econometrics case study

EconometricsBenjaminCroftIndividualProject

Fall2014

FertilityRatesAroundtheWorld:APilotStudyoftheRegressionof14EndogenousVariablestoLocal

FertilityRates

TableofContents

Introduction

LiteratureReview

ExplanationofVariables

RegressionEquation

StatisticalSignificance

JointSignificance

Heteroskedasticity

Limitations

Conclusion

WorksCited

INTRODUCTION Ofthecriticalglobalissuesofthe21stcentury,populationgrowthisperhapsamongthegreatest.Thefearsofhumanoverpopulationareaboundingastheworldpopulationclockticksupwardsof7.2trillionpeopleasofDecember2nd,2014.Anewbabyisbornevery8seconds,whileapersondiesatestimated12secondintervals,showingthatthebirthratesurpassesthedeathrateleadingtoanetgaininthehumanpopulationevery16seconds.Micro-statisticsareastonishingintheabstract;however,theeffectofpopulationgrowthpermeatesverytangibleglobalissuesaswell.Fromfamine,resourcewars,ethnicconflict,landuse,airpollution,transportation,infrastructure,militaries,energydependency,climatechange,incomeinequality,racism,sexism,andmore,populationisaunifyingthemebehindmanyoftheworld’scurrentcrises.

Populationisrisingquickly,raisingseveralimportantquestionsaboutsustainedhumanexistence.Whatispopulation’seffectonenergyconsumptionandoildependency?Howcanarapidlygrowinghumanpopulationbeenvironmentallysustainable?Whereareallthenewpeoplegoingtolive?Wherearetheresourcesgoingtocomefromtosupportagrowingpopulation?Whatisthecarryingcapacityoftheearth?Whatcangloballeadersdotocurbrunawaypopulationgrowth? Toanswerthesequestions,itisimperativetotakeastepbackandanalyzewhatcontributestopopulationchange,bothpositivelyandnegatively.Thisstudyattemptstoprovidesomeexplanatorypowerforfertilityrates–thatis,therateatwhichawomangivesbirthwithinacountry.Fertilityratesprovidecriticalinsightintopopulationchangeastheyexplainthegrowthanddeclineofpopulationovertime.Byanalyzingthepredictorsoffertilityrates,policymakerscanthenbuildmoreeffectivelawsandprogramstostemapossiblyunsustainablepopulationgrowth.Aroundtheworld,fertilityratesarespikinginsomeareaswhilestabilizinginothers.Usingdatacollectedfromeverycountryaroundtheworld,thisstudyseekstoexplainwhy.

LITERATUREREVIEW

Foraglobalissue,fertilityratestudiesrangewidelyfromprojectionmodels,impactassessments,andpredictionequations.Assuch,theliteraturerangeswidelyonitsfocus.Twostudiesstandoutasrelevanttothestudyconductedinthispaper.(i)Berhan,Yifru,andBerhan(2014) Intheirarticle“ReasonsForPersistentlyHighMaternalandPerinatalMortalitiesinEthiopia:PartII–Socio-EconomicandCulturalFactors,”theauthorsseektoexplainthefertilityratesbyexaminingpredisposingfactorsforpregnancy-relatedhealthrisksinEthiopia.Theauthorsincludedthemoderncontraceptiveuse,harmfultraditionalpractice,adultliteracyrates,andlevelofincomeaspredictorsoffertilityratesandusedaregressionmodeltopredictthedependentvariable.TheyconcludethatthefertilityrateinEthiopiadecreasedduetoanincreaseincontraceptiveusebyafactorof7,decreaseduseofdangerousfemalecircumcision,andanincreaseintheadultliteracyratewhichimplieshigherpre-andpost-natalhealthcareandaccessibilitytoeconomicresources.(ii)Asamoah,Benedict,Agardh,andÖstergren(2013) Intheirarticle“InequalityinFertilityRateandModernContraceptiveUseAmongGhanaianWomenfrom1988-2008,”theauthorsseektoexaminetheuseofcontraceptivesbywomenwholiveinlowsocioeconomicplanesaswellasalackoffamilyplanningservicesavailabletothem.Usingsurveystocollectdataandanappliedregressionmodel,theauthorsconcludethatequalityincontraceptiveuseincreasedoverthestudy’stimespan.However,inequalityinfertilityrateincreasedaswell,whileeducationandincomeinequalitiesweremaintainedoverthesameperiod. Thesestudiesillustratetheimportanceofaneffective,comprehensiveapproachtopopulationstudies.Populationisacomplex,nuanced,overarchingconstructthatisdifficulttoapproachinanyway;assuch,theliteraturefocusesonnarrowaspectsonstateorregionallevels.Aglobalregressionapproachtoexplainingfertilityratesislackinginthecurrentliterature;suchanapproach,ofcourse,maynotexistduetoomittedvariablebias,lowexplanatorypower,andfundamentaldatacollectionobstaclesthatprecluderesearchersfromdevelopingaworldwideanalysisonthesubject.However,thestudyinthispaperseeksto,attheveryleast,createdataanalysesinapilotstudythatcouldtriggerfurtherevaluationbyotherresearchers.

ENDOGENOUSVARIABLESFertilityRate(Dependentvariable) Thisvariablemeasuresthenumberofchildbirthsperwomaninacountry.Itiscalculatedbyestimatinghowmanychildrenawomanwouldbeargiventhatshelivestotheendofherchildbearingyears.Thisvariableislog-transformedinordertodemonstratetheproportionalchangeoffertilityratesduetochangesintheratesofindependentvariables.ContraceptivePrevalence Thisvariableismostlikelythemostinfluentialpredictoroffertilityratesacrosstheworld.Thisvariablemeasuresthepercentageofwomenandtheirsexualpartnerswhoutilizeanyformofcontraception.Itismeasuredonlyforwomenages15-49.Withoutknowingtheestimationoutput,itisestimatedthisvariablewillhaveanegativecoefficient,asanincreaseinthecontraceptiveprevalenceratewillleadtoadecreaseinthebirthrate.GDPPerCapita GDPpercapitaisthegrossdomesticproductofacountrydividedbyitspopulation.DatainthisstudyareincurrentUnitedStatesdollars.Thisvariableislog-transformedinordertodemonstratetheeffectsofaproportionalchangeinGDP;withoutlogtransformingit,GDPistheonlyindependentvariablethatisnotinapercentorrate,andskewsthedataheavily.Withoutknowingtheestimationoutput,itisestimatedthisvariablewillhaveanegativecoefficient,asthericherthecountry,thelessneedforchildrenaslaborersorsocialsupportsystemsfortheelderly.HealthExpenditure Healthexpenditureisthesumofpublicandprivatehealthexpensesofacountry.ItismeasuredasapercentoftotalGDP.AccordingtotheWorldBank,itincludes“provisionofhealthservices(preventativeandcurative),familyplanningactivities,nutritionactivities,andemergencyaiddesignatedforhealthbutdoesnotincludeprovisionofwaterandsanitation.”Withoutknowingtheestimationoutput,itisestimatedthisvariablewillhaveanegativecoefficient,asanincreaseintheamountofhealthexpendituresofacountrywouldleadtoahealthierpopulace,accesstoprenatalcare,lessneedforchildrentosupportelderlyinpoorhealth,etc.allofwhichwouldreducethefertilityrate.InternetUsers Thisvariablemeasuresthepercentofacountry’spopulationwhohaveaccesstotheinternet.Withoutknowingtheestimationoutput,itisestimatedthatthisvariablewouldhaveanegativecoefficient,asaccesstotheworldwidenetworkwouldenablethedisseminationofculture,education,andideasregardinggeneralwellness,familyplanning,andreproductivehealth.Furthermore,accesstotheinternetwoulddenoteindividualshaveelectronicdeviceswhichcanaccessthe

network,implyingtheindividualshaveaneconomicstationwhichallowsthemtoaffordthosedevices.LaborForceParticipationRate–Female Thisvariablemeasurestheproportionofthepopulationthatiseconomicallyactive.TheWorldBankdatausesages15andup.Thisregressionmodeldemarcatesbetweenmaleandfemaleparticipationrate,sinceinsomecountries,culturalpracticesprecludewomenfromparticipatinginthelaborforce.Withoutknowingtheestimationoutput,itishypothesizedthatthecoefficientonthisvariableisnegative;asanincreasingnumberofwomenpursueworkorcareers,asmalleramountofwomenaresolelyassignedtochildbearingactivities.LaborForceParticipationRate–Male

Thisvariablemeasurestheproportionofthepopulationthatiseconomicallyactive.TheWorldBankdatausesages15andup.Thisregressionmodeldemarcatesbetweenmaleandfemaleparticipationrate,sinceinsomecountries,culturalpracticesprecludewomenfromparticipatinginthelaborforce.Withoutknowingtheestimationoutput,itishypothesizedthatthecoefficientonthisvariableisnegative;asanincreasingnumberofmenpursueworkorcareers,familyunitshavehighereconomicresourcesandthusrelylessonchildrenaslaborersorcaretakers.LiteracyRate–Female Thisvariablemeasuresthepercentofthepopulationwhocanbothreadandwriteatanelementarylevel.Thisregressionmodeldemarcatesbetweenmaleandfemaleparticipationrate,sinceinsomecountries,culturalpracticesprecludewomenfromeducation.Withoutknowingtheestimationoutput,itisestimatedthiscoefficientisnegative;amoreliteratefemalepopulationimpliesamoreeducatedfemalepopulation,whohavesocioeconomicmeansandaccesstogreaterhealthcareoptions.LiteracyRate–Male Thisvariablemeasuresthepercentofthepopulationwhocanbothreadandwriteatanelementarylevel.Thisregressionmodeldemarcatesbetweenmaleandfemaleparticipationrate,sinceinsomecountries,culturalpracticesprecludewomenfromeducation.Withoutknowingtheestimationoutput,itisestimatedthiscoefficientisnegative;amoreliteratemalepopulationwillimpliesamoreeducatedmalepopulation,whohavesocioeconomicmeansandaccesstogreaterhealthcareoptions.MalnutritionUnder5YearsofAge Thisvariablemeasuresthepercentageofchildrenundertheageof5“whoseheightforage(stunting)ismorethantwostandarddeviationsbelowthemedianfortheinternationalreferencepopulationages…ThedataarebasedontheWorldHealthOrganization’snewchildgrowthstandardsreleasedin2006.”Withoutknowingtheestimationoutput,itisestimatedthisvariableispositive.Thehigherthepercentageofmalnourishedchildren,thelesssocioeconomicresiliencetothe

environmentthereis,thelesshealthcarethereis,andthehigherrelianceonmorechildrentosustaintheworkandhomethereis. MobilePhoneSubscriptions Thisvariablemeasuresthepercentageofcellphonesthatareconnectedtoacellularnetworkinacountry.Withoutknowingtheestimationoutput,itisforecastedthatthiscoefficientisnegative;morecellphonesmeansamoreindustrialized,developednationwhichgrantsaccesstoeducation,healthcare,andinformation.MortalityUnder5YearsofAge Thisvariablemeasurestheprobabilityper1,000thatachildwilldiebeforeturningfiveyearsold.Withoutknowingtheestimationoutput,itisestimatedthiscoefficientwillbepositive;intheunfortunaterealitythatacountryhasahighmortalityrate,thereislikelymoresocioeconomicdistress,lesshealthcare,andmoreconflict.Thismeansahigherfertilityrateisneededtosustainthepopulation.PovertyRate Thepovertyratemeasurestherelativeratesofpovertyaccordingtoacountry’sstandardofpoverty.Thisvariablemustbeusedwithcaution,sincestandardsofpovertyarerelativeandreporteddifferentlyonacountry-by-countrybasis.Withoutknowingtheregressionoutput,itisestimatedthiscoefficientispositive;higherpovertyleadstoaneedforchildrenforlaborandtocarefortheelderlyastheyage.RatioofGirlstoBoysinPrimaryandSecondaryEducation Thisvariablemeasurestheratioofgirlstoboysinprimaryandsecondaryeducation,inbothpublicandprivateschools.Withoutknowingtheestimationoutput,itishypothesizedthatthiscoefficientispositive;thehigherinequalityineducation(thelessgirlseducatedincontrasttoboys),themoregenderinequalityandhigherfertilityratesacountrymighthave.RuralPopulation Thisvariablemeasuredtheproportionofacountry’spopulacethatlivesinruralareas.Itisestimatedthatthiscoefficientispositive;isolatedareaslackaccesstotheInternet,tohealthcareservices,tocontraceptionresources,andmore.Thiswouldpresumablyleadtoariseinfertilityrates.

REGRESSIONEQUATION

Thefollowingregressionequationisusedtocreatethemodelforthisstudy.𝐿𝑜𝑔 𝐹𝑒𝑟𝑡𝑖𝑙𝑖𝑡𝑦 𝑅𝑎𝑡𝑒

= 𝛽! + 𝛽! 𝐶𝑜𝑛𝑡𝑟𝑎𝑐𝑒𝑝𝑡𝑖𝑣𝑒 𝑃𝑟𝑒𝑣𝑎𝑙𝑒𝑛𝑐𝑒 + 𝛽!(log 𝐺𝐷𝑃 𝑃𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎+ 𝛽! 𝐻𝑒𝑎𝑙𝑡ℎ 𝐸𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒𝑠 + 𝛽! 𝐼𝑛𝑡𝑒𝑟𝑛𝑒𝑡 𝑈𝑠𝑒𝑟𝑠+ 𝛽! 𝐿𝐹𝑃𝑅 𝐹𝑒𝑚𝑎𝑙𝑒 + 𝛽! 𝐿𝐹𝑃𝑅 𝑀𝑎𝑙𝑒+ 𝛽! 𝐿𝑖𝑡𝑒𝑟𝑎𝑐𝑦 𝑅𝑎𝑡𝑒 𝐹𝑒𝑚𝑎𝑙𝑒 + 𝛽! 𝐿𝑖𝑡𝑒𝑟𝑎𝑐𝑦 𝑅𝑎𝑡𝑒 𝑀𝑎𝑙𝑒+ 𝛽! 𝑀𝑎𝑙𝑛𝑢𝑡𝑟𝑖𝑡𝑖𝑜𝑛 𝑢𝑛𝑑𝑒𝑟 5 + 𝛽!" 𝑀𝑜𝑏𝑖𝑙𝑒 𝑃ℎ𝑜𝑛𝑒 𝑆𝑢𝑏𝑠𝑐𝑟𝑖𝑝𝑡𝑖𝑜𝑛𝑠+ 𝛽!! 𝑀𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦 𝑢𝑛𝑑𝑒𝑟 5 + 𝛽!" 𝑃𝑜𝑣𝑒𝑟𝑡𝑦 𝑅𝑎𝑡𝑒+ 𝛽!" 𝑅𝑎𝑡𝑖𝑜 𝑜𝑓 𝐺𝑖𝑟𝑙𝑠 𝑡𝑜 𝐵𝑜𝑦𝑠 𝑖𝑛 𝑃𝑟𝑖𝑚𝑎𝑟𝑦 𝑎𝑛𝑑 𝑆𝑒𝑐𝑜𝑛𝑑𝑎𝑟𝑦 𝐸𝑑+ 𝛽!"(𝑅𝑢𝑟𝑎𝑙 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛)

InitialAnalysis

Uponinitialreviewoftheestimationoutput,itappearsthattheregressionmodeldoesafairlygoodjobatexplainingthevariationoffertilityratesacrossthe14selectedvariableswithanadjustedR-squaredisapproximately81.7%.Furthermore,theF-statisticoftheregressionis32.79,whichisverysubstantialforanF-statistic.Afewvariablesstandoutasimportant(wewilltesttheirsignificanceinthefollowingsection):contraceptiveprevalencerate,GDPpercapita,percentofpopulationwithaccesstointernet,thelaborforceparticipationrateformales,mortalityrateforchildrenunder5yearsold,andthepovertyrateofeachcountry.

STATISTICALSIGNIFICANCEFirst,wewillevaluatethestatisticalsignificanceofeachvariableusingthefollowingstructureofat-test:

𝐻!: 𝑡 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐 𝑜𝑓𝛽! < 𝐶𝑟𝑖𝑡𝑖𝑐𝑎𝑙 𝑡 𝑣𝑎𝑙𝑢𝑒𝐻!: 𝑡 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐 𝑜𝑓 𝛽! ≥ 𝐶𝑟𝑖𝑡𝑖𝑐𝑎𝑙 𝑡 𝑣𝑎𝑙𝑢𝑒

Ifthecriticaltvalueof𝛽! isgreaterthanthecriticaltvalue,werejectthenullhypothesis,and𝛽! isstatisticallysignificant.For101observations:T

Thecriticalt-valueis1.986at5%levelofstatisticalsignificance. Thecriticalt-valueis1.660at10%levelofstatisticalsignificance.

Variable t-value Conclusionat5% Conclusionat10% 1.986 1.660

ContraceptivePrevalence

-3.10 Statisticallysignificant Statisticallysignificant

Log(GDPPerCapita) -1.95 Statisticallyinsignificant(almostsignificant)

Statisticallysignificant

HealthExpenditure

-0.99 Statisticallyinsignificant Statisticallyinsignificant

InternetUsers

-1.69 Statisticallyinsignificant Statisticallysignificant

LFPR–Female


LFPR–Male

3.27 Statisticallysignificant Statisticallysignificant

LiteracyRate-Female

0.55 Statisticallyinsignificant Statisticallyinsignificant

LiteracyRate–Male


MalnutritionUnder5


MobilePhones


MortalityUnder5


PovertyRate


GirlstoBoysinPrim/SecEducation


RuralPopulationRate


JOINTSIGNIFICANCE

FtestsWewillconductF-testsonselectcombinationsofvariablestodeterminewhethertheyarejointlysignificantdespitebeingsingularlyinsignificant.F-testsfollowthetemplate:

𝐻!: 𝐹 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐 < 𝐶𝑟𝑖𝑡𝑖𝑐𝑎𝑙 𝐹 𝑣𝑎𝑙𝑢𝑒𝐻!: 𝐹 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐 ≥ 𝐶𝑟𝑖𝑡𝑖𝑐𝑎𝑙 𝐹 𝑣𝑎𝑙𝑢𝑒

Withonerestrictioninthenumeratorand(n-k-1)=(101–14–1)=86degreesoffreedominthedenominator,the5%Fvalueis3.97andthe10%Fvalueis2.77.F-test#1: LiteracyRateofFemales LiteracyRateofMales

Thejointvalueofmaleandfemaleliteracyratesisnotsignificant.

F-test#2: InternetUsers MobilePhoneSubscriptions

InternetUsersandMobilePhoneSubscriptionsarejointlysignificantatthe10%level.Thismeansthetwovariablestogetherhassomeexplanatoryvalue.F-test#3 Log(GDP_PC) LFPR_Female LFPR_Male

GDPPerCapita,LFPRoffemales,andLFPRofmalesarejointlysignificantatthe5%level.

HETEROSKEDASTICITYWithsuchalargedatasetthatspansallcountriesoftheworld,itisimperativewecheckforheteroskedasticity(unequalvariance).WewilldothisbyusingtheBreusch-PaganTestforHeteroskedasticity.Thistestappearsasfollows:

𝐻!: 𝐹 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐 < 𝐶𝑟𝑖𝑡𝑖𝑐𝑎𝑙 𝐵𝑃 𝑣𝑎𝑙𝑢𝑒𝐻!: 𝐹 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐 ≥ 𝐶𝑟𝑖𝑡𝑖𝑐𝑎𝑙 𝐵𝑃 𝑣𝑎𝑙𝑢𝑒

Usingp-values,thisisequivalentto:

𝐻!: 𝑃𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝐹 > 0.05𝐻!: 𝑃𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝐹 ≤ 0.05

Eitherway,ifwefailtorejectthenullhypothesis,thereisnotsubstantialevidenceofheteroskedasticity.Ifwedorejectthenullhypothesis,thereisevidenceofheteroskedasticity,andwewillhavetore-runourregressiontocontrolforit.

ByexaminingtheprobabilityvalueofFat0.1092,weseethatthereisnotevidenceofheteroskedasticityatthe0.05p-value,althoughasweincreasep-valueto0.11weriskseeingit.Assuch,weruntheheteroskedasticity-controlledWhiteOLSregressiontoensurehomoscedasticity.

WiththeWhiteOLSheteroskedasticity-controlledregression,wefindveryfewchangestoouroriginalregression,whichsuggeststhatheteroskedasticityisnotaconcern.OuradjustedR-squaredvalueremainsroughlythesame,andallthesignificancesofourindependentvariablesremainthesame,exceptfortwo:log(GDP_PC)andInternetUsers.Aftercontrollingforheteroskedasticity,wefindthatGDPPerCapitahasbecomesignificantatthe5%levelfort-tests,andInternetUsershasbecomelesssignificant.Whatthissuggestsisthatthevarianceonmostindependentvariableswashomoscedastic;however,onGDPandInternetUsersthevariancewasheteroskedastic.Bycontrollingforitsunequalvariance,ourregressionbecomesmoreaccurate.

LIMITATIONSWithanyambitiousstudy,therearecertainlimitationstoconsider.Mostconcernslayunderthreecategories:datacollectionandmethodology.Datacollection (i)Incompletedata.Collectingsuchalargesurveyofeverycountryintheworldisagrandundertaking.Giventhatthereare195*countriesintheworld(*somecountriessuchasTaiwanorSyriaarenotconsideredlegitimatecountriesincertaincircles),collectingdataonallofthemisdifficult.TheWorldBank,WorldHealthOrganization,andtheCIAWorldFactbookalldeliversubstantialamountsofdataregardinginternationalactors;however,duetosomecountries’inabilityorrefusaltocontributeaccuratestatisticstothesereportshinderstheabilityforresearcherstodrawcompleteconclusionsaboutallcountriesintheworld.Thedifficultydoesnotnecessarilyliewithonecountryfailingtoreportonevariable;thedifficultyisthatmanycountriesfailtoreportdifferentvariables.Thisstudymeasured14variables.Ifacountryfailedtoreportonanyofthose14,thestatisticalpackageusedwouldnotincludethatcountryinthefinalregressionoutput.Thisisalimitationontheconcludingpowerofthestudy;however,afteraround15hoursofdatacollectionandcleaning,theauthorfeelsasamplesizeof101countriesisagreatstartingpointforfurtherinvestigationorpilotstudy.

(ii)Omittedvariablebias.Aswithanystudymeasuringaverynuancedandcomplexdependentvariablesuchasfertilityratesacrosstheworld,thereareinfinitelymanyindependentvariablestochoosefromtopredictthedependentvariable.Thisstudystartedwithfiveindependentvariables,butrecognizedtheneedforamorecomprehensiveperspectivetofullyexplainthedependentvariable.Fromfivevariablestofourteen,thestudy’sR-squaredvaluegrewfrom45%to84%,whichmeans84%ofthevariationinfertilityratesisexplainedbytheendogenousvariablesinthestudy.However,itisclearthatmanymorevariablescouldbeexaminedtoincreasetheexplanatorypowerofthestudy.Itisimportanttonotethatmuchofthevarianceinfertilityratesisexplainedbyqualitativedatasuchasethnicity,religion,andculture,eachofwhichhavethousandsofpossiblevalues.AnR-squaredvalueof84%isquiteexcitingtodiscovergiventhe14includedvariables.Methodology (i)Multicollinearity.Astudyfocusedonfertilityratesiscomplex.Almosteveryincludedindependentvariableisnotexactlyindependent.Forexample,theratioofgirlstoboysinprimaryandsecondaryeducationisinfluencedbythelaborforceparticipationrateofeachgender.GDPisaffectedbytheamountofpeoplelivinginruralareasandtheliteracyratesofeachgender.Malnutritionandmortalityratesforchildrenunder5arehighlycorrelated.Themulticollinearityofthestudyneedstobetakenintoconsiderationbeforeconcludinganyresults.

CONCLUDINGREMARKS Thusfar,wehavedefinedourvariables,runtheregression,evaluatedstatisticalsignificance,evaluatedjointsignificance,andtestedandcorrectedforheteroskedasticity.Whatdoourresultstellus?RemarksonSignificantVariablesThemostsignificantvariablesinthisregressionmodel(inorderofsignificance)are:

(1)ContraceptivePrevalence.Thisindependentbeingthemostsignificantisrelativelystraightforward.Unlikeothervariables,whichhaveanindirectcausallinktoincreasingorreducingthefertilityrate,contraceptivesdirectlyreducefertilityrateastheirfunction.Everypercentincreaseincontraceptiveprevalencereducesoverallfertilityrateinacountrybyabout0.5%,ceterisparibus.Theoretically,byexpandingcontraceptiveusetoaquarterofthepopulationbetweenages15and49,thebirthratewouldbeloweredby12.5%.Themagnitudeofthischangeinitiallyseemssmall;however,byreducingfertilityateven5%acrossthehundredsofmillionsofpeopleinsomecountries,asubstantialunsustainableoverpopulationthresholdwouldbereduced.(2)MortalityRateUnder5YearsofAge.Thisindependentvariableisverytellingabouttheconditionsofglobalsocio-economy.Themortalityratepredictstherateatwhichchildrenundertheageof5die.Inthetragicsituationswherethisrateishigh,economicandhealthfactorsthatexistinthecountryareextremelypoor.Malnourishment,lackofaccesstoimmunizations,unsanitarymedicalandculturalpractices,conflict,andextremepovertyallcontributetoahighmortalityrate,especiallyforchildren.Incountriesthatsufferfromtheseproblems,theremaybealackofsocialsafetynetfortheelderly–withoutafoundationalgenerationofchildren,whowouldtakecareoftheelderly?Withoutnewgenerationsofchildren,whowillsustainthelaboreconomy?Theanswertothesequestionsisgenerallynoone;assuch,ahigherfertilityisneededtoensurenewgenerationscansustaintheold.Sinceitscoefficientispositive,a1%increaseinmortalityrateforchildrenunder5explainsa0.38%increaseinthefertilityrate.(3)LaborForceParticipationRate–Male.Thisindependentvariablewasamongthemostshockingvariablestotheauthoratinitialanalysis.Itisinterestingtonotethelaborforceparticipationrateoffemalesislesssignificantofavariablethanformales.However,usingaculturalparadigm,thismakessense;manyculturesprecludewomenfromenteringtheworkforceeithereconomicallyorsocially.Assuch,theamountofmenwhoparticipateinthelaborforcehasamuchheavierweight.AnincreaseintheLFPRformenexplainsalmosta1%increaseinthefertilityrate,incontrasttoanincreaseofwomeninthelaborforcewhichdecreasesfertilityrate(albeitatasmallermagnitude).

(4)PovertyRate.Thisindependentvariableexplainsmuchofthevarianceinfertilityrateastheeconomicconditionsofpovertycontributetotheunifyingholisticpictureoftheunderlyingdemandforahighfertilityrate,asdiscussedpreviouslyinthisreport.Anincreaseof1%inthepovertyrateexplainsanincreaseofmorethe0.4%inthefertilityrate.(5)GDPPerCapita.Whilebeinglesssignificantthantheaforementionedfourindependentvariables,GDPPerCapitaremainsaverysignificantpredictorforfertilityrates.Interestingly,whileitsstatisticalsignificanceislowerthanthepreviousfour,themagnitudeofitscoefficientismuchgreater.A1%increaseintheGDPPerCapitareducesfertilityratesby8.8%.Thismakessense;ascountriesgetricher,theygenerallyprovidemoresocialservicesandhealthcareservices,reducingtheneedforchildrentosustaintheeconomyandcarefortheelderlyorthesick.OverallConclusions OfthemanyWorldBankdevelopmentmeasures,thenumberofbirthsperwomanisaparamountindicatortounderstandingtheeconomichealthandwelfareofacountry.Thisstudysoughttocreateamultivariate,ordinaryleastsquaresregressionmodeltoexplainthevarianceoffertilityratesacrosstheworld.Using14variablesandcorrectingforheteroskedasticity,themodelexplain81.6to84.2%ofthevariationinfertilityratesacrosstheworld.Themostsignificantvariablesaffectingthefertilityratearecontraceptiveprevalence,mortalityrateofchildrenunder5,thelaborforceparticipationrateformales,thepovertyrate,andGDPPerCapita.Near-significantvariablesexplainingfertilityratesincludetheproportionofacitizenrywholiveinruralareas,thelaborforceparticipationrateforfemales,andtheproportionofapopulacethathasaccesstotheInternet. Theimplicationsofthisstudyarecauseforfurtherinvestigationintothesocioeconomicpredictorsofbirthratesaswellaspolicyimplicationsforlawmakersacrosstheworld.Furtherstudiesshouldseektocollectmorefulldata,reducetheriskofmulticollinearity,andperhapsstudyabroaderrangeofindependentvariables.However,usingtheinformationcollectedinthisstudy,theauthor’srecommendationisforgloballeaders,nonprofits,andinternationalagenciestoseektoprovidemoresafecontraceptionmethods,fundeducationforwomen,delivermoreeffectiveandlong-termaidpackages,andgiveaccesstohealthcaremeasurestodevelopingcountriesstrugglingwithunsustainablepopulationrates.Hopefully,theUNcanreachitsMillenniumDevelopmentgoalstostabilizetheglobalpopulationthenextfewdecades.

WorksCited

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Berhan,Yifru,andAsresBerhan."ReasonsForPersistentlyHighMaternalAnd

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