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Page 1: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

We considered a range of different issues around coordinating information on water

Page 2: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

User Interactions

User Interactions

Markets, Politics

Markets, Politics

UsabilityUsability

CostCost

Format Limitations

Format Limitations

Information Context

Information Context

LocationLocation AvailabilityAvailability PricePrice QualityQualityLaw of One PriceLaw of One Price

Collective Action ProblemsCollective Action Problems

Winners / LosersWinners / Losers

Gangs & GovernmentGangs & Government

We considered a range of different issues around coordinating information on water

Page 3: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

User Interactions

User Interactions

UsabilityUsability

CostCost

Format Limitations

Format Limitations

Page 4: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

User Interactions

User Interactions

UsabilityUsability

CostCost

Format Limitations

Format Limitations

• S MS• Flashing• V oice• U SSD

Page 5: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

User Interactions

User Interactions

UsabilityUsability

CostCost

Format Limitations

Format Limitations

• SMS• Flashing• V oice• USSD

(in Swahili)

Page 6: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Information Context

Information Context

LocationLocation AvailabilityAvailability PricePrice QualityQuality

Page 7: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Information Context

Information Context

LocationLocation AvailabilityAvailability PricePrice QualityQuality

Page 8: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Information Context

Information Context

LocationLocation AvailabilityAvailability PricePrice QualityQuality

Page 9: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Information Context

Information Context

LocationLocation AvailabilityAvailability PricePrice QualityQuality

• Describinglocations

Page 10: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

identifying landmarks

Page 11: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

identifying landmarks

Page 12: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Information Context

Information Context

LocationLocation AvailabilityAvailability PricePrice QualityQuality

• Describinglocations

Page 13: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Markets, Politics

Markets, Politics

Law of One PriceLaw of One Price

Collective Action ProblemsCollective Action Problems

Winners / LosersWinners / Losers

Gangs & GovernmentGangs & Government

Page 14: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Markets, Politics

Markets, Politics

Law of One PriceLaw of One Price

Collective Action ProblemsCollective Action Problems

Winners / LosersWinners / Losers

Gangs & GovernmentGangs & Government

Page 15: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Markets, Politics

Markets, Politics

Law of One PriceLaw of One Price

Collective Action ProblemsCollective Action Problems

Winners / LosersWinners / Losers

Gangs & GovernmentGangs & Government

Summer/Fall 2007: Mass disconnections

“It was a police operation to do away with illegal cartels…”

“More than 90% of vendors [in Mathare] steal our water…”

NWC, Crow & Obada 2009

Page 16: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Markets, Politics

Markets, Politics

Law of One PriceLaw of One Price

Collective Action ProblemsCollective Action Problems

Winners / LosersWinners / Losers

Gangs & GovernmentGangs & Government

Summer/Fall 2007: Mass disconnections

“It was a police operation to do away with illegal cartels…”

“More than 90% of vendors [in Mathare] steal our water…”

NWC, Crow & Obada 2009

New York Times, June 20 2007:

“Might Drink Your Blood, but Otherwise Not Bad Guys”

Page 17: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Markets, Politics

Markets, Politics

Law of One PriceLaw of One Price

Collective Action ProblemsCollective Action Problems

Winners / LosersWinners / Losers

Gangs & GovernmentGangs & Government

Information Context

Information Context

LocationLocation AvailabilityAvailability PricePrice QualityQuality

User Interactions

User Interactions

UsabilityUsability

CostCost

Format Limitations

Format Limitations

• SMS• Flashing• V oice• USSD

(in Swahili)

• Describinglocations

Page 18: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

suggestive evidence

• Better water information can improve clean water uptake (Jalan and Somanathan 2008; Luoto 2009; Madajewicz et al. 2007).

• Demand for water elastic at low prices but inelastic at high prices (Ashraf et al. 2007; Berry et al. 2008; Kremer et al. 2009).

• The poor are willing to pay slightly more “with their feet” for clean water (Kremer et al. 2009).

• Mobile phones can improve market efficiency (Aker 2010; Jensen 2007).

Page 19: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

randomized impact evaluation

Page 20: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

• Gold standard of impact evaluations

• Entail random assignment of individuals or groups into “treatment” and “control” groups

• Outcomes compared across treatment and control groups

• Randomization eliminates selection bias and supports causal inference

randomized impact evaluation

GOAL: Obtain convincing estimates of M-Maji’scost-effectiveness

Page 21: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

randomized impact evaluation

Page 22: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

randomized impact evaluation

Page 23: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

randomized impact evaluation

Page 24: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

User Feedback?Reduced Water Prices?

Lower Travel Costs?Higher Clean Water Uptake?

randomized impact evaluation

Page 25: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

www.umande.org

Our Community Partner

Page 26: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

www.umande.org

M- Maji

Page 27: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Baseline Data Collection

Household Survey(1000 randomly selected households asked about access to clean water and other public services)

Water Prices(Water prices sampled once a week in all villages, 10-20

weeks, following dry cycle)

Water Quality(Same 60 watering points tested for fecal contamination

every 3 weeks for 9 weeks)

GPS Study(Jerry cans used for collecting water rigged with GPS

data loggers to study water search paths)

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African Population and Health Research CenterCenter for Disease Control (CDC)

Center on Housing Rights and Evictions (COHRE)Herufi Library (Kenya Census)

Kenya Ministry of HousingKenya National Bureau of Statistics (KNBS)

Langata Stakeholders ForumNairobi Water and Sewerage Company

Map KiberaUmande Trust

UN HabitatWater and Sanitation for the Urban Poor (WSUP)

Page 29: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Baseline Data Collection

Household Survey(1000 randomly selected households asked about access to clean water and other public services)

Water Prices(Water prices sampled once a week in all villages, 10-20

weeks, following dry cycle)

Water Quality(Same 60 watering points tested for fecal contamination

every 3 weeks for 9 weeks)

GPS Study(Jerry cans used for collecting water rigged with GPS

data loggers to study water search paths)

Page 30: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

sampling design

Source: Kenya National Bureau of Statistics (2011)

Page 31: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Price for water normally and during shortages?Distance to closest watering point?Village where water is found during shortages?Importance of water price, distance, and quality?Knowledge of water-borne diseases?Water treatment habits and diarrheal infection rates?Access to electricity, toilets, schools, clinics, police?Access to mobile phones?Participation in community organizations and village meetings?Knowledge of local political leaders?Quality of roof, wall, floor?Education, Ethnicity?

Page 32: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Baseline Data Collection

Household Survey(1000 randomly selected households asked about access to clean water and other public services)

Water Prices(Water prices sampled once a week in all villages, 10-20

weeks, following dry cycle)

Water Quality(Same 60 watering points tested for fecal contamination

every 3 weeks for 9 weeks)

GPS Study(Jerry cans used for collecting water rigged with GPS

data loggers to study water search paths)

Page 33: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Source: Map Kibera (2011)

Source: W

orld Bank (2005)

Page 34: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Baseline Data Collection

Household Survey(1000 randomly selected households asked about access to clean water and other public services)

Water Prices(Water prices sampled once a week in all villages, 10-20

weeks, following dry cycle)

Water Quality(Same 60 watering points tested for fecal contamination

every 3 weeks for 9 weeks)

GPS Study(Jerry cans used for collecting water rigged with GPS

data loggers to study water search paths)

Page 35: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Courtesy: Ben Crow, UCSC

Page 36: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Baseline Data Collection

Household Survey(1000 randomly selected households asked about access to clean water and other public services)

Water Prices(Water prices sampled once a week in all villages, 10-20

weeks, following dry cycle)

Water Quality(Same 60 watering points tested for fecal contamination

every 3 weeks for 9 weeks)

GPS Study(Jerry cans used for collecting water rigged with GPS

data loggers to study water search paths)

Page 37: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

1. Advertisements (brochures, wall paintings)

2. Community meetings, women’s groups

3. Organize first group of test users

4. Formal M-Maji launch with community

M-Maji Launch (May/June 2011)

Welcome to M-Maji!

1. Request Water2. Sell Water3. Complain

OK < > Back

Page 38: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

M-Maji Launch (May/June 2011)

M‐Maji

helping residents of Kibera find clean, affordable water

on their mobile phones

WHO WE ARE

M-Maji is a team of American and Kenyan students,

professors, and designers. We are based at Stanford

University and the University of Nairobi, and in Kibera we are collaborating with local

community partner Umande Trust. We are committed to

increasing access to clean, safe water in Kibera, Kenya.

For more information visit: http://mmaji.wordpress.com

or e-mail: [email protected]

HOW IT WORKS

M-Maji collects advertisements

from water sellers in Kibera and puts them into a location-

specific database.

Water buyers

can then access this database to see where there is water nearby.

M‐Maji

The M-Maji team solicits feedback and conducts

random quality testing to ensure that buyers get the quality of information they

deserve.

Page 39: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

M-Maji Launch (May/June 2011)

As a non‐profit project, M‐Maji seeks to save communi es me and money in their daily search for water, and empower them with knowledge about water quality. M‐Maji operates in close collabora on with Umande Trust and Stanford University (USA). Please visit M‐Maji at: h p://mmaji.wordpress.com/. Contact: Sunny Jeon, M‐Maji Project Lead Email: [email protected]; Tel: +254 (0)701 320 995

Communi es in Kibera lack consistent access to clean and affordable drinking water. But they do not lack access to mobile phones. M‐Maji uses this insight to develop an innova ve, cost‐effec ve IT solu on to help clean water delivery systems in Kibera.

Sample Screenshot for Water Vendor

Sample Screenshots for Water Buyers

Please describe the location you are selling water using less than 160 characters.

OK < > BACK

In what village are you looking

for water?

1. Kisumu Ndogo 2. Kambi Muri 3. Makina

OK < > BACK

KSH2 per jerry can @ Salome’s next to St. Marys Church, facing

mobile charging station.

OK < > BACK

M-Maji

Page 40: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

1. Advertisements (brochures, wall paintings)

2. Community meetings, women’s groups

3. Organize first group of test users

4. Formal M-Maji launch with community

M-Maji Launch (May/June 2011)

Welcome to M-Maji!

1. Request Water2. Sell Water3. Complain

OK < > Back

Page 41: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Welcome to M-Maji!

1. Request Water2. Sell Water3. Complain

OK < > Back

User Feedback?Reduced Water Prices?

Lower Travel Costs?Higher Clean Water Uptake?

M-Maji

Page 42: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

1. Model for sustainability

4 key challenges

Page 43: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

1. Model for sustainability

2. Partnering with telecomm companies

4 key challenges

Page 44: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access
Presenter
Presentation Notes
Safaricom 4 hops = Ksh 6 per USSD, Ksh 1.5 per SMS; means 58,800 + 12,000 =
Page 45: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

3 Villages, 20,000 households

10% of households adopt M-Maji 1/week, 150 vendors use 3/week

9,800 USSD sessions/monthand 8,000 SMS/month

Presenter
Presentation Notes
Safaricom 4 hops = Ksh 6 per USSD, Ksh 1.5 per SMS; means 58,800 + 12,000 =
Page 46: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

3 Villages, 20,000 households

10% of households adopt M-Maji 1/week, 150 vendors use 3/week

9,800 USSD sessions/monthand 8,000 SMS/month

On YU:

KSH 13,800 = $172/month

On SAFARICOM:

KSH 70,800 = $885/month

Both Networks: $363/month service charge

Presenter
Presentation Notes
Safaricom 4 hops = Ksh 6 per USSD, Ksh 1.5 per SMS; means 58,800 + 12,000 =
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1. Model for sustainability

2. Partnering with telecomm companies

3. Test and refine prototype

4 key challenges2KSH @ George’s

Watering Point, near St. Mary’s

Primary School in Gatwekera

OK < > Back

LAI

Presenter
Presentation Notes
m-maji connects sellers like Samuel with buyers like Salome and brings better information to the Kibera water market. Sunny’s going to tell you a bit more about the design choices behind m-maji, and some of the issues we considered.
Page 48: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

1. Model for sustainability

2. Partnering with telecomm companies

3. Test and refine prototype

4. Navigate through Kibera NGO industry

4 key challenges

Page 49: We considered a range of different issues around coordinating … · 2018-05-14 · Baseline Data Collection. Household Survey (1000 randomly selected households asked about access

Special ThanksLeann Bankoski

Prof. Michele BarryAidah Binale

Prof. Joshua CohenProf. Ben Crow

Prof. Rob DainesProf. Larry Diamond

Erica EstradaWendy Ju

Kelvin Bai LugakaProf. Edward Miguel

Sally MadsenCaleb Ogara

Josiah OmottoProf. Dan OrwaJackie Sargent

Prof. Peter WaiganjoProf. Terry Winograd

andour amazing collaborators

at Umande and the Univ. of Nairobi