Why social networks are important in development

Stockholm Doctoral Course Program in Economics

Development Economics II: Lecture 3

Social Networks

Masayuki Kudamatsu IIES, Stockholm University

Why social networks are

important in development

• Peer effects on welfare / behavior

• Impact of network characteristics(e.g. class size on test score)

• Impact of network behavior (e.g. technology adoption)

• Help contract enforcement • Informal insurance

• Group lending

• Relational contract

This lecture

1. Identify the effect of network characteristics (Munshi 2003)

2. Identify the effect of network behavior

• How to overcome the reflection problem (Manski 1993; Conley & Udry 2010)

3. Network characteristics that enhance contract enforcement

1. Impact of Network

Characteristics

• Empirical challenge: omitted variable bias

• People in the same network share many things in common

• Geography

Digression

Positive assortative matching: suppose

• Two types of agents (high and low ability): ui ∈ {H,L},(H > L)

• i’s payoff from forming a pair w/ k:

Digression (cont.)

• Ability of each pair: positively correlated if

Vi(H,H)−Vi(H,L) > Vi(L,H)−Vi(L,L)

Digression (cont.)

• Dating back to Gary Becker’s analysis on marriage

• Applications in development: Ghatak (2000) for microfinance; Ackerberg & Botticini (2002) for sharecropping

1. Impact of Network

Characteristics (cont.)

⇒ Need an exogenous variation in network characteristics

• Munshi (2003) uses rainfall shock in Mexico as an IV for the migrant

1.1 Research question

Does a larger network size of Mexican migrants in US increase the probability of employment of its members?

• Why important?

• What’s original?

1.1 Research question (cont.)

Why important?

• # of people living outside country of birth: 175,000,000 in 2000

• 2.9% of world population (2.2% in 1965)

• Remittances: huge & growing cf. See Yang (2008)’s lecture note for

Remittances vs. ODA, FDI

(1991-2005)

Source: World Development Indicators 2007. Data are in current US$.

1.1 Research question (cont.)

What’s original?

• Many have studied this question

• But identification in these studies not credible

1.1 Research question (cont.)

Is it feasible?

• Mexican Migration Project: a

cross-sectional survey with recall data

• A cross-sectional survey of Mexican communities (survey year differs)

• 200 hh heads in each community surveyed for retrospective history of migration and employment

⇒ Panel data

1.2 Background

Network members for migrants: Those from the same origin community

• Where to migrate varies a lot across

origin communities (Table II.B)

1.3 Theory

Role of network: overcome information asymmetry

• Worker’s ability: unobservable to firms

• Incumbent workers have better info.

on the ability of their network members

⇒ Firms rely on referrals from

1.3 Theory (cont.)

Established migrants play key role

• More likely to be employed

• Have been at destination for longer

• More to lose if they are fired

• Have developed firm-specific skills

⇒ Incentive to refer high ability workers from their network

1.3 Theory (cont.)

Who benefits more from referrals

• Those w/ unfavorable observed

characteristics:

• women, older men, less educated

1.4 Empirical specification

Sample: person-years located in US

Yict = αMNct +βMEct + µi +ηt + εict

Yict: Dummy for being employed / for

non-agricultural job

• For migrant i from community c in year t

• Nonagricultural job: higher-paying

1.4 Empirical specification (cont.)

Yict = αMNct +βMEct +µi +ηt +εict

• MNct: Ratio of surveyed members

of community c located in US for 1-3 yrs by year t

• MEct: Same as MNct but for 4+ yrs

• Prediction:

• α = 0,β > 0

1.5a Identification issue (1)

• People from some origin

communities may be better skilled for US jobs

⇒ More people migrate

⇒ Network size bigger & each

1.5a Identification issue (1)

(cont.)

Controlling for individual FE ability (µi)

solves this problem

• US jobs are as low-skilled as those

in Mexico (Tables I.B & II.A)

1.5b Identification issue (2)

• Business environment at

destination affects BOTH network size & employment probability (εict)

• More people migrate ifεict ↑

• With serial correlation in business environment at destination, even # of established migrants (MEct)

correlated with εict

1.5b Identification issue (2)

(cont.)

⇒ Use rainfall at origin as an IV for network size

• Rainfall ↓ ⇒ # of migrants ↑

1.5b Identification issue (2)

(cont.)

• Rainfall in Mexico doesn’t affect employment opportunities in US

• Corr. coeff. btw. rainfall in Mexico & US: 0.01 (p. 570)

• Origin community is too small to affect labor market conditions at the

destination

1.5b Identification issue (3)

• Negative rainfall shock in the past

⇒ Among migrants in US today, # of those staying long ↑

⇒ Mechanically, they’re more likely to be employed (more opportunities to find a job)

1.6 IV estimation

MNct & MEct: instrumented by

• Mean rainfall in community c over years t to t −2

• Mean rainfall in community c over years t − 3 to t − 6

* Results robust to a different cut-off (btw.t −3 &

Digression: reduced-form / 1st

stage

• Always show 1st stage results • Reader can check if instruments are

not weak

• Better to report reduced-form results as well

• Reduced-form coefficients: proportional to IV coefficients

1.8 Results: OLS vs IV (Table VI)

• Similar finding for having

1.8 Results: OLS vs IV (cont.)

Why |βˆIV| > |βˆOLS|?

• Endogeneity due to return migration • Attenuation bias due to

measurement error

• Size of network based on random sample of individuals from community

• Heterogeneous treatment effect (fn

33)

1.9 Heterogenous treatment

effects

Network size effect: larger for

• New migrants (arrive in t −1 or t)

(VI(4))

• Women for employment (VI(6)) • But not for occupation (IX(7))

• Older men (above 45 yrs old) for

employment (VI(7))

• But not for occupation (IX(8))

• Less educated (<10 yrs of

2. Impact of network behavior

• Important to distinguish the impact of network behavior from the one of network characteristics

• If network behavior matters

⇒ changing a few people’s behavior is enough to induce many more in the network to change their behavior.

• If only characteristics matters

2. Impact of network behavior

(cont.)

• Early studies regress each person’s behavior on the average behavior of their network members

• Manski (1993): this methodology is

wrong due to the reflection problem • Does the mirror image cause the

person’s movement or reflect them?

Reflection problem

To answer why an individual tends to behave in a similar way to his/her

network members, we want to estimate:

y = α + βE(y|x_{) + E(}z_|x₎�_{γ +}z�_{η + u}

• x_{: membership indicators}

• z_{: individual-level determinants of y} observed by econometricians

• u: individual-level determinants of y

Reflection problem (cont.)

y = α + βE(y|x_{) + E(}z_|x₎�_{γ +} z�_{η + u}

• β: “endogenous effect”

ie. Impact of network members’ behavior

• γ: “contextual effect”

ie. Impact of network characteristics

Reflection problem (cont.)

• Suppose E(u|x_,z_{) =} x�_δ (ie. average u differs across networks)

⇒ If δ �= 0, network members behave in the same way because their

unobservable characteristics that directly affect behavior are the same.

• Two reasons for this:

• Network members share the same

environment (simultaneity bias)

e.g. Geography, weather, business cycle

• Endogenous network formation (selection bias)

Reflection problem (cont.)

y = α + βE(y|x_{) + E(}z_|x₎�_{γ +} z�_{η +u} Take expectation both sides of the outcome equation conditional on x

Reflection problem (cont.)

• So E(y|x_{) is a linear function of}

E(z_|x₎

• This is true even ifδ = 0 (ie. no omitted variable bias)

• y = α+βE(y|x_{) +E(}z_|x₎�_{γ +}z�_{η +u} cannot be estimated due to

Reflection problem (cont.)

Solutions (Manski 2000, p. 129): specify endogenous effect as

1. Dynamic (ie. lagged mean) 2. Nonlinear function of mean

3. Not mean behavior but, say, median behavior

• Conley & Udry (2010) follow these three

4. Some members affected by randomized treatment

• Randomized treatment approach is popular by now

• But there is a caveat

• Network may change in response to treatment

• Carrell, Sacerdote& West (2013): a policy designed by experimental

Conley and Udry (2010)

• Detailed data collection by

long-term fieldwork (every 6 weeks for 2 years)

• Knowledge of agriculture (how pineapple grows)

2.1 Research Question

Do pineapple farmers learn from their friends about the optimal usage of fertilizer?

• Interesting?

• If yes, only a few farmers need to be subsidized for universal adoption

• Original?

• Overcome the reflection problem

• Feasible?

2.2 Background & Data

• Panel household surveys (every six

week in 1996-98) in 3 villages of southern Ghana

• Pineapple recently introduced in the study area (Figure 3)

2.2 Background & Data (cont.)

• Pineapple takes 5 survey rounds to

mature after fertilizer is applied

⇒ Once applied, farmer cannot change the use of fertilizer in the same plot until harvest

• Pineapple grows throughout the year

⇒ Not everyone plants at the same time

2.2 Background & Data (cont.)

• Outcome variable: Changes in

amount of fertilizer used

• Sample: 107 plantings by 47

pineapple farmers whose previous planting is also observed (closed circles in Figure 2)

2.2 Background & Data (cont.)

• Each farmer’s network (“information neighbors”): obtained by asking

• Among 7 other farmers randomly chosen from the sample,

• Whom they turn to for advice on their farm

cf. Previous studies often treat everyone else in the same village as network members

⇒ Median # of info neighbors: 2

• Location of all plots: collected by

GPS receivers

2.3 Theory

Basic ideas:

• Info. neighbors’ behavior per se shouldn’t matter

• What matters is information each farmer obtains from their info. neighbors

• Farmer i updates E[πi,t(xi,t−5)] by

observing neighbor j’s profit

πj,s(xj,s−5)

• tp < s ≤ t where tp is the period of i’s

previous planting

• How does ∆xit ≡ xi,t − xi,tp respond

to πj,s(xj,s−5)?

Implications 1 & 2

⇒ If bad news, i will take different behavior from j’s

Implication 3

For good news on xj,s �= xi,tp,

theory also predicts the direction of behavior change:

• Good news on x > xi,tp

⇒ xi,t − xi,tp > 0

• Good news on x < xi,tp

Implication 4

2.4 Measuring good (bad) news

• For i’s expectation on πj,s(xj,s−5),

(fortp < s ≤ t)

use median of πk,τ(xk,τ−5) where

• k: plots within 1km radius ofi

• τ ∈ {s−3,s −2,s −1,s}

• 1(xk,τ−5 > 0) =1(xj,s−5 > 0)

• If πj,s(xj,s−5) exceeds this, it is a

good news on x = xj,s−5; otherwise

• So theory tells us that farmer i’s behavior is a highly non-linear function of i’s network member behavior

2.5 Testing implications 1-2

Use logit estimation:

Pr(∆xit �= 0) = Λ

2.5 Testing implications 1-2

Use logit estimation:

Pr(∆xit �= 0) = Λ

• Theoretical predictions:

2.5 Testing implications 1-2

Use logit estimation:

Pr(∆xit �= 0) = Λ

• S.E.: Conley (1999)’s spatial GMM • Stata ado files: downloadable at Tim

Changes in growing conditions

˜Γit ≡ |x_itclose − xitp|

x_itclose: Average of xks where:

• k: plots within 1km of plot i

• s ∈ {t − 3,t − 2,t − 1,t}

Other controls

z

_it

• Wealth

• Soil characteristics

• Dummies for • Clan

• Village

• Survey round

2.5 Testing implications 1-2

(cont.)

• 1SD ⇑ in share of bad news on xitp

⇒ Prob. of fertilizer use change ⇑ by 15%pt

• For bad news on x �= xitp, ⇓ by 9%pt

• Mean prob. of fertilizer use change: 13%)

• Robust to how to measure ∆xit �= 0

2.6 Testing implication 3

Implication 3 says:

2.6 Testing implication 3 (cont.)

Therefore, define

Mi,t ≡

GoodNews(xj,s−5) × (xj,s−5 − xi,tp)

Experienceit

• GoodNews(xj,s−5): dummy for πj,s(xj,s−5) above i’s expectation • Experienceit: How many plantings i

2.6 Testing implication 3 (cont.)

OLS estimation of

∆xit = β1Mit + β2Γit +z�_itβ3 + ν_it

• Γit ≡ x_itclose − xitp: Changes in

growing conditions for farmer i at time t

• z_{it: same as before, plus Γit defined} from financial neighbors

2.6 Testing implication 3 (cont.)

∆xit = β1Mit + β2Γit +z�itβ3 + ν_it

2.6 Testing implication 3 (cont.)

2.6 Testing implication 3 (cont.)

• 1SD ⇑ in Mi,t ⇒ xi,t ⇑ by 4 cedis per

plant, larger than median level

• Effect: bigger for novice pineapple

farmers

• Consistent w/ Implication 4

2.7 Robustness Checks

• Endogeneous network formation drives the result?

• Info. shocks: uncorrelated with z_it,

conditional on growing conditions (page 54)

• Info. neighbors: measured att = 0

2.8 Additional findings

• Own learning effect: equally important (Table 6 A)

• Impact on labor use: similar result

for pineapple while no learning for maize-cassava (Table 7 A-B)

• Good news in geographic

neighborhood: misleading results (Table 7 C)

⇒ Measuring the ACTUAL network:

2.9 Future research

• If learning is important, info network must be endogenous

⇒ Who will connect to early adopters?

• Those who value info high

• Those who incur low cost to link up with them (gender, wealth, religion, etc.)

Banerjee et al. (2013)

• How diffusion of new technology (joining microfinance) depends on who was first informed about it?

• Estimate the model in which

• If informed, joining is a function of own characteristics & fraction of informed neighbors joining

• Information diffusion probability differs by whether informed join or not

Banerjee et al. (2013) (cont.)

• Fraction of informed neighbors joining does not matter

• Informed pass info even if not joining

BenYishay and Mobarak (2013)

3. Network Structure &

Enforcement

• In developing countries, legal institutions are weak

⇒ Contracts cannot be enforced by third party

• Social networks play a role of enforcement

• Grief (1993): Mediterranean traders in 11th century

• Early studies treat network structure as a black box

• Some recent attempts to unpack the black box

• Bloch, Genicot, & Ray (2008): informal insurance

• Jackson, Rodriguez, & Tan (2012): favor exchange

3.1 Research Question

Jackson, Rodriguez, & Tan (2012) ask

Why important?

• Without external enforcement (very

relevant to developing countries), favor

exchanges to achieve higher welfare (e.g. informal insurance, credit)

require repeated interactions between a pair of individuals

Why important? (cont.)

• Favor exchanges can still be

sustained if failing to do a favor to someone leads to no opportunity to receive favors from other people in the future

Why important? (cont.)

• This is unlikely.

• Some people may prefer not

punishing based on their own interest.

• The deviator may not ask a favor to everybody else in the future.

• So we want to know which pair of

individuals in a community needs to be connected to sustain favor

Why original?

• Provide a new concept of network structure (“support”) that is key to sustain favor exchanges

• This concept is derived from game theory analysis

cf. Sociologists asked the same question and came up with a concept of

“clustering” (how likely two of your friends know each other) without no formal theoretical justification

Why feasible?

• Mathematical skills...

• Unique data on favor exchanges in

3.2 Model

• n players

• Discrete time (t ∈ {1,2, ...}) • Linked players can do favors for

each other

Model (cont.)

• Prob. that i needs a favor from j in period t (if i & j are linked): p

• Value of a favor: v

• Cost of a favor: c

• Assume v > c > 0 (ie. Doing a favor is socially optimal)

Model (cont.)

• Agents choose to keep or delete a link at each period

• Keep a link ⇒ Doing a favor to the linked agent when called upon

In each period

t

• Agents decide whether to keep their

links

• Links are retained if mutually agreed.

• At most one agent it is called upon

to do a favor for jt ∈ Ni(gt)

• gt: Network in period t

• Ni(gt): Set of agents linked to i

(“neighbors”) in network gt

• it decides whether to do a favor

• If not doing a favor, the linkij is deleted

3.3 Analysis

Consider a case of n = 2

• Expected value of a relationship per period

pv − pc

• Present value of keeping a relationship

p(v −c)

⇒ Doing a favor is preferred if

δp(v −c)

Analysis:

n

=

3

• Consider 3 agents connected to each other (a “triad”)

• If failing to do a favor causes

ostracism, doing a favor is preferred if

2 · δp(v − c) 1 − δ > c

Analysis: Equilibrium refinements

• Consider smallest m that satisfies

m · δp(v − c) 1 −δ > c

• Any network where all agents have at least m links is sustainable as a SPE

• If any favor is ever refused, all agents delete all links

• Once a favor is refused, some other agents may want to deviate from the punishment strategy, in order to obtain higher payoff

• The punishment strategy (or

Definition: renegotiation-proof

A network g is renegotiation-proof if

• g is sustainable as a SPE

Definition: Renegotiation-proof

(cont.)

• Illustrate this concept for the case

where n = 4 and m = 2

• Suppose player 1 fails to do a favor

to player 2.

• Everyone prefers the original network to the empty network that would result from failing to do a favor

• Now consider this network

• Suppose player 1 needs to do a favor to 3.

• But the following network Pareto-dominates the empty

network (and is renegotiation-proof as we just saw)

One more equilibrium selection

criterion

• Some people may fail to give a favor just by chance

• If this leads to a huge change in network structure, we should not observe the original network so often in reality

Definition: Robust network

A network is robust against social contagion if

• It is renegotiation-proof

• For any network sustained in some

Definition: Supported links

A link ij ∈ g is supported if

• There exists k such that ik ∈ g and

jk ∈ g

Theorem 3

If

• No pair of players could sustain favor exchange in isolation

• A network is robust against social

contagion then

Proof of Theorem 3

• Suppose otherwise. (We will show this

implies that the network is not robust.) Then

there exists a link ij ∈ g that is not supported.

• Consider player h ∈ {/ i,j}

• Deleting a link involving h results in

a robust network that includes link ij

Proof of Theorem 3 (cont.)

• If h is not connected to either i or j

• Deleting a link of h leads to a robust network with ij being part of it

Proof of Theorem 3 (cont.)

• If h is connected to either i or j

• Deleting a link of h leads to a robust network with ij being part of it

• Repeat the deletion of a link of a

player other than i and j.

• Remember robustness requires local link deletion for any network

sustained in some continuation from the original network

• Only the link ij will remain

• But this link is not a robust network

⇐ No pair of players could sustain favor exchange in isolation

3.4 Take it to data

• Are links in favor networks “supported” in reality?

• Data from 75 villages in Karnataka,

India. (downloadable from Esther Duflo’s website)

• Stratified random sample of HHs (half of the population) based on a full census

• Each HH: asked to name

• friends, those they visit/invite,

borrow/lend kerosene or rice or money, give/receive advice, ask for medical help, relatives, go together to

• HHs i and j are measured as having a link if either of i or j

mentions the other

• If HHs outside the sample are

mentioned, such information is thrown away

• Measurement error causes

underestimation of # of supported links

• Only half of HHs interviewed

• HHs may forget mentioning a link

• For biases caused by measuring a network

• A link is measured as supported if a relationship of one type is

supported by relationships of any other type

• Calculate the fraction of favor

network links (borrow/lend, advice, medical help) that are supported in each of 75 villages

⇒ In most villages, the fraction exceeds 50% (Figure 6)

x-axis : percentile of villages in terms of the fraction of supported favor network links

Other supporting evidence

• A link is more likely to be formed if supported, even conditional on the

geographic distance between the pair (sec VI.F)

• Ratio of fraction of pairs having a common connected HH btw. linked & unlinked pairs in favor network: higher for pairs w/

4. Further readings

Recent papers on social networks in development

• Angelucci et al. (2010): extended family in Mexico & secondary school enrolment

• Khwaja et al. (2011): business

network in Pakistan & credit access

4. Further readings (cont.)

Social network can backfire

• Banerjee & Newman (1998): a theory

• Munshi & Rosenzweig (2006): men

in India trapped in caste network while women benefit from trade liberalization

Social network may solve poverty trap

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