Wanting to and Getting to Study Business Studies Or Economics At

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WANTINGTOANDGETTINGTOSTUDYBUSINESSSTUDIESOR ECONOMICSATSCHOOL 

PeterDavies1,3 and MarcoG.Ercolani2,3[correspondingauthor]

1 [email protected], School of Education, University of Birmingham 2 [email protected], Birmingham Business School, University of Birmingham 3 Centre for Higher Education Equity and Access, University of Birmingham



Keywords

Educationaleconomics,humancapital,wagedifferentials,EconomicsandBusinessStudies



Abstract

Wepresentresultsfromƒuniquesurveyon3,279schoolstudents’subjectpreferencesand subsequentchoiceofsubjectstostudybetweentheagesof16and18.Wefocus,inparticular, onthedecisiontostudyEconomicsorBusinessStudies.Wefindschoollevelandstudentlevel effectsonsubjectchoices.Ourevidencesuggestsschoolsinfluencesubjectchoicesthrough(a) whetherƒschooloffersƒsubjectand(b)differencesbetweenstudents’declaredintentionsand actualchoices.Weinterprettheseresultsinthecontextofcompetitionbetweenschoolsand expressedpreferencesofresearchintensiveuniversitiesregardingso-called‘hard’and‘soft’ subjects. 

Thefollowingdatawerecollectedinouruniquesurvey:(i)Beliefsaboutgraduatesalariesby subjectarea,strengthofpreferencestowardsdifferentsubjects,motivationtowardschoiceof subject;and(ii)actualchoiceofsubjectstostudyinthefinaltwoyearsofschooling.Background datawerecollecteddirectlyfromstudentsandmatchedwithƒrangeofdataavailablefromthe NationalPupilDatabase.

 

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1. INTRODUCTION

Subjectchoicesinschoolsmatterforfutureemployment,socialmobilityandthebalanceof knowledgeandskillsavailablefortheeconomy.WhilstonlythestudyofMathematicsatschool hasbeenshowntobeassociatedwithhigherfutureincomeintheUKandtheUS(Levine and Zimmerman 1995, Dolton and Vignoles 2002, Arcidiacono 2004, Rose and Betts 2004)ǡthesubjects whichstudentschoosetostudyinsecondaryschoolssetthemontrajectoriestowardsdifferent universities,differentdegreesubjectsanddifferentemploymentprospects(Chevalier2011). Schools’freedomtochoosewhichsubjectstoofferandstudents’freedomtochoosewhich subjectstostudyvariesconsiderablybetweencountriesandwithincountriesovertime.This hassubstantialimplicationsfortheroleofeducationinsociety.Forexample,intheUSstudents facechoicesbetweenbundlesofsubjectsindifferentcurriculumtracksleadingtodifferent collegeandnon-collegetrajectories(ZietzandJoshi2005).IntheUK,socialmobilityis associatedwithsubjectchoicethroughattendanceat‘elite’RussellGroupuniversities. AttendingƒRussellGroupuniversityisassociatedwithachievingemploymentinhighstatus professionandwithhigherearnings(SuttonTrust2004,Boliver2013,Greggetal.2013).The RussellGroupofuniversitieshaspublishedadvicetoschoolstudentsaboutwhichsubjectsto study.

Therelativedifficultyofdifferentsubjectshasreceivedconsiderableattentionindebateabout subjectchoices(CEM2008).Itissometimesargued(e.g.Skelton2012)thatalladvancedlevel (A-level)subjectsshouldbeequallydifficult.Inlightofthis,in2014,theexaminationsregulator inEnglandannouncedƒreductioninthenumberofsubjecttitles,partlyonthegroundsthatA- levelsshouldbe‘robustandinternationallycomparable’(OfQUAL2014,p.11).Oneconsequence ofthisreviewwasthat‘EconomicsandBusinessStudies’wasdiscontinuedasƒcombined subject.bigadvantageofstrictcomparabilitybetweensubjectsisthatitmakesiteasierto compareschools.Thisobjectivehasalsopromptedcalls(e.g.Anderson2104)fortheA-level choicestobereplacedbyƒbaccalaureatecurriculuminwhichstudentsarerequiredtostudyƒ rangeoftraditionalsubjectswhichwouldexcludeso-called‘soft’subjects.Asidefromwhether strictcomparabilityisachievable,thereisalsoƒquestionaboutwhetheritisdesirable.

Restrictingchoicereducestheopportunityforstudentstostudysubjectsinwhichtheyhaveƒ relativeadvantage(Daviesetal.2009).cullof‘soft’subjectsmaybeproblematicfromthe perspectiveofwideningparticipationinhighereducation.Studentswhoachievedminimum gradesforuniversityentrancein‘soft’subjectsarelesslikelytoachievethisstandardin‘hard’ subjects(WilkinsandMeeran2012).Studentswhofallintothiscategoryaremorelikelythan otherstudentstocomefrom‘under-represented’groupsinhighereducation.

Ourstudypresentsƒbroadcomparisonbetweenchoicesof‘hard’and‘soft’choiceswithƒ specificfocusontwosubjects,EconomicsandBusinessStudies.EconomicsandBusiness Studiesprovideƒusefulpointofcomparison(asnotedinpreviousstudiessuchasWilkinsand Meeran2012).First,theRussellGroup(2011)hascategorisedBusinessStudiesasƒ‘soft’ subjectandEconomicsasƒ‘hard’subject.Second,thetwosubjectshaveoverlappingcontentto theextentthatanadvancedlevelqualificationhasbeenavailablein‘EconomicsandBusiness Studies’.Third,eachofEconomicsandBusinessStudiesisofferedasƒsubjectbysome,butnot all,schools(Jinetal.2013).Thisenablesinsightsintotheroleofschoolsinshapingstudents’ choices. 2 Version 12

Thestudybuildsonpreviousresearchinseveralways.Firstwehavedataonstudents’intended subjectchoicesandtheiractualchoicesandthisenablesananalysisofdifferencesbetween intentionsandoutcomes.Nopreviousstudyhasreportedthiscomparison.Second,wehave uniquedataonstudents’expectedandactualGeneralCertificateinSecondaryExamination (GCSE)gradesinMathematicsandEnglish.Thisallowsustoidentifyassociationsbetween unexpectedexaminationresultsandthedifferencebetweenintendedandactualsubjects studied.Third,sincewehave48schoolsinoursampleweareabletoexaminedifferences betweenschoolsinwayswhicharenotpossiblewithstudiesincludingonlyƒfewschools.The nextsectionreviewstheoryandevidenceabouttheroleofschoolsinshapingstudents’choice ofsubjects.Thisfollowedbyanaccountofmethod,resultsandourconclusions.

2. ADVANCEDLEVELSUBJECTDIFFICULTYANDINDIVIDUALCHOICE

Schoolexitexaminationsprovidethecredentialsonwhichapplicationstouniversitycourses arejudgedinmanycountries.InEnglandthesejudgementsaremadeonthebasisofƒtariff systemwhichawardspointstogradesachievedindifferenttypesofexamination.Discussionof applicationstouniversityislargelyconductedintermsofgradesachievedinA-levelcoursesfor whichthe2014tariffpointswere:A*(140),(120),(100),(80),(60),E(40).Most studentssitexaminationsinthreeA-levels.Studentsmaximisetheirchanceofentryinto universitybymaximisingtheirA-levelgradepoints.Aswellasenablinggreaterchoicebetween institutions,highergradesenablestudentstoreachentrancecriteriasetbyselective institutions.Forexample,coursesatRussellGroupuniversitiestypicallyrequiredatleastgrades ABB(320points)(RussellGroup2011).WhilstHussainetal.(2009)reportedƒsmalladditional wagepremiumforgraduatesfromeliteuniversitiesintheUK,WalkerƬZhu(2013)findno differencebetweenwagepremiatograduatesofdifferenttypesofuniversity.

TheproblemforeachstudentiistomaximisehisorhertotaltarifffromthreeA-levelgrades:

max  ଷ ௘ ௜௝ ෍௝ୀଵ ܩ where = ( , , Ȍand ௘ ௧ିଵ ௜௝ ௜௝ ௜௝ ௝ isܩstudent݂ ܣi’spreviousܵܧ ܦattainmentinsubjectŒ ௧ିଵ ௜௝ ܣ isstudenti’sself-efficacy(confidence)insubjectj

ܵܧ௜௝isthedifficultyofsubjectjrelativetoothersubjects.

Studentsܦ௝ willchoosesubjectsinwhichtheyhaveƒrelativeadvantage(confirmedbyDavieset al.2009)withtheprovisothatallstudentswillbediscouragedfromchoosingsubjectsinwhich itishardertoachievetopgrades.Coeetal.(2008)revieweddifferentestimatesofthedifficulty ofschoolsubjects.Theyfoundthat,atA-level,scienceandmodernforeignlanguagesare relativelyhardandthatmostappliedsubjectssuchascommunicationstudiesandtheatre studiesarerelativelyeasier.Theyalsoreportedthatthesedifferenceshavebeenstableover time.

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However,theRussellGroupuniversities(2011,2013)havealsoexpressedtheirpreferencefor somesubjectsratherthanothers.Theydescribedtheirpreferredsubjectsas‘facilitating’.They distinguishedthesesubjectsfromothersubjectswhichwere‘notfacilitating’,butequallyhard andothersubjectswhichwere‘soft’.Althoughin2013laterdroppedtheterm‘soft’,theirthree- foldclassificationremainedthesame:

 Hardtraditional(‘facilitating’):Biology,Chemistry,EnglishLiterature,Geography, History,Mathematics,ModernForeignLanguages,Physics.

 Hardnon-traditional(butnotfacilitating):Classics,ComputerScience,Economics,Law, Music,Non-EuropeanLanguages,OtherScience,PhilosophyandReligion,Psychology.

 SoftǣArt,Beauty,BusinessStudies,ChildDevelopment,DesignandTechnology,Health andSocialCare,MediaStudies,PerformingArts,Photography,PhysicalEducation, Sociology,StudySkills,TravelandTourism.

Consequently,studentswhowishtoapplytothemostselectiveuniversitiesfaceƒfurther constraintintheirsubjectchoices.If η320,thenƒstudentwillmaximisetheir universitychoicebystudyingatleasttwoଷ ‘facilitating௘ subjects’ifthisdoesnotprejudicetheir σ௫ୀଵ ܩ௜௝ expectationofachievingƒtotaltariffofatleast320.Otherwisetheyshouldsimplyaimto maximisetheirgrades,withtheimplicationthatonaveragetheyarelesslikelythanother studentstochoose‘facilitating’subjects.

InJuly2012TheDepartmentforEducation(2014)announcedthatitwouldbeincludingƒ measureofschoolperformanceintermsoftheproportionofstudentsachievinggradesAABin ‘facilitating’subjects.Thisintroducedƒstrongincentiveforschoolstoencouragehighachieving studentstooptforfacilitatingsubjects.Thisannouncementcameafterthestudentsinthis samplehaddeclaredtheirintentionsandafterschoolshaddeterminedtheircurriculumfor 2012/13.

3. SCHOOLEFFECTSINSUBJECTCHOICE

AccordingtoƒreviewbyJinetal.(2013)relativelylittleoftheliteratureonsubjectchoicehas consideredschooleffects.Inthissectionweconsidertwoprocessesthroughwhichschoolsmay affectsubjectchoice:theprivateversusstateschooleffectandthecompetitionbetweenstate schoolseffect.

differencebetweenprivateschoolsandstateschoolsarisesfromƒdifferencebetween schools’objectives.Privateschoolsfocusontheproportionoftheirstudentswhoprogressto eliteuniversities(Dunneetal.2013,Jones2013)whilststateschoolsfocusontheexamination gradesachievedbytheirstudents(Daviesetal2002,Wilsonetal.2006).relativelyhigh proportionofprivatelyeducatedstudentsattendeliteuniversitiesinEngland(SuttonTrust 2004,Manganetal.2010,Greggetal.2013),whichaccordingtoDunneetal.(2013)andBoliver (2013),reflectsthefocusofcareersguidanceinprivateschools.Weinterpretthisfocusasƒ reflectionofwhatparentsarebuyingwhentheysendtheirchildrentoprivateschools:an increasedlikelihoodthattheirchildwillenterƒwell-paidprofessiononthebasisofthe advantagesconferredbygraduatingfromaneliteuniversity(Hussainetal.2009,McKnightet 4 Version 12 al.2002).InordertosecureƒplaceataneliteuniversityintheUKitisimportanttochoose somesubjectsratherthanothersinthefinalyearsofschooling.

ResearchersintheUKandelsewherehaveconsistentlyfoundƒstrongassociationbetween attendingprivateschoolsandstudyingtraditional,‘hard’subjects.studyofsubjectchoicesin Australianschools(FullartonandAinley2000)foundƒpositivebivariateassociationbetween attendingƒprivateschoolandchoosingtostudyscienceorlanguagesintheuppersecondary school.AnotherAustralianstudybyLambandBall(1999)foundƒpositivebivariateassociation betweenchoiceofMathematicsorlanguagesinuppersecondaryschoolandthelikelihoodof goingtouniversity.Studieswhichcontrolforothervariablestendtofindweakerassociations. GillandBell(2013)analysedfactorsassociatedwithchoosingtostudyPhysicsatA-levelin England.Theyfoundthatgirlsattendingprivateschoolsweresubstantiallymorelikelytostudy Physicsthangirlsattendingƒstateschool.Buttheyincludednodataonsocio-economicstatus sotheycouldnotdistinguishbetweenanassociationledbythetypeofparentschoosingtosend theirchildrentoƒprivateschoolandƒ‘school-effect’ofprivateschools.UsingdatafromUCAS (theUKuniversityadmissionsprocess),Boliver(2013)foundthatstudentseducatedinprivate schoolsweretwiceaslikelyasstateschoolstudentstoapplytoaneliteuniversity,after controllingforA-levelgrades.Whensheaddedƒcontrolforgradesin‘facilitatingsubjects’this privateschooleffectismoderated(theoddsratioofstate/privateschoolsincreasesfrom0.48/1 to0.58/1).Boliverusedparentaloccupationashersolemeasureofsocialclass.Onceshe controlledforgradesinfacilitatingsubjectstheassociationbetweenthismeasureandapplying toaneliteuniversitybecamelessexact,withonlychildrenwithparentsinmanualoccupations beinglesslikelytoapplytoaneliteuniversity(atthe5%levelofsignificance).

Thesecondprocessunderpinningschooleffectsisschoolbehaviourinthecontextofquasi- markets.Althoughcompetitionbetweenschoolsisfrequentlyfocusedonwhenstudents transferfromprimarytosecondaryschoolsatage11,thereisalsosubstantialmovementof studentsbetweenschoolsatage16whentheyhavefinishedcompulsoryschoolingandchoose whethertocontinueschoolingforƒfurthertwoyears(Manganetal.2001).AdnettandDavies (2000)developedanaccountofthepredictionsofeconomictheoryforschools’curriculum designinlocalmarkets.Theynotedthatlocalschoolingmarketsareusuallycharacterisedby oligopoly,wheretheeffectonƒschoolofanychangesitmakestoitscurriculumdependonthe responsesofcompetitors.Standardanalysisofoligopolisticmarketssuggeststhatschools’ objectivesmaynotbealignedwithstudents’objectivesandthatschoolsnearthetopoflocal hierarchiesfaceweakincentivestoinnovate.Conversely,whilstschoolsnearthebottomoflocal hierarchieshavestrongincentivestoinnovate,theyareheldbackbysunkhumancapital(the rangeofexpertiseoftheircurrentstaff),theeffectoffallingenrolmentontheircapacityto changeandtheirdesiretomaintainthebreadthofthecurriculumwhichfitswiththeirexisting conceptionofthecurriculumƒschoolshouldoffer.Followupstudies(Daviesetal.2002,2003) supportedthesepredictions,withƒgradualemergenceofcurriculuminnovationbyschools whichwouldbejudgedas‘lesssuccessful’intermsoflocalleaguetablesandenrolment. Comparedwith‘moresuccessfulschools’theseschoolshadhigherproportionsofstudentsfrom lowersocio-economicgroups.Innovationinthese‘lesssuccessful’tendedtobeintheformof appliedandvocationalcourseswhichwereseenasmoreattractivetostudentsandmoreuseful preparationforthefutureswhichtheseschoolsenvisagedfortheirstudents.

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4. METHOD

Wesurveyedover5,000studentsaged15-16intheirfinalyearofcompulsoryschooling(Year 11)inEngland.Afterdatacleaningwewereleftwith3,279completestudentrecords.During theirfinalyearstudentschoosewhethertocontinueinfull-timeeducationandwhichsubjects tostudyintwofurtheryearspriortouniversity.Moststudentsbetweentheagesof16and18 studythreetofiveA-levelsubjectswhicharedesignedaspreparationforundergraduate universitystudy.Byfocusingonstudentsatthisageweaimtocaptureexpectationsand evidenceofdecision-makingatƒkeytransitionpointinstudents’education.Thebaseline surveygathereddataonintentionstowardssubjectsofstudyandexpectedgrades.Wematched thesedatawithactualgradesfromtheNationalPupilDatabaseandinformationonsubjects studiedprovidedbytheschools(forthosestudentswhocontinuedtheirstudiesatthesame school).

MODELLING Mostsubjectsofferedbyƒschoolareweakalternativesforeachother(withsmallnegative bivariatecorrelations).However,somesubjects(notablyinscience)arestrongcomplements (withpositivebivariatecorrelationsofbetween.3and.4).Wemodelstudents’choiceof subjectsusingƒmultinomialfunctioninwhichwedistinguishbetweenchoicesaccordingtothe proportionof‘hardsubjects’instudents’choiceofcourses.Wefollowthethree-fold classificationofsubjectsusedbytheRussellGroupofuniversities(2011,2013).

Students’choicestostudyEconomicsorBusinessStudiesareframedbythisbroad categorisation.Tableͳpresentsananalysisoftheothersubjectsstudiedbystudentswhohad chosentostudyeitherEconomicsorBusinessstudies(orboth).Comparedwithstudentswho studiedneitherEconomicsnorBusinessStudies,studentswhochoseEconomicsalsotendedto studyotherhardsubjectsandstudentswhochoseBusinessStudiesalsotendedtostudyother softsubjects.

Tableͳ HowchoiceofEconomicsandBusinessStudiesisframedbythehard/softdistinction

 Studentswhohavestudied  Economics Business Economics Neither Studies and Economics Business nor Studies Business Studies n 504 346 46 2383 Ψofsubjects‘hardtraditional’ 76.3 40.6 25.9 63.7 (facilitating) (26.0)ͳ (33.5) (23.1) (29.4) Ψofothersubjects‘hardbut 11.1 24.3 54.3 16.5 non-traditional’ (18.0) (27.3) (23.4) (18.5) Ψofothersubjects‘soft’ 12.6 35.2 19.7 19.8 (19.9) (31.5) (21.5) (23.0)  100% 100% 100% 100% ͳFiguresinbracketsshowstandarddeviations.

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Thesecomparisonsprovidetherationaleforplacingstudentsinoneofthefollowingseven categoriesaccordingtotheirsubjectmixinadvancedlevelsubjectschosen:

1. NeitherEconomicsnorBusinessStudiesAND70%ormoreofsubjectsaretraditional (i.e.hard) 2. NeitherEconomicsnorBusinessStudiesANDlessthan70%ofsubjectsaretraditional 3. BusinessStudies(notEconomics)AND50%ormoreofothersubjectsaresoft 4. BusinessStudies(notEconomics)ANDlessthan50%ofothersubjectsaresoft 5. Economics(notBusinessStudies)AND70%ormoreofothersubjectsaretraditional 6. Economics(notBusinessStudies)ANDlessthan70%ofothersubjectsaretraditional 7. EconomicsandBusinessStudies(sampletoosmallforƒtraditional/softsplit) 

Usingthesecategoriesweestimatedƒmultinomiallogitmodelwithclusteringatschoollevelto takeaccountofschoollevelfactors.Ourestimatedmodelis

( = 1, =1…7) ( + + )

ଶ variable , equal to one ifܠwhere subject choice for eachܲ of݌௞ k subjects݇ is indicatedൌȦ byߙ଴ theߙ multinomialଵݔଵ ઺Ԣ the student studied subject combination k and zero otherwise. Covariate x took the value 1 for state 1 ௞ schools and 0 for private schools. The other covariates, capturing individual student ݌characteristics were included in the vector .

ܠଶ DATA Werecruitedƒstratifiedrandomsampleof48schoolsfromƒselectionofpostcodeareasin Englandfrommetropolitan,urbanandrurallocalities.Wehaveƒrichsetofbackgroundand post-interventiondata,includingdataonintentions,socioeconomicstatus,andacademic attainment.

Werestrictedoursampletoschoolswithatleast100studentsintheir‘sixthforms’(thefinal twoyearsofsecondaryschool)andthatenrolledstudentsbetweentheagesof13and18.This yieldedƒtotalof958stateschoolsand195privateschools.Wecreatedtwo(stateandprivate) randomizedlistsofschoolsandinvitedschoolstoparticipateintheorderofeachlist.We stratifiedthesampletoinclude19privateschoolstoenableƒgoodcomparisonbetween practiceintheprivateandstatesectors.Oursampleselectioncriteriafavouredschoolswhich werelargerthanaverage,withhigherthanaveragelevelsofachievementandlowerthan averageproportionsofstudentsfromlowersocio-economicbackgrounds.Infact,over95%of studentsinthesampleexpectedtogaingradesatage16whichareregardedasminimumentry levelsforuniversity(atleastgradeinGCSEMathandEnglish).Thiscontrastswithnational figures:64%ofallstudentsgainedatleastgradeinGCSEEnglishand58%ofallstudents gainedatleastgradeinGCSEMathematics.Moreover,inoursample,52%and56%of studentsexpectedeitherƒgradeA*orƒgradeinGCSEEnglishandMathematicsrespectively. Theseproportionsaremorethandouble(23%forEnglishand26%forMathematics)the equivalentratiosforallstudentsinthecountry.Therefore,theproportionofthestudentsinour samplewhowillenrolat‘elite’universitiesandwhoshouldexpecttoachievehighdegree classificationsislikelytobewellabovethenationalaverage.Intotalwecontacted189schools bythetimethat50schoolsfirmlycommittedthemselvestoparticipationintheproject.Within 7 Version 12 ourtwomaingroupsofschools(stateandprivate)wecomparedschoolswhichacceptedthe invitationtoparticipatewiththosewhichdeclinedtoparticipateintheproject.Wecompared meansfor:Schoolsize,numbersofstudentsinthesixthform,ΨofstudentsgainingͷgradesA*- atGCSE,valueaddedperformancebetweenages16and18,Ψofstudentseligibleforfree schoolmealsandtheindexofmultipledeprivationoftheschools’studentspostcodes.Wefound onedifferenceforthestateschools:schoolswhichagreedtoparticipatehadslightlylower recordedvalueaddedscores(991comparedto1004,p=.004).

WeonlyhavedataonactualA-levelsubjectsforstudentswhocontinuedwiththeireducationat thesameschoolafterage16.Ouranalysisisrestrictedtothese3,279students.Thesestudents hadsignificantlyhigherGCSEgradesinMathematics(byjustoveronegrade)andEnglish(by roughlyonegrade)andhighculturalcapital(justunderhalfƒstandarddeviation)thanstudents whocontinuededucationandtrainingatanotherinstitution.logisticregressionfoundthatthe likelihoodofcontinuinginthesameschoolafterage16waspositivelyassociatedwithbeing male,GCSEgradesinMathematicsandEnglish,andhavingƒprofessionalfather.Wefoundno associationbetweenstayinginthesameschoolandtheintentionto(i)studyhardorsoft subjectsor(ii)studyeitherEconomicsorBusinessStudies.

Inaddition,weusedthemultipleimputationroutinesinStata-mi-toimputemissingindividual datapointsusingthefollowingvariables:wage expectations, family background, expected exam results, Key Stage 2 and 3 exam results, whether the students intended to study each subject at A- level, and the students’ actual A-level choicesWeimputed20valuesforeachmissingindividual value.

THESURVEYINSTRUMENT ParticipatingschoolswereaskedtoissueallYear11studentswithƒquestionnaire.We collecteddataonstudents’characteristicsincludingparentaleducationandoccupation, ethnicity,andexpectedexaminationgradesatage16.Wealsoaskedstudentsforpermissionto linktheirdatawithinformation(suchasgender,achievementgradesandeligibilityforfree schoolmeals)intheNationalPupilDatabase.Summarystatisticsforourcontrolvariableson students’andschools’characteristicsarepresentedinTable2.Culturalcapitalhasbeen frequentlycited(e.g.DiMaggio1982,NobleandDavies2009)asanimportantfactorinstudents’ highereducationchoices.Weincludedthreemeasuresofculturalcapitalusingitemsfrom previousstudies(Evansetal.2010,NobleandDavies2009,TramonteandWilms2010).The divisionintothreedimensionsofculturalcapitalisbasedonanexploratoryfactoranalysis reportedelsewhere(Daviesetal.2014).

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Tableʹ SummaryStatisticsonControlVariables

 mean s.d. Demographics   StateSchool(=1) 0.67 0.47 Male(=1) 0.51 0.50 White(=1) 0.75 0.44 PeerEffect 0.00 1.00 (normalizedSchoolAverageA-levelPointScore) Grades   ExpectedGCSEGradeMaths 6.50 1.10 ExpectedGCSEGradeEnglish 6.80 1.20 Expected–ActualGradeMaths 0.08 0.70 Expected–ActualGradeEnglish 0.24 0.75 Socio-economicbackground   Mothergraduate 0.52 0.50 Fathergraduate 0.57 0.50 Motherprofessionalormanagerialjob 0.46 0.50 Fatherprofessionalormanagerialjob 0.64 0.48 Culturalcapital(includingbooks) 39.8 6.90 

5. REGRESSIONRESULTS

OurregressionanalysisofintentionsandactualchoicesofEconomicsorbusinessinstudents’ finaltwoyearsofschoolingispresentedintables͵and4.Table͵presentsanalysesconducted onthewholesampleandanalysesconductedonƒsamplerestrictedtoschoolsofferingeach subject.Table͵presentsassociationsbetweenourindependentvariablesandintendingto study,oractuallystudying,ƒsubject.Wecommentontheresultsfirstintermsofƒcomparison betweenBusinessStudiesandEconomics.Therewasƒstrongpositiveassociationbetween attendingƒprivateschoolandstudyingEconomics.Therewasƒschoollevelassociationwith privateschoolsmorelikelytoofferEconomicsasanoptiontostudents.Therewasalsoƒ studentlevelassociationwheretheprobabilitythatƒstudentstudiedEconomicswasmuch higherintheprivateschoolsifthesampleisrestrictedtothoseschoolsthatdoofferEconomics.

TherewasƒstrongpositiveassociationbetweenexpectedachievementinMathematicsand choosingtostudyEconomicswhilstthereisƒnegativeassociationbetweenactualchoiceof BusinessStudiesandexpectedachievementinMathematics.Whenthesamplewasrestrictedto schoolsofferingEconomics,theexpectedEnglishgradehadƒstrongnegativeassociationwith actuallystudyingBusinessStudiesandƒmodestnegativeassociationwithactuallystudying Economics.StudentswhoachievedhigherthanexpectedGCSEgradesinMathematicswere morelikelytostudyEconomicswhilststudentswhoachievedlowerthanexpectedGCSEgrades inEnglishweremorelikelytostudyBusinessStudies.Malesweremuchmorelikelytostudy Economicsthanfemales,withnoindicationthatthisisanassociationwithwhetherschools offerEconomics.Thereisƒsuggestionthatmaleswereslightlymorelikelythanfemalesto actuallystudyBusinessStudies.Studentsfromwhiteethnicbackgroundswerelesslikelythan otherstudentstostudyeithersubject.Studentswhosefatherwasinƒprofessionalor 9 Version 12 managerialoccupationweremorelikelythanotherstudentstointendtostudyBusinessStudies orEconomics,butfather’soccupationwasnotassociatedwithactuallystudyingthesubject. Thissocialclassbackgroundinfluenceappearstohavebeenlessstrongthanschoolinfluence.In contrast,students’withgraduatefatherswerelesslikelythanotherstudentstointendtostudy BusinessStudies.Studentswithhigherlevelsofculturalcapitalwerelesslikelytoactuallystudy BusinessStudies.Insummary,largedifferencesbetweenEconomicsandBusinessStudieswere observedintheassociationswithattendingƒprivateschool,expectedgradesandgender.There waslittleobservabledifferenceinrelationtoethnicbackground.

Thedatashedsomelightontheroleofschoolsinsubjectchoiceinthreeways:(1)throughthe differencemadebyattendingƒprivateschool;(2)throughwhetherschoolsoffersubjectsand (3)throughthedifferencebetweenintentionsandactuallystudyingƒsubjectatschoolswhich offerthatsubject.Curiously,wefoundnodifference,usingFisher’sExactTest,betweenschools thatdidordidnotofferBusinessStudiesinthelikelihoodthatƒstudentintendedtostudy BusinessStudies.InschoolswhichofferedBusinessStudiesinthesixthform,theproportionof studentswhocontinuedtheirstudiesattheschoolwaslittledifferentbetweenthosewho intendedtostudyBusinessStudies(63%)andthosewhodidnot(65%).Inschoolswhichdid notofferBusinessStudiestheproportionofstudentswhocontinuedtheirstudiesattheschool wasslightlylower(78%)thanthosewhodidnot(85%)(Fisher’sExactTest,p=0.06).These figuresalsoindicateƒnegativerelationshipbetweenthelikelihoodthatƒschoolwilloffer BusinessStudiesandtheproportionofstudentscontinuingtheirstudiesattheschool.The proportionofstudentsdeclaringthattheyintendedtostudyEconomicswasmuchlowerin schoolswhichdidnotofferEconomics(7%)thaninschoolswhichdidofferEconomics(31%).



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Table 3a: Logit regressions on intending to or actually studying Business Studies (BS)

(1) (2) (3) (4) BS BS BS BS Intended, Intended, Actual, Actual, Full Schools Full Schools sample offering sample offering BS BS main State School -0.29 -0.35 -0.43* -0.60** (-1.62) (-1.63) (-1.82) (-2.47) School Peer Effect -0.11 -0.18 -0.42** -0.33** (-1.11) (-1.57) (-3.54) (-2.64) Expected A-level Maths Grade -0.34** -0.32** -0.46** -0.39** (-3.57) (-3.10) (-3.82) (-3.32) Expected A-level English Grade -0.01 0.01 -0.34** -0.32** (-0.17) (0.11) (-3.49) (-3.28) Actual-Expected GCSE Maths Grade 0.19* 0.19* (1.76) (1.80) Actual-Expected GCSE English Grade 0.32** 0.26** (3.01) (2.43) Male 0.10 0.16 0.15 0.20 (0.69) (1.00) (0.92) (1.18) White -0.53** -0.52** -0.52** -0.53** (-3.39) (-2.94) (-2.74) (-2.78) Mother professional -0.09 -0.07 0.08 0.08 (-0.60) (-0.45) (0.45) (0.48) Father professional 0.40** 0.38** 0.12 0.09 (2.51) (2.21) (0.68) (0.52) Mother Univ. Graduate 0.23 0.24 0.23 0.20 (1.34) (1.26) (1.14) (1.02) Father Univ. Graduate -0.48** -0.65** -0.40** -0.36* (-2.90) (-3.48) (-2.05) (-1.83) Family Cultural Capital incl. books -0.02** -0.02 -0.03** -0.03** (-2.08) (-1.61) (-2.02) (-2.09) Constant 2.04** 1.66** 4.68** 4.49** (2.74) (2.08) (5.41) (5.25) Observations 1863 1488 1863 1488 Pseudo R2 0.038 0.043 0.145 0.106 Notes: Estimated parameters t statistics in parentheses * p < 0.10, ** p < 0.05 

 

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Table 3b: Logit regressions on intending to or actually studying Economics

(1) (2) (3) (4) Economics Economics Economics Economics Intended, Intended, Actual, Actual, Full Schools Full Schools sample offering sample offering Economics Economics

State School -0.48** -0.41** -0.46** -0.30** (-3.27) (-2.73) (-3.01) (-1.97) School Peer Effect 0.36** 0.21** 0.29** -0.03 (4.02) (2.06) (3.10) (-0.32) Expected A-level Maths Grade -0.22** -0.24** -0.30** -0.27** (-2.49) (-2.55) (-3.05) (-2.69) Expected A-level English Grade 0.45** 0.49** 0.43** 0.45** (4.94) (5.01) (4.45) (4.54) Actual-Expected GCSE Maths Grade -0.21* -0.26** (-1.92) (-2.21) Actual-Expected GCSE English Grade 0.15* 0.15 (1.67) (1.59) Male 0.71** 0.76** 0.88** 1.02** (5.33) (5.43) (6.33) (7.08) White -0.59** -0.60** -0.29** -0.32** (-4.25) (-4.18) (-2.02) (-2.17) Mother professional 0.06 0.10 0.21 0.17 (0.48) (0.75) (1.50) (1.19) Father professional 0.37** 0.36** 0.11 0.09 (2.43) (2.26) (0.73) (0.57) Mother Univ. Graduate -0.31** -0.34** -0.25 -0.23 (-2.01) (-2.11) (-1.57) (-1.47) Father Univ. Graduate -0.15 -0.18 -0.10 -0.09 (-1.00) (-1.11) (-0.62) (-0.53) Family Cultural Capital incl. books 0.01 0.01 -0.00 -0.01 (1.24) (1.18) (-0.16) (-0.63) Constant -3.34** -3.44** -2.45** -2.45** (-4.27) (-4.14) (-3.06) (-2.99) Observations 1863 1492 1863 1492 Pseudo R2 0.129 0.100 0.105 0.074 Notes: Estimated parameters t statistics in parentheses * p < 0.10, ** p < 0.05  

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TableͶ[NEEDSUPDATING] ProbabilityofintendingtooractuallystudyingBusinessStudiesorEconomics atschoolsofferingthesubject

 Intention Actually Intention Actually tostudy Studying toStudy Studying Business Business Economics Economics Studies Studies ͳStateschool,averagegrades,male, 0.13 0.12 0.19 0.21 white,highSES†  ʹPrivateschool,highgrades,male,white, 0.13 0.07 0.36 0.32 highSES†   ͵Stateschool,lowgrades,male,white, 0.20 0.32 0.10 0.20 lowSES  ͶStateschool,averagegrades,female, 0.12 0.10 0.10 0.08 white,highSES,  ͷPrivateschool,highgradesǡfemale, 0.12 0.06 0.21 0.13 whitehighSES   ͸Stateschool,lowgrades,female,white, 0.18 0.27 0.05 0.08 lowSES Ș‘HighSES’deϐinedasmotherandfatherprofessionalandgraduatesandwithculturalcapital ones.d.abovethemean.‘LowSES’definedasneithermotherandfatherprofessionalor graduatesandwithculturalcapitalones.d.belowthemean.

Tableͷ[NEEDSUPDATING] Factorsassociatedwiththelikelihoodthatƒstudentwillcontinuetheirstudiesatthe sameschoolafterage16

 Coefficient (pvalueinbrackets) State(1)orPrivate(0)School .18(.20) ExpectedMathsGrade .29(<.001) ExpectedEnglishGrade .37(<.001) Actual-ExpectedMathsGrade .40(<.001) Actual–ExpectedEnglishGrade .27(<.001) Gender(Male=1) .50(<.001) Ethnicity(White=1) -.16(.22) Motherprofessional -.30(.01) Fatherprofessional .22(.06) MotherGraduate -.06(.63) FatherGraduate .12(.36) CulturalCapitalTotal -.004(.61) Businessstudiesofferedinsixthformbyschool .07(.71) Economicsofferedinsixthformbyschool .43(<.001) Constant -3.47(<.001) N 2455 Loglikelihood 2405.9 

 

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Onequestionofinteresttoschoolsiswhetherofferingƒparticularsubjectmakesitmorelikely thatstudentswillcontinuetheirsixthformstudiesattheschool.Tableͷpresentsresultsfromƒ logisticregressionwhichaddressesthisquestion.Aftertakingaccountoftypeofschool attended,students’achievementandotherpersonalcharacteristics,wefoundthatstudents weremorelikelytocontinuetheirstudiesatthesameschoolifEconomicswasoffered,butnot ifBusinessStudieswasoffered.

IfweconcentrateontheschoolsofferingBusinessStudiesorEconomicsweobservesome differencesbetweenstudents’declaredintentionsandthesubjectstheystudied.We investigatedthesedifferencesbycreatingdummyvariableswhichtookthevalueͳwhenƒ studentstudiedƒsubjecttheyintendedandͲwhentheydidnotfollowthroughƒdeclared intentiontostudyƒsubject.Weusedthesamesetofvariablesontherighthandsideasshown inTable5.Therewasƒmodestnegativerelationship(significantatthe10%level)between expectedgradesandthelikelihoodofƒstudentactuallystudyingBusinessStudiesafter expressinganintentiontodoso.Studentswithmothersinprofessionalormanagerial occupationswerealsomorelikelytofollowuptheirintentionstostudyBusinessStudies (p=.014).TherewerestrongassociationsbetweenexpectedgradeinMaths(pα,001)andbeing male(pα.009)andfollowingthroughanintentiontostudyEconomics.Thevariationin patternsbetweensubjectsmayindicateƒschoolinfluenceindiscouraginghighachingstudents fromstudyingBusinessandencouragingthemtostudyEconomics.

6. CONCLUSIONS

Weaddressedtheshortageofevidenceonschooleffectsonsubjectchoice(Jinetal.2013)using ƒmoderatelylargesamplewhichincludesƒwiderangeofvariablesthathavenotbeenavailable to any previous studies. We have presented evidence indicating strong school influences on subject choices operating through (a) whether ƒ school offers ƒ subject and (b) differences between students’ declared intentions and actual choices. These findings are consistent with twoaspectsofcompetitionbetweenschools.First,privateschoolsarepaidbyparentstomake it likely that their children will secure ƒ place at an elite university. Giventhe guidance from elite universities about subject choice, this creates an incentive for private schools to offer EconomicsandtoencouragehighachievingstudentstostudyEconomics.Thisisexactlywhat wefindinourevidence.Stateschoolshavebeenencouragedbygovernmentpolicytomaximise students’ grades. This has created an incentive for schools to encourage lower achieving studentstostudy‘soft’subjectssuchasBusinessStudies.

Onewayinwhichgovernmentpolicycouldrespondtothisproblemisbyrequiringallstudents tostudyƒparticularrangeofsubjectsthrough,forexample,ƒbaccalaureatesystem.Thereare two disadvantages of this policy. First, it means that grades will fall (unless there is grade inflation) since we know that some students are better at some subjects than others and we knowthatgiventhechoice,studentstendtochoosetocontinuethesubjectsatwhichtheyhave been more successful (Davies et al. 2009). Second, it means that depth of learning will be sacrificed for breadth. Studies of breadth of curriculum choice have, thus far, found no advantage to mixing ƒ broad range of subjects. Indeed, the higher education system is predicated on in-depth study of ƒ few subjects, ƒ model which the Bologna process has encouraged the rest of Europe towards. An alternative would be to change the incentives for 14 Version 12 schools by making them focus on what students do after leaving school rather than on their examinationgradesatschool.Giventhatsomuchofrecenteducationpolicyhasbeenbasedon tryingtoemulatetheprivatesector,itseemsoddthatthishasnotbeendonealready.

REFERENCES

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