Do Family Values Shape the Effectiveness of Labour-to-Work

Do Family Values Shape the Effectiveness of Labour-to-Work Policies? An Analysis of a Recent Parental Leave Policy Reform in Germany Mireia Borrell-Porta

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Dofamilyvaluesshapetheeffectivenessoflabour‐to‐workpolicies?Ananalysisof

arecentparentalleavepolicyreforminGermany

MireiaBorrell‐Porta1

Early draft – please do not circulate

Abstract

Theeffectivenessofpolicyreform isnot independentof familyvalues.To test thisargument,

thispaperexplorestheeffectofaparentalleavepolicyreforminGermany(2007)onthereturn

toworkofmotherswhoholddifferentfamilyvalues.Thepolicyincentivizedanearlierreturnto

work by reducing the paid parental leave subsidy from two to one year.Although itwould

encompassdeliveringchildcare,thereturnwasexpectedtogeneratean incomeeffect inthe

secondyearwhichwouldmainlyaffect low‐incomemothers.Thepaperreliesonaregression

discontinuitystrategytoestimatetheimpactofthepolicyanditshowsthatthemagnitudeof

the incomeeffect issubjecttothe familyvaluesheldbymothersaswellastotheireducation

attainment.Specifically,thepaperfindsthathighly‐educatedmothersupholdingliberalfamily

valuesaremorewilling toaccelerate theirreturn toworkafter thepolicyreformandhence

benefitfromthelabour‐to‐workpolicy.

I. Introduction

Severalscholarshavedevelopedtheideathatfamilyvaluesshapeeconomicdecisions.Reher

(1998)givesahistoricalaccountofthedifferencesinfamilytiesacrossEuropetohighlight

their relevance for social and economic outcomes. Specifically, he suggests that much of

socio‐economicdisparitieswillremaininplacedespitesimilarmodernizationprocesses,and

that familystructure isarelevantdriver.Esping‐Andersen(1990,1999)acknowledgesthe

role of the family in certain conservative welfare states of southern Europe, a core trait

1 PhD Candidate at the European Institute, London School of Economics and Political Science.

Email: [email protected]

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which–forsomeauthorssuchasNaldini(2003)andLeibfried(inFerge&Kolberg,1992)‐

sets thebasis foradistinct typeofwelfare regime. Ferrera’spaper(1996)goes further in

characterizingthe‘southernmodelofwelfarestate’,highlightingthefamilyroleasa“social

clearinghouse” in southernEurope.Although taking amore empirical approach, economic

scholars(seeforinstanceAghionetal.,2011;Aghionetal.,2010;Alesinaetal.,2010;Algan&

Cahuc, 2007) have too made the case for a key role of family values on labour market

institutions and policies. They hypothesise the co‐evolution between values, demand for

regulation (behaviour) and policy design. Values, it is argued, influence the demand for

certain types of regulation, which, at the same time reinforce certain attitudes. In this

context twopossible equilibria emerge; a ‘good’ equilibriumwith low levels of regulation

andhighlevelsofsocialcapitalanda‘bad’equilibriumwiththeoppositeresults.

Thispaper ismotivatedbythe latterandattempts togoonestep furtherbyanalysingthe

interactionbetweenapolicy reformand family values and their impact onpreferences to

work.Iaimatshowingevidencethatasocialpolicyreformdependsontheexistingfamily

valuestobeeffective.

Theempiricalstrategyisthatofexaminingtheimplementationofaparentalleavepolicyin

Germanywhichaimedatacceleratingthepaceofreturntoworkaftermaternityinacontext

where different family values co‐exist. The paper relies on a regression discontinuity

strategytoestimatetheimpactofthepolicyanditshowsthatitseffectivenessissubjectto

thefamilyvaluesheldbymothersaswellastotheireducationattainment.

Thepapercontributestoaliteraturewhichusesthebroaderterm‘culture’andlooksatits

interaction with economic outcomes. Specifically, Barro and McCleary (2003), Tabellini

(2010),Guiso,etal., (2004,2009), IchinoandMaggi (2000)amongmanyothershavecast

light on the multiple ways culture can affect social preferences and economic outcomes.

However,thereiscontroversyonthedeterminismofthe‘cultural’argument.Whereassome

authorsperceivecultureandsocialpreferencesas ratherstaticandslow‐movingconcepts

(see for instanceRoland, 2004), others – such asRaquel Fernandez ‐ explicitly reject this

notion of culture and preferences as something “irrational, static or slow

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changing”(Fernandez et al., 2004, p. 4) and argue that their change “depends on the

environment broadly speaking, including the opportunities which determine individual’s

learning pace, their interactions with others, and particular historical experiences”(p. 4).

Hence,policiescaninfluencetheenvironment.Similarly,thispapercontributestothedebate

onpolicyconvergenceataEuropeanlevel.Althoughthechoiceofpolicyisultimatelyleftto

member states, there is littledoubt that theseEU recommendationshave an effect on the

policy direction adopted by each state(Featherstone & Radaelli, 2003). These policies,

however, and following the argument stated above,may have different results depending

not only on the existing institutional framework but also on its cultural framework and,

more precisely, on the family values in place (Fernandez, 2007, p. 306). These ones,

therefore,cannotbeignoredatthetimeofdesigninglabourmarketandsocialpolicies.

The rest of the paper is organized as follows. Section II describes the policy reform and

section IIIgivesanaccountof thedataandthemethodologyused.Section IVpresents the

results,followedbythediscussioninsectionV.Finally,sectionVIconcludes.

II. The2007policyreform

ThenumerousparentalpolicyreformsthattookplaceinGermanyinthepastthreedecades

reflect a conflictive equilibriumbetween the traditional breadwinnermodel and thedual‐

earner‐carer model. The introduction of maternal leave dates back to 1979, when the

coalition of Social‐democrats and Liberal devised a maternal leave policy grounded on

mothers’ health and well‐being issues (Leitner, 2010), which, in the eyes of the policy‐

makerscouldhavepromisingeffectsinincreasingemploymentratesamongmothers.The

policy implemented a six months paid maternal leave period which enabled formerly

employedmotherstoreceiveacappedearnings‐relatedbenefit.Thisbenefitdidnotaccount

forpartners’earnings,andtargetedformerlyemployedmothers,whichsuggestsadeparture

from the predominant existing breadwinner model. However, part‐time work was not

included,giventhatitwasnotinlinewithhealthgoalsofthepolicy.

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In1986thecoalitionofChristian‐democratsandLiberalspromotedasecondreform,which

revertedbacktotheoldbreadwinnermodel(Leitner,2010).Firstly,thepre‐existingcapped‐

earningsbenefitwassubstitutedbyaflat‐ratebenefitavailabletobothemployedandnon‐

employedmothersaswellasfathers.Thebenefit,however,wassolowthatitdidnotattract

fathers. Secondly, breadwinner’s earnings were taken into account and could reduce the

benefit (i.e. it was a means‐tested benefit). Thirdly, the paid maternal leave period was

increasedfirstlytotenmonthsandlateron,in1993,totwoyears.Additionally,theoverall

leaveperiodwasextended to threeyears. Implicitly themodelwas thereforepromotinga

breadwinnermodel,thetraditionalfamilymodel(Leitner,2010).Part‐timing,however,was

permittedupto18‐19hoursperweek.In2000theSocialdemocratsandtheGreenparty2,

implemented another reform which slightly departed from the breadwinner model

(Fleckenstein,2011),acknowledgedthe individualrighttoparental leavebyallowingboth

parentstotaketheleavesimultaneously,althoughthebenefitremainedameans‐testedone.

It also allowedpart‐timeworkup to 30hours perweek and it included thepossibility of

havingahigherflat‐ratebenefit ifthebenefitspanwasreducedfromtwotooneyear.The

impactofthereform,however,wasweakenedbythelackofinstitutionalchildcarefacilities.

In2005theso‐calledRed‐Greencoalitionmadeanattempttotacklethisissuebypassinga

lawwhichcommitted to theexpansionof childcare facilities forchildren less than3years

old.

Finally, in2007, theSocial‐democrats andChristian‐Democrats tookabig step forward to

shift from a breadwinnermodel towards a dual earner‐carermodel (Fleckenstein, 2011).

The reform – called Elterngeld ‐ replaced the flat‐rate benefit with a wage‐replacement

benefit up to 67% of earnings beforematernity leave, funded by the federal government

throughpublictaxation(Blum,2012).Acapof€1800andaminimumof€300wassetand

thenon‐employedwere entitled to thisminimum. Importantly, the reformalsodecreased

thebenefitspanfromtwotooneyearsanddevotedresourcestotheexpansionofchildcare

places.

2 Thesepartieswereencouragedbythe1996EUdirectiveonparentalleave(96/34/EC)

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Theexpectedbenefitsofthe2007parentalleavereform

The design of the policy suggests that low‐income mothers are the group which should

experiencealargerchangeintheirworkbehaviour.Beforethepolicytheywereentitledtoa

maximumof€300,whereasafterthepolicytheyareentitledto67%oftheirpre‐maternal

earningswithaminimumof€300,asubstantialincreaseofthebenefit.Inthesecondyear,

however, by design, employed low‐income mothers experience a total decrease of the

benefit.High‐incomeemployedmothers,instead,donotseetheirincentivesmuchchanged

bythepolicy,especiallyinthesecondyear.Beforethepolicyhigh‐incomemothersdidnot

receiveanybenefitsotheincentivestoreturntoworkaftermaternityleavewerehigh.After

thepolicythissituationchangesandtheyreceive67%oftheirearningsduringthefirstyear

(withacapof€1800)andnothinginthesecondyear.

Theargumentof thepaper is that theseexpectationsare likely tobe influencedby family

valuesinthecaseofaworkingmother.AsBorkstatesinhispaper(2011),attitudestowards

workingmothers inGermanyhavebeenrathernegativeovertheyears,especially inWest

Germany. A termhas been coined ‐ ‘Rabenmütter’ (ravenmother) ‐ to designateworking

motherswithyoungchildren.Fleckenstein(2011)makesasimilarpointinhispaperwhen

he argues that, despite a decline in traditional family values, ‘West Germany remains

relatively conservative by international standards’ (p. 548). With the aim of testing the

argumentempirically,Ithereforeexpectthepolicytoworkonlyforthosemotherswhohold

liberalfamilyvalues.

III. Dataandmethods

InthissectionIshow,firstly,theintuitionbehindtheempiricalstrategyfollowed.Secondly,I

explainthechoiceofthemethodused:theRegressionDiscontinuityDesign(RDD).Thirdly,I

describe the nature of the variables, treatment and control, placing special attention to

familyvalues.Finallythesectioncontainssomedescriptivestatisticsandsomepreliminary

evidence.

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Empiricalstrategy

The paper examines the effects of the 2007 policy on the decision to return‐to‐work for

motherswithdifferent familyvalues.Thepaper runsa seriesof logit specificationsof the

followingtype:

P(Yit=1) = α + β1tit + β2fvj + β3titfvj+ β4Xi +ε;

whereYit is theprobabilityofpreferringa fast return toworkaftermaternity fora given

individual i. It takes value1 if themother states that shewill goback towork as soonas

possibleorwithinoneyear,and0ifshestatesthatitwilltaketwoormoreyearstogoback

towork.titstandsforthetreatmentgroup,thatis,theindividualsaffectedbythepolicy,and

isatimedummyvariablewhichtakesvalue0iftheobservationbelongstotheperiodbefore

thepolicywasimplemented(1stJanuary2007)and1afterthepolicy.Dataspacesfrom2005

until2009,thatis,twoyearsbeforethepolicyandthreeyearsafterthepolicy.Thechoiceof

yearsaccountsforapotentialdelayinimplementation.fvjisaproxyofthefamilyvaluesof

eachindividual,whichareassigneddependingonthecountryofmigration(seesubsection

below for an explanation of the construction of the variable family values). titfvj is the

interaction between the time dummy and the family values. Finally,Xit includes a set of

individual characteristics as controls. This is themain specification corresponding to the

hypothesisof thepaper. InadditionIalsorunothersimilarspecificationswhereI test the

effectofthepolicyondifferentsubgroupsofpeopleaccordingtotheirincomeandeducation.

In these cases the interaction termhappensbetweenoneof the control variables and the

timedummy.

Methodology

The method used in this paper is the Regression Discontinuity Design (RDD) with a

Difference‐in‐Differences (DD) specification. The RDD method is used to estimate causal

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effectsofaninterventionbyexaminingcomparableobservationsbeforeandafterthecut‐off

point. Itreliesontheassumptionthattheinterventionisrandomlyassignedandtherefore

observationsaroundthecut‐offpointarecomparable.Observationsbeforethecut‐offpoint

(the implementation of the policy) can then be treated as a control group (Green et al.,

2009).ThepolicyinterventionanalysedinthispaperseemssuitedtoaRDDmethod.Firstly,

theinterventioncanbearguedtoberandomlyassigned,giventhatthetreatmentwouldbe

available for all new‐mothers from 1 January 2007. The cut‐off point, therefore, did not

dependonany individualcharacteristicsof themother,onlyonthebirthrateof thechild.

Althoughitcanbearguedthatmotherscouldhaveattemptedtochangetheirbehaviourand

delaymaternity, this argument is ratherweakdue to the speed of the legislationprocess.

ThemainfeaturesofthereformwerediscussedinMay2006,draftedinJune, the lawwas

passedinSeptember2006anditbecameeffectiveon1January2007(Kluve,2009).Figure1

supportsthisargumentbyshowingthatthemonthlynumberofbirthratesdidnotchange

significantlyfrom2005to2007.

Figure1:Birthratesacrossyears

Source:,BergemannandRiphahn(2011),fromtheGermanFederalStatisticaloffice

Theobservationsintheanalysisarethereforedividedbetweenyears2005and2006,which

belongtothecontrolgroup,andyears2007to2009,whichbelongtothetreatmentgroup3.

3Seethesection‘Discussion’forananalysisofthechoiceofthenumberofyearstaken.

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Moreover,IuseaDDspecification–reflectedintheinteractionterm‐comparingdifferent

subgroupsofindividualsaccordingtotheirfamilyvalues,educationorincome.

ThechoiceoftheRDDmethodhasanumberofadvantagesworthmentioning.Tostartwith,

intheanalysisofthepapertheoverallRDDchoicesubstitutesthedifference‐in‐difference‐

in‐differencesmethod(DDD)–whoseresultsarealwaysmoredifficulttointerpret.Inthis

paper this is a great advantage because, aside from a control and treatment group that

allowsmetoinfercausality,Ialsoneedacontrolandtreatmentgrouptoassesstheimpactof

familyvalues(i.e.theimpactofthepolicydependingonvaluesneedsacomparisonbefore

andafterandbetweentraditionalandliberalfamilyvalues).Secondly,inaRDDcontextthe

control group is probablymore similar to the treatment group than in aDD context. And

thirdly, the RDD specification reduces the probability of havingmeasurement errors: my

dependentvariableisbasedonaquestionintheGSOEPdatabaseaboutthe ‘willingnessto

return to work’ (see data and descriptive statistics section for more information), which

leadstoafourcategoriesresponsewhichIcodeasbinary:fastreturnorslowreturn.This

choicewouldnothavebeenpossiblewithaDDDspecification,giventhatmycontrolgroup

wouldhaveprobablybeenworkingmothers(childlessorwitholderchildren),forwhomit

makesnosensetoaskaquestiononthewillingnesstoreturntowork(giventhattheyare

already working). Alternatively, an option for the DDD would have been to look at the

numberofhoursworkedbymotherswithone‐yearoldchild,andcomparethosebeforeand

after thepolicy,withworkingmothersorwomenas a control variable, forwhich Iwould

alsolookatnumberofhoursworked.Thedata,however,makesitdifficulttoknowtheage

of the child as it only provides data for the mother, and this could possibly lead to

measurementerrors.

The identification strategy follows Fernandez (2007) to evaluate the impact of the policy

reform on the decision to return‐to‐work across individuals with different family values.

Thisapproachusesmigrantstoovercometheproblemofdisentanglingtheeffectsofculture

or values from economic and institutional environment. Migrant groups face the same

institutionalandeconomicenvironmentofthenativeindividualsinthecountryofresidence

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but they are assumed to preserve, to a certain extent, family values of their country of

ancestry. With this approach I then analyse the effects of the policy for different female

migrants groups, comparing their outcome within these groups and between them and

nativegroups(EastandWestGermans).Thebasisforclusteringtheindividualsindifferent

groups will be family values. That is, I cluster them according to whether they hold

traditional or liberal family values (family values can also be – and will be – rated on a

continuum).Inordertoavoidproblemsofreversecausality(i.e.currentattitudesmayhave

been influencedbyprevious economicoutcomes), I use aproxy for current family values,

whicharevaluesexpressedbyindividualsinthemigrant’scountryofancestryinprevious

years.

Dataanddescriptivestatistics

IusetheGermanSocio‐economicpaneldata(GSOEP)4,alongitudinaldatasetrunningyearly

since1984until2011(thelatestwave)whichinterviewsallthemembersofthehousehold,

newcomersandfollowstheleaversinnewhouseholds.TheGSOEPhasgraduallyincreased

its sample up to nine times, with some of these samples being focused onmigrants (see

Appendix1forarelationoftheexistingsamples).Intotal–from1984to2011‐itcontains

around600.000observations.ForthepresentanalysisIselectwomenwhoworkandhave

hadachildinoneoftheyearsfrom2005to2009.Toknowwhethertheyhadachild,thereis

a questionwhich asks ‘Hasyour family situationchangedafterDecember31,200X?’ (200X

belongs to n‐2, i.e. if the questionnaire belongs to year 2008, the question will refer to

December 31, 2006). One of the answers is ‘Yes, had a child’ and for each answer the

respondentisaskedwhetherthiswasinyearnorn‐1(i.e.inthequestionnairebelongingto

year2008, theoptionsare:2007and2008).Given that the interviewshappen indifferent

monthsoftheyearforeachrespondent,itcanbethecasethattheyareaskedthisquestion

4ThedatausedinthispaperwereextractedusingtheAdd‐OnpackagePanelWhizv4.0(Oct2012)forStata.PanelWhizwaswrittenbyDr.JohnP.Haisken‐DeNew([email protected]).ThePanelWhizgeneratedDOfiletoretrievetheSOEPdatausedhereandanyPanelwhizPluginsareavailableuponrequest.Anydataorcomputationalerrors in thispaperaremyown.Haisken‐DeNewandHahn (2010)describePanelWhiz indetail.

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beforetheyhavehadachild(e.g.therespondentisinterviewedinJanuary2007andshehas

achildinDecember2007).Toavoiddroppingwomenwhohaveactuallyhadachild,Irely

ontheanswersfromyearn‐1.Inthecaseofmultiplebirths,Ihaveonlykepttheobservation

belonging to last birth.After droppingmissing observations and coding all the variables I

need,Iendupwithatotalof491observations.

Mydependent variable is basedon the followingquestion from theGSOEPquestionnaire:

‘Whenapproximately,wouldyouliketostartwithpaidemployment?’Theanswerscanbe:‘1)

Assoonaspossible,2)Nextyear,3) In thenext two to fiveyears,4) In thedistant future, in

more than fiveyears’. I code themas fast return (dummy=1) if the answer is ‘as soonas

possible’or‘nextyear’andslowreturnifotherwise(dummy=0).Ithereforehaveabinary

dependentvariablewhichistheintentiontoreturntowork.Appendix2explainsindetailthe

codingofthedependentvariable.

Myindependentvariable–individualfamilyvalues‐is,asstatedabove,proxiedbysocietal

familyvaluesexpressedbyindividualsinthemigrant’scountryofancestryintheyear2000.

Thesocietalfamilyvaluesareconstructedasfollows:Itaketheanswersfromthefollowing

question fromtheWVS:Doyouagreewith the followingstatement?:Aworkingmothercan

establish justaswarmandsecurearelationshipwithherchildrenasamotherwhodoesnot

work’andrunanindividuallogitregression,withthisquestionbeingthedependentvariable

and my main independent variable being country dummies. The base region is ‘West

Germany’. The country coefficients then tell me the likelihood that an individual from a

certaincountryorregionwillagreewiththepreviousstatementcomparedtoanindividual

fromWest Germany. I control for age, age squared, size of town, marital status, sex and

education.Thecountrydummycoefficientswillbetheinstrumentusedformyindependent

variable. All coefficients happen to be statistically significant (most of them at 1%

significancelevel)exceptforPortugal.Figure2depictstheresults.

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Figure2:effectsofcountryoforiginon‘workingmother’acceptance

Inmyanalysisthesevalueswillbeusedasacontinuousvariableaswellasabinaryvariable.

Ithereforecreateadummyvariablewhichisadichotomizationofthevariablefamilyvalues.

TocarryoutthisdichotomizationIhavecalculateditsmeanvalueandIhavecategorizedall

observations below themean as traditional family values and all observations above the

meanasliberalfamilyvalues.

Both family values and the dummy variable are then assigned to each ofmy observation

depending on theirmigrant origin. The GSOEP database allowsme to knowwhether the

observation has ‘nomigrant background’, a ‘directmigrant background’ (first‐generation

migrant)oran‘indirectmigrantbackground’(second‐generationmigrant)andittellsmethe

countryoforigin for thosewhoare categorizedasmigrants. SeeAppendix2 fordetailson

howIhavecodedthecountryoforiginforeachmigrant.ToseparateEastfromWestGerman

observations,Ihaveusedtheinformationfromthevariable‘sample’.SampleCincludesonly

observations from East Germany. I have also used the variable ‘sample’ to drop some

observationswhichbelongedtosampleswherethemotherwasGermanbutthefatherwasa

migrant.Thatis,Ihavedroppedtheobservationsforwhichthemigrationbackgroundofthe

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mother was ‘no migration background’ and it belonged to sample B (foreigners), D

(Immigrants)orF (Innovation)whereat leastonememberof thehousehold isamigrant.

The following table, Table 1, shows the migration background and the number of

observations.

Table1:Countryoforiginofobservations

Countryoforigin

Nomigrationbackground

Directmigrationbackground

Indirectmigrationbackground

TOTAL

EastGermany 234 234WestGermany 125 125Turkey 10 20 30Russia 11 0 11Greece 1 5 6Poland 6 0 6Macedonia 2 3 5Croatia 4 0 4Italy 2 1 3Rumania 3 0 3Ukraine 3 0 3Portugal 2 0 2Serbia 2 0 2USA 2 0 2Belarus 1 0 1Brazil 1 0 1Canada 1 0 1China 1 0 1CzechRepublic 1 0 1France 1 0 1Hungary 1 0 1Iran 1 0 1Philippines 1 0 1Slovakia 1 0 1TOTAL 359 71 32 462Source:ownelaborationbasedonGSOEPNote:Ihavedroppedtheobservationswithmissinginformationonanycrucialvariabletopursuetheanalysis.

Several controls are included in the regression. Household income is coded as 0 if it is

greaterthan€2000/monthand1ifitislowerthanthisthreshold.Thereasontocodehigh

incomeas0andlowincomeas1–whichmightseemunusual–isjustifiedbythefactthat

thepolicywasexpectedtoinfluencemainlylowincomemothers,andtherefore,this isthe

‘treatment’ group that I am interested in. In order to dichotomise it I have calculated the

meanvalueof incomeand Ihave treatedall observationsbelow themeanas low income.

Education,measured in ‘yearsof education’ is another control variable. It hasbeen coded

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bothasacontinuousvariableandasabinaryvariable.Inthelattercase,thetwocategories

are low and high education, with the threshold being A‐levels. Low education, therefore,

includesobservationswithnoA‐levels,thatis,withlessthantwelveyearsofeducation.High

educationincludesobservationswiththirteenormoreyearsofeducation.Someregionsin

GermanyneedtwelveyearsofeducationtoachieveA‐levels,whereasothersneedthirteen.

Given thisdifference,whenusingeducationasabinaryvariable,observationswith twelve

years of education have been dropped. Another control is marital status. This one takes

value0ifthemotherisnotmarriedand1ifsheismarried.Finally,Iuseregionaldataonthe

percentage of zero to three year‐old babies that attend childcare as a proxy for regional

childcareavailability.

Table2presentssomebasicdescriptivestatisticsofthedatabeforeandafterthepolicy.The

data reveals that there are no statistically significant differences in the means of the

variables between the two periods (before and after the policy). Themeans for the ‘fast

return’ variable, the dependent variable, indicates that in both periods thewillingness to

quicklyre‐enterthejobmarketislow.Thedataalsoshowsthattheaveragematernalageis

32yearsold,andthatmostoftheobservationsinbothperiodsaremarried.Withregardsto

years of education, the average is 13 years, which is the A‐levels threshold. Average

household income amounts to approximately €2.700 and average family values aremore

traditional.

Table2:Descriptivestatistics

Number of children born

Variables mean SD mean SD

fast return (0/1) 0,45 0,5 0,48 0,5

maternal age 31,6 6,87 31,7 5,32

married (0/1) 0,67 0,47 0,7 0,46

years of education 13,14 2,65 13,26 2,69

household income 2644 1419 2764 1212

family values 0,35 0,48 0,29 0,45

childcare 0,17 0,15 0,16 0,14

Note: t‐tests indicate no statistically significant difference between subgroups at 1% and 5% levels.

Before the policy reform

2005‐2006

187 303

2007‐2009

After the policy reform

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IV. Empiricalanalysisandresults

Tables3to5presenttheresultsoftheempiricalanalysis. Inallregressionsthedependent

variable is the probability of preferring a fast return to work after maternity. I start by

looking at the entire sample. Given that the policy was mainly targeted at shaping the

behaviour of low‐income mothers, I also show the results of the effect of the policy for

mothers living in low‐incomehouseholds.Table3 is therefore relevant toprovideuswith

information on how well the policy reached its target, which will then motivate the

introductionoffamilyvalues.

Thebaselineregression(col.1)regressestheprobabilityofpreferringafastreturntowork

on a time dummy variable and a set of individual characteristics, which include marital

status,educationasacontinuousvariable,incomeandaproxyofchildcareavailability.The

resultsshowthatthepolicyhashadapositiveeffectonshapingpreferencestowardsafast

return to work, although strictly speaking the effect is small (of almost 2%) and not

statistically significant. Instead, the four controls appear tobe statistically significant.One

year increase ineducation increases theprobabilityofpreferringa fast return toworkby

almost3%,significantat1%level.Beingmarrieddecreasestheprobabilityofpreferringfast

returntoworkbyabitmorethan10%,significantat10%level.Onepossibleinterpretation

isthatmarriedmothersarelikelytohaveothersourcesof income(e.g. fromthehusband)

which allow them to delay the return to work. An increase of one percent in childcare

availabilityleadstoanimpactonthepreferencesofafastreturntoworkof32%,significant

at10%level.Havingalowhouseholdincomealsodecreasestheprobabilityoffastreturnto

workbyapproximately16%,significantat1%level.

An interaction between the policy (time dummy) and income is added in a difference‐in‐

differencefashion(col.2).Thisinteractionisinterestingbecause,aspointedoutinsectionII,

thepolicydesignmakesitmoreexpensiveforlowincomehouseholds‐whowereentitledto

the parental leave means‐tested benefits before the policy ‐ to stay at home. These

householdswouldseeafterthepolicyaprobableincreaseofthebenefitinthefirstyearof

childbirthfollowedbyatotaleliminationofthebenefitinthesecondyear.Itwastherefore

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expectedthatitwouldgenerateanincomeeffectinthesecondyearwhichwouldincentivise

lowincomemotherstoreturnfastertowork.Theresultsdepictedincolumn2,however,do

notquitesupportthisexpectation.Thecoefficientofthevariableincomesuggestthatbefore

thepolicya lowincomemotherwas24%less likely toprefera fastreturntoworkthana

high incomemother, a resultwhichmotivates the introductionof thepolicy.Nonetheless,

theinteractiontermtellsusthatthissituationdidnotchangesignificantlyafterthepolicy.

Althoughafterthepolicyalowincomemotherwas14%morelikelytopreferafastreturnto

work than before the policy, its effect appears to be statistically insignificant at 10%

significance level, implying that the preference of fast return to work for low income

individualsdidnotchangeafterthepolicyreform.Thisresultisrobustevenwhenonlythe

individualswith the lowest income5 in the scale are taken into account and its income is

interactedwiththepolicy.Theresultisshownincolumn3,whichshowsthattheinteraction

term remains statistically insignificant. Both in column 2 and 3 income, education and

maritalstatusaresignificant,withsimilareffectsasincolumn1,whereastheavailabilityof

childcareisonlysignificantincolumn2.Thisfirstanalysis,onthebasisoftheresultsinTable

3,suggeststhatthepolicyhasnothadavisibleeffect.

5Ihavetakenthefirstquartileofincome.Thatis,Ihavedichotomizedthevariable,withthelowestquartilecodedas1andtherestcodedas0

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Table3:Averagemarginaleffects:effectofthepolicyandeffectofthepolicyinteractedwithlowincomemothers,entiresample.

Variables (1) (2) (3)

timedummy 0.017 ‐0.024 ‐0.017 (0.738) (0.685) (0.766)

education(c) 0.029*** 0.029*** 0.033*** (0.002) (0.002) (0.000)

maritalstatus ‐0.104* ‐0.106* ‐0.096* (0.061) (0.056) (0.087)

Childcare 0.319* 0.299* 0.247 (0.078) (0.099) (0.173)

Income ‐0.158*** ‐0.243*** (0.007) (0.006)

time*income 0.143 (0.196)

income,lowestpercent. ‐0.171* (0.061)

time*lowestpercent.income 0.135 (0.238)

Observations 403 403 403pvalinparentheses

***p<0.01,**p<0.05,*p<0.1 Nonetheless, it is plausible that the effect of the policy is concealed by some kind of

heterogeneitywithin the population.One plausible source of heterogeneitywhich goes in

line with the hypothesis of the paper is family values. These ones are likely to play a

significant role in shaping the outcomeof aparental leavepolicy and thus, the lattermay

appearineffectiveiffamilyvaluesarenottakenintoaccount.TheresultsinTable4suggest

that this might be the case, although they are not very robust. Family values, when

categorized as a binary variable (family values b, col. 4), seem to matter for the policy

effectivenessat10%significance level.After thepolicy,motherswith liberal familyvalues

are21%more likely toprefera fastreturn towork thanbefore thepolicy.This isnot the

case for mothers with more traditional values, as the insignificance of the time dummy

coefficientshows.Nonetheless,thisresultisnotrobustwhenfamilyvaluesaremodelledasa

17

continuousvariable(familyvalues(c)),shownincolumn5.Heretheinteractionbetweenthe

time dummy variable and family values is insignificant, as it is the time dummy and the

family values variable. The only significant variables are marital status, income and

education.

Table4:Averagemarginaleffects:impactofthepolicyinteractedwithfamilyvalues,impactofthepolicyinteractedwitheducation;entiresample.Impactofincomeandvalues,highly‐educatedsample.

Variables (4) (5) (6) (7) (8) (9)

timedummy ‐0.050 ‐0.072 ‐0.023 0.028 ‐0.007 ‐0.245 (0.431) (0.554) (0.786) (0.719) (0.938) (0.201)

maritalstatus ‐0.128** ‐0.132** ‐0.090 ‐0.097 ‐0.130 ‐0.126 (0.029) (0.024) (0.137) (0.238) (0.130) (0.141)

Childcare 0.379* 0.258 0.281 0.080 ‐0.031 0.003 (0.092) (0.230) (0.163) (0.764) (0.926) (0.992)

Education1 0.028*** 0.029*** 0.094 (0.004) (0.003) (0.296)

time*educ 0.093 (0.400)

Income ‐0.171*** ‐0.167*** ‐0.158** ‐0.170 ‐0.120 ‐0.115 (0.007) (0.009) (0.014) (0.234) (0.229) (0.249)

time*income 0.166 (0.367)

familyvalues(b) ‐0.130 ‐0.065 (0.166) (0.648)

time*values(b) 0.211* 0.267* (0.054) (0.088)

familyvalues(c) ‐0.003 ‐0.073 (0.954) (0.507)

time*values(c) 0.065 0.227* (0.390) (0.068)

Observations 364 364 340 206 191 191pvalinparentheses

***p<0.01,**p<0.05,*p<0.1

18

Another source of heterogeneity might be education. The results on the previous tables

suggest that education is a highly influential variable for the probability to return fast to

work,bothinstatisticandeconomicterms6.Moreover,educationisrecognizedbyandlarge

in the literature as one of the most decisive factors in influencing female labour market

participation (see, for example, OECD 2004:4). I would then expect education to play a

positive role in the effectiveness of policy, because highly‐educated individuals are more

likely tobeawareof thebenefits for theirworkingcareerofreturning faster towork.The

effectsofeducation,however,maybe limitedbytwodifferent factors. Firstly, itwouldbe

logical to think thatonly thehighly‐educatedmotherswith low incomereact to thepolicy

(given that the policy is targeting low income individuals). Figure 3 shows that this

hypothesis is worth testing given that although there is a positive correlation between

incomeandeducation,thehighertheeducationlevel,themorevarianceincomeexperiences.

Secondly,andfollowingthemainargumentstatedinthispaper,theeffectofeducationmight

fadewhen family values are taken into account.Ahighly‐educatedmother coming froma

strongfamilytraditionandamarkedpreferenceforhomechildrearingislikelytopreferto

stay at home with the child for a longer period of time, regardless of any economic

incentives.

6 Marital status is also very relevant, although less statistically significant than education.Unfortunately, the possibility of analysing the policy only for married or lone mothers isunfeasibleduetothereductionofthesamplesize.

19

Figure3:Correlationbetweenhouseholdincomeandeducation,entiresample.

Column6ofTable4 starts bypresenting the impact of the policy and its interactionwith

educationontheprobabilityofafastreturntowork.Theinteractiontermsuggeststhatthe

effectof thepolicy is insignificantboth forhighand low‐educatedmothers,controlling for

marital status, childcare availability and income. Column 7 uses the restricted sample of

highly‐educatedmothers7and testswhether the low‐income–highly‐educated individuals

react to the policy. Again, the interaction term between time dummy and level of income

showsthatthisisnotthecase.Inotherwords,itappearsthathighly‐educatedmotherswith

lowincomeexhibitthesamepreferencestoreturnfasttoworkbeforeandafterthepolicy.

Column8and9testthehypothesisinwhichonlythosehighly‐educatedmotherswithliberal

valuesreacttothepolicy.Theresultsconfirmthehypothesis,regardlessofwhetherfamily

valuesaretreatedasabinary(column8)orcontinuous(column9)variable.Accordingtothe

interaction termsof themodels,motherswith liberal familyvaluesarebetween22%and

27%morelikelytopreferafastreturntoworkafterthepolicy.Theresult issignificantat

10%significancelevel.Thisisnotthecaseformotherswithmoretraditionalvalues,asthe

7Athree‐wayinteractionbetweenthethreevariableswouldbepossible,althoughitwouldresultincoefficientsdifficulttointerpret.

02

000

400

06

000

800

01

0000

5 10 15 20Amount Of Education Or Training In Years

dincome Fitted values

20

insignificance of the time dummy coefficient in bothmodels shows. Therefore, it appears

thateducationisrelevanttotheeffectivenessofthepolicy,butitisnotsufficienttochange

mother’s behaviour. The relevance of family values reveals that it is only those highly‐

educated mothers coming from a liberal family tradition who are more likely to be

influencedbythepolicyreformandadjusttheirdecisiononre‐enteringthelabourmarketat

afasterpace.

Thepredictedprobabilitiesofpreferringafastreturntoworkforhighly‐educatedmothers

holdingdifferentfamilyvaluesareshowninTables5and6.Whenclassifyingfamilyvalues

dichotomously,(Table5),theprobabilityofpreferringafastreturntoworkisaround50%

formothersholdingtraditionalfamilyvalues,bothbeforeandafterthepolicy(assuggested

abovewith the timecoefficients fromTable4 small change inprobabilitybeforeandafter

thepolicyisinsignificantformotherswithtraditionalfamilyvalues).Withregardtomothers

withliberalfamilyvalues,theprobabilityofpreferringafastreturntoworkamountto46%

beforethepolicy,whereasitincreasesupto71%afterthepolicy.

Table5:Predictedprobabilitiesofpreferringafastreturntowork,dichotomousfamilyvaluesandhigh‐educatedmothers’sample

Before the policy After the policy

Family values = 0 0,53 0,52


Table6:Predictedprobabilitiesofpreferringafastreturntowork,dichotomousfamilyvaluesandhigh‐educatedmothers’sample

Before the policy After the policy

Family values = 0,5 0,57 0,44





Note:thetableonlydepictsfivevaluesoffamilyvalues,butthevaluesrangefrom5to25.

21

Beforeturningtothediscussion,andgiventhestrongimpactofeducationontheanalysed

policy and the significant interaction of education and family values, there is the need to

confirm that it is education thatmatters, andnot other correlatedomittedvariables.Two

maincandidatesaretobeconsidered:incomeandoccupation.

With regard to the first one – income– it is reasonable to think that it is correlatedwith

education, and that therefore only low income mothers with liberal family values (as

opposedtothosehighly‐educatedwithliberalfamilyvalues)reacttothepolicy.Column10

fromTable7restrictsthesampletothelow‐incomemothersandinteractsthetimedummy

withfamilyvalues.Theresultsrejectthehypothesisandshowthatlowincomemotherswith

liberalfamilyvaluesdonotsignificantlyreacttothepolicy.

Occupation is the other variablewhich is likely to be correlatedwith education. I expect

mothers in occupationswhich donot involvemuch physical effort to bewilling to return

fastertowork.Thecorrelationbetweenoccupationandourdependentvariableamountsto

24%8.Ihavethenincludedoccupationinthemodelandinteractitwiththepolicy,asshown

in column 11. The results suggest that occupation is significant at 10% level before the

policy,andthatwhite‐collarworkersarethreetimesmorelikelytoexperienceafastreturn

toworkthanblue‐collarpeers.Thisoutcomedoesnotchangeafterthepolicyinasignificant

way. This result, however, is not robust to the inclusion of marital status and education

controls,asshownincolumn12.Inotherwords,onceeducationisincludedinthemodel,the

effect of occupation disappears. This suggests, therefore, that education stands as a

significantvariable.

8Ihavecodedoccupationasabinaryvariablethattakesvalue0ifitis‘blue‐collar’and1ifitis‘white‐collar’. I have dropped the ‘self‐employed’, ‘apprentices’ and ‘civil servant’ categories,sinceitwashardertoclassifythemaccordingtothephysicaleffortrequired.

22

Table 7: Average marginal effects: impact of the policy on low-income sample, effect of the policy with occupation.

Variables (10) (11) (12)

timedummy 0.099 0.025 0.101 (0.370) (0.899) (0.616)

familyvalues(b) 0.121 (0.424)

time*values(b) 0.093 (0.596)

maritalstatus ‐0.140 0.064 (0.118) (0.414)

education(c) 0.043*** 0.027* (0.005) (0.077)

childcare 0.085 0.659*** (0.795) (0.009)

occupation 0.266* 0.200 (0.086) (0.218)

time*occupation ‐0.070 ‐0.119 (0.745) (0.581)

Observations 125 195 184pvalinparentheses

***p<0.01,**p<0.05,*p<0.1

23

V. Discussion

This paper has attempted to testwhether family values act as a filter to the success of a

familypolicy.Theresultshaveshownthatthepolicyhasbeeneffectiveforhighly‐educated

motherswithliberalfamilyvalues.Onthisbasis,thissectiongivesanoverviewoftheresults

and turns to adiscussionof anumberofmethodological anddata issues arising from the

analysisaswellaspolicyimplications..

Resultsandrobustnesschecks

Thepolicyanalysed in thispaperwasdesignedtoaccelerate thereturn toworkmainlyof

motherswith low income.Nonetheless, the results from theprevious section suggest that

thepolicydidnotsignificantlychangethepreferencesofthisgrouptoreturnfasttowork.

Instead,theyshowthattwoothervariables–educationandfamilyvalues–arerelevantto

explainwhoreactedtothepolicy.

Previous literature has always pointed out at education as a key variable to understand

different labour‐market outcomes. Indeed it is reasonable to think that highly‐educated

mothers aremore aware of the benefits of returning faster towork for their professional

careers.Theresultsof thisanalysis,however,addtothe literatureshowingthateducation

alone isnotasignificant factor for thesuccessof thepolicy. Itappearsthatamonghighly‐

educated mothers, upholding liberal values increases the probability of preferring a fast

return to work by around 23% to 27%. This is not the case amongmothers frommore

traditionalvaluesbackgrounds,forwhichnoevidenceisfoundofachangeinpreferencesto

return towork. This result is robust to the specification of family values as a continuous

variable,anditissignificantat10%level.Thisfindingisinlinewiththehypothesisofthe

paper and suggests that highly‐educatedmothers with traditional family valuesmay still

prefertochildrearingathomefora longerperiodanddelaytheirreturntowork; i.e.that

financial incentives are less relevant. Instead, highly‐educatedmotherswith liberal family

valuesmayprefertore‐enterthejobmarketassoonaspossible.

24

Some robustness checks are carried out to validate the argument. One alternative

explanationtotheoneofferedwhichwouldrulefamilyvaluesoutcouldbethatitisactually

highly‐educatedand low incomemotherswho react to the policy ( as opposed to highly‐

educatedwithliberalfamilyvaluesbackground).Thismightbe,Iargue,becausewithinthe

highly‐educated theremightbe lowandhigh income individuals, forwhomthepolicyhas

income effects which work in opposite directions, as explained in section II. But even

restricting the sample to the highly‐educated and calculating the impact of the policy

dependingonincomedoesnotrevealanyimpactofeducationtogetherwithincomeonthe

probabilityoffastreturntowork.

Another alternative explanation could be that the power of education is overstated in

detriment to other correlated variables with potentially equal power, such as income or

occupation.Withregard to income,onemight think that it isnothighly‐educatedmothers

withliberalvaluestheoneswhoreactbettertothepolicybutlow‐incomemothers(atwhich

thepolicywastargeted)withliberalfamilyvalues.Ifthisisthecase,theimplicationswould

be that thepolicywouldhavepartially succeed, although the significanceof valueswould

pointtotheneedtotakefamilyvaluesseriouslyifthepatternsofreturntoworkaretobe

changed.Results,however,donotsupportthisargument;lowincomemotherswithliberal

familyvalueshavenotsignificantlyreactedtothepolicy.Turningtooccupation,itwouldbe

logical to think that mothers with less physically demanding jobs return faster to work.

Althoughresultspointoutattheimportanceofoccupationfortheprobabilityoffastreturn

beforethepolicy,thissignificancedisappearsonceIcontrolforeducation.

Policyimplications

Several policy implications stem from these findings. In order to enhance policy

effectiveness,ashiftfromeconomicincentivestowardseducationisneeded.However,asthe

findingsreveal,notallhighly‐educatedmothersreactsimilarlytothepolicy.Itisonlyhighly‐

educatedmotherswith liberalvalueswhoaremore likely tochangetheirpreferencesand

accelerate theirreturn toworkafter thepolicy.Thissuggests that familyvaluesshouldbe

25

takenonboardforpolicydesign.Inotherwords,whattheresultsareimplyingisthatsimilar

socialandlabour‐marketpoliciesarelikelytohavedifferenteffectsdependingonthefamily

valuesofthesociety inwhichthepolicy is implemented.Asnotedinthe introduction, this

result is of great significance for the process of European integration and in the present

European context. Although the design and implementation of labour‐market and social

policiesfallwithinthestateresponsibility,theEuropeanUnionisencouragingconvergence

ofcertainpoliciesandoutcomesrelatedtochildcareand,morebroadly,tosocialcare.Thisis

even more relevant in current times, when the current economic crisis has led some

countries to adopt labour‐market policies on the basis of success in other European

countries.Althoughsomeofthesepoliciesmightbeneeded,itsimplementationincountries

where family ties and values are different may lead to unexpected outcomes. Notably,

ineffectivenessofsuchpoliciesindifferentcountriesmaysubsequentlybeseenasanissue

of implementation failure or of lack of administrative capacity, whereas, as the results

suggest,differencesineffectivenessmayinfactbedrivenbycountryheterogeneityinfamily

values.

These results, however, provide an alternative picture to other findings. Bergemann and

Riphahn(2011)analysethe2007policyreformandfindthattherehasbeenanimpactfor

low‐income mothers at 10% significance level. They highlight a substantially higher

propensityofEastGermanwomentoreturnfastertowork.KluveandTamm’spaper(2009),

whichalsoanalysesthesamepolicyreform,takenon‐employedmotherswithintheirsample

aswell,andconcludethattheimpactofthereformhasonlybeenpositiveforEastGermany

mothersandwomenwhowerenotpreviouslyemployed.TherelevanceoftheEastGerman

mothersinbothpapersmayreflect,assuggestedinthispaper,differencesinfamilyvalues.

Tofullyunderstandthereasonswhytheresultsoftheseotherpapersdifferfromthisone,

thepresentpapercouldcarryoutdifferentrobustnesscheckswhicharespelledout inthe

nextsection.

26

Methodologyanddataissues

The choice of RDD has some advantages which have already been stated in section III.

Nonetheless, RDD also suffers from some drawbacks which are worth discussing. One of

themandprobablythemostimportantisthetrade‐offbetweenthenumberofobservations

andtheaccuracyofthecontrolandtreatmentgroup.IntheRDDspecification,thecloserthe

observationsaretothecut‐offpoint,thelowertheriskthatthetreatmenteffectsuffersfrom

omitted variable bias(Green et al., 2009). On the other hand, having a narrow timeframe

leads to fewer number of observations, which increases the sampling variability. In this

paperthetimeframehasbeentwoyearsbeforeandthreeyearsafterthepolicyinorderto

allowanacceptablenumberofobservations.Thischoice,however,ismadeattheexpenseof

increasing the bias. At least two alternatives exist to correct for thepotential bias: one of

themwould be to increase the number of control variables. I could control for childcare

facilities,tenureatthejob,macroeconomiccontrolssuchasemploymentlevels,numberof

childrenathome,and theLänderamongothers.However,addingcontrolshas tobedone

withcare,giventherelativelysmallnumberofobservationsIhave,especiallyinthehighly‐

educatedsample.Anotheroption is tocarryoutaDDDanalysisandcomparetheoutcome

with that of theRDDanalysis.Despite the above‐mentioned setbacksofDDD,using ithas

severaladvantages.Firstly,itwouldallowmetoenlargethetimeframeandasaconsequence

increasethenumberofobservations.Secondly,andgiventhatIwouldhaveacontrolgroup

for thewhole period, I could avoidmany controls andwin degrees of freedom. Finally, it

wouldallowme toexpandmyanalysis and test forpotential ‘delay’ effects and long‐term

effectsofthepolicy.Withregardtothe‘delay’effects,itmightbethecasethatittakessome

time for the individuals to adjust to the policy, something that the DDD analysis could

account for by omitting the observations closer to and past the cut‐off point. This is not

possiblewiththeRDDanalysis,giventhatittakespreciselyobservationsclosetothecut‐off

point. DDDcanalsotest forwhetherthe long‐termeffectsof thepolicyaredifferent from

the short‐termones. For example, this paper suggests that the policy does notworkwith

individuals with low‐education and traditional values. However, it might be that family

27

valuesareshapedby thepolicyandexperimentashift in themedium‐longrun.This isan

effectthatDDDismorelikelytocapture,whereasRDDdoesnotallowfortesting.Another

relatedeffectthatRDDcouldpotentiallybemissing–althoughthetestsinthedatasection

suggest that it is not – is an anticipation effect. DDD analysis would complement the

mentionedtestsbyomittingtheobservationscloserandprevioustothecut‐offpoint.

Someofthevariablesincludedintheanalysis–especiallythedependentvariableandfamily

values variable – deserve special attention. With regard to the dependent variable, one

alternativeistotake‘actualreturntowork’asopposedto‘willingnesstogobacktowork’.

This, however,has twomaindrawbacks. Firstly thismeasure ismuchmore influencedby

factorssuchas thehazardsof findinga joborotherpersonal factors thanthequestionon

‘willingness to return towork’. Secondly, although thepanel characteristics of thedataset

wouldhaveallowedmetofollowindividualsacrosstimeandthereforetracethetimethatit

takesforthemtogobacktowork,thefactthattheindividualdataisyearly–asopposedto

monthly–andthatinterviewstakeplaceindifferentmonthsforeveryindividualandevery

year,wouldhaveprobablyleadtomeasurementerrorprobleminmydependentvariable.

Family values areworth discussing, not only because of its salience in thepaper but also

becauseofthemanypotentialwaysofmeasuringit.Inordertoreinforcetheresultsofthe

paperIcouldcarryoutalternativeanalysiswhereotherwaysofproxyingfamilyvaluesare

used.Theapproachusedinthispaperfollowsseveralpaperswhichhaveexamined‘culture’

and‘familyvalues’,andwhichusuallyusequestionsfromdatabasessuchastheWorldValue

Survey(WVS),EuropeanValueSurvey(EVS)ortheInternationalSocialSurveyProgramme

(ISSP).Therearemultiplequestionsandstatementsinthesedatabasesthatrefertovalues,

and this paper chose a statement from theWorld Value Survey – ‘aworkingmother can

establishasecureandwarmrelationshipwithherchildasamotherwhodoesnotwork’ ‐

whichwasstronglylinkedtochildcare.Otherquestionsorstatements,however,couldalso

be used as proxies and therefore test the robustness of my results. One that it is a very

28

relevant statement to childcare is the following: ‘A pre‐school child suffers if themother

works’.Theonlysetbackof thisstatement is thereducednumberofcountrieswhichhave

been asked the question. Yet, it would still be an interesting robustness check. Another

alternative‐andonethat fittheepidemiologicalapproachusedinthepaper‐wouldbeto

takethefemalelabourforceparticipationofthemigrantscountryofancestryinthepast.It

is assumed that this figure reflects the institutions and values of the country of ancestry.

Therefore,ifitcorrelateswiththefemalelabourforceparticipationofthesecondgeneration

migrantsintheanalysedcountry(Germanyinthiscase)it isagoodmeasureofthevalues

andattitudesofmigrantswhichinfluencetheirwillingnesstore‐enterthejobmarketaswell

asthetimeandcircumstances.Anotherpertinentissuetodiscussistheroleofthepartner’s

family values in shaping women’s attitudes towards work. Addressing this issue could

broadentheunderstandingoffemalelabour‐marketdecisionsandproviderobustnesstothe

results. The idea is based on the assumption that working decisions are not made by

individualsbutbyhouseholdunits,andthepartnerisakeycomponent,whosefamilyvalues

and attitudes towardswomen’swork can have asmuch influence toworking patterns of

mothers as their own values. A similar issue arises when the parents of the observed

individualcomefromdifferentcountriesofancestry,andthereforehavepotentiallydifferent

familyvalues.Toknowhowtheseonesare transmitted to the individual isadifficult task

and any decision involves some assumptions. So far, this paper has taken a conservative

approach by including only the second generation individuals whose parents come from

same country. Nonetheless, finding a way to include the individuals with parents from

differentcountriesofancestrycouldrevealinterestingresults.

To sumup, exploring both differentmethodologies and avenues to quantify family values

wouldbeagoodexercisetotesttherobustnessoftheresultsandtounderstandwhatfactors

are relevant fora childcarepolicy tobeeffectiveandaccelerate return toworkaswell as

boostingfemaleemploymentlevels.

29

VI. Conclusion

In this paper I suggest that the impact of labour‐market related policies is dependent on

familyvalues.Theempiricalresultsof thepolicyanalysedpartiallyconfirmthisargument,

findingthattheimpactofafamilypolicywhichattemptedtoacceleratethereturntoworkof

mothershasonlybeensignificantforhigh‐educatedmotherswithliberalfamilyvalues.The

mainconclusionsarethereforethreefold:firstly,itisrevealedthateconomicincentivesare

notsufficienttoacceleratereturnofmotherstowork.Secondly,thepapergoesinlinewith

the already existing evidence of the relevant role of education in shaping labour‐market

outcomes, andmore specifically, in shaping thepaceof return towork.At the same time,

however,thepapersuggeststhateducationisnotasufficientfactortosecurethesuccessof

thisfamilypolicy,andthatfamilyvaluesplayasignificantrole.

Policy implications can be drawn from these findings. Firstly, policies directed towards

accelerating the return towork ofmothers should concentrate not only on the economic

incentives, but they should consider other factors such as the level of education of the

mother. Secondly, the inclusionof family values increases theunderstandingof individual

behaviourwhenitcomestodecisionsaboutworkandleisureandcanthusimprovepolicy

effectiveness and ultimately labour‐market outcomes. Thirdly, the findings contribute

significantlytothedebateonpolicytransferabilitydebateintheEuropeanUnion.Thepaper

suggests that similar social and labour‐marketpolicies are likely tohave adifferent effect

dependingonthefamilyvaluesofthesocietyinwhichthepolicyisimplemented.

Finally, thepaperhas provideda discussiononmethodology andotherdata issueswhich

canenhancetherobustnessofresults.RDDhasproventobeagoodmethodalthoughthere

are some shortcomings which could be overcome with a complementary analysis of the

policyusingDDD.Furthermore,ananalysisofthekeyvariableofthepaper–familyvalues–

reveals that thereareotherwaysofproxying familyvalueswhichwouldbe interesting to

examine.

30

Thereisstillalongwaytogotofullyunderstandhowfamilyvaluesandpolicyinteract,but

hopefully this paper has provided some substantial evidence of the relevance of family

valuestolabour‐marketrelatedpolicies,whicharecurrentlyatthecentrestageofEuropean

politics.

31

Appendix

Appendix1

GSOEPsamplesrelation

Source:SOEPSamplesOverview–2011/Wave28 Appendix2–Codingofvariables

Dependentvariable

Two problems arise in the coding of the dependent variable. Firstly, given that the

interviewsweredone indifferentmonthsof theyears, thereexists thepossibility that the

questionisaskedbeforethewomenhashadachild,inwhichcasetheanswerwouldbe‘no

apply’.GiventhatIhaveinformationonwhethertheyhavehadachild(seeabove),ifthisis

thecaseIcheckattheanswerofthisquestionforthenextyear.Secondly,theremightbea

biggapbetweenthebirthdateandtheinterviewdate,especiallyinthecasewhereweget

theanswer to thedependentvariable fromthe followingyear.Thisposesan inconvenient

for thecodingof thedependentvariable if theanswer is ‘withinayear’.This isbecause if,

say,thegapbetweenthebirthandinterviewdateisof10months,ananswer‘withinayear’

shouldbecodedasslowreturn(asopposedto fastreturn),giventhat the totalamountof

timetherespondentwouldhavebeenonparentalleavewouldbetwoyears.Toaccountfor

this,Itakeinformationaboutthebirthandinterviewmonth,substractthemandforanswers

‘withinayear’ I lookatthedifferencebetweenbirthandinterviewrate.Ifthedifferenceis

32

elevenortwelvemonths,Icodetheanswerasslowreturn.Ifthedifferenceamountstoone

tosixmonths,Icodeitasfastreturn.Ifthedifferenceamountstoseventotenmonths,itis

quiteambiguous,soIdroptheobservation.

Codingofcountryoforiginsubjecttomigrationbackground

For those observations which have ‘direct migration background’, I look at the variable

‘countryoforigin’.Fortheobservationswith‘indirectmigrationbackground’theprocessto

tracebackthecountryoforiginismorecomplex.Firstly,Ilookatthevariable‘motherand

father countryoforigin’. If thisone isexistent, I checkwhether they come from the same

country.Whenthatisthecase,Icodetheobservationwiththepertinentcountryoforigin.

Whenthecountryoforiginforthefatherisdifferentfromthemother,Idroptheobservation

(furtheranalysiscouldbepursuedtotryto includetheseobservations). If themotherand

fathercountryoforiginisnotavailable,Ilookatthemotherandfatherpersonalnumber,I

look for themin thedatasetand I lookat thevariable ‘countryoforigin’or ‘nationality’, if

this one is not Germany. From 60 observations with ‘indirect migration background’, 43

haveaknowncountryoforigin.Fromthose,32havethesamecountryoforiginfrommother

andfather.

33

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