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 ≡ |xitclose − xitp|
xitclose: 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 + νit
• Γit ≡ xitclose − xitp: Changes in
growing conditions for farmer i at time t
• zit: same as before, plus Γit defined from financial neighbors
2.6 Testing implication 3 (cont.)
∆xit = β1Mit + β2Γit +z�itβ3 + νit
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 zit,
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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