1) The study examines how job referral networks form in urban Ethiopia through a field experiment. 2) The experiment tests whether people link to others for self-regarding reasons like getting referrals, or other-regarding reasons like helping others get jobs. 3) Results show people in self-interest treatments linked to less connected others for self-interested reasons like getting referrals. But in other-regarding treatments, people did not link to help others. 4) The study suggests policies could encourage employers to ask referrals from a more diverse range of people to strengthen peripheral groups' network positions.

1. ETHIOPIAN DEVELOPMENT
RESEARCH INSTITUTE
The Formation of Job Referral Networks
Evidence from a Field Experiment in Urban EthiopiaEvidence from a Field Experiment in Urban Ethiopia
A. Stefano Caria1 and Ibrahim Worku2
IFPRI ESSP‐II
Ethiopian Economic Association Conference
July 18 2013July 18, 2013
Addis Ababa
1
1University of Oxford, Centre for the Study of African Economies
2IFPRI‐Ethiopia Support Strategy Programme II

2. Design Predictions Data Results Conclusions
Social interactions matter for labour market outcomes
• Strong influence on labor market outcomes, through
information and referrals (Granovetter 1995, Topa 2011)
• In Ethiopia referrals common in flower sector (Mano et al 2010)
and network advice is popular search strategy (Seernels 2007)
• In our sample:
• 41pct of workers have first heard of their current job from social ties
• 29pct have received a referral
• Exclusion from referral networks is likely to be a
substantial disadvantage in labour market

3. Design Predictions Data Results Conclusions
Figure 1: The job contact network of a neighborhood in urban Ethiopia

4. Design Predictions Data Results Conclusions
Empirical degree distribution is quite unequal
Figure 2: Distribution of degree in job contact networks

5. Design Predictions Data Results Conclusions
• Theory suggests agents have both self-regarding and other
regarding reasons to link with the so far poorly connected
• This prediction does not fit the real data
• Models could be misconstruing the incentives in the field, or the
decision making process. We focus on decision making
1 Would agents include peripheral peers when this maximises
the chance of getting a referral?
2 Do agents also have other-regarding reasons to include
peripheral peers?

6. Design Predictions Data Results Conclusions
• We devise an AFE to test for these hypotheses, based on
Beaman Magruder (2012)
• We find evidence for self-regarding but not for other regarding
motives to link with peripheral agents

7. Design Predictions Data Results Conclusions
Outline
1 Design
2 Predictions
3 Data
4 Results
5 Conclusions

8. Design Predictions Data Results Conclusions
The game
• Subjects add two links to an exogenous undirected network
• Specify a partner or ask that one is randomly drawn for them
• The network determines who can refer whom
• A lottery determines whether participants get a lab-job
• Lab-job holders make one referral to a random unemployed tie

9. Design Predictions Data Results Conclusions
The protocol
1 Network positions are randomly assinged
2 Dictator game
3 Test for understanding
4 Linking decisions
5 Jobs are drawn
6 The network is updated
7 Referrals are given

10. Design Predictions Data Results Conclusions
Treatments isolate motives for linking behaviour
• In SELF treatments network updated with links of one randomly
drawn unemployed player
• Other regarding concerns switched off
• Second order, strategic considerations switched off
• In OTHER treatments we implement the links of one randomly
drawn employed player
• Other-regarding concerns primed, self-regarding switched off
• 2x2 design: we also vary anonymity (decisions remain private)
• 5th treatment checks understanding at the end to limit priming

11. Design Predictions Data Results Conclusions
The network
A
I F B G
E C
D
H
Figure 3: ID letters

12. Design Predictions Data Results Conclusions
Jobs are drawn
A
I F B G
E C
D
H
Figure 4: Bold IDs have jobs

13. Design Predictions Data Results Conclusions
SELF treatment
A
I F B G
E C
D
H
Figure 5: Network augmented with links of one unemployed person

14. Design Predictions Data Results Conclusions
OTHER treatment
A
I F B G
E C
D
H
Figure 6: Network augmented with links of one employed person

15. Design Predictions Data Results Conclusions
Outline
1 Design
2 Predictions
3 Data
4 Results
5 Conclusions

16. Design Predictions Data Results Conclusions
Theory suggests two mechanisms of inclusion
1 Models of strategic network formation posit agents consider
costs and benefits of each link (Jackson Wolinsky 1996, Bala Goyal
2000)
When people compete for referrals, links with peripheral people
are very valuable (Calvo Armengol, 2004)
2 Other regarding preferences may also motivate linking choices
• If agents are altruistic (efficiency minded or inequity averse) they will
also try to maximise the chance that peers are referred for a job
• In our game, this implies linking to the peripheral agents
• Directed altruism in non anonynous treatment

17. Design Predictions Data Results Conclusions
We derive four predictions
1 Subjects in SELF treatments will create new links with peripheral
agents
2 Subjects in OTHER treatments will be create new links with
peripheral agents
3 DG giving correlated with link decisions in OTHER, but not in SELF
treatments
4 Subjects in OTHERn will be more likely to refer those whom they
know in real life. Decisions of subjects in SELFn will not be affected

18. Design Predictions Data Results Conclusions
We analyze the data with the following dyadic regression model:
rij = α + βc2j + γc3j + uij (1)
• Unit of observations is all initially unlinked dyads
• Linea probability model
• Standard errors are clustered at session level
• The coefficients on c2j and c3j will provide the basic test for
hypotheses 1 and 2
Include interactions for treatments, understanding and DG giving:
rij = α + βc2j + γc3j + δti + θti ∗ c2j + λti ∗ c3j + uij (2)

19. Design Predictions Data Results Conclusions
Outline
1 Design
2 Predictions
3 Data
4 Results
5 Conclusions

20. Design Predictions Data Results Conclusions
The experiment
• A 50k town in northern Ethiopia with a growing industrial sector
• Randomly sampled blocks and interviewed all individuals 20-40
• Everyone invited to play game: 447/518 subjects participated
• 10 sessions of SELFa OTHERa OTHERn, 11 sessions of SELFn, 9
sessions of SELFawp
1 Covariate balance across assigned network centrality is good
2 Some observable differences (at 10pct s.l) across session treatments
3 Understanding was high and uncorrelated with treatment

21. Design Predictions Data Results Conclusions
Outline
1 Design
2 Predictions
3 Data
4 Results
5 Conclusions

22. Design Predictions Data Results Conclusions
Result 1
Subjects in SELF treatments are more likely to link with less
central peers
Result 2
Linking behaviour in SELF treatments is highly correlated with
understanding, and not correlated with giving in the DG

23. Design Predictions Data Results Conclusions
Table 1: LPM: SELF treatments
Base Controls Treatments
(1) (2) (3)
j centrality = 2 -.167 -.179 -.195
(.039)∗∗∗ (.068)∗∗∗ (.060)∗∗∗
j centrality = 3 -.198 -.200 -.340
(.047)∗∗∗ (.075)∗∗∗ (.072)∗∗∗
Non anonymous -.023
(.048)
Non anonymous X c = 2 .020
(.079)
Non anonymous X c = 3 .160
(.073)∗∗
No probabilities -.035
(.087)
No prob X c = 2 .005
(.116)
No prob X c = 3 .038
(.136)
Const. .397 .407 .436
(.028)∗∗∗ (.046)∗∗∗ (.043)∗∗∗
Obs. 1594 1528 1528

24. Design Predictions Data Results Conclusions
Table 2: LPM: SELF treatments
Understanding1 Understanding2 OtherRegarding
(1) (2) (3)
j centrality = 2 -.016 -.261
(.137) (.086)∗∗∗
j centrality = 3 -.010 -.241
(.097) (.087)∗∗∗
Understanding .131 .125
(.033)∗∗∗ (.054)∗∗
Understand X c = 2 -.199 -.187
(.043)∗∗∗ (.110)∗
Understand X c = 3 -.229 -.222
(.064)∗∗∗ (.087)∗∗
DG sent -.007
(.006)
Sent X c = 2 .012
(.009)
Sent X c = 3 .006
(.009)
Const. .290 .298 .453
(.020)∗∗∗ (.070)∗∗∗ (.058)∗∗∗
Obs. 1517 1517 1528

25. Design Predictions Data Results Conclusions
Result 3
Subjects in OTHER treatments are NOT more likely to link with
less central peers
Result 4
Linking behaviour in OTHER treatments is uncorrelated with
understanding or giving in the dictator game

26. Design Predictions Data Results Conclusions
Table 3: LPM: OTHER treatments
Base Controls Treatments
(1) (2) (3)
j centrality = 2 -.065 -.101 -.159
(.059) (.081) (.098)
j centrality = 3 .012 -.085 -.129
(.076) (.088) (.122)
Non anonymous -.033
(.077)
Non anonymous X c = 2 .119
(.112)
Non anonymous X c = 3 .089
(.144)
Const. .302 .336 .352
(.041)∗∗∗ (.052)∗∗∗ (.071)∗∗∗
Obs. 1072 1022 1022

27. Design Predictions Data Results Conclusions
Result 5
In non anonymous treatments, subjects are more likely to link with
known peers
SELFn OTHERn
(1) (2)
j centrality = 2 -.177 .002
(.052)∗∗∗ (.076)
j centrality = 3 -.165 .058
(.044)∗∗∗ (.102)
i knows j .101 .241
(.051)∗∗ (.123)∗
Same gender -.055 -.003
(.024)∗∗ (.035)
Sum age .002 -.002
(.002) (.001)
Diff age -.002 .003
(.001)∗∗ (.001)∗∗
Const. .303 .320
(.086)∗∗∗ (.096)∗∗∗
Obs. 563 452
Table 4: LPM: Non anonymous treatments

28. Design Predictions Data Results Conclusions
Outline
1 Design
2 Predictions
3 Data
4 Results
5 Conclusions

29. Design Predictions Data Results Conclusions
Hiring policies can re-direct network formation
• Individuals may have both self and other regarding motives in the
formation of job contact networks
• We find strong evidence in support of self-regarding motives
• We are unable to find evidence of other-regarding motives
• Policy can target incentives in network formation processes
• Employers can be incentivized to ask more referrals from members
of peripheral groups. This would strengthen the latter’s position in
job networks