Forschungsinstitut zur Zukunft der ArbeitInstitute for the Study of Labor

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Population Policy:Abortion and Modern Contraception Are Substitutes

IZA DP No. 9809

March 2016

Grant MillerChristine Valente

Population Policy: Abortion and

Modern Contraception Are Substitutes

Grant Miller Stanford University

and NBER

Christine Valente

University of Bristol and IZA

Discussion Paper No. 9809 March 2016

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IZA Discussion Paper No. 9809 March 2016

ABSTRACT

Population Policy: Abortion and Modern Contraception Are Substitutes*

There is longstanding debate in population policy about the relationship between modern contraception and abortion. Although theory predicts that they should be substitutes, the existing body of empirical evidence is difficult to interpret. What is required is a large‐scale intervention that alters the supply (or full price) of one or the other – and importantly, does so in isolation (reproductive health programs often bundle primary health care and family planning – and in some instances, abortion services). In this paper, we study Nepal’s 2004 legalization of abortion provision and subsequent expansion of abortion services, an unusual and rapidly‐implemented policy meeting these requirements. Using four waves of rich individual‐level data representative of fertile‐age Nepalese women, we find robust evidence of substitution between modern contraception and abortion. This finding has important implications for public policy and foreign aid, suggesting that an effective strategy for reducing expensive and potentially unsafe abortions may be to expand the supply of modern contraceptives. JEL Classification: J13, N35 Keywords: abortion, contraception, Nepal Corresponding author: Christine Valente Department of Economics University of Bristol 8 Woodland Road BS8 1TN, Bristol United Kingdom E-mail: christine.valente@bristol.ac.uk

* The authors thank the Center for Research on Environmental Health and Population Activities for their assistance in collecting Comprehensive Abortion Care Center Data and to the Family Health Division of the Ministry of Health and Population of Nepal for granting access to Technical Committee for Implementation of Comprehensive Abortion Care records. We also thank five anonymous referees for their useful suggestions. Christine Valente gratefully acknowledges funding from ESRC grant No. RES‐000‐22‐3740.

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1. Introduction

There is longstandingdebate in reproductivehealth circles about the relationship

betweenmoderncontraceptionandabortionuse.Overseveraldecades,populationscholars

have documented concomitant increases in both contraceptive prevalence and abortion

ratesaroundtheworldinsettingsasdiverseasCuba,SouthKorea,Bangladesh,Singapore,

Netherlands,Denmark,andtheUnitedStates(NobleandPotts1996;Rahmanetal.2001;

MarstonandCleland2003).Thisphenomenoniscommonlyattributedtorapidreductions

indesiredfertility,whichinturnincreasedemandforallmethodsofbirthcontrol(Marston

andCleland2003).

However,theorypredictsthatholdingdemandforbirthcontrolconstant(andabsent

absolute moral or religious constraints), women (couples) 1 should use modern

contraceptivesandabortion interchangeably– that is, theyaresubstitutes(Westoffetal.

1981;KaneandStaiger1996;BongaartsandWestoff2000;Westoff2000;Rahmanet al.

2001;MarstonandCleland2003).2Arelativeincreaseintheaffordability,availability,or

acceptabilityofoneshouldleadwomenwishingtoregulatetheirfertilitytosubstituteaway

1Becausewestudymarriedwomeninapatriarchalsociety,thechoiceofcontraceptionislikelytobetheresultofintra‐householdbargaining.Wedonottheoreticallyorempiricallydistinguishindividualpreferencesfromthechoicesthatresultfromthisbargainingprocess.However,ourreduced‐formestimatesisolateimportant–andpolicyrelevant–parametersofinterest.Recognizingthispoint,werefertocontraceptivedecisionsaswomen’sdecisionsforsimplicitythroughoutthepaper.2 A separate strand of economic theory, which studies the response of risk‐taking behavior to perceivedchangesintheconsequencesofabadoutcome(e.g.,increasedautomobilesafety,availabilityoftreatmentforamedicalcondition),suggestsanadditionalmechanismthroughwhichwomenwhodonotwanttobecomepregnantmay reduce contraceptive usewhen access to abortion improves. There is indeed evidence thatreducing thecostofabadoutcomemay increase risk‐taking inotherhealthareas.ExamplesarePeltzman(1975)ontheeffectofautomobilesafetyondangerousdriving,Dilleyetal.(1997)onHIVtreatmentandrisk‐takingamongmenwhohavesexwithmen,andPeltzman(2011)ontheeffectofmedicalbreakthroughs(e.g.,newtreatmentsforheartdisease)onoffsettingbehavior(e.g.,obesity).

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from the other.3Since themid‐1990s (as declining fertility rates have plateaued), global

contraceptive prevalence has continued to rise, while abortion rates have declined – a

relationshipconsistentwithsubstitution.

Debateabouttherelationshipbetweencontraceptionandabortionhasfundamental

implications forpublicpolicyandforeignaid. Importantly, ifmoderncontraceptivesand

abortionsaresubstitutes,thenaneffectivestrategyforreducingexpensiveandpotentially

life‐threatening abortions may be to boost the supply4of modern contraceptives. Two

recentanalysesoftheUnitedStates’“MexicoCityPolicy”(MCP)suggestthatbyreducing

fundingforfamilyplanningprograms,theMCPmayhaveactuallyreducedtheavailabilityof

moderncontraceptivesrelativetoabortionandthusincreasedabortionrates(Bendavidet

al.2011,Jones2011).5

Understanding the trade‐off between contraception andabortionwould also shed

light on ways to prevent maternal deaths. Research on the determinants of maternal

mortalityworldwidesuggeststhatunsafeabortionplaysaquantitativelyimportantrole.In

LatinAmericanandCaribbeancountries, a systematic review found thatunsafeabortion

accounts for roughly 50% more maternal deaths than better‐known complications like

sepsis(Khanetal.2006).TheWorldHealthOrganizationestimatesthat13%ofmaternal

3The theoretical discussionof the tradeoff between contraception and abortion among demographers hastendedtofocusontheeffectofchangingcontraceptiveprevalenceontheabortionrate(e.g.,BongaartsandWestoff2000).Intheeconomicsliterature,thetradeoffisunderstoodasgoingbothways(seeKaneandStaiger1996foradiscussionoftheeffectofachangeinthecostofabortiononcontraceptiveuse).Thedifferencestemsfromtheeconomicmodelingofcontraceptivechoicesasdependingonthecostofcontraceptionrelativetothecostofnotusingcontraception,whichinturndependsonthecostofabortion(seeSection4).4Changesinsupplyincludebothchangesinavailabilityandchangesinthefullpriceofcontraception(monetary,social,andemotionalprice).Forbrevity,werefertothesechangescollectivelyas‘supply’changesthroughout.5FirstannouncedinMexicoCityin1984byPresidentReagan’sadministration,the‘MexicoCityPolicy’requiresallnon‐governmentalorganizationsoperatingabroadtorefrainfromperformingorcounselingwomenaboutabortionasameansoffertilitycontrolasaconditionforreceivingU.S.federalfunding.

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deathsworldwidearelinkedtounsafeabortion(WHO2010).Givenconcernsaboutunder‐

reporting, existing evidence is also suspected to underestimate mortality from unsafe

abortion(Gerdtsetal.2013).

What isneededtoestablishwhetherornot theuseofmoderncontraceptivesand

abortionsarecomplementsorsubstitutesisalarge‐scaleinterventionthataltersthesupply

ofeitheroneortheother–andimportantly,doessoinisolation.Todate,findingsuchcases

hasbeenchallengingbecausereal‐worldreproductivehealthprogramsgenerallydelivera

bundleofservicestogether,makingitdifficulttodisentangletheeffectofsupplyofmodern

contraceptivesorabortionfromotherprogramcomponents. Asacaseinpoint,thewell‐

knownMatlabFamilyPlanningExperimentbundledtheprovisionofmoderncontraceptives

withtheprovisionofbothabortionservices(menstrualregulation)andchildhealthservices,

makingitdifficulttoisolatetheeffectofcontraceptivesupply(Rahmanetal.2001;Miller

andSingerBabiarz2013).

Thispaperstudiesanunusualpolicychangewell‐suitedtoassessingtherelationship

between theuseofmodern contraceptives and abortion. Starting inMarch2004,Nepal

legalizedtheprovisionofabortionbyselectedexistinghealthserviceproviders.Inaddition

toitsscale,whatdistinguishesthispolicyisthatindoingso,Nepaldidnotexpandthesupply

ofmoderncontraceptives,bundlethelegalizationofabortionwithchangesintheprovision

ofanyothertypeofservice,orexpandthehealthcareworkforce.Weutilizeunusuallyrich

individual‐leveldatarepresentativeoffertileageNepalesewomencollectedinfourwaves

both before and after the legalization of abortion to estimate how the use of modern

contraceptives(andotherreproductivebehaviors)respondedtothispolicy.

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Wefindthattheadditionofalegalabortioncenterinone’sdistrictisassociatedwith

a2.6%decreaseintheoddsofusinganycontraceptive[OR:0.974,95%CI:(0.961;0.987)],

implying that a move from zero to the mean number of centers post‐legalization was

associatedwithareductionincontraceptiveprevalenceof2percentagepoints‐6%ofthe

pre‐legalizationprevalencerate.Decomposing thiseffectamongtraditionalcontraceptive

methods(suchaswithdrawalandtherhythmmethod),femalesterilization,andreversible

modernmethods,we find that thedecreaseoccursprincipally among reversiblemodern

methods.

2. Background

2.1. GlobalandRegionalTrends

Globally,contraceptiveuseandabortionrateshavebeeninverselyrelatedoverthe

pastseveraldecades.Contraceptiveprevalencehasincreasedsteadilyoverthepasttwenty

years, rising from 54.8% to 63.3% between 1990 and 2010 (Alkema et al. 2013).

Simultaneously, there has been a steady decline in abortion rates, falling from 35 to 28

abortionsper1,000womenonaverageworldwidebetween1995and2008(Sedghetal.

2012). These global trends are of course consistent with substitution of modern

contraceptionforabortion,butanumberofpotentiallyimportantconfoundingfactorshave

alsobeenatworkovertime(changesindesiredfertility,forexample).

Theinverserelationshipbetweenabortionandcontraceptionisparticularlyevident

informerlysocialistEasternEuropeancountries.Undercommunism,abortionwasamajor

(ifnottheprincipal)methodofbirthcontrolacrossmuchofEasternEuropeandCentralAsia

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(Frejka1983).6Afterthecollapseofcommunism,abortionratesdeclinedsteeplywiththe

diffusionofmoderncontraceptivesduring the1990s (Westoff etal.1998;Westoff2000;

Pop‐Eleches2010)againsuggestingthatcontraceptionandabortionmayhavebeenused

interchangeably.

On the other hand, concomitant increases in both contraceptive prevalence and

abortionrateshavebeenobservedinavarietyofcountriesfurtherbackintime,including

Cuba,SouthKorea,Bangladesh,Singapore,theNetherlands,Denmark,andtheUnitedStates

(Noble andPotts1996;Rahmanet al. 2001;Marston andCleland2003). Bongaarts and

Westoff(2000)andMarstonandCleland(2003)suggestthatthesesimultaneousincreases

mayoccurduringtransitionstolowerfertilityifthesupplyofmoderncontraceptivesfailsto

keep pace with the reduction in desired fertility. Then, as desired fertility plateaus,

substitution betweenmodern contraceptives and abortion should becomemore evident

(MarstonandCleland2003).Thisisconsistentwithglobaltrendssincethemid‐1990s,as

theworldwidedeclineinfertilitydecelerated(WorldDevelopmentIndicators2014).

2.2. PreviousEstimatesofSubstitutionbetweenAbortionandContraception

Manystudiesoftherelationshipbetweencontraceptionandabortionindeveloping

countriesarelimitedtoinformalanalysesoftheirco‐movement.Onlyahandfulofstudies

haveattempted to estimate the causal relationshipbetween the two.Two recent studies

investigate changes in abortion and contraceptiveuse inducedby theMexicoCity Policy

(MCP).Bendavidetal.(2011)comparechangesinabortionandcontraceptiveuseovertime

in countries highly exposed to the Mexico City Policy (MCP) relative to less exposed

6TheSovietUnionwasthefirstcountrytolegalizeabortionin1920.

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countries.Theauthors findthatmoreexposedcountriesexperiencedslower increases in

contraceptiveprevalenceandhigherincreasesinabortionafterthere‐enactmentoftheMCP,

suggestingthatreducedcontraceptivesupplymayhaveincreasedtheincidenceofabortion.

Jones(2011)comparesabortionratesamongwomeninGhanaduringperiodsinwhichthe

MCPisbothenforcedandnotenforced.Shefindsthatruralwomenaremorelikelytohave

an abortion during periods of enforcement,which she links to the increased number of

unwantedpregnanciesfollowingthereductionincontraceptivesupplyunderthepolicy.

Rahman et al. (2001) analyzes changes in abortion linked to the Matlab Family

PlanningExperimentintervention.Theauthorsshowthatabortionratesfell intreatment

villages relative to control villages between 1979 and 1998 (despite increasing secular

trends in both contraceptive use and abortion). However, the experimental treatment

bundled menstrual regulation 7 services together with the provision of modern

contraceptivesbetween1977and1983(donorsthenstoppedsupportingthiscomponentof

theprogram).Mostoftherelativedeclineinabortionintreatmentareasoccurredaround

1983–andsoisplausiblyduetotheendofabortionservices.Antenatalandchildhealth

serviceswerealsobundledtogetherwiththeprovisionofmoderncontraceptivesbeginning

in1978(Phillipsetal.1984),makingitdifficulttodisentangletheindependentcontribution

ofcontraceptivesupplyfromimprovementsinchildsurvival.8

Evidence fromwealthycountries isalsothin. AnanatandHungerman(2012) find

thattheavailabilityoforalcontraceptivesstartingatage16isassociatedwithareductionin

7Theterm“menstrualregulation”referstomanualvacuumaspirationprocedurescarriedoutafteramissedperiodbutbeforepregnancyisclinicallyconfirmed.8Forinstance,Phillipsetal.(27)findthatcontraceptiveprevalenceisindependentlycorrelatedwithsomeofthematernalandchildhealthcomponentsoftheMatlabprogramme,andthatthesignofthecorrelationvarieswiththetypeofintervention.

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theprobabilityofreportinghavinghadanabortionbetweenages16and19.Glasieretal.

(2004)findnochangeinabortionratesinScottishcommunitiesfollowingfreedistribution

of advance emergency contraception to women ages 16‐29. Finally, Durrance (2013)

analyzes the diffusion of emergency contraception through pharmacies in the state of

Washington,findingnochangeintheabortionrate.

3. TheNepaleseNaturalExperiment

3.1. TheLegalizationofAbortioninNepal

Prior to 2002, Nepalese women who terminated their pregnancies faced

imprisonment for infanticide. 9 On September 27, 2002, the King of Nepal signed a bill

legalizingabortionpriortothetwelfthweekofpregnancy,priortotheeighteenthweekin

casesofrapeorincest,andatanygestationalagewithappropriatemedicaladvice(toprotect

thehealthofthemotheror incasesofseverebirthdefects, forexample)(MOHP,WHO&

CREHPA 2006). When this law was enacted, however, Nepalese reproductive health

providerswereneither permittednor adequately trained to begin offering safe abortion

services.Consequently,therewasverylittleincreaseinabortion,ifany,followingthislawin

2002(Valente2014).

Nepal’sfirstlegalabortionserviceswereofferedinMarch2004,andthenumberof

healthcentersregisteredtoprovidethemgrewrapidlyovertime,risingto141inJune2006

and291byFebruary2010.Toplacethisexpansionintocontext,thenumberofregistered

abortionprovidersgrewfromnonetonearlytwiceasmanyproviderspercapitaasinthe

9Althoughimprisonmentwasnotacommonoutcomeamongwomenwhohadanabortion,amongthesmallpopulationoffemaleinmatesinNepaleseprisons(405in1997),asubstantialproportionisbelievedtohavebeenconvictedonabortion‐relatedcharges(Ramaseshan1997).

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UnitedStatesby2010–overaperiodofjustsixyears.10Thislarge‐scalepolicychangehas

been hailed by advocates as a success, and, according to observers, “Nepal's experience

making high‐quality abortion care widely accessible in a short period of time offers

importantlessonsforothercountriesseekingtoreducematernalmortalityandmorbidity

fromunsafeabortion”(Samandarietal.2012,p.1).

Underthepolicy,seniorgynecologistsfromcentralandregionalhospitalsaswellas

from someNGO and private clinicswere trained to becomeboth the first legal abortion

providers as well as safe abortion trainers themselves. With the aim to result in rapid

nationalscale‐up,trainingthencascadedfromregionalandzonalhospitalstopublicdistrict

hospitals(Samandarietal.2012).Theprivatesector(primarilyMarieStopesInternational

andtheFamilyPlanningAssociationofNepal)also“fill(s)animportantnicheinurbanareas”

(Samandarietal,2012)andislessprevalentinruralareas–whichwerehometo83%ofthe

Nepalesepopulationaccordingtothe2011populationcensus.Asaresult,morepopulous

districts,districtsinthemoreaccessibleregionsofthecountry,andurbanareasweremore

likelytohavelegalabortionservicesinearlyyears.11

Although illegalabortionshavealwaysbeenavailable tosomedegree, legalization

greatlyreducedtheeffective(quality‐adjusted)fullprice.Thecostofalegalabortionranges

10291 abortion centers in 2010 relative to a totalNepal population of 26.49Million reported in the 2011Nepalesepopulationcensusimpliesonecenterper91,031inhabitants.IntheUnitedStates,therewere1,793abortion providers in 2008 (Guttmacher Institute 2013) relative to a total population of 305 Million(PopulationReferenceBureau2008),implyingoneproviderper170,106inhabitants.11Inaddition,NepalexperiencedaMaoistinsurgencyin1996,whichledtoa10‐yearlow‐tomedium‐intensityconflict,peakingin2002.Conflictareasbetween2004and2006mayhavealsoexperiencedslower,lessintenseincreasesinthesupplyoflegalabortion.Ifareasinwhichabortionsupplygrewmoreslowlyhadpre‐existingtrenddifferencesincontraceptiveuse,thiscouldbiasourestimates.InSection7,weshowthatourresultsarerobusttoallowingformorepopulousdistricts,districtsinmoreaccessibleregionsofthecountry,andurbanareastoexperiencedifferentialtimetrendsincontraception–aswellastocontrollingforconflictintensity.

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fromRs800toRs2000(USD11.33toUSD28.33)(MOHP&CREHPA2006)relativetomean

annual income Rs51,978 in 2004 (Central Bureau of Statistics 2004, p.37). Government

policy stipulates that poor women are entitled to abortion services free of charge, but

eligibilitycriteriahavenotbeenclearlydefined,andinpractice,theytendnottoreceiveany

preferentialtreatment(CREHPA2007,Samandarietal.2012).Comparisonstothecostof

illegalabortionsaredifficult; fivecasestudies inMOHPetal. (2006) report considerable

variation(Rs200,Rs500,Rs700,Rs3000,Rs8000).However,legalabortionsaremuchsafer,

reducing the likelihood of maternal death and post‐abortion complications requiring

expensivemedical care (MOHP et al. 2006).12 Consistentwith legalization reducing the

effective(quality‐adjusted)priceonanabortion,Valente(2014)showsthathavingalegal

abortioncenternearbyatthestartofapregnancyreducestheprobabilityofcarryingthe

pregnancytotermby8.1%.13

Incontrasttoabortion,contraceptionservicesareavailablefreeofchargethrough

governmentfacilities(andatasubsidizedpricethroughsocialmarketingorganizationslike

PSI – and at full price in private facilities) (Shrestha et al. 2012). Condoms, pills, and

injectablesareprovidedbyalllevelsofgovernmentfacilitiesandproviders,whileIUDsand

implants can be obtained in selected hospitals, primary health centers, and health posts

(Shrestha et al. 2012). In the latest Demographic and Health Survey (DHS 2011), 55%

(47.5%)ofsterilizedwomen(men)weresterilizedinagovernmenthospitalorclinic,while

19.4%(32.5%)weresterilizedthroughagovernment‐runmobileclinic.

12MOHPetal.(2006)reportsthatcostsforpost‐abortionemergencycarecanrangefromRs2000toRs5000.13Thisfigureisbasedonabinarydefinitionofproximitytoalegalabortioncentrecorrespondingtothemediandistance to the nearest legal abortion centre (28.6 kilometers). Various robustness checks for differentdefinitionsofaccesstoalegalabortioncentrearepresentedinValente(2014).

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AuniquefeatureofNepal’slegalizationofabortionisitsnarrowfocus.Inparticular,

itwasnotaccompaniedbyameaningfulincreaseinthesupplyofmoderncontraceptives,an

expansionofthereproductivehealthworkforce,orimprovementsintheprovisionofother

healthservices.Instead,pre‐existingreproductivehealthcareprovidersweretrainedand

licensedtoofferabortionservicesaspartoftheirexistingpractices.14ThisfeatureofNepal’s

policychangeallowsustoisolatechangesintheuseofmoderncontraceptiveslinkeddirectly

toexpansionsinabortionsupply(thatarenotattributabletosimultaneouschangesineither

health service delivery or contraceptive supply that often accompany such changes in

abortionpolicyasin,e.g.,Pop‐Eleches(2010)).15

3.2. TrendsinModernContraceptiveUseandAbortioninNepal

Figure1showsthecontraceptiveprevalenceandabortionratesinNepalovertime.

Afterarapid,sustained,increaseintheuseofmoderncontraceptivesfromthelate1970s

untilthemid2000s(fromonly2%to48%),contraceptiveprevalencethenplateauedwith

thelegalizationofabortionin2004(Figure1Panel1).Asinothercountries,thispatternof

co‐movement is consistentwith substitution (and occurred during a period of declining

fertility,withNepal’stotalfertilityratefallingfrom4.6in1996to2.6in2011(MOHPetal.

2012)).

14AsurveyofComprehensiveAbortionCare(CAC)providersconductedin2009revealedthatonly8of139surveyedprovidersintroducednewcontraceptiveservicesormaternalandchildhealthservicesataroundthesametimeastheystartedprovidingabortionservices(Valente2014).15Ofcourse,mostabortionclientsreceivepost‐abortioncontraceptivecounselling(MOHP&CREHPA2006).Thiscouldleadustounderestimatetheextentofthesubstitutionawayfromcontraceptionifwomenwhouseabortionservicesaremorelikelytousecontraceptionafterhavinghadanabortion.However,theresultsofourstatisticalanalysisarevirtuallyunchangedwhenexcludingwomenwhoreporthavinghadanabortionwithinoneyearofthesurvey,thussuggestingthatpost‐abortionchangesincontraceptiveusearenotinfluencingourfindings(fullresultsareavailableonrequest).

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However,theseaggregatetrendsmayreflectchangesincontraceptiveuseunrelated

tothelegalizationofabortion.Abettertestofwhetherornottheplateauingofcontraceptive

prevalenceislinkedtoNepal’sincreaseinabortionsupplywouldusedistrict‐levelvariation

in themagnitude of abortion supply. Figure 2 shows the concentration of legal abortion

center across Nepal’s districts, illustrating that there is substantial geographic variation.

SplittingNepal’s 75 districts into terciles of legal abortion center concentration in 2010,

Figure1Panel2showsthatplateauing incontraceptiveprevalence isgreater indistricts

withhigherconcentrationsoflegalabortioncenters.Figure1Panel2alsoshowsthatareas

with fewer abortion centers initially had lower contraceptive prevalence rates. Our

estimation strategy accounts for these baseline differences across districts (due to both

observable and unobservable, time‐invariant factors), assuming that there are no time‐

varyingomittedvariablescorrelatedwithboth the increase in legalabortioncentersand

contraceptiveuse. InSection7,wereportanumberofrobustnesstestsshowingthatour

resultsareunlikelytobedrivenbytime‐varyingomittedvariables.

4. ConceptualFramework

Beforeturningtoourdataandmethodsusedtoestimatetherelationshipbetween

abortion supply and contraceptive use in Nepal, we first present a simple conceptual

frameworktoclarifythehypothesistestedinthispaper.

To fix ideas, consider the choice between using contraception and not using

contraceptionfacedbyawoman(couple)whodoesnotwanttohaveachildnow(Figure

A2). Define , a dummy variable equal to one if woman uses contraception, and zero

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otherwise; is a dummy variable equal to one if woman has an abortion, and zero

otherwise.Finally,define ,theprobabilityoffailureofthecontraceptivemethodusedby

the woman (and so 0 1 ). For simplicity, we assume that, in the absence of

contraception, the woman becomes pregnant with probability one. Assuming a strictly

positive probability of less than one does not change the qualitative implications of the

model,nordoesallowingforimperfectpredictionsoftheprobabilitiesofbecomingpregnant

withandwithoutcontraception.16Ifawomanusescontraception,thenwithprobability1

, she does not become pregnant and therefore never aborts.With probability , she

becomespregnantandeitherabortsornot. Ifawomandecidestonotusecontraception,

thenshebecomespregnantandeitherabortsornot.

Nowdefinethecosts(financialandpsychological)attachedtousingcontraceptionas

,thedirectcostsattachedtohavinganabortionas ,andthenetpresentvalueofthecosts

attachedtohavinganunwantedchildas ,whichareallallowedtovaryacrosswomen.

Conditionalonbeingpregnantwithanunwantedpregnancy,woman abortsifandonlyif

. Woman will use contraception if and only if her expected cost from using

contraceptionislowerthanthatfromnotusingcontraception,i.e.,ifandonlyif17:

1 1

16Theonlydifferencewhenassumingaprobabilityofbecomingpregnantintheabsenceofcontraceptioninferiortooneisthatinequality(1)becomes: 1 min , ,where istheprobabilityofbecomingpregnantintheabsenceofeffectivecontraception,and istheprobabilityofbecomingpregnantdespiteusingcontraceptionwhencontraceptiveprotectionisneeded(whichisthecasewithprobability ).Allowingwomentoholderroneousbeliefsintermsof and changestherangesofcostsoverwhichwomendecidetousecontraceptionornot,butdoesnotalterthequalitativeconclusionsofthemodelabouttheeffectofadecreaseinthecostofabortionrelativetocontraception.17Notethatwhen ,theexpectedcostofusingcontraceptionis: 1 andthecostofnotusingcontraceptionis .When ,theexpectedcostofusingcontraceptionis:

1 andthecostofnotusingcontraceptionis .

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Insummary,awomanwillusecontraceptionifandonlyif:

1 min , (1)

Ourhypothesisisthatwhenalegal,safe,andaffordableabortioncenteropensina

woman’s district of residence, decreaseswhile all the other parameters of themodel

remainconstant,andhencemin , eitherdecreasesorstaysthesame.Therefore,given

that1 is positive, inequality (1) becomes less likely to hold and fewer women use

contraception,resultinginsubstitutionofabortiontocontraception.

Previous studies estimating the trade‐off between contraceptive use and abortion

generally analyze how abortion use responds to changes in contraceptive supply. This

approachreliesheavilyontheaccuracyofabortionreporting,whichisknowntobepoorin

surveydata(JonesandForrest1992). Incontrast,ourstudy investigateshowtheuseof

moderncontraceptivesrespondstotheprovisionoflegalabortioncenters.Indoingso,we

provideatestofwhetherornotwomendecidenottousecontraceptionup‐frontwhenitis

lessdifficult/costlytohaveanabortion(ratherthanwhetherornottheyarelesslikelyto

haveanabortionex‐postwhenthesupplyofcontraceptivesincreases).

5. DataandMethods

5.1. DataonNepaleseWomenandLegalAbortionCenters

TomeasuremoderncontraceptiveuseamongNepalesewomen,weusefourwaves

fromtheNepaleseDemographicandHealthSurveys (DHS), twopre‐legalizationand two

post‐legalization (Demographic and Health Surveys of Nepal 1996‐2011). Collecting

nationally representative data from fertile‐age women (defined as ages 15‐49) in 1996,

2001, 2006, and 2011, these surveys provide the best available information about

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reproductivebehavioramongNepalesewomen.Eachwave includes ahousehold survey

(collecting general information about household composition and socio‐economic

characteristics),andanindividualsurveyadministeredtoallfertile‐agewomen(including

questionsaboutcurrentandretrospectivefertilityregulationpracticesoverthepreceding

four or five years – as well as complete retrospective fertility histories detailing all

pregnancies, including those that did not end in a livebirth). We restrict the sample to

marriedwomen(becausethe1996and2001surveysonlyincludedmarriedwomen),but

wealsoassesstherobustnessofourresultstoalternativeapproaches.18Ourpooledsample

acrossthesefoursurveywavesincludes32,098women.19

Abriefnoteabouttheuseofcontemporaneousdata(fromsurveyyearsonly)versus

retrospectivecontraceptivehistorydata(foryearspriortothesurveyyear,asrecalledby

respondents in survey years) is warranted. An important virtue of using only

contemporaneousdataisthatitminimizesmeasurementerrorinreporteduseofmodern

contraceptives.20Thedrawbacksofusingonlycontemporaneousdataarethepossibilityof

lowerstatisticalpower(becauseofsmallersamplesizes)andlessflexibilitytoexaminethe

evolutionofcontraceptiveuseovertimerelativetotheexpansionoflegalabortioncenters.

18Forexample,wealsorestrictoursample towomenages25andabove,amongwhommarriage isnearlyuniversal – 97.3% of respondents ages 25 and above in the 2006 and 2011weremarried at the time ofinterview.FocusingonmarriedwomenisconsistentwiththecompositionoflegalabortionserviceclientsinNepal.AsurveycarriedoutbyCREHPAin2006indicatesthatnearly98%ofclientswereorhadbeenmarried,closeto60%werebetween20and29yearsold(only5%wereunder20),andlessthan7%hadnolivingchild(23%,40%,and31%,hadone,two,andthreeormorelivingchildren,respectively)(MOHP&CREHPA2006).AninternationalcomparisonoflegalabortionserviceclientcharacteristicssuggeststhatthecharacteristicsofNepaleseclientsaregenerallycomparabletothoseobservedinotherlessdevelopedcountries(Bankoleetal.1999,MOHP&CREHPA2006).1940,622womenwereinterviewedintotal(8,429in1996,8,726in2001,10,793in2006and12,674in2011).Afterdropping2,175womenwhoarenotusualresidentsofthehouseholdinwhichtheyareobserved,6,348unmarriedwomen interviewed in2006and2011,andonewomanwhose levelofeducation ismissing,weobtainthefinalsampleof32,098.20ThequestionaskedintheDHSis“Areyoucurrentlydoingsomethingorusinganymethodtodelayoravoidgettingpregnant?,”

16

Although we cannot be certain about how much measurement error exists in the

retrospective recall data about contraceptive use, studies of contraceptive history recall

error suggest substantial limitations in the use of such recall data (Strickler et al. 1997,

Beckett et al. 2001).21Beyond contraceptiveuse,more recent research suggests that the

quality of recall data deteriorates very rapidly and that the length of the recall period

influences self‐reported morbidity and use of health services in ways not previously

demonstrated(Dasetal.2012).22Giventheseconcerns,theavailabilityofanunusuallylarge

numberofDHSwavesforouranalysis(four),andthefactthatwehaveadequatepowerto

examinethecorrelationbetweentrendsincontraceptiveuseandtheintensityofabortion

supply(asshowninSection6),wefocusoncontemporaneousdatainouranalysis.

Weusethetotalnumberoflegalabortioncentersineachdistrict,month,andyearto

measuretheintensityofabortionsupply.Weconstructedthismeasureusingadministrative

records from the Nepalese Technical Committee for Implementation of Comprehensive

AbortionCare(TCIC2010)containingexactregistrationdatesforeachlegalabortionfacility

authorized before February 2010. We then assign intensity of abortion supply to each

21Strickleretal.(1997)andBeckettetal.(2001)comparecontraceptiveusebythesamewomanforthesameperiodsoftime–butreportedattwodifferentsurveydates–andfindthattherearesubstantialinconsistenciesatthedisaggregatedlevel.Specifically,Strickleretal.(1997)findthat,amongMoroccanwomenreportingatleastoneperiodofcontraceptiveuse,only45.1%reportedperiodsofuseandnon‐useinthesameorderintwodifferentsurveys,andonly29.3%reportedthesamesequenceandlengthofcontraceptiveuse.Similarly,whencomparingindividualreportsofcontraceptiveuseincontemporaneousandrecalldataforMalaysia,Beckettetal(2001)obtainaKappacoefficientofonly0.38.Inthepresenceofmisclassification(e.g.,reportingnotusingcontraceptionwheninfactusingandvice‐versa),Hausmanetal.(47)showthatestimatesareinconsistentandtheirprecisioncanbeoverstated.22NotethatrecallerrorforthevariablesexaminedbyDasetal.(2012)–whichincludevisitstothedoctorandself‐medication–isveryrelevanttorecallerrorincontraceptiveuse,butlesssoformoresalienteventssuchasthebirthofachild.

17

individualwomaninourpooledDHSsampleatthedistrict‐month‐yearlevel(accordingto

herinterviewdate).23

Table1reportsdescriptivestatisticsbothforourpooledsampleandseparatelyfor

eachsurveyyear.Thefirstrowreportsthemeannumberoflegalabortioncentersinthe

woman’sdistrictineachsurveywave.Theintensityofabortionsupplyvariesconsiderably

both across survey waves and across districts within each post‐legalization wave. On

average,womeninterviewedin2006had2.72centersintheirdistrict(s.d.:2.997),andthis

numberrisesto6.34(s.d.:6.702)by2011.

The next eight rows then summarize modern and traditional contraception and

abortion.Moderncontraceptiveuseincreasesbetweeneachsurveywaveuntil2006(from

27% in 1996 to 46% in 2006) but then ceases to rise between 2006 and 2011. Among

modernmethods,themostcommonisfemalesterilization,butreversiblemethodsaccount

formostoftheincreaseincontraceptiveprevalencebetweensurveywaves.In1996,2%of

womenreporteverhavinganabortion,24risingto8%by2011.Desiredfertilityalsodeclined

acrosssurveywaves.Forexample,theaverage“ideal”numberofchildrenfellfrom2.95in

1996to2.24in2011.25

23Bydefinition,anabortioncenterisonlylegalifitis“listed”withtheTechnicalCommitteeforImplementationofComprehensiveAbortionCare(TCIC),andthedatausedherearebasedonthelistofallfacilitiesincludedontheTCIClistuptoFebruary2010.24Foreachpregnancyinthepregnancyhistoryofthewoman,sheisaskedwhetherthebaby[was]“bornalive,borndead,orlostbeforebirth”;thenwhenthepregnancyendedandhowlongitlasted;andthenonly“didyou[therespondent]orsomeoneelsedosomethingtoendthispregnancy?”.Thesequenceofquestionsisthesameforthefoursurveys,butthevariablesavailablechangedin2011.Before2011,wecountasanabortionanypregnancy not ending in live birth forwhich thewoman either says that somethingwas done to end thepregnancyorrefusestoanswerthelastquestion.Inthe2011survey,thedatasetdoesnotcontainthenecessaryraw data to apply the exact same rule, but contrary to the previous surveys, provides a classification ofpregnanciesaslivebirth/stillbirth/miscarriage/abortionbasedonthesamesurveyquestions.25PanelCshowsthatoursampleisapredominantlyruralandwithlowlevelsofeducation(especiallyamongwomen)thatincreaserapidlyacrossstudywaves.

18

5.2. StatisticalMethods

We estimate logitmodels of the following general form forwoman i in districtd

observedinsurveys:

1 (2)

Where / 1 isthecumulativelogisticdistribution.Here isadichotomous

indicatorforvariousmeasuresofcontraceptiveuse(equalto1ifwomanireportsusinga

givenmethodofcontraception,and0otherwise), isthenumberoflegalabortioncenters

inthedistrictatthetimeofthesurvey, isavectorofindividualcharacteristics(urban

dummy, age, religiondummies, education attainmentdummies), is a vectorof district

dummy variables, is a vector of (3) DHS wave dummies (equivalent to year dummy

variables). 26 We estimate equation (2) using survey weights and allowing for error

correlationofanarbitrarynaturewithindistrict.Equation(2)implementsa`difference‐in‐

difference’estimationstrategyinwhich capturestheeffectofeachlegalabortioncenterin

awoman’sdistrictoncontraceptiveuse,controllingforbaselinedifferencesincontraceptive

usebetweendistricts( )andtimetrendscommontoalldistricts( ).Thevalidityofour

estimatesthusreliesontheassumptionofnomeaningfuldifferencesinpre‐existingfertility

regulationtrendsacrossdistrictswithvaryingincreasesinthesupplyoflegalabortions.In

Section7,wereportevidenceconsistentwiththisassumption.

26Note thatCds is codedusingabortion facilitydata asof February2010 for the2011DHSwavesince theadministrativerecordswehavehadaccesstoendinFebruary2010.

19

6. Results

The first six columns of Table 2 report odds ratios estimates of the effect of the

numberoflegalabortioncenters(α)forvariousindicatorsofcontraceptiveuse(shownat

thetopofeachcolumn)obtainedbyestimatingEquation(2).Thefirstcolumnshowsresults

foruseofanyformofcontraception(modernortraditional):theadditionofalegalabortion

centerinawoman’sdistrictofresidenceisassociatedwitha2.6%reductionintheoddsof

usinganycontraceptive[OR:0.974,95%CI:(0.961;0.987)].27Thisoddsratiocorrespondsto

adecreaseintheprobabilityofusinganyformofcontraceptionof0.5percentagepointsper

legalabortioncenter[95%CI:(‐0.007;‐0.002)],a2percentagepointreductionfromthepre‐

legalizationmeanof35%associatedwithfourlegalabortioncenters–themeannumberof

centersinthetwopost‐legalizationsurveywaves).28

Columns (2) and (6) report separate estimates for use of any modern and any

traditional method of contraception (respectively). 29 The odds of using modern

contraceptivesdecreaseby2.6%withanadditionalabortioncenter,whiletheoddsratiofor

useoftraditionalmethodsisindistinguishablefromone[OR:0.974,95%CI:(0.960;0.989)

andOR:0.992,95%CI:(0.978;1.006),respectively].Takentogether,theseresultssuggest

thatwhenalegalabortionfacilityopensinawoman’sdistrict,shereducesheruseofmodern

contraceptives,whiletraditionalcontraceptionremainsunchanged.Analyzingtheeffectof

anadditionalabortioncenteronmoderncontraceptiveusebyagegroup,wefindthelargest

27ThisandtheotherpointestimatesofinterestinTable2arenearlyunchangedwhenthelinearagevariableisreplacedwithseven5‐yearagecategories.Resultsareavailableonrequest.28Marginaleffectsreportedinthepaperarecomputedatthemodeofallcategoricalcovariates,themeanofmaternalage,andthemeannumberofabortioncentersperdistrictinthetwopost‐legalizationsurveys.29Traditionalmethodssuchaswithdrawalandtherhythmmethodareonlyusedby4%ofwomeninourpooledsample,buttheirusehasincreasedovertime,from2%in1996to7%in2011.

20

decreaseincontraceptiveuseamongthe15‐19andthe30‐34agegroup,whiletheeffectis

statisticallysignificantforallgroupsuptoages35‐39(TableA1).

Columns(3),(4),and(5)ofTable2thenanalyzehowsubstitutionawayfrommodern

contraceptionwiththeopeningof legalabortioncentersvariesbetweensterilizationand

reversible modern methods.30 Column (3) shows that an additional abortion center is

associated with a 2.2% reduction in the odds of female sterilization [OR: 0.978, 95%

CI:(0.957;0.999)], implyinga0.23percentagepointdecrease in theprevalenceof female

sterilization. On the contrary, we find that abortion centers have no effect on male

sterilization (Column (4)). The estimated change in odds of using reversible modern

methodsreportedinColumn(5)issimilartothatofusingfemalesterilization,decliningby

2.4%witheachadditionallegalabortionfacility[OR:0.976,95%CI:(0.968;0.984)].31

IfourinterpretationoftheestimatesinthefirstsixcolumnsofTable2iscorrect,the

expansion of legal abortion centers should also be associated with an increase in the

probability thatwomen abort (although an effect on contraceptive usemay be detected

before theeffectonabortion isrealized). TheseventhcolumnofTable2reports results

obtainedbyre‐estimatingEquation(2)usingadichotomousindicatorforwhetherornota

womanreportseverhavinganabortion(definedasapregnancythatdidnotresultinalive

birthandforwhichsomeonehasdonesomethingtoendthepregnancy).Eachadditional

30Amongreversiblemodernmethods,condomsmayrequiremorenegotiationwithmalepartners.Repeatingourestimationseparatelyforcondomuse(OR:.989;95%CI:.979;1.0004)andforotherreversiblemethods(OR:.976;95%CI:.967;.984),wefindalargerassociationforotherreversiblemodernmethods,althoughthedifferencebetweenthemisnotstatisticallysignificant.31Oddsratiosontheothercovariatesgenerallyhavetheexpectedsigns:theindicatorsforeachDHSsurveycapturetheoveralltrendsincontraceptiveusedescribedinSection5.1,andconfirmthaturban,bettereducated,andolderwomenaremorelikelytousecontraception.Coefficientsonreligiousaffiliationvariablesarealsoreasonable(e.g.,MuslimsaresignificantlylesslikelytousecontraceptionthanHindus).Itisinterestingtonotethattheeducationgradientisverysteepfortraditionalmethodsbutmuchlesssoforuseofanytypeofcontraception.

21

legalabortioncenterinawoman’sdistrictisassociatedwitha1.3%increaseinoddsofever

havinganabortion,which isstatisticallysignificantat the90%level (OR:1.013;95%CI:

0.998; 1.029), and implies a 4 %‐increase relative to the pre‐legalization proportion

reportingeverhavinganabortionforfourlegalabortioncenters.Becausethelikelihoodof

everhavinganabortionpartlydependsonthenumberofpastpregnancies,weconfirmthat

the estimates in column (7) are not driven by changes in fertility by using the share of

pregnancies aborted by the respondent as the dependent variable (estimating a linear

specificationbyordinaryleastsquares).Column(8)showsthattheabortioncenterestimate

is again positive and statistically significant (linear coefficient: 0.0019; 95% CI:

0.0015;0.0022).

7. AssessmentofRobustnessandExtensions

7.1TestingforPre‐ExistingTrendDifferences

Althoughour‘difference‐in‐difference’estimationframeworkaccountsforbaseline

differences in contraceptive prevalence across districts, it assumes that districts with

varyingconcentrationsofabortionfacilitieshadparalleltrendsincontraceptiveprevalence

priortothelegalizationofabortion.Totestwhetherornotthenumberofabortioncenters

wastargetedtodistrictswithpre‐existingtrenddifferencesincontraceptiveprevalence,we

conducttworelated“placeboexperiments”.

Inthefirst,weassignadistrict‐levelmeasureofthefuturenumberofabortioncenters

(thenumberofcentersatthetimeofnextsurvey)toeachwomaninthe1996and2001DHS

waves (i.e., before any legal abortion center opened). Re‐estimating Equation (2) using

22

futurenumberofabortioncentersinlieuofthecurrentnumberofcenters,Table3reports

estimatesfortheparameter intheequation

1 . Consistent with our assumption of

‘paralleltrends,’noneoftheseestimatedoddsratiosaresignificantlydifferentfrom1(nor

istheestimateforfuturenumberofabortioncentersestimatedbyordinaryleastsquaresin

Column(8)significantlydifferentfromzero).

Thesecondplaceboexperimentrepeats the firstwith twodifferences: italsouses

data from the 2006DHSwave, and it includes both current and future number of legal

abortionfacilities(becausesomecenterswereoperatingin2006).Table4showsestimates

for future and current number of legal abortion facilities, again suggesting that current

contraceptiveprevalenceandpastabortionbehaviorarenotcorrelatedwithfutureabortion

supply.Overall,theseresultssuggestnotargetingofabortioncenterstodistrictswithpre‐

existing trend differences in contraceptive prevalence – and are consistent with our

interpretation of Table 2 showing evidence that abortion and the use of modern

contraceptivesaresubstitutes.

7.2OtherRobustnessTests

For completeness, we also estimate variants of equation (2) using recall data

containedinthe2006and2011DHSfertilityhistoriesandreportourresultsinTableA2.

Ourspecificationsusewoman‐monthobservationsfromApril2000toFebruary2010and

excludes women who were sterilized or whose husbands were sterilized by March

2004; isreplacedby ,thenumberoflegalabortioncentersinthedistrictforeach

monthandyear.Wefindanegative,statisticallysignificantrelationshipbetweenthenumber

23

ofabortioncentersinawoman’sdistrictandheroddsofreportinguseofanycontraceptive

method,confirmingourinferencesfromcontemporaneousdata(Column1).Thisestimateis

robusttocontrollingforlinear,quadraticorcubicdistrict‐specifictrends(Columns2,3,and

4,respectively).Whenaddingaplacebotreatmentvariableequaltothenumberofabortion

centersinthedistrict12monthsinthefuture,theresultpersists,andtheeffectoftheplacebo

treatmentvariableisstatisticallyinsignificant(Column5).

We then investigate the robustnessofourmainresults toaddressingavarietyof

otherpotentialconcerns:

1. First,wecontrolforanumberofadditionalregressorsinPanelAofTable5.

Specifically,wecontrolforrespondents’idealnumberofchildren;numberof

conflictcasualtiesintheyearprecedingthesurveyinrespondents’districts

(per 1991 district population, the year of the last pre‐conflict population

census);whetherornotrespondentsreporthavingheardafamilyplanning

message on the radio in the lastmonth;whether or not respondentswere

visitedbyafamilyplanningworkerintheprevious12months;whetherornot

respondents had heard of AIDS; and socio‐economic status (measured by

quintileinthedistributionofhouseholdassetownership).Ourconclusionsdo

notchangeafterweincludetheseadditionalcontrols.32

2. Second,inPanelB,werestrictthesampleanalyzedinPanelAtowomenwho

werenotsterilizedandwhosehusbandswerenotsterilizedasofMarch2004.

32WealsoestimatetherobustnesstestshowninTable5,PanelAincludingboththenumberofchildrenborntoawomanandwhetherornotawomanhadajobinthepast12months.Theresultingestimateschangeverylittle(andinsignificantlyso)whenincludingtheseadditionalcovariates.Theseresultsareavailableuponrequest.

24

Theresultsconfirmthesign,significance,andmagnitudeofthemainestimates

forallmoderncontraceptionandfortemporarymethods.33

3. Third,inPanelC,wefurtherscalethenumberofabortioncentersbydistrict

population(asof2001,thedateofthelastpre‐legalizationpopulationcensus).

Ourestimatesbecomemore imprecise (thestandarderrorsnearlydouble),

but the negative association between legal abortion centers and the

prevalence of any modern contraception – and specifically temporary

methods–remainsstatisticallysignificant.34

4. Fourth,inPanelsD,E,andF,weexplicitlyallowtimetrendstovarybypre‐

legalization district population (Panel D), region (Panel E), rural/urban

location(PanelF),andwealthquintile (PanelG).35Morepopulousdistricts,

districts in the more accessible regions of the country, and urban areas

experienced earlier/more intense expansions of legal abortion supply.

Additionally,privateprovidersaremoreprevalentinurbanareas,andthese

private providers may be more responsive to local demand than public

facilities. The twomain national health and population programs in place

during therelevantperiod(theNepalFamilyHealthProgramduring2001‐

33Notethatinthisrestrictedsample,bydefinition,thereisnovariationinsterilizationstatusinthe1996and2001DHS(asthosesterilizedby1996or2001aredroppedfromthesample),sothatwecanonlyusethe2006and2011DHSsurveysfortheanalysisofthesterilizationoutcomes.Thefindingsonsterilizationoutcomesusingonlythelasttwosurveyssuggestastatisticallyinsignificantdecreaseinfemalesterilization,andamarginallysignificantincreaseinmalesterilization(butthetotaleffectonmoderncontraceptionisstillsignificantlyandconsistentlynegativeoverall).34Althoughresultsarelessprecise,thenumberofabortioncentersperinhabitantisverysimilartoourmainmeasureofprogramtreatment:thenumberofabortioncenters.Thecorrelationbetweenthesetwovariablesis0.71.35Aregionisdefinedastheinteractionbetweenaneconomicregion(ofwhichtherearefiveinNepal)andanecologicalbelt(terai,hillormountain),with13regionsdefinedintheDHS.

25

2006andtheNepalHealthSectorProgramImplementationPlanduring2004‐

2009) also aimed to prioritize the poor and those living in remote areas

(MOHP et al., 2012). Interacting DHS wave and initial population, region,

urban location, andwealth quintile in Panels D, E, F, and G show that our

conclusionsareunchangedwhenallowingforsystematictrenddifferencesin

contraceptiveusebythesecharacteristics.

Finally, we explore the robustness of our conclusions to a variety of weighting,

functional form, and sample considerations and find that our results are robust to using

unweighted‐ratherthanweightedlogitmodels(PanelAofTable6),toreplacingourlogit

specificationwitha linearprobabilitymodel (PanelBofTable6), toexcludingeachDHS

surveyinturntoinvestigateifourconclusionsdependonanyindividualsurvey(Table7)36,

and to limiting the sample to allwomen ages 25‐49 insteadof restrictingour sample to

marriedwomen(Table8).37

7.3ConsiderationofChangesinTemporaryModernMethodsvs.Sterilization

Theresultspresentedsofarsuggestthattheincreaseinthesupplyoflegalabortions

affected the use of temporary modern contraceptive methods, but its effect on new

sterilizationsislessclear.Oneplausibleexplanationforreductionsinthecostofabortionto

36TheonlyoutcomeforwhichtheconclusionsvarywhenindividualDHSsurveysareexcludedismalesterilization,whichsignificantlyincreaseswithanadditionalabortioncenterifweexcludethe1996survey,andsignificantlydecreasesifweexcludethe2011survey,whereasithasastatisticallyinsignificanteffectifweincludeallfoursurveysorexcludethe2001or2006surveys.WethereforeconcludefromtheresultsinTable7thatthereisnorobustevidenceofachangeinmalesterilization,asinthemainanalysis.37WealsorepeatedtheanalysisexcludingthecapitalKathmandu,whichhasthelargestnumberofabortionfacilitiesofalldistricts.Theestimatedoddsratiosareverysimilartothoseobtainedwiththewholesample,butestimatesbecomemuchlesspreciselyestimatedduetothelossinvariationinourabortionsupplyvariable,andthereforemostoddsratiosbecomestatisticallyinsignificant.Fullresultsareavailableonrequest.

26

affecttemporarycontraceptionbutnotsterilizationcanbeunderstoodbyreturningtoour

conceptualframeworkinSection4.Re‐arrangingInequality(1)bydividingeachsideby1

andallowingformorethanonetypeofcontraceptivemethoddenotedbym,womaniwill

choose the contraceptivemethodwith the lowest perceived ratio of cost to success rate

( aslongasthevalueofthisratioislessthanmin , .Ifchangesinabortionsupply

onlyaffectthedecisionsofwomenforwhomtheperceivedratioofcosttosuccess( is

higher for sterilization than for temporary methods, the relevant trade‐off is between

temporarymethodsandnocontraception.Thiscouldbethecaseifwomenwhofaceahigh

costofhavinganabortion(regardlessofwhetherornotitislegallyandsafelyprovided–

duetomoralconsiderationsorhightransportcosts,forexample)werealsomorelikelyto

havealowerperceivedcost‐to‐successrateofsterilizationrelativetotemporarymethods.

Ifthiswerethecase,thenlegalabortioncenterswouldnotdecrease sufficientlytoaffect

min , forwomenwhowouldchoosesterilizationovertemporarymethods.

8. Conclusion

Althoughscholarshavewrittenextensivelyabouttherelationshipbetweentheuseof

moderncontraceptivesandabortion–andhavegenerallyreportedaninverserelationship

betweenthetwo,thecausalrelationshipbetweenthetwohasbeendifficulttoisolate.Akey

difficultyisthefactthatreproductivehealthprogramsoftenaltermanyaspectsofservice

deliverysimultaneously–expandingthereproductivehealthworkforce,bundlingtogether

new contraception and abortion services, and improving the quality of health services

generally.EventhefamousMatlabfamilyplanningexperimentintegratedtheprovisionof

27

moderncontraceptiveswiththeprovisionofbothabortionservices(menstrualregulation)

and antenatal and child health services, making it difficult to isolate the effect of

contraceptivesupply.

Thisstudyanalyzestherelationshipbetweencontraceptiveuseandabortionduring

therapidscale‐upoflegalabortionservicesacrossNepal–a“naturalexperiment”inwhich

abortion services were not accompanied by changes in contraceptive supply or other

potentiallyconfoundinghealthpolicychanges. UsingfourDHSsurveywaves(twobefore

andtwoafterlegalization)andanofficialcensusofalllegalabortioncenters,wefindthat

eachlegalabortioncenterinawoman’s(couple’s)districtofresidencewasassociatedwith

a2.6%reductionintheoddsofusinganycontraceptive.Forthemeannumberofcenters

perdistrictinthepost‐legalizationperiod(four),ourestimatesimplythatNepal’sexpansion

of abortion supply was associated with a 2 percentage point decline in the use of

contraceptives – a 6% decrease relative to the pre‐legalization mean. 38 This decline in

contraceptiveuseoccursamongmodern(butnottraditional)methodsandisdrivenmost

robustlybychangesintheuseofreversiblemodernmethods(primarilyinjectionsand,toa

lesser extent, condoms and the pill). Our direct assessments of the “parallel trends”

assumption underlying our difference‐in‐difference study design also strengthens the

interpretation that our estimates provide evidence of true substitution between use of

moderncontraceptivesandabortion.

We emphasize two important policy implications of our findings. First, policies

aiming to reduce the full cost of abortion (financial, social, psychological, etc.) shouldbe

38Theeffectestimatedhereisbasedonthevariationovertimeinlocalavailabilityofabortioncentersacrossdistricts.Itmaythereforebeanunderestimateofthetruesubstitutioneffectbecausewomenmaybeabletotraveltoabortioncenterslocatedoutsidetheirdistrict(e.g.,inthecapitalKathmandu).

28

accompaniedbymeasurestoalsoreducethefullcostofcontraceptiveuse(broadlydefined

to includesocialandpsychologicalcosts) ifpolicymakerswishtoavoidsubstitutionfrom

contraceptiontoabortion.Second,indemonstratingatrade‐offbetweencontraceptionand

abortion,ourfindingsalsosuggestthatreductionsinthecostofcontraceptionmayreduce

theincidenceofabortion.

29

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35

Figures

Figure1:AbortionandContraceptionTrendsinNepalPanel1

Panel2

Sources:Panel1:abortion:Sedghetal.(2011);contraception:1970‐1987fromMauldinandSegal(1988), 1990‐1995 from United Nations (2004), and 1996‐2011 from MOHP (2012). Panel 2:authors’ calculations based on Demographic and Health Surveys of Nepal (1996‐2011)(contraception) and Technical Committee for Implementation of Comprehensive Abortion Care(2010)(abortionfacilities).

36

Figure2:District‐levelCoverageofAbortionCenters

Source:TechnicalCommitteeforImplementationofComprehensiveAbortionCare(2010). 

37

TablesTable1–SummaryStatistics

  (1)  (2)  (3)  (4)  (5) 

  DHS 1996  DHS 2001  DHS 2006  DHS 2011  Pooled 

  mean  sd  N  mean  sd  N  mean  sd  N  mean  sd  N  mean  sd  N 

Panel A: abortion supply and contraception Number of Legal Abortion Centers in District of Residencea  0.00  0.000  7496  0.00  0.000  7842  2.72  2.997  7776  6.34  6.702  8984  2.45  4.689  32098 

Any Method  0.29    7496  0.41    7842  0.50    7776  0.51    8984  0.43    32098 

Modern Method  0.27    7496  0.37    7842  0.46    7776  0.44    8984  0.39    32098 

Traditional Method  0.02    7496  0.04    7842  0.04    7776  0.07    8984  0.04    32098 

Modern Method Other than Sterilization  0.09    7496  0.14    7842  0.20    7776  0.21    8984  0.16    32098 

Female Sterilization  0.13    7496  0.16    7842  0.19    7776  0.16    8984  0.16    32098 

Male Sterilization  0.06    7496  0.07    7842  0.07    7776  0.08    8984  0.07    32098 

Ever Had an Abortion  0.02    7496  0.02    7842  0.04    7776  0.08    8984  0.04    32098 

Share of Pregnancies  Abortedb  0.00  0.041  6798  0.00  0.039  7138  0.01  0.074  7204  0.03  0.111  8228  0.01  0.075  29368 

Panel B:  Fertility preferences                               

Ideal Number of Children  2.95  1.059  7337  2.65  0.879  7712  2.43  0.830  7762  2.24  0.788  8960  2.55  0.927  31771 

Panel C: Covariates                               

Urban  0.08    7496  0.10    7842  0.15    7776  0.13    8984  0.12    32098 

Age  30.58  8.968  7496  30.95  8.897  7842  31.47  8.923  7776  31.68  8.600  8984  31.20  8.847  32098 

Hindu (excluded category)  0.87    7496  0.85    7842  0.86    7776  0.85    8984  0.86    32098 

Buddhist  0.06    7496  0.07    7842  0.08    7776  0.08    8984  0.07    32098 

Muslim  0.05    7496  0.05    7842  0.04    7776  0.04    8984  0.04    32098 

Christian  0.00    7496  0.01    7842  0.01    7776  0.02    8984  0.01    32098 

Other Religion  0.01    7496  0.02    7842  0.01    7776  0.01    8984  0.02    32098 

No education (excluded cat.)  0.80    7496  0.72    7842  0.63    7776  0.49    8984  0.65    32098 

Primary Education  0.11    7496  0.15    7842  0.17    7776  0.19    8984  0.15    32098 

Secondary Education  0.08    7496  0.12    7842  0.18    7776  0.27    8984  0.16    32098 

Tertiary Education  0.01    7496  0.01    7842  0.02    7776  0.06    8984  0.03    32098 

Statisticsweightedusingsurveyweights.Sampleofmarriedwomenaged15‐49whousuallyresideinthehousehold.Source:Authors'calculationsusingDemographicandHealthSurveysof Nepal (1996‐2011) for all variables except number of legal abortion centers in district of residence, which is based on data from Technical Committee for Implementation ofComprehensiveAbortionCare(2010).aNumberoflegalabortioncentersindistrictofresidencecodedusingabortionfacilitydataasofFebruary2010forthe2011DHSwavesincetheadministrativerecordswehavehadaccesstoendinFebruary2010.bDefinedonlyforwomenwithatleastonepregnancy

38

Table2–EffectofAvailabilityofLegalAbortionCentersonContraceptiveUseandSelf‐ReportedAbortions  (1)  (2)  (3)  (4)  (5)  (6)  (7)  (8) 

 

Any Method Modern Method 

Female Sterilization 

Male Sterilization 

Modern Method Other 

than Sterilization 

Traditional Method 

Ever Had an Abortion 

Share of all pregnancies aborted 

Number of Abortion Centers in District 

0.974***  0.974***  0.978**           0.999  0.976***  0.992  1.013*  0.002*** 

  (0.0065)  (0.0072)  (0.0109)  (0.0106)  (0.0041)  (0.0071)  (0.0080)  (0.0002) 

DHS 2001  1.759***  1.678***  1.339***  1.184  1.886***  1.552***  0.879  ‐0.002** 

  (0.1112)  (0.0977)  (0.0817)  (0.1226)  (0.1452)  (0.2408)  (0.1302)  (0.0007) 

DHS 2006  2.343***  2.293***  1.699***  1.105  2.681***  1.421**  1.900***  0.000 

  (0.2003)  (0.1962)  (0.2224)  (0.1670)  (0.2289)  (0.2276)  (0.3310)  (0.0017) 

DHS 2011  2.656***  2.410***  1.591***  1.289  2.987***  2.099***  3.216***  0.007*** 

  (0.2303)  (0.2249)  (0.2330)  (0.2110)  (0.3140)  (0.3676)  (0.5636)  (0.0025) 

District Dummies Included?  

Yes  Yes  Yes  Yes  Yes  Yes  Yes  Yes 

Maternal characteristics included? 

Yes  Yes  Yes  Yes  Yes  Yes  Yes  Yes 

Observations  32098  32098  31620  32078  32098  31657  31371  29368 

No. of districts  75  75  72  74  75  70  70  75 

(Pseudo) R‐squared  0.1102  0.1014  0.1982  0.1604  0.0971  0.0818  0.1327  0.0626 

Mean value of dependent variable 

0.431  0.388  0.158  0.068  0.163  0.044  0.043  0.015 

Maternal characteristics: urban residence, age, religion dummies (Hindu (omitted), Buddhist, Muslim, Christian or Other), education dummies (no education(omitted),primaryeducation,secondaryeducation,tertiaryeducation).Columns(1)to(7)reportoddsratiosfromalogitmodel.Column(8)presentscoefficientsfromalinearregressionincludingaconstant(coefficientnotreportedhere).District‐correlatedrobuststandarderrorsinparentheses.Regressionsweightedusingsurveyweights.Sampleofmarriedwomenaged15‐49whousuallyresideinthehousehold.Excludedreligiouscategoryis"Hindu",excludededucationcategoryis"Noeducation". Someobservationsaredropped inColumns (3), (4), (6),and(7)due to lackofvariation in thevalueof thedependentvariablewithindistrict.ObservationsforwomenwhohaveneverhadanypregnancyaredroppedinColumn(8)sincetheshareofabortedpregnanciesisnotdefinedforthesewomen.Source:Authors'calculationsusingDemographicandHealthSurveysofNepal(1996‐2011)andTechnicalCommitteeforImplementationofComprehensiveAbortionCare(2010).***p<0.01,**p<0.05,*p<0.10.

39

Table3‐ControlExperiment1:EffectofAvailabilityofFutureLegalAbortionCentersBeforeAnyCenterOpened  (1)  (2)  (3)  (4)  (5)  (6)  (7)  (8) 

 

Any Method Modern Method 

Female Sterilization 

Male Sterilization 

Modern Method Other 

than Sterilization 

Traditional Method 

Ever Had an Abortion 

Share of all pregnancies aborted 

Number of Abortion Centers at Next Survey Date 

1.011  1.004  1.000  0.928  0.994  0.986  0.967  ‐0.000 

(0.0386)  (0.0341)  (0.0220)  (0.0453)  (0.0190)  (0.0355)  (0.0243)  (0.0002) 

                 

Observations  15338  15338  14324  15338  15310  14601  12994  13936 

No. of districts  72  72  61  72  71  63  50  72 

(Pseudo) R‐squared 

0.1243  0.1219  0.1698  0.1640  0.1198  0.0800  0.0749  0.0207 

Mean value of dependent variable 

0.352  0.319  0.149  0.062  0.116  0.034  0.019  0.005 

Outputomittedforthefollowingvariables:threedummyvariableforDHS2001,districtfixed‐effects,andcontrolsforurbanlocation,ageatinterview,religion,andeducationsummarizedinTable1PanelC.Columns(1)to(7)reportoddsratiosfromalogitmodel.Column(8)presentscoefficientsfromalinearregressionincludingaconstant(coefficientnotreportedhere).District‐correlatedrobuststandarderrorsinparentheses.Regressionsweightedusingsurveyweights.Sampleofmarriedwomenaged15‐49whousuallyresideinthehousehold.SomeobservationsaredroppedinColumns(3),(4),(6),and(7)duetolackofvariationinthevalueofthedependentvariablewithindistrict.ObservationsforwomenwhohaveneverhadanypregnancyaredroppedinColumn(8)sincetheshareofabortedpregnanciesisnotdefinedforthesewomen.Source:Authors'calculationsusingDemographicandHealthSurveysofNepal(42)–DHS1996and2001only–andTechnicalCommitteeforImplementationofComprehensiveAbortionCare(2010).***p<0.01,**p<0.05,*p<0.10.

40

Table4‐ControlExperiment2:EffectofAvailabilityofFutureLegalAbortionCentersOverandAbovetheEffectofCurrentAvailability  (1)  (2)  (3)  (4)  (5)  (6)  (7)  (8) 

 

Any Method Modern Method 

Female Sterilization 

Male Sterilization 

Modern Method Other 

than Sterilization 

Traditional Method 

Ever Had an Abortion 

Share of all pregnancies aborted 

Number of Abortion Centers 

0.928***  0.921***  0.909**  0.969  0.966  1.043  1.057  0.001 

  (0.0257)  (0.0291)  (0.0374)  (0.0783)  (0.0223)  (0.0469)  (0.0609)  (0.0009) 

Number of Abortion Centers at Next Survey Date 

1.020  1.022  1.035  0.970  1.001  0.982  0.958*  ‐0.000 

(0.0173)  (0.0177)  (0.0235)  (0.0372)  (0.0104)  (0.0231)  (0.0245)  (0.0004) 

                 

Observations  23114  23114  22343  23063  23114  22730  22260  21140 

No. of districts  75  75  68  73  75  69  66  75 

(Pseudo) R‐squared  0.1215  0.1173  0.1971  0.1546  0.1144  0.0692  0.1003  0.0318 

Mean value of dependent variable 

0.400  0.366  0.161  0.063  0.146  0.035  0.026  0.008 

Outputomittedforthefollowingvariables:twodummyvariablesforDHS2001and2006,districtfixed‐effects,andcontrolsforurbanlocation,ageatinterview, religion, and education summarized in Table 1 Panel C. Columns (1) to (7) report odds ratios from a logitmodel. Column (8) presentscoefficients from a linear regression including a constant (coefficient not reported here). District‐correlated robust standard errors in parentheses.Regressionsweightedusingsurveyweights.Sampleofmarriedwomenaged15‐49whousuallyresideinthehousehold.SomeobservationsaredroppedinColumns(3),(4),(6),and(7)duetolackofvariationinthevalueofthedependentvariablewithindistrict.ObservationsforwomenwhohaveneverhadanypregnancyaredroppedinColumn(8)sincetheshareofabortedpregnanciesisnotdefinedforthesewomen.Source:Authors'calculationsusingDemographicandHealthSurveysofNepal(42)–DHS1996,2001,and2006only–andTechnicalCommittee for ImplementationofComprehensiveAbortionCare(2010).***p<0.01,**p<0.05,*p<0.10.

41

Table5:RobustnessChecks  (1)  (2)  (3)  (4)  (5)  (6)  (7)  (8) 

 

Any Method Modern Method 

Female Sterilization 

Male Sterilization 

Modern Method Other 

than Sterilization 

Traditional Method 

Ever Had an Abortion 

Share of all pregnancies aborted 

Panel A: Include further controls (see notes below table for details) Number of Abortion Centers 

0.980***  0.980***  0.983*  1.001  0.981***  0.996  1.014*  0.002*** 

(0.0055)  (0.0062)  (0.0100)  (0.0089)  (0.0041)  (0.0074)  (0.0073)  (0.0002) 

                 

Observations  31762  31762  31288  31743  31762  31325  31045 29063 

 

                 

Panel B: Further controls + restrict sample to non‐sterilized couples as of March 2004 (results in Columns (3) and (4) restricted to DHS 2006 and 2011) Number of Abortion Centers 

0.973***  0.970***  0.987  1.141*  0.977***  0.994  1.011  0.002*** 

(0.0058)  (0.0067)  (0.0265)  (0.0833)  (0.0044)  (0.0073)  (0.0076)  (0.0002) 

                 

Observations  25890  25890  12095  12026  25890  25517  25264 23214 

 

 

Panel C: As Panel B + Scale number of abortion centers by district population Number of Abortion Centers 

0.983  0.976*  1.001  1.063*  0.982**  1.007  1.012  0.001*** 

(0.0106)  (0.0122)  (0.0238)  (0.0380)  (0.0083)  (0.0134)  (0.0104)  (0.0004) 

                 

Observations  25890  25890  12095  12026  25890  25517  25264 23214 

 

                 

Panel D: As Panel B + Allow for time trends to vary by district population Number of Abortion Centers 

0.965***  0.955**  0.991  1.102  0.979**  1.015  1.030**  0.002*** 

(0.0125)  (0.0173)  (0.0366)  (0.0932)  (0.0083)  (0.0156)  (0.0142)  (0.0004) 

                 

Observations  25890  25890  12095  12026  25890  25517  25264 23214 

 

42

                  

Panel E: As Panel B + Allow time trends to vary by (13) region Number of Abortion Centers 

0.965***  0.960***  0.941  1.142  0.971***  1.009  1.018  0.002*** 

(0.0077)  (0.0094)  (0.0491)  (0.1005)  (0.0094)  (0.0128)  (0.0166)  (0.0003) 

                 

Observations  25890  25890  12095  12026  25890  25517  24898 23214 

 

                 

Panel F: As Panel B + Allow time trends to differ in rural and urban areas Number of Abortion Centers 

0.977***  0.976***  0.994  1.142  0.977***  0.993  1.017**  0.001*** 

(0.0059)  (0.0063)  (0.0245)  (0.0950)  (0.0050)  (0.0086)  (0.0084)  (0.0003) 

                 

Observations  25890  25890  12095  12026  25890  25517  25264  

23214 

                 

    Panel G: As Panel B + Allow time trends to differ by wealth quintile     

Number of Abortion Centers 

0.986**  0.986*  0.974  1.149*  0.984***  0.994  1.011  0.001*** 

(0.0068)  (0.0072)  (0.0264)  (0.0965)  (0.0044)  (0.0088)  (0.0082)  (0.0002) 

                 

Observations  25890  25890  12095  12026  25890  25517  25264  23214 

                 

Outputomittedforthefollowingvariables:threedummyvariablesforDHS2001,2006,and2011,districtfixed‐effects,andcontrolsforurbanlocation,ageat interview, religion, education, ideal number of children, control for the number of conflict casualties in the year preceding the survey (per districtpopulationasof1991,thelastpre‐conflictpopulationcensus),forwhetherornotthewomanreportshavingheardafamilyplanningmessageontheradiointhelastmonth,whethershewasvisitedbyafamilyplanningworkerintheprevious12months,whethershehasheardofAIDS,andfortheSESgrouptowhichshebelongs(asmeasuredbytheherquintileinthedistributionofhouseholdlivingstandard).Columns(1)to(7)reportoddsratiosfromalogitmodel.Column(8)presentscoefficientsfromalinearregressionincludingaconstant(coefficientnotreportedhere).District‐correlatedrobuststandarderrorsinparentheses.Regressionsweightedusingsurveyweights.Sampleofmarriedwomenaged15‐49whousuallyresideinthehousehold.SomeobservationsaredroppedinColumns(3),(4),(6),and(7)duetolackofvariationinthevalueofthedependentvariablewithindistrict.InPanelsBtoF,Columns(3)and(4)excludeobservationsfor1996and2001,sincebydefinitionthereisnovariationinsterilizationstatusinthesesurveysafterdroppingthosesterilizedbeforeMarch2004.ObservationsforwomenwhohaveneverhadanypregnancyaredroppedinColumn(8)sincetheshareofabortedpregnanciesisnotdefinedforthesewomen.Source:Authors'calculationsusingDemographicandHealthSurveysofNepal(42)–DHS1996and2001only–andTechnicalCommitteeforImplementationofComprehensiveAbortionCare(2010).***p<0.01,**p<0.05,*p<0.10.

43

Table6:FurtherRobustnessChecks  (1)  (2)  (3)  (4)  (5)  (6)  (7)  (8) 

  Any Method Modern Method 

Female Sterilization 

Male Sterilization 

Modern Method Other 

than Sterilization 

Traditional Method 

Ever Had an Abortion 

Share of all pregnancies aborted 

Panel A: Unweighted regressions Number of Abortion Centers 

0.968***  0.967***  0.979**  0.987  0.972***  0.995  1.003  0.002*** 

(0.0058)  (0.0063)  (0.0096)  (0.0109)  (0.0043)  (0.0067)  (0.0067)  (0.0002) 

                 

Observations  32098  32098  31620  32078  32098  31657  31371  29368 

                 

Panel B: Linear probability model Number of Abortion Centers 

‐0.005***  ‐0.005***  ‐0.002**  0.000  ‐0.003***  ‐0.000  0.003***   

(0.0016)  (0.0018)  (0.0011)  (0.0008)  (0.0007)  (0.0004)  (0.0006)   

                 

Observations  32098  32098  32098  32098  32098  32098  32098   

Outputomittedforthefollowingvariables:threedummyvariablesforDHS2001,2006and2011,districtfixed‐effects,andcontrolsforurbanlocation,ageatinterview,religion,andeducationsummarizedinTable1PanelC.SeealsoNotesunderTable2.***p<0.01,**p<0.05,*p<0.10.  

44

  Table7:RobustnessoftheEffectofAbortionCenterstoExcludingOneSurveyataTime  (1)  (2)  (3)  (4)  (5)  (6)  (7)  (8) 

Excluded data 

Any Method Modern Method 

Female Sterilization 

Male Sterilization 

Modern Method Other 

than Sterilization 

Traditional Method 

Ever Had an Abortion 

Share of all pregnancies aborted 

DHS 1996  0.975***  0.977***  0.980*  1.024*  0.977***  0.993  1.024**  0.002*** 

  (0.0065)  (0.0073)  (0.0120)  (0.0140)  (0.0048)  (0.0093)  (0.0097)  (0.0002) 

  24602  24602  24228  24202  24602  24238  24056  22570 

DHS 2001  0.976**  0.976**  0.979*  0.993  0.977***  0.990  1.013*  0.002*** 

  (0.0110)  (0.0107)  (0.0122)  (0.0158)  (0.0055)  (0.0076)  (0.0080)  (0.0002) 

  24256  24256  23846  23902  24256  23529  23684  22230 

DHS 2006  0.974***  0.974***  0.981*  0.996  0.974***  0.990  1.004  0.002*** 

  (0.0070)  (0.0077)  (0.0109)  (0.0111)  (0.0043)  (0.0074)  (0.0079)  (0.0002) 

  24322  24322  23858  24322  24322  23810  23422  22164 

DHS 2011  0.957***  0.951***  0.953*  0.924***  0.968***  1.016  0.986  0.000 

  (0.0123)  (0.0144)  (0.0239)  (0.0245)  (0.0104)  (0.0212)  (0.0260)  (0.0005) 

  23114  23114  22343  23063  23114  22730  22260  21140 

Outputomittedforthefollowingvariables:twodummyvariablesindicatingDHSwaves,districtfixed‐effects,andcontrolsforurbanlocation,ageatinterview,religion,andeducationsummarizedinTable1PanelC.SeealsoNotesunderTable2.***p<0.01,**p<0.05,*p<0.10.  

45

 Table8–Robustnessoftheeffectofabortioncenterstoincludingallinterviewedwomenage>=25insteadofrestricting2006and2011surveystoever‐marriedwomen  (1)  (2)  (3)  (4)  (5)  (6)  (7)  (8) 

 

Any Method Modern Method 

Female Sterilization 

Male Sterilization 

Modern Method Other 

than Sterilization 

Traditional Method 

Ever Had an Abortion 

Share of all pregnancies aborted 

Number of Abortion Centers 

0.977***  0.979***  0.979*  1.001  0.977***  0.988  1.007  0.002*** 

(0.0051)  (0.0060)  (0.0106)  (0.0099)  (0.0044)  (0.0078)  (0.0092)  (0.0003) 

                 

Observations  25174  25174  24843  25150  25174  24848  24454  24268 

No. of clusters  75  75  72  74  75  70  69  75 

Pseudo R‐squared  0.0665  0.0618  0.1607  0.1177  0.1006  0.0828  0.1259  0.0736 

Mean Y  0.479  0.435  0.196  0.085  0.155  0.045  0.050  0.016 

Outputomitted for the followingvariables: threedummyvariable forDHS2001,district fixed‐effects,andcontrols forurban location,ageat interview,religion,andeducation.Columns(1) to (7)reportoddsratios froma logitmodel.Column(8)presentscoefficients froma linearregression includingaconstant(coefficientnotreportedhere).District‐correlatedrobuststandarderrorsinparentheses.Regressionsweightedusingsurveyweights.Sampleofwomenaged25‐49whousuallyreside inthehousehold, irrespectiveof theirmaritalstatus.Excludedreligiouscategory is"Hindu",excludededucationcategoryis"Noeducation".SomeobservationsaredroppedinColumns(3),(4),(6),and(7)duetolackofvariationinthevalueofthedependentvariablewithindistrict.ObservationsforwomenwhohaveneverhadanypregnancyaredroppedinColumn(8)sincetheshareofabortedpregnanciesisnotdefinedforthesewomen.Source:Authors'calculationsusingDemographicandHealthSurveysofNepal(1996‐2011)andTechnicalCommitteeforImplementationofComprehensiveAbortionCare(2010).***p<0.01,**p<0.05,*p<0.10.

46

AppendixFigureA1:AbortionandContraceptionTrendsinFormerCommunistCountrieswithCompleteAbortionData

Sources:Abortion:1975‐1996fromHenshawetal.(1999),1996‐2003fromSedghetal.(2007).Contraception:1970‐1987fromMauldinandSegal(1988),1990‐2000fromUnitedNations(2004).Contraceptionfiguresfor1970‐1987labeled“CzechRepublic”areaggregatefiguresforCzechoslovakia.

47

FigureA2:AnalyticalFramework

C=1

1 nopregnancy A=0

pregnancy A=0 C=0 A=1

48

TableA1:ResultsbyagegroupDependent variable: =1 if Modern Method, 0 otherwise 

(1)  (2)  (3)  (4)  (5)  (6)  (7) 

Age Group:  15‐19  20‐24  25‐29  30‐34  35‐39  40‐44  45‐49 

Number of Abortion Centers 

0.948***  0.971***  0.977***  0.956***  0.974*  1.000  0.994 

(0.0170)  (0.0076)  (0.0076)  (0.0078)  (0.0146)  (0.0109)  (0.0150) 

               

Observations  2578  5868  6415  5435  4678  3852  2982 

Outputomittedforthefollowingvariables:threedummyvariablesforDHS2001,2006and2011,districtfixed‐effects,andcontrolsforurbanlocation,ageatinterview,religion,andeducationsummarizedinTable1PanelC.SeealsoNotesunderTable2.***p<0.01,**p<0.05,*p<0.10.

49

TableA2:ResultsObtainedUsingRecallDataDependent Variable: =1 if any method, 0 otherwise 

    District‐Specific Trends   

  (1)  (2)  (3)  (4)  (5) 

 Baseline  Linear Trends  Quadratic 

Trends Cubic Trends 

Placebo Test 

Number of Abortion Centers 

         

0.977*** 0.970***  0.969**  0.984**  0.965** 

in the woman’s district  (0.0045)  (0.0111)  (0.0143)  (0.0082)  (0.0138) 

Number of Abortion Centers 12 months later          1.008 

          (0.0120) 

           

District Dummies  Yes  Yes  Yes  Yes  Yes 

Covariates  Yes  Yes  Yes  Yes  Yes 

Observations  923886  923886  923886  923886  757050 

Sampleofmarriedwomeninterviewedinthe2006and2011DHSsurveyswhowerenotsterilizedandwhosehusbandswerenotsterilizedpriortoMarch2004.Periodincluded:April2000(startofthecalendarperiodforthe2006DHS)toFebruary2010(lastmonthforwhichwehavedataonregistrationofabortioncenters).Outputomittedacrossallcolumnsforthefollowingvariables:adummyvariableforDHS2011,districtfixed‐effects,month/yeardummies(e.g.,May2008),and controls for urban location, age at interview, religion, and education. The sample in Column (5) is smaller as itexcludesthelast12calendarmonthsforwhichwehavedataoncurrentabortioncentersbutnotfutureabortioncenters.Source:Authors'calculationsusingDemographicandHealthSurveysofNepal(42)–DHS1996and2001only–andTechnicalCommitteeforImplementationofComprehensiveAbortionCare(43).***p<0.01,**p<0.05,*p<0.10.