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D O C U M E N T O

D E T R A B A J O

Instituto de EconomíaD

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I N S T I T U T O D E E C O N O M Í A

www.economia.puc.cl • ISSN (edición impresa) 0716-7334 • ISSN (edición electrónica) 0717-7593

Parental Monitoring ande Children´s Internet Use: The Role of Information,Control, and Cues

Francisco Gallego, Ofer Malamud, Crsitian Pop-Eleches.

4962017

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ParentalMonitoringandChildren'sInternetUse:TheRoleofInformation,Control,andCues

FranciscoGallegoPontificiaUniversidadCatólicadeChile

OferMalamudNorthwesternUniversity

CristianPop‐ElechesColumbiaUniversity

October2017

Abstract*Thispaperexploreshowasymmetricinformationbetweenparentsandchildrenanddirectparentalcontrolscaninfluencechildren’sinternetuseinChile.Wedesignedandimplementedasetofrandomizedinterventionswherebyapproximately7700parentsweresentweeklySMSsmessageswith(i)specificinformationabouttheirchildren’sinternetuse,and/or(ii)encouragementandassistancewiththeinstallationofparentalcontrolsoftware.WeseparatetheinformationalcontentfromthecueassociatedwithSMSmessagesandvarythestrengthofthecuesbyrandomlyassigningwhetherparentsreceivedmessagesinapredictableorunpredictablefashion.Ouranalysisyieldsthreemainfindings.First,wefindthatmessagesprovidingparentswithspecificinformationreducechildren’sinternetuseby6‐10percentandhelpparentsmitigatetheproblemofasymmetricinformationinthehousehold.Second,wedonotfindsignificantimpactsfromhelpingparentsdirectlycontroltheirchildren’sInternetaccesswithparentalcontrolsoftware.Third,thestrengthorsalienceofthecueassociatedwithreceivingamessagehasanindependentimpactoninternetuse.

* We would like thank Ramon Rodriguez and his staff at the Ministry of Education for providing the data and technical assistance necessary to conduct this study. We are especially grateful to Jaime Bellolio who collaborated with us on an evaluation of the “Yo Elijo mi PC” program that is the setting of the present study. Cristian Larroulet, Jose Ignacio Cuesta, Antonia Asenjo, Magdalena Bennet, Ana Mendoza, Dario Romero, Sebastian Otero, and Alejandro Saenz provided excellent research assistance. We would like to thank Paloma Acevedo, Peter Bergman, Samuel Berlinski, Marianne Bertrand, Lucas Coffman, Stefano Dellavigna, Jeanne Lafortune, Philip Oreopoulos, as well as seminar participants at Columbia Teacher’s College, the Inter-American Development Bank, Princeton University, and PUC-Chile for comments and suggestions. We would like to thank FONDECYT (Project 1141111), J-PAL, the Columbia-Chile fund, and the Population Research Center, grant # R24 HD051152-05 from the National Institute of Child Health and Human Development for financial support. All errors are our own.

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1. Introduction

Economistshavelongbeeninterestedinhowparentscanaffecttheirchildren’sactions.

Becker(1974,1981)introducedhisfamous“RottenKidTheorem”toshowthat,under

certainassumptions,altruisticparentscancontroltheirchildren’sactionsindirectly

throughex-posttransfers.However,asBergstrom(1989)pointedout,theRottenKid

theoremdoesnotholdinthepresenceofmoralhazardduetoasymmetricinformation

—i.e.whenparentscannotobservetheirchildren’sactions.BursztynandCoffman

(2012)provideevidenceforsuchasymmetricinformationamongparentsinBrazil.

Nevertheless,evenintheabsenceofasymmetricinformation,parentsmaynotbeable

tocontroltheirchildren’sactionsbecausetheyareunableorunwillingtomake

negativetransfersthatimposelargecostsonthechild(Weinberg,2001;Berry,2015).

Inthesecases,parentsmaywishforthepossibilityofcontrollingtheirchildren’sactions

directly.

Themainmotivationforthispaperistoexploretheroleofdirectcontrolsand

asymmetricinformationbetweenparentsandchildreninthecontextofhome

computersandinternetuse.Tothisend,wedesignedandimplementedasetof

randomizedinterventionstotesttheimpactofsendingparentsweeklySMSmessages

containingspecificinformationabouttheirchildren’srecentinternetuseand/or

encouragementandassistancewithinstallingparentalcontrolsoftware.Providing

parentswithinformationabouttheirchildren’sinternetuseshouldhelpalleviate

informationalasymmetriesandthereforereducethepotentialformoralhazard.

Encouragingparentstoinstallparentalcontrolsoftwarecanhelpparentsbypassthe

needtoincentivizetheirchildrenorenforcerulesrelatedtocomputeruse,assuming

thatparentsareabletoinstallandoperatesuchparentalcontrolsoftware.

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Exploringparent-childinteractionswhennavigatingtechnologyisespecially

instructivebecausetheinformationalasymmetriesarelikelytobepronouncedand

implementingdirectcontrolscanbedifficult;childrenareoftenquickertoadapttonew

technologiesandparentsmayencounterchallengesinunderstandinghowchildrenuse

technology.ArecentstudybyMalamudandPop-Eleches(2011)foundthatparental

rulesabouthomeworkandcomputeruseattenuatedthenegativeeffectsofcomputer

ownership,suggestingthatparentalmonitoringandsupervisionmaybeimportant

mediatingfactors.Buttheseresultswereonlysuggestivebecausetheruleswerenot

randomlyassignedandmayhavereflectedotherparentalcharacteristics.

Asecondmotivationforthispaperistounderstandhowandwhytheprovision

ofinformationaffectsbehavior.Tothisend,wedesignedourinterventionstoseparate

theinformationalcontentfromthecueassociatedwiththeSMSmessages,and

attemptedtovarythestrengthorsalienceofthecuesbyrandomlyassigningwhether

parentsreceivedmessagesinapredictableorunpredictablefashion.Thiswasbasedon

researchinneurosciencewhichsuggeststhathumanresponsesmayberelatedtothe

predictabilityornoveltyofthestimuli(Parkin,1997;Bernsetal.,2001;Fenkeretal.,

2008).Itisalsocloselyrelatedtoresearchinpsychologyonhowdifferentschedulesof

reinforcementaffectbehavior(FersterandSkinner,1957).

Thereisalargeliteratureexaminingtheimpactofprovidinginformationto

consumers,voters,students,parents,etc.(AlcottandRogers,2014;FinanandFerraz,

2011;Jensen,2010;Dizon-Ross,2016).Moreover,acoupleofrecentpapersstudythe

effectofsendingSMSmessagestoparentswithinformationabouttheirmissed-

assignment,attendance,andgrades(Bergman,2016;Berlinskietal.,2017).However,

thesestudieshavenotseparatedtheeffectonbehaviorduetotheinformationalcontent

ofthemessagevs.thecuethatisassociatedwithgettingthemessageitself.Thereisalso

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researchexamininghowSMSmessagescanserveasreminderstopromotebeneficial

changestobehaviorineducation(Castleman,2015),health(Pop-Elechesetal.,2011)

andsavings(Karlanetal.,2014)).Someofthesestudiesalsoconsidertheroleoflimited

attentioninexplainingtheeffectivenessofSMSmessagesandreminders.1Nevertheless,

ourunderstandingofthesalienceorstrengthofthesetypeofmessagesremains

limited.2

Wefocusonasampleofchildrenin7thand8thgradewhoreceivedfree

computersand12monthsoffreeinternetsubscriptionsthroughChile’s“YoElijomiPC”

(YEMPC)programin2013.Wehavedataontheintensityofinternetuseatthedaily

levelfromtheinternetserviceprovider(ISP)whichservedallofthecomputers

providedtothechildreninoursample.Accordingtothisdata,childrendownloaded

approximately150MBofinternetcontentdailywhichtranslatestoabout3hoursof

internetuseonadailybasis.GiventhattheAmericanAcademyofPediatrics(AAP)

recommendsnomorethan2hoursofscreentimeforchildren(AAP,2016),this

suggestsaroleforparentalsupervisionofInternetuseregardlessofthetypeofuse.

Ouruniquedataenabledustointroduceatreatmentthatprovidedparentswith

informationabouttheirchildren’sinternetuse.Forthis“information”treatment,we

sentparentsweeklySMSmessagesprovidingspecificinformationfromtheISPabout

theintensityofinternetuse,intermsofMBsuploaded/downloaded,overtheprevious

week.Forthe“parentalcontrol”treatment,wesentparentsweeklySMSsoffering

assistancewiththeinstallationofWindows8(W8)parentalcontrolsoftware.Wealso

incorporatedatreatmentarmthatincludedbothISPinformationandassistancewith

W8parentalcontrolstotestforpossibleinteractionsbetweenthesetreatments.To

1Taubinsky(2014)andEricson(2017)providetheoreticalanalysesofhowlimitedattentionandpresentbiascanaffecttheeffectivenessofmessagesandreminders.2Bertrandetal.(2010)considertheeffectofdifferentcuesinthecontextofamarketingfieldexperiment.

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disentangletheinformationalcontentandtheofferofassistancefromthecue

associatedwithSMSmessages,wecomparethesetreatmentstoacontrolgroupin

whichparentsreceivedweeklySMSsremindingthemthatchildrenshouldmakegood

useoftheircomputers,amessagethatwasincludedineverytreatment.Inaddition,we

attemptedtovarythestrengthofthecuewithineachofourtreatmentarmsby

randomlyassignparentstoeitherreceivetheSMSsonthesamedayoftheweek(the

“fixed”subgroups)oraonrandomdayoftheweek(the“random”subgroups).Allof

theseinterventionslastedfor14weeks.

Wehavethreemainsetsofresults.First,wefindthathouseholdsinwhich

parentsreceivedISPinformationaboutinternetusehada6to10percentlower

intensityofinternetuseduringthetreatmentperiodrelativetohouseholdsinthe

controlgroup.Theseeffectspersistintheweeksandmonthsaftertreatmentended.

Thissuggeststhatourtemporaryinterventionprovidinginformationoninternetuse

mayhavealteredthepermanentintra-householdequilibriumandhelpedparentssolve

theproblemofasymmetricinformationinamorepermanentway.Wealsoshowthat

therearestatisticallysignificantreductionsinusepreciselyonthedaysimmediately

afterreceivingtheISPinformationandthiseffectismorerelevantintheearlyweeksof

theexperiment.Moreover,itistheSMSmessageswhichconveythe“badnews”,i.e.that

childrenusedmoreinternetthanthereferencegroupinaspecificweek,whichproduce

amuchlargerdeclineininternetuse.Thus,thesefindingsconfirmthatitisthespecific

informationprovidedtoparentsabouttheirchildren’sinternetusewhichleadstoa

significantreductionofinternetuse.

Second,wedonotfindsignificantimpactsfromhelpingparentsdirectlycontrol

theirchildren’sinternetaccess.Inparticular,wedonotfindadifferenceininternetuse

betweenparentswhowereencouragedandprovidedassistancetoinstallparental

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controlsoftwareascomparedwiththoseinthecontrolgroupwhoonlyreceiveda

genericcue.Take-upofthisinterventionwasonly15percentwhenmeasuredinterms

oftheparentswhoactuallyrespondedtoourmessages.However,forthosetakingup

theintervention,wedonotfindchangesinInternetuseevenonthedaysimmediately

afterinstallingtheparentalcontrolsettings.Webelievethisfindingreflectsthe

considerableobstaclesfacedbylow-incomeparentsinimplementingtechnological

solutionsformonitoringandsupervisingtheirchildren.

Third,wehaveseveralresultsthathelpusopenthe“blackbox”ofhowmessages

thatcontaininformationaffectbehavior.Asmentionedabove,bysendingmessagesthat

varytheamountofinformationwecanshowtheimportantroleofamessage’s

informationalcontentonreducingInternetuse.Butouranalysisalsoshowstwo

additionalfindingsthatsuggesttheimportanceofsalience.Whenweexperimentally

variedthestrengthofthecue,wefindthathouseholdswhoreceivedSMSsonarandom

scheduleexperiencedsignificantlygreaterreductionsininternetusethanthoseon

fixedschedules,aneffectsimilarinmagnitudetothemaineffectassociatedwith

receivingtheISPinformation.Furthermore,wefindthateventheSMSmessagessentto

thecontrolgrouphadshort-termimpactsonInternetuseinthefirstweeksofthe

experiment,perhapsduetothenoveltyofthemessage.

Ourpapermakesseveralcontributions:First,weidentifyreal-timeimpactsof

theprovisionofinformationoninternetuse,animportantdimensionofchildren’s

behaviorathomethatisoftenimperfectlyobservedtoparents.Second,weuse

experimentalvariationtoisolatethecausaleffectofprovidingparentswithspecific

informationabouttheirchildren’sbehaviorandhelpingparentsexercisedirectcontrol

overtheirchildren’sbehavior.Thisdealswiththepotentialendogeneityassociatedwith

observationalcomparisonsofparentswhohaveaccesstomoreorlessinformationand

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controlovertheirchildren’sbehavior.Third,weisolatetheimpactofprovidingparents

withspecificinformationfromtheeffectofacuefromreceivingamessagebyusinga

controlgroupinwhichparentsreceivedmessageswithoutthisinformation.Suchcues

arelikelytobeanimportantpartofanyattempttoprovideparents(orothereconomic

actors)withinformation.Fourth,weattempttolearnmoreabouttheroleofthesecues

byintroducingexperimentalvariationinhowandwhenparentsreceivedthemessages.

Thus,thisstudyprovidesauniqueopportunitytobetterunderstandthenatureofintra-

familydynamicsbetweenparentsandchildrenandtobetterunderstandhowandwhy

messagescontaininginformationaffectbehavior.

Thepaperisorganizedasfollows:Section2providessomebackgroundonthe

YoElijomiPCprogram.Section3introducestheexperimentaldesign.Section4

describesthedatausedforthestudy.Section5explainstheempiricalstrategy

underlyingtheanalysis.Section6presentsthemainfindings.Section7discussesand

interpretsthesefindings.Finally,Section8concludes.

2. Background

Wedesignedandimplementedourexperimentinthecontextofthe2013cohortofthe

YEMPCprogram.YEMPCisaChileangovernmentprogramthatprovidescomputersto

7thgraderswithhighacademicachievementfromdisadvantagedhouseholds.Students

areeligiblefortheprogramiftheyattainedasufficientlyhighgradepointaverage

(GPA)in4th,5th,andthefirstsemesterof6thgradeandiftheirhouseholdscoredbelow

acertainlevelonafocalizationinstrumentcalledFichadeProtecciónSocial(FPS).3

Figure1presentsthetimelineoftheprocessesassociatedwiththe2013cohort

oftheYEMPCandwiththeexperimentweimplementedwithpartofthestudentsofthis

3Carneiroetal.(2014)presentadetaileddescriptionoftheFPSinstrument.

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cohort.ThetimelineofYEMPCforeachroundisasfollows:Eligiblestudentsare

identifiedbasedontheirFPSandGPAscoresinSeptember-Octoberoftheyearpriorto

receiptofthecomputer(2012inourcase).Inprinciple,eachstudentwhomeetsthe

FPSandGPArequirementsiseligibletoreceiveacomputer;thereisnoapplication

process.However,studentsarethenrequiredtoselectacomputerinNovember-

December.Anumberofdifferentoptionsareavailableeachyear,includinglaptopsand

desktopswithdifferentfeaturesandspecifications.Eachcomputerisequippedwith

Windows8andMicrosoftOffice.Computersaredistributedtostudentsduringthe

monthsofAprilandMay.Thecomputersaregiveninschools,inaceremonyorganized

bythemunicipalitywherethestudentisenrolled.Beginningwiththe2011cohort,

studentswithsometypesofcomputersalsoreceived12monthsoffreeInternetservice.

Ourexperimentconsidersstudentswhoenterto7thgradein2013andwere

selectedtotheprograminNovember2012.Wefocusinthebeneficiariesofthiscohort

whoreceivedfreeinternetaccesswiththeircomputersstartingfrommid-2013.

Internetisprovidedthroughaprivateprovider.32,270beneficiariesareinthis

category(outofthe52,122beneficiariesoftheprograminthe2013cohort).

Afteraprocessthatinvolvedcontactingfamiliesbyphone(usingadministrative

data)andaskingthemtoparticipateintheexperimentandtocompleteatelephone

baselinesurvey,weendedupwithasampleof9,636parentswithavalidcellphone(to

receivetheSMSs)andwhoconsentedtoparticipateintheexperiment.4

In terms of access to computers and Internet at home before receiving the

YEMPCprogram,Gallegoetal.(2017)documentthat40%ofbeneficiarieshadaPCat

home,23%hadInternetaccessathome,and6%haveacellphonewithInternetaccess.

4AppendixTable1comparesthestudentsinourexperimentalsamplewithabroadersampleofthosewhoappliedforacomputerwithaninternetconnectionunderthe“YoElijoMiPC”program.Wedonotidentifyeconomicallyrelevantdifferencesbetweentheexperimentalsampleandthebroadersample.

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Intermsofwheretheyhaveaccesstocomputer(differentfromhome):87%hadaccess

toacomputeratschool,30%inacyber-café,and68%canaccessacomputer´sfriend.

ThisimpliesthatmostofthekidsinoursamplehadaccesstoacomputerandInternet

before theprogramstartedbut that amuch smaller fractionhadpersonal computers

and personal Internet access. However, in terms of total Internet use, the median

student reported that timeusewas "access to Internet some times in theweek".This

contrasts with an average time of about three hours of Internet use per day in the

baseline of our experiment that followed the distribution of computers to recipients.

ThissuggeststheYEMPCprogramincreaseddramaticallyInternetuse.

3. ExperimentalDesign

Werandomized7,707familiesintothedifferentexperimentalgroupspresentedin

Table1.5Inadditiontothedatacomingfromthetelephonebaselinesurveywehave

administrativedataoninternetusestartingfromSeptember,2013.Usingthis

information,weimplementedastratifiedrandomizationconsideringthefollowing

strata:(i)guardian'seducation(NoHigh-School,HighSchool,College),(ii)parent

perceptionofwhetherthestudentstaystoolonginfrontofthecomputer(YesorNo,

usinginformationfromabaselinesurvey),and(iii)InternetUseastotalMBs

downloadedbetweenSeptemberandDecember15,2013(usingadministrativedata

fromtheISPprovider).

Theexperimentconsistsofthedeliveryofweeklytextmessagestotheparents

includedinthesample.TheSMSsdifferedintermsofcontentandthedayoftheweekin

5Wealsohavea“referencegroup”of1,929familieswhichweusetocomputeInternetuseforthepurposesofcomparisontooneoftheexperimentalarms.

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whichtheyweredelivered.Intermsofcontents,wesentthreetypesofSMSsusingthe

followingtexts:

• SMS-only:“WehopeyourchildmakesgooduseoftheYoElijoMiPClaptopthat

he/shewon”.

• ISP:“Wehopeyourchildmakesgooduseofthelaptopthathe/shewon.Yourchild

downloadedXXMBstheweekoftheDD-MMM,{“morethan",or“similarto",or“less

than"}whatatypicalchilddownloaded:YYMBs.”6

• W8:“Wehopeyourchildmakesgooduseofthelaptopthathe/shewon.The

ParentalControlprogramofWindows8canhelpyousuperviseyourchild's

computeruse.CallusatXXX-XXXXforassistance.”

GroupT0receivedtheSMS-onlymessage,T1receivedtheISPmessage,T2receivedthe

W8message,andT3receivedboththeISPandW8messages(inthatorder).Ineach

groupabouthalfofthefamiliesreceivedthetreatmentsinfixed-daysoftheweek(and

werandomizethedayinwhichtheyreceivedthemessage)andabouthalfofthe

familiesreceivedthemessagesonrandomdaysofeachweek.

Themessagesweresentweeklybetween4pmand5pmondifferentweekdays

betweenDecember23,2013andApril6,2014.Thisperiodcoversthesummervacation

(fromDecember,232013toMarch6,2014)andtheschoolperiod(fromMarch7,

2014).WediscusstheactualshareofSMSsreceivedineachtreatmentarminSection

6.1.

4. Data

ThemainsourceofadministrativeinformationforourstudyisinformationonInternet

useforeachbeneficiarycollectedbytheInternetprovider.Thisincludesdaily

6The“referencegroup”isusedtocomputetheweeklyaverageofMBsdownloadedbyatypicalchild.

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informationonMBsdownloadedanduploaded.Wereceivedinformationforeach

beneficiaryfortheperiodcoveredbetweenSeptember22,2014andJune,17,2015.

ThisimpliesthatwehaveinformationonInternetusefortheperiodbeforetheSMS

treatmentsstarted,fortheperiodinwhichtheSMSsweredelivered,andalsofor12

weeksafterthetreatmentwasdiscontinued.

Inaddition,weuseinformationfromthebaselinesurveyforthestratified

randomizationandtocontrolforseveralbaselinecharacteristicsinourmain

specifications.Theseincludehouseholddatawithinformationaboutthestudentsand

parentssuchas,studentgender,guardianage,familycomposition,numberofsiblings,

parents’education,parents’workingconditions,andguardian´sperceptionsofinternet

andcomputeruse.Wehaveinformationforalltheindividualsincludedinthesample,

asthiswaspartoftheenrollmentprocess.

Table2summarizesstudentandparentalcharacteristicsforourmainanalytical

sample.ThedailymeanMBsdownloadinthe3monthsofthepre-treatmentperiodwas

approximately150MBwhichcorrespondedto186minutesofpredictedinternetuse.

Almostallofthechildreninoursamplelivewiththeirmothers,andoversixtypercent

alsolivewiththeirfathers.Moreover,approximatelythree-quartershaveasiblingliving

withthemwhilefifteenpercentalsolivewithagrandparent.Oursampleofstudentsis

43percentfemaleandtheyhaveanaverageof1.7siblings.Theaverageageofthe

guardianis40yearsold.Mostoftheguardianshavesecondaryeducation,withalmost

halfhavingcompleteditandanotherfifteenpercenthavesomesecondaryeducation.

Theremainderhaveonlyanelementaryeducationwithjustfourpercenthavingsome

highereducation,whichisnotsurprisinggiventhetargetpopulationoftheYoElijomi

PCprogram.

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Duringthetreatmentperiod,wewereabletogatherinformationaboutwhether

theSMSssentwerereceivedonthecellphonesoftreatedparents.Thisservesto

measurethetechnicalpartofthetake-up,asrelatedtotheactualdeliveryofthe

messages.WealsocollecteddataontheinstallationofW8parentalcontrolsetting

throughourcallcenter.Thus,thismeasure,capturesthetake-upoftheW8treatment

directlyfromus,thoughparentscouldinstallparentalcontrolsoftwarebyothermeans.

WediscussandusethisinformationinTable4.

Then,afterthedeliveryofthetreatmentwasended,weappliedabriefphone

interviewbetweenAprilandearlyMay2014toexaminesomepotentialmechanisms

underlyingourestimatedimpactsoninternetuse.Wewereabletocontact5,001

parentswhoconsentedtoparticipateinthesurvey.Thisisequivalentto57%ofthe

originalsample.Thisismostlyaconsequenceofthedifficultytoreachparentsonthe

phone,astherejectionrateofthesurveywasjustabout14%.Thesurveyincludesa

seriesofquestionsaboutparentrecollectionsofreceivingSMSs,theusefulnessofthe

SMSs,andthedecisiontoinstalltheparentalcontrolsoftware.7Weusethisinformation

inTable5.

Finally,inordertoeasetheinterpretationoftheresults,weconstructedaproxy

fortimeuse(inseconds)usinginformationfromstudentsofthe2012YEMPCcohort.

WecollectedinformationonbothMBs(downloadedanduploaded)andtimeofinternet

connectionforasampleof48,920studentsfor125days(frommid-Apriltoearly

December,2012).UsingthisinformationweestimatedOLSregressionsmodelsinwhich

timeofInternetuseisanon-linearfunctions(includinginteractions)ofMb

downloaded,Mbuploaded,dummiesforthedayoftheweek,dummiesforholidays,and

7AppendixTable1presentsacomparisonofbaselinecharacteristicsbetweenthemainsampleandthesurveysample.

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dummiesfordiscretelevelsofuse(fourcategoriesthatreflecthigher,high,normal,low

use).WeusetheestimationwiththehighestR2(0.621)toimputetimeofInternetuse

foroursample.Wekeeptheseestimatesjustasreferencefortheinterpretationofthe

mainresultsinthepaperandpresentimpactestimatesintheAppendixasrobustness

checks.

Table3showsbalanceinthemaindemographiccharacteristicsforoursample

acrosseachofourtreatmentarms,T1,T2,andT3relativetothecontrolgroupT0.The

F-testpresentedinthelastcolumnrejectsbalanceacrossthetreatmentsandthe

controlgroupatthe10%levelforjustonevariable.Thiscorrespondstowhetherthe

childliveswiththeirmother.8Still,thedifferencesinaveragesforthisvariableacross

groupsdonotseemtobeeconomicallyrelevant.Wecontrolforthisvectorofcovariates

insomeofourregressionspecificationsand,notsurprisinglygiventhebalanceacross

treatmentarms,ourcoefficientsremainlargelyunchanged.

5. Empiricalstrategy

Wepresenttwoalternativeregressionapproachesforestimatingtheimpactofour

maininterventionsonInternetuse.First,weanalyzetheimpactofthedifferent

interventionsbycomparingaverageInternetuseacrosshouseholdsallocatedtothe

differentgroups.ThisallowsustoidentifytheaverageeffectonInternetuseoverthe

entiretreatmentperiod.Second,wealsoanalyzetheeffectsoftheactualreceptionof

theSMSmessagesusinganeventstudyanalysisinwhichweexploitwithin-event

8AppendixTable2presentsbalancetestsfortherandom/fixedschedulesub-treatmentsandAppendixTable3presentsbalancetestsforthesurveysample.Weonlyobservetwounbalancedvariablesfortherandom/fixedschedulecomparisons(outof15),andwithsmalldifferences:therandom-schedulehouseholdshaveslightlylessparentswithcompletehighereducation(1p.p.)andwerepresentwithahigherprobabilityintheothercategorygroupforcurrentemploymentstatus(1.p.p.).

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variationinInternetuseonadailybasis.Thisallowstobetterunderstandthe

mechanismsbehindthechangesinbehavior.

Inthefirstapproach,weadoptthestandardspecificationusedtoanalyze

randomizedexperimentsbyseparatelyidentifyingtheimpactsofeachtreatmentarm,

T1,T2,orT3relativetoourcontrolgroupT0:

!! = !′!! + !!!1! + !!!2! + !!!3! + !!

where!! isameasureofinternetuseforhousehold!,.Forsomespecificationswealso

includeasetofcontrolvariables(!!).ThecoefficientonT1capturestheeffectof

receivingISPinformationwithrespecttothecontrolgroupT0,thecoefficientonT2

capturestheeffectofreceivinginformationonhowtoinstallparentalcontrolswith

respecttothecontrolgroup,andT3capturestheeffectofreceivinginformationonboth

ISPandhowtoinstallparentalcontrols.TotheextentthatnotalloftheSMSssentare

actuallyreceived,thesecoefficientswillreflectsintention-to-treat(ITT)parameters.To

theextentthatthereisimperfectcomplianceonthismargin,thesecoefficientscanbe

scaledupbythefractionofmessagesreceived(althoughasshownbelow,thevast

majorityofSMSssentwereactuallyreceived).

Tofurtherimproveprecision,wealsoconsideranalternativeregressionmodel

thataccountsforthefactthatgroupT3effectivelyreceivesbothofthetreatments

providedtogroupsT1andT2:

!! = !′!! + !!!"#_!"#$%&'()$"! + !!!"#$%&"'()%&#)'*! + !!

where!! and!! aredefinedasbefore,!"#_!"#$%&'()$"! isanindicatorforhouseholds

thatareeitheringroupT1orT3,and!"#$%&"'()%&#)'*! isanindicatorforhouseholds

thatareeitheringroupT1orT3.Thus,weestimatetheimpactofparentsreceivingISP

informationregardinginternetusewhethertheyareinT1orT3andtheimpactof

receivinginformationaboutW8parentalcontrolswhetherornottheyareinT2orT3.

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Notethatthisspecificationdoesrequireustoassumethatthereareno

complementaritiesbetweenthetwoseparatetreatments.Aswillbeseenbelow,wedo

notobserveanysignificanteffectsforT2relativetothecontrolgroupandtheestimated

impactsforT3aresimilartothoseestimatedforT1.Therefore,wethinkthatthe

assumptionunderlyingthisalternativemodelisfairlyinnocuous.

Second,wetakeadvantageofthefactthatwehavedailyinformationoninternet

usetoestimatethedynamiceffectofeachSMS“event”ondailyInternetuseonthedays

immediatelyprecedingandfollowingthedayonwhichthemessagewassent.Westack

alltheeventsforeachsub-treatmentandestimatethefollowingmodel:

!!"! = !!!!!!

!!!!+ !!!!

!

!!!+ !!!"#!! ∗ !"#!

!!

!!!!+ !!!"#!! ∗ !"#!

!

!!!

+ !!!"!! ∗ !"!!!

!!!!+ !!!"!! ∗ !"!

!

!!!+ !! + !!"!

or,withabuseofnotation(becausewearenotexplicitlyexcludingd=-1)

!!"# = !!!!!

!!!!+ !!!"#!! ∗ !"#!

!

!!!!+ !!!"!! ∗ !"!

!

!!!!+ !! + !!"#

whereYandiaredefinedasbefore,dreferstotheday,ereferstotheevent,ISPisa

dummythattakesavalueof1forhouseholdsintheISPinformationgroup,PCisa

dummythattakesavalueof1forhouseholdsintheparentalcontrolsgroup,Drefersto

daydummyvariables,andµe denotetheeventfixedeffects.Thisapproachallowsusto

estimateavectorofcoefficientsthatcapturedifferencesinInternetusewithrespectto

day-1(i.e.,thedaybeforeactuallyreceivingthetreatment)forallthetreatmentgroups.

Forinstanceθ-3measuresthedifferenceinInternetthreedaysbeforereceivingthe

messagewithrespecttothedaybeforethemessagewasreceivedforthecontrolgroup,

(θ-3+θ-3ISP)isthesameeffectforhouseholdsintheISPinformationgroup,and(θ-3+θ-

3PC)fortheparentalcontrolgroup.

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Wealsoestimatetheimpactofoursub-treatmentsinwhichwevarywhetherthe

SMSsaresentinapredictableorunpredictablefashion.Todothis,weestimatethe

followingregressionmodel:

!! = !′!! + !!"#$%&! + !!

whereRandomiequals1iftheSMSsweresentonarandomdayoftheweekand0ifthe

SMSsweresentonthesamedayofeachweek.Thecoefficient!capturestheimpactof

receivingthemessageonarandomdayrelativetoafixeddayoftheweek.

Finally,weconsideraspecificationthatallowsfortheinteractionofourmain

treatmentsthatprovideISPinformationorparentalcontrolswithoursub-treatments

whichvarywhethertheSMSsaresentinapredictableorunpredictablefashion:

!! = !!!! + !!!"!_!"#$! + !!!"#$%&"'()%&#)'*! + !!!"#$%&!

+ ! !"!_!"#$! ∗ !"#$%&! + ! !"#$%&"'()%&#)'*! ∗ !"#$%&! + !!

Thecoefficientsηandθindicatewhetherprovidinginformationandparentalcontrols

arecomplements(orsubstitutes)withthestrengthorsalienceofthecue.

6. Take-Up

Webeginbyshowingthepatternsoftake-upusingouradministrativedatainTable4.

Columns(1)and(2)confirmthathouseholdswerecorrectlytargetedtoreceiveSMSs

providinginformationaboutinternetusefromtheISPprovider.FromPanelA,thosein

groupsT1andT3receivedapproximately82percentoftheseSMSswhereasthosein

groupT2andthecontrolgroupdidnotreceivethem.Thisisalsoapparentwhenusing

ouralternativeregressionmodelinPanelBtoestimatethecombinedimpactof

providingISPInformationfromT1andT3.Similarly,columns(3)and(4)confirmthat

householdswerecorrectlytargetedtoreceiveSMSsregardingtheWindows8(W8)

parentalcontrolsoftware.ThoseingroupsT2andT3received83percentand81

17

percentoftheseSMSswhilethoseingroupT1andthecontrolgroupdidnotreceive

thematall.9Theimperfectcomplianceintheadministrativedatarepresentscasesin

whichtheSMSmessageswerenotdeliveredduetotechnicalissues(i.e.server

problems,lackofreception,etc.).However,asshowninAppendixTable4,thevast

majorityofparentsreceivedatleastonemessage(98%inthecaseofT1andT2and

97%inthecaseT3).

Finally,columns(5)and(6)ofTable4showthatabout14percentofhouseholds

intreatmentgroupT2and16percentofhouseholdsingroupT3receivedassistance

fromuswithinstallingtheW8parentalcontrolsoftware;again,asexpected,theserates

werezerointreatmentgroupT1andthecontrolgroup.10

WealsoaskedparentsabouttheirrecollectionsofreceivingSMSs,theusefulness

oftheSMSs,andtheirdecisiontoinstallparentalcontrolsoftware.Column(1)ofPanel

AinTable5indicatesnosignificantdifferencesinwhetherparentsrecalledever

receivinganSMSacrossthedifferenttreatmentarmsT1,T2andT3,relativetoabaseof

86percentinthecontrolgroup.Thisisnotsurprisinggiventhatallhouseholdswere

sentaweeklySMS(thoughPanelBdoessuggestthatslightlyfewerparentswho

receivedtheParentalControlinterventionsreporttohaveeverreceivedanSMS).

However,column(2)indicatesthat,amongparentsingroupT2whoonlyreceivedan

SMSregardingW8parentalcontrols,significantlyfewerrememberedwhattheSMS

actuallysaidascomparedtothecontrolgroup.Incontrast,amongparentsingroupsT1

andT3whoreceivedanSMSregardingtheISPinternetuse,significantlymore

rememberedwhattheSMSactuallysaidascomparedtothecontrolgroup.This

9PanelBdoesshowasmallbutsignificanteffectofthecombinedimpactofISPInformationfromT1andT3onthelikelihoodofreceivingSMSsregardingW8parentalcontrolsoftware.Thisisaresultofthesmalldifferencesintake-upbetweenT2andT3.10Again,PanelBshowsasmallbutsignificanteffectofthecombinedimpactofISPInformationfromT1andT3onthelikelihoodofinstallingtheW8parentalcontrolsoftwareasaresultofthesmalldifferencesintake-upbetweenT2andT3.

18

differentialrateofrecallmayalsoexplainsomeofthedifferencesintheimpacts

betweentheIPSandW8interventions.

Columns(3)-(7)showwhether,conditionalonreportingthereceiptofanSMS,

parentsfoundtheSMSsusefulandforspecificpurposesrelativetothecontrolgroup.

Forthemostpart,thepatternsinPanelAandBarenotsurprisingandpresentevidence

thathelpustounderstandthepotentialimpactsofthetreatments.Parentsingroups

T1andT3whoreceivedSMSsregardingtheISPinternetuseweresignificantlymore

likelytofindthesemessagesusefulforbeinginformedaboutinternetuse.Important

forourstudy,whileabout20%ofparentsinthecontrolgroupdiscussedtheSMSswith

theirkids,thispercentagemorethandoublesforparentsingroupsthatreceived

information.Incontrast,parentswhoreceivedinformationabouttheparentalcontrols

SMSswereslightlylesslikelytodiscussthemessageswiththeirkid.

ParentsingroupsT2andT3whoreceivedSMSsabouttheW8parentalcontrols

weresignificantlymorelikelytofindthemusefulforlearningabouttoolsthatwouldbe

helpfultomonitoruse.ButmoreparentsingroupT2foundtheSMSstobeofnouseat

all.Thus,forsomereason,informationabouttheW8softwarewasnotveryusefulto

parents,evenincontrasttothegeneralmessagereceivedbyparentsinthecontrol

group.Wealsofindthat,despitethefactthatalltreatmentarmsalsocontaineda

sentenceremindingparentstoensuretheirchildrenmadegooduseofthecomputer,

fewerhouseholdsintreatmentgroupsT1,T2,andT3reportedthattheirmessageswere

usefulforthispurposeascomparedtothecontrolgroupwhichcontainedonlythis

sentence.Thismaypresentafirstindicationforthelimitedattentionofparents;

includingadditionalcontentintheSMSmayhaveledparentstopaylessattentiontothe

firstpartoftheSMS.

19

Finally,column(8)indicatesthatparentsingroupsT2andT3whoreceived

informationabouttheW8parentalcontrolssoftwareweremorelikelytoinstallit.Yet

thereisalsoevidencethatsomeparentsingroupT1andthecontrolgroupsucceededin

installingparentalcontrolsoftwaredespitenotreceivinganyassistancefromus.

Overall,ouradministrativeandsurveydatasuggestthattheinterventionsworkedas

intendedandthattheactualcontentoftheSMSsdidmatter.Nevertheless,take-up

associatedwithinstallingtheWindows8(W8)parentalcontrolsoftwarewasquitelow.

7. MainResults

Thissectiondescribesourmainresultsontheroleofinformation,parentalcontrols,

andcuesforparentalmonitoringandsupervision.Theanalysisofinformationand

parentalcontrolismainlybasedonTable6andFigure2;thatofcuesisbasedonTable

7andFigure5.Bothtablesarestructuredinasimilarfashiontothetablesshowing

take-up:PanelAdisplaystheimpactseparatelyforeachtreatmentarmT1,T2,andT3

relativetothecontrolgroup;PanelBdisplaysthecombinedimpactofprovidingISP

InformationfromT1andT3aswellasthecombinedeffectofprovidingParental

ControlsoftwarefromT2andT3.Ineachpanel,weshowtheoverallimpactandthe

separateimpactsforweekdaysandweekends.

7.1 Information

7.1.1 Aggregateimpacts

WebeginwithadiscussionoftheaggregateimpactsofprovidingparentswithISP

informationontheintensityofinternetuse.AcrossthedifferentspecificationsinPanel

AofTable6,thereisevidencethathouseholdsingroupT1inwhichparentsreceived

theISPinformationaboutinternetusehadlowerintensityofinternetuseoverthe

20

treatmentperiod.Thedailyreductionof11-16megabytesdownloadedrepresentsa6-

10percentdecreaserelativetothecontrolgroup.Theestimatesaremoreprecisewith

theinclusionofthecontrolvariables,andonlyslightlylargerinmagnitudeforthe

weekendascomparedtoweekdays.TheimpactsforhouseholdsingroupT3,inwhich

parentswereprovidedwithbothinformationaboutinternetuseandhelpinstalling

parentalcontrols,arenegativebutsomewhatsmallerinmagnitudeandlesssignificant

thanthoseforT1.AbroadlysimilarpatternisobservedinPanelBwheretheincreased

precisionyieldsaconsistentlysignificant(combined)impactofprovidingISP

InformationfromT1andT3.

Theseresultssuggestthatprovidingparentswithspecificinformationabout

theirchildren’sinternetusedoesleadtoasignificantreductionof6-10percentin

contemporaneousinternetuse.Thistranslatestoadailyreductionofabout7minutes

ofinternetuseinhouseholdswhichreceivedtheISPinformationintervention(see

AppendixTable5forestimatesintermsofpredictedminutesofuse).

Forthemostpart,theimpactofourinterventionsareverysimilaracross

weekdaysandweekends.Thismaybebecausethepatternsofinternetuseandparental

monitoringdonotvarybetweenweekdaysandweekends.Indeed,wedonotseelarge

differencesininternetuseforthecontrolgroupbetweenweekdaysandweekends;

averageinternetuseis169.4and167.6forweekdaysandweekendsrespectively.

However,itisalsopossiblethattherearecountervailingforcesatplay.Forexample,

children’sdemandforinternetmaybehigherduringweekendsbuttheabilityof

parentstomonitortheirchildren’sinternetusemayalsobecorrespondinglygreater.As

aresult,theequilibriumlevelofusebothwithandwithoutourinterventionsmayend

upbeingquitesimilaracrossweekendsandweekdays.Alternatively,thesimilarityin

treatmenteffectsacrossweekdaysandweekendscanbeaconsequencethatour

21

interventionsaffectedbehaviorthatchangeInternetuseinapermanentwayacrossall

daysoftheweek.

7.1.2 Eventstudy

Wealsoexplorethehigh-frequencydynamicsofourinterventionsbyimplementingan

eventstudyanalysisthatexploitsthetimingofthemessageswithineachweek.The

resultsarepresentedinPanelAofFigure2whichplotscoefficientestimatesforthe

controlgroup(SMS-only),theinformationtreatment(ISPinfo)group,andtheparental

controltreatments(PC).Day0marksthedayonwhichtheSMSsarereceivedeach

week,althoughthemessageswerereceivedintheafternoonsowemayexpectlarger

impactsonthefollowingday(day1).Foreaseofcomparison,wenormalizeallof

coefficientstoequalzeroonthedaypriortoreceiptoftheSMS(day-1).These

coefficientsarealsopresentedinAppendixTable6alongwiththeirstatistical

significance.

Wedonotobserveatrendforanyofthegroupsinthedaysprecedingreceiptof

theSMS(days-3to-1).However,wedoseesignificantdifferenceemergeastheSMSs

arereceivedbythehouseholds.InternetusestartsdecliningfortheISPInfogroupon

day0,declinesfurtheronday1,andremainsbelowtheinternetuseinthepre-

treatmentperiod.Theplotsforthecontrolgroupdoesnotfollowthesamepattern.

Whilethereisasmalldecreaseinday1,thisquicklyrevertstothelevelofthedays

beforethemessagesweresent.Thissupportstheresultsoftheprevioussectionby

confirmingthatitisthereceiptoftheSMSmessagesthemselvesthatleadstoa

discernibleeffectoninternetuse.

Theimpactassociatedwiththeactualcontentofthemessagereceivedbythe

ISP-infogroupisshowninPanelsBandCofFigure2.Inparticular,wesplitthesample

22

betweenthosereceivingamessagestatingthatInternetuseinthepreviousweekwas

“above”themeanofthereferencegroupandthosestatingthatInternetuseinthe

previousweekwasthe“sameorbelow”themean.Theseresultsindicatethatthe

observedeffectsinPanelAaredrivenbythoseSMSmessagescontaining“badnews”for

theparents.Wedonotseeasimilarpatternfortheothertwogroups,whichsuggests

thisisnotexplainedbymean-reversioninInternetuse.Thisfurtherconfirmsthatitis

theactualcontentofthemessagemattersandnotsimplybecauseofthereceiptofthe

SMSmessages.

Tosummarize,theseresultssuggestthatprovidingparentswithinformation

abouttheirchildren’sInternetusehelpstoalleviatetheinformationalasymmetries

withinthefamily.ByhavingacontrolgroupthatalsoreceivesanSMSmessage,wecan

isolatetheimpactofinformationfromthecueassociatedwiththemessageitself.

Moreover,theevidencefromoureventstudyanalysisthatthemessagecontentdrives

theimpactsonlyservestoreinforcetheviewthatitistheinformationitselfwhich

generatesthecausalimpactonInternetuse.

7.2 ParentalControls

Inthissection,weuseTable6andtheeventstudyframeworkinFigure2toconsider

theimpactofofferingassistancewithinstallingparentalcontrolsoftwareonthe

intensityofinternetuse.

LookingatPanelAofTable6,weseenosignificanteffectsforhouseholdsin

groupT2inwhichparentswereprovidedwithinformationaboutinstallingparental

controls;ifanythingthecoefficientsareslightlypositive.Therearealsonosignificant

effectsinPanelBwhereweestimatethe(combined)impactofofferingparentalcontrol

softwarefromT2andT3;thepointestimatesareallclusteredclosetozero.Thus,the

23

aggregatedataindicatesthatofferingparentswithassistancetoinstallparentalcontrol

softwareisnotaneffectivewayofchangingbehavior.Thisconclusionisalsoconfirmed

bytheresultsfromtheeventstudyanalysis.Incontrasttothepatternsobservedforthe

informationaltreatment,Figure2showsnodiscernableimpactofparentalcontrol

interventiononintensityofinternetuseinthedaysimmediatelyfollowingreceiptof

theSMSmessage.

Asafurtherexercise,weconsideranalternativeevent-studyanalysisinwhich

weestimatetheshort-termimpactofactuallyinstallingW8parentalcontrolsoftware.

Sinceweprovidedassistancewithinstallingparentalcontrolsoftwaretofamiliesin

treatmentgroupsT2andT3,weknowtheprecisedateonwhicheachofthe564

parentswhocalledreceivedthisassistance.ThesedatesarestaggeredthroughJanuary

(afterwhichnomorecallswerereceived)whichallowsustoestimateaneventstudy

thatcontrolsforseasonality,similartothoseusedinestimatingtheimpactofreceiving

anSMS.TheresultsofthisanalysisareshowninFigure3andAppendixTable7which

indicatenosignificantshort-termimpactsinthedaysimmediatelyafterinstallationof

theW8parentalcontrolsoftware.Giventhatthedecisiontoinstallparentalcontrol

softwarecouldbeendogenoustointernetuse,thesefindingsneedtobeinterpreted

withcare.However,theyareconsistentwiththelong-termestimatesfromcomparisons

acrossourmaintreatmentgroups.

Theabsenceofsignificantimpactsfromprovidingassistanceforinstalling

parentalcontrolsoftwarecouldindicatethatparentsalreadyhaveaccesstoother

meansofcontrollingtheirchildren’scomputeruse.Thismayalsoexplainthelowrateof

take-upforthisintervention.Alternatively,thelowrateoftake-upcouldreflectthe

considerableobstaclesfacedbylow-incomeparentsinimplementingtechnological

solutionsformonitoringandsupervisingtheirchildren.Asnotedpreviously,parentsin

24

thistreatmentarmweremorelikelytoreportlearningabouttoolsthatcanbehelpfulin

monitoringtheirchildren.Butperhapssuchparentsneedmorehands-onassistanceto

actuallyinstallanduseparentalcontrolsoftwareontheirchildren’scomputers.11

Moreover,installingandoperatingparentalcontrolsoftwarecanimposesubstantial

timecostswhichmayleadtoprocrastination,status-quobias,andotherbiasesthat

arisewiththedemandforcommitmentdevices(seeBryanetal.,2010forareview).

7.3 Cues

Asexplainedabove,ourinterventionsweredesignedtoseparatetheinformational

contentandtheofferofassistancewithparentalcontrolsoftwarefromthecue

associatedwiththeSMSmessages.Thissectionpresentsadditionalevidencesuggesting

thatthecuesthemselvesalsoplayanimportantroleinaffectedparentalbehavior.

First,weuseoureventstudyframeworktoshowthatSMSmessagessenttothe

controlgrouphadshort-termimpactsinthefirstweeksoftheexperiment.Appendix

Table8presentstheimpactsfromtheeventstudyforeachtreatmentgroupduringthe

firstandsecondhalfofthetreatmentperiod.Wewilldiscusstheimplicationsofthese

patternsforthepersistenceofourmaininterventionsinthesubsequentsection.

However,itisnotablethatthereisanegativeandstatisticallysignificantdecreaseof

about10MBsonedayaftertheSMSmessagewasreceivedforthecontrolgroupduring

thefirsthalfofthetreatmentperiod.Thissuggeststhatthesalienceofthemessagealso

matterssincetheSMSmessageswithoutspecificinformationoninternetusearelikely

tobemoresalientatthebeginningoftheexperiment.

11Wehaveexaminedwhichparentalcharacteristicspredicttake-upoftheW8parentalcontrolsoftware.Thestrongestpredictorsarethegenderofthestudent(lesslikelytoinstallforfemales)andthestatedintentiontoinstallparentalcontrolsoftwareinthebaselinesurvey.

25

Second,weconsidertheeffectofvaryingthestrengthofthecueassociatedwith

messagesbysendingthemineitherapredictableorunpredictablefashion.Foreach

treatmentarm,arandomsubsetofhouseholdsreceivedSMSsonthesamedayofthe

week(thisfixeddaywasrandomlydrawnamonghouseholds)whiletheremainderof

householdsreceivedSMSsonarandomdayoftheweek.12Table7examinestheeffectof

receivingSMSsonarandomversusafixedscheduleforallthetreatmentarms

combined.ThereisstrongevidencethathouseholdswhoreceivedSMSsonarandom

schedulehadgreaterreductionsof10-15MBsindailyinternetusethanhouseholdson

fixedschedules.Thisissimilarinmagnitudetothemaineffectassociatedwithreceiving

ISPinformationrelativetothecontrolgroup,andsuggeststhatthestrengthofthecue

associatedtothemessageisasimportantasthemessageitself.13

Webelievethesefindingsareconsistentwithresearchinneuroscienceand

psychologyfindingthatunpredictableandnoveltystimulihavelargerimpacts(e.g.

Parkin,1997;Bernsetal.,2001;Fenkeretal.,2008).Theyarealsorelatedtoresearchin

behavioraleconomicsthatemphasizestheroleofinattentioninthecontextof

reminders(e.g.,Karlan,etal.2014,Taubinsky2014,Ericsson,2017).Onealternative

explanationforthesepatternsisthatrandomschedulesallowformoreflexible

responsesbyparentswhenreceivingamessageisnotasconvenientonsomedays(and

theimpactofrepeatedmessagesisnon-linear).Inthiscase,wewouldexpecttofind

heterogeneoustreatmenteffectsbythedayoftheweekinwhichthemessagewas

12AppendixTable10presentsregressionsoftake-upfortherandom/fixedschedulesubtreatments.Resultsimplysmalldifferencesintake-upusingadministrativedataininstallationofW8inrandomschedulegroupwithintheT2treatmentgroup,andnegativedifferencesinthepercentageofmessagesreceivedagainsttherandomschedulehouseholdsintheT3group.13InAppendixTable11,weestimateaspecificationthatestimatestheeffectofrandomvs.fixedschedulesseparatelyforeachtypeoftreatment.ReceivingSMSsonarandomscheduleleadstonegativeimpactsineverytreatmentarm,althoughitissmallerandinsignificantamonghouseholdsthatreceivedinformationonparentalcontrols.Thefactthatthecontrolgroupalsoshowsdifferentialeffectsbyrandomvs.fixedschedulessuggeststhatthestrengthofthecueprovidedbytherandomschedulealsoaffectsmessageswithoutspecificinformationalcontent.

26

delivered.However,wedonotfindstatisticallysignificantdifferencesacrossdays.A

secondalternativeexplanationisthatparentspaymoreattentiontoSMSssentona

randomschedulebecausethey(mistakenly)interpretthemasbeingsentactivelyby

someonemonitoringtheirinternetuse,whereasSMSmessagessentonafixedschedule

aremorelikelysentthroughanautomatedprocess;inreality,allofthemessageswere

automated.Whilewecannotcompletelyruleoutthispossibility,itisworthnotingthat

thedifferencesbetweenrandomandfixedschedulesareevenlargerinmagnitudefor

thecontrolgroup(asdiscussedinSection8.3),wherethemessagesdonotcontainany

specificinformationabouttheirchildren’sinternetuse.

8. FurtherResults

8.1 Dynamics

Didtheimpactsassociatedwithourinterventionsdisplaydifferentdynamicsduringthe

treatmentperiod?WebeginwithabroadlookatthepatternsovertimeinFigure4

whichshowsthetreatmenteffectsforeachweekinthepre-treatmentperiod,treatment

period,andpost-treatmentperiod(relativetothecontrolgroup).Weobservetheinitial

impactsoftheinformationtreatmentbuildupduringthefirst4weeksoftreatmentand

thenappeartostabilizethroughtherestofthetreatmentperiod.Theseresultsare

confirmedinTable8,whichpresentsthecoefficientestimatesfortheimpactofeach

treatmentforthefirst-halfofthetreatment(fromweeks1to7)andforthesecondhalf

ofthetreatment(weeks8to14).

Furthermore,Figure5presentstheimpactsfromtheeventstudyforthefirst

andsecondhalfofthetreatmentperiod(andthepost-treatmentperiod)forthefixed

27

schedulesub-treatment.14Resultsinbothpanelsindicatethatthedynamiceffectsof

providingISPinformationwerestrongerinthefirst-halfofthetreatmentperiod.

AppendixTable8presentstheeconometricresultscorrespondingtothesetables.They

showsizeabledecreasesininternetuseofapproximately13megabytesonthedaythe

SMSwasreceivedand20megabytesonedayafterreceiptoftheSMS.Incontrast,the

effectsinthesecondhalfofthesample,whilestillnegative,arenotstatistically

significant.

Theseresultsprovidecomplementaryevidenceonwhythecuesassociatedwith

anSMSmessagehaveanimpactinouranalysis.Thedifferentdynamicsofrandom

versusfixedmessagesduringthefirstandsecondpartoftheinterventionareconsistent

withtheviewthattheincreasedstrengthofthecueforrandom-schedulemessages

shouldbemorerelevantinthesecondpartoftheinterventionafterrecipientsonthe

fixed-schedulehavelikelybecomeaccustomedtoreceivingtheirmessagesonthesame

dayeveryweek.

8.2 Persistence

Ifourinterventionsprovidedparentswithnewtoolstoaddressthechallengeof

monitoringandsupervisingtheirchildren,wewouldexpecttheseimpactstopersist.On

theotherhand,ifparentsdependontheSMSsthemselvestohelpthemmonitorand

supervisetheirchildren,theseeffectswouldlikelydisappearwhentheystopreceiving

theirSMSs.Inordertoanswerthisimportantquestion,weanalyzetheimpactofour

treatmentsduringtheperiodaftertheinterventionshadended(i.e.the“post-treatment

period”).

14Wejustuseinformationfortheindividualswhoreceivedmessagesonafixeddayoftheweekbecauseitisnotobvioushowtoshow“placebo”impactsinthepost-treatmentperiodforthesubsampleofindividualswhoreceivedmessagesonarandomdayoftheweek.

28

InFigure4,whichalsoshowsthetreatmenteffectsforeachweekinthe

posttreatmentperiod(relativetothecontrolgroup),weobservethatthetreatment

effectsremainatasimilarlevelevenaftertheinterventionsconcludeinweek14.This

evidencesuggeststhatourmainimpactsdidpersistfollowingthetreatmentperiod.A

similarpictureemergesintheregressionresultspresentedinTable8,whichconfirm

thatintheposttreatmentperiod(weeks15to26)therearesignificantimpactseven

afterthetreatmentends,atroughlythesameorderofmagnitudeasthetreatment

impactsduringthelastweeksofthetreatmentperiod.15Notethatwedonotfindany

significanteffectintheposttreatmentperiodwhentheSMSswerenotactuallyreceived.

Theseresultsshedlightonthemechanismsunderlyingourmainresults.Thefact

thatourmainimpactsdidpersistfollowingthetreatmentperiod,suggeststhatthe

interventionaffectedinternetusenotsimplythroughadirecteffectoftheSMSsthat

mighthelpparentstobettermonitorandsupervisetheirchildren.Rather,thetools

providedbythisinterventionmayhavechangedtheequilibriumuseovertheinitial

interventionperiodand,therefore,ledthetreatmenteffectstopersistevenafterthe

SMSsstopped.

8.3 Interactionsbetweentreatments

WealsoconsidertheinteractionbetweenourmaintreatmentsthatsentSMSs

providingISPInformationaboutinternetuseandreminders/assistanceforinstalling

ParentalControlssoftwarewithoursub-treatmentsthatvariedwhetherthoseSMSs

15Theexperimenttookplaceduringboththevacationperiod(fromDecember,2013toearlyMarch2014)andtheschoolperiod(fromearlyMarchonwards).Thishasanimportantoverlapwiththeanalysesweperforminthissection.AppendixTableA13estimatestreatmenteffectsforthelasttwoweeksofthevacationperiodandthefirsttwoweeksoftheschoolperiodinordertocomparetheeffectofthetreatmentwhileinvacationandwhileinschool,Theseresultssuggestthetreatmenteffectsarenotsubstantiallydifferentforthevacationandschoolperiodand,therefore,weconcludethatthedynamiceffectswepresentinthissectionareprobablyunrelatedtothisalternativeexplanation.

29

werereceivedonarandomorafixedschedule.Theseinteractionseffectsaredisplayed

inTable9forourcombinedtreatmentsandinAppendixTable10fortheseparate

treatments.

Ineithercase,weobservemaineffectsthataresimilartotheonesestimatedin

previoustables:receivingSMSswithISPInformationaboutinternetuse(forthoseona

fixedschedule)leadstosignificantlylowerinternetuse;receivingreminders/assistance

forinstallingParentalControlssoftware(forthosewhoreceivedthemonafixed

schedule)hasanegativebutstatisticallyinsignificantimpactoninternetuse;and

receivingSMSsonarandomscheduleleadstoverylargeandsignificantreductionsin

internetuse.

TheinteractioneffectsbetweenISPinformationandindicatorsforarandom

scheduleareconsistentlypositive,albeitnotsignificant(asimilarpatternholdswith

respecttotheinteractionbetweentherandomscheduleandtheparentalcontrols

treatment).ThissuggeststhatISPinformationandanycueassociatedwitharandom

schedulearesubstitutes,notcomplements.Inotherwords,providingspecific

informationappearstocrowdouttheeffectofthecueassociatedwiththemessage.

9. Conclusion

Parentsareoftenconfrontedwiththechallengeofmonitoringandsupervisingtheir

children’sactions.Thischallengehasbecomeevenmorepressingwiththeincreasing

availabilityofinternetaccessathomewhichmaydisplaceproductiveactivities(and

exposestudentstoinappropriatecontent).Inthepaper,weexaminedtheimportant

roleofasymmetricinformationandthepotentialfordirectparentalcontrolsinaffecting

children’sinternetuse.Theinformationalasymmetriesinthiscontextarelikelytobe

30

especiallypronouncedbecausechildrenareoftenquickertoadapttonewtechnologies

andparentsmaynotbeawareofhowchildrenareusingtechnology.

Wedesignedandimplementedasetofrandomizedexperimentstotestwhether

theintensityofchildren’sinternetuserespondstotheprovisionofspecificinformation

aboutchildren’sinternetuseandtotheofferofassistancewiththeinstallationof

parentalcontrolsoftware.Thesampleincludeschildrenin7thand8thgradewho

receivedfreecomputersand12monthsoffreeinternetsubscriptionsthroughChile’s

“YoElijomiPC”(YEMPC)programin2013,andwetakeadvantageofdetailed

informationontheintensityofinternetuseatthedailylevelfromtheinternetservice

provider(ISP)whichservedallofthecomputersprovidedtothechildreninoursample.

Ourresultsshowthatprovidingparentswithinformationabouttheirchildren’s

internetuseleadstosubstantialreductionsinuse:householdsinwhichparents

receivedISPinformationaboutinternetusehadsignificantlylowerintensityofinternet

useduringthetreatmentperiodascomparedtohouseholdsinthecontrolgroup.We

observestatisticallysignificantreductionsinusepreciselyonthedaysimmediately

afterreceivingtheISPinformation.Furthermore,itisthoseSMSmessagesindicating

thatchildrenusedmoreinternetthanthereferencegroupinaspecificweek,which

producethelargestdeclinesininternetuse.Wefindnoimpactofreceivingassistance

withtheinstallationofparentalcontrolsoftwareontheintensityofinternetuse.

Moreover,wedonotobserveshort-termimpactsofactuallyinstallingparentalcontrol

softwareamongthefamiliesthatreceivedassistance.

Takentogether,thesefindingsindicatethatprovidingparentswithspecific

informationabouttheirchildren’sinternetuseaffectbehaviorwhileprovidingparents

withparentalcontrolsoftwaredoesnot.Thefactthattheimpactsofinformationeffects

persistaftertreatmentendssuggeststhatourtemporaryinterventionmayhavealtered

31

theequilibriumofinternetuseandhelpedparentssolvetheproblemofasymmetric

informationinamorepermanentway.

WealsofindstrongevidencethathouseholdswhoreceivedSMSswithan

unpredictablescheduleexperiencedsignificantlygreaterreductionsininternetuse

thanthoseonpredictableschedules,aneffectsimilarinmagnitudetothemaineffect

associatedwithreceivingtheISPinformation.Inaddition,wefindthattheSMS

messagessenttothecontrolgrouphadshort-termimpactsonInternetuseinthefirst

weeksoftheexperiment,perhapsduetothenoveltyofthemessage.Thesefindings

suggestthatthecuesassociatedwithmessageshaveanindependenteffectonbehavior

andthatthestrengthofsuchcuesisanimportantdeterminantofouroutcomes.Thus,

ourstudyshedslightontheroleofinformationandcuesinaffectingbehavior.

32

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Table 1: Sample Size by Treatment and Subtreatment

Treatment Fixed RandomGroup Day Day Total

T1 ISP 963 964 1927T2 W8 965 963 1928T3 ISP + W8 962 962 1924T0 SMS-only 964 964 1928

Total 3853 3854 7707

Notes: The sample was stratified by Guardian’s education (No High-School, High School, College), Parent perception of whether the stu-dent stays too long in front of the computer (Yes or No) and InternetUse as the total MB downloaded between September and December(the 15th).

Table 2: Summary Statistics

Mean S.D.

Panel A: Student CharacteristicsDaily mean download, pre 153.51 179.77Average daily use in minutes, predicted, pre 186.37 162.81Live with mother 0.96 0.20Live with father 0.62 0.49Live with Brother/Sister 0.76 0.42Live with Grandfather/Grandmother 0.15 0.36Female 0.43 0.49Number of siblings 1.72 1.28

Panel B: Guardian CharacteristicsGuardian Age 40.42 7.78What is your education level?

Elementary incomplete 0.10 0.30Elementary complete 0.14 0.35Secondary incomplete 0.15 0.36Secondary complete 0.47 0.50High incomplete 0.04 0.20High complete 0.09 0.29

What is your current employment status?Working full time 0.33 0.47Working part-time 0.13 0.33Not working looking for a job 0.06 0.23Not working not looking for a job 0.47 0.50Other 0.02 0.14

Notes: This table presents estimated means (Column 1) and stan-dard deviations (Column 2) for students included in the experimentalsample.

Table 3: Balance by Treatment

(1) (2) (3) (4) (5)T1 T2 T3 SMS-Only P-Value(F-Test)

Panel A: Student CharacteristicsDaily mean download, pre 153.75 153.96 153.12 153.36 0.999

( 180.83) ( 177.29) ( 174.63) ( 183.42)Average daily use in minutes, predicted, pre 185.26 188.04 188.17 184.60 0.862

( 161.91) ( 164.42) ( 164.60) ( 160.53)Live with mother 0.95 0.96 0.97 0.95 0.085

( 0.21) ( 0.19) ( 0.18) ( 0.21)Live with father 0.62 0.62 0.61 0.62 0.931

( 0.49) ( 0.49) ( 0.49) ( 0.49)Live with Brother/Sister 0.76 0.78 0.78 0.75 0.112

( 0.43) ( 0.42) ( 0.41) ( 0.43)Live with Grandfather/Grandmother 0.16 0.15 0.15 0.15 0.766

( 0.37) ( 0.36) ( 0.36) ( 0.35)Female 0.42 0.42 0.42 0.45 0.361

( 0.49) ( 0.49) ( 0.49) ( 0.50)Number of siblings 1.69 1.73 1.73 1.74 0.625

( 1.25) ( 1.30) ( 1.31) ( 1.28)

Panel B: Guardian CharacteristicsGuardian Age 40.29 40.64 40.49 40.50 0.587

( 7.82) ( 7.98) ( 7.92) ( 7.67)What is your education level?

Elementary incomplete 0.09 0.10 0.10 0.10 0.961( 0.29) ( 0.30) ( 0.30) ( 0.30)

Elementary complete 0.13 0.14 0.14 0.15 0.773( 0.34) ( 0.35) ( 0.35) ( 0.35)

Secondary incomplete 0.16 0.15 0.15 0.15 0.691( 0.37) ( 0.36) ( 0.36) ( 0.36)

Secondary complete 0.47 0.47 0.47 0.47 0.994( 0.50) ( 0.50) ( 0.50) ( 0.50)

High incomplete 0.04 0.04 0.05 0.04 0.329( 0.19) ( 0.21) ( 0.22) ( 0.20)

High complete 0.10 0.09 0.09 0.09 0.727( 0.30) ( 0.29) ( 0.28) ( 0.29)

What is your current employment status?Working full time 0.33 0.33 0.34 0.32 0.707

( 0.47) ( 0.47) ( 0.47) ( 0.47)Working part-time 0.13 0.14 0.13 0.13 0.758

( 0.33) ( 0.34) ( 0.33) ( 0.34)Not working looking for a job 0.05 0.05 0.06 0.06 0.570

( 0.22) ( 0.22) ( 0.23) ( 0.24)Not working not looking for a job 0.47 0.46 0.46 0.47 0.790

( 0.50) ( 0.50) ( 0.50) ( 0.50)Other 0.02 0.02 0.02 0.02 0.575

( 0.14) ( 0.15) ( 0.14) ( 0.13)

Note: This table presents estimated di↵erences between students in the di↵erent experimental groups. Columns1 to 4 present means and stadard deviations in parentheses. Column 5 presents the p-value of a of joint testfor di↵erences between the T1, T2 and T3 and SMS-only groups.

Table 4: Take-up: using Administrative Data

(1) (2) (3) (4) (5) (6)SMS ISP SMS ISP SMS W8 SMS W8 W8 installed W8 installed

Panel A: T1, T2, T3T1 0.821*** 0.820*** 0.000 -0.000 -0.001 -0.000

(0.006) (0.006) (0.005) (0.005) (0.008) (0.008)T2 -0.000 -0.001 0.832*** 0.832*** 0.135*** 0.135***

(0.006) (0.006) (0.005) (0.005) (0.008) (0.008)T3 0.816*** 0.816*** 0.815*** 0.815*** 0.156*** 0.157***

(0.006) (0.006) (0.005) (0.006) (0.008) (0.008)

Observations 7,707 7,707 7,707 7,707 7,707 7,707Control Mean 0 0 0 0 0 0BL Controls X X X

Panel B: ISP Info and Parental ControlsISP Information 0.818*** 0.818*** -0.008** -0.008** 0.011* 0.011*

(0.004) (0.004) (0.004) (0.004) (0.006) (0.006)Parental Controls -0.002 -0.003 0.824*** 0.823*** 0.146*** 0.146***

(0.004) (0.004) (0.004) (0.004) (0.006) (0.006)

Observations 7,707 7,707 7,707 7,707 7,707 7,707Control Mean 0 0 0 0 0 0BL Controls X X X

Note: This table presents estimated e↵ects on take-up for di↵erent treatment groups with respect to the control group. Columns 1 and 2 presentestimates on the reception of SMSs including ISP information. Columns 3 and 4 present estimates on the reception of SMSs including an o↵erof help to install parental control settings. Columnd 5 and 6 present estimates on the installation of parental control settings through the callcenter of the experiment. Odd-numbered columns present estimates without controls and even-numbered columns present estimates includingbaseline control variables. Control variables include the baseline values of mean of MBs of Internet use; guardian gender, age, educationlevel and employment status; number of siblings; and dummies for family composition (indicating whether the child lives with mother, father,step-mother or father’s partner, step-father or mother’s partner, uncle/aunt, brother/sister, grandfather/grandmother, other relatives, andother non-relatives). Robust estimated standard errors are reported in parentheses. *** Significant at the 1 percent level. ** Significant at the5 percent level. * Significant at the 10 percent level.

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Table 6: Impact of Treatments on Intensity of Internet Use

(1) (2) (3) (4) (5) (6)Period: All All Weekdays Weekdays Weekend Weekend

Panel A: T1, T2, T3T1 -12.422 -13.178* -11.154 -11.982* -15.658* -16.230**

(7.978) (7.086) (8.115) (7.215) (8.279) (7.471)T2 0.058 -0.578 1.281 0.569 -3.062 -3.504

(8.218) (7.512) (8.232) (7.562) (8.800) (8.060)T3 -9.594 -11.744 -9.865 -12.143* -8.902 -10.723

(7.782) (7.148) (7.785) (7.168) (8.450) (7.824)

Observations 7,707 7,707 7,707 7,707 7,707 7,707Control Mean 168.9 168.9 169.4 169.4 167.6 167.6BL Controls X X X

Panel B: ISP Information and Parental ControlISP Information -11.038** -12.172** -11.150** -12.348** -10.751* -11.724**

(5.439) (4.848) (5.498) (4.893) (5.716) (5.200)Parental Controls 1.443 0.429 1.285 0.204 1.844 1.001

(5.469) (4.855) (5.527) (4.906) (5.748) (5.191)


Note: This table presents estimated e↵ects on Internet use measured as daily average MBs uploaded and down-loaded. Odd-numbered columns present estimates without controls and even-numbered columns present estimatesincluding baseline control variables. Control variables include the baseline values of mean of MBs of Internetuse; guardian gender, age, education level and employment status; number of siblings; and dummies for familycomposition (indicating whether the child lives with mother, father, step-mother or father’s partner, step-father ormother’s partner, uncle/aunt, brother/sister, grandfather/grandmother, other relatives, and other non-relatives).Robust estimated standard errors are reported in parentheses. *** Significant at the 1 percent level. ** Significantat the 5 percent level. * Significant at the 10 percent level.

Table 7: Impact of Random on Intensity of Internet Use


Random -14.530*** -12.401** -15.294*** -13.123*** -12.582** -10.557**(5.458) (4.873) (5.516) (4.921) (5.735) (5.216)

Observations 7,707 7,707 7,707 7,707 7,707 7,707BL Controls X X X

Note: This table presents estimated e↵ects of the random sub-treatment (with respect to the fixed sub-treatmentgroup) on Internet use measured as daily average MBs uploaded and downloaded. Odd-numbered columns presentestimates without controls and even-numbered columns present estimates including baseline control variables.Control variables include the baseline values of mean of MBs of Internet use; guardian gender, age, education leveland employment status; number of siblings; and dummies for family composition (indicating whether the child liveswith mother, father, step-mother or father’s partner, step-father or mother’s partner, uncle/aunt, brother/sister,grandfather/grandmother, other relatives, and other non-relatives). Robust estimated standard errors are reportedin parentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10percent level.

Table 8: Impact of Treatments on Intensity of Internet Use Across Periods


Panel A: 1st Half of TreatmentISP Information -11.096** -12.286*** -9.724* -10.957** -14.822*** -15.894***


(4.974) (4.405) (5.044) (4.470) (5.357) (4.865)


Panel B: 2nd Half of TreatmentISP Information -10.978 -12.055* -12.655* -13.816** -6.952 -7.831

(6.912) (6.375) (7.057) (6.505) (7.287) (6.827)Parental Controls -1.771 -2.873 -1.696 -2.854 -1.949 -2.920

(6.946) (6.383) (7.091) (6.523) (7.326) (6.819)

Observations 7,707 7,707 7,707 7,707 7,707 7,707Control Mean 174 174 172.7 172.7 177.1 177.1BL Controls X X X

Panel C: Post-TreatmentISP Information -11.273** -12.126*** -11.101** -12.007*** -11.710** -12.428**

(4.905) (4.452) (4.847) (4.383) (5.400) (4.999)Parental Controls 0.032 -0.717 -0.621 -1.436 1.694 1.112

(4.921) (4.455) (4.863) (4.387) (5.418) (5.002)



Table 9: Interactions of Treatments with Random


ISP Info -12.256 -15.980** -11.470 -15.291** -14.263* -17.739**(8.266) (7.286) (8.472) (7.442) (8.369) (7.588)

PC -7.149 -5.672 -7.584 -6.117 -6.039 -4.536(8.279) (7.278) (8.488) (7.444) (8.376) (7.554)

Random -24.341** -22.340** -24.484** -22.419** -23.976** -22.140**(10.114) (9.293) (10.141) (9.367) (10.893) (10.023)

ISP Info ⇥ Random 2.428 7.608 0.631 5.878 7.014 12.020(10.911) (9.677) (11.030) (9.773) (11.460) (10.373)

PC ⇥ Random 17.193 12.259 17.749 12.701 15.775 11.133(10.908) (9.613) (11.028) (9.709) (11.456) (10.312)



Figure 1: Timeline

Figure 2: Event Study

-30

-25

-20

-15

-10

-50

510

15

-3 -2 -1 0 1 2 3day

All Events

-30

-25

-20

-15

-10

-50

510

15

-3 -2 -1 0 1 2 3day

Above Events

-30

-25

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-15

-10

-50

510

15

-3 -2 -1 0 1 2 3day

Not-Above Events

ISP Info PCSMS-Only

Notes:This figure presents estimated e↵ects for each treatment group in day around the reception of an SMS message.Day 0 marks the day on which the SMSs are received each week. See Appendix Table 6 for more details.

Figure 3: W8 Install Event Study, controlling for seasonality

-70

-40

-10

2050

80M

B U

se-3 -2 -1 0 1 2 3

Day

Parental Controls

-70

-40

-10

2050

80M

B U

se

-3 -2 -1 0 1 2 3Day

T2

-70

-40

-10

2050

80M

B U

se

-3 -2 -1 0 1 2 3Day

T3

MB Use

Notes:This figure presents estimated e↵ects for the installation of the W8 parental control settings on Internet use,measured using MBs downloaded and uploaded. Day 0 marks the day on which the program was installed. SeeAppendix Table 7 for more details.

Figure 4: Impact on MB Use Across Weeks, with controls

-60

-40

-20

020

Impa

ct

-20 -10 0 10 20 30Week

ISP Information

-40

-20

020

40Im

pact

-20 -10 0 10 20 30Week

Parental Controls

Notes: This table presents estimated e↵ects (and confidence intervals) for each treatment group (with respect to thecontrol group) for each week of the experiment on Internet use measured as daily average Control variables includethe baseline values of mean of MBs of Internet use; guardian gender, age, education level and employment status;number of siblings; and dummies for family composition (indicating whether the child lives with mother, father, step-mother or father’s partner, step-father or mother’s partner, uncle/aunt, brother/sister, grandfather/grandmother,other relatives, and other non-relatives).

Figure 5: Event Study, Post-Treatment for Fixed Subgroup

-30

-25

-20

-15

-10

-50

510

15

-3 -2 -1 0 1 2 3day

1st Half of Treatment

-30

-25

-20

-15

-10

-50

510

15

-3 -2 -1 0 1 2 3day

2nd Half of Treatment

-30

-25

-20

-15

-10

-50

510

15

-3 -2 -1 0 1 2 3day

Post Treatment

ispinfo_fixed_1half pc_fixed_1halfsmsonly_fixed_1half

Notes: This figure presents estimated e↵ects for each treatment group in day around the reception of an SMSmessage. Day 0 marks the day on which the SMSs are received each week. See Appendix Table 11 for more details.

Table A1: Sample Comparisons

Sample: Full Experiment Survey

Panel A: Student CharacteristicsDaily mean download, pre 157.49 153.55 155.18

( 183.55) ( 179.04) ( 185.56)Average daily use in minutes, predicted, pre 189.51 186.52 185.67

( 164.21) ( 162.85) ( 162.49)Female 0.41 0.43 0.43

( 0.49) ( 0.49) ( 0.49)ADHD 14.21 14.48 14.52

( 3.94) ( 4.14) ( 4.20)Number of siblings 1.77 1.72 1.69

( 1.32) ( 1.28) ( 1.26)Rural School 0.15 0.15 0.14

( 0.36) ( 0.35) ( 0.35)Computer Skills 8.69 8.72 8.75

( 2.06) ( 2.02) ( 2.00)

Panel B: Mother CharacteristicsMother’s Education Level

Elementary incomplete 0.12 0.10 0.10( 0.32) ( 0.30) ( 0.29)

Elementary complete 0.14 0.14 0.14( 0.35) ( 0.34) ( 0.34)

Secondary incomplete 0.16 0.15 0.15( 0.36) ( 0.36) ( 0.36)

Secondary complete 0.45 0.47 0.48( 0.50) ( 0.50) ( 0.50)

High incomplete 0.03 0.04 0.04( 0.18) ( 0.19) ( 0.19)

High complete 0.06 0.06 0.06( 0.23) ( 0.23) ( 0.24)

Mother’s Employment StatusEmployed 0.34 0.32 0.30

( 0.47) ( 0.47) ( 0.46)Unemployed 0.04 0.04 0.04

( 0.21) ( 0.20) ( 0.20)Home-maker 0.58 0.61 0.63

( 0.49) ( 0.49) ( 0.48)Retired 0.00 0.00 0.00

( 0.07) ( 0.06) ( 0.07)

Panel C: Household CharacteristicsRules Index 4.02 4.06 4.08

( 1.11) ( 1.08) ( 1.07)Student has Computer at Home 0.40 0.41 0.42

( 0.49) ( 0.49) ( 0.49)Student has Internet at Home 0.24 0.25 0.25

( 0.43) ( 0.43) ( 0.43)

Sample Size 29833 7707 5001

Note: This table presents estimated means for students in the YEMPC program who applied for a computer withInternet connection (Column 1), students in the experimental sample (Column 2), and students in the sample ofthe phone survey (Column 3). Estimated standard deviations are reported in parentheses.

Table A2: Balance by Treatment

(1) (2) (3)Random Fixed P-Value

Panel A: Student CharacteristicsDaily mean download, pre 153.47 153.54 0.970

( 177.62) ( 181.21)Average daily use in minutes, predicted, pre 186.12 186.54 0.832

( 162.75) ( 162.87)Live with mother 0.96 0.96 0.497

( 0.20) ( 0.19)Live with father 0.61 0.62 0.113

( 0.49) ( 0.48)Live with Brother/Sister 0.76 0.77 0.296

( 0.43) ( 0.42)Live with Grandfather/Grandmother 0.15 0.15 0.444

( 0.36) ( 0.36)Female 0.43 0.42 0.757

( 0.49) ( 0.49)Number of siblings 1.71 1.72 0.519

( 1.28) ( 1.27)

Panel B: Parents CharacteristicsGuardian Age 40.58 40.30 0.262

( 8.15) ( 7.52)What is your education level?

Elementary incomplete 0.10 0.10 0.443( 0.30) ( 0.30)

Elementary complete 0.14 0.14 0.866( 0.35) ( 0.35)

Secondary incomplete 0.15 0.16 0.525( 0.36) ( 0.36)

Secondary complete 0.47 0.47 0.990( 0.50) ( 0.50)

High incomplete 0.04 0.05 0.072( 0.19) ( 0.21)

High complete 0.10 0.09 0.162( 0.30) ( 0.29)

What is your current employment status?Working full time 0.33 0.32 0.374

( 0.47) ( 0.47)Working part-time 0.13 0.13 0.342

( 0.33) ( 0.34)Not working looking for a job 0.06 0.06 0.515

( 0.23) ( 0.23)Not working not looking for a job 0.46 0.48 0.289

( 0.50) ( 0.50)Other 0.02 0.02 0.049

( 0.15) ( 0.13)

Note: This table presents estimated di↵erences between students in the di↵erent experimentalgroups. Columns 1 and 2 present means and stadard deviations in parentheses. Column 3 presentsthe p-value of a t-test for di↵erences between the Random day and Fixed day groups.

Table A3: Balance by Treatment for the FU Survey Sample

(1) (2) (3) (4) (5)T1 T2 T3 SMS-Only P-Value(F-Test)

Panel A: Student CharacteristicsDaily mean download, pre 149.38 158.72 151.25 163.15 0.305

( 177.19) ( 184.23) ( 171.92) ( 205.38)Average daily use in minutes, predicted, pre 180.47 191.39 185.34 187.61 0.505

( 159.27) ( 166.27) ( 162.66) ( 162.93)Live with mother 0.96 0.97 0.97 0.95 0.041

( 0.19) ( 0.17) ( 0.17) ( 0.22)Live with father 0.65 0.65 0.64 0.62 0.345

( 0.48) ( 0.48) ( 0.48) ( 0.49)Live with Brother/Sister 0.77 0.79 0.80 0.74 0.030

( 0.42) ( 0.41) ( 0.40) ( 0.44)Live with Grandfather/Grandmother 0.16 0.14 0.14 0.15 0.637

( 0.37) ( 0.35) ( 0.35) ( 0.36)Female 0.42 0.42 0.42 0.46 0.205

( 0.49) ( 0.49) ( 0.49) ( 0.50)Number of siblings 1.62 1.75 1.74 1.71 0.109

( 1.20) ( 1.30) ( 1.33) ( 1.28)

Panel B: Guardian CharacteristicsGuardian Age 40.54 40.47 40.54 40.85 0.693

( 7.62) ( 7.26) ( 7.79) ( 7.77)What is your education level?

Elementary incomplete 0.09 0.09 0.10 0.09 0.856( 0.28) ( 0.29) ( 0.30) ( 0.29)

Elementary complete 0.12 0.14 0.15 0.16 0.139( 0.33) ( 0.35) ( 0.36) ( 0.36)

Secondary incomplete 0.17 0.16 0.14 0.16 0.315( 0.37) ( 0.37) ( 0.34) ( 0.37)

Secondary complete 0.49 0.48 0.48 0.46 0.724( 0.50) ( 0.50) ( 0.50) ( 0.50)

High incomplete 0.03 0.04 0.04 0.03 0.290( 0.17) ( 0.20) ( 0.21) ( 0.18)

High complete 0.11 0.09 0.09 0.09 0.543( 0.31) ( 0.29) ( 0.29) ( 0.29)

What is your current employment status?Working full time 0.31 0.30 0.31 0.29 0.632

( 0.46) ( 0.46) ( 0.46) ( 0.45)Working part-time 0.12 0.14 0.13 0.14 0.510

( 0.33) ( 0.34) ( 0.33) ( 0.35)Not working looking for a job 0.05 0.05 0.06 0.05 0.916

( 0.22) ( 0.23) ( 0.23) ( 0.22)Not working not looking for a job 0.50 0.49 0.48 0.50 0.809

( 0.50) ( 0.50) ( 0.50) ( 0.50)Other 0.02 0.02 0.02 0.01 0.572

( 0.14) ( 0.14) ( 0.15) ( 0.12)

Note: This table presents estimated di↵erences between students in the di↵erent experimental groups who participated in the follow-uptelephone survey. Columns 1 to 4 present means and stadard deviations in parentheses. Column 5 presents the p-value of a of joint test fordi↵erences between the T1, T2 and T3 and SMS-only groups.

Table A4: Take-up: Extensive Margin using Administrative Data

(1) (2) (3) (4)SMS ISP SMS ISP SMS W8 SMS W8

T1 0.976*** 0.975*** 0.000 -0.000(0.004) (0.004) (0.004) (0.004)

T2 0.000 -0.000 0.978*** 0.978***(0.004) (0.004) (0.004) (0.004)

T3 0.967*** 0.967*** 0.967*** 0.967***(0.004) (0.004) (0.004) (0.004)

Observations 7,707 7,707 7,707 7,707Control Mean 0 0 0 0BL Controls X X

Note: This table presents estimated e↵ects for di↵erent treatmentgroups with respect to the control group. Columns 1 and 2 presentestimates on the reception of at least one SMS including ISP informa-tion. Columns 3 and 4 present estimates on the reception of at leastone SMS including an o↵er of help to install parental control settings.Odd-numbered columns present estimates without controls and even-numbered columns present estimates including baseline control vari-ables. Control variables include the baseline values of mean of MBs ofInternet use; guardian gender, age, education level and employment sta-tus; number of siblings; and dummies for family composition (indicatingwhether the child lives with mother, father, step-mother or father’s part-ner, step-father or mother’s partner, uncle/aunt, brother/sister, grand-father/grandmother, other relatives, and other non-relatives). Robustestimated standard errors are reported in parentheses. *** Significantat the 1 percent level. ** Significant at the 5 percent level. * Significantat the 10 percent level.

Table A5: Impact of Treatments on Time of Internet Use


Panel A: T1, T2, T3T1 -7.290* -7.641** -7.039* -7.360** -7.929** -8.356**

(3.770) (3.388) (3.750) (3.373) (3.976) (3.599)T2 3.227 0.834 3.240 0.879 3.194 0.720

(3.831) (3.459) (3.806) (3.437) (4.058) (3.697)T3 -2.563 -5.507 -2.780 -5.688* -2.011 -5.045

(3.696) (3.386) (3.663) (3.351) (3.944) (3.651)


Panel B: ISP Information and Parental ControlISP Information -6.540** -6.991*** -6.530** -6.963*** -6.568** -7.060***


(2.637) (2.393) (2.619) (2.375) (2.796) (2.562)


Note: This table presents estimated e↵ects on Internet use measured as predicted time connected to Internet. Odd-numbered columns present estimates without controls and even-numbered columns present estimates includingbaseline control variables. Control variables include the baseline values of mean of MBs of Internet use; guardiangender, age, education level and employment status; number of siblings; and dummies for family composition(indicating whether the child lives with mother, father, step-mother or father’s partner, step-father or mother’spartner, uncle/aunt, brother/sister, grandfather/grandmother, other relatives, and other non-relatives). Robustestimated standard errors are reported in parentheses. *** Significant at the 1 percent level. ** Significant at the5 percent level. * Significant at the 10 percent level.

Table A6: Event Study of SMS messages by Treatment

(1) (2) (3) (4) (5) (6) (7)Day -3 Day -2 Day -1 Day 0 Day 1 Day 2 Day 3

Panel A: Treatment e↵ectISP Info 1.347 -0.542 0 -6.613 -10.660** -4.793 -4.885

( 4.753) ( 4.660) (0) ( 4.526) ( 4.529) ( 4.542) ( 4.566)PC -1.603 -0.256 0 -1.550 -4.419 -3.175 -3.960

( 4.759) ( 4.661) (0) ( 4.522) ( 4.526) ( 4.539) ( 4.564)SMS-Only 2.243 1.295 0 0.834 -5.519 1.101 -1.135

( 4.691) ( 4.596) (0) ( 4.463) ( 4.467) ( 4.479) ( 4.504)

Panel B: Above EventsISP Info 9.420 4.375 0 -17.484** -27.921*** -18.247** -21.621***

( 8.134) ( 7.979) (0) ( 7.757) ( 7.764) ( 7.783) ( 7.823)PC -2.243 -0.889 0 -3.002 -4.930 -4.679 -4.502

( 5.847) ( 5.727) (0) ( 5.556) ( 5.561) ( 5.577) ( 5.608)SMS-Only 1.457 0.178 0 -0.148 -7.422 0.215 -2.856

( 5.778) ( 5.664) (0) ( 5.503) ( 5.507) ( 5.522) ( 5.552)

Panel C: Not-Above EventsISP Info -5.504 -5.422 0 0.814 1.036 2.702 5.447

( 4.929) ( 4.828) (0) ( 4.687) ( 4.691) ( 4.705) ( 4.731)PC -0.097 1.911 0 -0.899 -4.638 -1.119 -3.600

( 4.186) ( 4.099) (0) ( 3.976) ( 3.980) ( 3.991) ( 4.014)SMS-Only 0.596 -0.852 0 0.543 -4.954 -0.583 -1.208

( 4.117) ( 4.034) (0) ( 3.917) ( 3.920) ( 3.931) ( 3.953)

Note: This table presents estimated e↵ects on Internet use measured as MBs downloaded and uploaded for di↵erentdays around the reception of a SMS. Day 0 marks the day on which the SMSs are received each week. The coe�cientfor Day -1 is imposed to be 0 for each treatment group. Regressions also include dummies for each event. Robustestimated standard errors are reported in parentheses. *** Significant at the 1 percent level. ** Significant at the5 percent level. * Significant at the 10 percent level.

Table A7: W8 Parental Control Settings Installation, Event Study

(1) (2) (3) (4) (5) (6) (7)Day -3 Day -2 Day -1 Day 1 Day 2 Day 3

Panel A: Parental ControlsMB Use -12.852 -10.555 -21.823 -21.960 -15.711 -1.548

(28.384) (28.375) (28.367) (28.362) (28.372) (28.382)Panel B: T2MB Use -43.390 -22.280 -22.451 -14.188 -0.901 25.217

(49.181) (49.166) (49.153) (49.143) (49.153) (49.166)Panel C: T3MB Use 13.607 -0.367 -21.302 -28.914 -29.069 -25.277

(31.493) (31.483) (31.474) (31.470) (31.486) (31.500)

Notes: This table presents estimated e↵ects on Internet use measured as MBs downloaded and uploaded fordi↵erent days around the installation of W8 parental control settings.. Day 0 marks the day on which theprogram was installed. The coe�cient for Day 0 is imposed to be 0 for each treatment group. Regressionsalso include dummies for weeks in which the installation took place. Robust estimated standard errors arereported in parentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level. *Significant at the 10 percent level.

Table A8: Event Study for 1st and 2nd Half of Treatment


Panel A: 1st HalfISP Info -3.677 -4.411 0 -13.005** -19.689*** -4.398 -10.291*

( 6.254) ( 6.135) (0) ( 5.808) ( 5.849) ( 5.902) ( 5.939)PC -7.752 -0.765 0 -4.521 -7.171 -4.359 -5.746

( 6.262) ( 6.055) (0) ( 5.822) ( 5.863) ( 5.919) ( 5.953)Placebo -1.173 -1.818 0 -0.415 -10.337* 2.670 -2.645

( 6.186) ( 6.059) (0) ( 5.729) ( 5.767) ( 5.822) ( 5.860)

Panel B: 2nd HalfISP Info 4.832 2.515 0 -0.461 -3.282 -8.304 -2.453

( 7.598) ( 7.354) (0) ( 7.020) ( 6.983) ( 6.967) ( 7.001)PC 7.169 1.322 0 0.771 -3.606 -5.047 -5.145

( 7.569) ( 7.330) (0) ( 6.990) ( 6.956) ( 6.939) ( 6.975)Placebo 6.551 6.456 0 1.133 -2.687 -3.744 -2.818

( 7.489) ( 7.253) (0) ( 6.929) ( 6.896) ( 6.878) ( 6.911)


Table A9: Take-up by Subtreatment: using Administrative Data

(1) (2) (3) (4) (5) (6)SMS ISP SMS ISP W8 ISP W8 ISP W8 Installed W8 Installed

Panel A: Within Separate TreatmentsWithin T1

Random -0.007 -0.006 0.000 0.000 0.000 0.000( 0.011) ( 0.011) ( 0.000) ( 0.000) ( 0.000) ( 0.000)


Within T2

Random 0.000 0.000 0.002 0.003 0.034** 0.033**( 0.000) ( 0.000) ( 0.011) ( 0.011) ( 0.016) ( 0.016)


Within T3

Random -0.025** -0.027** -0.023** -0.024** 0.000 0.002( 0.011) ( 0.011) ( 0.011) ( 0.011) ( 0.017) ( 0.017)


Panel B: Within Joint TreatmentsWithin ISP Information

Random -0.016** -0.016** -0.011 -0.012 0.000 0.000( 0.008) ( 0.008) ( 0.014) ( 0.014) ( 0.009) ( 0.009)


Within Parental Controls

Random -0.013 -0.014 -0.010 -0.009 0.017 0.017( 0.014) ( 0.014) ( 0.008) ( 0.008) ( 0.011) ( 0.011)


Note: This table presents estimated e↵ects on take-up for the random-day sub-treatment group with respect tothe fixed-day sub-treatment group. Columns 1 and 2 present estimates on the reception of SMSs including ISPinformation. Columns 3 and 4 present estimates on the reception of SMSs including an o↵er of help to installparental control settings. Columnd 5 and 6 present estimates on the installation of parental control settingsthrough the call center of the experiment. Odd-numbered columns present estimates without controls and even-numbered columns present estimates including baseline control variables. Control variables include the baselinevalues of mean of MBs of Internet use; guardian gender, age, education level and employment status; number ofsiblings; and dummies for family composition (indicating whether the child lives with mother, father, step-motheror father’s partner, step-father or mother’s partner, uncle/aunt, brother/sister, grandfather/grandmother, otherrelatives, and other non-relatives). Robust estimated standard errors are reported in parentheses. *** Significantat the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level.

Table A10: Interactions of Treatments with Random

(1) (2) (3) (4) (5) (6)Period: All All Week Week Weekend Weekend

T1 -10.943 -17.147 -9.138 -15.498 -15.549 -21.357*(12.714) (11.061) (13.154) (11.433) (12.553) (11.187)

T2 -5.835 -6.839 -5.250 -6.324 -7.326 -8.152(12.244) (11.523) (12.457) (11.782) (12.631) (11.854)

T3 -19.407 -21.653* -19.057 -21.408* -20.300 -22.279*(12.603) (11.340) (12.828) (11.559) (12.917) (11.771)

Random -21.642* -22.497** -22.146* -22.986** -20.357 -21.250*(12.044) (11.212) (12.037) (11.290) (13.098) (12.130)

T1 ⇥ Random -2.972 7.921 -4.047 7.014 -0.228 10.233(15.947) (14.141) (16.218) (14.358) (16.555) (14.999)

T2 ⇥ Random 11.796 12.572 13.072 13.836 8.541 9.348(16.419) (15.057) (16.455) (15.154) (17.568) (16.165)

T3 ⇥ Random 19.627 19.867 18.384 18.577 22.797 23.159(15.569) (14.205) (15.573) (14.249) (16.907) (15.565)



Table A11: Impact for Fixed Subgroup for 1st Half/2nd Half/After Treatment


Panel A: 1st Half of TreatmentISP Info -10.773 -9.457 0 -7.499 -23.529*** -3.387 -8.632

( 8.307) ( 8.253) (0) ( 8.052) ( 8.109) ( 8.163) ( 8.169)PC -8.570 1.832 0 -7.039 0.391 -9.177 -9.394

( 8.345) ( 8.137) (0) ( 8.141) ( 8.200) ( 8.257) ( 8.262)SMS-Only -6.137 -1.255 0 1.381 -5.785 3.008 -7.202

( 8.202) ( 8.159) (0) ( 7.962) ( 8.017) ( 8.070) ( 8.074)

Panel B: 2nd Half of TreatmentISP Info 7.684 2.810 0 -8.582 -3.671 -8.477 1.099

( 10.650) ( 10.495) (0) ( 10.348) ( 10.289) ( 10.233) ( 10.232)PC 8.267 2.850 0 5.434 0.924 7.317 -1.573

( 10.620) ( 10.465) (0) ( 10.315) ( 10.264) ( 10.208) ( 10.206)SMS-Only 6.203 13.041 0 -0.109 -0.379 5.897 1.154

( 10.479) ( 10.320) (0) ( 10.180) ( 10.128) ( 10.075) ( 10.071)

Panel C: After TreatmentISP Info 7.589 -0.275 0 3.744 7.274 -5.752 1.996

( 6.122) ( 6.073) (0) ( 6.052) ( 6.055) ( 6.064) ( 6.018)PC 0.542 -6.553 0 2.427 4.968 4.880 0.848

( 6.124) ( 6.074) (0) ( 6.053) ( 6.055) ( 6.065) ( 6.018)SMS-Only 1.187 -2.627 0 6.528 10.034* -1.346 0.384

( 6.009) ( 5.971) (0) ( 5.949) ( 5.953) ( 5.962) ( 5.911)


Table A12: Impact of Treatments on Intensity of Internet Use, Pre/Post School

(1) (2)Pre-School Post-School

Panel A: T1, T2, T3T1 -12.992 -22.580**

(12.056) (9.927)T2 1.017 -8.536

(12.889) (10.766)T3 -12.551 -10.991

(12.220) (10.532)

Observations 7,707 7,707BL Controls X XControl Mean 204.9 153.7

Panel B: ISP Information and Parental ControlISP Information -13.280 -12.514*

(8.429) (6.755)Parental Controls 0.729 1.527

(8.489) (6.787)

Observations 7,707 7,707BL Controls X XControl Mean 204.9 153.7

Note: :This table presents estimated e↵ects on Internet use measuredas daily average MBs uploaded and downloaded. Column 1 presents es-timates for the last two weeks of the vacation period and Column 2 forthe first two weeks of the school period. Control variables include thebaseline values of mean of MBs of Internet use; guardian gender, age, ed-ucation level and employment status; number of siblings; and dummiesfor family composition (indicating whether the child lives with mother,father, step-mother or father’s partner, step-father or mother’s partner,uncle/aunt, brother/sister, grandfather/grandmother, other relatives,and other non-relatives). Robust estimated standard errors are reportedin parentheses. *** Significant at the 1 percent level. ** Significant atthe 5 percent level. * Significant at the 10 percent level.

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