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

Peer eﬀects and externalities in technology adoption:
Evidence from community reporting in Uganda
Romain Ferrali1
Guy Grossman2
Melina Platas Izama3
Jonathan Rodden4
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

Overview
Motivation
Developing countries: persistent poor public service delivery

Overview
Motivation
Developing countries: persistent poor public service delivery ← frontline
providers not suﬃciently monitored / supervised by public oﬃcials

Overview
Motivation
Possible solution: community monitoring

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

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

Overview
Motivation
Take-up: matters for both eﬃciency and equity reasons

Overview
Motivation
Variable adoption rates

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

Overview
Technology adoption literature: networks matter!
Social learning from peers (friends, family, co-workers) facilitates the
adoption of new technologies

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

Overview
Costs of communication with network ties are lower and their opinion is
generally more trustworthy

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

Overview
Networks shown to support diﬀusion of technologies from agriculture best
practices (Conley & Udry 2010) to medical innovations (Coleman 1966), etc.
Open question: do networks support the adoption of political
communication technologies, such as community reporting platforms?

Overview
Research design in nutshell
We study how social ties contribute to the adoption of a new mobile-based
political communication platform in a low-income country setting

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

Overview
The political communication platform was introduced to 130 Ugandan
villages using a ﬁeld experimental design

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

Overview
Consistent with past research on “ICT for better governance,” adoption rates
among the treatment villages were highly uneven
We explore the role of social diﬀusion by collecting ‘whole’ network data from
16 treatment villages as well as data on knowledge and usage of the platform

Overview
Findings and Theory in Nutshell
1 Empirics I
on average, networks facilitated adoption...

Overview
1 Empirics I
but had no eﬀect in half the villages.

Overview
1 Empirics I
2 Theory: networks eﬀects depend on the goods’ externalities

Overview
1 Empirics I
past work demonstrated peer eﬀects on the adoption of goods with minimal
externalities...

Overview
1 Empirics I
externalities...
networks have no eﬀect on the adoption of goods characterized by signiﬁcant
externalities (e.g. PCTs) ...

Overview
1 Empirics I
externalities...
unless the community enforces truthful communication...

Overview
1 Empirics I
externalities...
which crucially depends on informal institutions and leadership structure

Overview
1 Empirics I
externalities...
which crucially depends on informal institutions and leadership structure
3 Empirics II
ﬁnd support for the model’s testable implications

Research Design
Research Design

Research Design
District local governments in Uganda
Districts: highest tier of subnational government, responsible for
administering local public services (e.g. health, education, water)

Research Design
Setting
130 randomly selected villages in Arua are encouraged to use platform

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

Research Design
Setting
Inception community meetings in 24 treatment clusters (October 2014)

Research Design
Setting

Research Design
Setting
Relatively high, but variable technology take-up across villages

Research Design
Setting
0
2500
5000
7500
10000
12500
2014−07 2015−01 2015−07
Date
Cumulativenumberofmessagesreceived
Type
all
relevant
actionable

Research Design
Setting
0
.02
.04
.06
Density
0 20 40 60 80 100
relevant messages per 100 villagers
Variability in message intensity

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

Research Design
Setting
Conducted a full census in 16 villages (summer 2016):
8 high performing and 8 low performing wrt uptake (residuals)
Total of 3, 182 villagers

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

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

Research Design
Figure: Graphical representation of the union network of two villages in the study area.

Research Design
Descriptive statistics of sample
Variable Sample Low uptake High uptake ∆ min max
Outcome % 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 1.00
income -0.45 -0.54 -0.36 0.19∗
-2.00 2.00
secondary 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 1.00
political 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 1.00
mean pro-sociality 0.28 0.28 0.29 0.01 0.00 1.00
Network degree 8.79 8.36 9.22 0.86 0.00 217.00
betweenness 140.60 132.16 149.01 16.85 0.00 23850
clustering coeﬃcient 0.38 0.40 0.37 -0.03 0.00 1.00
mean size 199.00 198.62 199.38 0.75
Village adult population 269.38 264.25 274.50 10.25 32 429
ethnic 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 0.57
poverty score -0.07 -0.09 -0.05 0.03 -0.48 0.47
N 3184 1589 1595 6

Research Design
Estimation
Main speciﬁcation. Spatial Autoregressive Regression (SAR)
y = λMy + Xβ +

Research Design
Estimation
y = λMy + Xβ +
y, vector of outcomes: adopt ∈ {0, 1}

Research Design
Estimation
y = λMy + Xβ +
M, spatial matrix: union network → # adopting neighbors

Research Design
Estimation
y = λMy + Xβ +
X, matrix of controls
Network: degree centrality
Demographics: age, gender, secondary education, immigrant
Design: usage of phone, meeting attendance
Politics: political participation, leadership position
Spatial inﬂuence: autoregressive term with M inverse log-distance
Within-village comparison: models include village-level FE

Research Design
Estimation
y = λMy + Xβ +
X, matrix of controls
Network: degree centrality
Demographics: age, gender, secondary education, immigrant
Design: usage of phone, meeting attendance
Politics: political participation, leadership position
Spatial inﬂuence: autoregressive term with M inverse log-distance
Within-village comparison: models include village-level FE
Robustness checks

Main results
Main results

Main results
There is peer inﬂuence
Dependent variable: adopt
Absolute 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
Controls
Observations 3,184 3,019 3,184 3,019
R2 0.139 0.245 0.116 0.231
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

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

Main results
Robustness checks
Test Issue Result Notes
Selection model
Pr(adopt) = Pr(hear)×Pr(adopt|hear)
(Larson & Lewis 2017)
1 peers aﬀect both stages of diﬀu-
sion; adoption variability larger

Main results
Robustness checks
Selection model
Instrumental variable
(An 2016)
2 zj → yj → yi
Instrument: distance from meeting
location

Main results
Robustness checks
Selection model
(An 2016)
2 zj → yj → yi
location
Non-parametric controls for degree
(Aronow & Samii, nd)
3 degree strata & GAM

Main results
Robustness checks
Selection model
(An 2016)
2 zj → yj → yi
location
Non-parametric controls for degree
(Aronow & Samii, nd)
3 degree strata & GAM
Matching
(Aral et al 2009)
2, 3 full matching on network covariates
and most important predictors of
uptake

Main results
Wide variation across villages
q
q
q
q
q
q
q
−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
AverageMarginalEffect
Uptake
q High
Low

Model
Model

Model
Theory
Adopting a new technology is risky

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

Model
Theory
Lying about beneﬁts of technology is costly

Model
Theory
Technologies vary in whether adoption generates externalities:

Model
Theory
Externalities in adoption: whether the adoption decision of one agent
aﬀects the payoﬀ from adoption of another agent

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

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

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

Model
A model: adoption without externalities
N agents are the nodes of a social network g
Each agent i decides whether to adopt a new technology, yi ∈ {0, 1}.
ui (yi , θ) = qθ(yi ) − yi ci
Not adopting gives a payoﬀ of zero: qθ(0) = 0
Adoption 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

Model
Learning and communication
t = 0: nature draws the state θ ∈ {H, L}

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

Model
probability pi .
pi ≡ expertise

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

Model
probability pi .
pi ≡ expertise
t = 3: agents update their belief about the state, and decide whether to
adopt if suﬃciently conﬁdent they are in high state

Model
probability pi .
pi ≡ expertise
yi = 1 ⇐⇒
Pr(θ = H|si , {mji })
Pr(θ = L|si , {mji })
likelihood ratio
≥ ai

Model
probability pi .
pi ≡ expertise
yi = 1 ⇐⇒
Pr(θ = H|si , {mji })
Pr(θ = L|si , {mji })
likelihood ratio
≥ ai = f (ci , πi )

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

Model
When do you get truthful communication? (Setup)
The case without externalities
ui =
ui (yi , θ) = qθ(yi ) − yi ci , without externalities
ui (yi , y−i , θ) = qθ yi + j=i yj − yi ci , with positive externalities

Model
ui =
Additional assumption:

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

Model
ui =
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=i
1{mij = si }

Model
When do you get truthful communication?
No externalities:
Lying brings no beneﬁts and generates costs
TC is an equilibrium for any κ ≥ 0
TC is the unique equilibrium for any κ > 0

Model
When do you get truthful communication?
No externalities:
Lying brings no benefits and generates costs
TC is an equilibrium for any κ ≥ 0
TC is the unique equilibrium for any κ > 0
Positive externalities:
lying brings benefit and TC is not equilibrium
peer effects depend on making cost of lying high enough
TC is an equilibrium iff κ ≥ ¯κ1 ← informal institutions!
TC is the unique equilibrium iff κ ≥ ¯κ2
0 ≤ ¯κ1 ≤ ¯κ2 ≤ 1

Empirical implications

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

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

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

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

4 Strong ties will be more eﬀective than weak ties in supporting truthful
communication, and therefore, in supporting diﬀusion
5 Informal institutions should support adoption in high-uptake villages

Findings
externalities

Findings
q
q
q
q
q
q
q
−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
AverageMarginalEffect
Uptake
q High
Low

Findings
externalities
There are peer eﬀects, but not in all villages.

Findings
externalities

Findings
High uptake
village
Low uptake
village
0.00 0.03 0.06 0.09
Average marginal effect of one adopting neighbor on adoption
Effect
satisfaction
communication
contagion

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

Findings
externalities
3 Experts will have a stronger peer eﬀect than novices

Findings
q
q
q
q
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
source

Findings
externalities
Leaders exert more inﬂuence than citizens in high-uptake villages

Findings
externalities

Findings
q
q
q
q
q
q
q
q
q
q
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
Variable

Findings
externalities
Public goods games and concentrated leadership

Conclusions
Conclusion
1 We qualify a long-standing argument: “peer eﬀects are ubiquitous in the
process of technology adoption.”
2 For technologies with strong externalities, there are no peer eﬀects if
communities do not manage to enforce truthful communication.
3 This may explain variation, and overall low rates of adoption of ICTs for
political communication.

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 Health
Centre”

Appendix
High variation in number of users per village

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

Recommended

Recommended

More Related Content

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

Similar to Peer effects and externalities in technology adoption: Evidence from community reporting in Uganda (20)

More from Stockholm Institute of Transition Economics

More from Stockholm Institute of Transition Economics (20)

Recently uploaded

Recently uploaded (20)

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