Do Job  NetworksDisadvantage  Women?    BKMMotivationExperiment           Do Job Networks Disadvantage Women?Set-upMain Re...
Do Job  NetworksDisadvantage  Women?                                                               Motivation    BKMMotiva...
Do Job  NetworksDisadvantage  Women?                                 What about hiring processes?    BKM                  ...
Do Job  NetworksDisadvantage  Women?                                             Women and Networks    BKM                ...
Do Job  NetworksDisadvantage  Women?                                       Why would networks leave women out?    BKM     ...
Do Job  NetworksDisadvantage  Women?                                                      Our experiment    BKM           ...
Do Job  NetworksDisadvantage  Women?                                                                Preview    BKM        ...
Do Job  NetworksDisadvantage  Women?                                             Outline of rest of talk    BKMMotivationE...
Do Job  NetworksDisadvantage  Women?                                                    Our Experiment    BKMMotivation   ...
Do Job  NetworksDisadvantage  Women?                                               Our Experiment (2)    BKM              ...
Do Job  NetworksDisadvantage  Women?                                                         CA men and women are    BKM  ...
Do Job  NetworksDisadvantage  Women?                                   Experiment: Referral Rounds    BKMMotivation       ...
Do Job  NetworksDisadvantage  Women?                                  Do Referral Systems disadvantage    BKM             ...
Do Job  NetworksDisadvantage  Women?                                                      A simple model    BKMMotivation ...
Do Job  NetworksDisadvantage  Women?                                                                                      ...
Do Job  NetworksDisadvantage  Women?                                                  What we observe    BKMMotivationExpe...
Do Job  NetworksDisadvantage  Women?                             Are men and women connected?    BKM                    • ...
Do Job  NetworksDisadvantage  Women?                                                         Are men less likely to know  ...
Do Job  NetworksDisadvantage  Women?                                       How would different network    BKM              ...
Do Job  NetworksDisadvantage  Women?                                                                                    Si...
Do Job  NetworksDisadvantage  Women?                                                                                      ...
Do Job  NetworksDisadvantage  Women?                                                                                    Si...
Do Job  NetworksDisadvantage  Women?                                                                              Higher q...
Do Job  NetworksDisadvantage  Women?                                                                                    Si...
Do Job  NetworksDisadvantage  Women?                                                                                   Sha...
Do Job  NetworksDisadvantage  Women?                                                                                    Si...
Do Job  NetworksDisadvantage  Women?                                                     Worse information about women    ...
Do Job  NetworksDisadvantage  Women?                                                    Model predictions    BKMMotivation...
Do Job  NetworksDisadvantage  Women?                                 Closest gender, quality & social    BKM              ...
Do Job  NetworksDisadvantage  Women?                                  Characteristics of closest referrals    BKMMotivatio...
Do Job  NetworksDisadvantage  Women?                                                    Do men refer similar male and    B...
Do Job  NetworksDisadvantage  Women?                                                     What about women’s referrals?    ...
Do Job  NetworksDisadvantage  Women?                                                      Summary so far    BKMMotivation ...
Do Job  NetworksDisadvantage  Women?                                        Is Women’s Disadvantage    BKM                ...
Do Job  NetworksDisadvantage                                                       less information about women           ...
Do Job  NetworksDisadvantage  Women?                                            Low info and screening    BKMMotivation   ...
Do Job  NetworksDisadvantage  Women?    BKM                                                                 Table 4: Refer...
Do Job  NetworksDisadvantage  Women?                                                  Screening Results    BKMMotivation  ...
Do Job  NetworksDisadvantage  Women?                                 Screening with choice of gender    BKM             Wh...
Do Job  NetworksDisadvantage  Women?                               What exactly is being screened?    BKMMotivation       ...
Do Job  NetworksDisadvantage  Women?    BKMMotivation                                                                     ...
Do Job  NetworksDisadvantage  Women?    BKMMotivation                                                                     ...
Do Job  NetworksDisadvantage  Women?                                                           Conclusions    BKMMotivatio...
Do Job  NetworksDisadvantage  Women?                                         Conclusions: Economics    BKMMotivation      ...
Do Job  NetworksDisadvantage  Women?                                                   Conclusions: Policy    BKM         ...
Do Job  NetworksDisadvantage  Women?                                             Comment on Attrition    BKM             •...
Do Job  NetworksDisadvantage  Women?                                   Can work experience explain    BKM                 ...
Do Job  NetworksDisadvantage  Women?                                                          Competition    BKMMotivation...
Do Job  NetworksDisadvantage  Women?                                                 Cross-randomization    BKMMotivationE...
Do Job  NetworksDisadvantage  Women?    BKM                                          Appendix Table 3: Competition incenti...
Do Job  NetworksDisadvantage  Women?    BKM                                            Figure 2: Gender choice in referral...
Do Job  NetworksDisadvantage  Women?    BKM                                                    Figure 3: Referral qualific...
Do Job  NetworksDisadvantage  Women?    BKM                                             Figure 6: Referral Qualifies , by ...
Do Job  NetworksDisadvantage  Women?    BKM                                         Figure 7: Referral Qualifies , by Fema...
Do Job  NetworksDisadvantage  Women?                           Social Payments and Qualification    BKMMotivation          ...
Do Job  NetworksDisadvantage  Women?                             Unbiased Expectations of Quality    BKMMotivationExperime...
Do Job  NetworksDisadvantage  Women?                                 Biased Expectations of Quality    BKM                ...
Do Job  NetworksDisadvantage  Women?                    Selection rule even with positive relationship    BKMMotivationExp...
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11.08.2012 - Lori Beaman

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Do Job Networks Disadvantage Women? Evidence from a Recruitment Experiment in Malawi

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11.08.2012 - Lori Beaman

  1. 1. Do Job NetworksDisadvantage Women? BKMMotivationExperiment Do Job Networks Disadvantage Women?Set-upMain Result Evidence from a recruitment experiment inTheoryNetwork MalawistructureConnectionsHeterogeneousNetworks Lori Beaman, Niall Keleher, and Jeremy MagruderSocialIncentivesScreening Northwestern, IPA, and UC-BerkeleyScreeningEither Genderversus RestrictedConclusions November, 2012Bonus SlidesComment 1Comment 2Comment 3Comment 4Comment 5
  2. 2. Do Job NetworksDisadvantage Women? Motivation BKMMotivation • In Malawi, as in much of the world, women areExperiment disadvantaged in labor marketsSet-upMain Result • underrepresented in the formal sectorTheory • earn lessNetworkstructureConnections • There are a litany of possible explanations, e.g.HeterogeneousNetworks • taste-based or statistical discriminationSocial • differences in baseline human capitalIncentives • differences in preferencesScreeningScreening • differences in tenure/experience profilesEither Genderversus Restricted • and so onConclusions • Current policy interventions focus on closing the genderBonus SlidesComment 1 gap in educational attainmentComment 2CommentComment 3 4 • Question: will that be enough?Comment 5
  3. 3. Do Job NetworksDisadvantage Women? What about hiring processes? BKM • Much less research on whether the hiring process causesMotivation (dis)advantagesExperimentSet-up • About half of jobs are found through networksMain Result • In developing countries, networks are key for risk sharing,Theory credit in addition to labor market accessNetworkstructure • Several advantages for employersConnectionsHeterogeneousNetworks • relatively costless way to circulate infoSocial • (some) workers may have useful screening info aboutIncentives friends and relatives (Montgomery 1991, Beaman andScreeningScreening Magruder 2012)Either Gender • tied contracts between reference and referral may solveversus RestrictedConclusions moral hazard problems (Heath 2012)Bonus Slides • But do they disadvantage groups?Comment 1Comment 2Comment 3 • Calvo-Armengol and Jackson (2004): the use of networksCommentComment 4 5 can lead to disadvantages between groups • Are women one of these groups?
  4. 4. Do Job NetworksDisadvantage Women? Women and Networks BKM • Priors are not so clear - potential advantages andMotivation disadvantages.ExperimentSet-up • Could help women, if e.g. resume characteristics are scarceMain ResultTheory and hard-to-observe characteristics are more important • Or, could leave women out: sociologists emphasizeNetworkstructure gender-homophily in networksConnectionsHeterogeneousNetworks • necessary condition for Calvo-Armengol and JacksonSocial (2004) mechanismIncentives • However: as a stylized fact from observational data,ScreeningScreening women are less likely to get networked jobsEither Genderversus Restricted • In U.S. unemployed women are less likely to report usingConclusionsBonus Slides their friends and relatives for help in search (27% of menComment 1 vs. 20% of women) (Ioannides and Loury,2004)Comment 2Comment 3Comment 4 • Based on observational data - could be confounded byComment 5 differences in occupations, reporting choices, etc.
  5. 5. Do Job NetworksDisadvantage Women? Why would networks leave women out? BKM • It may be more costly for firms to access female referralsMotivation • Men (or women) may not be connected to (high quality)ExperimentSet-up womenMain ResultTheory • Sociology lit: women’s networks may be less organizedNetwork around work (e.g. Smith 2000)structureConnections • Or men (or women) may have those connections, butHeterogeneousNetworks prefer to refer menSocialIncentives • If it is easier to get (high quality) male referrals thanScreening female referrals because of network characteristics, thenScreeningEither Gender cost-minimizing firms may end up hiring more menversus Restricted through referralsConclusionsBonus Slides • Firms may get more out of using referral systems for maleComment 1Comment 2 hiresComment 3CommentComment 4 5 • References may be better able or more willing to screen men
  6. 6. Do Job NetworksDisadvantage Women? Our experiment BKM • We conducted a recruitment experiment as part of a hiringMotivation drive for enumerators in MalawiExperimentSet-up • Survey firm wanted to hire more womenMain ResultTheory • Two waves: people encouraged to apply themselves andNetwork people then asked to make a referralstructureConnections • All applicants complete skills assessmentHeterogeneousNetworks • Competitive job between genders:SocialIncentives • 38% of people who apply themselves are women andScreening perform similarly to menScreeningEither Gender • One type of position, so differences in occupational sortingversus Restricted cannot affect results. Reporting clear, too.ConclusionsBonus Slides • Referral phase randomized whether applicants could referComment 1Comment 2 only men, only women, or anyone, and also terms ofComment 3Comment 4 contractComment 5 • Fixed finders fees or performance incentive
  7. 7. Do Job NetworksDisadvantage Women? Preview BKM • The use of referral systems disadvantages highly skilledMotivation womenExperiment • Only 30% of referrals (versus 38% of applicants) areSet-upMain Result women when people have a choiceTheory • 2 reasons: men systematically refer menNetwork • Women’s referrals (both men and women) are less likely tostructureConnections qualifyHeterogeneousNetworks • However, when we restrict gender choices, men andSocialIncentives women make references at the same rate under allScreening contracts regardless of which gender they must referScreeningEither Gender • Men and women are connected to suitable men and womenversus RestrictedConclusions • We develop and test a model to find out whichBonus Slides characteristics of networks lead to disadvantagesComment 1Comment 2 • Social incentives rather than productivity differences leadCommentComment 3 4 to disadvantagesComment 5 • Screening potential of networks is maximized when men refer men
  8. 8. Do Job NetworksDisadvantage Women? Outline of rest of talk BKMMotivationExperimentSet-upMain Result 1 Describe experimental designTheory 2 Test whether women are (dis)advantaged by referralNetworkstructure systemsConnectionsHeterogeneousNetworks 3 Discuss a model of optimal referral choices under differentSocial network characteristicsIncentivesScreening 4 Test whether men and women are connected to suitableScreeningEither Gender womenversus RestrictedConclusions 5 Test for gender differences in network characteristicsBonus SlidesComment 1Comment 2Comment 3Comment 4Comment 5
  9. 9. Do Job NetworksDisadvantage Women? Our Experiment BKMMotivation • IPA-Malawi regularly hires a large number of enumeratorsExperiment for several projectsSet-upMain Result • We posted fliers indicating a hiring drive at a number ofTheory visible places in Lilongwe and BlantyreNetworkstructure • Applicants were instructed to appear at a localConnectionsHeterogeneous employment center at a specific date and time, with aNetworksSocial resume.Incentives • Upon arrival, applicants given an id card and resumesScreeningScreening collectedEither Genderversus Restricted • Applicants completed a written testConclusions • Several math problems, ravens matrices, English skillsBonus SlidesComment 1 assessment, job comprehension component, computer skillsComment 2Comment 3 assessmentCommentComment 4 5 • 2 similar versions of test to limit cheating
  10. 10. Do Job NetworksDisadvantage Women? Our Experiment (2) BKM • Following, applicants completed a practical skillsMotivation assessmentExperimentSet-up • IPA enumerators act as survey respondents, applicants actMain Result as enumeratorsTheory • To test for hard-to-observe abilities, we made a number ofNetworkstructure incorrect answers to questions - i.e. inconsistent householdConnectionsHeterogeneousNetworks size, implausible values for household acreageSocial • Actors instructed to give the right answer if the applicantsIncentives press themScreeningScreening • 2 versions of incorrect answersEither Genderversus Restricted • We measure the number of traps that the applicantsConclusions caughtBonus Slides • Total score on all components averaged. ApplicantsComment 1CommentComment 2 3 informed of qualification threshold.Comment 4Comment 5 • Qualified individuals called for enumerator positions as positions open
  11. 11. Do Job NetworksDisadvantage Women? CA men and women are BKM competitiveMotivationExperiment Figure 1: CA Ability by Gender .03Set-upMain ResultTheory kernel density estimateNetworkstructure .02ConnectionsHeterogeneousNetworksSocialIncentives .01ScreeningScreeningEither Genderversus RestrictedConclusions 0Bonus SlidesComment 1 20 40 60 80 100Comment 2 CAs overall (corrected) scoreComment 3Comment 4 Male CAs Female CAsComment 5
  12. 12. Do Job NetworksDisadvantage Women? Experiment: Referral Rounds BKMMotivation • Finally, applicants asked to make a referralExperiment • Randomly assigned to one of following treatments:Set-upMain Result • Asked at random to make a referral who was male, aTheory referral who was female, or a referral who could be male orNetwork femalestructureConnectionsHeterogeneous • Cross-randomized the finder’s fee:NetworksSocial • A fixed fee of either 1000 MWK or 1500 MWK ($6 orIncentives $10).Screening • A performance incentive of 500 MWK if their referral doesScreeningEither Gender not qualify or 1800 MWK if their referral does qualifyversus RestrictedConclusions • All treatments fully blinded from the perspective ofBonus Slides evaluatorsComment 1CommentComment 2 3 • Referrals attend recruitment session 3 or 4 days later.Comment 4Comment 5 Complete same skills assessment.
  13. 13. Do Job NetworksDisadvantage Women? Do Referral Systems disadvantage BKM women?Motivation Table 1: Gender Distributions of CAs and ReferralsExperimentSet-up (1) (2) (3) (4)Main Result Female  Diff: p Theory All CAs Male CAs CAs valueNetwork A. CA CharacteristicsstructureConnections Fraction of CAs 100% 62% 38%Heterogeneous CA is qualified 53% 56% 48% 0.047Networks N 767 480 287Social B. CA Characteristics: Made Referral, Either Gender TreatmentsIncentives Fraction of CAs 100% 61% 39%Screening CA is qualified 57% 62% 49% 0.061ScreeningEither Gender N 217 130 87versus Restricted C. Referral Characteristics:  Either Gender TreatmentsConclusions Referral is Female 30% 23% 43% 0.002Bonus Slides Referral is Qualified 49% 56% 38% 0.019Comment 1 Referral is Qualified Male 34% 43% 22% 0.002Comment 2Comment 3 Referral is Qualified Female 14% 13% 17% 0.456Comment 4 N 195 117 78Comment 5
  14. 14. Do Job NetworksDisadvantage Women? A simple model BKMMotivation • Suppose conventional applicants (CAs) each know aExperimentSet-up collection of potential referrals, some men and women.Main ResultTheory • Each of these potential referrals has a social transfer theyNetwork will give the applicantstructureConnections • Each also has an actual quality and an observed expectedHeterogeneousNetworks qualitySocialIncentives • Focus on individuals the CA might actually choose:ScreeningScreening • For each perceived probability of qualifying, the personEither Genderversus Restricted who maximizes social paymentsConclusions • Therefore expected quality is decreasing in social paymentsBonus Slides • Observe referral choice under two contact types: fixed feeComment 1CommentComment 2 3 and performance payComment 4Comment 5
  15. 15. Do Job NetworksDisadvantage Women? Today, graphically BKM sample similar networks 20MotivationExperiment 15Set-upMain ResultTheory 10Networkstructure Social PaymentConnectionsHeterogeneousNetworks 5SocialIncentives 0ScreeningScreeningEither Genderversus Restricted -5ConclusionsBonus SlidesComment 1 -10Comment 2 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Comment 3 Perceived Probability of QualifyingComment 4Comment 5 Note: Diamonds: women, Circles: men
  16. 16. Do Job NetworksDisadvantage Women? What we observe BKMMotivationExperimentSet-up • Whether someone chooses to make a referralMain ResultTheory • For those who make a referral, we see 2 nodes in theNetworkstructure gender-specific network for each gender:ConnectionsHeterogeneousNetworks • Characteristics of person who maximizes social incentivesSocial • Characteristics of person who maximizes expected payIncentivesScreening +social incentives under performance incentive contractScreeningEither Genderversus RestrictedConclusionsBonus SlidesComment 1Comment 2Comment 3Comment 4Comment 5
  17. 17. Do Job NetworksDisadvantage Women? Are men and women connected? BKM • One reason women may be disadvantaged by referralMotivation system is if (suitable) women are not integrated into men’sExperimentSet-up networksMain Result • Men and women make a decision to make a referral if theTheoryNetwork expected payoffs are greater than 0structureConnections • Under fixed fees, this means that they know a man orHeterogeneousNetworks woman whose social payment is not too negativeSocial • Under perf pay, this means that they know a man orIncentives woman whose total package of fixed fees + expected perfScreening payScreeningEither Genderversus Restricted • A stronger question: Are there men who only knowConclusions suitable women?Bonus SlidesComment 1 • Are men in “either” treatments more likely to return withComment 2Comment 3 a referral than men in “male” treatments?Comment 4Comment 5 • [Later] does screening behavior look different in “either” treatments versus restricted male referrals?
  18. 18. Do Job NetworksDisadvantage Women? Are men less likely to know BKM suitable women?MotivationExperiment Table 2: Probability of Making a ReferralSet-up (1) (2) (3) (4)Main ResultTheory Female Treatment ‐0.004     ‐0.055     ‐0.004     ‐0.042Network          (0.038)     (0.054)     (0.050)     (0.074)structure Either Gender Treatment 0.014     0.017     ‐0.052     ‐0.024Connections          (0.040)     (0.055)     (0.052)     (0.071)Heterogeneous Performance Pay                           ‐0.148 *** ‐0.113Networks                                    (0.056)     (0.080)Social Perf Pay * Female Treatment                           0.004     ‐0.013Incentives                                    (0.076)     (0.111)Screening Perf Pay * Either Treatment                           0.152 *   0.086Screening                           (0.079)     (0.110)Either Genderversus Restricted Observations 506     310     506     310Conclusions CA Gender Men Women Men WomenBonus Slides NotesComment 1 1 The dependent variable is an indicator for whether the CA makes a referral.Comment 2 2 All specifications include CA visit day dummies.Comment 3Comment 4Comment 5
  19. 19. Do Job NetworksDisadvantage Women? How would different network BKM characteristics affect referralMotivation choicesExperimentSet-upMain Result We identify four dimensions of heterogeneity:TheoryNetwork 1 Maximal Social payment received: “Closest gender”structureConnectionsHeterogeneous 2 Expected quality of closest person: “Quality”NetworksSocialIncentives 3 Slope of social payment/expected quality tradeoff:Screening “Network Shallowness”ScreeningEither Genderversus Restricted 4 Variance of actual quality relative to expected quality:Conclusions “Information”Bonus SlidesComment 1Comment 2Comment 3Comment 4Comment 5
  20. 20. Do Job NetworksDisadvantage Women? Similar Networks BKM sample similar networks 20MotivationExperimentSet-up 15Main ResultTheoryNetwork 10structureConnections Social PaymentHeterogeneousNetworks 5SocialIncentivesScreeningScreening 0Either Genderversus RestrictedConclusions -5Bonus SlidesComment 1Comment 2Comment 3Comment 4 -10Comment 5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Perceived Probability of Qualifying
  21. 21. Do Job NetworksDisadvantage Women? Closer men BKM higher male social payments 20MotivationExperimentSet-up 15Main ResultTheoryNetwork 10structureConnections Social PaymentHeterogeneousNetworks 5SocialIncentivesScreeningScreening 0Either Genderversus RestrictedConclusions -5Bonus SlidesComment 1Comment 2Comment 3Comment 4 -10Comment 5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Perceived Probability of Qualifying
  22. 22. Do Job NetworksDisadvantage Women? Similar Networks BKM sample similar networks 20MotivationExperimentSet-up 15Main ResultTheoryNetwork 10structureConnections Social PaymentHeterogeneousNetworks 5SocialIncentivesScreeningScreening 0Either Genderversus RestrictedConclusions -5Bonus SlidesComment 1Comment 2Comment 3Comment 4 -10Comment 5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Perceived Probability of Qualifying
  23. 23. Do Job NetworksDisadvantage Women? Higher quality men BKM higher male quality 20MotivationExperimentSet-up 15Main ResultTheoryNetwork 10structureConnections Social PaymentHeterogeneousNetworks 5SocialIncentivesScreeningScreening 0Either Genderversus RestrictedConclusions -5Bonus SlidesComment 1Comment 2Comment 3Comment 4 -10Comment 5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Perceived Probability of Qualifying
  24. 24. Do Job NetworksDisadvantage Women? Similar Networks BKM sample similar networks 20MotivationExperimentSet-up 15Main ResultTheoryNetwork 10structureConnections Social PaymentHeterogeneousNetworks 5SocialIncentivesScreeningScreening 0Either Genderversus RestrictedConclusions -5Bonus SlidesComment 1Comment 2Comment 3Comment 4 -10Comment 5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Perceived Probability of Qualifying
  25. 25. Do Job NetworksDisadvantage Women? Shallower women BKM Shallower female network 20MotivationExperimentSet-up 15Main ResultTheoryNetwork 10structureConnections Social PaymentHeterogeneousNetworks 5SocialIncentivesScreeningScreening 0Either Genderversus RestrictedConclusions -5Bonus SlidesComment 1Comment 2Comment 3Comment 4 -10Comment 5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Perceived Probability of Qualifying
  26. 26. Do Job NetworksDisadvantage Women? Similar Networks BKM sample similar networks 20MotivationExperimentSet-up 15Main ResultTheoryNetwork 10structureConnections Social PaymentHeterogeneousNetworks 5SocialIncentivesScreeningScreening 0Either Genderversus RestrictedConclusions -5Bonus SlidesComment 1Comment 2Comment 3Comment 4 -10Comment 5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Perceived Probability of Qualifying
  27. 27. Do Job NetworksDisadvantage Women? Worse information about women BKM less information about women 20MotivationExperimentSet-up 15Main ResultTheoryNetwork 10structureConnections Social PaymentHeterogeneousNetworks 5SocialIncentivesScreeningScreening 0Either Genderversus RestrictedConclusions -5Bonus SlidesComment 1Comment 2Comment 3Comment 4 -10Comment 5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Perceived Probability of Qualifying
  28. 28. Do Job NetworksDisadvantage Women? Model predictions BKMMotivationExperiment 1 Under fixed fees: only differences in closeness affect whichSet-upMain Result referral is chosenTheoryNetwork 2 Higher quality increases returns under performance paystructureConnections • Quality (of person who gives highest social payment) isHeterogeneousNetworks revealed under fixed feesSocialIncentives 3 Worse info, more shallow networks can both lead to lowerScreeningScreening response to performance payEither Genderversus RestrictedConclusionsBonus SlidesComment 1Comment 2Comment 3Comment 4Comment 5
  29. 29. Do Job NetworksDisadvantage Women? Closest gender, quality & social BKM incentivesMotivationExperiment Men may know women, but would they share opportunities?Set-upMain ResultTheory • Prediction 1 from the model: The person referred underNetwork fixed fees is the closest person in the networkstructureConnectionsHeterogeneous • If men are closer to men (or women), should see menNetworks referred systematically under fixed fee - unrestrictedSocialIncentives treatmentScreeningScreening • The restricted-gender fixed fee treatments also let usEither Genderversus Restricted observe the quality of the closest people in the network:Conclusions • If men’s networks of men are higher quality than men’sBonus SlidesComment 1 networks of women, should see fixed fee restricted maleCommentComment 2 3 referrals being higher quality than fixed fee restrictedCommentComment 4 5 female
  30. 30. Do Job NetworksDisadvantage Women? Characteristics of closest referrals BKMMotivationExperimentSet-upMain Result C. Referral Characteristics:  Either Gender TreatmentsTheory Referral is Female 30% 23% 43% 0.002 Referral is Qualified 49% 56% 38% 0.019Networkstructure Referral is Qualified Male 34% 43% 22% 0.002Connections Referral is Qualified Female 14% 13% 17% 0.456HeterogeneousNetworks N 195 117 78Social D. Referral Characteristics:  Either Gender, Fixed Fee TreatmentsIncentives Referral is Female 32% 25% 43% 0.042Screening Referral is Qualified 50% 60% 37% 0.012ScreeningEither Gender Referral is Qualified Male 34% 44% 20% 0.007versus Restricted Referral is Qualified Female 16% 16% 16% 0.983Conclusions N 117 68 49Bonus SlidesComment 1Comment 2Comment 3Comment 4Comment 5
  31. 31. Do Job NetworksDisadvantage Women? Do men refer similar male and BKM female network members?Motivation Figure 2: Mens Fixed Fee ReferralsExperiment .03Set-upMain ResultTheory Kernel density estimateNetworkstructure .02ConnectionsHeterogeneousNetworksSocialIncentives .01ScreeningScreeningEither Genderversus Restricted 0Conclusions 20 40 60 80 100Bonus Slides Referrals overall (corrected) scoreComment 1Comment 2 Men referring men Men referring womenComment 3Comment 4Comment 5 Note: figure compares men in restricted treatments only
  32. 32. Do Job NetworksDisadvantage Women? What about women’s referrals? BKMMotivation Figure 3: Womens Fixed Fee ReferralsExperiment .03Set-upMain Result Kernel density estimateTheory .02NetworkstructureConnectionsHeterogeneousNetworks .01SocialIncentivesScreeningScreeningEither Gender 0versus Restricted 20 40 60 80 100Conclusions Referrals overall (corrected) scoreBonus Slides Women referring men Women referring womenComment 1Comment 2Comment 3CommentComment 4 5 Note: figure compares women in restricted treatments only
  33. 33. Do Job NetworksDisadvantage Women? Summary so far BKMMotivation • By design, we only observe clean evidence of differences inExperiment social incentives for men or women who maximize socialSet-upMain Result incentives (who are revealed through the fixed feeTheory treatments)Networkstructure • For those people:ConnectionsHeterogeneousNetworks • Men tend to maximize men’s incentivesSocial • Low ability people tend to maximize women’s incentivesIncentives • Closest women are low abilityScreeningScreening • Closest men however are not systematically low abilityEither Genderversus Restricted • Can conclude: at least among socially closest people, menConclusions and women have different social incentivesBonus SlidesCommentComment 1 2 • Social incentives make it cheaper to (a) get male referralsCommentComment 3 4 from men and (b) use men to get high quality referralsComment 5
  34. 34. Do Job NetworksDisadvantage Women? Is Women’s Disadvantage BKM Productive?MotivationExperimentSet-upMain Result • If employers encourage referral hires, they likely gainTheory something from their useNetworkstructure • One thing which has been emphasized is screening (e.g.ConnectionsHeterogeneous Montgomery (1990), Beaman and Magruder (2012))NetworksSocial • If employees see hard to observe characteristics, canIncentives improve outcomes for employerScreeningScreeningEither Gender • If men (women) are less able to screen women, it may leadversus Restricted to employers discouraging female referralsConclusionsBonus Slides • From the model: CAs will screen if and only if they haveCommentComment 1 2 good information, and networks are not too shallowComment 3Comment 4Comment 5
  35. 35. Do Job NetworksDisadvantage less information about women 20 Women? BKMMotivation 15ExperimentSet-upMain Result 10Theory Social PaymentNetworkstructureConnections 5HeterogeneousNetworksSocialIncentives 0ScreeningScreeningEither Genderversus Restricted -5ConclusionsBonus SlidesComment 1 -10Comment 2 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Comment 3 Perceived Probability of QualifyingComment 4Comment 5
  36. 36. Do Job NetworksDisadvantage Women? Low info and screening BKMMotivation • Low info makes the tradeoffs “steeper” - it becomes moreExperimentSet-up expensive and more infeasible to find very high qualityMain Result referralsTheoryNetwork • Essentially, most referral probabilities of qualificationstructureConnections pushed towards 1/2HeterogeneousNetworks • this increases the payoffs to referring someone who youSocialIncentives think is relatively low ability under perf pay incentivesScreeningScreening • and decreases the payoffs to referring someone who youEither Genderversus Restricted think is relatively high ability under perf payConclusions • Empirically, if men (women) have lower ability to screenBonus SlidesComment 1 women, should observe a smaller increase in performanceComment 2CommentComment 3 4 in response to perf payComment 5
  37. 37. Do Job NetworksDisadvantage Women? BKM Table 4: Referral PerformanceMotivation Referral Qualifies (1) (2) (3) (4)Experiment Female Referral Treatment ‐0.030    ‐0.190 **  0.068    ‐0.181    Set-up          (0.062)    (0.083)    (0.081)    (0.113)    Main Result Either Gender Treatment 0.071    ‐0.231 *** 0.227 *** ‐0.242 ** Theory          (0.066)    (0.082)    (0.084)    (0.107)    Network Performance Pay                        0.267 *** 0.021    structure                                 (0.093)    (0.122)    Connections Perf Pay * Female Treatment                        ‐0.248 *   ‐0.022    Heterogeneous                                 (0.127)    (0.171)    Networks Perf Pay * Either Treatment                        ‐0.383 *** 0.032    Social                                 (0.132)    (0.169)    Incentives Observations 390    227    390    227    Screening CA Gender Men Women Men WomenScreening NotesEither Gender 1 The dependent variable is an indicator for the referral qualifying.versus Restricted 2 All specifications include CA visit day dummies.ConclusionsBonus SlidesComment 1Comment 2Comment 3Comment 4Comment 5
  38. 38. Do Job NetworksDisadvantage Women? Screening Results BKMMotivation • Men can screen menExperimentSet-up • Men cannot screen women (or, at least, won’t at theseMain ResultTheory levels of incentives)Network • Allowing the option to refer women eliminates thestructureConnections screening premiumHeterogeneousNetworks • Suggests that employers who want to maximize screeningSocialIncentives may discourage men from making female referrals.Screening • Some evidence that difference is info and not shallowness:ScreeningEither Genderversus Restricted men are more likely to make a low quality referral underConclusions perf pay-female treatments than under fixed fee-femaleBonus Slides • Women show less ability to screen men or women overallComment 1Comment 2Comment 3 • Some not quite sig evidence that they can screen womenComment 4Comment 5
  39. 39. Do Job NetworksDisadvantage Women? Screening with choice of gender BKM Why is there a lower screening premium when we allow eitherMotivation gender?Experiment • Under performance pay, one maximizes the sum of socialSet-upMain Result incentives and expected perf incentiveTheory • This makes the theoretical effect ambiguousNetworkstructureConnections • Considering either gender in general allows you to “buy”HeterogeneousNetworks quality with giving up a lower amount of social incentivesSocialIncentives • Increases both chance that you observe someone who hasScreening a high chance of qualifying and gives you OK socialScreening incentivesEither Genderversus Restricted ⇒ May increase performance premiumConclusionsBonus Slides • Also ↑ chance that you observe someone who has an OKCommentComment 1 2 chance of qualifying but gives you great social incentivesComment 3Comment 4 ⇒ May decrease performance premiumComment 5 • Happens, in particular, when info is bad about one gender
  40. 40. Do Job NetworksDisadvantage Women? What exactly is being screened? BKMMotivation • Have much richer data than is being used here - detailedExperiment assessments of different referral characteristicsSet-upMain Result • Men are screening in some ways across a broad category ofTheory characteristicsNetworkstructure • Women are screening, too -ConnectionsHeterogeneousNetworks • significantly, women screen women on language scores andSocial cognitive skills.Incentives • Women screen men on survey experienceScreeningScreeningEither Gender • The former is probably more valuable as screening forversus RestrictedConclusions employers. May be a role for encouraging female referralsBonus Slides of womenComment 1Comment 2 • still, if employers use referrals for screening, biggest returnsCommentComment 3 4 are to get men to refer menComment 5
  41. 41. Do Job NetworksDisadvantage Women? BKMMotivation Table 5: Screening of Male CAs on Different Characteristics Survey  Tertiary  Math  Language  Ravens  Computer  Practical  Feedback Experiment exp Education Score Score score Score Exam Score points (1) (2) (3) (4) (5) (6) (7) (8)Set-upMain Result Female Referral  ‐0.033     0.045     ‐0.017     ‐0.115     ‐0.092     0.062     1.033     3.003 *** Treatment (0.069)     (0.074)     (0.142)     (0.207)     (0.194)     (0.371)     (0.661)     (1.044)    Theory Either Gender  0.040     0.072     0.009     0.087     0.089     0.623     1.378 **  1.856 *   Treatment (0.072)     (0.077)     (0.148)     (0.215)     (0.203)     (0.387)     (0.689)     (1.089)    Network Performance Pay 0.080     0.067     0.134     ‐0.005     0.230     0.943 **  0.496     1.883    structure          (0.080)     (0.085)     (0.164)     (0.238)     (0.224)     (0.428)     (0.757)     (1.197)    Connections Perf Pay * Female  ‐0.075     0.025     ‐0.259     ‐0.027     ‐0.293     ‐0.915     ‐0.950     ‐2.443    Heterogeneous Treatment (0.108)     (0.116)     (0.223)     (0.325)     (0.305)     (0.583)     (1.026)     (1.622)    Networks Perf Pay * Either  ‐0.165     ‐0.083     ‐0.065     ‐0.169     ‐0.367     ‐0.856     ‐1.768 *   ‐3.371 ** Social Treatment (0.113)     (0.121)     (0.232)     (0.338)     (0.318)     (0.607)     (1.069)     (1.696)    Incentives Observations 386     390    390    390    390    390    383    382    Notes 1 The dependent variable is an indicator for the referral qualifying.Screening 2 All specifications include CA visit day dummies.ScreeningEither Genderversus RestrictedConclusionsBonus SlidesComment 1Comment 2Comment 3Comment 4Comment 5
  42. 42. Do Job NetworksDisadvantage Women? BKMMotivation Table 6: Screening of Female CAs on Different Characteristics Tertiary  Math  Language  Ravens  Computer  Practical  Feedback  Survey expExperiment Education Score Score score Score Exam Score pointsSet-up (1) (2) (3) (4) (5) (6) (7) (8)Main Result Female Referral  0.032     0.151     ‐0.332     ‐1.140 *** ‐0.435     ‐0.627     0.972     2.152     Treatment (0.091)     (0.110)     (0.216)     (0.342)     (0.270)     (0.538)     (0.963)     (1.349)    Theory Either Gender  0.040     0.017     ‐0.189     ‐0.246     ‐0.172     ‐0.139     0.015     0.879     Treatment (0.086)     (0.104)     (0.205)     (0.324)     (0.256)     (0.509)     (0.910)     (1.274)    Network Performance Pay 0.264 *** 0.143     ‐0.400 *   ‐0.465     ‐0.175     0.419     1.832 *   1.604    structure          (0.098)     (0.119)     (0.234)     (0.370)     (0.293)     (0.582)     (1.056)     (1.479)    Connections Perf Pay * Female  ‐0.320 **  ‐0.292 *   0.402     1.330 **  0.551     0.232     ‐2.164     ‐2.134    Heterogeneous Treatment (0.138)     (0.166)     (0.326)     (0.515)     (0.408)     (0.811)     (1.468)     (2.055)    Networks Perf Pay * Either  ‐0.270 **  ‐0.052     0.368     0.500     ‐0.260     ‐0.372     ‐1.625     ‐4.511 ** Social Treatment (0.136)     (0.164)     (0.323)     (0.510)     (0.403)     (0.802)     (1.448)     (2.027)    Incentives Observations 226     227    227    227    227    227    222    222    NotesScreening 1 The dependent variable is indicated in the column heading. 2 All specifications include CA visit day dummies.ScreeningEither Genderversus RestrictedConclusionsBonus SlidesComment 1Comment 2Comment 3Comment 4Comment 5
  43. 43. Do Job NetworksDisadvantage Women? Conclusions BKMMotivationExperiment • Stylized Fact: women are less likely to receive job referralsSet-upMain Result than men (from data in US and Europe)TheoryNetwork • Using a recruitment experiment in Malawi, we confirmstructureConnections that women are disadvantaged by referral systemsHeterogeneousNetworks • Men choose not to refer women, when given the choiceSocialIncentives • Women choose women at about the population average,ScreeningScreening but make on average low quality referralsEither Genderversus RestrictedConclusionsBonus SlidesComment 1Comment 2Comment 3Comment 4Comment 5
  44. 44. Do Job NetworksDisadvantage Women? Conclusions: Economics BKMMotivation • We test several network constraints that could drive thisExperimentSet-up resultMain ResultTheory • Men and women are equally likely to be connected to menNetwork and womenstructureConnections • Men are closest to men, but have high quality male andHeterogeneousNetworks female contactsSocialIncentives • Women are not socially closer to one gender than theScreening other, but have low quality networks of womenScreeningEither Gender • Men can screen men well, cannot screen women; womenversus Restricted can screen both men and women to a lesser extentConclusionsBonus SlidesComment 1Comment 2Comment 3Comment 4Comment 5
  45. 45. Do Job NetworksDisadvantage Women? Conclusions: Policy BKM Permitting women’s disadvantage in referral rates has threeMotivation benefits to employers:ExperimentSet-upMain Result • It is lower cost for men to refer men than for men to referTheory women (since social incentives are higher)NetworkstructureConnections • It is lower cost to get high quality referrals if men areHeterogeneousNetworks making referralsSocialIncentives • Screening benefits of referral systems are maximized whenScreeningScreening men are encouraged to refer only menEither Genderversus Restricted • All in all, a hard problem to solveConclusionsBonus Slides • Current policies to address gender gap - such as investingComment 1Comment 2 in girls’ education - will not be enough to overcome thisComment 3Comment 4 • Maybe a role for quota systems in hiring policyComment 5
  46. 46. Do Job NetworksDisadvantage Women? Comment on Attrition BKM • 80% of applicants make a referralMotivation • Reference rate is always really similar across genders,Experiment different across treatmentsSet-upMain Result • Differences in referral quality across gender, withinTheory treatment can be taken at (close to) face value for thoseNetwork who make referralsstructureConnections • Difference in referral quality across treatment will be theHeterogeneousNetworks combined effect of some attrition + population averageSocial choicesIncentives • For employers (and to understand actual trends inScreeningScreening references), the net effect (including attrition) is theEither Genderversus Restricted relevant dimension in any eventConclusions • Implications for e.g. ability to screen are the same ifBonus SlidesComment 1 individuals attrit because they know their options are badComment 2Comment 3 • We also simulate the model and recover the sameComment 4Comment 5 predictions on the attrition decision and results within made referrals
  47. 47. Do Job NetworksDisadvantage Women? Can work experience explain BKM results?MotivationExperimentSet-upMain ResultTheory • Men are more likely to have worked at a survey firm in theNetwork past than womenstructureConnections • Working at a survey firm may both enhance your networkHeterogeneousNetworks and give you better informationSocialIncentives • While it does not affect any of the interpretations - orScreening disadvantages women face - it may be an underlyingScreeningEither Gender mechanismversus RestrictedConclusions • We find no differential response among people who haveBonus Slides worked at a survey firm in the past.Comment 1Comment 2Comment 3Comment 4Comment 5
  48. 48. Do Job NetworksDisadvantage Women? Competition BKMMotivation • Niederle and Vesterlund (2007) find that women areExperimentSet-up averse to competition relative to menMain Result • Making a reference involves introducing the employer to aTheoryNetwork potential competitor for the jobstructureConnections • May have an incentive to refer someone bad (though, aHeterogeneousNetworks marginal incentive for an informed decision maker -Social referral is one additional applicant among many)IncentivesScreening • May have been particularly salient in our context, asScreeningEither Gender applicants not yet hiredversus Restricted • However, certainly a relevant incentive in on-the-jobConclusions referrals, tooBonus SlidesComment 1 • Again, suggests a mechanism, without affectingComment 2Comment 3 interpretations or policy prescriptionsComment 4Comment 5
  49. 49. Do Job NetworksDisadvantage Women? Cross-randomization BKMMotivationExperiment • We cross-randomized a treatment designed to make theSet-upMain Result competitiveness more salientTheory • CAs were told the qualification threshold was eitherNetworkstructureConnections 1 Absolute: scoring better than 60HeterogeneousNetworks 2 Relative: scoring in the top half of applicantsSocialIncentives • We hypothesize that the relative treatment makes theScreening competition more salient (since CAs compete directly withScreeningEither Gender referrals to be in the top half)versus RestrictedConclusions • (admittedly, somewhat weak test)Bonus Slides • Look just at fixed fee referrals to isolate social incentivesComment 1Comment 2Comment 3Comment 4Comment 5
  50. 50. Do Job NetworksDisadvantage Women? BKM Appendix Table 3: Competition incentives among fixed fee referralsMotivation (1) (2) (3) (4) (5) (6)Experiment CA  Referral  Referral  CA  Referral  Referral  Dependent Variable Qualifies Qualifies Qualifies Qualifies Qualifies QualifiesSet-upMain Result Competitive Treatment 0.021 0.072     0.052     0.014 0.090     0.227          (0.062) (0.069)     (0.121)     (0.086) (0.095)     (0.165)Theory Female Treatment              0.094                  ‐0.024Network                       (0.116)                  (0.177)structure Either Treatment              0.175                  ‐0.160Connections                       (0.123)                  (0.169)Heterogeneous Competitive * Female               0.007                  ‐0.263Networks                       (0.166)                  (0.236)Social Competitive * Either               0.103                  ‐0.142Incentives              0.176                  (0.236)Screening Observations 287 232     232     166 133     133Screening CA Gender Men Men Men Women Women WomenEither Genderversus RestrictedConclusionsBonus SlidesComment 1Comment 2Comment 3Comment 4Comment 5
  51. 51. Do Job NetworksDisadvantage Women? BKM Figure 2: Gender choice in referrals, by CA performance .8MotivationExperimentSet-up .6Main Result Referral is FemaleTheoryNetwork .4structureConnectionsHeterogeneousNetworks .2SocialIncentives 0ScreeningScreening 20 40 60 80 100Either Gender CAs overall (corrected) scoreversus Restricted Referrals of Male CAs Referrals of Female CAsConclusionsBonus SlidesComment 1Comment 2Comment 3Comment 4Comment 5
  52. 52. Do Job NetworksDisadvantage Women? BKM Figure 3: Referral qualification rate, by CA performance 1MotivationExperiment Referrals qualification rateSet-up .8Main ResultTheory .6NetworkstructureConnections .4HeterogeneousNetworksSocialIncentives .2ScreeningScreening 20 40 60 80 100Either Gender CAs overall (corrected) scoreversus Restricted Referrals of Male CAs Referrals of Female CAsConclusionsBonus SlidesComment 1Comment 2Comment 3Comment 4Comment 5
  53. 53. Do Job NetworksDisadvantage Women? BKM Figure 6: Referral Qualifies , by Male CA performance .8MotivationExperimentSet-up .6 Referral qualifiesMain ResultTheory .4NetworkstructureConnections .2HeterogeneousNetworksSocialIncentives 0Screening 20 40 60 80 100Screening CAs overall (corrected) scoreEither Genderversus Restricted Men referring women, fixed Men referring men, fixed Men referring women, perf Men referring men, perfConclusionsBonus SlidesComment 1Comment 2Comment 3Comment 4Comment 5
  54. 54. Do Job NetworksDisadvantage Women? BKM Figure 7: Referral Qualifies , by Female CA performanceMotivationExperiment .8Set-up Referral qualifiesMain Result .6TheoryNetwork .4structureConnectionsHeterogeneous .2NetworksSocialIncentives 0Screening 20 40 60 80 100Screening CAs overall (corrected) scoreEither Genderversus Restricted Women referring women, fixed Women referring men, fixed Women referring women, perf Women referring men, perfConclusionsBonus SlidesComment 1Comment 2Comment 3Comment 4Comment 5
  55. 55. Do Job NetworksDisadvantage Women? Social Payments and Qualification BKMMotivation • Possible (reasonable?) that social payments increase withExperiment qualification in the ambient networkSet-upMain Result • Referrals give you better social transfers if they get the jobTheoryNetwork • Consistent with our modelling assumptionsstructureConnections • No assumption made about the joint distribution of αg , Qjg in the ambient networkHeterogeneousNetworks jSocial • Selection rule still leads to decreasing relationship amongIncentivesScreening non-dominated referralsScreeningEither Gender • However, may change interpretation of social paymentsversus RestrictedConclusions • Incentives aligned with employerBonus Slides • differences in quality expectations may lead to women’sComment 1Comment 2 disadvantage if men expect men to be higher quality,CommentComment 3 4 women have wrong quality expectationsComment 5
  56. 56. Do Job NetworksDisadvantage Women? Unbiased Expectations of Quality BKMMotivationExperimentSet-up • Model assumed εg was mean 0 - allowed us to estimate Q1 j gMain ResultTheory • Already showed that men’s fixed fee referrals of men ARENetwork NOT higher ability than men’s fixed fee referrals of womenstructureConnections • And women’s (low quality) fixed fee referrals ARE NOTHeterogeneousNetworks the highest quality people they know (they know highSocial quality men)IncentivesScreening • So, if CA’s have unbiased expectations: can conclude thatScreeningEither Gender expectations of quality ARE NOT source of women’sversus RestrictedConclusions disadvantageBonus Slides • But, expectations of quality could be biasedComment 1Comment 2Comment 3Comment 4Comment 5
  57. 57. Do Job NetworksDisadvantage Women? Biased Expectations of Quality BKM • If expected social incentives increase in expected referralMotivation qualification and expectations are biased (for now, againstExperiment women)Set-upMain Result • Incentives to refer a qualified person are still strictly largerTheory under perfNetworkstructure • Would expect to see even more men referred under perfConnectionsHeterogeneous (We don’t)Networks • Would expect to see men restricted to refer women attritSocialIncentives more under perf (We don’t)ScreeningScreening • Moreover, some evidence that social incentives are notEither Genderversus Restricted strongly correlated with expected referral performanceConclusions • Men referring other men are choosing not to refer the bestBonus Slides men they know under fixedComment 1Comment 2 • Men do respond to incentivesComment 3CommentComment 4 5 • Similar argument holds for women referring low ability people.
  58. 58. Do Job NetworksDisadvantage Women? Selection rule even with positive relationship BKMMotivationExperimentSet-upMain ResultTheoryNetworkstructureConnectionsHeterogeneousNetworksSocialIncentivesScreeningScreeningEither Genderversus RestrictedConclusionsBonus SlidesComment 1Comment 2Comment 3Comment 4Comment 5

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