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The Formation of Job Referral Networks: Evidence from a Field Experiment in Urban Ethiopia
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The Formation of Job Referral Networks: Evidence from a Field Experiment in Urban Ethiopia

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International Food Policy Research Institute (IFPRI) and Ethiopian Development Research Institute (EDRI) in collaboration with Ethiopian Economics Association (EEA). Eleventh International Conference ...

International Food Policy Research Institute (IFPRI) and Ethiopian Development Research Institute (EDRI) in collaboration with Ethiopian Economics Association (EEA). Eleventh International Conference on Ethiopian Economy. July 18-20, 2013

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    The Formation of Job Referral Networks: Evidence from a Field Experiment in Urban Ethiopia The Formation of Job Referral Networks: Evidence from a Field Experiment in Urban Ethiopia Presentation Transcript

    • 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
    • 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
    • Design Predictions Data Results Conclusions Figure 1: The job contact network of a neighborhood in urban Ethiopia
    • Design Predictions Data Results Conclusions Empirical degree distribution is quite unequal Figure 2: Distribution of degree in job contact networks
    • 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?
    • 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
    • Design Predictions Data Results Conclusions Outline 1 Design 2 Predictions 3 Data 4 Results 5 Conclusions
    • 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
    • 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
    • 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
    • Design Predictions Data Results Conclusions The network A I F B G E C D H Figure 3: ID letters
    • Design Predictions Data Results Conclusions Jobs are drawn A I F B G E C D H Figure 4: Bold IDs have jobs
    • 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
    • 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
    • Design Predictions Data Results Conclusions Outline 1 Design 2 Predictions 3 Data 4 Results 5 Conclusions
    • 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
    • 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
    • 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)
    • Design Predictions Data Results Conclusions Outline 1 Design 2 Predictions 3 Data 4 Results 5 Conclusions
    • 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
    • Design Predictions Data Results Conclusions Outline 1 Design 2 Predictions 3 Data 4 Results 5 Conclusions
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • Design Predictions Data Results Conclusions Outline 1 Design 2 Predictions 3 Data 4 Results 5 Conclusions
    • 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