Applying benefit-cost analysis to an environmental health program: the case of

sanitation

Joe Cook

(Washington State University)

“Benefit-Cost Analysis of Community-Led Total Sanitation: Incorporating Results from Recent Evaluations” by Mark Radin, Marc Jeuland, Hua

Wang, and Dale Whittington. Guidelines for BCA Project Working Paper , 2019

Rural Open Defecation (percent of population)

0.1%-0.6%

0.6%-3.3%

3.3%-11.5%

11.5%-23%

23%-76%

CLTS RCTs (2009-2017)Reference Location Intervention Baseline Endline Control/

Treatment Villages

Inclusion criteria

Clasen et al. (2014)

India TSC Sept–Oct 09

Oct –Dec 13

50/50 Villages w/ <10% latrine coverage & improved water. HHs w/ children <4 or pregnant females.

Hammer & Spears (2016)

India TSC Feb 04 Aug 05 30/30 District purposefully selected.

Pattanayak et al. (2009)

India TSC Aug 05 Aug 06 20/20 Villages w/ low latrine coverage, adequate water, roads, and 70-500 HHs. HHs w/ children <5.

Patil et al. (2014)

India TSC May–Jul 09

Feb –Apr 11

40/40 Willing villages and HHs w/ children < 2.

Gutieras et al. (2015)

Bangladesh CLTS w/ and w/o subsidies

Dec 11–Feb 12

May –Jul 13

66/(115,49)

Villages w/ low latrine coverage & no WASH interventions.

Cameron et al. (2013)

Indonesia CLTS & TSSM

Aug–Sept 08

Nov 10 – Feb 11

80/80 Villages purposefully sampled. HHs w/ children < 2.

Briceño et al. (2017)

Tanzania CLTS & TSSM

May –Nov 12

96/94 HH w/ children <5 & in village since 09.

Elbers et al. (2012)

Mozambique CLTS 08 10 20/20 District purposefully selected.

Pickering et al. (2015)

Mali CLTS Apr –Jun 11

Mar –May 13

61/60 Villages w/ <60% latrine coverage & between 30-70 HHs. HHs w/ children ≤ 10.

TSC= Indian Total Sanitation Campaign, CLTS plus supply side intervention, TSM = World Bank Total Sanitation and Marketing, CLTS plus supply side intervention.

Community-Led Total

Sanitation (CLTS)

Household Latrine

ConstructionLatrine

Use/Reduced Open

Defecation

Community Social Norm

Diarrhea Outcomes

Documented Change: Briceño et al. (2017)Cameron et al. (2013)Clasen et al. (2014)Elbers et al. (2012)Gutieras et al. (2015)Hammer & Spears (2016)Pattanayak et al. (2009)Patil et al. (2014)Pickering et al. (2015)

Documented Change: Briceño et al. (2017)Cameron et al. (2013)Elbers et al. (2012)Gutieras et al. (2015)Pattanayak et al. (2009)Patil et al. (2014)Pickering et al. (2015)

Documented Change:Cameron et al. (2013) (diarrhea)Hammer and Spears (2016) (diarrhea)

Documented Change:Pickering et al. (2015)

Other Health Outcomes

Non-Health Outcomes

Cameron et al. (2013) (Mucus/blood in stool)Hammer and Spears (2016) (Height-for-age)Pattanayak et al. (2009)/Dickinson et al. (2015) (mid-arm circumference, height-for-age, weight-for-age)Patil et al. 2014 (acute lower respiratory illness, enteric parasite)Pickering et al. (2015) (height-for-age, blood in stool, diarrhea mortality)

Pattanayak et al. (2009)/Dickinson et al. (2015) (time savings)Pickering et al. (2015) (women’s privacy and safety at night when defecating)

Causal Chain: CLTS ➙ Outcomes

Benefit-Cost Analyses of Sanitation InterventionsReference Intervention BCR % Health

BenefitsHutton and Haller (2004) Water and sanitation MDG ~6.5-11.7 ~40%Hutton et al. (2007) Water and sanitation MDG 5-46 ~40%Whittington et al. (2009) CLTS 2.7-3 ~80%Winara et al. (2011) CLTS 1.7-2.3 ~50%Heng et al. (2012) CLTS 0.84-1.4 ~50%Rijsberman and Zwane (2012) CLTS++ 4 - 7

Whittington et al. (2012) CLTS 0.6 -10 ~85%

Hutton (2015) Universal access 4.5 – 7.3 ~30%Larsen (2016) Private improved sanitation 1.1 – 2.6 ~50%Sklar (2017) Pit latrines with septic tanks 0.5-2 ~55%Whittington et al. (2017) CLTS 0.5-3 ~66%Larsen (2018) Hardware/ Behavior change 2.2-9 25-40%Hutton et al. (2018) Swachh Bharat Mission <1-12.4 30-40%Larsen (2018) Hardware/ Behavior change 1.8-7.8 30-55%

Hypothetical Rural District in Sub-Saharan AfricaPopulation of 100,000

• 200 villages• Each village has 100 households• Each household has five members: two adults, two

children 5-14, one child < 5 years• Low-, medium- and high-uptake villages• 10 year planning horizon

Main BCA categories Benefits

Mortality reduction benefits (VSL) Ref. case guidanceMorbidity reduction benefits =

(Cost of illness approach) Ref. case guidanceTime savings Ref case guidance

CostsProgram delivery (“software”) (>> capital costs)Latrine constructionHousehold time costs for participating in the CLTS program (often ignored)

Valuing time savings (Whittington & Cook 2019 JBCA)

Multiply “time saved” (hours per month) by “shadow value of time” (VOT)…

Benefits per month = [Hours saved per month] x VOT

What is VOT?

Value of time (VOT): Two Observations

1) Likely to vary across activities (sectors)2) Heterogeneity across individuals engaged in

the same activity

What’s different about low-income countries?

1) More activities outside the formal sector ➜ empirical estimates of VOT using nonmarket valuation methods more important (less reliance on theory)

2) Fewer, lower taxes on income ➜ distinction between before and after-tax wages is less important

3) Data on time savings and wage rates may be harder to obtain from secondary sources ➜ greater need for some primary data collection

Where do guideline recommendations come from?

Two nonmarket valuation approaches have been used:1) Revealed preference (e.g. travel cost)2) Stated preference (e.g. contingent valuation)

Review of 10 empirical VOT studies in low- and middle-income countries. • Most related to travel time• Evidence consistent with “rule of thumb” of 50% of wage rate.

Most in range of 25-75%. • Few direct estimates of VOT for waiting.

Recommendations for valuing time

Step 1 - see if the majority of time changes are being devoted to income generating activities ➜ use the average household after-tax (i.e. take home) wage rate as the value of time Step 2 – if most of the time savings are not devoted to income-generating activities ➜ do a sensitivity analysis to see if VOT between 25-75% of the after-tax market wage has a significant effect on the results of the benefit-cost analysis. If not, primary data collection is probably not warranted.Step 3 – If changes in the values of VOT between 25-75% do affect the results ➜consider doing primary data collection to estimate VOTStep 4 – If the distribution of benefits and costs is especially important in the benefit-cost analysis ➜ consider doing primary data collection to estimate heterogeneity in the VOT across households

CLTS Results

Distribution of benefits: high-uptake villages

Sensitivity Analysis – Monte Carlo

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Cumulative Distribution of BCR without Externality

Conclusions• CLTS likely to pass BCA in many situations, but not

very attractive BCR.• Large $$ resources towards RCTs of CLTS, but oddly

still large uncertainty on effect on diarrhea, incl. externality (only modest impact here).

• Little use for additional “desktop” reviews. Hard work of local primary data collection to facilitate targeting

Thank you!