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THEACHIEVEMENTGAPBETWEENGOVERNMENTANDPRIVATESCHOOLSINPAKISTAN
AThesis
submittedtotheFacultyofthe
GraduateSchoolofArtsandSciences
ofGeorgetownUniversity
inpartialfulfillmentoftherequirementsforthe
degreeof
MasterofPublicPolicy
inPublicPolicy
By
MaryamAkmal,B.A.
Washington,DC
April12,2016
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Copyright2016byMaryamAkmal
AllRightsReserved
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THEACHIEVEMENTGAPBETWEENGOVERNMENTANDPRIVATESCHOOLSINPAKISTAN
MaryamAkmal,B.A.
ThesisAdvisor:AdamThomas,Ph.D.
ABSTRACT
LearningoutcomesinPakistanhavetraditionallybeenpoor.However,overthelasttwo
decades,theeducationalmarketplacehaschangedsubstantially.Inparticular,enrollmentin
privateschoolshasincreaseddramaticallyacrossabroadrangeofincomegroupsinbothurban
andruralareas.GiventheimportantroleofprivateschoolsinPakistan'seducationallandscape,
thereisanincreasingfocusonthelearninggapbetweengovernmentandprivateschools.Using
household-leveldatafromruralandurbanareasofPakistan,thisstudyestimatestheextentto
whichprivateschoolstudentsperformbetterthangovernmentschoolstudents.
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IamgratefultoProfessorAdamThomasforhisguidance,supportandencouragementthroughoutthisproject.
Manythanks,MaryamAkmal
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TABLEOFCONTENTS
Introduction..................................................................................................................................1
Background...................................................................................................................................3
LiteratureReview..........................................................................................................................4
ConceptualFrameworkandHypothesis.......................................................................................8
DataandMethods......................................................................................................................11
DescriptiveStatistics...................................................................................................................15
Results.........................................................................................................................................20
Discussion...................................................................................................................................30
References..................................................................................................................................34
Appendix.....................................................................................................................................38
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LISTOFTABLES
Table1:DefinitionsofVariables.................................................................................................14
Table2:DescriptiveStatisticsforDependent,KeyIndependentandControlVariables............17
Table3:KeyCharacteristicsDisaggregatedbySchoolType.......................................................19
Table4:RegressionResultsforMathTestScores......................................................................21
Table5:RegressionResultsforEnglishReadingScores..............................................................24
Table6:RegressionResultsforLocalReadingScores.................................................................27
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INTRODUCTION
Pakistanhasthesecondhighestnumberofout-of-schoolchildrenintheworld(UNESCO,
2015).1Evenamongchildrenwhoareenrolledinschool,learninglevelsarelow.Estimates
suggestthatonly43percentofgrade-fivestudentscanperformgrade-twoleveldivision,while
only50percentofstudentsingradefivecanreadgrade-twolevelsentencesinPakistan's
nationallanguage,Urdu(ASERPakistan,2013).Basedonthecurrentstateofeducation,evenif
allPakistanichildrenwereenrolledinschool,manyofthestudentswouldstillbefunctionally
illiterateandinnumerate(Dasetal.,2006).
InordertoinstituteeffectivepolicyreforminPakistan'seducationsector,itisimportant
toidentifykeyfactorscontributingtothepoorlearninginsidetheclassrooms.Thecasefor
focusingonstudentlearningmaybestrengthenedevenfurtherbythepossiblelinkbetween
qualityofeducationandenrollment;improvedlearninginschoolsmayboostenrollmentand
retention,asparentsandchildrengainahigherreturnontheirinvestmentoftimeand
resources.
Educationisakeydriverofindividualearningsandnationaleconomicgrowth(Hanushek
andWößmann,2007).AccordingtothePakistanBureauofStatistics(PBS),morethanone-third
ofPakistan'spopulationisbelowtheageoften.Manypolicymakersbelievethatsustained
nationalgrowthwillrequirethatthissignificantsegmentofthepopulationreceivesahigh-
qualityeducation.ThegovernmentofPakistanhasdemonstrateditscommitmenttoimproving
educationaccessandqualitybymakingeducationakeypriorityofPakistan'sNationalPlanof
Action(2013).Pakistan’seducationbudget,whichhasaveragedataround2percentofGDPin
1AccordingtoUNESCO’smostrecentestimates,6.7millionPakistanichildrenareoutofschool,ofwhichmorethanhalfarefemale.
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recentyears,isprimarilyusedforteachersalaries.AccordingtotheWorldBank(2008),90
percentofPakistan'seducationbudgetisspentonthesalariesofapproximately1.5million
teachers.However,notwithstandingthisinvestmentinimprovingthequalityofclassroom
instruction,studentlearningoutcomescontinuetobedisappointing.
Numerousstudieshaveexploredtheimpactofvariouseducationalinputs,suchas
teachers,facilitiesandcurricula,onstudentlearning.Hanushek(2003)criticizestheemphasis
on"input-based"ratherthan"incentive-based"educationpolicies,arguingthatincreasing
resourcesdoesnotsignificantlyimprovestudentlearning.InPakistan,governmentandprivate
schoolshavedifferentincentivestructures.Forexample,teachersingovernmentschoolstend
tobepaidbetterandhavemoretrainingandexperiencethanprivateschoolteachers.
However,salariesofteachersingovernmentschoolsareusuallytiedtoeducationandseniority
ratherthantostudentlearningoutcomes(Andrabietal.,2010).Keepinginmindthedifferent
incentivestructures,thisstudyanalyzeseducationoutcomesingovernmentandprivateschools
acrossPakistanusingsurveydatafromtheAnnualStatusofEducationReport(ASER)2014.The
surveycovers144districtsofPakistanandprovidesinformationaboutstudentlearning,family
characteristicsandhouseholdinformation.2
Theremainderofthispaperisorganizedasfollows.Inthenextsection,Iprovide
backgroundontheevolutionofprivateschoolinginPakistan.Ithenreviewtherelevant
literatureanddescribemyconceptualframework,data,econometricmethodsanddescriptive
statistics.Lastly,Idiscussmyfindingsandresults.
2ASERselectsdistrictsbasedonthepresenceoflocalcollaboratingpartners(ASER,2010).Whileatpresentonly144districtsaresurveyed,theultimategoalofASERistocoverall157districtsofPakistan.
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BACKGROUND
Approximatelyone-thirdofallstudentsinPakistanattendprivateschools(Nguyenand
Raju,2014).Theseschoolsservearangeofincomegroupsandareprevalentinbothruraland
urbanareas(Andrabietal.,2002).Whilethereareeliteprivateschoolscateringtohigh-income
groupsinPakistan,thispaperfocusesonthemajorityofprivateschoolsthatarelow-cost
enterprisesservinglow-andmiddle-incomecommunities.Thesebusinessesarelargely
unregulatedandreceivealmostnogovernmentsupport(Andrabietal.,2010).Theytendto
havelowoperationalcostsandareoftenrunoutoftheowners'homes(Andrabietal.,2010).
Privateschoolsalsoemploymoreuntrainedstaffthangovernmentschools,whereteachersare
paidsignificantlymore(Andrabietal.2010;FrenchandKingdon2010).
AccordingtoAndrabietal.(2006),inmanydevelopingcountries,theper-childcostsin
privateschoolsaresignificantlylowerthantheper-childcostsingovernmentschoolsduetothe
payscalesforgovernmentteachersalaries.Privateschoolstendtoemployteacherswithlower
academicqualificationsandtraining.Asaresult,basedonqualificationsalone,teacherquality
seemslowerintheprivatesector.However,teachereffectivenessisafunctionofboth
qualificationsandmotivation,thelatterofwhichishardtomeasure.Itispossiblethatthe
differentincentivestructuresingovernmentandprivateschoolsmayinfluenceteachers'effort
andmotivation,andasaresult,thequalityoflearning(FrenchandKingdon,2010).Despite
theirlowercostsandinferiorhiringpractices,privateschoolsarelargelyperceivedasproviding
betterqualityeducationthantheirgovernmentcounterparts,asevidencedbytherising
demandforprivateeducationinPakistan.
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ThenumberofprivateschoolsinPakistanmorethandoubledfrom30,000inthe1990s
to70,000in2008(NguyenandRaju,2014).3Inthelasttwodecades,enrollmentinprivate
schoolshasincreasedacrossabroadrangeofincomegroups,includinghigh-income,urban
householdsandlow-income,ruralones(Ibid.).UsingLEAPS(LearningandEducational
AchievementsinPunjabSchools)datacollectedbetween2004and2007inthreedistrictsof
Punjab,Andrabietal.(2010)concludethataveragestudentachievementissignificantlyhigher
inprivateschoolsthaningovernmentschools.Suchfindingscorroboratethecurrent
perceptionofsuperioreducationalqualityinprivateschools.
LITERATUREREVIEW
EvidenceforpoorlearningoutcomesacrossschoolsinPakistaniswell-documented.
Poorstudentperformanceisobservedacrossalltypesofschools,includinggovernmentand
privateschools(Andrabietal.2007;ASER2013).However,fewerstudiescontributedirectlyto
thegrowingdebateaboutthedifferenceineducationoutcomesbetweenpublicandprivate
schoolsinPakistan.Whiletherehasbeenincreasingrecognitionofthepotentialforlow-cost
privateschoolstoimproveeducationaccessandqualityinPakistan,mostexistingstudiesareof
restrictedgeographicalscopeduetolimiteddata.
DemandforPrivateSchoolsinPakistan
Thereissubstantialevidencedocumentingtheriseindemandforprivateeducationin
Pakistansincethe1980s.Andrabietal.(2005)findevidenceofsignificantgrowthinthenumber
ofprivateschoolsinruralareas,thusdispellingearlierassertionsbyJimenezandTan(1987)
3Enrollmentinprivateschoolsincreasedfromlessthan5percentin1990to35percentin2005(Andrabietal.,2010).In1999alone,8000newprivateschoolswerecreated.Roughlyhalfoftheseprivateschoolswereinruralareas(Andrabietal.,2006).
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thatprivateschoolsareanurbanphenomenonandcatertoarelativelywealthyclientele(Arif
andSaqib2003;NguyenandRaju2014).Onthecontrary,Andrabietal.(2002)findthatmost
Pakistaniprivateschoolsarecateringtolow-andmiddle-incomepopulationsinruralareas,
ratherthantotheelite.MuralidharanandKremer(2006)observeasimilartrendinIndia,
whereprivateschoolsarewidespreadinruralareas.Furthermore,Andrabietal.(2010)find
thatthecostofeducatingaPakistanichildis40percentlowerinprivateschoolsthanin
governmentschools.Aldermanetal.(2001)alsofindevidenceofhighdemandforprivate
educationamonglow-incomehouseholdsinPakistan.Theirstudyshowsthatmanypoor
householdsuseprivateschoolsevenwhenfacedwithhighertuitioncosts,duetoparents'
preferencefortheperceivedhigherqualityofinstructioninprivateschools.
LearningOutcomesinGovernmentandPrivateSchoolsinPakistan
Severalstudiesfindthatprivateschoolspositivelyaffectlearningoutcomes.The
strongeststudyonlearninggapsbetweengovernmentandprivateschoolsisbyAndrabietal.
(2010)whoinstrumentforprivateschoolenrollmentusingthedistancetoaprivateschool
relativetothedistancetoagovernmentschool.Theyfindasignificantrelationshipbetween
privateschoolattendanceandlearningoutcomesinthreedistrictsofruralPunjabinPakistan.
Theirstudyrevealsthatforchildrenwithsimilarcharacteristicstestscoresare0.8to1standard
deviationshigheringovernmentschoolsthaninprivateschools.Inaddition,privateschool
studentshavehigherscoresontestsofcivicvaluesthatmeasureunderstandingofconcepts
suchasdemocracyandgenderequality.Whilethestudyislimitedingeographicalscope(asitis
confinedtothreedistrictsinruralPunjab),itpresentsthemostcredibleevidencetodatein
supportoftheclaimthatprivateschoolsproducebetterlearningoutcomes.
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Dasetal.(2006),usingdatafromthePunjabprovinceofPakistan,findevidenceofpoor
academicoutcomesinbothgovernmentandprivateschools.Theauthorsfindthatatleast50
percentofthevariationinlearningoutcomescanbeaccountedforbydifferencesinschool
type,suchasgovernmentversusprivateschools,or"bad"governmentschoolsversus"good"
governmentschools.Forexample,thegapinEnglishtestscoresbetweenstudentsfrom
governmentandprivateschoolsistwelvetimeslargerthanthegapbetweenchildrenfromrich
andpoorfamilies.Inanotheranalysisthatuseshouseholdsurveydatafromlow-incomeareas
ofLahoreCity,Aldermanetal.(2001)findthatchildreninprivateschoolsoutperform
governmentschoolstudentsaftercontrollingforfamilycharacteristicsandschoolinputs.Using
thenationwideASERPakistan2011data,AmjadandMacLeod(2011)findthatthequalityof
educationispooracrossgovernmentschools,privateschoolsandschoolswithpublic-private
partnerships.Inaddition,theyfindthatstudentsfromprivateschoolsoutperformstudents
fromgovernmentschools.
Aslam(2009)usesaHeckmantwo-stepproceduretoovercomethepossibilityofsample
selectionbiasamongchildrenwhogotopublicandprivateschoolsinLahoreCity.Sample
selectionbiascouldariseifparentssendingchildrentoprivateschoolshaveagreaterinterest
intheirchild'sacademicsuccess,whichcouldaffectthestudent'sachievementregardlessof
thetypeofschoolattended.Theauthor'sHeckman-correctedresultsarenotsignificantly
differentfromherOrdinaryLeastSquares(OLS)estimates.Theseresultscorroborateearlier
findingsbyAndrabietal.(2010),whichshowthatprivateschoolsaremoreeffectiveatteaching
mathandliteracyskillsthangovernmentschools.
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Notallevidencesupportsthenotionthatdifferencesinstudentperformancecanbe
attributedtoschooltype.ArifandSaqib(2003)sample50schoolsacrossthecountryandfind
evidenceofbetterlearningoutcomesamongprivateschoolstudents,buttheyalsofindthat
thedifferencecanlargelybeexplainedbyfamilybackgroundandschoolcharacteristics,suchas
teacherqualificationandstudent-teacherratio.Performancealsovariesbydistrict,with
governmentschoolsperformingbetterthanprivateschoolsinsomedistricts.Inaddition,the
authorsarguethatlow-incomehouseholdstendtosendtheirchildrentogovernmentschools,
asprivateeducationislargelyunaffordableforthem.
LearningOutcomesinGovernmentandPrivateSchoolsinOtherDevelopingCountries
Studiescomparingoutcomesacrossgovernmentandprivateschoolsinotherdeveloping
countrieslargelyprovidesupportfortheclaimthatstudentshavebetterlearningoutcomesin
privateschools.FrenchandKingdon(2010)examineASERdatafromIndiatoestimatethe
effectofprivateschoolenrollmentonlearningoutcomes.Theyuseavarietyoftechniquesto
estimatetheeffectofprivateschools,suchasOLSestimation,cross-sectionfixedeffect
techniquesatthelevelofstate,district,villageandhouseholds,andpaneldataanalysisusing
villageandtimefixedeffects.Theauthorsfindthatthereisaprivateschooleffectonchild
achievementof0.17standarddeviations.AnotherstudybyMuralidharanandKremer(2006)in
Indiafindsevidenceofasizeableandsignificantassociationbetweenprivateschoolattendance
andlearningoutcomesaftercontrollingforfamilyandhouseholdcharacteristics.
AdescriptivestudybyTooleyandDixon(2005)findsthatprivateschoolsinIndia,
Ghana,NigeriaandKenyaarepopularamonglow-incomesegments,havebetterachievement
scoresandhavelowerteachercosts.Jimenezetal.(1991)usedatafromColombia,the
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DominicanRepublic,thePhilippines,TanzaniaandThailand,toshowthatprivateschool
studentsperformbetteronstandardizedmathandEnglishteststhangovernmentschool
students,evenaftercontrollingforincome.Inaddition,theyfindevidencethatprivateschools
havelowerper-unitcoststhangovernmentschools,substantiatingclaimsoflowerprivate
schoolcostsinPakistanbyAndrabietal.(2010).
ThePresentStudy
Manystudieshaveexaminedthelearninggapbetweengovernmentandprivateschools
invariousdevelopingcountries.However,onlyafewhavefocusedonlearningoutcomesin
governmentandprivateschoolsinPakistan.Todate,themostinformativestudiesfrom
Pakistanhaveutilizeddatafromarestrictedgeographicalarea(Andrabietal.2006;Dasetal.
2006).ThisstudyusesASERsurveydatafrom2014,coveringmostregionsofPakistan,to
presentacomprehensivenation-wideassessmentoflearningingovernmentandprivate
schools.
CONCEPTUALFRAMEWORKANDHYPOTHESIS
Basedonthefindingsintheexistingliterature,Ihypothesizethatattendingprivate
schoolsispositivelycorrelatedwithlearningoutcomes.Inotherwords,Ipredictthatthereisa
significantlearninggapbetweenstudentsattendingprivateschoolsandstudentsattending
governmentschools.Inaddition,Iexpectthesizeoftheprivateschooladvantagetovaryby
district(Dasetal.2006).Whileprivateschoolshavegrowninbothruralandurbandistricts,
theireffectsmayvaryduetothedifferentsocialandeconomiccharacteristicsofthedistricts.
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Whilethepurposeofthisstudyistoinvestigatetheeffectofschooltype(government
versusprivate)onlearninglevels,manyotherfactorsalsocontributetothedifferencein
achievementamongstudents.Thesefactors,whicharerelatedtobothlearninglevelsand
schooltype,canbebroadlycategorizedintoindividualchildcharacteristics,parent
characteristicsandhouseholdcharacteristics.Inordertoisolatetherelationshipbetween
schooltypeonlearningoutcomes,itisimportanttocontrolforthesefactors.Thisstudyuses
controlvariablesfromthenationallyrepresentativeASERPakistansurvey,whichprovideschild-
,parent-andhousehold-leveldataacrossruralandurbandistrictsofPakistan.
ChildCharacteristics
Apartfromthewell-documentedlinkbetweenachild'snaturalcognitiveabilityand
learningoutcomes,otherchildcharacteristics,suchasnumberofsiblings,genderandaccessto
tutoringoutsideofschoolarealsorelatedtoeducationalachievement.Numerousstudiesshow
aninverserelationshipbetweenfamilysizeandlearningoutcomes(Alwin1991;Shavitand
Pierce1991;Downey1995).Onepossibletheoryexplainingthisrelationshipisthe"resource
dilutioneffect":asparent'sfiniteresources,suchasincomeandtime,arespreadoutamong
moresiblings,thelowerresourcesperchildarerelatedtolowereducationoutcomes(Downey,
2001).GiventhestronggenderbiasinenrollmentandlearninginPakistan,parentsmaychoose
tospendmoreonamalechild'seducation.4Forexample,Aslam(2009)findsthatboysin
Pakistanaremorelikelytobeenrolledincomparativelyhigh-qualityprivateschoolsthangirls.
4Accordingtoa2014reportbytheWorldEconomicForum,Pakistanranks132outof142countriesintermsofthegendergapineducationalattainment.AnotherstudybytheBrookingsInstitution(KingandWinthrop,2015)findsthatthereare74girlsforevery100boysenrolledinsecondaryschoolinPakistan.
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Privatetutoringoutsideofschoolisprevalentamongstudentsfrombothruraland
urbanareas(Dundaretal.,2014).Aslam(2009)positsthatpartofthereasonfortheuptakeof
privatetutoringcouldbethepoorqualityofeducationprovidedinschools,withqualitybeing
worseingovernmentschools.Theprobabilityofreceivingextratutoringisalsorelatedto
income,genderandmaternaleducation(Macphersonetal.,2014).
HouseholdCharacteristics
Manystudiesfromhigh-incomecountrieshavedocumentedthepositiverelationship
betweenincomeandtestscores(DahlandLochner2012;DuncanandMagnuson2005;Davis-
Kean2005).Dasetal.(2006)findasimilarrelationshipbetweenparentalincomeandchild's
educationalachievementinPakistan.However,theauthorsfindthattheachievementgap
attributabletoparents'incomeandeducationissignificantlyreducedonceoneaccountsfor
differencesinschooltype.Incomealsoaffectsavailabilityofelectricity,waterandotheritems
thatfacilitateaproductivelearningenvironmentathome.Furthermore,incomeand
transportationavailabilitydeterminewhetherschoolsarephysicallyaccessible.InPakistan,
wherefemalemobilityisrestrictedduetoculturalexpectations,distancefromschoolaffects
girlsmorethanboys(Aldermanetal.,2001).Andrabietal.(2006)findthatprivateschoolshelp
tomitigatethenegativeeffectofdistanceonschoolattendanceandboostoverallenrollment
forbothboysandgirls.
ParentCharacteristics
Thereisampleevidencedocumentingapositiverelationshipbetweenparents'levelof
educationandchildren'slearningoutcomes(Davis-Kean2005;Dubow2010).InPakistan,
Andrabietal.(2009)findthatthetestscoresofchildrenwhosemothershavesomeeducation
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are0.24to0.35standarddeviationshigherthanchildrenwithuneducatedmothers.
Furthermore,motherswithsomeeducationaremorelikelytospendtimehelpingchildrenwith
homework(Ibid.).Itispossiblethateducatedparentsarealsomorelikelytochoosebetween
publicandprivateschoolsbasedonperceivedquality.
DATAANDMETHODS
MyempiricalanalysesusedatafromtheAnnualStatusofEducationReport(ASER)
Pakistan2014surveycoveringchildrenaged3to16yearsin123ruraldistrictsand21urban
centersofPakistan.ASERassessmenttoolstestchildren'slearningthroughagradeonetotwo
leveltestonarithmetic,Englishandreading(Urdu,SindhiorPashto).Eachstudentisassigneda
learninglevelbetweenone(lowest)tofive(highest).Thegoaloftheassessmentistoobtain
informationonthebasicreadingandarithmeticabilitiesofchildren.Forthepurposesofthis
study,Irecodethetestscorevariablesintodichotomousvariablestakingthevalueofoneif
studentsscoreabovetheaverageforthesubjectandzeroifstudentsscorebelowtheaverage.
Thisapproachallowsmetoproducemoreeasilyinterpretableregressionresults.
SincetheASERtestisonlyadministeredtochildrenabovefiveyearsofage,Iexcludeall
childrenwhoarebelowfiveyearsofagefrommyanalysis.Furthermore,Iexcludeanychildren
whowenttoreligiousorothertypesofschoolsfromthesample,asthisstudyisfocusedonly
ondifferencesinlearningoutcomesbetweenstudentsenrolledingovernmentandprivate
schools.5
5Religiousschoolsormadrassahs,unlikepublicandprivateschools,primarilyteachreligioussubjects.However,somemadrassahsalsoteachnon-religioussubjectssuchasmathincombinationwithreligiousstudies.
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Icontrolforvariablesthatareplausiblyrelatedtobothlearningoutcomes(the
dependentvariable)andtheprobabilityofgoingtoprivateschoolsovergovernmentschools
(thekeyindependentvariable).Asisimpliedinthediscussionintheprevioussectionthese
controlvariablescanbecategorizedintothreebroadcategories:childcharacteristics,
householdcharacteristicsandparentcharacteristics(SeeTable1).
Toanalyzetherelationshipbetweenschooltype(adummyvariablethattakesona
valueofoneifthechildgoestoaprivateschoolandzerootherwise)andlearningoutcomes(a
dummyvariablethattakesonavalueofoneifthechildscoresabovetheaverageandzero
otherwise),IestimateaLinearProbabilityModel(LPM)withdistrict-levelfixedeffects.TheLPM
allowsmetopredictthechangeinprobabilityofscoringabovetheaveragegivenachangein
schooltypefromgovernmenttoprivate.Myfixedeffectsspecificationcontrolsfortime-
invariantcharacteristicsoftheindividualdistricts–forexample,unobservedadministrative
differencesbetweendistrictsthatcouldaffectresourcesallocatedtoschools.Thisapproachis
consistentwiththatofAslam(2009),whoestimatedavillage-levelfixed-effectsmodeltostudy
therelativeeffectivenessofschooltypesinLahore,Pakistan.Isubsequentlyapplymore
stringentfixedeffectscontrolsatthevillageandsiblinglevel.
IcannotuseasimpleOLSmodeltoestimatetheeffectofprivateschoolingonlearning
outcomesduetosampleselectionbias.Itispossiblethatthechoiceofprivateschoolsisrelated
tounobservedcharacteristicsofthechildandthefamilythatarealsorelatedtoachievement
scores.AssuggestedbyFrenchandKingdon(2010),OLSestimatesprovideanupperboundof
theprivateschooleffect,astheyincludetheeffectofunobservablefactorsinadditiontothe
privateschooleffect.Toapplystrictercontrols,Iusedistrict-levelfixedeffects.
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Asimpleversionofmyregressionequationisasfollow:
LearningLevelid=β0+β1Privateid+βxxXXid+αd+uid
whereXXrepresentsallthecontrolvariableslistedinTable1.Thesubscripts"i"and"d"
representeachindividualchildanddistrictrespectively.
Iestimatethreeseparateregressionequations,witheachequationusingoneofthree
dependentvariablesmeasuringlearningoutcomes:math,Englishandreadinginthelocal
language.
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Table1:DefinitionsofVariables
DependentVariableLearningLevel
MathHighestLevel
Adichotomousvariableindicatingwhetherthechildscoredabovetheaverageinmath(0=no,1=yes).6
EnglishReading
AdichotomousvariableindicatingwhetherthechildscoredabovetheaverageinEnglish(0=no,1=yes).7
LocalReading
Adichotomousvariableindicatingwhetherthechildscoredabovetheaverageinlocallanguage:Urdu,Sindhi,Pashto(0=no,1=yes).8
IndependentVariable
PrivateAdichotomousvariableindicatingthetypeofschoolattendedbythechild(0=government,1=private).
ChildCharacteristics
FemaleAdichotomousvariableindicatingthechild’sgender(0=male,1=female).
AgeAcontinuousvariableindicatingthechild’sage(between5-16years).
Grade Acontinuousvariableindicatingthegradeofthechild:0-12.
ChildTakingPaidTutoringAdichotomousvariableindicatingwhetherthechildtakespaidtutoringoutsideofschool(0=no,1=yes).
TuitionFee9Acontinuousvariableindicatingtheschool'stuitionfeeinPakistaniRupees.
HouseholdCharacteristics
HouseholdSizeAcontinuousvariableindicatingthetotalnumberofmembersinthesurveyedhousehold.
HouseTypeMixedAdichotomousvariableindicatingthattheconstructiontypeofhouseismixed(0=notmixed,1=mixed).
HouseTypeSolid Adichotomousvariableindicatingthattheconstructiontypeof
6Theaveragescoreformathis3.450onascaleof1to5(1=beginner/nothing,2=recognitionof1-9,3=recognitionof10-99,4=subtraction,5=division).7TheaveragescoreforEnglishreadingis3.468onascaleof1to5(1=beginner/nothing,2=capitalletters,3=smallletters,4=words,5=sentences).8Theaveragescoreforlocallanguagereadingis3.476onascaleof1to5(1=beginner/nothing,2=letters,3=words,4=sentences,5=story).9GovernmentschoolsinPakistantendtochargeasmalltuitionfee(Kattan&Burnett,2004).ForthegovernmentschoolssampledinASER2014data,tuitionfeesrangefromRs.20($0.19)toRs.8000($75.88)permonth.
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houseissolid(0=notsolid,1=solid).
HouseOwnedAdichotomousvariableindicatingwhetherthehouseisownedbytheresidents(0=no,1=yes).
ElectricityConnectionAvailable
Adichotomousvariableindicatingwhetherthehousehasanelectricalconnection(0=no,1=yes).
MobileAvailableAdichotomousvariableindicatingwhetherthereisamobilephoneavailableforusebyhouseholdmembers(0=no,1=yes).
TVAvailableAdichotomousvariableindicatingwhetherthereisatelevisioninthehousehold(0=no,1=yes).
NearestSchoolDistanceAcontinuousvariableindicatingtheone-waydistancefromthehousetothenearestschoolinkilometers.
ParentCharacteristicsMother'sAge Acontinuousvariableindicatingtheageofthechild'smother.Mother'sEducationSecondary
Adichotomousvariableindicatingwhetherthechild'smotherhascompletedsecondaryschool(0=no,1=yes).
Mother'sEducationHighSchool
Adichotomousvariableindicatingwhetherthechild'smotherhascompletedhighschool(0=no,1=yes).
Mother'sEducationSomeCollege
Adichotomousvariableindicatingwhetherthechild'smotherhascompletedsomecollege(0=no,1=yes).
Father'sAge Acontinuousvariableindicatingtheageofthechild'sfather.Father'sEducationSecondary
Adichotomousvariableindicatingwhetherthechild'sfatherhascompletedsecondaryschool(0=no,1=yes).
Father'sEducationHighSchool
Adichotomousvariableindicatingwhetherthechild'sfatherhascompletedhighschool(0=no,1=yes).
Father'sEducationSomeCollege
Adichotomousvariableindicatingwhetherthechild'sfatherhascompletedsomecollege(0=no,1=yes).
DESCRIPTIVESTATISTICS
Table2providesdescriptivestatisticsforthedependentandkeyindependentvariables,
andforchild,householdandparentcharacteristics.AppendixAsupplementsTable2by
providingdescriptivestatisticsbyregiontype(urbanversusrural).Table3presentsdifferences
inkeycharacteristicsbyschooltype(governmentversusprivate).1011
10Alldescriptivestatisticsareunweighted.AccordingtoASERsurveydocumentation,weightedestimatesarethesameasunweightedestimatesatthedistrictlevelduetotheuseofprobability-basedsampling(PBS).
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AsshowninTable2,averagelearninglevelsamongstudentsinbothgovernmentand
privateschoolsarelow.Themeanvalueforallthreelearningoutcomevariablesis
approximately0.52,implyingthatmoststudents'ASERtestscoresarebelowthebasic
proficiencylevelsexpectedingradeonetotwo.Furthermore,themeanandstandard
deviationsforstudentoutcomesaresimilaracrossmath,readinginEnglishandreadinginlocal
language,whichshowsthatstudentstendtodopoorlyacrossalltests.Inlinewiththenational
gapinmale-femaleliteracyrates,fatherstendtobemoreeducatedthanmothers.12The
averagetuitionfeeforgovernmentandprivateschoolsisRs.359($3.42)permonth.However,
thereissubstantialvariationintuitionfees,rangingfromRs.20($0.19)permonthtoRs.8,000
($75.88)permonth.13Themaximumhouseholdsizeis74,implyingthatsomefamiliesinthe
sampleresideinsharedhouseholds.14
11Theoriginalsamplehad238,843observations.Missingvaluesfortuitionfeewerederivedusingregressionmeanimputation.Intheoriginalsample,213,018valuesweremissingandonly25,825observationswereavailable.Amongthe213,018imputedvalues(89.19%oftheobservations),anyimputedvaluesbelowthevalueof20(minimumvalueforthetuitionfeevariableacrossprivateandgovernmentschools)ornegativevaluesweredropped(275observations).Lastly,allmissingobservationsforthedependent,keyindependentandcontrolvariables(exceptforthetuitionfeevariable)weredroppedfromthesample.Fordependentvariables,Idroppedthefollowingmissingvalues:1,134for"MathHighestLevel,"1,027for"EnglishReading"and46,214for"LocalReading."Idropped27,234observationsfor"Private"(keyindependentvariable).Idropped44,993observationsforthemissingcontrolvariables(excluding"TuitionFee").Asaresult,thefinalsamplesizeis117,966.12AccordingtoUNICEF,theyouthliteracyrateformalesin79.1percent.Thecomparablefigureforfemalesis61.5percent.(UNICEF,2013)13AccordingtothePakistanBureauofStatistics,Pakistan'sannualpercapitaincomeis$1,513,implyingthattheaveragePakistanimakesRs.13,292($126)permonth.(PakistanBureauofStatistics,2015)14Twoextremelyhighhouseholdsizevaluesof7773and307werecodedasmissinganddropped,astheyareextremeoutliers.
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Table2:DescriptiveStatisticsforDependent,KeyIndependentandControlVariables
Mean SD Min MaxDependentVariables
MathHighestLevel 0.50 0.50 0 1EnglishReading 0.55 0.50 0 1LocalReading 0.51 0.50 0 1
KeyIndependentVariable Private 0.34 0.48 0 1
ChildCharacteristics Female 0.36 0.48 0 1Age 9.75 3.19 5 16Grade 3.97 2.90 0 12ChildTakingPaidTutoring 0.16 0.37 0 1TuitionFee 358.55 227.37 20 8000
HouseholdCharacteristics HouseholdSize 7.27 3.54 1 74HouseTypeMixed 0.30 0.46 0 1HouseTypeSolid 0.34 0.47 0 1HouseOwned 0.91 0.29 0 1ElectricityConnectionAvailable 0.91 0.29 0 1MobileAvailable 0.84 0.36 0 1TVAvailable 0.65 0.48 0 1NearestSchoolDistance(km) 1.53 14.02 0 2200
ParentCharacteristics Mother'sAge 36.47 7.20 16 97Mother'sEducationSecondary 0.17 0.38 0 1Mother'sEducationHighSchool 0.13 0.34 0 1Mother'sEducationSomeCollege 0.03 0.17 0 1Father'sAge 41.25 7.98 18 92Father'sEducationSecondary 0.21 0.40 0 1Father'sEducationHighSchool 0.26 0.44 0 1Father'sEducationSomeCollege 0.12 0.32 0 1Thesamplesizeis117,966,with15,681observationsforurbanareasand102,285observationsforruralareas.Fordescriptivestatisticsdisaggregatedbyregiontype,refertoAppendixA.
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Table3showsthedifferenceincharacteristicsbetweenstudentswhoattend
governmentandprivateschools.Mostdifferencesarestatisticallysignificantattheonepercent
level.Thetableshowsthat,onaverage,privateschoolstudentspayhighermonthlyfees(Rs.
452.20versusRs.309.33)forschoolsandarealsomorelikelytotakepaidtutoringlessons
outsideofschool.Privateschoolstudentsaremorelikelytohaveasolidhouseandhaveaccess
toelectricity,mobilephoneandtelevisionathome.Onaverage,parentsofchildrenwhogoto
privateschoolshavecompletedagreaternumberofyearsofschoolingthanparentsofchildren
whogotogovernmentschools.Itisnoteworthythatmanyofthedifferencesinkey
characteristicsbetweenprivateandgovernmentschoolsaresimilartothedifferencesinkey
characteristicsbetweenurbanandruralareas(AppendixA).
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Table3:KeyCharacteristicsDisaggregatedbySchoolType
Private Government Difference SEDependentVariables
MathHighestLevel 0.51 0.49 0.02*** 0.00EnglishReading 0.58 0.53 0.04*** 0.00LocalReading 0.51 0.51 0.00ns 0.01
ChildCharacteristics Female 0.39 0.35 0.04*** 0.00Age 9.22 10.02 -0.81*** 0.02Grade 3.52 4.20 -0.68*** 0.02ChildTakingPaidTutoring 0.33 0.08 0.25*** 0.00TuitionFee 452.20 309.33 142.27*** 1.33
HouseholdCharacteristics HouseholdSize 6.83 7.50 -0.66*** 0.02HouseTypeMixed 0.27 0.31 -0.03*** 0.00HouseTypeSolid 0.52 0.25 0.26*** 0.00HouseOwned 0.88 0.92 -0.04*** 0.00ElectricityConnectionAvailable 0.96 0.88 0.08*** 0.00MobileAvailable 0.92 0.80 0.12*** 0.00TVAvailable 0.78 0.59 0.19*** 0.00NearestSchoolDistance(km) 1.72 1.44 0.28** 0.09
ParentCharacteristics Mother'sAge 35.52 36.93 -1.41*** 0.04Mother'sEducationSecondary 0.20 0.15 0.05*** 0.00Mother'sEducationHighSchool 0.23 0.08 0.15*** 0.00Mother'sEducationSomeCollege 0.06 0.01 0.05*** 0.00Father'sAge 40.50 41.65 -1.15*** 0.00Father'sEducationSecondary 0.19 0.22 -0.03*** 0.03Father'sEducationHighSchool 0.34 0.22 0.12*** 0.00Father'sEducationSomeCollege 0.19 0.08 0.11*** 0.00Totalsamplesizeis117,966,with77,345observationsforgovernmentschoolsand40,621observationsforprivateschools.
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RESULTS
MyregressionresultsaresummarizedinTables5,6and7,whichusemath,Englishand
localreadingtestscoresasthedependentvariable,respectively.Eachregressionalsocontrols
forchild,parentandhouseholdcharacteristics.Myanalysisusescross-sectionfixedeffects
techniques,andIapplyprogressivelystringentfixedeffectscontrolsatthedistrict,villageand
siblinglevels.Ineachtable,Model1reportstheresultsofmyLPMregressionwithoutfixed
effects,providinganupperlimitfortheprivateschoolrelationshipwithstudentachievement.
Model2displaystheresultsofregressionsthatincludedistrict-levelfixedeffects,whichcontrol
forthepermanentcharacteristicsofeachdistrict,bothobservableandunobservable,suchas
thelevelofurbanizationortheextentofcorruptionwithinlocaleducationbodiesandother
fixedsocial,politicalandgeographicdifferences.Model3includestheresultsofvillage-level
fixedeffectsregressions,whichcontrolforpermanentcharacteristicsofthevillage,suchas
schoolquality(FrenchandKingdon,2010).Model4testswhethertheeffectofprivate
schoolingdiffersbygenderusinginteractions.Finally,Model5showstheresultsofasibling-
levelfixedeffectsmodel.Bycontrastingchildrenwhoareenrolledinprivateschoolswiththeir
siblingswhoarenot,Icontrolfortheeffectoffamilybackgroundontestoutcomes.Robust
standarderrorsarereportedbeneathallcoefficients.
Overall,theresultsreportedinTables5,6and7fortheLPM,district-levelandvillage-
levelfixedeffectsspecificationsconfirmmyhypothesisthatprivateschoolinghasasmall,
significantandpositiveassociationwithtestscores.However,therelationshipbetweenprivate
schoolingandtestscoresdisappearswhenincludingsiblingfixedeffectsintheregression.
Nevertheless,itisadvisabletoexercisecautionwheninterpretingsiblingfixedeffects,asthe
informationusedtoestimatetheprivateschoolcoefficientisderivedfromlessthanfive
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percentofthesamplememberswholiveinhouseholdswithatleasttwosiblings,andinwhich
atleastonesiblingattendsprivateschoolandatleastonesiblingattendspublicschool.
Table4:RegressionResultsforMathTestScores
15Thedummyvariable"MathHighestLevel"takesonavalueof1ifthestudentscoresabovetheaveragemathscoreof3.450andtakesonavalueof0ifthestudentscoresbelowtheaverage.
DependentVariable MathHighestLevel(Score0-1)15 (1) (2) (3) (4) (5)
LPM DistrictFE VillageFE
VillageFEw/
Interaction SiblingFEKeyIndependent
Variable Private 0.0554*** 0.0180*** 0.0149*** 0.0120*** 0.00355 (0.00264) (0.00277) (0.00290) (0.00336) (0.00540)
ChildVariables Female -0.00726*** -0.0101*** -0.00984*** -0.0125*** -0.00593* (0.00229) (0.00223) (0.00217) (0.00272) (0.00315)Female*Private
0.00742*
(0.00440)Age 0.0171*** 0.0200*** 0.0282*** 0.0282*** 0.0445***
(0.000823) (0.000816) (0.000844) (0.000844) (0.00141)
Grade 0.0938*** 0.0883*** 0.0806*** 0.0805*** 0.0667***
(0.000946) (0.000959) (0.00101) (0.00101) (0.00174)
ChildTakingPaidTutoring 0.0388*** 0.0360*** 0.0382*** 0.0383*** 0.0553***
(0.00320) (0.00325) (0.00362) (0.00363) (0.00881)
TuitionFee/100 0.000244 0.000391 -0.00108 -0.00106 -0.00396***
(0.000711) (0.000696) (0.000705) (0.000704) (0.00149)
HouseholdVariables HouseholdSize 0.000303 -0.000392 -0.0416 -0.0387
(0.000320) (0.000353) (0.000394) (0.000394)HouseTypeMixed 0.0378*** 0.0164*** 0.00749** 0.00748**
(0.00291) (0.00311) (0.00354) (0.00354)
HouseTypeSolid 0.0585*** 0.0294*** 0.0157*** 0.0158***
(0.00321) (0.00352) (0.00406) (0.00406)
HouseOwned -0.0188*** 0.00178 0.0113** 0.0114**
(0.00382) (0.00395) (0.00443) (0.00443)
ElectricityConnectionAvailable 0.0321*** -0.00441 -0.00951 -0.00949
(0.00419) (0.00465) (0.00615) (0.00615)MobileAvailable 0.0190*** 0.0189*** 0.00676 0.00676
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22
Robuststandarderrorsareinparentheses.P-valuesaregiveninparenthesesunderF-statisticsforjointhypothesistests.***p<0.01,**p<0.05,*p<0.1
(0.00353) (0.00392) (0.00460) (0.00460)
TVAvailable -0.0182*** 0.00802*** 0.00366 0.00369
(0.00282) (0.00298) (0.00324) (0.00324)
NearestSchoolDistance(km) 0.000186** 0.0357 0.0637 0.0657
(0.0534) (0.0476) (0.000119) (0.000119)ParentVariables
Mother'sAge 0.00172*** 0.00174*** 0.000136 0.000137 -0.00193
(0.000301) (0.000302) (0.000317) (0.000317) (0.00291)
Mother'sEducationSecondary 0.0112*** 0.00483 0.00810** 0.00810** 0.00847
(0.00328) (0.00331) (0.00335) (0.00335) (0.0376)
Mother'sEducationHighSchool 0.0333*** 0.0206*** 0.0146*** 0.0145*** 0.0769
(0.00397) (0.00408) (0.00420) (0.00420) (0.0561)
Mother'sEducationSomeCollege 0.0350*** 0.0295*** 0.0271*** 0.0269*** -0.0863
(0.00722) (0.00724) (0.00735) (0.00735) (0.0788)
Father'sAge -0.00142*** -0.00152*** -0.0332 -0.0332 0.00340
(0.000271) (0.000271) (0.000283) (0.000283) (0.00267)
Father'sEducationSecondary -0.00128 -0.0157*** -0.00795** -0.00793** -0.0170
(0.00308) (0.00309) (0.00321) (0.00321) (0.0343)
Father'sEducationHighSchool 0.0158*** 0.00579* 0.0109*** 0.0109*** -0.0446
(0.00319) (0.00319) (0.00333) (0.00333) (0.0369)
Father'sEducationSomeCollege 0.0287*** 0.0278*** 0.0222*** 0.0221*** -0.00784 (0.00441) (0.00449) (0.00475) (0.00475) (0.0490)Constant -0.132*** -0.0940*** -0.127*** -0.126*** -0.256***
(0.00902) (0.00939) (0.0107) (0.0107) (0.0605)
Observations 117,966 117,966 117,966 117,966 117,966R-squared 0.437 0.476 0.561 0.561 0.768F-statisticforjointhypothesis:private=female*private=0
24.16***(0.0000)
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23
MathTestScores
ThesimpleLPMmodelinTable5showsthatgoingtoaprivateschoolinsteadofa
governmentschoolisassociatedwitha5.54percentagepointincreaseintheprobabilityof
scoringabovetheaverageformathtestscores.Applyingdistrict-level(orvillage-level)fixed
effectsreducesthemagnitudeofthisassociationconsiderably.ThisimpliesthatourinitialLPM
estimatewaspositivelybiasedduetoomissionofcontrolsforthecharacteristicsofvillages(for
example,whetheravillageisclosertoanurbancenter,hasbetterqualityschoolsorlower
levelsofcorruptionwithinlocaladministrativebodies)thatarelinkedtomathtestscoresand
probabilityofprivateschoolattendance.Forthedirectionofthebiastobepositive,village
characteristicsmusteitherbepositivelyassociatedwithbothmathtestscoresandthe
probabilityofattendingprivateschools,ortherelationshipofvillagecharacteristicswithboth
variablesmustbenegative.Forexample,avillagewithproximitytoanurbancenterislikelyto
havebetteraccesstoelectricity,whichcouldthereforebelinkedtobettertestscores.Sucha
villagecouldalsohaveabetterroadinfrastructure,whichcouldbepositivelylinkedtoprivate
schoolattendance.Ontheotherhand,itispossiblethatavillagewithhighlevelsofcorruption
doesnotspendenoughfundsonteachertrainingorschoolfacilities,whichcouldcontributeto
poortestscores.Similarly,highcorruptioncouldbeassociatedwithdifficultyinestablishing
privateschoolsduetolicensinghurdles,therebynegativelyaffectingprivateschoolattendance.
Bothscenarioswouldresultinapositivebiasintheprivateschoolcoefficient.Lastly,theresults
showthattheassociationbetweenprivateschoolingandmathscoresoffemalestudentsis
positiveandhighlysignificant.Inaddition,theassociationbetweenprivateschoolattendance
andmathtestscoresislargerforfemalesthanformales.
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Table5:RegressionResultsforEnglishReadingScores
16Thedummyvariable"EnglishReading"takesonavalueof1ifthestudentscoresabovetheaverageEnglishreadingscoreof3.468andtakesonavalueof0ifthestudentscoresbelowtheaverage.
DependentVariable EnglishReading(Score0-1)16 (1) (2) (3) (4) (5)
LPM DistrictFE VillageFE
VillageFEw/
Interaction SiblingFEKeyIndependent
Variable Private 0.0686*** 0.0203*** 0.0111*** 0.00738** -0.0186*** (0.00271) (0.00283) (0.00299) (0.00346) (0.00552)
ChildVariables Female -0.00455* -0.0107*** -0.0131*** -0.0165*** -0.0127*** (0.00235) (0.00229) (0.00223) (0.00278) (0.00321)Female*Private
0.00953**
(0.00456)
Age 0.0204*** 0.0233*** 0.0291*** 0.0291*** 0.0457***
(0.000837) (0.000823) (0.000855) (0.000855) (0.00142)
Grade 0.0837*** 0.0780*** 0.0726*** 0.0726*** 0.0578***
(0.000963) (0.000966) (0.00102) (0.00102) (0.00175)
ChildTakingPaidTutoring 0.0534*** 0.0462*** 0.0394*** 0.0396*** 0.0541***
(0.00324) (0.00332) (0.00370) (0.00370) (0.00891)
TuitionFee/100 0.00138* -0.000224 -0.00116 -0.00113 -0.00469*** (0.000732) (0.000698) (0.000715) (0.000715) (0.00146)HouseholdVariables HouseholdSize 0.00245*** -0.0414 0.0393 0.0430
(0.000336) (0.000371) (0.000408) (0.000408)HouseTypeMixed 0.0351*** 0.0188*** 0.0108*** 0.0108***
(0.00300) (0.00318) (0.00365) (0.00365)
HouseTypeSolid 0.0608*** 0.0353*** 0.0165*** 0.0165***
(0.00330) (0.00359) (0.00417) (0.00417)
HouseOwned -0.0229*** -0.00155 0.00485 0.00492
(0.00390) (0.00407) (0.00458) (0.00458)
ElectricityConnectionAvailable 0.0444*** 0.00507 0.00137 0.00139
(0.00433) (0.00475) (0.00632) (0.00632)MobileAvailable 0.0225*** 0.0227*** 0.0112** 0.0112**
(0.00364) (0.00398) (0.00472) (0.00472)
TVAvailable -0.0125*** 0.00998*** 0.00607* 0.00611*
(0.00292) (0.00305) (0.00333) (0.00333)
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Robuststandarderrorsareinparentheses.P-valuesaregiveninparenthesesunderF-statisticsforjointhypothesistests.***p<0.01,**p<0.05,*p<0.1
NearestSchoolDistance(km) 0.000172** 0.0206 0.000102 0.000103
(0.0567) (0.0455) (0.000123) (0.000123)ParentVariables
Mother'sAge 0.000495 0.00123*** 0.000117 0.000119 -0.00265
(0.000306) (0.000308) (0.000330) (0.000330) (0.00314)
Mother'sEducationSecondary 0.0219*** 0.00687** 0.00497 0.00497 -0.0840**
(0.00336) (0.00337) (0.00343) (0.00343) (0.0380)
Mother'sEducationHighSchool 0.0479*** 0.0237*** 0.0139*** 0.0138*** -0.00527
(0.00405) (0.00414) (0.00430) (0.00430) (0.0560)
Mother'sEducationSomeCollege 0.0513*** 0.0218*** 0.0176** 0.0174** -0.149*
(0.00745) (0.00746) (0.00754) (0.00754) (0.0779)
Father'sAge -0.000533* -0.00133*** -0.000245 -0.000245 0.00421
(0.000276) (0.000276) (0.000293) (0.000293) (0.00286)
Father'sEducationSecondary 0.0149*** -0.00353 0.000958 0.000990 0.000160
(0.00316) (0.00315) (0.00330) (0.00330) (0.0362)
Father'sEducationHighSchool 0.0141*** 0.00816** 0.0142*** 0.0142*** -0.0192
(0.00327) (0.00326) (0.00343) (0.00343) (0.0380)
Father'sEducationSomeCollege 0.0127*** 0.0248*** 0.0242*** 0.0241*** 0.0271 (0.00447) (0.00454) (0.00485) (0.00484) (0.0516)Constant -0.114*** -0.0433*** -0.0576*** -0.0566*** -0.163**
(0.00924) (0.00958) (0.0109) (0.0109) (0.0650)
Observations 117,966 117,966 117,966 117,966 117,966R-squared 0.402 0.448 0.536 0.536 0.758F-statisticforjointhypothesis:private=female*private=0
16.98***(0.0000)
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EnglishReadingTestScores
ThemagnitudeoftherelationshipbetweenprivateschoolingandEnglishtestscoresis
higheracrossthedifferentregressionspecificationsascomparedtothemagnitudeofthe
relationshipbetweenprivateschoolattendanceandmathtestscores.ThesimpleLPMmodelin
Table6showsthatgoingtoaprivateschoolinsteadofagovernmentschoolisassociatedwitha
6.86percentagepointincreaseintheprobabilityofscoringaboveaverageforEnglishtest
scores.Applyingdistrict-levelfixedeffectsreducesthemagnitudeoftherelationshipto2.03
percentagepoints.Village-levelfixedeffectsfurtherreducethemagnitudeoftherelationship
to1.11percentagepoints.SimilartotheresultsforMathscores,capturingpermanentdistrict
andvillagecharacteristicslowerstheestimatedcoefficientonprivateschooling,implyingthat
thesimpleLPMmodelfailedtoaccountforpermanentdistrictandvillagecharacteristicsthat
haveasimilarlysignedrelationshipwithEnglishreadingtestscoresandtheprobabilityof
attendingprivateschools.However,afterIapplysibling-levelfixedeffects,Ifindthatthereisa
negativeassociationbetweenprivateschoolattendanceandtestscores.Theimplicationsof
thisfindingarediscussedingreaterdetailinthenextsection.Lastly,consistentwiththe
findingsformathscores,theresultsshowapositiveandsignificantassociationbetweenprivate
schoolattendanceandEnglishtestscoresoffemalestudents.Inaddition,theassociation
betweenprivateschoolattendanceandEnglishtestscoresislargerforfemalesthanformales.
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Table6:RegressionResultsforLocalReadingScores
DependentVariable LocalReading(Score0-1)17 (1) (2) (3) (4) (5)
LPM DistrictFE VillageFEVillageFEw/Interaction SiblingFE
KeyIndependentVariable
Private 0.0477*** 0.0142*** 0.00979*** 0.00727** -0.00758 (0.00263) (0.00277) (0.00292) (0.00339) (0.00541)
ChildVariables
Female 0.000719 -0.00547** -0.00762*** -0.00999*** -0.00778**
(0.00228) (0.00224) (0.00218) (0.00274) (0.00315)
Female*Private
0.00654
(0.00441)
Age 0.0220*** 0.0239*** 0.0290*** 0.0290*** 0.0448***
(0.000822) (0.000820) (0.000849) (0.000849) (0.00142)
Grade 0.0897*** 0.0865*** 0.0820*** 0.0819*** 0.0682***
(0.000945) (0.000960) (0.00102) (0.00102) (0.00174)
ChildTakingPaidTutoring 0.0511*** 0.0449*** 0.0441*** 0.0442*** 0.0538***
(0.00319) (0.00327) (0.00361) (0.00361) (0.00882)
TuitionFee/100 -0.00169** -0.00139** -0.00114 -0.00113 -0.00406*** (0.000720) (0.000701) (0.000736) (0.000736) (0.00145)
HouseholdVariables
HouseholdSize 0.000260 -0.000460 0.000106 0.000109
(0.000327) (0.000364) (0.000402) (0.000402)
HouseTypeMixed 0.0412*** 0.0272*** 0.0106*** 0.0106***
(0.00290) (0.00313) (0.00358) (0.00358)
HouseTypeSolid 0.0593*** 0.0322*** 0.0126*** 0.0127***
(0.00318) (0.00352) (0.00409) (0.00409)
HouseOwned -0.0222*** 0.00330 0.00743* 0.00749*
(0.00383) (0.00398) (0.00447) (0.00447)
ElectricityConnectionAvailable 0.0248*** 0.00521 0.00216 0.00218
(0.00422) (0.00468) (0.00620) (0.00620)MobileAvailable 0.00911*** 0.0118*** 0.0129*** 0.0129***
(0.00352) (0.00390) (0.00467) (0.00467)
TVAvailable -0.0146*** 0.000367 0.000912 0.000939
17Thedummyvariable"LocalReading"takesonavalueof1ifthestudentscoresabovetheaveragelocalreadingscoreof3.476andtakesonavalueof0ifthestudentscoresbelowtheaverage.
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(0.00280) (0.00296) (0.00326) (0.00326)
NearestSchoolDistance(km) 0.000214*** 0.0385 0.000101 0.000103
(0.0548) (0.0470) (0.000121) (0.000121)ParentVariables
Mother'sAge 0.00230*** 0.00160*** 0.000468 0.000469 -0.00432
(0.000300) (0.000305) (0.000326) (0.000326) (0.00301)
Mother'sEducationSecondary 0.0192*** 0.00639* 0.00869*** 0.00869*** -0.0256
(0.00326) (0.00329) (0.00336) (0.00336) (0.0385)
Mother'sEducationHighSchool 0.0406*** 0.0251*** 0.0213*** 0.0212*** 0.0430
(0.00395) (0.00405) (0.00420) (0.00420) (0.0512)
Mother'sEducationSomeCollege 0.0435*** 0.0323*** 0.0355*** 0.0353*** -0.0466
(0.00732) (0.00735) (0.00749) (0.00749) (0.0758)
Father'sAge -0.00103*** -0.00109*** -0.000279 -0.000279 0.00683**
(0.000270) (0.000274) (0.000291) (0.000291) (0.00276)
Father'sEducationSecondary 0.00629** -0.00106 0.000959 0.000981 0.00203
(0.00306) (0.00309) (0.00323) (0.00323) (0.0352)
Father'sEducationHighSchool 0.00872*** 0.0107*** 0.0117*** 0.0117*** -0.0374
(0.00316) (0.00318) (0.00334) (0.00334) (0.0356)
Father'sEducationSomeCollege 0.0134*** 0.0311*** 0.0219*** 0.0219*** -0.00640 (0.00438) (0.00449) (0.00475) (0.00475) (0.0491)Constant -0.170*** -0.129*** -0.145*** -0.144*** -0.299***
(0.00897) (0.00936) (0.0107) (0.0107) (0.0606)
Observations 117,966 117,966 117,966 117,966 117,966R-squared 0.440 0.473 0.558 0.558 0.767F-statisticforjointhypothesis:private=female*private=0
12.14***(0.0005)
Robuststandarderrorsareinparentheses.P-valuesaregiveninparenthesesunderF-statisticsforjointhypothesistests.***p<0.01,**p<0.05,*p<0.1
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LocalReadingTestScores
Themagnitudeoftherelationshipbetweenprivateschoolingandlocalreadingtest
scoresisloweracrossthedifferentregressionspecificationsascomparedtothemagnitudeof
therelationshipbetweenprivateschoolingandmathandEnglishtestscores.ThesimpleLPM
modelinTable7showsthatgoingtoaprivateschoolinsteadofagovernmentschoolis
associatedwitha4.77percentagepointincreaseintheprobabilityofscoringabovetheaverage
forlocalreadingtestscores.Applyingdistrict-levelfixedeffectsreducesthemagnitudeofthe
relationshipto1.42percentagepoints.Controllingforvillage-levelfixedcharacteristicsreduces
themagnitudeoftherelationshipevenfurtherto0.98percentagepoints.Therelativelysmall
relationshipbetweenprivateschoolattendanceandlocalreadingtestscorescouldbe
explainedbythefactthatprivateschoolstendtofocusonlearninginEnglishmorethan
governmentschools.Infact,parentstendtoassociatethehigherqualityofprivateschool
educationwithinstructioninEnglish(SocietyforAdvancementofEducation,2015).Therefore,
itispossiblethatlocallanguageinstructioninprivateschoolsisnotashigh-qualityasthe
instructioningovernmentschools.Lastly,consistentwiththefindingsformathandEnglishtest
scores,theresultsshowapositiveandsignificantassociationbetweenprivateschool
attendanceandlocalreadingtestscoresoffemalestudents.Inaddition,theassociation
betweenprivateschoolattendanceandlocalreadingtestscoresislargerforfemalesthanfor
males.
ControlVariables
Myestimatesoftherelationshipbetweenprivateschoolingandgender,andbetween
privateschoolingandindicatorsoffamily'swealth,areunsurprising.Boystendtoscorehigher
inmathandEnglishthangirlsacrossthevariousspecifications.Takingpaidtuitionoutsideof
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regularschoolhasastatisticallysignificantandpositiveassociationwithmath,Englishandlocal
readingtestscores.Havingabrickhouseinsteadofahutisalsosignificantlypositively
associatedwithtestscoresacrossthethreesubjects,underscoringtheimportantroleoffamily
income.Inaddition,itisnotablethatparentaleducationlevelsplayanimportantrolein
studentperformance.InlinewiththefindingsbyAndrabietal.(2009),childrenwithmothers
whohavesecondary-leveleducationperformsignificantlybetterthanchildrenwithmothers
whohavenoeducation.Similarly,childrenwhosefathershavesomecollegeeducationtendto
havesignificantlyhighertestscores.
DISCUSSION
OverviewofFindings PrivateschoolsinPakistanhaveexpandeddramaticallyoverthepastfewdecades.In
lightofthisfact,policymakershavequestionedtheroleofprivateschoolinginimprovingaccess
toqualityeducationinPakistan.Whileasubstantialbodyofliteraturefindsthatprivate
schoolinghasapositiverelationshipwithtestscores,themajorityofstudiesusedatathathas
limitedgeographicalscope.MyresearchusesdatafromthenationallyrepresentativeASER
surveytoanalyzetherelationshipbetweenprivateschoolsandtestscores.
Theresultsofmyprimaryspecificationssuggestthat,consistentwithpreviousresearch
(Aldermanetal.2001;Andrabietal.2010;Aslam2009;Dasetal.2006;FrenchandKingdon
2010),privateschoolshaveamodestandpositiverelationshipwithtestscores.Theregression
resultsforallthreetests(math,readinginEnglishandreadinginlocallanguage)reveala
significantandpositiveassociationbetweenprivateschoolingandtestscores.However,this
relationshipdisappearswhensiblingfixedeffectsareincludedinthemodel.Whiletheresults
formathandlocalreadingtestscoresarepositiveandinsignificant,theresultsforEnglishtest
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scoresrevealthatgoingtoprivateschoolsisassociatedwitha1.86percentagepointdecrease
inprobabilityofscoringabovetheaverage,implyingthatthereisaprivateschool
disadvantage.Thisresultissurprisingconsideringprivateschoolsareperceivedasfocusing
moreoninstructioninEnglishthantheirgovernmentcounterparts.However,asexplained
previously,mysiblingfixedeffectsspecificationsrelyonmuchlessvariationthanmyother
specificationstoestimatetherelationshipbetweenprivateschoolattendanceandtestscores.
Themixedresultsofthesiblingfixedeffectsmodelhighlighttheneedformoresophisticated
identificationstrategiesusingnationallyrepresentativedatatogainabetterunderstandingof
therelationshipbetweenprivateschoolattendanceandtestscores.
MyresultsareparticularlyintriguingwhenIexaminetherelationshipbetweenprivate
schoolingandtestscoresseparatelybygender.Forfemales,goingtoaprivateschoolinsteadof
agovernmentschoolisassociatedwithalargerchangeinprobabilityofscoringabovethe
average(incomparisontomales)acrossallthreetesttypes.Theassociationbetweenprivate
schoolingandtestscoresoffemalestudentsisconsistentlysignificantattheonepercentlevel.
AccordingtoAndrabietal.(2006),low-costprivateschoolstendtohirefemaleteachers
residinginthelocalareastokeepsalarycostslow.Itispossiblethatthegreaterpresenceof
femaleteachersinprivateschoolsfacilitatesproductivein-classinteractionforfemalestudents
thatmayleadtobettertestperformance.Inaddition,previousevidencebyAslam(2009)has
suggestedthatgirlstendtohavepooreraccesstoprivateschoolingthanboys.However,no
priorstudiesfromPakistanhavepresentedevidenceforthedifferentialrelationshipbetween
privateschoolingandtestscoresforboysandgirls.Thisfindingprovidesanopportunityfor
furtherresearch.
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Althoughmyanalysiscontrolsforvariouschild-,parent-andhousehold-level
characteristics,myresultsmaybesubjecttoomittedvariablebias.Factorssuchasparental
motivationarehardtomeasureandarelikelylinkedtobothprivateschoolattendanceandtest
scoreoutcomes.Moremotivatedparentsarelikelytosendtheirchildrentoschoolsthatare
perceivedashavinghigherquality,thatis,privateschools.Similarly,parentswithhigherlevels
ofmotivationarelikelytoinvestmoretimeandresourcesintheirchild'seducation,for
example,byhelpingtheirchildwithhomework.Theabsenceofthisvariablefromtheanalysis
wouldupwardlybiasourcoefficient.
Furthermore,parentalattitudesandexpectationscansignificantlydifferdependingon
thechild'sgender.Forexample,someresearchhassuggestedthatparentshavemore
confidenceinamalechild'smathabilitiesthanintheabilitiesofafemalechild(Stockdale,
1995).Itispossiblethatparentswhosendafemalechildtoaprivateschoolarelikelytohave
lessdiscriminatoryattitudestowardsgirls'education,whichcouldexplainthestrongerprivate
schooleffectforgirlsthanforboys.Becausemymodeldoesnotcaptureunobservableparental
attitudesandexpectationstowardsfemaleeducation,itispossiblethatthecoefficienton
privateschoolingisupwardlybiased:parentalsupportandencouragementislikelytobe
positivelylinkedtoprivateschoolattendanceforfemalesaswellasbettertestscores.
PolicyImplications
Thepolicyimplicationsofmystudyaremixed.Inlightoftheriseofprivateschoolsand
poorqualityoflearningingovernmentschools,thedebateregardingtheroleofthepublicand
privatesectorsineducationremainsunresolved.However,itisclearthatalargeanddiverse
privatesector,comprisingbotheliteandlow-feeprivateschools,isanintegralpartofPakistan's
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educationallandscape.Inlinewiththestanceofsupportersofprivatizededucation,thisstudy
presentssomeevidencethatprivateschoolingcanhelpimproveaccesstoqualityeducation.
Myfindingssuggestthattheimpactofprivateeducationonfemalelearningoutcomes
maybemeaningfulandsignificant.Therefore,privateschoolsmayplayanimportantrolein
narrowingthegenderdisparitiesinachievement.Thereisaneedforbettercollectionand
utilizationofgender-disaggregateddatatofurtherexaminetherelationshipbetweenprivate
schoolingandfemaleachievement.
IrrespectiveofthemarginalsuperiorityofprivateschoolsinPakistan,childreninboth
governmentandprivateschoolshavepoorlearningoutcomesonaverage.Thepolicysolution
toPakistan'seducationcrisisistounderstandwhybothgovernmentandprivateschoolsare
failingtodeliverqualityeducation.Thereisaneedtobetterutilizeexistingdatatounderstand
thereasonsforpoorlearningtakingplaceinPakistanischools.Oneimportantsteptowardsthat
goalistogathermoredetailedandin-depthsurveyinformationtounderstandhowprivate
schoolscanbeusedtoprovideaccessibleandqualityeducation.
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APPENDIX
AppendixA:DescriptiveStatisticsDisaggregatedbyRegionType
Urban Rural Difference SEDependentVariables
MathHighestLevel 0.63 0.48 0.15*** 0.00EnglishReading 0.70 0.53 0.17*** 0.00LocalReading 0.65 0.49 0.16*** 0.00
ChildCharacteristics Age 10.01 9.71 0.30*** 0.03Female 0.44 0.35 0.08*** 0.00Grade 4.57 3.88 0.70*** 0.02ChildTakingPaidTutoring 0.24 0.13 0.11*** 0.00TuitionFee 518.79 333.95 184.83*** 1.86
HouseholdCharacteristics HouseholdSize 5.57 7.53 -1.96*** 0.03HouseTypeMixed 0.19 0.31 -0.13*** 0.00HouseTypeSolid 0.77 0.28 0.50*** 0.00HouseOwned 0.75 0.93 -0.18*** 0.00ElectricityConnectionAvailable 0.99 0.89 0.10*** 0.00MobileAvailable 0.99 0.82 0.17*** 0.00TVAvailable 0.95 0.61 0.34*** 0.00NearestSchoolDistance(km) 1.41 1.55 -0.14ns 0.12
ParentCharacteristics Mother'sAge 36.16 36.51 -0.35*** 0.06Mother'sEducationSecondary 0.20 0.17 0.03*** 0.00Mother'sEducationHighSchool 0.38 0.10 0.28*** 0.00Mother'sEducationSomeCollege 0.10 0.02 0.08*** 0.00Father'sAge 40.70 41.34 -0.64*** 0.07Father'sEducationSecondary 0.13 0.22 -0.09*** 0.00Father'sEducationHighSchool 0.39 0.24 0.16*** 0.00Father'sEducationSomeCollege 0.29 0.09 0.19*** 0.00Totalsamplesizeis117,966,with15,681observationsforurbanareasand102,285observationsforruralareas.