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Peer effects and externalities in technology adoption: Evidence from community reporting in Uganda Romain Ferrali 1 Guy Grossman 2 Melina Platas Izama 3 Jonathan Rodden 4 1 Princeton 2 UPenn 3 NYU Abu-Dhabi 4 Stanford December 15, 2017 SITE – Stockholm School of Economics Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 1 / 29

Peer effects and externalities in technology adoption: Evidence from community reporting in Uganda

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Peer effects and externalities in technology adoption:Evidence from community reporting in Uganda

Romain Ferrali1 Guy Grossman2 Melina Platas Izama3 Jonathan Rodden4

1Princeton 2UPenn 3NYU Abu-Dhabi 4Stanford

December 15, 2017

SITE – Stockholm School of Economics

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 1 / 29

Overview

Motivation

Developing countries: persistent poor public service delivery

← frontlineproviders not sufficiently monitored / supervised by public officials

Possible solution: community monitoring – monitoring costs can be high(time, possible retaliation) while benefits are diffused

Modified solution: ICTs platforms that support community reporting(immediate, inexpensive, anonymous, “comparative advantage”)

Take-up: matters for both efficiency and equity reasons

Variable adoption rates → why do community reporting ICT platforms (andPCTs more generally) get adopted in some places and not in other?

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 2 / 29

Overview

Motivation

Developing countries: persistent poor public service delivery ← frontlineproviders not sufficiently monitored / supervised by public officials

Possible solution: community monitoring – monitoring costs can be high(time, possible retaliation) while benefits are diffused

Modified solution: ICTs platforms that support community reporting(immediate, inexpensive, anonymous, “comparative advantage”)

Take-up: matters for both efficiency and equity reasons

Variable adoption rates → why do community reporting ICT platforms (andPCTs more generally) get adopted in some places and not in other?

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 2 / 29

Overview

Motivation

Developing countries: persistent poor public service delivery ← frontlineproviders not sufficiently monitored / supervised by public officials

Possible solution: community monitoring

– monitoring costs can be high(time, possible retaliation) while benefits are diffused

Modified solution: ICTs platforms that support community reporting(immediate, inexpensive, anonymous, “comparative advantage”)

Take-up: matters for both efficiency and equity reasons

Variable adoption rates → why do community reporting ICT platforms (andPCTs more generally) get adopted in some places and not in other?

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 2 / 29

Overview

Motivation

Developing countries: persistent poor public service delivery ← frontlineproviders not sufficiently monitored / supervised by public officials

Possible solution: community monitoring – monitoring costs can be high(time, possible retaliation) while benefits are diffused

Modified solution: ICTs platforms that support community reporting(immediate, inexpensive, anonymous, “comparative advantage”)

Take-up: matters for both efficiency and equity reasons

Variable adoption rates → why do community reporting ICT platforms (andPCTs more generally) get adopted in some places and not in other?

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 2 / 29

Overview

Motivation

Developing countries: persistent poor public service delivery ← frontlineproviders not sufficiently monitored / supervised by public officials

Possible solution: community monitoring – monitoring costs can be high(time, possible retaliation) while benefits are diffused

Modified solution: ICTs platforms that support community reporting(immediate, inexpensive, anonymous, “comparative advantage”)

Take-up: matters for both efficiency and equity reasons

Variable adoption rates → why do community reporting ICT platforms (andPCTs more generally) get adopted in some places and not in other?

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 2 / 29

Overview

Motivation

Developing countries: persistent poor public service delivery ← frontlineproviders not sufficiently monitored / supervised by public officials

Possible solution: community monitoring – monitoring costs can be high(time, possible retaliation) while benefits are diffused

Modified solution: ICTs platforms that support community reporting(immediate, inexpensive, anonymous, “comparative advantage”)

Take-up: matters for both efficiency and equity reasons

Variable adoption rates → why do community reporting ICT platforms (andPCTs more generally) get adopted in some places and not in other?

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 2 / 29

Overview

Motivation

Developing countries: persistent poor public service delivery ← frontlineproviders not sufficiently monitored / supervised by public officials

Possible solution: community monitoring – monitoring costs can be high(time, possible retaliation) while benefits are diffused

Modified solution: ICTs platforms that support community reporting(immediate, inexpensive, anonymous, “comparative advantage”)

Take-up: matters for both efficiency and equity reasons

Variable adoption rates

→ why do community reporting ICT platforms (andPCTs more generally) get adopted in some places and not in other?

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 2 / 29

Overview

Motivation

Developing countries: persistent poor public service delivery ← frontlineproviders not sufficiently monitored / supervised by public officials

Possible solution: community monitoring – monitoring costs can be high(time, possible retaliation) while benefits are diffused

Modified solution: ICTs platforms that support community reporting(immediate, inexpensive, anonymous, “comparative advantage”)

Take-up: matters for both efficiency and equity reasons

Variable adoption rates → why do community reporting ICT platforms (andPCTs more generally) get adopted in some places and not in other?

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 2 / 29

Overview

Technology adoption literature: networks matter!

Social learning from peers (friends, family, co-workers) facilitates theadoption of new technologies

Early adopting peers (network ties) reduce uncertainty over costs and benefitsof using a new technology

Costs of communication with network ties are lower and their opinion isgenerally more trustworthy

Networks shown to support diffusion of technologies from agriculture bestpractices (Conley & Udry 2010) to medical innovations (Coleman 1966), etc.

Open question: do networks support the adoption of politicalcommunication technologies, such as community reporting platforms?

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 3 / 29

Overview

Technology adoption literature: networks matter!

Social learning from peers (friends, family, co-workers) facilitates theadoption of new technologies

Early adopting peers (network ties) reduce uncertainty over costs and benefitsof using a new technology

Costs of communication with network ties are lower and their opinion isgenerally more trustworthy

Networks shown to support diffusion of technologies from agriculture bestpractices (Conley & Udry 2010) to medical innovations (Coleman 1966), etc.

Open question: do networks support the adoption of politicalcommunication technologies, such as community reporting platforms?

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 3 / 29

Overview

Technology adoption literature: networks matter!

Social learning from peers (friends, family, co-workers) facilitates theadoption of new technologies

Early adopting peers (network ties) reduce uncertainty over costs and benefitsof using a new technology

Costs of communication with network ties are lower and their opinion isgenerally more trustworthy

Networks shown to support diffusion of technologies from agriculture bestpractices (Conley & Udry 2010) to medical innovations (Coleman 1966), etc.

Open question: do networks support the adoption of politicalcommunication technologies, such as community reporting platforms?

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 3 / 29

Overview

Technology adoption literature: networks matter!

Social learning from peers (friends, family, co-workers) facilitates theadoption of new technologies

Early adopting peers (network ties) reduce uncertainty over costs and benefitsof using a new technology

Costs of communication with network ties are lower and their opinion isgenerally more trustworthy

Networks shown to support diffusion of technologies from agriculture bestpractices (Conley & Udry 2010) to medical innovations (Coleman 1966), etc.

Open question: do networks support the adoption of politicalcommunication technologies, such as community reporting platforms?

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 3 / 29

Overview

Technology adoption literature: networks matter!

Social learning from peers (friends, family, co-workers) facilitates theadoption of new technologies

Early adopting peers (network ties) reduce uncertainty over costs and benefitsof using a new technology

Costs of communication with network ties are lower and their opinion isgenerally more trustworthy

Networks shown to support diffusion of technologies from agriculture bestpractices (Conley & Udry 2010) to medical innovations (Coleman 1966), etc.

Open question: do networks support the adoption of politicalcommunication technologies, such as community reporting platforms?

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 3 / 29

Overview

Research design in nutshell

We study how social ties contribute to the adoption of a new mobile-basedpolitical communication platform in a low-income country setting

The communication platform enables citizens to report service deliveryproblems via text-messages to their (Ugandan) local government

The political communication platform was introduced to 130 Ugandanvillages using a field experimental design

Consistent with past research on “ICT for better governance,” adoption ratesamong the treatment villages were highly uneven

We explore the role of social diffusion by collecting ‘whole’ network data from16 treatment villages as well as data on knowledge and usage of the platform

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 4 / 29

Overview

Research design in nutshell

We study how social ties contribute to the adoption of a new mobile-basedpolitical communication platform in a low-income country setting

The communication platform enables citizens to report service deliveryproblems via text-messages to their (Ugandan) local government

The political communication platform was introduced to 130 Ugandanvillages using a field experimental design

Consistent with past research on “ICT for better governance,” adoption ratesamong the treatment villages were highly uneven

We explore the role of social diffusion by collecting ‘whole’ network data from16 treatment villages as well as data on knowledge and usage of the platform

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 4 / 29

Overview

Research design in nutshell

We study how social ties contribute to the adoption of a new mobile-basedpolitical communication platform in a low-income country setting

The communication platform enables citizens to report service deliveryproblems via text-messages to their (Ugandan) local government

The political communication platform was introduced to 130 Ugandanvillages using a field experimental design

Consistent with past research on “ICT for better governance,” adoption ratesamong the treatment villages were highly uneven

We explore the role of social diffusion by collecting ‘whole’ network data from16 treatment villages as well as data on knowledge and usage of the platform

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 4 / 29

Overview

Research design in nutshell

We study how social ties contribute to the adoption of a new mobile-basedpolitical communication platform in a low-income country setting

The communication platform enables citizens to report service deliveryproblems via text-messages to their (Ugandan) local government

The political communication platform was introduced to 130 Ugandanvillages using a field experimental design

Consistent with past research on “ICT for better governance,” adoption ratesamong the treatment villages were highly uneven

We explore the role of social diffusion by collecting ‘whole’ network data from16 treatment villages as well as data on knowledge and usage of the platform

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 4 / 29

Overview

Research design in nutshell

We study how social ties contribute to the adoption of a new mobile-basedpolitical communication platform in a low-income country setting

The communication platform enables citizens to report service deliveryproblems via text-messages to their (Ugandan) local government

The political communication platform was introduced to 130 Ugandanvillages using a field experimental design

Consistent with past research on “ICT for better governance,” adoption ratesamong the treatment villages were highly uneven

We explore the role of social diffusion by collecting ‘whole’ network data from16 treatment villages as well as data on knowledge and usage of the platform

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 4 / 29

Overview

Findings and Theory in Nutshell

1 Empirics I

on average, networks facilitated adoption...

but had no effect in half the villages.

2 Theory: networks effects depend on the goods’ externalities

past work demonstrated peer effects on the adoption of goods with minimalexternalities...

networks have no effect on the adoption of goods characterized by significantexternalities (e.g. PCTs) ...

unless the community enforces truthful communication...

which crucially depends on informal institutions and leadership structure

3 Empirics II

find support for the model’s testable implications

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 5 / 29

Overview

Findings and Theory in Nutshell

1 Empirics I

on average, networks facilitated adoption...

but had no effect in half the villages.

2 Theory: networks effects depend on the goods’ externalities

past work demonstrated peer effects on the adoption of goods with minimalexternalities...

networks have no effect on the adoption of goods characterized by significantexternalities (e.g. PCTs) ...

unless the community enforces truthful communication...

which crucially depends on informal institutions and leadership structure

3 Empirics II

find support for the model’s testable implications

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 5 / 29

Overview

Findings and Theory in Nutshell

1 Empirics I

on average, networks facilitated adoption...

but had no effect in half the villages.

2 Theory: networks effects depend on the goods’ externalities

past work demonstrated peer effects on the adoption of goods with minimalexternalities...

networks have no effect on the adoption of goods characterized by significantexternalities (e.g. PCTs) ...

unless the community enforces truthful communication...

which crucially depends on informal institutions and leadership structure

3 Empirics II

find support for the model’s testable implications

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 5 / 29

Overview

Findings and Theory in Nutshell

1 Empirics I

on average, networks facilitated adoption...

but had no effect in half the villages.

2 Theory: networks effects depend on the goods’ externalities

past work demonstrated peer effects on the adoption of goods with minimalexternalities...

networks have no effect on the adoption of goods characterized by significantexternalities (e.g. PCTs) ...

unless the community enforces truthful communication...

which crucially depends on informal institutions and leadership structure

3 Empirics II

find support for the model’s testable implications

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 5 / 29

Overview

Findings and Theory in Nutshell

1 Empirics I

on average, networks facilitated adoption...

but had no effect in half the villages.

2 Theory: networks effects depend on the goods’ externalities

past work demonstrated peer effects on the adoption of goods with minimalexternalities...

networks have no effect on the adoption of goods characterized by significantexternalities (e.g. PCTs) ...

unless the community enforces truthful communication...

which crucially depends on informal institutions and leadership structure

3 Empirics II

find support for the model’s testable implications

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 5 / 29

Overview

Findings and Theory in Nutshell

1 Empirics I

on average, networks facilitated adoption...

but had no effect in half the villages.

2 Theory: networks effects depend on the goods’ externalities

past work demonstrated peer effects on the adoption of goods with minimalexternalities...

networks have no effect on the adoption of goods characterized by significantexternalities (e.g. PCTs) ...

unless the community enforces truthful communication...

which crucially depends on informal institutions and leadership structure

3 Empirics II

find support for the model’s testable implications

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 5 / 29

Overview

Findings and Theory in Nutshell

1 Empirics I

on average, networks facilitated adoption...

but had no effect in half the villages.

2 Theory: networks effects depend on the goods’ externalities

past work demonstrated peer effects on the adoption of goods with minimalexternalities...

networks have no effect on the adoption of goods characterized by significantexternalities (e.g. PCTs) ...

unless the community enforces truthful communication...

which crucially depends on informal institutions and leadership structure

3 Empirics II

find support for the model’s testable implications

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 5 / 29

Overview

Findings and Theory in Nutshell

1 Empirics I

on average, networks facilitated adoption...

but had no effect in half the villages.

2 Theory: networks effects depend on the goods’ externalities

past work demonstrated peer effects on the adoption of goods with minimalexternalities...

networks have no effect on the adoption of goods characterized by significantexternalities (e.g. PCTs) ...

unless the community enforces truthful communication...

which crucially depends on informal institutions and leadership structure

3 Empirics II

find support for the model’s testable implications

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 5 / 29

Research Design

Research Design

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 6 / 29

Research Design

District local governments in Uganda

Districts: highest tier of subnational government, responsible foradministering local public services (e.g. health, education, water)

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 7 / 29

Research Design

District local governments in Uganda

Districts: highest tier of subnational government, responsible foradministering local public services (e.g. health, education, water)

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 7 / 29

Research Design

Setting

130 randomly selected villages in Arua are encouraged to use platform

U-Bridge effect on service delivery is discussed in a companion paper

Inception community meetings in 24 treatment clusters (October 2014)

Relatively high, but variable technology take-up across villages

Conducted a full census in 16 villages (summer 2016):

8 high performing and 8 low performing wrt uptake (residuals)

Total of 3, 182 villagers

0

2500

5000

7500

10000

12500

2014−07 2015−01 2015−07

Date

Cum

ulat

ive

num

ber

of m

essa

ges

rece

ived

Type

all

relevant

actionable

0

.02

.04

.06

Den

sity

0 20 40 60 80 100relevant messages per 100 villagers

Variability in message intensity

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 8 / 29

Research Design

Setting

130 randomly selected villages in Arua are encouraged to use platform

U-Bridge effect on service delivery is discussed in a companion paper

Inception community meetings in 24 treatment clusters (October 2014)

Relatively high, but variable technology take-up across villages

Conducted a full census in 16 villages (summer 2016):

8 high performing and 8 low performing wrt uptake (residuals)

Total of 3, 182 villagers

0

2500

5000

7500

10000

12500

2014−07 2015−01 2015−07

Date

Cum

ulat

ive

num

ber

of m

essa

ges

rece

ived

Type

all

relevant

actionable

0

.02

.04

.06

Den

sity

0 20 40 60 80 100relevant messages per 100 villagers

Variability in message intensity

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 8 / 29

Research Design

Setting

130 randomly selected villages in Arua are encouraged to use platform

U-Bridge effect on service delivery is discussed in a companion paper

Inception community meetings in 24 treatment clusters (October 2014)

Relatively high, but variable technology take-up across villages

Conducted a full census in 16 villages (summer 2016):

8 high performing and 8 low performing wrt uptake (residuals)

Total of 3, 182 villagers

0

2500

5000

7500

10000

12500

2014−07 2015−01 2015−07

Date

Cum

ulat

ive

num

ber

of m

essa

ges

rece

ived

Type

all

relevant

actionable

0

.02

.04

.06

Den

sity

0 20 40 60 80 100relevant messages per 100 villagers

Variability in message intensity

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 8 / 29

Research Design

Setting

130 randomly selected villages in Arua are encouraged to use platform

U-Bridge effect on service delivery is discussed in a companion paper

Inception community meetings in 24 treatment clusters (October 2014)

Relatively high, but variable technology take-up across villages

Conducted a full census in 16 villages (summer 2016):

8 high performing and 8 low performing wrt uptake (residuals)

Total of 3, 182 villagers

0

2500

5000

7500

10000

12500

2014−07 2015−01 2015−07

Date

Cum

ulat

ive

num

ber

of m

essa

ges

rece

ived

Type

all

relevant

actionable

0

.02

.04

.06

Den

sity

0 20 40 60 80 100relevant messages per 100 villagers

Variability in message intensity

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 8 / 29

Research Design

Setting

130 randomly selected villages in Arua are encouraged to use platform

U-Bridge effect on service delivery is discussed in a companion paper

Inception community meetings in 24 treatment clusters (October 2014)

Relatively high, but variable technology take-up across villages

Conducted a full census in 16 villages (summer 2016):

8 high performing and 8 low performing wrt uptake (residuals)

Total of 3, 182 villagers

0

2500

5000

7500

10000

12500

2014−07 2015−01 2015−07

Date

Cum

ulat

ive

num

ber

of m

essa

ges

rece

ived

Type

all

relevant

actionable

0

.02

.04

.06

Den

sity

0 20 40 60 80 100relevant messages per 100 villagers

Variability in message intensity

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 8 / 29

Research Design

Setting

130 randomly selected villages in Arua are encouraged to use platform

U-Bridge effect on service delivery is discussed in a companion paper

Inception community meetings in 24 treatment clusters (October 2014)

Relatively high, but variable technology take-up across villages

Conducted a full census in 16 villages (summer 2016):

8 high performing and 8 low performing wrt uptake (residuals)

Total of 3, 182 villagers

0

2500

5000

7500

10000

12500

2014−07 2015−01 2015−07

Date

Cum

ulat

ive

num

ber

of m

essa

ges

rece

ived

Type

all

relevant

actionable

0

.02

.04

.06

Den

sity

0 20 40 60 80 100relevant messages per 100 villagers

Variability in message intensity

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 8 / 29

Research Design

Setting

130 randomly selected villages in Arua are encouraged to use platform

U-Bridge effect on service delivery is discussed in a companion paper

Inception community meetings in 24 treatment clusters (October 2014)

Relatively high, but variable technology take-up across villages

Conducted a full census in 16 villages (summer 2016):

8 high performing and 8 low performing wrt uptake (residuals)

Total of 3, 182 villagers

0

2500

5000

7500

10000

12500

2014−07 2015−01 2015−07

Date

Cum

ulat

ive

num

ber

of m

essa

ges

rece

ived

Type

all

relevant

actionable

0

.02

.04

.06

Den

sity

0 20 40 60 80 100relevant messages per 100 villagers

Variability in message intensity

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 8 / 29

Research Design

Setting

130 randomly selected villages in Arua are encouraged to use platform

U-Bridge effect on service delivery is discussed in a companion paper

Inception community meetings in 24 treatment clusters (October 2014)

Relatively high, but variable technology take-up across villages

Conducted a full census in 16 villages (summer 2016):

8 high performing and 8 low performing wrt uptake (residuals)

Total of 3, 182 villagers0

2500

5000

7500

10000

12500

2014−07 2015−01 2015−07

Date

Cum

ulat

ive

num

ber

of m

essa

ges

rece

ived

Type

all

relevant

actionable

0

.02

.04

.06

Den

sity

0 20 40 60 80 100relevant messages per 100 villagers

Variability in message intensity

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 8 / 29

Research Design

Setting

130 randomly selected villages in Arua are encouraged to use platform

U-Bridge effect on service delivery is discussed in a companion paper

Inception community meetings in 24 treatment clusters (October 2014)

Relatively high, but variable technology take-up across villages

Conducted a full census in 16 villages (summer 2016):

8 high performing and 8 low performing wrt uptake (residuals)

Total of 3, 182 villagers

0

2500

5000

7500

10000

12500

2014−07 2015−01 2015−07

Date

Cum

ulat

ive

num

ber

of m

essa

ges

rece

ived

Type

all

relevant

actionable

0

.02

.04

.06

Den

sity

0 20 40 60 80 100relevant messages per 100 villagers

Variability in message intensity

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 8 / 29

Research Design

Constructing the network: four types of ties

Four undirected networks: tie if i names j and j names i

Family

Friends

Lender

Problem solver

Undirected, weighted union network

tie if tie in any of the four networks

weight is number of ties in the four networks

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 9 / 29

Research Design

Constructing the network: four types of ties

Four undirected networks: tie if i names j and j names i

Family

Friends

Lender

Problem solver

Undirected, weighted union network

tie if tie in any of the four networks

weight is number of ties in the four networks

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 9 / 29

Research Design

Figure: Graphical representation of the union network of two villages in the study area.Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 10 / 29

Research Design

Descriptive statistics of sample

Variable Sample Low uptake High uptake ∆ min maxOutcome % adopters 0.04 0.02 0.07 0.05∗∗∗ 0.00 1.00

% heard 0.31 0.23 0.38 0.14∗∗∗ 0.00 1.00% satisfied 0.39 0.22 0.44 0.22∗∗ 0.00 1.00

Individual age 37.39 37.22 37.55 0.33 18 101% females 0.58 0.59 0.56 -0.03∗∗ 0.00 1.00income -0.45 -0.54 -0.36 0.19∗ -2.00 2.00secondary education 0.23 0.18 0.28 0.09∗∗ 0.00 1.00% use phone 0.62 0.58 0.66 0.08∗ 0.00 1.00% immigrants 0.49 0.48 0.50 0.02 0.00 1.00% leaders 0.14 0.12 0.16 0.04∗∗ 0.00 1.00political participation 0.00 -0.00 0.00 0.00 -1.23 1.77% attend meeting 0.08 0.05 0.11 0.06∗∗∗ 0.00 1.00mean pro-sociality 0.28 0.28 0.29 0.01 0.00 1.00

Network degree 8.79 8.36 9.22 0.86 0.00 217.00betweenness 140.60 132.16 149.01 16.85 0.00 23850clustering coefficient 0.38 0.40 0.37 -0.03 0.00 1.00mean size 199.00 198.62 199.38 0.75

Village adult population 269.38 264.25 274.50 10.25 32 429ethnic fractionalization 0.04 0.02 0.07 0.05 0.00 0.41% employed 0.86 0.89 0.84 -0.05 0.68 1.00% non-agriculture 0.22 0.19 0.25 0.06 0.00 0.57poverty score -0.07 -0.09 -0.05 0.03 -0.48 0.47

N 3184 1589 1595 6

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 11 / 29

Research Design

Estimation

Main specification. Spatial Autoregressive Regression (SAR)

y = λMy + Xβ + ε

y , vector of outcomes: adopt ∈ {0, 1}

M, spatial matrix: union network → # adopting neighborsX , matrix of controls

Network: degree centralityDemographics: age, gender, secondary education, immigrantDesign: usage of phone, meeting attendancePolitics: political participation, leadership positionSpatial influence: autoregressive term with M inverse log-distanceWithin-village comparison: models include village-level FE

Robustness checks

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 12 / 29

Research Design

Estimation

Main specification. Spatial Autoregressive Regression (SAR)

y = λMy + Xβ + ε

y , vector of outcomes: adopt ∈ {0, 1}

M, spatial matrix: union network → # adopting neighborsX , matrix of controls

Network: degree centralityDemographics: age, gender, secondary education, immigrantDesign: usage of phone, meeting attendancePolitics: political participation, leadership positionSpatial influence: autoregressive term with M inverse log-distanceWithin-village comparison: models include village-level FE

Robustness checks

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 12 / 29

Research Design

Estimation

Main specification. Spatial Autoregressive Regression (SAR)

y = λMy + Xβ + ε

y , vector of outcomes: adopt ∈ {0, 1}

M, spatial matrix: union network → # adopting neighbors

X , matrix of controlsNetwork: degree centralityDemographics: age, gender, secondary education, immigrantDesign: usage of phone, meeting attendancePolitics: political participation, leadership positionSpatial influence: autoregressive term with M inverse log-distanceWithin-village comparison: models include village-level FE

Robustness checks

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 12 / 29

Research Design

Estimation

Main specification. Spatial Autoregressive Regression (SAR)

y = λMy + Xβ + ε

y , vector of outcomes: adopt ∈ {0, 1}

M, spatial matrix: union network → # adopting neighborsX , matrix of controls

Network: degree centralityDemographics: age, gender, secondary education, immigrantDesign: usage of phone, meeting attendancePolitics: political participation, leadership positionSpatial influence: autoregressive term with M inverse log-distanceWithin-village comparison: models include village-level FE

Robustness checks

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 12 / 29

Research Design

Estimation

Main specification. Spatial Autoregressive Regression (SAR)

y = λMy + Xβ + ε

y , vector of outcomes: adopt ∈ {0, 1}

M, spatial matrix: union network → # adopting neighborsX , matrix of controls

Network: degree centralityDemographics: age, gender, secondary education, immigrantDesign: usage of phone, meeting attendancePolitics: political participation, leadership positionSpatial influence: autoregressive term with M inverse log-distanceWithin-village comparison: models include village-level FE

Robustness checks

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 12 / 29

Main results

Main results

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 13 / 29

Main results

There is peer influence

Dependent variable: adoptAbsolute threshold Fractional threshold

Parsimonious Baseline Parsimonious Baseline(1) (2) (3) (4)

# adopting neighbors 0.035∗∗∗ 0.027∗∗∗

(0.005) (0.005)% adopting neighbors 0.325∗∗∗ 0.213∗∗∗

(0.052) (0.048)degree 0.002∗∗∗ 0.001∗ 0.004∗∗∗ 0.003∗∗∗

(0.001) (0.001) (0.001) (0.001)Village FE X X X XControls X XObservations 3,184 3,019 3,184 3,019R2 0.139 0.245 0.116 0.231

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 14 / 29

Main results

Robustness checks

Threats to identification:1 Networks may matter differently for “hearing” and “adopting”2 Peer effects may be spurious (homophily, shared context, ...)3 Exposure to encouragements is endogenous to network position

Test Issue Result NotesSelection modelPr(adopt) = Pr(hear)×Pr(adopt|hear)(Larson & Lewis 2017)

1 X peers affect both stages of diffu-sion; adoption variability larger

Instrumental variable(An 2016)

2 X zj → yj → yiInstrument: distance from meetinglocation

Non-parametric controls for degree(Aronow & Samii, nd)

3 X degree strata & GAM

Matching(Aral et al 2009)

2, 3 X full matching on network covariatesand most important predictors ofuptake

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 15 / 29

Main results

Robustness checks

Threats to identification:1 Networks may matter differently for “hearing” and “adopting”2 Peer effects may be spurious (homophily, shared context, ...)3 Exposure to encouragements is endogenous to network position

Test Issue Result NotesSelection modelPr(adopt) = Pr(hear)×Pr(adopt|hear)(Larson & Lewis 2017)

1 X peers affect both stages of diffu-sion; adoption variability larger

Instrumental variable(An 2016)

2 X zj → yj → yiInstrument: distance from meetinglocation

Non-parametric controls for degree(Aronow & Samii, nd)

3 X degree strata & GAM

Matching(Aral et al 2009)

2, 3 X full matching on network covariatesand most important predictors ofuptake

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 15 / 29

Main results

Robustness checks

Threats to identification:1 Networks may matter differently for “hearing” and “adopting”2 Peer effects may be spurious (homophily, shared context, ...)3 Exposure to encouragements is endogenous to network position

Test Issue Result NotesSelection modelPr(adopt) = Pr(hear)×Pr(adopt|hear)(Larson & Lewis 2017)

1 X peers affect both stages of diffu-sion; adoption variability larger

Instrumental variable(An 2016)

2 X zj → yj → yiInstrument: distance from meetinglocation

Non-parametric controls for degree(Aronow & Samii, nd)

3 X degree strata & GAM

Matching(Aral et al 2009)

2, 3 X full matching on network covariatesand most important predictors ofuptake

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 15 / 29

Main results

Robustness checks

Threats to identification:1 Networks may matter differently for “hearing” and “adopting”2 Peer effects may be spurious (homophily, shared context, ...)3 Exposure to encouragements is endogenous to network position

Test Issue Result NotesSelection modelPr(adopt) = Pr(hear)×Pr(adopt|hear)(Larson & Lewis 2017)

1 X peers affect both stages of diffu-sion; adoption variability larger

Instrumental variable(An 2016)

2 X zj → yj → yiInstrument: distance from meetinglocation

Non-parametric controls for degree(Aronow & Samii, nd)

3 X degree strata & GAM

Matching(Aral et al 2009)

2, 3 X full matching on network covariatesand most important predictors ofuptake

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 15 / 29

Main results

Robustness checks

Threats to identification:1 Networks may matter differently for “hearing” and “adopting”2 Peer effects may be spurious (homophily, shared context, ...)3 Exposure to encouragements is endogenous to network position

Test Issue Result NotesSelection modelPr(adopt) = Pr(hear)×Pr(adopt|hear)(Larson & Lewis 2017)

1 X peers affect both stages of diffu-sion; adoption variability larger

Instrumental variable(An 2016)

2 X zj → yj → yiInstrument: distance from meetinglocation

Non-parametric controls for degree(Aronow & Samii, nd)

3 X degree strata & GAM

Matching(Aral et al 2009)

2, 3 X full matching on network covariatesand most important predictors ofuptake

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 15 / 29

Main results

Wide variation across villages

●●

●●

−0.05

0.00

0.05

0.10

B(236)

H(102)

E(263)

C(159)

M(195)

G(163)

D(282)

K(204)

L(228)

P(191)

O(187)

I(168)

F(205)

N(223)

J(185)

Village

Ave

rage

Mar

gina

l Effe

ct

Uptake

● High

Low

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 16 / 29

Model

Model

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 17 / 29

Model

Theory

Adopting a new technology is risky

Potential adopters rely on peers to learn about the costs and benefits

Lying about benefits of technology is costly

Technologies vary in whether adoption generates externalities:

Externalities in adoption: whether the adoption decision of one agentaffects the payoff from adoption of another agent

No externalities (private good). truthful communication is an equilibrium ⇒networks foster learning

Positive externalities (political participation). no truthful communication ⇒networks are ineffective... unless the community can enforce truthfulcommunication.

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 18 / 29

Model

Theory

Adopting a new technology is risky

Potential adopters rely on peers to learn about the costs and benefits

Lying about benefits of technology is costly

Technologies vary in whether adoption generates externalities:

Externalities in adoption: whether the adoption decision of one agentaffects the payoff from adoption of another agent

No externalities (private good). truthful communication is an equilibrium ⇒networks foster learning

Positive externalities (political participation). no truthful communication ⇒networks are ineffective... unless the community can enforce truthfulcommunication.

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 18 / 29

Model

Theory

Adopting a new technology is risky

Potential adopters rely on peers to learn about the costs and benefits

Lying about benefits of technology is costly

Technologies vary in whether adoption generates externalities:

Externalities in adoption: whether the adoption decision of one agentaffects the payoff from adoption of another agent

No externalities (private good). truthful communication is an equilibrium ⇒networks foster learning

Positive externalities (political participation). no truthful communication ⇒networks are ineffective... unless the community can enforce truthfulcommunication.

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 18 / 29

Model

Theory

Adopting a new technology is risky

Potential adopters rely on peers to learn about the costs and benefits

Lying about benefits of technology is costly

Technologies vary in whether adoption generates externalities:

Externalities in adoption: whether the adoption decision of one agentaffects the payoff from adoption of another agent

No externalities (private good). truthful communication is an equilibrium ⇒networks foster learning

Positive externalities (political participation). no truthful communication ⇒networks are ineffective... unless the community can enforce truthfulcommunication.

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 18 / 29

Model

Theory

Adopting a new technology is risky

Potential adopters rely on peers to learn about the costs and benefits

Lying about benefits of technology is costly

Technologies vary in whether adoption generates externalities:

Externalities in adoption: whether the adoption decision of one agentaffects the payoff from adoption of another agent

No externalities (private good). truthful communication is an equilibrium ⇒networks foster learning

Positive externalities (political participation). no truthful communication ⇒networks are ineffective... unless the community can enforce truthfulcommunication.

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 18 / 29

Model

Theory

Adopting a new technology is risky

Potential adopters rely on peers to learn about the costs and benefits

Lying about benefits of technology is costly

Technologies vary in whether adoption generates externalities:

Externalities in adoption: whether the adoption decision of one agentaffects the payoff from adoption of another agent

No externalities (private good). truthful communication is an equilibrium ⇒networks foster learning

Positive externalities (political participation). no truthful communication ⇒networks are ineffective... unless the community can enforce truthfulcommunication.

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 18 / 29

Model

Theory

Adopting a new technology is risky

Potential adopters rely on peers to learn about the costs and benefits

Lying about benefits of technology is costly

Technologies vary in whether adoption generates externalities:

Externalities in adoption: whether the adoption decision of one agentaffects the payoff from adoption of another agent

No externalities (private good). truthful communication is an equilibrium ⇒networks foster learning

Positive externalities (political participation). no truthful communication ⇒networks are ineffective...

unless the community can enforce truthfulcommunication.

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 18 / 29

Model

Theory

Adopting a new technology is risky

Potential adopters rely on peers to learn about the costs and benefits

Lying about benefits of technology is costly

Technologies vary in whether adoption generates externalities:

Externalities in adoption: whether the adoption decision of one agentaffects the payoff from adoption of another agent

No externalities (private good). truthful communication is an equilibrium ⇒networks foster learning

Positive externalities (political participation). no truthful communication ⇒networks are ineffective... unless the community can enforce truthfulcommunication.

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 18 / 29

Model

A model: adoption without externalities

N agents are the nodes of a social network gEach agent i decides whether to adopt a new technology, yi ∈ {0, 1}.

ui (yi , θ) = qθ(yi )− yici

Not adopting gives a payoff of zero: qθ(0) = 0Adoption is costly: ci ∈ (0, 1)Adoption is risky:

at t = 0, nature draws state of the world θ ∈ {H, L}.i is more likely gets benefit B = 1 in the high state: qH(1) > qL(1) = 0

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 19 / 29

Model

Learning and communication

t = 0: nature draws the state θ ∈ {H, L}

t = 1: each agent gets a signal si ∈ {H, L} that matches the state withprobability pi .

pi ≡ expertiset = 2: communication. Each agent i sends a message mij ∈ {H, L} abouttheir signal to each of their neighbors. j ∈ Ni (g).t = 3: agents update their belief about the state, and decide whether toadopt if sufficiently confident they are in high state

yi = 1 ⇐⇒ Pr(θ = H|si , {mji})Pr(θ = L|si , {mji})︸ ︷︷ ︸

likelihood ratio

≥ ai︸︷︷︸ = f (ci , πi )

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 20 / 29

Model

Learning and communication

t = 0: nature draws the state θ ∈ {H, L}t = 1: each agent gets a signal si ∈ {H, L} that matches the state withprobability pi .

pi ≡ expertiset = 2: communication. Each agent i sends a message mij ∈ {H, L} abouttheir signal to each of their neighbors. j ∈ Ni (g).t = 3: agents update their belief about the state, and decide whether toadopt if sufficiently confident they are in high state

yi = 1 ⇐⇒ Pr(θ = H|si , {mji})Pr(θ = L|si , {mji})︸ ︷︷ ︸

likelihood ratio

≥ ai︸︷︷︸ = f (ci , πi )

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 20 / 29

Model

Learning and communication

t = 0: nature draws the state θ ∈ {H, L}t = 1: each agent gets a signal si ∈ {H, L} that matches the state withprobability pi .

pi ≡ expertise

t = 2: communication. Each agent i sends a message mij ∈ {H, L} abouttheir signal to each of their neighbors. j ∈ Ni (g).t = 3: agents update their belief about the state, and decide whether toadopt if sufficiently confident they are in high state

yi = 1 ⇐⇒ Pr(θ = H|si , {mji})Pr(θ = L|si , {mji})︸ ︷︷ ︸

likelihood ratio

≥ ai︸︷︷︸ = f (ci , πi )

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 20 / 29

Model

Learning and communication

t = 0: nature draws the state θ ∈ {H, L}t = 1: each agent gets a signal si ∈ {H, L} that matches the state withprobability pi .

pi ≡ expertiset = 2: communication. Each agent i sends a message mij ∈ {H, L} abouttheir signal to each of their neighbors. j ∈ Ni (g).

t = 3: agents update their belief about the state, and decide whether toadopt if sufficiently confident they are in high state

yi = 1 ⇐⇒ Pr(θ = H|si , {mji})Pr(θ = L|si , {mji})︸ ︷︷ ︸

likelihood ratio

≥ ai︸︷︷︸ = f (ci , πi )

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 20 / 29

Model

Learning and communication

t = 0: nature draws the state θ ∈ {H, L}t = 1: each agent gets a signal si ∈ {H, L} that matches the state withprobability pi .

pi ≡ expertiset = 2: communication. Each agent i sends a message mij ∈ {H, L} abouttheir signal to each of their neighbors. j ∈ Ni (g).t = 3: agents update their belief about the state, and decide whether toadopt if sufficiently confident they are in high state

yi = 1 ⇐⇒ Pr(θ = H|si , {mji})Pr(θ = L|si , {mji})︸ ︷︷ ︸

likelihood ratio

≥ ai︸︷︷︸ = f (ci , πi )

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 20 / 29

Model

Learning and communication

t = 0: nature draws the state θ ∈ {H, L}t = 1: each agent gets a signal si ∈ {H, L} that matches the state withprobability pi .

pi ≡ expertiset = 2: communication. Each agent i sends a message mij ∈ {H, L} abouttheir signal to each of their neighbors. j ∈ Ni (g).t = 3: agents update their belief about the state, and decide whether toadopt if sufficiently confident they are in high state

yi = 1 ⇐⇒ Pr(θ = H|si , {mji})Pr(θ = L|si , {mji})︸ ︷︷ ︸

likelihood ratio

≥ ai︸︷︷︸

= f (ci , πi )

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 20 / 29

Model

Learning and communication

t = 0: nature draws the state θ ∈ {H, L}t = 1: each agent gets a signal si ∈ {H, L} that matches the state withprobability pi .

pi ≡ expertiset = 2: communication. Each agent i sends a message mij ∈ {H, L} abouttheir signal to each of their neighbors. j ∈ Ni (g).t = 3: agents update their belief about the state, and decide whether toadopt if sufficiently confident they are in high state

yi = 1 ⇐⇒ Pr(θ = H|si , {mji})Pr(θ = L|si , {mji})︸ ︷︷ ︸

likelihood ratio

≥ ai︸︷︷︸ = f (ci , πi )

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 20 / 29

Model

The benefits of truthful communication

Truthful communication fosters learning:

1 More peers ⇒ better learning

2 Outcomes of peers are correlated

3 Agents put more weight on experts

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 21 / 29

Model

When do you get truthful communication? (Setup)

The case without externalities

ui ={

ui (yi , θ) = qθ(yi )− yici , without externalitiesui (yi , y−i , θ) = qθ

(yi +

∑j 6=i yj

)− yici , with positive externalities

Additional assumption: qL(y) = 0 ≤ qH(y) ≤ qH(y + 1).Introducing a cost of lying κ ≥ 0:

ui = ui (yi , ., θ,mi ) = ui (yi , ., θ)− κ∑j 6=i

1{mij 6= si}

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 22 / 29

Model

When do you get truthful communication? (Setup)

The case without externalities

ui ={

ui (yi , θ) = qθ(yi )− yici , without externalitiesui (yi , y−i , θ) = qθ

(yi +

∑j 6=i yj

)− yici , with positive externalities

Additional assumption:

qL(y) = 0 ≤ qH(y) ≤ qH(y + 1).Introducing a cost of lying κ ≥ 0:

ui = ui (yi , ., θ,mi ) = ui (yi , ., θ)− κ∑j 6=i

1{mij 6= si}

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 22 / 29

Model

When do you get truthful communication? (Setup)

The case without externalities

ui ={

ui (yi , θ) = qθ(yi )− yici , without externalitiesui (yi , y−i , θ) = qθ

(yi +

∑j 6=i yj

)− yici , with positive externalities

Additional assumption: qL(y) = 0 ≤ qH(y) ≤ qH(y + 1).Introducing a cost of lying κ ≥ 0:

ui = ui (yi , ., θ,mi ) = ui (yi , ., θ)− κ∑j 6=i

1{mij 6= si}

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 22 / 29

Model

When do you get truthful communication? (Setup)

The case without externalities

ui ={

ui (yi , θ) = qθ(yi )− yici , without externalitiesui (yi , y−i , θ) = qθ

(yi +

∑j 6=i yj

)− yici , with positive externalities

Additional assumption: qL(y) = 0 ≤ qH(y) ≤ qH(y + 1).Introducing a cost of lying κ ≥ 0:

ui = ui (yi , ., θ,mi ) = ui (yi , ., θ)− κ∑j 6=i

1{mij 6= si}

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 22 / 29

Model

When do you get truthful communication?

No externalities:Lying brings no benefits and generates costsTC is an equilibrium for any κ ≥ 0TC is the unique equilibrium for any κ > 0

Positive externalities:lying brings benefit and TC is not equilibriumpeer effects depend on making cost of lying high enoughTC is an equilibrium iff κ ≥ κ̄1 ← informal institutions!TC is the unique equilibrium iff κ ≥ κ̄20 ≤ κ̄1 ≤ κ̄2 ≤ 1

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 23 / 29

Model

When do you get truthful communication?

No externalities:Lying brings no benefits and generates costsTC is an equilibrium for any κ ≥ 0TC is the unique equilibrium for any κ > 0

Positive externalities:lying brings benefit and TC is not equilibriumpeer effects depend on making cost of lying high enoughTC is an equilibrium iff κ ≥ κ̄1 ← informal institutions!TC is the unique equilibrium iff κ ≥ κ̄20 ≤ κ̄1 ≤ κ̄2 ≤ 1

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 23 / 29

Empirical implications

Empirical implications

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 24 / 29

Empirical implications

Empirical implications

1 Variation across networks in the support of diffusion of goods withexternalities, above what can be explained by variation in hearing rates

2 Discounting of positive signals (peers’ recommendations) when truthfulcommunication is not enforced

3 Experts will have a stronger peer effect than novices iff a network supportsdiffusion, as their signal carries greater weight

4 Strong ties will be more effective than weak ties in supporting truthfulcommunication, and therefore, in supporting diffusion

5 Informal institutions should support adoption in high-uptake villages

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 25 / 29

Empirical implications

Empirical implications

1 Variation across networks in the support of diffusion of goods withexternalities, above what can be explained by variation in hearing rates

2 Discounting of positive signals (peers’ recommendations) when truthfulcommunication is not enforced

3 Experts will have a stronger peer effect than novices iff a network supportsdiffusion, as their signal carries greater weight

4 Strong ties will be more effective than weak ties in supporting truthfulcommunication, and therefore, in supporting diffusion

5 Informal institutions should support adoption in high-uptake villages

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 25 / 29

Empirical implications

Empirical implications

1 Variation across networks in the support of diffusion of goods withexternalities, above what can be explained by variation in hearing rates

2 Discounting of positive signals (peers’ recommendations) when truthfulcommunication is not enforced

3 Experts will have a stronger peer effect than novices iff a network supportsdiffusion, as their signal carries greater weight

4 Strong ties will be more effective than weak ties in supporting truthfulcommunication, and therefore, in supporting diffusion

5 Informal institutions should support adoption in high-uptake villages

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 25 / 29

Empirical implications

Empirical implications

1 Variation across networks in the support of diffusion of goods withexternalities, above what can be explained by variation in hearing rates

2 Discounting of positive signals (peers’ recommendations) when truthfulcommunication is not enforced

3 Experts will have a stronger peer effect than novices iff a network supportsdiffusion, as their signal carries greater weight

4 Strong ties will be more effective than weak ties in supporting truthfulcommunication, and therefore, in supporting diffusion

5 Informal institutions should support adoption in high-uptake villages

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 25 / 29

Empirical implications

Empirical implications

1 Variation across networks in the support of diffusion of goods withexternalities, above what can be explained by variation in hearing rates

2 Discounting of positive signals (peers’ recommendations) when truthfulcommunication is not enforced

3 Experts will have a stronger peer effect than novices iff a network supportsdiffusion, as their signal carries greater weight

4 Strong ties will be more effective than weak ties in supporting truthfulcommunication, and therefore, in supporting diffusion

5 Informal institutions should support adoption in high-uptake villages

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 25 / 29

Empirical implications

Findings

1 Variation across networks in the support of diffusion of goods withexternalities

There are peer effects, but not in all villages.

2 Discounting of positive signals (peers’ recommendations) when truthfulcommunication is not enforced

i is more likely to adopt if j is satisfied in high uptake villages

3 Experts will have a stronger peer effect than novices

Leaders exert more influence than citizens in high-uptake villages

4 Informal institutions should support adoption in high-uptake villages

Public goods games and concentrated leadership

●●

●●

−0.05

0.00

0.05

0.10

B(236)

H(102)

E(263)

C(159)

M(195)

G(163)

D(282)

K(204)

L(228)

P(191)

O(187)

I(168)

F(205)

N(223)

J(185)

Village

Ave

rage

Mar

gina

l Effe

ct

Uptake

● High

Low

High uptakevillage

Low uptakevillage

0.00 0.03 0.06 0.09

Average marginal effect of one adopting neighbor on adoption

Effect

satisfaction

communication

contagion

Low uptake

High uptake

−0.01 0.00 0.01 0.02 0.03 0.04 0.05

Leader

Peer

Leader

Peer

Average Marginal Effect

sour

ce

Ethnic concentration (12)

Pct. strong ties (15)

Pct. leaders (15)

Degree (15)

Diffusion potential (15)

Population (15)

Religious concentration (15)

Pro−sociality − dictator (15)

Pro−sociality − public good (15)

Leadership concentration (14)

−5.0 −2.5 0.0 2.5

Standardized effect size

Var

iabl

e

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 26 / 29

Empirical implications

Findings

1 Variation across networks in the support of diffusion of goods withexternalities

There are peer effects, but not in all villages.

2 Discounting of positive signals (peers’ recommendations) when truthfulcommunication is not enforced

i is more likely to adopt if j is satisfied in high uptake villages

3 Experts will have a stronger peer effect than novices

Leaders exert more influence than citizens in high-uptake villages

4 Informal institutions should support adoption in high-uptake villages

Public goods games and concentrated leadership

●●

●●

−0.05

0.00

0.05

0.10

B(236)

H(102)

E(263)

C(159)

M(195)

G(163)

D(282)

K(204)

L(228)

P(191)

O(187)

I(168)

F(205)

N(223)

J(185)

Village

Ave

rage

Mar

gina

l Effe

ct

Uptake

● High

Low

High uptakevillage

Low uptakevillage

0.00 0.03 0.06 0.09

Average marginal effect of one adopting neighbor on adoption

Effect

satisfaction

communication

contagion

Low uptake

High uptake

−0.01 0.00 0.01 0.02 0.03 0.04 0.05

Leader

Peer

Leader

Peer

Average Marginal Effect

sour

ce

Ethnic concentration (12)

Pct. strong ties (15)

Pct. leaders (15)

Degree (15)

Diffusion potential (15)

Population (15)

Religious concentration (15)

Pro−sociality − dictator (15)

Pro−sociality − public good (15)

Leadership concentration (14)

−5.0 −2.5 0.0 2.5

Standardized effect size

Var

iabl

e

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 26 / 29

Empirical implications

Findings

1 Variation across networks in the support of diffusion of goods withexternalities

There are peer effects, but not in all villages.

2 Discounting of positive signals (peers’ recommendations) when truthfulcommunication is not enforced

i is more likely to adopt if j is satisfied in high uptake villages

3 Experts will have a stronger peer effect than novices

Leaders exert more influence than citizens in high-uptake villages

4 Informal institutions should support adoption in high-uptake villages

Public goods games and concentrated leadership

●●

●●

−0.05

0.00

0.05

0.10

B(236)

H(102)

E(263)

C(159)

M(195)

G(163)

D(282)

K(204)

L(228)

P(191)

O(187)

I(168)

F(205)

N(223)

J(185)

Village

Ave

rage

Mar

gina

l Effe

ct

Uptake

● High

Low

High uptakevillage

Low uptakevillage

0.00 0.03 0.06 0.09

Average marginal effect of one adopting neighbor on adoption

Effect

satisfaction

communication

contagion

Low uptake

High uptake

−0.01 0.00 0.01 0.02 0.03 0.04 0.05

Leader

Peer

Leader

Peer

Average Marginal Effect

sour

ce

Ethnic concentration (12)

Pct. strong ties (15)

Pct. leaders (15)

Degree (15)

Diffusion potential (15)

Population (15)

Religious concentration (15)

Pro−sociality − dictator (15)

Pro−sociality − public good (15)

Leadership concentration (14)

−5.0 −2.5 0.0 2.5

Standardized effect size

Var

iabl

e

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 26 / 29

Empirical implications

Findings

1 Variation across networks in the support of diffusion of goods withexternalities

There are peer effects, but not in all villages.

2 Discounting of positive signals (peers’ recommendations) when truthfulcommunication is not enforced

i is more likely to adopt if j is satisfied in high uptake villages

3 Experts will have a stronger peer effect than novices

Leaders exert more influence than citizens in high-uptake villages

4 Informal institutions should support adoption in high-uptake villages

Public goods games and concentrated leadership

●●

●●

−0.05

0.00

0.05

0.10

B(236)

H(102)

E(263)

C(159)

M(195)

G(163)

D(282)

K(204)

L(228)

P(191)

O(187)

I(168)

F(205)

N(223)

J(185)

Village

Ave

rage

Mar

gina

l Effe

ct

Uptake

● High

Low

High uptakevillage

Low uptakevillage

0.00 0.03 0.06 0.09

Average marginal effect of one adopting neighbor on adoption

Effect

satisfaction

communication

contagion

Low uptake

High uptake

−0.01 0.00 0.01 0.02 0.03 0.04 0.05

Leader

Peer

Leader

Peer

Average Marginal Effect

sour

ce

Ethnic concentration (12)

Pct. strong ties (15)

Pct. leaders (15)

Degree (15)

Diffusion potential (15)

Population (15)

Religious concentration (15)

Pro−sociality − dictator (15)

Pro−sociality − public good (15)

Leadership concentration (14)

−5.0 −2.5 0.0 2.5

Standardized effect size

Var

iabl

e

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 26 / 29

Empirical implications

Findings

1 Variation across networks in the support of diffusion of goods withexternalities

There are peer effects, but not in all villages.

2 Discounting of positive signals (peers’ recommendations) when truthfulcommunication is not enforced

i is more likely to adopt if j is satisfied in high uptake villages

3 Experts will have a stronger peer effect than novices

Leaders exert more influence than citizens in high-uptake villages

4 Informal institutions should support adoption in high-uptake villages

Public goods games and concentrated leadership

●●

●●

−0.05

0.00

0.05

0.10

B(236)

H(102)

E(263)

C(159)

M(195)

G(163)

D(282)

K(204)

L(228)

P(191)

O(187)

I(168)

F(205)

N(223)

J(185)

Village

Ave

rage

Mar

gina

l Effe

ct

Uptake

● High

Low

High uptakevillage

Low uptakevillage

0.00 0.03 0.06 0.09

Average marginal effect of one adopting neighbor on adoption

Effect

satisfaction

communication

contagion

Low uptake

High uptake

−0.01 0.00 0.01 0.02 0.03 0.04 0.05

Leader

Peer

Leader

Peer

Average Marginal Effect

sour

ce

Ethnic concentration (12)

Pct. strong ties (15)

Pct. leaders (15)

Degree (15)

Diffusion potential (15)

Population (15)

Religious concentration (15)

Pro−sociality − dictator (15)

Pro−sociality − public good (15)

Leadership concentration (14)

−5.0 −2.5 0.0 2.5

Standardized effect size

Var

iabl

e

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 26 / 29

Empirical implications

Findings

1 Variation across networks in the support of diffusion of goods withexternalities

There are peer effects, but not in all villages.

2 Discounting of positive signals (peers’ recommendations) when truthfulcommunication is not enforced

i is more likely to adopt if j is satisfied in high uptake villages

3 Experts will have a stronger peer effect than novices

Leaders exert more influence than citizens in high-uptake villages

4 Informal institutions should support adoption in high-uptake villages

Public goods games and concentrated leadership

●●

●●

−0.05

0.00

0.05

0.10

B(236)

H(102)

E(263)

C(159)

M(195)

G(163)

D(282)

K(204)

L(228)

P(191)

O(187)

I(168)

F(205)

N(223)

J(185)

Village

Ave

rage

Mar

gina

l Effe

ct

Uptake

● High

Low

High uptakevillage

Low uptakevillage

0.00 0.03 0.06 0.09

Average marginal effect of one adopting neighbor on adoption

Effect

satisfaction

communication

contagion

Low uptake

High uptake

−0.01 0.00 0.01 0.02 0.03 0.04 0.05

Leader

Peer

Leader

Peer

Average Marginal Effect

sour

ce

Ethnic concentration (12)

Pct. strong ties (15)

Pct. leaders (15)

Degree (15)

Diffusion potential (15)

Population (15)

Religious concentration (15)

Pro−sociality − dictator (15)

Pro−sociality − public good (15)

Leadership concentration (14)

−5.0 −2.5 0.0 2.5

Standardized effect size

Var

iabl

e

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 26 / 29

Empirical implications

Findings

1 Variation across networks in the support of diffusion of goods withexternalities

There are peer effects, but not in all villages.

2 Discounting of positive signals (peers’ recommendations) when truthfulcommunication is not enforced

i is more likely to adopt if j is satisfied in high uptake villages

3 Experts will have a stronger peer effect than novices

Leaders exert more influence than citizens in high-uptake villages

4 Informal institutions should support adoption in high-uptake villages

Public goods games and concentrated leadership

●●

●●

−0.05

0.00

0.05

0.10

B(236)

H(102)

E(263)

C(159)

M(195)

G(163)

D(282)

K(204)

L(228)

P(191)

O(187)

I(168)

F(205)

N(223)

J(185)

Village

Ave

rage

Mar

gina

l Effe

ct

Uptake

● High

Low

High uptakevillage

Low uptakevillage

0.00 0.03 0.06 0.09

Average marginal effect of one adopting neighbor on adoption

Effect

satisfaction

communication

contagion

Low uptake

High uptake

−0.01 0.00 0.01 0.02 0.03 0.04 0.05

Leader

Peer

Leader

Peer

Average Marginal Effect

sour

ce

Ethnic concentration (12)

Pct. strong ties (15)

Pct. leaders (15)

Degree (15)

Diffusion potential (15)

Population (15)

Religious concentration (15)

Pro−sociality − dictator (15)

Pro−sociality − public good (15)

Leadership concentration (14)

−5.0 −2.5 0.0 2.5

Standardized effect size

Var

iabl

e

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 26 / 29

Empirical implications

Findings

1 Variation across networks in the support of diffusion of goods withexternalities

There are peer effects, but not in all villages.

2 Discounting of positive signals (peers’ recommendations) when truthfulcommunication is not enforced

i is more likely to adopt if j is satisfied in high uptake villages

3 Experts will have a stronger peer effect than novices

Leaders exert more influence than citizens in high-uptake villages

4 Informal institutions should support adoption in high-uptake villages

Public goods games and concentrated leadership

●●

●●

−0.05

0.00

0.05

0.10

B(236)

H(102)

E(263)

C(159)

M(195)

G(163)

D(282)

K(204)

L(228)

P(191)

O(187)

I(168)

F(205)

N(223)

J(185)

Village

Ave

rage

Mar

gina

l Effe

ct

Uptake

● High

Low

High uptakevillage

Low uptakevillage

0.00 0.03 0.06 0.09

Average marginal effect of one adopting neighbor on adoption

Effect

satisfaction

communication

contagion

Low uptake

High uptake

−0.01 0.00 0.01 0.02 0.03 0.04 0.05

Leader

Peer

Leader

Peer

Average Marginal Effect

sour

ce

Ethnic concentration (12)

Pct. strong ties (15)

Pct. leaders (15)

Degree (15)

Diffusion potential (15)

Population (15)

Religious concentration (15)

Pro−sociality − dictator (15)

Pro−sociality − public good (15)

Leadership concentration (14)

−5.0 −2.5 0.0 2.5

Standardized effect size

Var

iabl

e

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 26 / 29

Empirical implications

Findings

1 Variation across networks in the support of diffusion of goods withexternalities

There are peer effects, but not in all villages.

2 Discounting of positive signals (peers’ recommendations) when truthfulcommunication is not enforced

i is more likely to adopt if j is satisfied in high uptake villages

3 Experts will have a stronger peer effect than novicesLeaders exert more influence than citizens in high-uptake villages

4 Informal institutions should support adoption in high-uptake villages

Public goods games and concentrated leadership

●●

●●

−0.05

0.00

0.05

0.10

B(236)

H(102)

E(263)

C(159)

M(195)

G(163)

D(282)

K(204)

L(228)

P(191)

O(187)

I(168)

F(205)

N(223)

J(185)

Village

Ave

rage

Mar

gina

l Effe

ct

Uptake

● High

Low

High uptakevillage

Low uptakevillage

0.00 0.03 0.06 0.09

Average marginal effect of one adopting neighbor on adoption

Effect

satisfaction

communication

contagion

Low uptake

High uptake

−0.01 0.00 0.01 0.02 0.03 0.04 0.05

Leader

Peer

Leader

Peer

Average Marginal Effect

sour

ce

Ethnic concentration (12)

Pct. strong ties (15)

Pct. leaders (15)

Degree (15)

Diffusion potential (15)

Population (15)

Religious concentration (15)

Pro−sociality − dictator (15)

Pro−sociality − public good (15)

Leadership concentration (14)

−5.0 −2.5 0.0 2.5

Standardized effect size

Var

iabl

e

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 26 / 29

Empirical implications

Findings

1 Variation across networks in the support of diffusion of goods withexternalities

There are peer effects, but not in all villages.

2 Discounting of positive signals (peers’ recommendations) when truthfulcommunication is not enforced

i is more likely to adopt if j is satisfied in high uptake villages

3 Experts will have a stronger peer effect than novicesLeaders exert more influence than citizens in high-uptake villages

4 Informal institutions should support adoption in high-uptake villages

Public goods games and concentrated leadership

●●

●●

−0.05

0.00

0.05

0.10

B(236)

H(102)

E(263)

C(159)

M(195)

G(163)

D(282)

K(204)

L(228)

P(191)

O(187)

I(168)

F(205)

N(223)

J(185)

Village

Ave

rage

Mar

gina

l Effe

ct

Uptake

● High

Low

High uptakevillage

Low uptakevillage

0.00 0.03 0.06 0.09

Average marginal effect of one adopting neighbor on adoption

Effect

satisfaction

communication

contagion

Low uptake

High uptake

−0.01 0.00 0.01 0.02 0.03 0.04 0.05

Leader

Peer

Leader

Peer

Average Marginal Effect

sour

ce

Ethnic concentration (12)

Pct. strong ties (15)

Pct. leaders (15)

Degree (15)

Diffusion potential (15)

Population (15)

Religious concentration (15)

Pro−sociality − dictator (15)

Pro−sociality − public good (15)

Leadership concentration (14)

−5.0 −2.5 0.0 2.5

Standardized effect size

Var

iabl

e

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 26 / 29

Empirical implications

Findings

1 Variation across networks in the support of diffusion of goods withexternalities

There are peer effects, but not in all villages.

2 Discounting of positive signals (peers’ recommendations) when truthfulcommunication is not enforced

i is more likely to adopt if j is satisfied in high uptake villages

3 Experts will have a stronger peer effect than novices

Leaders exert more influence than citizens in high-uptake villages

4 Informal institutions should support adoption in high-uptake villages

Public goods games and concentrated leadership

●●

●●

−0.05

0.00

0.05

0.10

B(236)

H(102)

E(263)

C(159)

M(195)

G(163)

D(282)

K(204)

L(228)

P(191)

O(187)

I(168)

F(205)

N(223)

J(185)

Village

Ave

rage

Mar

gina

l Effe

ct

Uptake

● High

Low

High uptakevillage

Low uptakevillage

0.00 0.03 0.06 0.09

Average marginal effect of one adopting neighbor on adoption

Effect

satisfaction

communication

contagion

Low uptake

High uptake

−0.01 0.00 0.01 0.02 0.03 0.04 0.05

Leader

Peer

Leader

Peer

Average Marginal Effect

sour

ce

Ethnic concentration (12)

Pct. strong ties (15)

Pct. leaders (15)

Degree (15)

Diffusion potential (15)

Population (15)

Religious concentration (15)

Pro−sociality − dictator (15)

Pro−sociality − public good (15)

Leadership concentration (14)

−5.0 −2.5 0.0 2.5

Standardized effect size

Var

iabl

e

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 26 / 29

Empirical implications

Findings

1 Variation across networks in the support of diffusion of goods withexternalities

There are peer effects, but not in all villages.

2 Discounting of positive signals (peers’ recommendations) when truthfulcommunication is not enforced

i is more likely to adopt if j is satisfied in high uptake villages

3 Experts will have a stronger peer effect than novicesLeaders exert more influence than citizens in high-uptake villages

4 Informal institutions should support adoption in high-uptake villagesPublic goods games and concentrated leadership

●●

●●

−0.05

0.00

0.05

0.10

B(236)

H(102)

E(263)

C(159)

M(195)

G(163)

D(282)

K(204)

L(228)

P(191)

O(187)

I(168)

F(205)

N(223)

J(185)

Village

Ave

rage

Mar

gina

l Effe

ct

Uptake

● High

Low

High uptakevillage

Low uptakevillage

0.00 0.03 0.06 0.09

Average marginal effect of one adopting neighbor on adoption

Effect

satisfaction

communication

contagion

Low uptake

High uptake

−0.01 0.00 0.01 0.02 0.03 0.04 0.05

Leader

Peer

Leader

Peer

Average Marginal Effect

sour

ce

Ethnic concentration (12)

Pct. strong ties (15)

Pct. leaders (15)

Degree (15)

Diffusion potential (15)

Population (15)

Religious concentration (15)

Pro−sociality − dictator (15)

Pro−sociality − public good (15)

Leadership concentration (14)

−5.0 −2.5 0.0 2.5

Standardized effect size

Var

iabl

e

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 26 / 29

Conclusions

Conclusion

1 We qualify a long-standing argument: “peer effects are ubiquitous in theprocess of technology adoption.”

2 For technologies with strong externalities, there are no peer effects ifcommunities do not manage to enforce truthful communication.

3 This may explain variation, and overall low rates of adoption of ICTs forpolitical communication.

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 27 / 29

Appendix

Example messages

Not relevant:“Hi ubridge”“We are for election”

Relevant:“I greet you all, but our major problem is sickness”“The tobbacco farmers are misserable how can Ubridge help them?”

Actionable:“The Only Borehole in Ogboa Village is broken”“NURSES DONT ATTEND PATIENTS DURING SAT AND sun in Opia HealthCentre”

Grossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 28 / 29

Appendix

High variation in number of users per village

FigureGrossman (UPenn) Peer effects and externalities in technology adoption December 15, 2017 29 / 29