THE ACHIEVEMENT GAP BETWEEN GOVERNMENT AND PRIVATE …

THEACHIEVEMENTGAPBETWEENGOVERNMENTANDPRIVATESCHOOLSINPAKISTAN

AThesis

submittedtotheFacultyofthe

GraduateSchoolofArtsandSciences

ofGeorgetownUniversity

inpartialfulfillmentoftherequirementsforthe

degreeof

MasterofPublicPolicy

inPublicPolicy

By

MaryamAkmal,B.A.

Washington,DC

April12,2016

ii

Copyright2016byMaryamAkmal

AllRightsReserved

iii

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.

iv

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.

2

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.

17

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.

18

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).

19

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.

20

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

21

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

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)

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.

24

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)


(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)

25


NearestSchoolDistance(km) 0.000172** 0.0206 0.000102 0.000103


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)


16.98***(0.0000)

26

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.

27

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)


(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.

28

(0.00280) (0.00296) (0.00326) (0.00326)

NearestSchoolDistance(km) 0.000214*** 0.0385 0.000101 0.000103


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)


12.14***(0.0005)


29

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

30

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

31

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.

32

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

33

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.

34

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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.

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