preference for the workplace, investment in human capital ... filepreference for the workplace,...

Preference for the Workplace, Investment inHuman Capital, and Gender

Matthew Wiswall - ASU; UW MadisonBasit Zafar - NY Fed

The views expressed in this paper reflect those of the authors, and not necessarily those of the New York

Fed or the Federal Reserve System.

Males and females work in different sectors

Males Females

Science 19.4 6.0

Health 15.9 28.9

Business 28.7 17.4

Government 13.2 8.4

Education 22.8 39.3Based on the 2010-2012 CPS, collegegraduates between the ages of 25-60.

Work sectors differ in their characteristics

% of % of Annual Hrs/wk Prop. of Yearlymales females earnings for part-time firingworkers workers for full-time full-time workers rate

Science 19.4 6.0 82.7 44.2 0.16 3.7%

Health 15.9 28.9 67.9 43.8 0.27 3.9%

Business 28.7 17.4 77.1 45.0 0.20 4.1%

Government 13.2 8.4 67.5 43.3 0.16 1.5%

Education 22.8 39.3 60.6 43.9 0.30 1.9%

p-value 0.00 0.00 0.00 0.00 0.00 0.00

Understanding determinants of job choiceChallenging to answer with observational data because:

• Observed choices may not reflect worker preferences only• if firms discriminate against certain workers and offer jobs to asubset only

• if labor market frictions prevent workers from matching withtheir most preferred jobs

• Can only evaluate the observable aspects of jobs.• estimates of job attribute preferences biased if unobservedcharacteristics correlated with observed ones (as in the case ofcompensation differentials, Rosen, 1984)

We get around this problem by exogenously varying certain aspectsof jobs in hypothetical scenarios, and asking college students forthe likelihood of choosing particular jobs [this approach hassimilarity with "conjoint analysis" and "contingent valuation"methods]

Example: Hypothetical choice scenario

You have been offered the following 3 jobs when you are aged 30.

Earnings at Annual % inc. Works hrs Part-timeage 30 in earnings per week available

Job 1 $96,000 3% 52 YesJob 2 $95,000 2% 45 YesJob 3 $89,000 4% 42 No

These jobs are otherwise identical in all other aspects.Now consider the situation where you are given the jobs offeredabove when you are aged 30, and you have decided to accept oneof these jobs.What is the percent chance (or chances out of 100) that you willchoose each of these jobs?

Gender Differences in College Majors

Women have surpassed men in college attendance, but choosedifferent majors (Gemici and Wiswall, 2011)

Males Fem

Some College 49.2 46.5

Bachelor’s in:Business 13.0 10.3

Engineering 11.7 3.2

Humanities 19.6 30.5

Natural Science 6.5 9.5Based on the 2013 ACS, 25-40yr oldswith at least some college.

Majors differ in earnings and other dimensionsShares Annual Hrs/wk % UE % Not

Males Fem Earnings for Part time Rate Empl.

Some College 49.2 46.5 42.6 43.5 43.8 7.3 13.8

Bachelor’s in:Business 13.0 10.3 77.4 45.5 27.0 4.3 8.4

Engineering 11.7 3.2 83.1 45.0 21.7 3.5 6.0

Humanities 19.6 30.5 59.3 44.4 38.6 3.9 10.1

Natural Science 6.5 9.5 72.9 45.0 34.3 2.3 8.6

F-test 0 0 0 0 0 0 0

Literature on college major choice finds a limited role of earningsand ability, with "tastes" being a dominant factor (Arcidiacono,2004; Zafar, 2013; Wiswall and Zafar, 2015; Altonji et al., 2015).

Could workplace preferences be a factor in college major choice?

This paper

• Using hypothetical job choices, we estimate preferences forworkplace attributes of college students

• "panel" data at the student level allows us to(semi-nonparametrically) estimate the distribution of jobpreferences

• Using estimated workplace preferences, combined with surveydata on students’perceived mapping of college majors toworkplace characteristics, we investigate the role of workplacepreferences in college major choice.

Preview of Results• Students, on average, have a dis-taste for higher job dismissal and ataste for workplace hours flexibility

• Substantial heterogeneity, and notable differences by gender

• Estimated preferences predicted of actual workplace characteristics4 years later (for a subset of respondents)

• Students perceive considerable differences in job characteristicsconditional on college major.

• Job attributes matter in major choice, with females being moresensitive to non-monetary returns in choice of major

• The (expected) earnings gender gap would be at least a thirdsmaller if there were no differences in workplace preferences- part ofthe earnings gender gap is a compensating differential

Outline for the talk

1. Literature Review

2. Model of Job Choice

3. Data description

4. Empirical estimates for job preferences

5. Model of major choice, and the role of job preferences

6. Conclusion

Literature Review: Occupation Choice

Study of occupation choice has a long history in economics (Roy,1951; Blau et al., 1955).

Adam Smith writes:“The five following are the principal circumstances which, so far asI have been able to observe, make up for a small pecuniary gain insome employments, and counterbalance a great one in others:first, the agreeableness or disagreeableness of the employmentsthemselves; secondly, the easiness and cheapness, or the diffi cultyand expense of learning them; thirdly, the constancy or inconstancyof employment in them; fourthly, the small or great trust whichmust be reposed in those who exercise them; and, fifthly, theprobability or improbability of success in them." (Wealth ofNations, 1776, Book 1, Chapter 10).

Literature review: Gender differences in occupation choice

Various hypotheses:

• Gender differences in preferences for competition (Niederleand Vesterlund, 2007; Flory et al., 2015)

• Gender differences in workplace flexibility (Goldin and Katz,2011; Flabbi and Moro, 2012; Goldin, 2014; Wasserman,2015)

• Gender differences in risk preferences (Croson and Gneezy,2009)

• Differences in tastes for certain occupations (Blau and Kahn,2012)

Challenging to disentangle due to omitted variables (Blau andKahn, 2006).

Literature Review: Compensating Differentials

• Theoretical framework for hedonic pricing: Rosen (1974;1986).

• Mixed results: Thaler and Rosen, 1975; Gronberg and Reed,1994; Van Ommeren et al., 2000; Stern, 2004; Dale-Olsen,2006.

• Search frictions could imply small equilibrium wagedifferentials even if preferences for non-wage amenities arestrong (Bonhomme and Jolivet, 2009; Hwang et al., 1992)

• Recent papers: Hall and Mueller (2015), Sorkin (2015), Taberand Vejlin (2016).

Literature review: College major choice and Gender

• Observables (such as ability, earnings expectations) play asmall role in major choice (Arcidiacono et al., 2012; Wiswall andZafar, 2015; Altonji et al., 2015)

• choice elasticity with regards to earnings is small. Tastes arethe dominant factor

• Gender differences in major choice largely due to tastes orpreferences for non-wage factors (Zafar, 2013; Wiswall andZafar, 2015; Bronson, 2015)

• Complementarities between college majors and occupations(Arcidiacono et al., 2015)

Canonical random utility model of job choice

Each job is characterized by a finite vector of K attributesXj = [Xj1, . . . ,XjK ]. In our context, this consists of :

• earnings at age 30• earnings growth• work hours per week• job flexibility (part-time availability)• probability of being fired• bonus pay (based on performance), on top of base salary• proportion of men at the job

Utility from job j is: Uij = X ′j βi + ε ij . Utility linear and separablein X’s.ε ij is the job-specific preference component, reflecting all remainingattributes.

Identification: realized job choice data

• Fraction of the population choosing job j :

qj =∫1{Uij > Uij ′}dF (βi , ε i1, . . . , ε iJ ).

F (βi , ε i ) is the joint distribution of preferences in the population.

• If ε′s are i.i.d. Type I extreme value

qj =∫ exp(X ′j βi )

∑j ′={1,..,J )

exp(X ′j ′βi )dG (βi ).

G (βi ) is the distribution of preferences in the population.

Limitations of realized job choice data

• Omitted unobserved job characteristics that are potentiallycorrelated with observed characteristics. X = [XObs ,XUnobser ]

ln(qjqj ′) = (XOj − XOj ′ )′βOi + (XUj − XUj ′ )′βUi

ln(qjqj ′) = (XOj − XOj ′ )′βOi + ηj .

• Demand side restrictions in jobs offered (equivalent toXU → −∞ if a job is not offered)

• Limited flexibility in the distribution of preferences in thepopulation, G (βi ): degenerate; Normality, etc.

Identification: Hypothetical Job Choice Data• Probability of individual i choosing job j :

pij =∫1{X ′j βi + ε ij > X ′j ′βi + ε ij ′}∀j ′ 6=jdHi (ε i1, . . . , ε iJ ),

Hi (·) is individual i’s belief about the distribution of ε i1, . . . , ε iJ ;resolvable uncertainty as in Blass et al. (2010).

• If ε′s are i.i.d. Type I extreme value

pij =exp(X ′j βi )

∑j ′={1,..,J )

exp(X ′j ′βi ).

No omitted variable bias.No parametric restriction needed for preferences (for a long panel).Estimates free from considering the equilibrium job allocationmechanism and the preferences of employers.

Study design

• 257 undergraduates at NYU. Drop 10 respondents withmissing data

• Participate in an experiment and survey in the CESSComputer Lab, in May 2012

• Experiment designed in ztree, analyzed in Ernesto et al.(forthcoming); survey taken online (constructed usingSurveyMonkey)

• Total study time was 90 minutes (30 minutes for the survey)• Compensation: $10 show-up fee; $20 for completing thesurvey; other experimental earnings.

Students presented with a total of 16 choice scenarios.

Descriptive statistics

All Males FemalesNumber of respondents 247 86 161

School Year:Freshmen 10.9% 9.3% 11.8%Sophomore 10.9% 11.6% 10.6%

Junior 36.4% 32.6% 38.5%Senior or more 41.7% 46.5% 39.1%

Age 21.49 21.69 21.37

Mean Parents’Income ($1000s) 137 141 135

SAT Math Score 696.0 717.7 684.3***SAT Verbal Score 674.0 677.0 672.5GPA 3.5 3.5 3.5

Intended/Current MajorEconomics/Business 31.2% 48.8% 21.7%***

Engineering 4.9% 8.1% 3.1%***Humanities and Soc Sciences 47.8% 30.2% 57.1%***

Natural Sciences/Math 16.2% 12.8% 18.0%

Choice Scenario

In each of the 8 scenarios below, you will be shown hypotheticaljobs offers. Each job offer is characterized by:A1A2A3A4These jobs are otherwise identical in all other aspects.Look forward to when you are 30 years old. You have been offeredeach of these jobs, and now have to decide which one to choose.In each scenario, you will be asked for the percent chance (orchances out of 100) of choosing each of the alternatives. Thechance of each alternative should be a number between 0 and 100and the chances given to the three alternatives should add up to100.[intro on the use of percentages]

Job choice: Example 1

• Respondents asked for the probability of choosing each job.• Eight scenarios like the one below presented

Earnings at % inc. Work hrs Part-time Mean Probage 30 earnings per week available Males Females

Job 1 $96,000 3% 52 YesJob 2 $95,000 2% 45 YesJob 3 $89,000 4% 42 No

Now consider the situation where you are given the jobs offeredabove when you are aged 30, and you have decided to accept oneof these jobs. What is the percent chance (or chances out of 100)that you will choose each of these jobs?

Job choice: Example 1 (contd)


Job 1 $96,000 3% 52 Yes

Job 2 $95,000 2% 45 Yes 39.3(22.7)

Job 3 $89,000 4% 42 No 36.9(24.7)



Job 1 $96,000 3% 52 Yes 31.9 31.5(22.5) (21.4)

Job 2 $95,000 2% 45 Yes 31.2 39.3***(23.7) (22.7)

Job 3 $89,000 4% 42 No 36.9 29.2***(24.7) (22.6)

Job choice: Example 2

Another eight scenarios like the one below presented

Earnings Prob. of Bonus as Prop of Mean Probage 30 being fired % of earn. men Males Females

Job 1 $87,000 1% 5% 49%

Job 2 $84,000 6% 13% 67%

Job 3 $95,000 5% 5% 69%



Job 1 $87,000 1% 5% 49% 36.7(24.3)

Job 2 $84,000 6% 13% 67%

Job 3 $95,000 5% 5% 69% 42.8(24.7)

Job choice: Example 2 (Contd)


Job 1 $87,000 1% 5% 49% 30.3 36.7*(22.5) (24.3)

Job 2 $84,000 6% 13% 67% 26.9 30.3(23.7) (21.4)

Job 3 $95,000 5% 5% 69% 42.8 33.1***(24.7) (20.8)

Elicited probabilities for job 1

Estimation

• Error term is iid with extreme value dist.: pij =exp(X ′j βi )

∑j ′ exp(X′j ′ βi )

• Applying log-odds gives: ln( pijpij ′ ) = (Xj − Xj ′)βi +ωij , where

ωij is the error introduced because of rounding of probabilities.

• Assume ωi1, . . . ,ωiJ are i.i.d. and median zero, conditional onX1, . . . ,XJ .

M[ln(pijpij ′) | X

]= (Xj − Xj ′)βi

• Estimated by LAD, separately for each individual with 32unique observations. Conduct inference using block bootstrapby re-sampling the entire set of job hypotheticals for eachstudent.

EstimatesOverall Males Females

Age 30 log earnings 15.40*** 22.86*** 11.42***(1.65) (3.88) (1.43)

Probability of being fired -0.38*** -0.39*** -0.37***(0.04) (0.10) (0.04)

Bonus, as % of earnings 0.28*** 0.38*** 0.22***(0.03) (0.05) (0.03)

Prop of males 0.00 -0.01 0.005(0.00) (0.01) (0.01)

Annual earnings increase 0.55*** 1.09*** 0.27**(0.10) (0.22) (0.10)

Hours per week of work -0.15*** -0.21*** -0.12***(0.02) (0.05) (0.02)

Part-time option available 0.79*** 0.86*** 0.76***(0.11) (0.22) (0.12)

Observations 247 86 161

b1bxz01

Text Box

Estimates, by genderOverall Males Females

Age 30 log earnings 15.40*** 22.86*** 11.42***(1.65) (3.88) (1.43)

Probability of being fired -0.38*** -0.39*** -0.37***(0.04) (0.10) (0.04)

Bonus, as % of earnings 0.28*** 0.38*** 0.22***(0.03) (0.05) (0.03)

Prop of males 0.00 -0.01 0.005(0.00) (0.01) (0.01)

Annual earnings increase 0.55*** 1.09*** 0.27**(0.10) (0.22) (0.10)

Hours per week of work -0.15*** -0.21*** -0.12***(0.02) (0.05) (0.02)

Part-time option available 0.79*** 0.86*** 0.76***(0.11) (0.22) (0.12)


Heterogeneity in preferencesPreferences for prob of being fired

Overall Males Females

Mean -0.38*** -0.39*** -0.37***

Median -0.17*** -0.15*** -0.20***

25th percentile -0.42*** -0.39*** -0.44***

75th percentile -0.04** -0.01 -0.05***

Standard dev. 0.68*** 0.85*** 0.56***++

Skewness -2.02*** -1.49*** -2.59***+


Willingness to Pay

How much do students need to be compensated (as a percent ofage 30 earnings) to increase the outcome by a unit?

Consider a change in the level of attribute Xk from value Xk = xkto Xk = xk + ∆, with ∆ > 0. Given our linear utility function, theindifference condition in terms of earnings Y is

xk βik + βi1 ln(Y ) = βik (xk + ∆) + βi1 ln(Y +WTPik (∆))

Solving, WTP is given by

WTPik (∆) = [exp(−βik

βi1∆)− 1]× Y

WTP estimates

Overall Male Female

Prob of being fired 2.83%*** 0.62% 4.01%***+++

Bonus -1.41%*** -0.85%* -1.71%***

Proportion of males 0.06% 0.08% 0.04%

Annual raise at job -1.56% -3.38%** -0.59%

Hrs/Week of work 1.13%*** 0.78% 1.31%***

Part-time available -5.13%*** -1.09% -7.29%***++

Asterisks denote sig. of estimates+ denote gender differences significant

Women, on average, willing to give up higher earnings to obtainother (non-pecuniary) job attributes. Suggests part of gender gapin earnings is a compensating differential

Heterogeneity in WTP

Fired Bonus as Prop. Ann % Hrs/wk Part-timeProb Salary % Males Raise of work available

Parents’income:Above Median 1.33 -1.56*** 0.04 -2.59* 0.64 -2.69Below Median 4.27***++ -1.28** 0.08 -0.59 1.60***+ -7.45***+

Exp FertilityAbove Median 2.81** -0.97* 0.04 -0.81 1.10** -4.75**Below Median 2.86*** -1.87*** 0.07 -2.31** 1.17*** -5.50***

Asterisks denote sig. of estimate.+ denote sig. of differences in demographics

Multivariate regressions indicate that only 7% of the variation inthe WTP can be explained by individual correlates.

Are Preferences Related to Actual WorkplaceCharacteristics?

• They need not be if (1) preferences change over time, (2)labor market frictions/discrimination, etc.

• We had consent from 115 (of the 247) respondents forfollow-up surveys. Re-surveyed them in January 2016

• 70 of them (~61%) completed the follow-up survey• No evidence of selection on observables into the follow-up

Actual Job Characteristics

Overall Male Female p-val

Number of Observations 70 21 49Labor Force status (%):

Employed, full-time 64.3% 57.1% 67.3%Employed, part-time 12.9% 19.0% 10.2%

Self-employed 7.1% 14.3% 4.1%Not employed (in school) 15.7% 9.5% 18.4%

Characteristics for employedLog Income | full-time employed 11.2 11.8 11.1 0.001Bonus (as % of salary) 5.8 10.9 3.4 0.034Hours of work/week 44.6 47.9 43.1 0.245Fired Probability (over next 12 months) 10.4 13.3 9.1 0.311Fraction of male employees 50.9 59.5 46.8 0.044Annual % increase in earnings 7.3 8.1 7.0 0.775Part-time or Flex work available (%) 61 47 68 0.143

Job Characteristics and Estimated WTP

Prob. of Bonus Prop. Earnings Hours Flex WorkFired Perc. Males Growth Worked Option

WTP -0.07 -1.01*** -7.32** -0.02 -1.70** -0.009**(0.21) (0.37) (3.05) (0.09) (0.78) (0.004)

Constant 10.70*** 3.64** 52.60*** 7.32*** 46.37*** 0.56***(2.09) (1.77) (2.94) (1.75) (2.03) (0.07)

Effect Size -0.658 -4.35 -6.89 -0.32 -4.09 -0.15

p-value 0.000Mean dep var 10.4 5.8 50.9 7.3 44.6 0.61Std dv (14.72) (12.79) (22.79) (13.34) (14.76) (.492)R-squared .002 .16 .092 .0001 .077 .090Observations 59 59 59 59 59 59

Estimated preferences (economically and statistically) significantlyrelated with actual job characteristics.

Beliefs about Major-specific job attributesAggregate college majors into 5 groups: (1) Economics/Business;(2) Engineering and Computer Science; (3) Humanities/OtherSocial Sciences; (4) Natural Sciences and Math; (5) NeverGraduate/Drop out.

Elicit beliefs about perceived job characteristics if student were tocomplete each major.

• Self Earnings beliefs: “If you received a Bachelor’s degree ineach of the following major categories and you were workingfull time when you are 30 years old what do you believe is theaverage amount that you would earn per year?”

• Beliefs about being fired: “What do you believe would be thepercent chance of being fired or laid off in the next year frompositions similar to those from which you would receive joboffers at age 30 if you received a Bachelor’s degree in each ofthe following major categories?”

Major Choice and expected earnings

Prob Majoring Exp EarningsMale Female Male Female

Economics 43.4 23.3*** 135 100***

Engineering 8.4 5.7 102 92*

Humanities 28.9 52.6*** 68 60***

Natural Sci 16.5 17.2 86 76***

No Degree 2.8 1.3* 43 33***Asterisks denote gender differences sig.

Other major-specific job attribute beliefs

Prob Hrs/wk Part-time Bonus PayMajoring work Avail. Prob (% of base)

M F M F M F M F

Economics 43.4 23.3*** 56 53 24 29 46 39

Engineering 8.4 5.7 49 48 27 33** 19 26

Humanities 28.9 52.6*** 44 43 37 45*** 13 15

Natural Sci 16.5 17.2 48 46 29 36** 14 19

No Degree 2.8 1.3* 46 46 48 55 10 7

F-test 0 0 0 0 0 0 0 0

Variation in beliefs within and across majors.

Heterogeneity in Major-specific Beliefs

Perceptions regarding major-specific job attributes

• Are these perceptions "accurate"?• we can’t know• comparison with existing statistics is not the right test (thoughperceptions compare well under that comparison)

• "accuracy" of perceptions irrelevant for understandingdecisions

• however, inaccurate perceptions/beliefs imply room for policyinterventions [information experiment]

• Why do job characteristics differ across majors?• students’perceptions of entering different occupations/sectorsvary substantially by college major [table]

Model of Major ChoiceUtility for student i from major m is given by

Vim = X ′imαi + Z ′imγ+ ηim

• Xim is i’s perceived job attributes in major m• Zim is a vector of other major specific characteristics (e.g.ability and diffi culty of major coursework), including amajor-specific constant

• ηim captures the remaining unobservable attributes of eachmajor

• Assume each αik is proportional to the βik up to some freeparameter δ:

αik = βik δ

expect δ ≥ 0, that is, job characteristics have weakly the samedirection of relationship with major choice as with utilityspecifically about jobs.

Estimation

Under the assumption that ηim’s are iid and have a Type I extremevalue dist, and the log-odds transformation:

ln(qimqim ′

) = (Xim − Xim ′)αi + (Zim − Zim ′)γ+ωim .

Two-step estimation.

1. Estimate job characteristic preferences βi for each individual,β̂i . Create an individual specific scalar of weighted jobcharacteristics for each major m: Bim = X ′i ,m β̂i .

2. Pooled estimator using LAD over the whole sample, where weestimate the vectors δ and γ.

M[ln(qimqim ′

) | X]= (Bim − Bim ′)δ+ (Zim − Zim ′)′γ,

Model EstimatesLAD estimates

Job attributes 0.018**(0.007)

Ability rank -0.064***(0.006)

Study time -0.009(0.025)

Economics Dummy -0.583(0.435)

Engineering Dummy -1.155***(0.363)

Natural Sci Dummy -0.816**(0.381)

Total Observations 741Number of Individuals 247

Importance of job attributes in major choiceWe compute marginal effects for each attribute as in non-linearmodels. The likelihood of majoring in m, for a given value ofXk = xk and evaluated at the sample mean for the other attributesand preferences, is given as:

qm(xk ) =exp(xkαk + X ′−kmα+ Z ′mγ)

exp(xkαk + X ′−kmα+ Z ′mγ) + ∑m ′=/m

exp(X ′m ′α+ Z′m ′γ)

.

Evaluated at two distinct values of Xk . Marginal effects arecomputed for:

• a change from 1% to 10% in the probability of being fired; inbonus as percentage of base pay; in earnings growth

• no part-time availability to part-time availability;• a change from 30 hrs/week to 50 hrs/week for work hours• a change from 30% male colleagues to 70%• a change of 10% in age 30 earnings.

Marginal Effects

Fired Part-time Hours Bonus Earnings Prop EarningsProb. Avail. Growth Males

Panel A: MalesEconomics -3.7% 1.2% -5.8% 4.5% 11.5% -0.9% 25.6%Engineering -4.9% 1.5% -7.7% 5.4% 15.0% -1.1% 33.4%Humanities -2.2% 0.7% -3.5% 2.6% 7.0% -0.5% 15.5%Natural Sci -4.7% 1.5% -7.4% 5.2% 14.5% -1.1% 32.2%

Panel B: FemalesEconomics -5.0% 1.2% -3.7% 2.9% 3.0% 0.5% 15.7%Engineering -5.7% 1.3% -4.2% 3.2% 3.4% 0.6% 17.7%Humanities -1.9% 0.5% -1.5% 1.2% 1.2% 0.2% 6.3%Natural Sci -5.3% 1.2% -3.8% 2.9% 3.2% 0.5% 16.4%

For females, the effects for job hours and probability of being firedare nearly a third of the effects for earnings.Females more sensitive to non-pecuniary job aspects in majorchoice

Workplace Preferences and the Gender Gap

Workplace preferences may impact the gender wage gap through:

1. the impact on specialization/job choice within anoccupation/industry (without impacting major choice)

2. influencing choice of major

Workplace Preferences and the Gender Gap

Dependent Variable: Log(Age 30 Expected Earnings)

Female -0.35** -0.20*** -0.29*** -0.15***(0.06) (0.06) (0.07) (0.06)

Major Controls N Y N YPreferences Controls N N Y YMean of Dep. Var 11.26 11.26 11.26 11.26R-squared .1209 .3386 .2013 .3967Observations 247 247 247 247

• Gender gap in (expected and actual) earnings declines by25%-35%, once we control for workplace preferences.

• If females had same preferences as men, the major choicechannel would lead to a 5% decline in the gender gap.

At least a third of the gender wage gap is due to preferences(compensating differentials).

Conclusion• Use a novel methodology to robustly estimate individualpreferences for workplace attributes.• Females have a stronger taste for workplace flexibility and jobstability.

• Substantial heterogeneity, that is not captured by standardparametric assumptions or observables

• Estimated preferences systematically linked with actual futureworkplace characteristics

• We link these preferences directly to college major choice, bycollecting unique data on students’perceived mapping ofmajors to workplace characteristics• job attributes play a role in major choice, with females beingmore sensitive to non-pecuniary job aspects in major choice

Conclusion (contd)

• The (expected) earnings gender gap would be at least a thirdsmaller if there were no differences in workplace preferences-part of the earnings gender gap is a compensating differential

• Given that prior literature finds that the residual unobservedtaste component is the dominant factor in human capitalchoices, the method used in this paper can be used tounderstand the determinants of educational/career choices,and the underlying causes for the gender gap

Perceived mapping of majors to work sectors.

Science Health Business Govt. Educ.M F M F M F M F M F

Economics 14 13 9 12 59 54 11 14* 7 7

Engineering 55 56 9 12* 19 13*** 9 10 8 8

Humanities 14 13 18 18 20 15** 24 27 24 28

Natural Sci 39 39 22 26 16 11*** 9 9 14 15

F-testa 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Population versus Self Beliefs

• Population beliefs question example:“Among all male college graduates currently aged 30 whowork full time and received a Bachelor’s degree in each of thefollowing major categories, what is the average amount thatyou believe these workers currently earn per year?"’

• Self beliefs earnings question example:“If you received a Bachelor’s degree in each of the followingmajor categories and you were working full time when you are30 years old what do you believe is the average amount thatyou would earn per year?"

Accuracy of population beliefs can be assessed.

Population Beliefs About Earnings at Age 30Beliefs about Men

Belief Percent Error

(Truth - BeliefTruth *100)Actual Abs

Economics/Business mean 8.69 -16.59 42.65(std.) (7.58) (101.67) (93.75)

Engineering/Comp. Sci. mean 7.98 3.18 36.37(std.) (7.77) (94.30) (87.05)

Humanities/Arts mean 5.82 -9.91 33.87(std.) (3.96) (74.79) (67.40)

Natural Sciences mean 6.85 5.60 32.41(std.) (4.41) (60.70) (51.60)

Not Graduate mean 3.57 25.28 35.31(std.) (1.77) (37.01) (27.58)

Note: Earnings in $10,000. Error = Truth-Belief: Negative error is over-estimate, positive error is under-estimate.

Self Beliefs about Earnings

Self Self Absoluteearnings % revision Self %pre (Post-PrePre *100) revision

Econ/Bus mean 12.69 -12.12 27.93(std.) (14.17) (41.87) (33.43)

Eng/Comp Sci mean 9.78 -2.62 26.39(std.) (8.49) (40.79) (31.19)

Hum/Arts mean 6.87 2.70 25.45(std.) (6.81) (39.75) (30.63)

Natural Sci mean 9.34 -0.70 28.19(std.) (9.92) (43.11) (32.60)

Not Graduate mean 3.93 33.42 43.31(std.) (7.59) (59.51) (52.74)

Note: Earnings in $10,000.

Population Beliefs and Self Beliefs: Information TreatmentAffects Self Beliefs

Dependent Variable: Log Earnings Log EarningsPre-Treatment Revision

(Post-Pre)

Log Population 0.309***Earnings Beliefs (0.0251)

Earnings Errors 0.0726***log(Truth/Belief) (0.0138)

Note: Positive error indicates under-estimating, Negative errorover-estimating.

preference for the workplace, investment in human capital ... filepreference for the workplace,...

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