The value of the TREE dataset for exploring gender inequality · 3. Parenthood anticipation / Family formation Independent variables • Marriage status • Pre-labour market values

ThevalueoftheTREEdatasetforexploringgenderinequality

Dr.BenitaCombetLMUMünchen

Institutfü[email protected]

Source:SRF

Source:SRF

Source:SRF

Howcanweexplainthisdifference?

Possibleexplanations1.   Humancapitaltheory(Becker1964)

–Menandwomendifferintheirendowments(e.g.educationalcredentials)

2.   Divisionoflabourwithinhousehold(Becker1985)–  ♂:specializeinpaidwork,continuetoinvestinjob-specificskills–  ♀:specializeinchildcare,choosefamily-friendlyjobs

3.Personality-  Behavioralpreferences(risk,competition,cooperation)

(e.g.Croson/Gneezy2009)–  CoreSelf-Evaluations(self-efficacy,self-esteem,neuroticism)(e.g.

Judge/Bono2001)–  Values(Hakim1998,2002)







Source:CommentsontheGuardianarticle:I‘mbeyondanger–whythegreatpaygaprevealisanexplosivemomentforgenderequality.Published28.2.2018


Laysummary:DieLohnungleichheitzwischenMännernundFrauenbeginntlangevorderFamiliengründung/L'inégalitésalarialeentrehommesetfemmescommencebienavantlafondationd'unefamille.SocialChangeinSwitzerland,Number18,June2019.Media:Tagesanzeiger,DerBund,LeTemps,Swissinfo,LaLiberté,TribunedeGenève,LeNouvelliste,24heures,RTN,RadioLac,Reiso

Project#1

Previousresearch

Genderwagegapinthebeginningofthecareer:

-Germany:6%(Ochsenfeld2014)-Finland:10%(Napari2009)-Switzerland:7%(Bertschyetal2014)-U.K.:8%(Manning/Swaffield2014)-U.S.:10%(Goldin2014),14%(Fortin2008)

Contra-arguments:–  Behaviourdiffersbecauseparenthoodisanticipated

Ourcontribution

•  Focusonthewagedevelopmentinearlycareer.–  Genderwagegap–  Gendergapinwagegrowth

•  Differencetopreviousresearch:–  Controllingforparenthoodanticipation

•  Values(towardsworkandfamily)•  Behaviour(byrestrainingthesample)

–  Knowledgeaboutintellectualcapacities(PISA),extensiveknowledgeofeducationandjobcharacteristics

Dataset&Approach•  LongitudinaldatasetTREE

•  followingaschool-leavercohort(mostlyborn1984/1985)from2000to2014

•  emphasisonschool-to-worktransition

•  Dependentvariable: grossmonthlywageinSwissFrancs(CHF),

•  standardizedforafull-timejob(40hoursperweek)•  adjustedtoinflation•  logarithm

•  Focuson3channels1.  Initialpotentialofrespondents2.  Labourmarketbehaviour3.  Parenthoodanticipation/Familyformation

Threechannelsaffectingpaygap

1.Initialpotential:

Matchingwithentropybalancing(Hainmueller2012)–  socio-demographiccharacteristics–  generaleducationalability–  educationalcertificatesachievedbeforeenteringthelabour

market•  Numberofeducationalcertificates•  1st/2ndeducationalcredentialonuppersecondary/tertiarylevel•  Fieldofstudy/fieldsofvocationaleducation


2.LabourmarketAddingindependentvariables: a)Jobrelatedhumancapital:

–  Numberofjobs(squared)–  Additionallyacquirededucationalcertificates

b)Characteristicsofcurrentjob–  Occupation(ISCO1-digit),sector(NOGA),cantonofthefirm,sizeofthefirm,workinghoursperweek,numberofsubordinates,permanentorfixed-termcontract,worksituation(nightshifts,week-endshifts,strainsinjob,varietyoftasks,autonomyinjob)


3.Parenthoodanticipation/Familyformation

Independentvariables•  Marriagestatus•  Pre-labourmarketvaluesconcerningworkmotivation(intrinsicandextrinsic)andpartnership/family

Restrictiononobservationsmin.3yearspriorparenthood=>differingbehaviourb/cofparenthoodanticipation

MethodsSample:•  Individualsaftertheycompletedtheireducation.•  Observationsmin.3yearspriorparenthood

Analyses:•  Random-EffectModels:

Overallgenderwagegapandwagegrowth•  Blinder-Oaxacadecomposition

Differencesinendowmentsandfactorscontributingtoitin first1.5years

Results–I

Results–I

Paygapwithoutcontrollingforendowments:5.4%Paygapwithcontrolling:Around4%

Results–II

Results–II

Results–II

Summary

Canthegenderwagegapbeexplainedbypreferencesforfamilyformation?Ifyes:Nogenderwagegapbeforefamilyformationsetsinandanticipatorybehaviour/charactertraitsarecontrolled

Results:–  Genderwagegapalreadyatlabourmarketentry:around4%infavourofmen=>Life-stylepreferencesarenottoblame

– Mostlycausedbyunexplained/unobservedfactors=>Notexplainedbyhumancapitaldifferences

Summary

Cangenderwagegapbeexplainedbypreferencesforfamilyformation?Ifyes:Nogenderwagegapbeforefamilyformationsetsinandanticipatorybehaviour/charactertraitsarecontrolled

Results:–  Genderwagegapalreadyatlabourmarketentry:around4%infavourofmen=>Life-stylepreferencesarenottoblame

– Mostlycausedbyunexplained/unobservedfactors=>Notexplainedbyhumancapitaldifferences








Project#2Theinfluenceofpersonalitytraitsonthegenderwagegapatcareerentry(withAnjaGhettaandBarbaraZimmermann,UniversityofBern)

PreviousresearchExplained%ofgenderwagegapbypersonalitytraits:–  Germany:3%(Müller/Plug2006),4.9-13.6%(Braakmann2009)–  Netherlands:12.5%(Nyhus/Pons2012)–  Russia:8%(Semykina/Linz2007)–  U.K.:2.5–27.6%(Manning/Swaffield2008)–  U.S.:5.4–14.5%(Cattan2014),10%(Fortin2008)

♂:+valuemoney,+self-esteem,+riskseeking,+competitive,+self-confident,+internallocusofcontrol,–agreeable♀:+conscientious,+interpersonalskills,+agreeable

PreviousresearchExplained%ofgenderwagegapbypersonalitytraits:–  Germany:3%(Müller/Plug2006),4.9-13.6%(Braakmann2009)–  Netherlands:12.5%(Nyhus/Pons2012)–  Russia:8%(Semykina/Linz2007)–  U.K.:2.5–27.6%(Manning/Swaffield2008)–  U.S.:5.4–14.5%(Cattan2014),10%(Fortin2008)

♂:+valuemoney,+self-esteem,+riskseeking,+competitive,+self-confident,+internallocusofcontrol,–agreeable♀:+conscientious,+interpersonalskills,+agreeable

PersonalitytraitsandenvironmentSocietallyprescribedbehaviourofmenandwomen:

♀–Communalcharacteristics:affectionate,helpful,kind, sympathetic,interpersonallysensitive,nurturant,gentle

♂–Agenticcharacteristics:assertive,controlling,confident, aggressive,ambitious,dominant,forceful,independent,self- sufficient,self-confident,pronetoactasaleader

Rolecongruitytheory(Eagly/Karau2002;Eagly/Sczesny2008)•  Individualsfaceprejudice/punishmentintheirinteractionsbecauseof

inconcruencybetween–  prescribedcharacteristicsduetotheirgenderandassociated

attributeswithacertainrolethoughttorequire=>Womeninleadershippositions

OurapproachDecreasingunobservedheterogeneityasmuchaspossible1.  Careerentry=>controlledforexperienceanddifferentcareer

progression•  Wholeworkingpopulation–e.g.Braakmann2010;Heineck/

Anger2010

2.  FocusingonVET(60%ofacohortinCH)=>highlinkagebetweeneducationandskillsinlabourmarket(dualVET)=>lessroomforpaynegotiation•  Cohortdataset–e.g.Fortin2008;Manning/Swaffield2008•  Universitystudents–e.g.Abele/Spurk2009;Groveetal.2011

Dataset&Methods•  LongitudinaldatasetTREE:

•  followingaschool-leavercohort(mostlyborn1984/1985)from2000to2014inSwitzerland,emphasisonschool-to-worktransition

•  Samplerestriction:•  RespondentswhosefirsteducationisaVET•  Restrictiontoobservationsmax.3yearspriorparenthood=>differingbehaviourb/cofparenthoodanticipation

•  Method:•  OLSmodels•  Kitagawa/Blinder-Oaxacadecomposition•  OLSmodelsforbothgenderseparately,comparisonofcoefficientswithseeminglyunrelatedestimation(Zellner1962)

Variables•  Dependentvariable:

grossmonthlywageinSwissFrancs(CHF),earnedintheirfirstyear inthelabourmarket

•  standardizedforafull-timejob(40hoursperweek)•  adjustedtoinflation•  logarithm

•  Independentvariables:1.  PotentialofrespondentsbeforeenteringVET(cognitiveskills

andsocio-demographiccharacteristics)2.  Personalitytraits(averagedoverwavesbeforerespondents

enteredlabourmarket)3.  CharacteristicsofeducationandVETformation4.  Jobcharacteristicsofcurrentjobandexperience

ResultsOurinterests:a)  Isthereagenderwagegap?

=>analysisofeffectofgenderonsalaryconditionalonvariouscharacteristicsofthepersonandhis/herjob

b)  Dopersonalitytraitsaffectsalary?Doestheeffectdifferbetweenmenandwomen?=>formale/femalesubpopulationseparately:analysisofeffectofpersonalitytraitvariablesconditionalonvariouscharacteristics=>comparisonofestimatesacrosssubpopulations

Results–I

Results–I

Results–I

Results–II

Red:significantdifferencebetweenmaleandfemalecoefficients

Results–II

Weareinterestedinthoseeffectsthata)  aresignificantforoneeithermaleand/orfemaleparticipants=>***

ANDb)  aresignificantlydifferentfromeachother=>red


Results–II


Results–II


Summary

Cangenderwagegapbeexplainedbypersonalitytraits?Result:Notreally– Evenconditionalonpersonalitytraits:Genderwagegapofaround4-5%

– Explanatoryvalueofpersonalitytraitsisratherlimited

– Heterogeneouseffects:•  Self-efficacy(♂:+♀:0)•  Importancetoworkwithpeople/care(♂:–♀:0)

Otherexplanations?

Mostlikelystatisticaldiscrimination:–  ExperimentsshowthatHRrecruitersdiscriminateagainstyoungwomenwithsmallchildren(e.g.Correlletal.2007;Oeschetal.2017)

Otherexplanations?

Mostlikelystatisticaldiscrimination:–  ExperimentsshowthatHRrecruitersdiscriminateagainstyoungwomenwithsmallchildren(e.g.Correlletal.2007;Oeschetal.2017)

Itseemsasifunobservedgenderwagegapcannotbechangedbyindividual‘sbehaviour.Howabouttheendowments?

Femaledominated Maledominated MixedAreas %female Areas %female Areas %female

Languages 72.5% Engineering 14.2% Medicine(M.D.,vet.,pharmacy)

60.7%

SocialSciences 70.5% Exactscience(math,physics,IT)

20.5% Law 57.1%

Humanities 64.4% Technicalscience 24.7% Naturalscience 48.4%

Economics 34.2%

GenderdistributioninfieldsofstudyatSwissuniversities

STEMfields=Science,technology,engineering,mathematics

AdvantagesstudyingaSTEMfield:•  ShortageofindividualswithSTEMcredentials•  Lowunemploymentchances•  Verygoodcareerprospects•  Highincome

NewprojectwithTREE2Whichpreferencescausehorizontalgendersegregationinfieldsofstudies?

Project3

Mainconclusionsofpreviousresearch•  Observedpreferencesorskillsdonotreallyexplaingenderedfield

ofstudychoice

•  Mainexplanatoryfactoroffieldofstudychoiceinregressions:Respondents‘gender

E.g.Charles/Bradley2009,Ochsenfeld2015,Wiswall/Zafar2014,Xie/Shauman2003

Problemofpreviousresearchapproach

Wedonotknowwhichpreferencesareimportantforfieldofstudychoice.Mainproblem:Impossibletodiscriminatebetweensubjects‘preferencesRelevantcharacteristicsoffieldsareconfoundede.g.primaryschoolteacher=>part-timework,highsocialskills,mathskillsarenotrequired,nocompetitione.g.mechanicalengineer=>mathskillsimportant,technicalskillsimportant,highsalary,highcompetition,full-timework

Problemofpreviousresearchapproach

Wedonotknowwhichpreferencesareimportantforfieldofstudychoice.Mainproblem:Impossibletodiscriminatebetweensubjects‘preferencesRelevantcharacteristicsoffieldsareconfoundede.g.primaryschoolteacher=>part-timework,highsocialskills,mathskillsarenotrequired,nocompetitione.g.mechanicalengineer=>mathskillsimportant,technicalskillsimportant,highsalary,highcompetition,full-timeworkSolution:Survey-basedchoiceexperimentswithstudentsbeforetheytransitiontouniversity=>TREE2Enablesustodiscriminatebetweenseveralpossiblyinfluentialfactors

ChoiceExperiment–Design

Preferencefor:MathematicsThinkingstyleCompetitionRiskGender-typicalassociatedskillsIncomePrestigePart-timework

ChoiceExperiment–Design

Preferencefor:MathematicsThinkingstyleCompetitionRiskGender-typicalassociatedskillsIncomePrestigePart-timework

TypicalSTEMfield

ChoiceExperiment–Basicidea

6choicesetsoutof24

Testfor:„Baseline“CompetitionMathematicsThinkingstyle


!

Femalesubjects=>strongpreferenceforsocialskills

Malesubjects=>strongpreferencefortechnologicalskills


!

Femalesubjects=>strongpreferenceforsocialskills

Malesubjects=>strongpreferencefortechnologicalskills

Knowledgeofpreferencesongrouplevel

AdvantagesofTREEforresearchongenderinequality

A)  Paneldatathatobservesindividualsatimportanttransitionsin

theirlifes=>inequalitiesingenderoftenconsequenceofaccumulationofdecisions=>investigationofmechanism

B)  Varietyofvariablesthatallowinterdisciplinaryresearch-standardizedabilitytest(PISA)-personalitytraits-educationalhistory-jobmarketbehaviour

Consequence:Idealdatasourceforresearchquestionsongenderinequality

OutlookThepotentialofTREEforquestionsongenderinequalityMainadvantageofTREE:Longitudinalcharacter=>allowsalifecourseperspectiveQuestionsthatcanbeexaminedinthefuture⇒ Evolutionofgenderwagegap⇒ Effectofmaternityleave

Wishlist•  ContinuationofTREE‘sopenesstoincludeexperimentsthatare

relevantforotherresearchersaswell

•  Informationonpartner(e.g.salary)=>maternityleavedependentonrelativeshareoncouple‘sincome

•  Informationonvaluesofrespondent‘ssocialnetwork

•  Hereticalsuggestion:ConsideringtocollectDNAdataforfutureuse(polygenicscores)

Thanksalotforyourattention!Dr.BenitaCombetLMUMünchenInstitutfü[email protected]

Documents

The value of the TREE dataset for exploring gender inequality · 3. Parenthood anticipation / Family formation Independent variables • Marriage status • Pre-labour market values