11.08.2012 - Lori Beaman

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections

HeterogeneousNetworks

SocialIncentives

Screening

Screening

Either Genderversus Restricted

Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Do Job Networks Disadvantage Women?Evidence from a recruitment experiment in

Malawi

Lori Beaman, Niall Keleher, and Jeremy Magruder

Northwestern, IPA, and UC-Berkeley

November, 2012

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Motivation

• In Malawi, as in much of the world, women aredisadvantaged in labor markets

• underrepresented in the formal sector• earn less

• There are a litany of possible explanations, e.g.

• taste-based or statistical discrimination• differences in baseline human capital• differences in preferences• differences in tenure/experience profiles• and so on

• Current policy interventions focus on closing the gendergap in educational attainment

• Question: will that be enough?

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

What about hiring processes?

• Much less research on whether the hiring process causes(dis)advantages

• About half of jobs are found through networks• In developing countries, networks are key for risk sharing,

credit in addition to labor market access

• Several advantages for employers

• relatively costless way to circulate info• (some) workers may have useful screening info about

friends and relatives (Montgomery 1991, Beaman andMagruder 2012)

• tied contracts between reference and referral may solvemoral hazard problems (Heath 2012)

• But do they disadvantage groups?

• Calvo-Armengol and Jackson (2004): the use of networkscan lead to disadvantages between groups

• Are women one of these groups?

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Women and Networks

• Priors are not so clear - potential advantages anddisadvantages.

• Could help women, if e.g. resume characteristics are scarceand hard-to-observe characteristics are more important

• Or, could leave women out: sociologists emphasizegender-homophily in networks

• necessary condition for Calvo-Armengol and Jackson(2004) mechanism

• However: as a stylized fact from observational data,women are less likely to get networked jobs

• In U.S. unemployed women are less likely to report usingtheir friends and relatives for help in search (27% of menvs. 20% of women) (Ioannides and Loury,2004)

• Based on observational data - could be confounded bydifferences in occupations, reporting choices, etc.

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Why would networks leave women out?

• It may be more costly for firms to access female referrals

• Men (or women) may not be connected to (high quality)women

• Sociology lit: women’s networks may be less organizedaround work (e.g. Smith 2000)

• Or men (or women) may have those connections, butprefer to refer men

• If it is easier to get (high quality) male referrals thanfemale referrals because of network characteristics, thencost-minimizing firms may end up hiring more menthrough referrals

• Firms may get more out of using referral systems for malehires

• References may be better able or more willing to screenmen

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Our experiment

• We conducted a recruitment experiment as part of a hiringdrive for enumerators in Malawi

• Survey firm wanted to hire more women

• Two waves: people encouraged to apply themselves andpeople then asked to make a referral

• All applicants complete skills assessment

• Competitive job between genders:

• 38% of people who apply themselves are women andperform similarly to men

• One type of position, so differences in occupational sortingcannot affect results. Reporting clear, too.

• Referral phase randomized whether applicants could referonly men, only women, or anyone, and also terms ofcontract

• Fixed finders fees or performance incentive

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Preview

• The use of referral systems disadvantages highly skilledwomen

• Only 30% of referrals (versus 38% of applicants) arewomen when people have a choice

• 2 reasons: men systematically refer men• Women’s referrals (both men and women) are less likely to

qualify

• However, when we restrict gender choices, men andwomen make references at the same rate under allcontracts regardless of which gender they must refer

• Men and women are connected to suitable men and women

• We develop and test a model to find out whichcharacteristics of networks lead to disadvantages

• Social incentives rather than productivity differences leadto disadvantages

• Screening potential of networks is maximized when menrefer men

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Outline of rest of talk

1 Describe experimental design

2 Test whether women are (dis)advantaged by referralsystems

3 Discuss a model of optimal referral choices under differentnetwork characteristics

4 Test whether men and women are connected to suitablewomen

5 Test for gender differences in network characteristics

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Our Experiment

• IPA-Malawi regularly hires a large number of enumeratorsfor several projects

• We posted fliers indicating a hiring drive at a number ofvisible places in Lilongwe and Blantyre

• Applicants were instructed to appear at a localemployment center at a specific date and time, with aresume.

• Upon arrival, applicants given an id card and resumescollected

• Applicants completed a written test

• Several math problems, ravens matrices, English skillsassessment, job comprehension component, computer skillsassessment

• 2 similar versions of test to limit cheating

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Our Experiment (2)

• Following, applicants completed a practical skillsassessment

• IPA enumerators act as survey respondents, applicants actas enumerators

• To test for hard-to-observe abilities, we made a number ofincorrect answers to questions - i.e. inconsistent householdsize, implausible values for household acreage

• Actors instructed to give the right answer if the applicantspress them

• 2 versions of incorrect answers• We measure the number of traps that the applicants

caught

• Total score on all components averaged. Applicantsinformed of qualification threshold.

• Qualified individuals called for enumerator positions aspositions open

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

CA men and women arecompetitive

0.0

1.0

2.0

3ke

rnel

den

sity

est

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20 40 60 80 100CA's overall (corrected) score

Male CAs Female CAs

Figure 1: CA Ability by Gender

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Experiment: Referral Rounds

• Finally, applicants asked to make a referral

• Randomly assigned to one of following treatments:• Asked at random to make a referral who was male, a

referral who was female, or a referral who could be male orfemale

• Cross-randomized the finder’s fee:

• A fixed fee of either 1000 MWK or 1500 MWK ($6 or$10).

• A performance incentive of 500 MWK if their referral doesnot qualify or 1800 MWK if their referral does qualify

• All treatments fully blinded from the perspective ofevaluators

• Referrals attend recruitment session 3 or 4 days later.Complete same skills assessment.

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Do Referral Systems disadvantagewomen?

(1) (2) (3) (4)

All CAs Male CAsFemale

CAs

Diff: p

value

A. CA Characteristics

Fraction of CAs 100% 62% 38%

CA is qualified 53% 56% 48% 0.047

N 767 480 287

B. CA Characteristics: Made Referral, Either Gender Treatments

Fraction of CAs 100% 61% 39%

CA is qualified 57% 62% 49% 0.061

N 217 130 87

C. Referral Characteristics: Either Gender Treatments

Referral is Female 30% 23% 43% 0.002

Referral is Qualified 49% 56% 38% 0.019

Referral is Qualified Male 34% 43% 22% 0.002

Referral is Qualified Female 14% 13% 17% 0.456

N 195 117 78

Table 1: Gender Distributions of CAs and Referrals

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

A simple model

• Suppose conventional applicants (CAs) each know acollection of potential referrals, some men and women.

• Each of these potential referrals has a social transfer theywill give the applicant

• Each also has an actual quality and an observed expectedquality

• Focus on individuals the CA might actually choose:

• For each perceived probability of qualifying, the personwho maximizes social payments

• Therefore expected quality is decreasing in social payments

• Observe referral choice under two contact types: fixed feeand performance pay

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Today, graphically

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-10

-5

0

5

10

15

20

Perceived Probability of Qualifying

Soc

ial P

aym

ent

sample similar networks

Note: Diamonds: women, Circles: men

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

What we observe

• Whether someone chooses to make a referral

• For those who make a referral, we see 2 nodes in thegender-specific network for each gender:

• Characteristics of person who maximizes social incentives

• Characteristics of person who maximizes expected pay+social incentives under performance incentive contract

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Are men and women connected?

• One reason women may be disadvantaged by referralsystem is if (suitable) women are not integrated into men’snetworks

• Men and women make a decision to make a referral if theexpected payoffs are greater than 0

• Under fixed fees, this means that they know a man orwoman whose social payment is not too negative

• Under perf pay, this means that they know a man orwoman whose total package of fixed fees + expected perfpay

• A stronger question: Are there men who only knowsuitable women?

• Are men in “either” treatments more likely to return witha referral than men in “male” treatments?

• [Later] does screening behavior look different in “either”treatments versus restricted male referrals?

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Are men less likely to knowsuitable women?

(1) (2) (3) (4)

Female Treatment ‐0.004 ‐0.055 ‐0.004 ‐0.042

(0.038) (0.054) (0.050) (0.074)

Either Gender Treatment 0.014 0.017 ‐0.052 ‐0.024

(0.040) (0.055) (0.052) (0.071)

Performance Pay ‐0.148 *** ‐0.113

(0.056) (0.080)

Perf Pay * Female Treatment 0.004 ‐0.013

(0.076) (0.111)

Perf Pay * Either Treatment 0.152 * 0.086

(0.079) (0.110)

Observations 506 310 506 310

CA Gender Men Women Men Women

Notes

1 The dependent variable is an indicator for whether the CA makes a referral.

2 All specifications include CA visit day dummies.

Table 2: Probability of Making a Referral

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

How would different networkcharacteristics affect referral

choices

We identify four dimensions of heterogeneity:

1 Maximal Social payment received: “Closest gender”

2 Expected quality of closest person: “Quality”

3 Slope of social payment/expected quality tradeoff:“Network Shallowness”

4 Variance of actual quality relative to expected quality:“Information”

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Similar Networks

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-10

-5

0

5

10

15

20


Soc

ial P

aym

ent


Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Closer men

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-10

-5

0

5

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15

20


Soc

ial P

aym

ent

higher male social payments

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Similar Networks

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-10

-5

0

5

10

15

20


Soc

ial P

aym

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Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Higher quality men

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-10

-5

0

5

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15

20


Soc

ial P

aym

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higher male quality

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Similar Networks

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-10

-5

0

5

10

15

20


Soc

ial P

aym

ent


Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Shallower women

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-10

-5

0

5

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15

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Soc

ial P

aym

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Shallower female network

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Similar Networks

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-10

-5

0

5

10

15

20


Soc

ial P

aym

ent


Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Worse information about women

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-10

-5

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Soc

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less information about women

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Model predictions

1 Under fixed fees: only differences in closeness affect whichreferral is chosen

2 Higher quality increases returns under performance pay

• Quality (of person who gives highest social payment) isrevealed under fixed fees

3 Worse info, more shallow networks can both lead to lowerresponse to performance pay

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Closest gender, quality & socialincentives

Men may know women, but would they share opportunities?

• Prediction 1 from the model: The person referred underfixed fees is the closest person in the network

• If men are closer to men (or women), should see menreferred systematically under fixed fee - unrestrictedtreatment

• The restricted-gender fixed fee treatments also let usobserve the quality of the closest people in the network:

• If men’s networks of men are higher quality than men’snetworks of women, should see fixed fee restricted malereferrals being higher quality than fixed fee restrictedfemale

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Characteristics of closest referrals

C. Referral Characteristics: Either Gender Treatments





N 195 117 78

D. Referral Characteristics: Either Gender, Fixed Fee Treatments





N 117 68 49

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Do men refer similar male andfemale network members?

0.0

1.0

2.0

3K

erne

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20 40 60 80 100Referral's overall (corrected) score

Men referring men Men referring women

Figure 2: Men's Fixed Fee Referrals

Note: figure compares men in restricted treatments only

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

What about women’s referrals?

0.0

1.0

2.0

3K

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20 40 60 80 100Referral's overall (corrected) score

Women referring men Women referring women

Figure 3: Women's Fixed Fee Referrals

Note: figure compares women in restricted treatments only

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Summary so far

• By design, we only observe clean evidence of differences insocial incentives for men or women who maximize socialincentives (who are revealed through the fixed feetreatments)

• For those people:

• Men tend to maximize men’s incentives• Low ability people tend to maximize women’s incentives

• Closest women are low ability• Closest men however are not systematically low ability

• Can conclude: at least among socially closest people, menand women have different social incentives

• Social incentives make it cheaper to (a) get male referralsfrom men and (b) use men to get high quality referrals

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Is Women’s DisadvantageProductive?

• If employers encourage referral hires, they likely gainsomething from their use

• One thing which has been emphasized is screening (e.g.Montgomery (1990), Beaman and Magruder (2012))

• If employees see hard to observe characteristics, canimprove outcomes for employer

• If men (women) are less able to screen women, it may leadto employers discouraging female referrals

• From the model: CAs will screen if and only if they havegood information, and networks are not too shallow

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-10

-5

0

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15

20


Soc

ial P

aym

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less information about women

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Low info and screening

• Low info makes the tradeoffs “steeper” - it becomes moreexpensive and more infeasible to find very high qualityreferrals

• Essentially, most referral probabilities of qualificationpushed towards 1/2

• this increases the payoffs to referring someone who youthink is relatively low ability under perf pay incentives

• and decreases the payoffs to referring someone who youthink is relatively high ability under perf pay

• Empirically, if men (women) have lower ability to screenwomen, should observe a smaller increase in performancein response to perf pay

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

(1) (2) (3) (4)

Female Referral Treatment ‐0.030 ‐0.190 ** 0.068 ‐0.181

(0.062) (0.083) (0.081) (0.113)

Either Gender Treatment 0.071 ‐0.231 *** 0.227 *** ‐0.242 **

(0.066) (0.082) (0.084) (0.107)

Performance Pay 0.267 *** 0.021

(0.093) (0.122)

Perf Pay * Female Treatment ‐0.248 * ‐0.022

(0.127) (0.171)

Perf Pay * Either Treatment ‐0.383 *** 0.032

(0.132) (0.169)

Observations 390 227 390 227

CA Gender Men Women Men Women

Notes

1 The dependent variable is an indicator for the referral qualifying.


Referral Qualifies

Table 4: Referral Performance

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Screening Results

• Men can screen men

• Men cannot screen women (or, at least, won’t at theselevels of incentives)

• Allowing the option to refer women eliminates thescreening premium

• Suggests that employers who want to maximize screeningmay discourage men from making female referrals.

• Some evidence that difference is info and not shallowness:men are more likely to make a low quality referral underperf pay-female treatments than under fixed fee-female

• Women show less ability to screen men or women overall

• Some not quite sig evidence that they can screen women

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Screening with choice of genderWhy is there a lower screening premium when we allow eithergender?

• Under performance pay, one maximizes the sum of socialincentives and expected perf incentive

• This makes the theoretical effect ambiguous

• Considering either gender in general allows you to “buy”quality with giving up a lower amount of social incentives

• Increases both chance that you observe someone who hasa high chance of qualifying and gives you OK socialincentives

⇒ May increase performance premium

• Also ↑ chance that you observe someone who has an OKchance of qualifying but gives you great social incentives

⇒ May decrease performance premium

• Happens, in particular, when info is bad about one gender

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

What exactly is being screened?

• Have much richer data than is being used here - detailedassessments of different referral characteristics

• Men are screening in some ways across a broad category ofcharacteristics

• Women are screening, too -

• significantly, women screen women on language scores andcognitive skills.

• Women screen men on survey experience

• The former is probably more valuable as screening foremployers. May be a role for encouraging female referralsof women

• still, if employers use referrals for screening, biggest returnsare to get men to refer men

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Survey

exp

Tertiary

Education

Math

Score

Language

Score

Ravens

score

Computer

Score

Practical

Exam Score

Feedback

points

(1) (2) (3) (4) (5) (6) (7) (8)

‐0.033 0.045 ‐0.017 ‐0.115 ‐0.092 0.062 1.033 3.003 ***

(0.069) (0.074) (0.142) (0.207) (0.194) (0.371) (0.661) (1.044)

0.040 0.072 0.009 0.087 0.089 0.623 1.378 ** 1.856 *

(0.072) (0.077) (0.148) (0.215) (0.203) (0.387) (0.689) (1.089)

Performance Pay 0.080 0.067 0.134 ‐0.005 0.230 0.943 ** 0.496 1.883

(0.080) (0.085) (0.164) (0.238) (0.224) (0.428) (0.757) (1.197)

‐0.075 0.025 ‐0.259 ‐0.027 ‐0.293 ‐0.915 ‐0.950 ‐2.443

(0.108) (0.116) (0.223) (0.325) (0.305) (0.583) (1.026) (1.622)

‐0.165 ‐0.083 ‐0.065 ‐0.169 ‐0.367 ‐0.856 ‐1.768 * ‐3.371 **

(0.113) (0.121) (0.232) (0.338) (0.318) (0.607) (1.069) (1.696)

Observations 386 390 390 390 390 390 383 382

Notes

1 The dependent variable is an indicator for the referral qualifying.


Table 5: Screening of Male CAs on Different Characteristics

Perf Pay * Female

Treatment

Perf Pay * Either

Treatment

Female Referral

Treatment

Either Gender

Treatment

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Survey expTertiary

Education

Math

Score

Language

Score

Ravens

score

Computer

Score

Practical

Exam Score

Feedback

points

(1) (2) (3) (4) (5) (6) (7) (8)

0.032 0.151 ‐0.332 ‐1.140 *** ‐0.435 ‐0.627 0.972 2.152

(0.091) (0.110) (0.216) (0.342) (0.270) (0.538) (0.963) (1.349)

0.040 0.017 ‐0.189 ‐0.246 ‐0.172 ‐0.139 0.015 0.879

(0.086) (0.104) (0.205) (0.324) (0.256) (0.509) (0.910) (1.274)

Performance Pay 0.264 *** 0.143 ‐0.400 * ‐0.465 ‐0.175 0.419 1.832 * 1.604

(0.098) (0.119) (0.234) (0.370) (0.293) (0.582) (1.056) (1.479)

‐0.320 ** ‐0.292 * 0.402 1.330 ** 0.551 0.232 ‐2.164 ‐2.134

(0.138) (0.166) (0.326) (0.515) (0.408) (0.811) (1.468) (2.055)

‐0.270 ** ‐0.052 0.368 0.500 ‐0.260 ‐0.372 ‐1.625 ‐4.511 **

(0.136) (0.164) (0.323) (0.510) (0.403) (0.802) (1.448) (2.027)

Observations 226 227 227 227 227 227 222 222

Notes

1 The dependent variable is indicated in the column heading.


Table 6: Screening of Female CAs on Different Characteristics

Female Referral

Treatment

Either Gender

Treatment

Perf Pay * Female

Treatment

Perf Pay * Either

Treatment

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Conclusions

• Stylized Fact: women are less likely to receive job referralsthan men (from data in US and Europe)

• Using a recruitment experiment in Malawi, we confirmthat women are disadvantaged by referral systems

• Men choose not to refer women, when given the choice

• Women choose women at about the population average,but make on average low quality referrals

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Conclusions: Economics

• We test several network constraints that could drive thisresult

• Men and women are equally likely to be connected to menand women

• Men are closest to men, but have high quality male andfemale contacts

• Women are not socially closer to one gender than theother, but have low quality networks of women

• Men can screen men well, cannot screen women; womencan screen both men and women to a lesser extent

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Conclusions: Policy

Permitting women’s disadvantage in referral rates has threebenefits to employers:

• It is lower cost for men to refer men than for men to referwomen (since social incentives are higher)

• It is lower cost to get high quality referrals if men aremaking referrals

• Screening benefits of referral systems are maximized whenmen are encouraged to refer only men

• All in all, a hard problem to solve

• Current policies to address gender gap - such as investingin girls’ education - will not be enough to overcome this

• Maybe a role for quota systems in hiring policy

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Comment on Attrition• 80% of applicants make a referral• Reference rate is always really similar across genders,

different across treatments

• Differences in referral quality across gender, withintreatment can be taken at (close to) face value for thosewho make referrals

• Difference in referral quality across treatment will be thecombined effect of some attrition + population averagechoices

• For employers (and to understand actual trends inreferences), the net effect (including attrition) is therelevant dimension in any event

• Implications for e.g. ability to screen are the same ifindividuals attrit because they know their options are bad

• We also simulate the model and recover the samepredictions on the attrition decision and results withinmade referrals

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Can work experience explainresults?

• Men are more likely to have worked at a survey firm in thepast than women

• Working at a survey firm may both enhance your networkand give you better information

• While it does not affect any of the interpretations - ordisadvantages women face - it may be an underlyingmechanism

• We find no differential response among people who haveworked at a survey firm in the past.

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Competition

• Niederle and Vesterlund (2007) find that women areaverse to competition relative to men

• Making a reference involves introducing the employer to apotential competitor for the job

• May have an incentive to refer someone bad (though, amarginal incentive for an informed decision maker -referral is one additional applicant among many)

• May have been particularly salient in our context, asapplicants not yet hired

• However, certainly a relevant incentive in on-the-jobreferrals, too

• Again, suggests a mechanism, without affectinginterpretations or policy prescriptions

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Cross-randomization

• We cross-randomized a treatment designed to make thecompetitiveness more salient

• CAs were told the qualification threshold was either

1 Absolute: scoring better than 602 Relative: scoring in the top half of applicants

• We hypothesize that the relative treatment makes thecompetition more salient (since CAs compete directly withreferrals to be in the top half)

• (admittedly, somewhat weak test)

• Look just at fixed fee referrals to isolate social incentives

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

(1) (2) (3) (4) (5) (6)

Dependent VariableCA

Qualifies

Referral

Qualifies

Referral

Qualifies

CA

Qualifies

Referral

Qualifies

Referral

QualifiesCompetitive 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)

Female Treatment 0.094 ‐0.024

(0.116) (0.177)

Either Treatment 0.175 ‐0.160

(0.123) (0.169)

Competitive * Female 0.007 ‐0.263

(0.166) (0.236)

Competitive * Either 0.103 ‐0.142

0.176 (0.236)

Observations 287 232 232 166 133 133

CA Gender Men Men Men Women Women Women

Appendix Table 3: Competition incentives among fixed fee referrals

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

0.2

.4.6

.8R

efer

ral i

s F

emal

e


Referrals of Male CAs Referrals of Female CAs

Figure 2: Gender choice in referrals, by CA performance

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

.2.4

.6.8

1R

efer

ral's

qua

lific

atio

n ra

te


Referrals of Male CAs Referrals of Female CAs

Figure 3: Referral qualification rate, by CA performance

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

0.2

.4.6

.8R

efer

ral q

ualif

ies


Men referring women, fixed Men referring men, fixedMen referring women, perf Men referring men, perf

Figure 6: Referral Qualifies , by Male CA performance

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

0.2

.4.6

.8R

efer

ral q

ualif

ies


Women referring women, fixed Women referring men, fixedWomen referring women, perf Women referring men, perf

Figure 7: Referral Qualifies , by Female CA performance

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Social Payments and Qualification

• Possible (reasonable?) that social payments increase withqualification in the ambient network

• Referrals give you better social transfers if they get the job

• Consistent with our modelling assumptions

• No assumption made about the joint distribution ofαgj ,Qg

j in the ambient network

• Selection rule still leads to decreasing relationship amongnon-dominated referrals

• However, may change interpretation of social payments

• Incentives aligned with employer• differences in quality expectations may lead to women’s

disadvantage if men expect men to be higher quality,women have wrong quality expectations

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Unbiased Expectations of Quality

• Model assumed εgj was mean 0 - allowed us to estimate Qg1

• Already showed that men’s fixed fee referrals of men ARENOT higher ability than men’s fixed fee referrals of women

• And women’s (low quality) fixed fee referrals ARE NOTthe highest quality people they know (they know highquality men)

• So, if CA’s have unbiased expectations: can conclude thatexpectations of quality ARE NOT source of women’sdisadvantage

• But, expectations of quality could be biased

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Biased Expectations of Quality

• If expected social incentives increase in expected referralqualification and expectations are biased (for now, againstwomen)

• Incentives to refer a qualified person are still strictly largerunder perf

• Would expect to see even more men referred under perf(We don’t)

• Would expect to see men restricted to refer women attritmore under perf (We don’t)

• Moreover, some evidence that social incentives are notstrongly correlated with expected referral performance

• Men referring other men are choosing not to refer the bestmen they know under fixed

• Men do respond to incentives

• Similar argument holds for women referring low abilitypeople.

Do JobNetworks

DisadvantageWomen?

BKM

Motivation

Experiment

Set-up

Main Result

Theory

Networkstructure

Connections


SocialIncentives

Screening

Screening


Conclusions

Bonus Slides

Comment 1

Comment 2

Comment 3

Comment 4

Comment 5

Selection rule even with positive relationship

11.08.2012 - Lori Beaman

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