indicates that

droughts incidence and demographic characteristics are orthogonal (Column 2). With the base of

dummy variables as boys aged 5-12 years old, girls in the same age group, older children, and adult

women spend more time fetching water. These relationships correspond to the descriptive statistics

provided in Table 2.

In column 3, the drought variable is interacted with demographic characteristics. Figure 6

shows the coefficients of interaction terms. Girls aged 13-18 and adult women aged 19-59 spend

1.26 and 0.9 hours more fetching water, respectively. The effect of droughts on time spent fetching

water is not observed for other household members. This shows that women and girls are the ones

who spend more time fetching water in order to meet the additional burden.

The results exhibit a larger effect of droughts on fetching water for households interviewed in

the dry months. In Uganda, there are two rainy seasons. The end of December to February is

the first dry season, and July is the second dry season, which is milder than the first one.22 The

column (4) and (5) restrict the sample to the ones that are surveyed in the dry season. The effect

of water scarcity in the last 12 months appears to affect more in the dry season, and it increases

21Results from households living in the community with private tap water infrastructure are all non-significant(results upon request).

22I divide months into two groups. Six months with the lowest average precipitation are categorized into drymonths (January, February, June, July, August, December). Other six months are classified as rainy months.

14

girls’ labor supply by 2.4 hours (59 percent increase from its mean of 4.05 hours).

7 Conclusion

Although fetching water accounts for a significant share of domestic labor, there is little attention

for domestic work in labor economics compared to market labor activity. The failure to include

domestic activity in studies and thereby leave the burden of domestic work unrecognized leads to a

lack of policies for household members who are responsible for such tasks (Guarcello, Lyon, Rosati,

Valdivia, et al., 2005).

This paper finds that the distance to a water source from the dwelling and waiting time increases

due to rainfall scarcity. Of the best of my knowledge, there is no previous empirical research docu-

menting this relationship using household data. Reflecting inelastic nature of water consumption,

households do not reduce water consumption due to water supply shocks. Instead, households are

more likely to pay cost related to water consumption including time cost of fetching water. The

research provides first evidence that drought affects girls’ and women’s labor supply for fetching

water within a household.23

Although the calculation is the ballpark estimate, Appendix J provide the monetary value of

time loss due to drought. Given that the drought increases the time for fetching water by 1.27

hours per week for girls (equivalent to 60.8 hours per year), drought-affected girls aged 13-18 lose

time equivalent value of 7.8 USD per year. A similar calculation reveals that adult women lose

11 USD equivalent time per year when they are hit by drought. The results highlight the cost of

drought, together with an increase in the cost to access to the water source documented in Section

6.3.

Previous studies of fetching water focus on the effect of time use for fetching water on other

activities, including female wage employment and child schooling.24 This paper does not analyze

those relationships because drought affects wage employment and schooling directly other than the

channel through fetching water (example includes change in crop price, agriculture productivity

23However, the results do not mean girls and women are the only ones facing higher cost of climate change. Asdroughts affect many outcomes, it is possible that boys and adult male labor may respond in other domains such asagriculture labor.

24Akabayashi and Psacharopoulos (1999); Cockburn and Dostie (2007); Cooke (1998); Devoto, Duflo, Dupas,Pariente, and Pons (2012); Gebru and Bezu (2014); Gross et al. (2018); Ilahi (2001); Ilahi and Grimard (2000);Nankhuni and Findeis (2004); Ndiritu and Nyangena (2011).

15

etc). Appendix K provides discussions and estimation results of drought on wage employment for

adult women and schooling for children.

The adverse effect of an increase in the burden of fetching water due to rainfall scarcity is not

limited to an increase in time for work itself. Asaba, Fagan, Kabonesa, and Mugumya (2013);

Sorenson, Morssink, and Campos (2011) documents a negative health effect of work such as pro-

longed fatigue, chest pain, and headache as a result of carrying water. Similarly, qualitative studies

revealed that women and children are a target of assault when they walk to the water source (Lev-

ison, DeGraff, & Dungumaro, 2017). The effect of labor supply on fetching water activity should

not be considered as a simple increase in labor supply.

Another field of research is the effect of climate on the quality of water. Not only does severe

weather affect the distance to a water source, but drought may also affect the quality of water that

households consume as well (Damania, Desbureaux, Rodella, Russ, & Zaveri, 2019). Although the

study find supportive evidence that drought affects the incidence of waterborn illness (Appendix

C), future research should investigate the relationship of severe weather with health outcomes.

16

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Figure 1: Water Demand and Supply

Notes: Qmin represents the minimum water consumption per capita per day for daily activity. The demand curve isprice inelastic around minimum consumption level. Three supply curves represent: S (normal water supply proxiedby normal rainfall), S’ (water scarcity proxied by drought), and S” (water abundance proxied by excessive rainfall).

26

Figure 2: Distribution of Log Rainfall Deviation and Relationship to Reported Drought

1 2 3 4 5

01

23

4D

en

sity

−.5 −.15 −.07 .03 .13 .5Log Deviation Rainfall

Notes: The labels represent1: Less than 10th percentile of distribution (More than 15 percent reduction)2: 10th−30th percentile of the distribution (7−15 percent reduction)3: 30th−70th percentile of the distribution (7 percent reduction of the rainfall to 4 percent increase)4: 70th−90th percentile of the distribution (4−13 percent increase)5: More than 90th percentile of the distribution (More than 13 percent increase)

Kernel density estimate

0.247***

0.047**

0.000

−0.057***

−0.111***

−.2

−.1

0.1

.2.3

1Drought

2ModerateScarcity

3Normal

4Moderate

Abundance

5Abundance

Notes: Significance level at, *p < .10,**p < .05,***p < .01.Observations at the household level. Dependent variable is an indicator of one if the householdreported irregular rainfall/drought in the last 12 months, otherwise zero. The estimates includesthe same control variables as the main analysis including household fixed effects.

Rainfall Deviation and Reported Incidence of Drought

27

Figure 3: Location of the Households by Drought Characteristics

Notes: The map shows 3195 households where the samples have been collected. The color of the” ”dotdescribe the drought variable by fifth (red) tenth (orange) and fifteenth (beige) percentile cutoff.

28

Figure 4: Water Source Choice (0/1) by Rainfall Deviation from its Local MeanSample: Communities without Private Tap Infrastructure

0.000 0.000 0.000 0.000 0.000

.05

.03

.01

−.0

1−

.03

−.0

5

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

(a) Private Tap

0.017*

0.0030.000 0.001

−0.011

.05

.03

.01

−.0

1−

.03

−.0

5

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

(b) Public Tap

0.021

0.011

0.000

0.0090.012

.05

.03

.01

−.0

1−

.03

−.0

5

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

(c) Ground Water (Well/Borehole)

−0.018

−0.008

0.000

0.010

0.004

.05

.03

.01

−.0

1−

.03

−.0

5

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

(d) Surface Water (River, Dam, Pond, Lake)

Notes: Significance level at ***p < 0.01, **p < 0.05, *p < 0.1. Observations at the household level. The dependentvariable is an indicator of one if the household chose the water source as their main water source, otherwise zero.Drought corresponds to the last 12 months of rainfall reduction of more than 15% from its local mean. Thedefinition for “moderate scarcity” corresponds to bottom 10th-30th percentile (annual rainfall reduction of 7-15%),and “normal” means bottom 30th-70th percentile, and “moderate abundance” means bottom 70th-90th percentile,and “abundance” corresponds to top 10th percentile (annual rainfall increase of more than 13%).

29

Figure 5: Outcomes (y-label) by Deviation of Rainfall from its Local MeanSample: Communities without Private Tap Infrastructure

0.482

−0.042 0.0000.146

0.067

−.5

0.5

1

Pe

r ca

pita

Wa

ter

co

nsu

mp

tio

n (

ltr)

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

0.085**

−0.036

0.0000.012

−0.001

−.1

−.0

50

.05

.1.1

5

Dis

tan

ce

to

th

e m

ain

wa

ter

so

urc

e (

IHS

)

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

0.025**

−0.002 0.000 −0.002 −0.002

−.0

20

.02

.04

Pa

id u

se

r fe

es/ta

riff

(Bin

ary

)

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

1.913**

−0.4160.000

−0.392

0.571

−2

−1

01

23

HH

we

ekly

la

bo

r fo

rfe

tch

ing

wa

ter

(Ho

urs

)

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

Notes: Significance level at ***p < 0.01, **p < 0.05, *p < 0.1. Observations at the household level.The dependent variable is an indicator of one if the household chose the water source as their mainwater source, otherwise zero. Drought corresponds to the last 12 months of rainfall reduction of morethan 15% from its local mean. The definition for “moderate scarcity” corresponds to bottom10th-30th percentile (annual rainfall reduction of 7-15%), and “normal” means bottom 30th-70thpercentile, and “moderate abundance” means bottom 70th-90th percentile, and “abundance”corresponds to top 10th percentile (annual rainfall increase of more than 13%).

30

Figure 6: The Effect of Drought on Individual Time Spent on Fetching Water by Age and Gender

b=0.017

b=−0.179

b=0.150

b=1.260***

b=0.035

b=0.901***

b=0.045

−1

01

2H

ou

rs p

er

we

ek

Boysaged5−12

[Base]Drought

Girlsaged

5−12 ×Drought

Boysaged

13−18 ×Drought

Girlsaged

13−18 ×Drought

Men aged19−59 ×Drought

Womenaged

19−59 ×Drought

Senioraged 60above ×Drought

Notes: Significance level at, *p < .10,**p < .05,***p < .01.The coefficient is estimated as interaction of drought with demographic characteristics with boysaged 5−12 years old as the base of the interaction.

Notes: Significance level at ***p < 0.01, **p < 0.05, *p < 0.1. Observations at the individual level.The outcome variable is individual labor supply for fetching water in the last seven days.

31

Table 1: Household Characteristics in LSMS Uganda (2009-2012)

(1) (2) (3) (4) (5)

AcrossWholeSample

Communitieswith

Private TapInfrastructure

Communitieswithout

Private TapInfrastructure Min Max

Household size 5.90 5.69 5.95 1 23Urban 0.22 0.67 0.10 0 1Panel A: Water Source

Private tap 0.05 0.25 0 0 1Public tap 0.12 0.30 0.07 0 1Ground water (well/bore-hole) 0.73 0.38 0.82 0 1Surface water 0.06 0.03 0.07 0 1Other 0.04 0.05 0.04 0 1Panel B: Household Water Consumption

Daily consumption per person (ltr) 13.43 15.07 13 0 100Panel C: Water Payment

Pay user fee (0/1) 0.16 0.49 0.07 0 1Pay user fee (Cents: including zero) 94.80 329.32 33.71 0 3702.93Pay user fee (among paid) 610.74 675.51 493.75 4.93 3702.93Panel D: Fetching Activity and EnvironmentHousehold total weekly hoursfor fetch water 12.95 7.79 14.31 0 147Number of people participated 2.49 1.85 2.66 0 13Weekly hours for fetch wateramong participated 5.45 4.30 5.68 0.25 26Distance to water soure (km) 0.53 0.22 0.61 0 4.80Walking time to water source (mins) 21.64 11.13 24.41 0 110Waiting time at water source (mins) 12.98 7.89 14.28 0 160Panel E: Region

Central 0.30 0.48 0.25 0 1East 0.24 0.19 0.25 0 1North 0.26 0.16 0.28 0 1West 0.21 0.18 0.22 0 1

Observations 7788 1609 6154 7788 7788

Notes: The data in the table pool three rounds of survey (The household characteristics are similar over three rounds).The top one percent of the variables are trimmed to deal with extreme values (waiting/walking time, fetching waterlabor supply, distance to water source, water consumption, and user fee for water). Unit of the user fee for waterpayment is cents represented in price level of 2010 in the United States.

32

Table 2: Individual Characteristics: Labor Supply on Fetching Water

Boy Girl Male Female Senior

5-12 13-18 5-12 13-18 19-59 19-59 60 above

Age 8.46 15.31 8.45 15.31 33.92 34.30 67.87Marital Status

Married monogamously 0 0 0 0.03 0.50 0.47 0.41Married polygamous 0 0 0 0 0.13 0.18 0.17Divorced/Separated 0 0 0 0.01 0.04 0.11 0.09Widow/Widower 0 0 0 0 0.01 0.07 0.32Never married 1 0.99 1 0.95 0.33 0.17 0.01

Currently attending school 0.78 0.83 0.80 0.80 0.14 0.08 0Weekly hours for fetching water(including zero) 2.58 3.16 3.01 4.05 1.06 3.59 1.23Participation rate for fetchingwater 0.57 0.65 0.61 0.68 0.26 0.58 0.25Weekly hours for fetching water(only Participated) 4.52 4.90 4.91 5.93 4.05 6.17 4.94

Observations 6351 4184 6287 3920 7770 8829 1999

Notes: The participation rate is the percent of the sample with positive labor hours for fetching wateractivity.

33

Table 3: Summary Statistics of the Last 12 Months Rainfall

1 2 3 4 5

Average DroughtModerateScarcity Normal

ModerateAbundance Abundance Min Max

Log deviation 0 -0.27 -0.11 -0.01 0.09 0.18 -0.51 0.37Reported drought 0.32 0.62 0.39 0.30 0.23 0.19 0 1

Notes: 1: Less than 10th percentile of distribution (More than 15 percent reduction) 2: 10th-30th percentile ofthe distribution (7-15 percent reduction) 3: 30th-70th percentile of the distribution (7 percent reduction of therainfall to 4 percent increase) 4: 70th-90th percentile of the distribution (4-13 percent increase) 5: More than 90thpercentile of the distribution (More than 13 percent increase). Experience Drought is the binary variable thattakes 1 if the household reported experience of drought shock.

34

Table 4: Effect of Drought on Fetching Water Environment

Communities with Private TapInfrastructure

Communities without Private TapInfrastructure

Distance Waiting time Distance Waiting time

(1) (2) (3) (4) (5) (6)(IHS) (km) (Mins) (IHS) (km) (Mins)

Drought -0.109 -0.099 0.838 0.095∗∗∗ 0.158∗∗∗ 5.826∗∗∗

(0.090) (0.072) (3.494) (0.032) (0.043) (1.538)

Household FE Yes Yes Yes Yes Yes Yes

Control Yes Yes Yes Yes Yes Yes

Non-drought mean 0.2 0.2 7.2 0.5 0.6 12.9Adjusted R2 0.04 0.06 0.02 0.03 0.02 0.05Observation 1,577 1,573 1,566 6,095 6,036 6,009

Notes: Standard errors in parentheses, ∗p < .10,∗∗ p < .05,∗∗∗ p < .01. Observations at the household level.The drought is defined as the bottom tenth percentile (15 percent reduction of rainfall in the past 12 months)of the historical distribution of rainfall. Control includes rainfall of the interview weeks, month/year fixedeffect and local ten years historical mean rainfall of the interview week.

35

Table 5: Effect of Drought on Monthly User Fee for Water

Communities with Private TapInfrastructure

Communities without Private TapInfrastructure

(1) (2) (3) (4) (5) (6)Paid fee(Binary)

Paid fee(Cents)

Paid fee(Cents excl 0)

Paid fee(Binary)

Paid fee(Cents)

Paid fee(Cents excl 0)

Drought 0.025 -37.530 -144.388 0.026∗∗∗ 9.550 -149.701(0.042) (48.751) (96.740) (0.010) (6.550) (101.388)

Household FE Yes Yes Yes Yes Yes Yes

Control Yes Yes Yes Yes Yes Yes

Non-drought mean 0.48 321.36 671.26 0.07 33.69 511.67Adjusted R2 0.02 0.09 0.17 0.01 0.01 0.36Observation 1,592 1,585 774 6,132 6,130 414

Notes: Standard errors in parentheses, ∗p < .10,∗∗ p < .05,∗∗∗ p < .01. Observations at the household level. Thedrought is defined as the bottom tenth percentile (15 percent reduction of rainfall in the past 12 months) of thehistorical distribution of rainfall. Control includes rainfall of the interview weeks, month/year fixed effect and localten years historical mean rainfall of the interview week. Unit of the user fee for water payment is cents representedin price level of 2010 in the United States.

36

Table 6: Effect of Drought on Household Labor on Fetching Water

Sample: Communities without Private Tap Infrastructure(1) (2) (3)

Household totalweekly time spentfor fetching water

Number of householdmembers participated

for fetching water

Time spent forfetching water per

participated member

Drought 1.885∗∗∗ -0.012 0.811∗∗∗

(0.703) (0.077) (0.263)

Household FE Yes Yes Yes

Control Yes Yes Yes

Non-drought mean 14.22 2.70 5.53Adjusted R2 0.06 0.16 0.02Observation 6,132 6,132 5,597

Notes: Standard errors in parentheses, ∗p < .10,∗∗ p < .05,∗∗∗ p < .01. Observations at the household level.The drought is defined as the bottom tenth percentile (15 percent reduction of rainfall in the past 12 months)of the historical distribution of rainfall. Control includes rainfall of the interview weeks, month/year fixedeffect and local ten years historical mean of rainfall of the interview week.

37

Table 7: Effect of Drought on Individual Time Spent on Fetching Water

Sample: Communities without Private Tap InfrastructureDry season Rainy season

(1) (2) (3) (4) (5) (6) (7)Drought 0.375∗∗ 0.355∗∗ 0.017 0.583∗∗ -0.327 0.234 -0.050

(0.147) (0.147) (0.225) (0.274) (0.435) (0.295) (0.363)

Boys: Aged 13-18 2.769∗∗∗ 2.792∗∗∗ 2.927∗∗∗ 2.764∗∗∗

(0.183) (0.191) (0.276) (0.288)

Girls: Aged 5-12 0.485∗∗∗ 0.464∗∗∗ 0.403∗∗∗ 0.523∗∗∗

(0.075) (0.081) (0.107) (0.120)

Girls: Aged 13-18 3.954∗∗∗ 3.785∗∗∗ 4.038∗∗∗ 3.595∗∗∗

(0.200) (0.209) (0.295) (0.303)

Men: Aged 19-59 -0.626∗ -0.625 -1.197∗∗ -0.295(0.377) (0.399) (0.599) (0.711)

Women: Aged 19-59 2.375∗∗∗ 2.258∗∗∗ 1.832∗∗∗ 2.449∗∗∗

(0.386) (0.409) (0.607) (0.723)

Senior: Aged 60+ -0.288 -0.292 -0.470 -0.699(0.410) (0.428) (0.428) (0.855)

Boys: Aged 13-18 × Drought -0.179 0.563 -0.356(0.309) (0.617) (0.379)

Girls: Aged 5-12 × Drought 0.150 0.872∗ 0.010(0.250) (0.523) (0.311)

Girls: Aged 13-18 × Drought 1.260∗∗∗ 2.427∗∗∗ 0.979∗∗

(0.341) (0.681) (0.420)

Men: Aged 19-59 × Drought 0.035 0.662 -0.160(0.235) (0.489) (0.298)

Women: Aged 19-59 × Drought 0.901∗∗∗ 1.406∗∗ 0.915∗∗

(0.295) (0.567) (0.383)

Senior: Aged 60+ × Drought 0.045 0.456 0.006(0.289) (0.550) (0.393)

Household FE Yes Yes Yes Yes Yes Yes Yes

Control Yes Yes Yes Yes Yes Yes YesAdjusted R2 0.07 0.14 0.22 0.18 0.24 0.18 0.24Observation 30,510 30,510 30,510 15,669 15,669 14,841 14,841

Notes: Standard errors in parentheses, ∗p < .10,∗∗ p < .05,∗∗∗ p < .01. Observations at the individual level. Thedrought is defined as last 12 months rainfall reduction of more than 15 percent. Base of the dummy for thedemographic variable is boys aged 5-12. Control includes rainfall of the interview weeks, month/year fixed effectand local ten years historical mean of rainfall of the interview week. Dry season is the data collected at months ofJanuary, February, June, July, August, December. Other six months are classified as rainy seasons.

38

Appendices

A Minimum Water Consumption Per Capita

The average water consumption varies significantly by countries, villages, and even between house-

holds (Rosen et al., 1999). Two primary household characteristics are associated with water con-

sumption are a source and household size (Figure A.1). Consistent with the previous findings

(Lindskog & Lundqvist, 1989), the water consumption per capita decreases as the household size

increases in Uganda. Compared to other water sources, households using surface water has the

lowest water consumption on average.

Figure A.1: Daily Water Consumption per Person by Water Source and Household Size

22.5

19.3 18.8

16.3

12.1

8.9

22.521.1

17.0

12.8

9.8 9.2

22.7

20.3

16.4

12.210.7

8.7

18.817.2

13.3

10.38.3

4.4

05

10

15

20

25

05

10

15

20

25

1 2 3 4−8 9−14 15−23 1 2 3 4−8 9−14 15−23

1 2 3 4−8 9−14 15−23 1 2 3 4−8 9−14 15−23

Private tap Public tap

Ground water (well/bore−hole) Surface water

Da

ily c

onsu

mp

tio

n (

Ltr

) p

er

pe

rso

n

Graphs by source of water and household size.

Reasons for the association between water consumption and household size is largely categorized

in three categories; Demand for water (e.g., different household member require different amount

of water), availability of labor supply for fetching water (e.g. number of women or girls available

to carry water in a household), or the economy of scale.

Regarding water demand, White, Bradley, and White (2002) divide the water consumption into

39

three categories: 1) consumption for drinking (expected to be minimum of 2-7.5 liters), 2) hygiene

(expected minimum varies by environments), and 3) amenity use including car wash, lawn watering.

Rest of the section provides the literature review of the demand for water.

Personal hygiene includes hand washing, food washing, bathing, and laundry. Although water

consumption for drinking is critical for survival, water consumption larger than five liters are mostly

used for bathing and washing purpose (Figure A.2).25 A study from Sudan documents that bathing

and washing clothes take almost 60 percent of the total water consumption (Cairncross & Kinnear,

1992).

Figure A.2: Water Consumption in Uganda, Tanzania, Kenya (Thompson, 2001)

Source: The figures are cited from Thompson (2001) using data from Uganda, Tanzania, and Kenya.

A.1 Consumption for drinking and cooking

Reviewing several studies, two liters for drinking/cooking and five liters for hygiene is suggested as

the minimum consumption. This means 7.5 liters per capita per day is required combining both.

Drinking water

The minimum water consumption necessary for survival is the subject of change by body size, the

temperature, and activity that people engage in. The literature review from (Howard & Bartram,

2003) indicates that a minimum of two liters for average adults is at least necessary. However,

people working in a tropical climate are expected to take 4.5 liters for survival. Using data from

the US Army, White et al. (2002) report that people need 4.5 liters of water per day to maintain

25Use of water for amenity is less common in low income countries.

40

hydration at 25 degrees Celsius (77 degrees Fahrenheit), although it is 6 liters at the 30 degrees

(86 degrees Fahrenheit).

Another factor to consider is the body size. According to the WHO 1993 report, one liter of

water is necessary for children with 10 kg, and 0.75 liters are required for children of five kilograms.

Furthermore, lactating women are suggested to intake 0.75 liters to one liter more on top of her

consumption.

Cooking

Estimating water consumption for cooking is not easy. The necessary water consumption for cooking

largely depends on the crop. Gleick (1998) estimated that ten liters per capita per day is needed

for food preparation, and Thompson (2001) suggested the necessity of 4.2 liters for countries in

East Africa.

A.2 Hygiene (Personal and domestic cleanliness)

A study from Thompson (2001) document that the amount of water consumption for hygiene differs

by accessibility to the water source. Washing dishes and clothes takes 6.6 liters, and bathing takes

7.3 liters if the water source is located outside of the home. For households where water is connected

to the house, washing dishes and clothes take 16.3 liters, and bathing takes 17.4. Another factor

to consider is the place of bathing. In rural areas, it may be socially acceptable for people to bathe

and launder clothes at or close to the water source (Howard & Bartram, 2003).

41

B Timing of Data Collection and Seasonality

The data collection of LSMS (2009/2012) started in September 2009 and ended in December 2012

(Figure B.1). The survey “is carried out annually, over a twelve-month period on a nationally

representative sample of households, for the purpose of accommodating the seasonality associated

with the composition of and expenditures on consumption (UBS, 2017).”

The data were not collected in September 2010 and October 2010. However, the pauses on data

collection happened in all regions, and there is no clear association between drought incidence and

data collection.

Figure B.1: Number of Interviews Conducted by Year/Month/Region

050

100

150

050

100

150

2009 2010 2011 2012 2009 2010 2011 2012

2009 2010 2011 2012 2009 2010 2011 2012

1 2 3 4 5 6 7 8 9101112 1 2 3 4 5 6 7 8 9101112 1 2 3 4 5 6 7 8 9101112 1 2 3 4 5 6 7 8 9101112 1 2 3 4 5 6 7 8 9101112 1 2 3 4 5 6 7 8 9101112 1 2 3 4 5 6 7 8 9101112 1 2 3 4 5 6 7 8 9101112

1 2 3 4 5 6 7 8 9101112 1 2 3 4 5 6 7 8 9101112 1 2 3 4 5 6 7 8 9101112 1 2 3 4 5 6 7 8 9101112 1 2 3 4 5 6 7 8 9101112 1 2 3 4 5 6 7 8 9101112 1 2 3 4 5 6 7 8 9101112 1 2 3 4 5 6 7 8 9101112

Central East

North West

Nu

mb

er

of

Ho

use

ho

ld C

on

du

cte

d I

nte

rvie

w

Graphs by Region_categ

42

C Waterborne illness as a Measure of Water Quality

Diarrheal related diseases are the third leading cause of death for children younger than 5 years

old in Africa (Reiner Jr et al., 2018). Although effect of droughts on quality of water is relevant

question for this research, the survey does not contain information about water quality at home.

This section presents the result of experience in waterborne illness as a proxy for water quality.

The survey asks experience of sickness and illness in the last 30 days to each household member. If

the person reports illness/sickness, enumerator further asks the symptoms (up to three). Following

Frempong, Kitzmuller, and Stadelman (2019), I define the outcome of waterborne illness as one if

the person reported illness and reported the symptom of diarrhea (acute/chronic) or vomit.

Although 23 to 45 percent of respondents reported experience of illness/sickness in the last 30

days depends on the age group, only 0.64 percent of children and 0.95 percent of adults reported

waterborn illness as a symptom of sickness. Children age less than five years old shows relatively

high prevalence of waterborn illness and it is 6.25 percent.

Figure C.1 shows estimation results by age: children aged less than 5, 5-18 and adult aged

19-59. There seems increase in incidence of waterborne illness for children under age five living in

communities without private tap infrastructure. The future research should investigate the effect

of climate shocks on household water quality, and the possible effect for health outcomes.

Figure C.1: Waterborn illness in the Last 30 Days by Deviation of Rainfall from its Local Mean

−.094

.042

0

−.036

.044

−.024

.0062 0 −.0025 .00098−.01

.0084 0 .0077 .014

−.2

−.1

0.1

.2

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

Child: Less than 5 Child: Aged 5−18

Adult: Aged 19−59

Note: The mean prevalence of diarrehea is 8% for children age less than 5 years old, 1.67% forchildren age 5−18, and 1.21% for adult age 19−59.

(a) Communities with Private Tap Infrastructure

.041

.066

0.0066

.027

.01 .012

0.0037 .0014

−.0051−.00024 0

.0057−.000035

−.0

50

.05

.1

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

Child: Less than 5 Child: Aged 5−18

Adult: Aged 19−59

Note: The mean prevalence of diarrehea is 9% for children age less than 5 years old, 1.71% forchildren age 5−18, and 1.51% for adult age 19−59.

(b) Communities without Private Tap Infrastructure

Notes: Observations at the individual level. Dependent variable is a binary variable of experience in waterborne illness in thelast 30 days. Drought corresponds to the last 12 months rainfall reduction of more than 15 percent from its local mean. Theconfidence interval is at 90 percent levels.

43

D Descriptive Statistics by Water Source

Table D.1 shows the descriptive statistics by the main water source of the household. The average

household size does not differ by water sources. Payment for water is high for private and public

tap. The consumption of water is especially low for households using open water source. The

distance to the water source is long for the open water source, but open water source users do not

have to wait at the water source. The average waiting time is the longest for well/borehole, with

an average of 16 minutes.

Table D.1: Household Characteristics in LSMS Uganda by Water Source (2009-2012)

(1) (2) (3) (4) (5)

AveragePrivate

tapPublic

tapWell

Bore-holeSurfaceWater

Panel A: Household Water ConsumptionHousehold size 5.90 6.64 5.07 6.00 5.81Daily consumption per person (ltr) 13.43 15.75 14.68 13.29 11.24Panel B: Water PaymentPay user fee (0/1) 0.16 0.88 0.72 0.02 0.01Pay user fee (Cents: including zero) 94.80 733.29 370.25 9.47 7.15Pay user fee (among paid) 610.74 844.01 513.00 441.70 483.32Panel C: Fetching Activity and EnvironmentHousehold total weekly hours for fetch water 12.95 2.03 7.12 15.11 12.77Number of people participated 2.49 0.80 1.90 2.75 2.70Weekly hours for fetch water among participated 5.45 2.66 3.62 5.87 4.95Weekly hours for fetch water per person 2.83 0.47 1.65 3.27 2.81Distance to water soure (km) 0.53 0.01 0.14 0.63 0.74Walking time to water source (mins) 21.64 0.33 8.95 25.68 26.13Waiting time at water source (mins) 12.98 0.36 5.54 16.61 2.07Panel D: RegionCentral 0.30 0.59 0.50 0.23 0.41East 0.24 0.15 0.13 0.27 0.10North 0.26 0.09 0.11 0.31 0.17West 0.21 0.17 0.26 0.18 0.33Urban 0.22 0.82 0.66 0.12 0.03Observations 7788 409 926 5643 473

Notes: The data in the table pool three rounds of survey information. The top one percent of the variablesare trimmed to deal with extreme values (waiting/walking time, fetching water labor supply, distance to watersource, water consumption, and user fee for water). The user fee for water payment is represented as centsconsidering exchange rate for each year.

44

E Attrition: Relationship to Drought

Using the Ugandan census data in 2002, Strobl and Valfort (2013) show historical rainfall scarcity

(average of 5 years prior) induces net out-migration in Uganda. Although drought in this study is

defined differently, if the attrition pattern is affected by rainfall, the attrition creates a bias in the

estimator.

For both the second and the third round of the data collection, around eight percent of the

households were not followed (even though they appear in the previous round). In this section,

households that appear in the previous round but not observed in the subsequent round is reffered

as Attrite.

Figure E.1 (a) and (b) show attrition rate by regions. The regional difference of attrition could

be due to different rainfall pattern, but there are other factors to consider unrelated to rainfall

variation. People in the big cities like Kampala (Central), Entebee (Central), Mbarara (West) are

more mobile or less likely to be found at home. Enumerators are less likely to be able to conduct

surveys for those households.

Figure E.1: Attrition Rate for Round Two and Three by Regions

.02

.04

.06

.08

.1.1

2

Central East

North West

(a) Household Level: Round Two

Note: Sample with attrition is defined as respondents whoappeared in the first round but did not appear at the rest ofthe survey.

.02

.04

.06

.08

.1.1

2

Central East

North West

(b) Household Level: Round Three

Note: Sample with attrition is defined as respondents who donot appear in the third round but appeared in the secondround.

To study the attrition pattern in relation to rainfall, I run the following regression

(2)Attriteht = α+ β0Droughtht−1 +Xht−1 + ξr + θ1year + εht

Out of 5,466 households (observed in the first and the second round), 585 households left the

45

sample (10.7 percent) in the next rounds. If the household exit from the data, the Attriteht takes

one, zero otherwise. Given that the incidence of droughts differ by regions, the estimation controls

for eight regional fixed effects.26

Table E.1 shows estimation result of equation (2). Without including control variables, the

experience of drought in the previous round of the survey is negatively associated with attrition

(Column 1). However, after the inclusion of regional fixed effects, the drought incidence is not

correlated (Column 2). The similar non-significant result holds even the estimation is conducted

separately by sub-samples (Column 3 and 4).

Table E.1: Effect of Drought on Not Interviewed (Attrite) in the Next Round

Whole

Communities withPrivate Tap

Infrastructure

Communities withoutPrivate Tap

Infrastructure

(1) (2) (3) (4)

Drought -0.043∗∗∗ -0.010 -0.015 0.005(0.013) (0.014) (0.036) (0.014)

Control Yes Yes Yes Yes

Regional FEs (8) No Yes Yes Yes

Non-drought mean 0.11 0.11 0.11 0.11Adjusted R2 0.02 0.04 0.01 0.04Observation 5,431 5,431 1,127 4,227

Notes: Sample of this analysis is household observed in the previous round (For the second roundof data collection, household observed in the first round, and for the third round of data collec-tion, second round.). Observations at the household level. The drought is defined as the bottomtenth percentile of the historical distribution of rainfall. Control includes year fixed effect and thehousehold size. Standard errors in parentheses, ∗p < .10,∗∗ p < .05,∗∗∗ p < .01.

Table E.2 presents the regression result of equation (2) but using individual level data. Out of

23,253 individuals (4,879 households) observed in the first and the second round, there are 2,251

individual not observed in the subsequent round (9.6 percent).

The results are similar to household level analysis (Table E.1). After the control of regional

fixed effects the drought and exit pattern is not statistically significantly correlated with drought

variable.

26In the main analysis, the regional fixed effects are not included in the estimation as the household fixed effectscontrols for regional time-invariant factors.

46

Table E.2: Effect of Drought on Not Interviewed (Attrite) in the Next Round

Whole

Communities withPrivate Tap

Infrastructure

Communities withoutPrivate Tap

Infrastructure

(1) (2) (3) (4)

Drought -0.014∗∗ -0.006 -0.024 -0.006(0.006) (0.007) (0.018) (0.007)

Control Yes Yes Yes Yes

Regional FEs (8) No Yes Yes Yes

Non-drought mean 0.10 0.10 0.10 0.10Adjusted R2 0.01 0.01 0.01 0.01Observation 23,180 23,180 4,791 18,303

Notes: Sample of this analysis is household observed in the previous round (For the second roundof data collection, household observed in the first round, and for the third round of data collec-tion, second round.). Observations at the household level. The drought is defined as the bottomtenth percentile of the historical distribution of rainfall. Control includes year fixed effect and thehousehold size. Standard errors in parentheses, ∗p < .10,∗∗ p < .05,∗∗∗ p < .01.

47

F Concurrent Rainfall

Together with long-term rainfall scarcity, this paper investigates the effect of concurrent precipita-

tion on time use.

Using data from the American Time Use Survey, Connolly (2008) shows that people in the

United States spend 30 minutes more at the office on rainy days.27 However, there is little em-

pirical evidence of labor supply change due to concurrent precipitation from developing countries.

The effect of rainfall may be different in developing countries from what has been documented in

developed societies.

The paper estimates the following regression with additional focus on concurrent rainfall in

addition to the long-term rainfall (past 12 months rainfall):

(3)yht = α+ β0Droughtit + θ1year + θ2month + ξH +Xht

+ β1Rainfallht + θ3Past Rainfall (10 Years)ht + εht

where “Rainfallht” is the average daily rainfall of the past weeks prior to the interview date in

the Inverse Hyperbolic Sin (IHS) form.28 To specifically control for the rainfall seasonality of the

interview time, the ten years local rainfall of the interview week in the IHS form is included in the

estimation as well. Again the deviation captures the percent change of rainfall.

Figure F.1 shows coefficient plot of rainfall of the last few weeks on fetching water. The results

show that contemporaneous rainfall of the last two weeks saves time spent fetching water, and

scarcity of rainfall increases the time spent fetching water.

27The working time increase as workers value leisure outside less on rainy days. In a similar framework, Graff Zivinand Neidell (2014); Seppanen, Fisk, and Lei (2006) investigate the effect of weather (temperature) on labor supply.

28As the daily rainfall includes the case of zero, the log deviation form is not applied in the estimation of concurrentrainfall. To deal with a large number of zero and the possible non-linear relationship of the short-term rainfall effect,this paper applies the IHS transformation for precipitation of the weekly rainfall. The IHS transformation is analternative to natural log transformation (Burbidge, Magee, & Robb, 1988) One nice feature of IHS is that is isdefined at zero. IHS transformation has a similar form with the natural log transformation (except around zero).Therefore, the coefficient is interpreted in the same way as a standard lograrathmic transformation.

48

Figure F.1: Effects of Past Few Weeks Rainfall on Household Time Spent on Fetching Water

.2

−.35

−.23

−.13

.13

−.7

−.5

−.3

−.1

.1.3

.5H

ou

se

ho

ld w

ee

kly

la

bo

r fo

r fe

tch

ing

wa

ter

(Ho

urs

)

Rainfall of1−7 days

IHS

Rainfall of8−14 days

IHS

Rainfall of15−21 days

IHS

Rainfall of22−28 days

IHS

Rainfall of29−35 days

IHS

Notes: Label 1-7 (I) is rainfall of the 1-7 days prior to interview day. The unit of rainfall is mm and it istransformed to IHS form. Control includes rainfall of the interview weeks, month/year fixed effect and local tenyears historical mean rainfall of the interview week. The confidence interval is at 90 percent levels.

49

G Robustness Checks

G.1 Drought defined by fifth and 15th percentile

The definition of drought used in the paper is the bottom tenth percentile of the log rainfall

deviation distribution. To show that the main results are not subject of the cut-off threshold, this

section provides drought and abundance defined with the fifth and 15th of the top and bottom

percentile of the distribution.

Figure G.1 and G.2 show the result from fifth and 15th percentile cut-off, respectively. The

main findings of the paper do not differ significantly by the cut-off value choice.

50

Figure G.1: Outcomes with Fifth Percentile Definition for Drought and AbundanceSample: Communities without Private Tap Infrastructure

0.544

0.036 0.000 0.068

0.555*

−.5

0.5

11

.5

Pe

r ca

pita

Wa

ter

co

nsu

mp

tio

n (

ltr)

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

0.078**

−0.006 0.0000.009

0.020

−.0

50

.05

.1.1

5

Dis

tan

ce

to

th

e m

ain

wa

ter

so

urc

e (

IHS

)

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

0.020

0.006

0.000 −0.002 −0.001

−.0

20

.02

.04

Pa

id u

se

r fe

es/t

ariff

(Bin

ary

)

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

1.837**

0.2110.000 −0.136 0.005

−1

01

23

HH

we

ekly

la

bo

r fo

rfe

tch

ing

wa

ter

(Ho

urs

)

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

Notes: Observations at the household level. Dependent variable for each figure is shown as y-label.Drought corresponds to fifth percentile of the rainfall distribution. “moderate scarcity” (bottom 15th-30thpercentile), and “normal” (bottom 30th-70th percentile), and “moderate abundance” (bottom 70th-95thpercentile), and “abundance” (top fifth percentile). The confidence interval is at 90 percent levels.

51

Figure G.2: Outcomes with 15th Percentile Definition for Drought and AbundanceSample: Communities without Private Tap Infrastructure

0.267

0.052 0.000 0.008

0.265

−.5

0.5

1

Pe

r ca

pita

Wa

ter

co

nsu

mp

tio

n (

ltr)

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

0.087***

−0.064*

0.0000.012 0.005

−.1

−.0

50

.05

.1.1

5

Dis

tan

ce

to

th

e m

ain

wa

ter

so

urc

e (

IHS

)

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

0.020*

−0.0020.000

−0.002 −0.001

−.0

20

.02

.04

Pa

id u

se

r fe

es/t

ariff

(Bin

ary

)

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

1.716**

−0.705

0.000

−0.523

0.287

−2

−1

01

23

HH

we

ekly

la

bo

r fo

rfe

tch

ing

wa

ter

(Ho

urs

)

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

Notes: Observations at the household level. Dependent variable for each figure is shown as y-label.Drought corresponds to 15th percentile of the rainfall distribution. “moderate scarcity” (bottom 15th-30thpercentile), and “normal” (bottom 30th-70th percentile), and “moderate abundance” (bottom 70th-85thpercentile), and “abundance” (top 15th percentile). The confidence interval is at 90 percent levels.

52

G.2 Drought Defined with Z-score

The main analysis used the deviation of the log rainfall to define the drought. Figure G.1 shows the

analytical result using a standardized z-score (difference from the local mean divided by its standard

deviation). The main result of the paper remains the same, with only difference in magnitude of

the coefficients.

Estimates with z-score shows a potential increase in daily water consumption by 0.64 liters

per person at the time of water abundance. If households still have the margin to increase water

consumption even at the normal rainfall years, this result highlights costly water collection not only

for the time of drought but also for normal rainfall years.

53

Figure G.1: Outcomes with Z-score Definition for Drought and AbundanceSample: Communities without Private Tap Infrastructure

0.198

−0.1490.000

−0.246

0.666**

−.5

0.5

11

.5

Pe

r ca

pita

Wa

ter

co

nsu

mp

tio

n (

ltr)

Drought(Z−score)

ModerateScarcity

(Z−score)

Normal(Z−score)

ModerateAbundance(Z−score)

Abundance(Z−score)

0.115***

−0.035

0.000 0.0050.022

−.1

0.1

.2

Dis

tan

ce

to

th

e m

ain

wa

ter

so

urc

e (

IHS

)

Drought(Z−score)

ModerateScarcity

(Z−score)

Normal(Z−score)

ModerateAbundance(Z−score)

Abundance(Z−score)

0.021*

0.001 0.000 −0.002 −0.000

−.0

20

.02

.04

Pa

id u

se

r fe

es/t

ariff

(Bin

ary

)

Drought(Z−score)

ModerateScarcity

(Z−score)

Normal(Z−score)

ModerateAbundance(Z−score)

Abundance(Z−score)

1.585*

0.2360.000

−0.749

0.397

−2

−1

01

23

HH

we

ekly

la

bo

r fo

rfe

tch

ing

wa

ter

(Ho

urs

)

Drought(Z−score)

ModerateScarcity

(Z−score)

Normal(Z−score)

ModerateAbundance(Z−score)

Abundance(Z−score)

Notes: Observations at the household level. Dependent variable for each figure is shown as y-label.Drought corresponds to the bottom ten percentile of the z-score distribution. “Moderate Scarcity” (bottom10th-30th percentile), and “Normal” (bottom 30th-70th percentile), and “Moderate Abundance” (bottom70th-90th percentile), and “Abundance” (top 10th percentile). The confidence interval is at 90 percentlevels. 54

G.3 Self-reporting Bias and Perception of Distance

One common issue for time use data is reporting bias. Reporting bias may be significant for child

labor since parents have an incentive to underreport (Dammert & Galdo, 2013; Janzen, 2017).

However, reporting inaccuracy may also be attributed to the difference in the sense of time between

children and adults, especially in the absence of a clock (Levison et al., 2017).

To investigate the relationship between weather shocks and self-reporting, a regression with the

same specification, but the outcome changed to a binary variable of self-reporting, is provided in

Table G.1 (Column 1). The coefficients do not show any significant relationship between drought

and self-reporting. Therefore, the paper excludes the possibility that reporting bias skew the

findings in the main analysis.

Another concern for the estimation of the distance to the water source is that drought may

increase the perceived distance of walking. In other words, people might feel the distance is longer

when weather is harsh on them. To investigate the relationship between droughts and perceived

distance, Table G.1 (Column 2 and 3) provide the regression result on the distance to school, which

is not expected to change by drought experience. The outcome of the estimation is the distance

to school for children age 5-17 years old (the estimation controls for individual fixed effects). The

result of the estimation does not indicate that the main result of the paper is not driven by the

change in perception of distance from drought.

55

Table G.1: Effect of Drought on Self-report and Distance to School (False Specification)

Sample: Communities without Private Tap Infrastructure(1) (2) (3)

Self-Report Distance to school

Binary(0/1) (IHS) (km)

Drought 0.008 0.045 0.049(0.010) (0.031) (0.064)

Individual FE Yes Yes Yes

Control Yes Yes Yes

Non-drought mean 0.3 1.1 1.7Adjusted R2 0.01 0.01 0.01Observation 30,545 10,738 10,682

Notes: Observations at the individual level. The distance to schoolsample is restricted to children age less than 17 years old who go toschool. The drought is defined as the bottom tenth percentile (15percent reduction of rainfall in the past 12 months) of the historicaldistribution of rainfall. Control includes rainfall of the interview weeks,month/year fixed effect and local ten years historical mean rainfall ofthe interview week. Standard errors in parentheses, ∗p < .10,∗∗ p <.05,∗∗∗ p < .01.

56

H Water Source Choice for Households Living in the Communi-

ties with Private Tap Water Infrastructure

Figure H.1: Water Source Choice (0/1) by Deviation of Rainfall from its Local MeanSample: Communities without Private Tap Infrastructure

0.036

0.016

0.000

0.011

−0.016

.15

.1.0

50

−.0

5−

.1

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

(a) Private Tap

−0.018 −0.021

0.000

−0.022

−0.048

.15

.1.0

50

−.0

5−

.1Drought Moderate

ScarcityNormal Moderate

AbundanceAbundance

(b) Public Tap

−0.007−0.014

0.000

0.017

0.072*

.15

.1.0

50

−.0

5−

.1

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

(c) Ground Water (Well/Borehole)

−0.011

0.015

0.000−0.004

−0.018

.15

.1.0

50

−.0

5−

.1

Drought ModerateScarcity

Normal ModerateAbundance

Abundance

(d) Surface Water (River, Dam, Pond, Lake)

Notes: Observations at the household level. Dependent variable is a binary variable of one if the household chose the watersource as their main water source, otherwise zero. Drought corresponds to the last 12 months rainfall reduction of more than15 percent from its local mean. “moderate scarcity” (bottom 10th-30th percentile: annual rainfall reduction of 7-15 percent),and “normal” (bottom 30th-70th percentile), and “moderate abundance” (bottom 70th-90th percentile), and “abundance”(top 10th percentile: annual rainfall increase of more than 13 percent).

57

I Heterogeneous Effect on Water Consumption

To further investigate the elasticity, this section provides heterogeneous estimated results for water

consumption by level of household water consumption in the non-drought years. If water con-

sumption is inelastic around the minimum water consumption level, households with higher water

consumption in the non-drought year would reduce consumption when they are hit by drought.

However, households with low water consumption are not able to reduce their consumption.

Figure I.1 shows the distribution of daily water consumption per capita in the non-drought

years. It is normally distributed except a heap around 20 liters, which is suggested minimum

amount of water for survival, and the standard water holding for one jerry can typically be used

in Uganda. Approximately half of the sample consume more than ten liters per person. House-

holds that consume more than twenty liters per person are only 11 percent of the whole sample.

Water consumption is lower for people living in communities without private water infrastructure

throughout the distribution.

Table I.1 columns (1) and (5) show the overall effect of drought on daily water consumption

per capita on average. As Figure 5 shows, there is no effect of drought on water consumption. The

rest of the columns present the estimation results with the interaction and water consumption with

certain thresholds. Column (2) and (6) includes the interaction of drought with households that

consume more than 20 liters in non-drought years.

For households that consume less than 20 liters in non-drought years living in communities

with private water infrastructure, droughts increase consumption of water by 2.3 liters. The inter-

action term shows that the households with more than 20 liters of water consumption reduce their

consumption by 3.3 liters (=2.34-5.63) at the time of drought. A similar reduction of water con-

sumption is observed for households living in communities without private water infrastructure by

2.2 liters (column 6). The reduction of water consumption somewhat disappears when the cut-off

value is set at ten liters (columns (4) and (8)). Although the results of point estimate differ by

different cut-offs, results suggest that households with larger water consumption at non-drought

year reduce water consumption during drought (and there may be a potential increase in water

consumption for households with low water consumption).29

29Although it is hard to test, increase in water consumption at the time of drought can be explained by thenecessity of water at the time of drought. One factor that influences the minimum water consumption of drinking

58

Figure I.1: Distribution of Minimum Water Consumption per Person at Non-Drought Year

0.0

2.0

4.0

6.0

8.1

De

nsity

0 5 10 15 20 25 30 35 40Daily Water Cons per Person (Ltr)

Communities with privatetap infrastructure

Communities without privatetap infrastructure

Notes: The top one percent of the water consumption is trimmed. One household is further droppedfrom the sample as the minimum water consumption level was 80 ltrs and twice as large as thesecond largest value.

59

Table I.1: Effect of Drought on per capital Daily Water Consumption (Ltr) by Water Use of Non-Drought Years

Communities with Private TapInfrastructure

Communities without Private TapInfrastructure

(1) (2) (3) (4) (5) (6) (7) (8)

Drought 1.068 2.325∗∗ 2.705∗∗ 4.258∗∗∗ 0.451 0.930∗∗∗ 1.392∗∗∗ 2.223∗∗∗

(1.033) (1.042) (1.066) (1.251) (0.342) (0.330) (0.335) (0.420)

Drought× Cutoff 20 -5.784∗∗∗ -3.090∗∗∗

(1.704) (0.921)

Drought× Cutoff 15 -4.662∗∗∗ -3.589∗∗∗

(1.375) (0.646)

Drought× Cutoff 10 -4.781∗∗∗ -2.730∗∗∗

(1.314) (0.446)

Household FE Yes Yes Yes Yes Yes Yes Yes Yes

Control Yes Yes Yes Yes Yes Yes Yes YesNon-drought

mean 15.2 15.2 15.2 15.2 12.8 12.8 12.8 12.8Adjusted R2 0.17 0.19 0.19 0.19 0.12 0.13 0.13 0.13Observation 1,549 1,549 1,549 1,549 6,063 6,063 6,063 6,063

Notes: Observations at the household level. The drought is defined as the bottom tenth percentile (15 percentreduction of rainfall in the past 12 months) of the historical distribution of rainfall. Control includes rainfallof the interview weeks, month/year fixed effect and local ten years historical mean of rainfall of the interviewweek. The variable of Cutoff 20 is the household whose minimum daily water consumption per capita ismore than 20 litters. The same definition is applied to Cutoff 15, Cutoff 10. Standard errors in parentheses,∗p < .10,∗∗ p < .05,∗∗∗ p < .01.

water is temperature. The literature review of the water indicate that minimum of two liters for average adults, butit would be 4.5 liters for the people working in tropical climate (Howard & Bartram, 2003). Using data from the USArmy, White et al. (2002) report that people need 4.5 liters of water per day to maintain hydration at the 25 degreescelsius (77 degrees Fahrenheit), although it is 6 liters at the 30 degree (86 degrees Fahrenheit).

60

J (Monetary) Cost of Drought

The study highlights the increase in time for fetching water for drought experienced households,

especially for girls and adult women. How much does this time loss equivalent to in monetary

terms? Whittington, Mu, and Roche (1990) document that the value of time for water collection

is equivalent to the wage rate of unskilled workers in Kenya.

To compute the monetary value of time loss due to drought, I analyze the agriculture wage for

hired labor using payment information.30 The agriculture module of the survey collects information

of how many person-days hired worker the household employed (for men, women, and children,

separately) and how much they paid in total for those labors (including cash and in-kind) in

agriculture.

Table J.1 shows the summary result of the payment information. The average daily payment

(including in-kind) for adult women is 1.35 USD (2010 price level). For children, the average earning

is 0.73 USD per day.31 Given that children, on average, work 5.35 hours per day when they are

fired as agriculture labor, their hourly wage for agriculture labor is 13 cents. The computed hourly

wage of 13 cents is comparable to survey about the child labor earnings conducted by ILO Child

Labour (2007) in Uganda. The report documents that most of the children earn 17-29 cents at

2006 price level per hour in fishing and construction sectors (no information for agriculture sector).

Given that the drought increases the time for fetching water by 1.27 hours per week for girls

(equivalent to 60.8 hours per year), drought-affected girls aged 13-18 lose time equivalent value of

7.8 USD per year. A similar calculation reveals that adult women lose 11 USD equivalent time per

year when they are hit by drought.

30Another method of computing time value is the calculation of shadow wage using agriculture input and output(Jacoby, 1993; Rosenzweig, 1980; Skoufias, 1994). However, given that the estimation requires several assumptionsand information such as labor hours at a certain time of the agriculture season, this paper computes the monetaryvalue of time based on the payment for hired laborers.

31A study from Tanzania estimates that one day of child work increase production value of household by 0.89 USD(2016 price level) (Andre, Delesalle, & Dumas, 2017).

61

Table J.1: Agriculture Payment including the Value of In-kind (Price in 2010 level)

(1) (2) (3)Payment

Person DayAgricultureLabor Hours

Hourly Wage=Payment/Hours

Men 2.61 USD 6.67 hours per day 39 cents

Women 1.35 USD 5.27 hours per day 25 cents

Children 0.73 USD 5.35 hours per day 13 cents

Notes: The calculation drop the sample where households mix men, femaleand child labor for one plot. 2544 households hire only adult male, 1378households hire only adult female and only 65 households hire only children.

62

K Other Activities: Wage Employment and Education

Time use is largely classified into four categories: work at home, market work, leisure, and time

allocated to human capital investment (DeGraff & Bilsborrow, 2003; Skoufias, 1993).

K.1 Female Wage Employment and Fetching Water at the Time of Droughts

For women with a high domestic work burden, an increase in domestic labor means less time for

economic activities and leisure. This relationship of fetching water and wage employment has been

widely studied.32

Table K.1 and K.2 show estimated results for change in the main occupation by droughts. The

results suggest that adult women (and men) are more likely to leave farm labor and work in their

own account activities when they experience droughts.

However, this paper does not focus on the relationship between fetching water activity and wage

employment. This is because droughts affect adults employment in several mechanisms (not only

through an increase in burden of fetching water activity). Teasing out the mechanism of fetching

water activity on other labor outcomes will be carried out in a separate paper.

32Devoto et al. (2012); Gross et al. (2018) find that better access to water sources decreases the time for fetchingwater. However, those studies find an increase in time for only in leisure, but not for women’s income-generatingactivities.

63

Table K.1: Effect of Drought on Household Farm Labor Chosen as the Main Occupation

Sample: Communities without Private Tap InfrastructureDry season Rainy season

(1) (2) (3) (4) (5) (6) (7)Drought -0.036∗∗ -0.037∗∗ -0.008 -0.002 0.044 -0.022 0.010

(0.017) (0.017) (0.024) (0.036) (0.043) (0.031) (0.038)

Boys: Aged 13-18 0.568∗∗∗ 0.571∗∗∗ 0.576∗∗∗ 0.578∗∗∗

(0.019) (0.021) (0.028) (0.030)

Girls: Aged 5-12 -0.047∗∗∗ -0.049∗∗∗ -0.060∗∗∗ -0.031∗∗

(0.009) (0.010) (0.013) (0.015)

Girls: Aged 13-18 0.550∗∗∗ 0.552∗∗∗ 0.546∗∗∗ 0.571∗∗∗

(0.020) (0.021) (0.029) (0.030)

Men: Aged 19-59 0.488∗∗∗ 0.497∗∗∗ 0.380∗∗∗ 0.577∗∗∗

(0.048) (0.051) (0.070) (0.071)

Women: Aged 19-59 0.669∗∗∗ 0.675∗∗∗ 0.565∗∗∗ 0.745∗∗∗

(0.048) (0.051) (0.070) (0.071)

Senior: Aged 60+ 0.393∗∗∗ 0.407∗∗∗ 0.561∗∗∗ 0.270∗

(0.082) (0.085) (0.114) (0.147)

Boys: Aged 13-18 × Drought -0.019 -0.041 -0.005(0.031) (0.054) (0.040)

Girls: Aged 5-12 × Drought 0.014 0.065 -0.018(0.024) (0.043) (0.031)

Girls: Aged 13-18 × Drought -0.016 -0.014 -0.038(0.032) (0.055) (0.040)

Men: Aged 19-59 × Drought -0.071∗∗∗ -0.132∗∗∗ -0.074∗∗

(0.027) (0.047) (0.035)

Women: Aged 19-59 × Drought -0.051∗∗ -0.075∗ -0.054∗

(0.024) (0.042) (0.032)

Senior: Aged 60+ × Drought -0.082∗∗ -0.203∗∗∗ -0.015(0.038) (0.069) (0.051)

Household FE Yes Yes Yes Yes Yes Yes Yes

Control Yes Yes Yes Yes Yes Yes YesAdjusted R2 0.17 0.19 0.32 0.34 0.36 0.34 0.35Observation 30,932 30,932 30,932 15,873 15,873 15,059 15,059

Notes: Standard errors in parentheses, ∗p < .10,∗∗ p < .05,∗∗∗ p < .01. Observations at the individual level. Thedrought is defined as last 12 months rainfall reduction of more than 15 percent. Base of the dummy for thedemographic variable is boys aged 5-12. Control includes rainfall of the interview weeks, month/year fixed effectand local ten years historical mean of rainfall of the interview week. Dry season is the data collected at months ofJanuary, February, June, July, August, December. Other six months are classified as rainy seasons.

64

Table K.2: Effect of Drought on Own-account Labor Chosen as the Main Occupation

Sample: Communities without Private Tap InfrastructureDry season Rainy season

(1) (2) (3) (4) (5) (6) (7)Drought 0.037∗∗∗ 0.037∗∗∗ 0.005 0.016 -0.046∗∗∗ 0.039∗∗∗ 0.011

(0.006) (0.006) (0.008) (0.013) (0.016) (0.015) (0.016)

Boys: Aged 13-18 0.039∗∗∗ 0.035∗∗∗ 0.044∗∗∗ 0.030∗∗∗

(0.007) (0.007) (0.011) (0.011)

Girls: Aged 5-12 -0.000 0.001 0.001 0.000(0.003) (0.003) (0.004) (0.004)

Girls: Aged 13-18 0.032∗∗∗ 0.031∗∗∗ 0.041∗∗∗ 0.026∗∗

(0.007) (0.008) (0.011) (0.012)

Men: Aged 19-59 0.229∗∗∗ 0.220∗∗∗ 0.311∗∗∗ 0.125∗∗∗

(0.039) (0.042) (0.060) (0.044)

Women: Aged 19-59 0.152∗∗∗ 0.142∗∗∗ 0.222∗∗∗ 0.060(0.039) (0.041) (0.060) (0.044)

Senior: Aged 60+ 0.072 0.052 0.041 0.063(0.046) (0.047) (0.062) (0.065)

Boys: Aged 13-18 × Drought 0.022∗ 0.044∗ 0.019(0.012) (0.024) (0.016)

Girls: Aged 5-12 × Drought -0.009 0.009 -0.016(0.007) (0.015) (0.011)

Girls: Aged 13-18 × Drought -0.002 0.009 0.003(0.010) (0.020) (0.014)

Men: Aged 19-59 × Drought 0.061∗∗∗ 0.098∗∗∗ 0.066∗∗∗

(0.017) (0.034) (0.022)

Women: Aged 19-59 × Drought 0.067∗∗∗ 0.098∗∗∗ 0.063∗∗∗

(0.014) (0.029) (0.018)

Senior: Aged 60+ × Drought 0.121∗∗∗ 0.260∗∗∗ 0.073∗∗

(0.027) (0.056) (0.034)

Household FE Yes Yes Yes Yes Yes Yes Yes

Control Yes Yes Yes Yes Yes Yes YesAdjusted R2 0.10 0.11 0.21 0.21 0.22 0.20 0.21Observation 30,932 30,932 30,932 15,873 15,873 15,059 15,059

Notes: Standard errors in parentheses, ∗p < .10,∗∗ p < .05,∗∗∗ p < .01. Observations at the individual level. Thedrought is defined as last 12 months rainfall reduction of more than 15 percent. Base of the dummy for thedemographic variable is boys aged 5-12. Control includes rainfall of the interview weeks, month/year fixed effectand local ten years historical mean of rainfall of the interview week. Dry season is the data collected at months ofJanuary, February, June, July, August, December. Other six months are classified as rainy seasons.

65

K.2 Child Education and Domestic Labor Supply

For children, domestic labor burden comes out of playtime and leisure time. Significant workloads

deter their educational investment as well (Arends-Kuenning & Amin, 2001; Assaad, Levison, &

Zibani, 2010; King & Hill, 1993; Levison, 1998). The previous set of studies investigates the effect

of fetching water (and firewood collection) on child schooling outcomes.33 They conclude that the

distance to the water source is associated with higher child labor demand and lower performance

at school. A meta-analysis of nine countries provides the relationship between water access and

female labor and child outcomes (Koolwal & Van de Walle, 2013). Although strong claims cannot

be made about causal impacts as the authors point out, the study highlights the burden of water

consumption related activities on schooling for both boys and girls.

K.2.1 School Enrollment and Domestic Work

This section investigates the relationship between school enrollment and domestic works.

Figure K.1 shows the average time for domestic activity (include fetching water and agriculture

and other domestics activities) by current school enrollment status for children under age 19. For

children aged 5-12 (age for primary school), going to school is associated with a longer time for

domestic activity. This unintuitive relationship is observed and documented in the other study as

well. Levison et al. (2017) document that this relationship for fetching water is explained by the

nature of the task, that many children go to school with a bucket or jug to fill water in Tanzania.

Similarly, households with large agricultural land (higher wealth level) have both higher school

enrollment and higher demand for agriculture labor.34

Table K.3 provides the relationship of school enrollment and domestic labor in regression form.

Without individual fixed effects, the school enrollment is positively related to labor supply of

fetching water for children age 5-12, indicating going to school increase the time for domestic labor.

The coefficient become much smaller when individual fixed effects are included. The relationship

is only negative to children aged 13-18 and the results are still significantly large even after the

inclusion of individual fixed effects.

33Akabayashi and Psacharopoulos (1999); Cockburn and Dostie (2007); Cooke (1998); Gebru and Bezu (2014);Ilahi (2001); Ilahi and Grimard (2000); Nankhuni and Findeis (2004); Ndiritu and Nyangena (2011).

34This is called wealth paradox in the child labor literature.

66

Figure K.1: Number of hours per week on fetching water by age and school enrollment

2.4

6.2

13.3

9.0

2.6

6.1

12.5

7.9

05

10

15

Age 5−12 Age 13−18 Age 5−12 Age 13−18

Not In S

chool

In S

chool

Not In S

chool

In S

chool

Not In S

chool

In S

chool

Not In S

chool

In S

chool

Female MaleH

ou

rs s

pe

nt

in d

om

estic la

bo

r

Graphs by SEX

Table K.3: Number of Hours Spent in Domestic Work

Dependent Variable Number of Hours Spent in Domestic Work

Without Individual Fixed Effects With Individual Fixed Effects

(1) (2) (3) (4)Age 5-12 Age 13-18 Age 5-12 Age 13-18

Currently attending school 0.897∗∗∗ -4.284∗∗∗ 0.848∗∗∗ -3.288∗∗∗

(0.159) (0.409) (0.249) (0.771)

Control Yes Yes Yes Yes

Non-drought mean 5.05 8.68 5.05 8.68Adjusted R2 0.13 0.07 0.05 0.09Observation 12,182 7,788 12,182 7,788

Notes: Standard errors in parentheses, ∗p < .10,∗∗ p < .05,∗∗∗ p < .01. The outcome variable is total laborhours of fetching water, agriculture and firewood collection. The school variable is a binary variable of one ifthe respondent is currently attending school, zero otherwise. Control includes rainfall of the interview weeks,month/year fixed effect and local ten years historical mean rainfall of the interview week, and dummy variablesof age of respondents and household size.

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K.2.2 Effect of Drought on Current School Enrollment

Table K.4 shows the effect of drought on current school enrollment. Experience of drought does

not affect current school enrollment in any age group (Column 1-4). The current estimates do not

find any evidence of drought on school enrollment of the year. However, further investigation is

necessary to investigate whether the experience of drought (hence the increase in labor supply)

affects the educational performance or schooling outcomes over a longer timespan.35

Table K.4: The effect of drought on school enrollment

Age 5-12 Age 13-18

(1) (2) (3) (4)Boy Girl Boy Girl

Drought 0.031 0.027 -0.012 0.014(0.020) (0.023) (0.023) (0.024)

Individual FE Yes Yes Yes Yes

Control Yes Yes Yes Yes

Non-drought mean 0.80 0.78 0.80 0.83Adjusted R2 0.19 0.19 0.09 0.06Observation 6,173 6,212 3,829 4,114

Notes: Standard errors in parentheses, ∗p < .10,∗∗ p < .05,∗∗∗ p < .01. Thedrought is defined as the bottom tenth percentile of the historical distributionof rainfall. Control includes rainfall of the interview weeks, month/year fixedeffect and local ten years historical mean rainfall of the interview week, anddummy variables of age of respondents and household size.

35The LSMS from Uganda does not ask the study time nor school absence.

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