Women's Leadership and Policymaking in the U.S. Federal ...

Women’sLeadershipandPolicymakingintheU.S.FederalBureaucracy*

November2018

RachelAugustinePotter

CraigVolden

WordCount:10,450

ABSTRACT

Itiswellestablishedthattheleadershipstylesofmenandwomendifferin important ways. How these differences affect policy outcomes,however,isconditionalonpoliticalcircumstances.Weexplorewomen’sleadership in bureaucratic agencies that engage in rulemaking, apolicymaking activity that is conducted by nearly all bureaucraticagencies.Acrossthreepresidentialadministrations,wefindthatwomenare more likely to lead bureaus that regulate traditional “women’sissues.” Thismatch seems toworkwell for agency performance, withwomenleadersbeingparticularlyeffectiveinadvancingmoreimportantrulesand inshepherdingnewrules throughto finalization.Ourresultssuggest this enhanced performance may be driven by the pairingbetween female leaders’ interests and the substantive focus of theseagencies,ratherthanadifference in leaderqualityor in featuresof theworkenvironment.Ourfindingsareamongthefirsttoestablishaneffectofleadergenderonbureaucraticperformance.

*Forhelpfulfeedback,wethankDanCarpenter,GeorgeKrause,JenniferLawless,KiraSanbonmatsu,MicheleSwers,AlanWiseman,andDanaWittmerWolfe,aswellasseminarparticipantsattheUniversityofNorthCarolinaatChapelHillandattendeesoftheNewDevelopmentsintheStudyofPresidentialandExecutivePoliticsconferenceatVanderbiltUniversity.WethankAïtaSeckforresearchassistance.Potter:AssistantProfessor,DepartmentofPolitics,UniversityofVirginia([email protected]).Volden:ProfessorofPublicPolicyandPolitics,AssociateDeanforAcademicAffairs,FrankBattenSchoolofLeadershipandPublicPolicy,UniversityofVirginia([email protected]).

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Women’sLeadershipandPolicymakingintheU.S.FederalBureaucracy

“YousucceedinWashingtonbycollaborating.Youcan’tjustthinkaboutyourownagency,oryour own goals. You have to please both sides of the aisle, while making sure you’re notoutshiningotherofficials,andpersuadingemployeeswhodon’thavetoobeyyourorders…Ittakesalotofhumility.”1

Ø HankPaulson

TreasurySecretary(2006-2009)

Leadershipofgovernmentagenciesrequiresadistinctiveskillset,onethatinvolves

theabilitytonavigatebyzantineprocedures,whilealsoavoidingpoliticalquagmires.This

isnoeasytask;astheabovequotefromformerTreasurySecretaryHankPaulsonmakes

clear,itisajobthatrequiresbothcollaborationandhumility.Thesetwotraitsaremore

commonlyassociatedwithwomenthanmen(e.g.,Barnes2016;EaglyandJohnson1990),

suggestingthat,inthebureaucraticcontextinparticular,femaletraitsmaymakefor

particularlyeffectiveleaders.

Yet,despitethisconnection—anddespiteawealthofscholarshipsuggestingthat

womenhavedifferentleadershipstyles,whichfrequentlytranslateintoincreased

organizationalperformance(e.g.,Barnes2016;MatsaandMiller2013;Volden,Wiseman,

andWittmer2013)—theroleofwomenasbureaucraticleadersisnotwellunderstood.As

Dolan(2001)observedmorethanadecadeago,wehaveconsiderablymoreinsightinto

1QuotedinDuhigg(2017).

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therecruitmentpatterns2andqualificationsoffemaleagencyheads,thaninformation

aboutwhethertheirpresencemeaningfullyaffectspolicyoutputs.3

Inthispaper,weinvestigatewhetherthecircumstancesthatSecretaryPaulson

identifiesreallydofavorafemaleleadershipstyle.4Underwhatconditionsarewomen

moreeffectiveleadersofbureaucraticagencies?Andhowdoesfemaleagencyleadership

affectthesubstanceofthepoliciesthatbureaucraticagenciesproduce?Weexplorethese

questionsusinganewdatasetofmorethan700agencyleadersoverthreerecent

presidentialadministrations.Weexaminetheeffectofleadersontheregulationsproduced

bytheiragencies.Whileoutsidersoftenconsiderregulationtobeanesotericprocess,itis

anactivityundertakenbynearlyallbureaucraticagencies.Additionally,since

administrativeregulationscarrythefullforceandeffectoflaw,rulemakingisanavenueby

whichagencyleaderscanleavealastingmarkonpublicpolicy.Rulemakingisthusidealfor

studyingthesystematiceffectoffemaleleadersacrossabroadswathofagencies.

Ouranalysisshowsthatthereisanimportantfemaleleadershipdifferentialinthe

federalbureaucracy.Wefindthat,overall,womenaremorelikelytobeputinpositionsof

2Forinstance,womenareunderrepresentedatboththerank-and-fileandtheleadershiplevelsofthefederalbureaucracy.Whilewomenmadeupapproximately47%oftheworkforcenationwidein2016,theyconstitutedonly43%ofthefederalcivilianworkforce(OPMFedScope).Andin2017PresidentTrumpappointedjustfourwomen(16.7%)tothetop24CabinetandCabinet-equivalentpositions.WhileTrump’sappointmentstrategywasnotableforitslackofdiversity(Lee2017),nopresidenthascomeclosetoapproachinggenderparityinappointmentstotopagencyposts.3Thereis,however,considerableworkexaminingthe“representativebureaucracy”andthesymboliceffectsofhavingwomeninleadershiprolesinthebureaucracy(see,e.g.,Keiseretal.,2002).4Weimplicitlyassumethatwomenadoptfeminineleadershipstylesmorefrequentlythandomen.Whileindividualsmaydeviatefromthispattern(e.g.,awomanmayadoptamasculineleadershipstyleoramanmayadoptafeminineleadershipstyle),webelievethatonanaggregatelevelthisisareasonableassumption.Futureworkparsingwhethertheresultswereportaretheresultofsexorgenderiswelcome.

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powerinagenciesthatdealwithwhathaveclassicallybeenreferredtoas“women’spolicy

issues,”likeeducationorhealthcare.Oncethere,theycanhaveaconsiderableimpact.

Femaleleadersworkingintheseareassponsormoreimportantregulationsandaremore

likelytoshepherdthoseregulationsthroughtobecomebindinglaw.Thisperformance

premiumisbestexplainedbythepairingofwomenwiththeirsubstantiveareasofpolicy

interest.Wefindnoevidencethatthispremiumcanbeexplainedbywomeninthese

positionsbeingofahigherqualityingeneralorofthemrespondingtothegreater

proportionofwomenworkingwithintheseagencies.Ourfindingsarethefirstsystematic

empiricalevidenceofthesubstantiveeffectsofgenderonbureaucraticperformance.

TheFemaleLeadershipDifference

Acrossavarietyofdisciplines,scholarshaveprobedthedifferencesinleadership

stylesbetweenmenandwomen.Thesestudiesyieldasimilarsetofadjectivestodescribe

women’sapproachtoleadership,including:“altruistic”(AndreoniandVesterlund2001),

“collaborative”(Barnes2016),“collegial”(LawlessandTheriault2016),“cooperative”

(Lawless2015),“democratic”(EaglyandJohannesen-Schmidt2001),“long-termoriented,”

(Silverman2003),“participative”(EaglyandJohnson1990),and“results-oriented”

(Dittmaretal.2017).Men,ontheotherhand,tendtobedescribedusingtermslike

“aggressive”(Kathlene1994),“autocratic”(EaglyandJohannesen-Schmidt2001),

“competitive”(Duerst-Lahti2002),“decisive”(Rosener1990),“directive”(Eaglyand

Johnson1990),“instrumental”(Duerst-Lahti2002),and“transactional”(Rosener1990).

Despitethesedistinctiveapproaches,whenitcomestothesubstantiveeffectsof

femaleleadershiponorganizationalperformance,theresultsaremoremixed,suggesting

context-specificeffectsonoutcomes.Forexample,inastudyofgenderquotasforcorporate

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boardsinNorwegianfirms,MatsaandMiller(2013)findthat,afterwomenwerebrought

ontoboards,firmprofitabilityfellwhileemploymentlevelsrose.Meanwhile,Ferreiraand

Gyourko(2014)uncovernoeffectofhavingafemalemayoronoutputsrangingfromthe

sizeoflocalgovernmenttothecompositionofexpenditurestolocalcrimerates.However,

womenandmenmaynotalwaysbeselectedtoleadunderthesameconditions.For

instance,RyanandHaslam(2005)reportthatwomenaremorelikelytobepromotedinto

leadershippositionsafterafirmhasexperiencednegativeperformanceshocks.

Inthepoliticalrealm,theroleoffemaleleadershiphasbeenmostdeeplyexploredin

legislativesettings.Again,acontingenteffectoffemaleleadershipemerges.5Forinstance,

Volden,Wiseman,andWittmer(2013)findthatwomenaremoreeffectivelawmakersthan

men—intermsofintroducinglegislationandseeingitthroughtobecomelaw—when

comparingmenandwomenintheminorityparty.Theyfindunevenpatternsofgender

differencesinthemajorityparty.AndAnziaandBerry(2011)showthatwomenwhoface

higherbarrierstoentrywhenrunningforCongressperformbetter.Whenvotersareprone

tosexdiscrimination,womenaresubjecttoa“performancepremium”and,asaresult,

thosewhosurvivearehigherqualitycandidateswhodelivermoreporktotheirdistricts

andcosponsormorelegislation.

Theconditionaleffectofwomen’sleadershipisfurthermagnifiedinpolicyareas

thatareconsidered“women’sissues.”Whiletheprecisedefinitionofwhatconstitutesa

5Indeed,inabroadanalysisoftheunconditionaleffectofwomenlegislatorsonlegislativebehavior(i.e.,fact-findingtrips,cosponsoringlegislation,andinterfacingwithcongressionalprocedures),LawlessandTheriault(2016)detectnostatisticallysignificantdifferencesbetweenwomenandmenbeyondthosethatwouldhaveexistedbychancealone.Theydo,however,concludethatwomencontributetoamore“collegial”congressionalworkenvironment;womenaremorelikelythanmentoparticipateinSeersuckerThursday,SecretSanta,andthecongressionalbaseballteam,forexample.

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“women’sissue”varies,itgenerallyincludesissuesthatdisproportionatelyaffectwomen

andtheirlives(Barnes2016).Policyareasrangingfromhousingtosocialwelfaretothe

environmenthavebeencharacterizedaswomen’sissues.Focusingontheseissues,several

studieshaveshownthat,withinlegislatures,womentendtoprioritizewomen’sissues

morethandomen(Barnes2016;Swers2002,2013;Tamerius1995).Othershaveshown

thatwomenfeelmoreempoweredtospeakupwhenitcomestowomen’sissues(Pearson

andDancey2011)orwhentheyareoperatinginanenvironmentthatisfemale-dominated

(EaglyandJohnson1990,KarpowitzandMendelberg2014).

However,femaleactivisminthisareadoesnotnecessarilytranslateintosuccess.

WittmerandBouché(2013)suggestthat,whilegreaterinvolvementbyfemalelegislators

mayleadtogreaterspendinginwomen’spolicyareas,itmaycomeattheexpenseof

meaningfulpolicyimpact.Meanwhile,inlowerstatehouses,Thomas(1991)findsthatbills

aboutwomen,children,andfamiliesweremoresuccessful(29%passagerate)when

introducedbywomen,comparedtothoseintroducedbymen(13%).Incontrast,atthe

nationallevel,Volden,Wiseman,andWittmer(2016)findthat,relativetomen,legislative

proposalssponsoredbywomeninCongressinwomen’spolicyareasarelesslikelytogoon

tobecomebindinglaw.Whatemergesfromaconsiderationofwomenworkingonwomen’s

issues,then,isaconsensusthat,althoughwomentendtohavestrongandactivevoices,

theiradvocacycomeswithnoguaranteeofsuccessintermsofachievingpolicychange.

LeadershipinRegulatoryPolicymaking

Rulemakingisanopportunityforleaderstomakealastingmarkonpublicpolicy.

Thisisbecause,asformerEPAAdministratorAnneGorsuchBurford(1981-1983)putit,

“onceyouputaregulationonthebooks,itisunlikelythatanyonewillbeabletochangeit”

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(BurfordandGreenya1986,98).Whenitcomestorulemaking,women’sleadershipskills

mayproveparticularlyvaluable.Bynecessity,rulemakingisacollaborativeactivity.The

successfulinitiationandcompletionofarulemakingprojectinvolvesdozensofpeople:the

rule-writingteamwithinthebureau,othercomponentagencieswithinthedepartment,

departmentalleadership,aswellasactivestakeholdersoutsidetheagencysuchasinterest

groupleadersandofficialsattheOfficeofInformationandRegulatoryAffairs(OIRA),the

WhiteHouseclearinghouseforagencyrulemaking.Orchestratingarulemakingeffortis

thereforeaparticipatoryexercise,onetowhichwomen,giventheirtypicalleadership

styles,maybeparticularlywell-suited.

However,notallrulesarecreatedequal.Somerulesaddressimportantpolicy

topics,whileothersdealwiththemoremundanemattersoftheadministrativestate.A

bureaucraticleadermayadvanceanambitiousregulatoryagendathataccomplishesmajor

policygoalsormaydevotehertimetomorequotidianregulatoryfixes.6Giventhe

collaborative,long-termorientedleadershipneededtoovercomeobstaclestomajor

regulatoryreforms,perhapswomenaremorelikelytochoosethefirstoftheseapproaches.

FormerEPAAdministratorLisaJackson(2009-2013)presentsacaseinpoint.

Describedasa“regulatorywarrior”(Carey2011),she“[pushed]throughthetoughestnew

airandwaterpollutionrulesinovertwodecades”(Schiffman2013).Duringherfour-year

tenure,Jacksonoversawtheissuanceofnumerousmajorenvironmentalregulations,

includingproposalsto:regulatemercuryemissionsfromcoal-firedpowerplants,set

greenhousegasemissionstandardsforpassengervehicles,andestablishapermitting

programforthelargeststationaryemissionssources.Pursuingthisagendarequiredherto6Ourargumentbuildsfromthepremisethatmezzo-levelbureauleaderscanhaveanimportantandlastingimpactonabureau’strajectory(see,e.g.,Carpenter2001).

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overcomenumerousobstacles,includingsignificantcourtchallengesandevenopposition

fromPresidentObama.7AdministratorJackson’sexpansiveactivityintherulemaking

arenastandsinstarkcontrasttoherpredecessor,StephenJohnson,whoiswidelyviewed

tohaveoverseenacaretakerregime.Whilemanydifferencesdistinguishthesetwoagency

leaders,onenotabledifferenceisgender.IfJackson’sregulatoryprowesscanbeattributed

toamore“feminine”approachtoleadingtheagency,thenitmayfitintoabroaderpattern

ofagendereffectinbureaucraticpolicymaking.

However,leadershipdoesnotstopwiththegenerationofregulatoryproposals;

aftertheproposalstage,thereareanumberofproceduralstepsthatanagencymustfollow

inordertoensurethattheproposalgoesontobecomeabindingfinalrule.Thesesteps

includethesolicitationofpublicfeedbackviaacommentperiod,thedraftingofafinalrule

thatrespondstothosecomments,possiblereviewofthedraftfinalrulebyOIRA,and

publicationofthefinalruleintheFederalRegister.8Inadditiontotheseprocedural

hurdles,arulemustalsoovercomepotentialpoliticalroadblocksfromoverseersin

Congress,theWhiteHouse,andpossiblythecourts(seePotterandShipan2017).Inother

words,oncearuleisproposed,thereisnoguaranteethatitwillgoontobecomeabinding

finalruleand,indeed,manyruleslanguishindefinitelyattheproposedrulestage.

Agencyleaderscanaffectthesuccess—orfailure—oftheirrulemakingprojects.

Theycandeploygreaterorfewerresourcestowardsrulemaking.Whenaproposedruleis

prioritizedwithintheagency,itstandsagreaterchanceofreachingthefinalrulestage

morequickly.Additionally,leaderscanstrategicallyemployprocedurestohelpinsulatea7TheWhiteHousedidnotsupportJackson’seffortstoadoptmorestringentozoneairqualitystandards(Childers2012).8Thisisageneralizationofthepaththataproposedrulemightfollow;foranexplanationofpossibledeviationsfromthispath,seeKerwinandFurlong(2011).

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particularrulefrompoliticalinterventionandtohelpensurethatitultimatelybecomesa

bindingfinalrule.Forinstance,Potter(2017)arguesthatagenciesleveragetheircontrol

overthetimingofthepublicationoffinalrules,speedinguptheirpublicationwhenthe

politicalclimateisfavorable,andslowingitdownto“waitout”amoreadverseclimate(see

alsoNouandStiglitz2016).Again,theadvantagesofafemaleleadershipstyle(often

describedas“collegial,”“participative,”and“results-oriented”)mayyieldgreatersuccessin

accomplishingthiskindoftasksinceitinvolvesrallyingnumeroussubordinates

throughouttheagency.Incombination,thislogicsuggeststhefollowinghypothesis.

GenderedRulemakingHypothesis:Womenleadersproposemoreimportantrulemaking

projectsandseetheirrulemakingprojectsthroughtofinalizationatagreaterrate

thandomen.

“Women’sIssues”inRegulatoryPolicymaking WhileAdministratorJackson’ssuccessinenvironmentalrulemakingislikely

attributabletomanyofherpersonalqualities,thefactthatsheworkedinthe

environmentalarena—apolicydomainthatmanyscholarsconsidertobeawomen’spolicy

issue(e.g.,Thomas1991,1994;Reingold2000;Little,Dunn,andDeen2001)—mayalso

havecontributedtohersuccess.Asdiscussedpreviously,theextantliteraturesuggeststhat

women’sleadershipcapabilitiesmaybeenhancedinthecontextofwomen’spolicywork

(Barnes2016;EaglyandJohnson1990;KarpowitzandMendelberg2014;Swers2002,

2013).Whilethisempowermentdoesnotnecessarilytranslatetopolicysuccess(Volden,

Wiseman,andWittmer2016;WittmerandBouché2013),thelogicsuggeststhatworking

inawomen’spolicyareamayendowafemaleleaderwithacomparativeadvantageinthe

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regulatoryprocess.Leaders,inturn,maythenbeabletotranslatethisadvantageinto

policygains,motivatingthefollowingconditionalhypothesis:9

ConditionalRulemakingHypothesis:Comparedtoothers,womenleadersinagenciesthat

focusonwomen’sissuesproposemoreimportantrulemakingprojectsandseetheir

rulemakingprojectsthroughtofinalizationatagreaterrate.

MechanismsofConditionalRulemakingSuccess

TheConditionalRulemakingHypothesisdoesnotspecificallyaddresswhywomen

mightperformdifferentlyinthecontextofawomen’sissueagency.Wepositthree

explanationsthatmightleadtothisoutcome;althoughnotanexhaustiveaccountingofthe

mechanismsthatmaybeatwork,theseexplanationsareamongthemostlikely

mechanisms.Theyarebothplausibleandtestable.

First,womenmayperformbetterinwomen’sissuesagenciessimplybecausethey

arepassionateabouttheseissuesandtheyhavetheopportunitytofocusonthem

exclusivelyintheseagencies—whatwecallthepairingeffect.Ifwomenaremore

committedtothecausewhenitcomestotheissuesthattheseagenciesaddress,10theymay

bemorewillingtoputinthetimeandeffortneededtoinitiateandcompleteimportant

regulatoryactionsintheseareas.Whilepassionisitselfunobservable,wesuspectthatan9Notably,wemakethecomparisongroupsinthehypothesis“otherleaders.”Thisgroupincludeswomenleadingagenciesthatarenotfocusedonwomen’sissueareas,aswellasmenleadingagencies(bothwomen’sissueagenciesandothertypesofagencies).10Forinstance,inaqualitativestudyofthewomen’smovement,Banaszak(2010)chronicleshowactivistswhosubsequentlyheldleadershippositionswithintheU.S.federalbureaucracyoftenusedacceptedchannels—includingrulemaking—toadvancetheiragendas.Whileshearguesthatwomencanleavetheirmarkinmanypolicyareas,herstudynonethelessfocusesonwomen’sissueareas,becausetheyreflecttheareaswherewomenactivistshavetraditionallyhadthemostinfluenceoverpolicy.

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alignmentbetweenleaders’preferencesandtheissuestheyaddress(bothinsalienceand

ideologicalterms)positivelyaffectstheirperformance,hypothesizedasfollows.

PairedInterestsHypothesis:Womenleadersaremoresuccessfulwhentheirinterestsare

pairedwiththeiractivities.

Asecondplausiblemechanismiswhatwerefertoastheworkenvironmenteffect.

Thisbuildsofftheideathatwomentendtosucceedwhentheyareputincollaborative

environmentswithotherwomen(KarpowitzandMendelberg2014,Thomas1991).A

women’sissueagencymayattractmorerank-and-filewomentoitsworkforce,whichmay

inturnenablewomentoadoptamorefeminineleadershipstyle,andamoreconsensus-

basedworkplaceculturemayemerge(GardinerandTiggemann1999).11Whenawomanis

putinchargeofsuchanagency,sheissetuptosucceed;priorresearchhasdemonstrated

thatwhenwomenareplacedinwomen-dominatedenvironments,theymoreeffectively

overcomestructuralbarriersandattainpoliticalpower(Barnes2016),consistentwiththe

followinghypothesis.

WorkEnvironmentHypothesis:Womenleadersaremoresuccessfulingender-balancedor

feminineworkenvironmentsthaninmale-dominatedworkenvironments.

11Femalesubordinatesmayalsobemoreinvestedinthesuccessofafemaleleaderintheseworkenvironments.Forinstance,ThomsenandSwers(2017)findthatfemaleDemocraticdonorsaremorelikelytogivetoliberalfemalecandidatesoverandabovetheirstandingontraditionalpredictorsofgiving,suchaswhethertheyareinacompetitiveseatorapartofparty/committeeleadership.

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Finally,theremaybeacompetitivetalentpooleffectbywhichmoreeffectivewomen

arechosentoleadwomen’sagencies.12Whenchoosingawomantoleadawomen’sissue

agency,thepoolofhighlyqualifiedcandidatesmaybelarger.13Forexample,itiswell

knownthatwomenareoverrepresentedinthefieldofeducation,soawomanchosento

leadaneducationagencymayhaverefinedherskillsovertimeinaverycompetitivefemale

environment.Notably,thislogicrunscountertoestablishedwisdomaboutfemaleleader

selection,whichstatesthatthemostcompetentwomenrisetothetopintraditionallymale

environmentswhereonlythestrongestcansurvive(i.e.,the“JackieRobinson”effect,see

AnziaandBerry2011).Nevertheless,inahighlycompetitiveenvironmentwithmany

capableindividuals,onlythehighestcaliberleadersrisetothetop,assuggestedbythe

followinghypothesis.

TalentPoolHypothesis:Womenleaderschosenfromalargeandcompetitivepoolof

talentedwomenaremoresuccessfulthanthoseselectedfromaweakerpool.

CharacterizingBureaucraticLeadership

Toinvestigatetheeffectsoffemaleleadershiponagencyregulatoryperformance,

weidentifiedalistofallexecutivebranchagenciesthatissuedatleastoneproposedrule

overthetwentyyearsbetween1995-2014(inclusive).WereliedontheUnifiedAgendaof12Tamerius(1995)makesasimilarcasewithrespecttowomenlegislatorsinCongress,arguingthat“themostimportantdistinctiontobemadeaboutfemaleandmalemembersofCongresslieslessintheirdesiretoenactfeministpoliciesthanintheirwillingnessandabilitytoinitiateandguidethesepoliciesthroughthelegislativeprocess”(108).13Onobservabledimensions,thewomeninoursampledonotdramaticallydifferfromthemaleleaders(Table1),nordowomenleadingwomen’sagenciesdifferfromwomenleadingothertypesofagencies(TableA2).Nonetheless,thesefemaleleadersmaydiffermeaningfullyinwaysthatarenotobservable.

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RegulatoryandDeregulatoryActions(“UnifiedAgenda”),asemi-annualaccountingof

agencyrulemakingactivitiesthatispublishedintheFederalRegister.Fromtherewe

determinedtheagencythatsponsoredeachproposedrule,14andusedpubliclyavailable

resourcestoidentifytheleaderswhostaffedthoseagenciesduringthistimeperiod.The

resultingdatasetincludes726agencyleadersat145agencies.

Foreachleaderweidentifiedanumberofbackgroundcharacteristics,including

theirgender,age(atthetimetheytookuptheposition),educationalattainment,race,and

whethertheyhadpriorpublicmanagementexperience.Wealsoevaluatedthelengthof

time(inyears)thattheyservedastheagencyleaderandwhetherthepositionrequired

confirmationbytheSenate.15

Inaddition,weconsidereddifferencesinthetypeofagenciestheseleadersoversaw.

Inparticular,givenourhypothesesaboutfemaleleadersinwomen’spolicyareas,we

consideredwhethertheagencyinquestiondealtwithapolicyissuethatistraditionally

consideredawomen’spolicyissue.Todothis,wematchedeachagencywiththePolicy

AgendasProjectmajortopicarea(BaumgartnerandJones2016)thatmostclosely

characterizeditsfunction.Wethenidentifiedagenciesthatoperateineightpredominantly

14Weidentifiedthebureaubythefirstfourdigitsoftheproposedrule’sRegulatoryIdentificationNumber(RIN).Insomecases,theentitysponsoringtheproposedrulewasanoffice(e.g.,theOfficeoftheSecretaryofHealthandHumanServices)oradepartment(e.g.,theDepartmentofVeteransAffairs)ratherthanabureauperse.Forsimplicity,werefertoleadersasagencyheadsthroughoutthetext.15Toidentifyinformationaboutleaders,theirdatesofservice,andtheirdemographiccharacteristics,wereliedonacombinationofInternetresources,includingwww.congress.gov,theWaybackMachine(www.archive.org),LinkedIn,Wikipedia,andwww.allgov.com.Nearlyalloftheleadersinourdatasetwerepresidentialappointees,althoughnotallpostsrequiredSenateconfirmation.Becauseweareunawareofadatasourcethatcomprehensivelylistsallagencyleadersandtheirkeycharacteristicsovertime,ourapproachledtosomecasesofmissingdata.WediscussmissingdatainmoredetailinAppendixB.

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women’spolicyareasascharacterizedbythescholarlyliterature:civilrights&liberties;

education;environment;health;housing&communitydevelopment;labor,employment,

andimmigration;law,crime,andfamily;andsocialwelfare.16

Table1:DemographicDifferencesbetweenMenandWomenasAgencyLeaders

Men Women Difference?Agea 52.07 49.92 -2.15Minority 0.28 0.30 Educationlevelb 2.33 2.34 Workedinanotherdepartment 0.15 0.14 Priorexperienceinbureau 0.37 0.36 Previouspublicmanagementexperience 0.63 0.65 Senate-confirmedposition 0.79 0.83 Tenureinpositionc 3.37 3.57 Servinginwomen’sissuearea 0.37 0.51 0.14Notes: Table entries are group means. The “Difference” column indicates whether thedifferencesbetweenmenandwomenarestatisticallysignificantatthep<0.05level.aIndicatesleader’sageatthetimeofappointment.bWecodeeducationlevels(0-3)ashighschool,bachelors,masters,anddoctorate.cCalculatedasyearsservedinposition.

Demographicallyspeaking,ourdatasuggestthatfemaleagencyheadsarenotall

thatdifferentfrommaleheads.AsshowninTable1,thereisnostatisticallydiscernible

differencebetweenmenandwomenintermsoftheireducationlevel,minoritystatus,

positionrank(i.e.,whetherthepositionrequiredSenateconfirmationornot),lengthof

16SeeVolden,Wiseman,andWittmer(2016)foralengthydiscussionoftherelevantliteratureonwomen’sissueareasandtheirselectionoftheseeightareasoutofthe19putforthbythePolicyAgendasProject.TableA3intheAppendixoffersamappingbetweenagencies,PolicyAgendastopicareas,andwomen’sissueareas.Intheiranalysis,Volden,Wiseman,andWittmer(2016)uncoveronlysixwomen’sissueareasratherthanthefullsetofeightasmorelikelytobeputforwardbywomenthanbymenintheU.S.Congress(i.e.,theenvironmentandsocialwelfareissuesdonotshowupasstatisticallysignificant).Theresultswereportherearesubstantivelyunchangedbyfocusingonthisnarrowersetofissues,asshowninTablesC8andC9.

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timeserved,orpriorpublicmanagementexperience.Whilewomenleadersdotendtobe

slightlyyoungeronaveragethanmen,thedifferenceissmall—abouttwoyears.

Figure1:ProportionofWomenLeaders,byAgencyTypeandPresidential

Administration,1995-2014

Note: Numbers above bars indicate the total number of women relative to allappointeestothattypeofagencyineachpresidentialadministration.Differencesinthese agency staffing proportions are statistically significant (p < 0.05) in thepresidentialadministrationsofGeorgeW.BushandBarackObama.

However,thereisameaningfuldifferenceintermsofwheremenandwomenserve.

Womenaremuchmorelikelytoleadagenciesthatdealwithwomen’spolicyissues.Figure

1illustratesthisdistinctioninpolicy-areaagencyleadershipbyeachofthepresidential

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administrationscoveredbyourstudy.17Ineachcase,presidentsweremorelikelytoput

womeninchargeofagenciesthatdealtwithwomen’sissues,withthosedifferences

attainingstatisticalsignificance(p<0.05)intheBushandObamaadministrations.

Anotherpotentialdistinctionthatmayexistbetweenmenandwomenasleadersis

theirpropensitytooverseeruleproduction.Ourmethodofdatacollectionbeganwithaset

ofagenciesthatissuedproposedrulesandthenidentifiedtheseriesofleadersthatheaded

theseagenciesduringourtimeperiod.However,someoftheagenciesinourdataset

producedveryfewproposedrulesduringthistimeframe,andmanyoftheleaders(26.3%)

didnotoverseetheproductionofevenasingleproposedrule.

AsFigure2shows,thepropensitytoeschewregulatoryactionaltogetheris

concentratedinwomen’sissueagencies.Thissuggeststhatthesetypesofagenciesare

muchlesslikelytoberegulatoryagencies,oragenciesthataccomplishtheirmissionviathe

issuanceandenforcementofrules.Agenciesthatdealwithotherissues—suchas

agricultureortransportation—aremuchmorelikelytohaveleaderswhooverseethe

productionofrules.However,withineachagencytype,thereisnosignificantdifference

betweenmenandwomenleadersintheirwillingnesstotakeontheregulatoryprocess.

17WenotethatourdataexcludethefirsttwoyearsoftheClintonadministrationandthelasttwoyearsoftheObamaadministration.Thereforewhileitappearsthatthetotalnumberoffemaleleadershasremainedrelativelyfixed,wearehesitanttomakeanyclaimsabouttheaggregateleadershipfiguresgiventhistruncation.

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Figure2:ProbabilityLeaderProposedatLeastOneRule,byGenderandAgencyType

Note:Barsindicatetheproportionofleaders(bygender)thatoversawtheissuanceof at least one proposed rule during their tenure, by agency type. Within eachagency type, the differences in gender are not statistically significant (p < 0.05),accordingtoadifferenceinproportionstest.

ThecombinationofFigures1and2raisesthepossibilityofwomenbeingless

influentialthanmeninrulemakingoverallduetotheappointmentprocess.Womenareless

frequentlychosentoleadagenciesthanaremen.Moreover,whenselected,womenare

morelikelytoheadagenciesdealingwithwomen’spolicyissues,andtheseagencies,in

turn,arelesslikelytobeactiveintheregulatoryspace.18

18Thisraisesabroaderquestionaboutwhetherwomenface“glasswalls”thatpreventthemfromassumingleadershiprolesinsomeareasofthebureaucracy.Whileoutsideofthescopeofourargument,webelievethatthisisanimportantnormativeissue,intermsofrepresentation,butalsointermsofallowingmanagerialtalenttoflowfreely.

17

DataandResults

Ourhypothesesaddresswhathappenswhenfemaleleadersentertheregulatory

arena,specificallywhethertheyhaveanimpactonthesignificanceorthecompletionofthe

regulationsproduced.Theunitofanalysisforthetestofourmainhypothesesisthe

individualproposedrule.Togaugeeachproposedrule’ssignificance,werelyonameasure

ofproposedruleimportancedevelopedbyPotter(2017).Impactisacontinuousmeasure

ofeachproposedrule’sbroaderpolicyimpact.19Itisboundbetweenzeroandone

(inclusive),withvaluesclosertooneindicatingamoreimpactfulproposedrule.20Foreach

proposedrulewealsoconstructaseconddependentvariable,Finalization,whichisa

dichotomousvariablethattakesonavalueofonewhenthesameleaderwhoissuedthe

proposedruleoversawitsfinalization,andzerootherwise.21

ThemostimportantindependentvariablesinouranalysisareFemale,a

dichotomousindicatorofwhetherthebureau’sleaderisfemaleornot,andWomen’sIssue,19Morespecifically,todeveloptheImpactscore,Potter(2017)employsalatentvariableapproachbasedonnumerousattributesofeachproposedrule,includingwhethertheproposalwas“economicallysignificant,”whetheritaffectedsmallbusinessesorothergovernmentalentities,andwhetheritspublicationwascoveredbytheNewYorkTimes.Aproposedrulethathasbroadpolicyimpactandaffectsmanydifferenttypesofgroupswouldhaveahigherimpactscorethanaproposedrulethathadanarrowerscope.20Forinstance,aproposedruleissuedbytheOccupationalSafetyandHealthAdministrationduringtheClintonadministrationthatwouldhaverequiredemployersacrosstheUStoadoptergonomicstandardsintheworkplacesscoreshighintermsofImpact(0.716).Meanwhile,aproposedruleissuedbytheOfficeofPersonnelManagementduringtheBushadministrationthatadjustedthereimbursementrateforuniformpurchasesbyfederalemployeesscoreslowonImpact(0.080).21Thereare132fewerobservationsincludedinthesecondmodelthanthefirst.ThisisbecauseweomitrulesthatweremergedwithortransferredtoanotherRegulatoryIdentificationNumber(RIN)duringthecourseofourstudy.Additionally,weassumethatrulesthatwerecensored(i.e.,notfinalizedduringtheperiodunderourstudy)werenotfinalizedduringtheleaderinquestion’stenure(i.e.,hadavalueofzero).Relaxingthisassumptiondoesnotaffectthesubstantiveinterpretationofourresults;seeAppendixTableC5.

18

whichindicateswhethertheagency’spolicyareaisoneoftheeightwomen’sissuesnoted

above.TheinteractionbetweenthesetwovariablesisusedtotesttheConditional

RulemakingHypothesis.

Wealsoincludeanumberofthecovariatespreviouslymentioned:22Minority,a

binaryindicatorofwhethertheleaderwasnon-white;threeindicatorvariablesforthe

leader’shighesteducationalattainment(Bachelors,Masters,andPhD);23indicatorsforthe

leader’spublicmanagementexperiencewithinotherfederalagencies,thebureauitself,and

inthepublicsectormorebroadly;andanindicatorforwhethertheleaderservesina

Senate-ConfirmedPosition.WealsoincludeLeaderTenure(andLeaderTenureSquared),

measuringtheyears24thattheleaderhadbeeninofficeatthetimeoftheproposedrule’s

publication.TableA1intheAppendixprovidesdescriptivestatisticsforallvariablesinthe

models.25

Table2presentstheresults.ThefirsttwomodelsreportOrdinaryLeastSquares

analysesofthelevelofimpactofthe9,148rulesputforthacrossthebureausunder22WeexcludeAgefromthemaintableaswewereunabletocollectthesedataforseveraloftheleadersinourstudy.Includingthisvariableshrinksthenumberofobservations,butitdoesnotaffectthesubstantiveresults.23Highschoolistheomittedcase.24Althoughthesevariablesarequantifiedinyears,weobservetenureatthemonthlevelandthereforeincludefractionsofyearsinthecreationofthisvariable.25Lewis(2007)studiesleaderattributesonagencyperformanceoutcomes.Ourinclusionofthecovariatesdiscussedhere,aswellasourcodingofthevariables,largelyfollowshisapproach.Inadditiontothecovariatesincludedinourmodels,Lewisfindsstatisticallysignificanteffectsforthreeadditionalvariables:whethertheleaderservedforafixedterm,whethertheagencywascreatedunderdividedgovernment,andwhethertheagencywascreatedbyaDemocraticpresident.Includingthesevariablesinouranalysisdoesnotsubstantivelyaffectourresults,althoughitdoesleadtoadropinoursamplesizeduetomissingdata.SeeTableC6intheAppendix.Wealsoconsideredseveraladditionalcontrolvariables,includingthepresenceofdividedgovernment,thetimeleftinthepresidentialadministration,andwhethertherulehadanyassociatedstatutoryorjudicialdeadlines.Sincenoneofthesecontrolsaffectsthesubstantivetakeaway,weexcludethemhereforthesakeofparsimony.

19

examination.Thethirdandfourthmodelsreporttheresultsoflogitanalysesinwhichthe

dependentvariabletakesavalueof1iftheleaderwhoinitiatedtherulesawitthroughto

finalizationand0otherwise.Inallmodels,toaccountfordifferencesinrulemakingacross

time,weincludefixedeffectsbyyear(i.e.,theyearinwhichtheproposedrulewas

published).Werelyonrandomeffectsacrossagenciesinordertoallowforvarianceacross

timeandacrossagencies.26Thefirstandthirdmodelsdemonstratethatthereareno

statisticallysignificantdifferencesbetweenmaleandfemaleleadersgenerally,nor

betweenwomen’sissueareasandotherissues.Thisnulleffectdoesnotprovidesupport

foranunconditionaleffectoffemaleleadership,aspositedintheGenderedRulemaking

Hypothesis.Basedonthecontrolvariables,itappearsthatleadercharacteristicshavelittle

systematiceffectontheimpactorfinalizationofregulationsputforthbybureaus.

Turningtothesecondmodel,thefirstthreevariablesinthetablecollectivelyoffer

anopportunitytotesttheConditionalRulemakingHypothesis.Thebaselinecaseisamale

leaderworkinginabureaufocusedonnon-women’sissues.Relativetothatbaseline,the

negative(butnotstatisticallysignificant)coefficientonWomen’sIssueshowsmaleleaders

toadvanceslightlylessimpactfulrulesinwomen’sissueareas.ThecoefficientonFemalein

turnshowswomenleaderstobelessimpactfulinthenon-women’sissueareas.Consistent

withtheConditionalRulemakingHypothesis,theinteractedvariableshowsthatfemale

leadersinwomen’sissueareasputforththemostimpactfulrules.

26Randomeffectsarepreferabletofixedeffectsinthiscasebecausetherearenumerousbureausthathadnowomenleadersduringthetimeperiodunderstudy.Therefore,relyingonfixedeffectsdropsthesebureausfromouranalysis,whereasrelyingonrandomeffectsallowsustoleveragethebetweenbureaueffect,whilestillcontrollingforunobservedbureau-levelfactors.

20

Table2:EffectsofFemaleLeadershipofRulemakingOutcomes

(1) (2) (3) (4) Impact Impact Finalization Finalization Women'sIssue 0.002 -0.005 -0.023 -0.346 (0.009) (0.009) (0.219) (0.239)Female -0.001 -0.008** -0.023 -0.343 (0.004) (0.004) (0.186) (0.230)Women'sIssue×Female 0.026** 1.119*** (0.010) (0.345)Minority -0.007 -0.009 -0.100 -0.193 (0.006) (0.006) (0.121) (0.147)Bachelors 0.011 0.007 -0.376 -0.528 (0.029) (0.028) (0.532) (0.443)Masters 0.011 0.009 0.166 0.084 (0.029) (0.028) (0.494) (0.404)PhD 0.016 0.012 0.097 -0.067 (0.029) (0.028) (0.507) (0.418)WorkedinAnotherDepartment 0.013 0.009 0.222 0.022 (0.010) (0.010) (0.276) (0.260)BureauExperience -0.001 -0.003 0.356 0.296 (0.006) (0.006) (0.229) (0.231)PreviousPublicMgmtExperience -0.017** -0.015** -0.276 -0.177 (0.008) (0.008) (0.226) (0.235)Senate-ConfirmedPosition 0.005 0.004 -0.340 -0.349 (0.010) (0.010) (0.242) (0.252)LeaderTenure -0.001 -0.001 -0.558*** -0.566*** (0.002) (0.002) (0.108) (0.102)LeaderTenureSquared -0.000 -0.000 0.030*** 0.028*** (0.000) (0.000) (0.010) (0.010)Constant 0.136*** 0.143*** 2.178*** 2.470*** (0.030) (0.029) (0.587) (0.530)Observations 9,148 9,148 9,016 9,016Numberofagencies 144 144 144 144AgencyRandomEffects YES YES YES YESYearFixedEffects YES YES YES YESAdjustedR2/PseudoR2 0.05 0.06 0.18 0.18Notes:OrdinaryLeastSquaresforModels1and2,logitanalysesforModels3and4.Robuststandarderrorsclusteredontheagencyareinparentheses.Two-tailedtests:*p<0.05,**p<0.01,***p<0.001.

21

Acrosseachcategory,acoherentstoryemerges,asillustratedintheenhancedbar

graphinFigure3.27Asseeninthefigure,eachoftheothercombinationsofagencytypes

andleadersislessimpactful,onaverage,thanfemaleleadersinwomen’sissueareas.

Whilenotallofthedifferencesarestatisticallydistinguishable,theyareconsistentwitha

femaleleadershipdifferential.Forexample,thepredicteddifferencebetweenawoman

leadingawomen’sissuesagencycomparedtoawomanleadinganotherissuesagencyis

relativelylarge(Δ=0.021)andstatisticallysignificantatthe.10level,whereasthe

differencebetweenmaleleadersacrossthetwoagencytypesisrelativelysmall(Δ=-

0.005)andnotstatisticallydifferent.Overall,thereisasignificantfemaleleadership

effect;comparedtothedifferencebetweenmenandwomeninotherissuesagencies,

womenleadingwomen’sissuesagenciesexceedthebehaviorofmaleleaders(ΔΔ=0.026).

Again,thissuggeststhatthereissomethingdifferentaboutagenciesthatfocuson

women’sissues.Steppingback,theseresultsprovidestrongsupportfortheConditional

RulemakingHypothesisintermsoftheimpactoftherulesproposedbydifferentleaders.

27Figure3andFigure4displaymodelresultsinanenhancedbargraphformat;seeBerryandHauenstein(2017).

22

Figure3:MarginalEffectsofGenderandIssueAreaonRuleImpact

Note:EachbarshowstheestimatedruleimpactamongleadersworkingindifferentissueareasfromModel2inTable2.EachΔvaluenexttoaU-shapedarrowisafirstdifferenceinmeans;asolidarrowindicatesaneffectdeemedsubstantivelysignificant.Eachestimatedquantityofinterestisreportedalongwiththeboundariesfora95%confidenceintervalinparentheses.Anasterisk(*)indicatesstatisticalsignificanceatthe0.05level(two-tailedtest).

BasedonthefourthmodelinTable2,consistentwiththeConditional

RulemakingHypothesis,onceagainapatternemergeswhereinwomenleadersin

women’sissueareasarethemostlikelytofinalizetherulestheyadvance(68.5%

finalization),followedbymaleleadersinotherissueareas(60.2%).Leastlikelyto

followthroughtofinalizationarefemaleleadersinnon-women’sissueareas(53.2%

finalization)andmaleleadersinwomen’sissueareas(53.1%).Inotherwords,the

effectsherearequitelarge,withafifteen-percentagepointgapinfinalizingrules

Male LeaderOther Issues Agency

Female LeaderOther Issues Agency

Male LeaderWomen's Issues Agency

Female LeaderWomen's Issues Agency

(n = 4991) (n = 1828) (n = 1043) (n = 1286)

0.1840.176 0.179

0.196

Effect of Female Leader Given Other Issues Agency:

Δ = -.008* (-.016,-.001)

Effect of Female Leader Given Women's Issues Agency:

Δ = .018 (-.001, .036)

Difference Between Other Issues Agency and Women's Issues Agency:

ΔΔ = .026* (.006, .045)

.1.1

4.1

8.2

2.2

6Ru

le Im

pact

Effect of Women's Issues Agency Given Male Leader:Δ = -.005 (-.024, .013)

Effect of Women's Issues Agency Given Female Leader:Δ = .021 (-.004, .045)

23

basedonleadergenderandagencyissueareas.Againtheleadershipdifferential

betweenmaleandfemaleleadersisconcentratedinwomen’sissuesagencies,as

shownbytheseconddifference(ΔΔ=0.226).Figure4showstheseeffects,which

largelymirrorthosewithrespecttoruleimpactshowninFigure3.

Figure4:ProbabilityofLeaderRuleFinalizationbyGenderandIssueArea

Note:Eachbarshowsthepredictedprobabilityofrule finalizationamong leadersworkingindifferentissueareasfromModel4inTable2.EachΔvaluenexttoaU-shaped arrow is a first difference in means; a solid arrow indicates an effectdeemed substantively significant. Each estimated quantity of interest is reportedalong with the boundaries for a 95% confidence interval in parentheses. Anasterisk(*)indicatesstatisticalsignificanceatthe0.05level(two-tailedtest).

Onthewhole,acrosstheanalysesoftheimpactofrulesandthefinalization

ofrules,strongevidenceemergesthatthecombinationofwomenleadingbureaus

Male LeaderOther Issues Agency

Female LeaderOther Issues Agency

Male LeaderWomen's Issues Agency

Female LeaderWomen's Issues Agency

(n = 4890) (n = 1816) (n = 1043) (n = 1267)

0.602

0.532 0.531

0.685

Effect of Female Leader Given Other Issues Agency:

Δ = -.071 (-.163,.022)

Effect of Female Leader Given Women's Issues Agency:

Δ = .154* (.083, .225)

Difference Between Other Issues Agency and Women's Issues Agency:

ΔΔ = .226* (.097, .357)

.4.4

5.5

.55

.6.6

5.7

.75

.8Pr

obab

ility

of R

ule

Fina

lizat

ion

Effect of Other Issues Agency Given Male Leader:Δ = -.071 (-.168, .025)

Effect of Women's Issues Agency Given Female Leader:Δ = .153* (.024, .283)

24

thataddresswomen’spolicyissuesisassociatedwiththeproductionofimportant

regulatorychanges.Oneadditionalimplicationofourargumentcombinesthe

findingsaboutimpactandfinalization;ifaleaderoverseestheinitiationofan

importantproposedruleinapolicyareainwhichheorsheisparticularlyeffective,

itisnotunreasonabletoconceivethattheleaderwillbeabletoseethatsamerule

throughtofinalization.InTableC1andFigureC1oftheAppendix,weprovide

evidenceinsupportofthisimplication.Putsimply,notonlydowomenleaders

proposemoreimpactfulrulesinwomen’sissueareasandcarrymorerulesto

finalization,buttheyalsoachievethecombinationoffinalizingmoreimpactfulrules.

Wenotethat,acrossallthreeofthesetests,womenaremoststrongly

distinguishedfromwomenleadingothertypesofissueagencies,followedbymen

leadingwomen’sissueagencies.Whilewomenstilloutperformmenleadingother

issueagencies,theresultsarelessstark.Inotherwords,itappearsthatleaders—

whetherwomenormen—dobestwhentheyareputintoenvironmentswhere

traditionalgenderleadershipstylesarematchedwiththesubstantivepolicyarea.

Onepotentialconcernisthatideology,ratherthangender,maybedriving

ourresults.Numerousstudiesshowthat,intheaggregate,femaleelitestendtobe

moreideologicallyliberalthanmaleelites(see,e.g.,AnziaandBerry2011,Poggione

2004,ThomsenandSwers2017,Welch1985);giventhatrelationship,ourgender

measurecouldpotentiallybeproxyingforideology,suggestingthatourfindings

showthatmoreliberalleaders—ratherthanfemaleleadersperse—aremore

successfulrulemakers.Inordertoruleoutthispossibility,wesubstitutedameasure

ofindividual-levelideologyinlieuofourgendermeasure.Ifgenderissimplya

25

stand-inforideology,thenweshouldseesimilarresultstothosereportedinTable2

usingtheideologymeasure.Theresults,showninTableC2,confirmthatthisisnot

thecaseandincreaseourconfidencethatwhatwehaveuncoveredis,infact,

evidenceofagenderdifferential.

Overall,ourfindingsarerobusttoanumberofalternatespecifications,which

wereportintheAppendix.First,weconsidermoreparsimoniousmodelsthatonly

includethekeytheoreticalvariables(TableC3);doingsoallowsustoincreaseour

samplesizesincewewereunabletocollectdataforallofthebiographical

covariatesfortheagencyleadersinourdataset.Next,inTableC4weemployan

alternatemeasureofarule’simportanceinlieuofourImpactmeasure;Priorityisa

discretemeasureofarule’simportanceasself-reportedbytheagencyintheUnified

Agenda.InTableC5,weomitrulesthatwerenotfinalizedduringthestudy’stime

periodfromourcodingofrulesintheFinalizationmodels.Next,Lewis(2007)

suggestsanumberofagency-levelcovariatesthatcanpotentiallyaffectan

organization’sperformance(seealsoClintonandLewis2008);weincludethese

variablesinTableC6.InTableC7weaccountforthehierarchyinherentinourdata

(rulesnestedwithinbureausnestedwithindepartments)usingamultilevel

modelingapproach(GelmanandHill2006).Finally,inTablesC8andC9weconsider

analternatecategorizationofwomen’sissues.Importantly,supportforthe

ConditionalRulemakingHypothesisisunaffectedbyeachofthesemodifications.

26

WhatDrivesSuccessfulFemaleLeaders?

Ourresultsthusfardonotexplainwhywomendobetterinwomen’sissue

agencies.Inthissection,wetestobservableimplicationsofthethreemechanism-

basedhypothesesdiscussedearlier—thePairedInterestsHypothesis,theWork

EnvironmentHypothesis,andtheTalentPoolHypothesis.

Wetestthesehypothesesusingvariablescodedattheagencylevel.

Therefore,wehereutilizetheagency-yearasourunitofanalysis.Tobegin,we

createanewdependentvariable,AverageImpact,whichistheaverageruleimpact

scoreforeachagency-year.Model5inTable3showsthemainfindingsfromabove

toberobusttothisalternativeunitofanalysis,withahigheraverageimpactofrules

proposedbywomenleadingwomen’sissueagenciesthanbyothercombinationsof

leadersandagencytypes(p<0.05).Likethepreviousmodels,weincludeyear-level

fixedeffectsandagency-levelrandomeffects.

Thisnewfinding,similartopriorfindings,isconsistentwiththePaired

InterestsHypothesis,whereinanalignmentbetweeninterestsandactivitiesoffersa

recipeforsuccess.Additionally,ifwomenleadingwomen’sissueagenciesare

indeedmoredevotedtothecause,partofthatcommitmentmayarisefroman

ideologicalalignmentthatmaymanifestinareasbeyondwomen’sissueagencies.In

keepingwiththelogicofthePairedInterestsHypothesis,totheextentthatfemale

leadersaremoreliberalthanmen,28womenleadingagenciesthataremoreliberal-

28Aconsiderablebodyofresearchsuggeststhatfemaleelitesaremoreliberalthanmaleelites(see,e.g.,AnziaandBerry2011,Poggione2004,ThomsenandSwers2017,Welch1985).Thisfindingalsoholdsforthesubsetofleadersinourdatasetforwhichindividual-levelideologyscoresareavailable.Using“CFscores,”ameasureofideologybasedoncampaignfinancedata(Bonica,Chen,andJohnson

27

leaningmaybemorecommittedtorealizingimportantregulatorychanges.To

evaluatethis,werelyonmeasuresofagencyideologycreatedbyRichardson,

Clinton,andLewis(2018).Theirestimatesofideologyarereputation-based,

developedusingelitesurveysoffederalexecutives.Usingthesescores,wedevelop

anindicatorvariable,LiberalAgency,tocapturewhetheraparticularagencyhasa

reputationforbeingideologicallyleft-leaning.29

Model6inTable3showstheresultsofanOLSmodelwithaninteraction

effectbetweenLiberalAgencyandFemaleLeaderonAverageImpact.Thepositive

andstatisticallysignificantcoefficientonthisinteractiverelationshipsuggeststhat

womenleadingagenciesthatareideologicallyliberalaremorelikelytogenerate

proposedruleswithahigheraverageimpactscore.30Thisfindingprovidesfurther

evidenceinfavorofthePairedInterestsHypothesis.

2015),theaverageideologyofwomenleadersinourdatasetisfarthertotheleft(CFscore=-0.35)thantheaveragemaleleader(CFscore=-0.12),adifferencethatisstatisticallysignificantatthep<0.10level.29TheRichardsonetal.(2018)measureofagencyideologyrangesfrom-2to2,withnegative(positive)valuesindicatingamoreliberal(conservative)agency.TocreateourmeasureofLiberalAgency,weassignedanagencyavalueof“1”ifithadanegativeideologyscoreandacorrespondingcredibilityintervalthatdidnotincludezero.Allotheragencieswerecodedas“0.”WeobtainsimilarresultsusingRichardsonetal.’srawideologyscores.LiberalAgencyispositivelycorrelatedwithWomen’sIssue(ρ=0.43),butisconceptuallydistinct.30Asonemightexpect,theeffectswereportarelargelydrivenbytheserviceofwomenintheClintonandObamaadministrations.PresidentGeorgeW.Bushappointedmoreconservativewomentothebureaucraticposts,includingtoliberal-leaningagencies.SubsettingthedatatojusttheBushadministrationresultsinapositivesignfortheinteractionbetweenLiberalAgencyandFemale,butthemagnitudeofthecoefficientissmallandnotstatisticallysignificant.

28

Table3:TestingtheMechanismHypotheses (5) (6) (7) (8) (9) Average

ImpactAverageImpact

AverageImpact

%ΔBudget

%ΔEmployees

Women'sIssue -0.010 -0.685 2.605 (0.009) (0.903)(2.423)Women'sIssue×Female 0.024* 1.298 -1.255 (0.013) (1.596) (2.478)LiberalAgency 0.008 (0.013) LiberalAgency×Female 0.035** (0.014) PropWorkforceFemale 0.034 (0.042) PropWorkforceFemale 0.042 ×Female (0.055) Female 0.002 -0.012 -0.015 -1.458 -0.207 (0.007) (0.008) (0.028) (1.215) (1.496)Constant 0.162*** 0.144*** 0.138*** 2.147 -3.331 (0.031) (0.033) (0.033) (2.466) (4.095)Observations 1,291 823 1,095 1,413 1,791Numberofagencies 144 72 127 114 135ControlVariables YES YES YES YES YESAgencyRandomEffects YES YES YES YES YESYearFixedEffects YES YES YES YES YESAdjustedR2 0.09 0.11 0.09 0.10 0.03Notes:OrdinaryLeastSquaresregressionresults.Robuststandarderrorsclusteredontheagencyareinparentheses.Two-tailedtests:*p<0.05,**p<0.01,***p<0.001.

TheWorkEnvironmentHypothesissuggeststhatthereissomethingparticular

aboutwomen’sissueagenciesthatmakethemamoreproductivework

environmentforfemaleleaders.Toconsiderwhethertheagencyitselfisthekey

factor,welooktoanalternatemeasureofthereceptivityofanagencytowomen’s

leadership;ProportionWorkforceFemaleisthepercentageofanagency’semployees

29

whoarefemale.31Iftheworkenvironmentisadriverinfemaleleadershipsuccess,

weshouldexpecttoseewomensucceedinworkplaceswherethereisalargershare

offemalecoworkersandsubordinates.Model7inTable3presentstheresultsofan

agency-yearmodelofAverageImpactwithProportionWorkforceFemaleinlieuof

thewomen’sissueindicator.Wefindnostatisticallysignificantrelationship

betweenthisinteractionandruleimpact,andthereforedonotfindsupportforthe

WorkEnvironmentHypothesis.32

Finally,ifselectionfromamorecompetitivepoolofpotentialleadersresults

inwomeninwomen’sagenciesbeinghigherqualityleadersthanothers—as

suggestedbytheTalentPoolHypothesis—thenweshouldexpecttoseeimproved

agencyperformanceacrosstheboard.Thatis,weshouldseeapositivedifference

whenawomanheadsawomen’sissueagencyintermsofotheroutputsbeyond

rulemaking,specificallywhencomparedtowomenleadingothertypesofagencies.33

Toevaluatethis,foreachagency-yearwecompiledadatasetoftheamountof

moneytheagencywasbudgetedtospendandthetotalnumberofpersonnelinthe

agency.Budgetoutlaydata(inreal1995dollars)arefromtheOfficeofManagement

31Acrosstheagenciesinoursample,femaleemployeesaremuchmorelikelytoworkinwomen’sissuesagencies;onaverage,womenconstitute59%oftheworkforceattheseagencies,comparedto46%oftheworkforceatallotheragencies,adifferenceof13percentagepoints(p<0.001).32Arguably,thiseffectmaybeconcentratedinwomen’sissueagencies.However,aspecificationwithatripleinteractionbetweenFemale,Women’sIssueAgency,andProportionWorkforceFemale(notshown)alsofailstooffersupportfortheWorkEnvironmentHypothesis.33Thelogichereisthatwomenareselectedfromadeeperpoolofcandidatesinwomen’spolicyareas,comparedtootherpolicyareas.Accordingly,thecomparisongroupshouldbewomeninotherpolicyareas.

30

andBudget’sPublicBudgetDatabase.34PersonneldataarefromtheOfficeof

PersonnelManagement’sFedScopeDatabase.Eachofthesedimensionsreflects

traditionalmarkersofbureaucraticpower—bygrowingthesizeofthebudgetorthe

staff,leadersincreasetheagency’sinfluence(e.g.,Niskanen1971).Ifwomen

headingwomen’sissueagenciesareindeedsuperiorcandidates,thentheyshould

beabletogrowtheirorganizationsalongthesedimensions.Bothvariablesare

calculatedaspercentagechangefromthepreviousyearinordertoplaceall

agenciesonacommonscaleandtoaddresstheleader’sabilitytoadvancethe

agency’sinterestsovertraditionallevels.

Models8and9inTable3showtheresultsofOLSmodelsofthepercentage

changeinbudgetauthorityandthepercentagechangeinpersonnel.35Theresults

suggestthatfemaleleadersinwomen’sissueagenciesarenodifferentfromtheir

femalecounterpartsinothertypesofagencieswhenitcomestoaffectingthebudget

levelsorthenumberofemployees(noraretheydifferentfromtheirmale

counterparts,bothinwomen’sissueagenciesandinotherissuesagencies).These

resultsoffernosupportfortheTalentPoolHypothesis,sincewomenleadersin

women’sissueagenciesdonotconsistentlydemonstratehigherperformanceacross

allmetrics.Onthewholethen,themechanismmostlikelytobedrivingourresultsis

34Thebudgetdataincludesomeextremeoutliers,largelyowingtotheparticularitiesofbudgetaccountingrulesforrevenuegenerationortoone-timecashinfusions(e.g.,the2009stimulus).Todealwiththeseoutliers(andavoidimplausiblevalues),weexcludetheupperandlower5%ofthedatahere.However,thenullresultsholdwhentheentirerangeofvaluesisincluded.35Alternatespecificationsbasedontheloggedvaluesofthesevariablesyieldsimilarresults.Additionally,forthebudgetdata,wealsoexaminedwhetherwomenleadersinwomen’sissueagenciesweremoreeffectiveatpreventingbudgetsfromshrinking,butagainfoundnodifferenceamongleadersonthisdimension.

31

portrayedinthePairedInterestsHypothesis,whereinthealignmentbetweenthe

interestsandactivitiesofwomenleaderspresentsapowerfulcombinationinthe

U.S.federalbureaucracy.

Conclusion

Whenapresidentselectsamale-dominatedCabinetandbureaucratic

leadershipteam(asPresidentTrumphasdone),itintroducesobviousissuesin

termsoftheoverallrepresentativenessofthebureaucracy.Ourresultssuggestthat

suchachoicemayalsohaveimplicationsfortheperformanceofbureaucratic

agencies.Whenwomenaregivenachancetoleadagenciesthatoperateinpolicy

areasthataretraditionallyconsideredwomen’sareas,theyaremoresuccessfulat

introducingandadvancingimportantregulationsthroughthenotice-and-comment

rulemakingprocess.Thisfindingcontrastsstarklywithfindingsabouttherelative

successofwomeninlegislatures;whereasscholarstherefindthatwomenmaybe

lesssuccessfulwhentheyworkonwomen’sissues(e.g.,Volden,Wiseman,and

Wittmer,2016),wefindwomenintheregulatoryarenaaremoresuccessful.This

suggeststhattheinstitutionalcontextinwhichwomenoperatelikelyplaysan

importantroleindeterminingtheireffectiveness.36

Additionally,weareabletoprovidepreliminaryevidenceaboutthe

mechanismthatleadswomentosuccess.Ourdatasuggestthatwomenmaybemore

likelytosucceedbecauseoftheirenhanceddrivetomakedurablepolicychanges

36Oneimportantdistinctionthatemergesisthatfemaleagencyheadshaveaformalleadershiproleinaninstitutionalizedhierarchy.Incontrast,womeninCongressrarelyholdformalleadershiproles(e.g.,Speaker,whip,committeechair).

32

throughtherulemakingprocessinissueareasofgreatestimportancetothem.We

refertothisasthePairedInterestsHypothesisandfindsupportforitwithgreater

rulemakingimpactbothduringanalignmentbetweenfemaleleadersandwomen’s

issuesandunderanideologicalalignment.Weencouragefuturescholarship

derivingadditionalteststhatmayuncoverthedeeperfoundationsthatunderliethis

behavior.

Whiletherearepotentiallyimportantpolicyimplicationsarisingfromour

findings,theyaresubjecttoanimportantcaveatregardingthecausalnatureofthe

effectsuncoveredhere.Femaleleaderschoosewhethertoworkinwomen’spolicy

areasorinotherpolicyareas.Thisformofselectioniscertainlynotrandomand

theremaybe(likelyunobservable)differencesbetweenwomenwhochooseto

address“women’sissues”intheirleadershiprolesandthosewhodonot.

Nonetheless,thesefindingsareimportantandrobust,andtheymeritfurther

investigationonseveraldimensions.First,thefocusofourstudyhasbeenonagency

leaders,butotherpersonnelarealsoimportanttorulemakingprocess.Futurework

shouldfocusontheroleofwomeninlower-levelpositions,suchasfemale

membershiponrule-writingteams.Inasenseourresultsconstitutea“hardtest,”

sincetheanalysesfocusontheeffectoffemaleleadersabsentafullaccountingof

howotherwomeninthehierarchy—bothabove(e.g.,departmenthead)andbelow

(e.g.,rulemakingteam)—contributetothesuccessoftherulemakingendeavor.

Second,webelievethattherearelikelyadditionalconsequencesassociatedwith

femaleleadershipofbureaucraticagencies.Forinstance,inothercontexts,female

leadersareknownto“payitforward”bypromotingmorejuniorwomeninto

33

positionsofpower(Gorman2005).Intheregulatorycontext,thissuggests

additionaldownstreamimplicationsforfemaleleadership.Third,wehaveclassified

women’sissuesattheagencylevelratherthantherulelevel.Whilewebelievethisis

areasonableapproachtounderstandingthegeneralsubstanceofpolicymaking,

furtherworkdivingintothedirectionandsubstanceofindividualruleswould

certainlybevaluable(see,e.g.,Workman2015).Finally,ourworkhasfocusedon

gender,butthereareotherfeaturesofdescriptiverepresentation,suchasraceand

priorserviceinthemilitary(Lowande,Ritchie,andLauterbach2018),andofthe

issuesthemselves,suchaswhethertheyofferdiffuseorconcentratedbenefits

(WhitbyandKrause2001),thatlikelyaffectbureaucraticperformanceandare

certainlyworthyofstudy.

Politiciansfrequentlylamenttheinefficiencyofbureaucraticagencies,as

wellastheslowpaceoftheregulatoryprocess(seePotter2017).Thisresearch

pointstoafocusonthecharacteristicsofagencyleaders—andinparticular

matchingtheirstrengthsandinterestswiththeagency’ssubstantivearea—asaway

ofimprovingboththeefficiencyandtheimpactofregulatorypolicymaking.We

hopethatourfindingscontributetoanewandimportantlineofresearchonthe

roleofgenderinadministrativegovernance.

34

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1

APPENDICES

to“Women’sLeadershipandPolicymaking

intheU.S.FederalBureaucracy”

TobemadeavailableonlineA. DescriptionoftheData

• TableA1.DescriptiveStatisticsandSourcesforModelVariables• TableA2.DemographicDifferencesofWomenLeaders,byAgencyType• TableA3.AgenciesIncludedintheDataset,byPolicyAreaandWomen’sIssueArea

B. DescriptionofAgencyLeaderDataCollectionProcessandMissingDataC. RobustnessChecks

• TableC1.EffectsofFemaleLeadershiponFinalizedRuleImpact• FigureC1.MarginalEffectofLeadingaWomen’sIssueAgencyonFinalizedImpact• TableC2.EffectsofLeaderIdeologyonRulemakingOutcomes• TableC3.ModelswithoutControls• TableC4.ModelsofRuleImportanceusingPriorityMeasure• TableC5.ModelswithAlternateRuleFinalizationAssumptions• TableC6.ModelswithAdditionalBureau-LevelControls• TableC7.HierarchicalLinearModels• TableC8.ModelsofImpact,byWomen’sIssueAreasandOtherIssueAreas• TableC9.ModelsofFinalization,byWomen’sIssueAreasandOtherIssueAreas

2

APPENDIXA.DescriptionoftheData

TableA1:DescriptiveStatisticsandSourcesforModelVariablesVariableName Mean Std.Dev. Min. Max.Impact(Source:Seetext)

0.18 0.11 0 1

Finalization(Source:Seetext)

0.59 0.49 0 1

FinalizedImpact(Source:Seetext)

0.18 0.11 0 1

Women’sIssue(Source:SeeTableA3)

0.27 0.44 0 1

Female(Source:SeeAppendixB)

0.36 0.48 0 1

Age(Source:SeeAppendixB)

52.03 8.33 31 74

Minority(Source:SeeAppendixB)

0.16 0.37 0 1

Bachelors(Source:SeeAppendixB)

0.16 0.37 0 1

Masters(Source:SeeAppendixB)

0.32 0.47 0 1

PhD(Source:SeeAppendixB)

0.51 0.50 0 1

WorkedinAnotherDepartment(Source:SeeAppendixB)

0.10 0.29 0 1

PriorExperienceinBureau(Source:SeeAppendixB)

0.45 0.50 0 1

PreviousPublicMgmtExperience(Source:SeeAppendixB)

0.62 0.49 0 1

Senate-ConfirmedPosition(Source:LibraryofCongress)

0.89 0.32 0 1

LeaderTenure(Source:SeeAppendixB)

2.44 1.89 0 16.67

3

TableA2:DemographicDifferencesofWomenLeaders,byAgencyType

Women’sIssueAgency

OtherIssuesAgency

Difference?

Agea 50.26 49.57 Minority 0.38 0.21 0.17Educationlevelb 2.41 2.28 Workedinanotherdepartment 0.17 0.11 Priorexperienceinbureau 0.36 0.35 Previouspublicmanagementexperience 0.71 0.60 Senate-confirmedposition 0.87 0.80 Tenureinpositionc 3.87 3.26 0.61Notes: Table entries are group means. The “Difference” column indicates whether thedifferences between women leading the respective types of agencies are statisticallysignificantatthep<0.05level.aIndicatesleader’sageatthetimeofappointment.bWecodeeducationlevels(0-3)ashighschool,bachelors,masters,anddoctorate.cCalculatedasyearsservedinposition.

4

TableA3:AgenciesIncludedintheDataset,byPolicyAreaandWomen’sIssueArea

BureauNameDeptName

PolicyAgendasMajorTopicCode

Women'sIssueAgency?

AdministrationForChildrenAndFamilies HHS Health(3) YesAdministrationOnAging HHS SocialWelfare(13) Yes*AgencyForInternationalDevelopment IND

InternationalAffairsandForeignAid(19) No

AgriculturalMarketingService USDA Agriculture(4) NoAgriculturalResearchService USDA Agriculture(4) NoAlcoholAndTobaccoTaxTradeBureau TREAS Macroeconomics(1) NoAnimalAndPlantHealthInspectionService USDA Agriculture(4) NoAssistantSecretaryForPolicy,ManagementAndBudget DOI

PublicLandsandWaterManagement(21) No

BureauOfAlcohol,Tobacco,Firearms,AndExplosives DOJ

Law,Crime,andFamilyIssues(12) Yes

BureauOfAlcohol,Tobacco,Firearms,AndExplosives TREAS Macroeconomics(1) No

BureauOfEconomicAnalysis DOCBanking,Finance,andDomesticCommerce(15) No

BureauOfIndianAffairs DOIPublicLandsandWaterManagement(21) No

BureauOfIndustryAndSecurity DOCBanking,Finance,andDomesticCommerce(15) No

BureauOfLandManagement DOIPublicLandsandWaterManagement(21) No

BureauOfOceanEnergyManagement DOI


BureauOfPrisons DOJLaw,Crime,andFamilyIssues(12) Yes

BureauOfReclamation DOIPublicLandsandWaterManagement(21) No

BureauOfSafetyAndEnvironmentalEnforcement DOI Energy(8) NoBureauOfThePublicDebt TREAS Macroeconomics(1) No

CensusBureau DOCBanking,Finance,andDomesticCommerce(15) No

CentersForDiseaseControlAndPrevention HHS Health(3) YesCentersForMedicare&Medicaid HHS Health(3) Yes

5

Services

CivilRightsDivision DOJLaw,Crime,andFamilyIssues(12) Yes

ComptrollerOfTheCurrency TREAS Macroeconomics(1) NoCorporationForNationalAndCommunityService IND SocialWelfare(13) Yes*CustomsRevenueFunction TREAS Macroeconomics(1) NoDefenseAcquisitionRegulationsCouncil DOD Defense(16) No

DepartmentOfState STATEInternationalAffairsandForeignAid(19) No

DepartmentOfTheAirForce DOD Defense(16) NoDepartmentOfTheArmy DOD Defense(16) NoDepartmentOfTheNavy DOD Defense(16) NoDepartmentOfVeteransAffairs VA Defense(16) No

DrugEnforcementAdministration DOJLaw,Crime,andFamilyIssues(12) Yes

EconomicDevelopmentAdministration DOC

Banking,Finance,andDomesticCommerce(15) No

EmployeeBenefitsSecurityAdministration DOL LaborandEmployment(5) YesEmploymentAndTrainingAdministration DOL LaborandEmployment(5) YesEnergyEfficiencyAndRenewableEnergy DOE Energy(8) NoEnvironmentalProtectionAgency IND Environment(7) Yes*ExecutiveOfficeForImmigrationReview DOJ


FarmCreditSystemInsuranceCorporation IND Agriculture(4) NoFarmServiceAgency USDA Agriculture(4) NoFederalAviationAdministration DOT Transportation(10) No

FederalBureauOfInvestigation DOJLaw,Crime,andFamilyIssues(12) Yes

FederalEmergencyManagementAgency DHS


FederalEmergencyManagementAgency IND


FederalHighwayAdministration DOT Transportation(10) No

FederalHousingFinanceAuthority HUDCommunityDevelopmentandHousingIssues(14) Yes

FederalMotorCarrierSafetyAdministration DOT Transportation(10) NoFederalRailroadAdministration DOT Transportation(10) No

6

FederalTransitAdministration DOT Transportation(10) NoFinancialCrimesEnforcementNetwork TREAS Macroeconomics(1) NoFinancialManagementService TREAS Macroeconomics(1) NoFoodAndDrugAdministration HHS Health(3) YesFoodAndNutritionService USDA Agriculture(4) NoFoodSafetyAndInspectionService USDA Agriculture(4) NoForeignAgriculturalService USDA Agriculture(4) NoForestService USDA Agriculture(4) No

GeneralServicesAdministration INDGovernmentOperations(20) No

GovernmentNationalMortgageAssociation HUD

CommunityDevelopmentandHousingIssues(14) Yes

GrainInspection,PackersAndStockyardsAdministration USDA Agriculture(4) NoHealthResourcesAndServicesAdministration HHS Health(3) YesImmigrationAndNaturalizationService DOJ


InternalRevenueService TREAS Macroeconomics(1) No

InternationalTradeAdministration DOCBanking,Finance,andDomesticCommerce(15) No

MaritimeAdministration DOT Transportation(10) NoMineSafetyAndHealthAdministration DOL LaborandEmployment(5) YesNationalArchivesAndRecordsAdministration IND

GovernmentOperations(20) No

NationalHighwayTrafficSafetyAdministration DOT Transportation(10) NoNationalInstituteOfStandardsAndTechnology DOC


NationalInstitutesOfHealth HHS Health(3) YesNationalNuclearSecurityAdministration DOE Energy(8) NoNationalOceanicAndAtmosphericAdministration DOC


NationalParkService DOIPublicLandsandWaterManagement(21) No

NationalScienceFoundation INDSpace,Science,Technology,andCommunications(17) No

NationalTelecommunicationsAndInformationAdministration DOC


NaturalResourcesConservationService USDA Agriculture(4) No

7

OccupationalSafetyAndHealthAdministration DOL LaborandEmployment(5) YesOfficeForCivilRights ED Education(6) YesOfficeForCivilRights HHS Health(3) Yes

OfficeOfAdministration HUDCommunityDevelopmentandHousingIssues(14) Yes

OfficeOfAssistantSecretaryForHealthAffairs DOD Defense(16) NoOfficeOfCommunityPlanningAndDevelopment HUD


OfficeOfElementaryAndSecondaryEducation ED Education(6) YesOfficeOfFairHousingAndEqualOpportunity HUD


OfficeOfFederalContractCompliancePrograms DOL

GovernmentOperations(20) No

OfficeOfFederalStudentAid ED Education(6) YesOfficeOfGeneralCounsel DOE Energy(8) No

OfficeOfGovernmentEthics INDGovernmentOperations(20) No

OfficeOfHousing HUDCommunityDevelopmentandHousingIssues(14) Yes

OfficeOfInnovationAndImprovement ED Education(6) YesOfficeOfInspectorGeneral HHS Health(3) Yes

OfficeOfJusticePrograms DOJLaw,Crime,andFamilyIssues(12) Yes

OfficeOfManagement ED Education(6) Yes

OfficeOfNaturalResourcesRevenue DOIPublicLandsandWaterManagement(21) No

OfficeOfPersonnelManagement INDGovernmentOperations(20) No

OfficeOfPlanning,EvaluationAndPolicyDevelopment ED Education(6) YesOfficeOfPolicyDevelopmentAndResearch HUD


OfficeOfPostsecondaryEducation ED Education(6) YesOfficeOfProcurementAndPropertyManagement USDA Agriculture(4) No

OfficeOfPublicAndIndianHousing HUDCommunityDevelopmentandHousingIssues(14) Yes

OfficeOfPublicHealthAndScience HHS Health(3) YesOfficeOfSafeAndDrug-FreeSchools ED Education(6) YesOfficeOfSpecialEducationAnd ED Education(6) Yes

8

RehabilitativeServicesOfficeOfSurfaceMiningReclamationAndEnforcement DOI


OfficeOfTheAmericanWorkplace DOL LaborandEmployment(5) YesOfficeOfTheAssistantSecretaryForAdministrationAndManagement DOL LaborandEmployment(5) YesOfficeOfTheAssistantSecretaryForHealth HHS Health(3) YesOfficeOfTheAssistantSecretaryForVeterans'EmploymentAndTraining DOL LaborandEmployment(5) YesOfficeOfTheChiefFinancialOfficer ED Education(6) YesOfficeOfTheSecretary USDA Agriculture(4) No

OfficeOfTheSecretary DOCBanking,Finance,andDomesticCommerce(15) No

OfficeOfTheSecretary DOD Defense(16) NoOfficeOfTheSecretary ED Education(6) YesOfficeOfTheSecretary HHS Health(3) YesOfficeOfTheSecretary DHS Defense(16) No

OfficeOfTheSecretary HUDCommunityDevelopmentandHousingIssues(14) Yes

OfficeOfTheSecretary DOL LaborandEmployment(5) Yes

OfficeOfTheSecretary DOIPublicLandsandWaterManagement(21) No

OfficeOfTheSecretary DOT Transportation(10) NoOfficeOfTheSpecialTrusteeForAmericanIndians DOI


OfficeOfThriftSupervision TREAS Macroeconomics(1) No

ParoleCommission DOJLaw,Crime,andFamilyIssues(12) Yes

PatentAndTrademarkOffice DOCBanking,Finance,andDomesticCommerce(15) No

PipelineAndHazardousMaterialsSafetyAdministration DOT Transportation(10) NoPublicHealthService HHS Health(3) YesResearchAndInnovativeTechnologiesAdministration DOT Transportation(10) NoRuralBusiness-CooperativeService USDA Agriculture(4) NoRuralHousingService USDA Agriculture(4) NoRuralUtilitiesService USDA Agriculture(4) No

SmallBusinessAdministration INDBanking,Finance,andDomesticCommerce(15) No

SubstanceAbuseAndMentalHealthServicesAdministration HHS Health(3) Yes

9

TechnologyAdministration DOCBanking,Finance,andDomesticCommerce(15) No

TransportationSecurityAdministration DHS Defense(16) NoU.S.ArmyCorpsOfEngineers DOD Defense(16) NoU.S.CitizenshipAndImmigrationServices DHS Defense(16) NoU.S.CoastGuard DHS Defense(16) NoU.S.CoastGuard DOT Transportation(10) NoU.S.CustomsAndBorderProtection DHS Defense(16) NoU.S.ImmigrationAndCustomsEnforcement DHS Defense(16) NoUnitedStatesFishAndWildlifeService DOI


WageAndHourDivision DOL LaborandEmployment(5) YesNote:Agenciesmarkedwithanasteriskarethoseconsideredwomen’sissuesonlyunderthebroader characterization of the term (8 policy areas) rather than the narrower area (6policyareas).Seefootnote16inthemaintextofthemanuscript.

10

APPENDIXB.DescriptionofAgencyLeaderDataCollectionProcessandMissingData

Tocompileadatasettotestourhypotheses,webeganwithalistofallproposed

rulesfromexecutivebranchagenciesfor1995-2014accordingtotheUnifiedAgendaof

RegulatoryandDeregulatoryActions(UA).WerelyonPotter’s(2017)codingoftheUA.

Fromthislistofproposedrules,wethenusedthefirstfourdigitsofeachproposedrule’s

RegulatoryIdentificationNumber(RIN)tocreatealistofeveryoffice,bureau,or

departmentthatsponsoredaproposedrule.

Foreveryentityonthatlist,wethensoughttoidentifythesetofleaderswho

headedthoseorganizationsduringthetimeperiodunderstudy.ForSenate-confirmed

positions(whichaccountfornearly90%ofourpositions),wewereabletousetheLibrary

ofCongress’swebsite(www.congress.gov)toidentifyalistofconfirmedagencyleaders.In

othercases,wereliedonarchivedversionsoftheagency’swebsite(www.archive.org)to

identifytheagencyheadatdifferentpointsintime.Oncewehadaname,wecodedforthe

individual’sbiographicalinformation(i.e.,age,race,gender,education,priorworkhistory,

andtenureinposition).Weusedavarietyofsourcestogetthisinformation,including

biographiespostedonarchivedagencysites,therésumésiteLinkedIn,thewebsite

www.allgov.com,andnewspaperandtradepresscoverageoftheindustry.

Whilewewereabletoascertainthegenderofeachoftheleadersinourdataset,we

werenotabletocollectreliablebiographicalinformationforallofthecovariatesforevery

leader.However,asweshowinTableC3below,focusingonthefullsetofleadersand

proposedrules(andexcludingallofthebiographicalcovariates)doesnotaffectthe

substantivetakeawaysfromouranalysis.

11

VacantPositionsandMissingData

Wewerenotabletosystematicallyidentifyactingofficials(i.e.,thosepersonswho

temporarilyfilledavacancyinanagencyleadershipposition),soproposedrulessponsored

byanactingofficialareexcludedfromourdataset.

Additionally,insomecases,thesponsoringentitywasarelativelyobscure

administrativeunitandwecouldnotidentifyalloftheagencyleaderswhoheadedthe

organization.Forinstance,despiteourbestefforts,wewereunabletoidentifytheleaderof

theDefenseAcquisitionRegulationsCouncil(i.e.,theDeputyAssistantDirectorforDefense

AcquisitionRegulationsSystem)priortoFebruary2007.Incaseslikethis,sincewehadno

informationontheagencyleader,wecouldnotincludeproposedrulesissuedduringthe

missingleader’stenureinouranalyses.Thesemissingdataaredisproportionately

concentratedamongsmaller,lessprominentbureausandoffices.Totheextentthatsuch

missingobservationsaffectouranalysis,wemaybeunder-accountingfortheeffectof

women,whoaremorelikelytoserveinlower-leveloffices.

12

APPENDIXC.RobustnessChecks

C1.ModelsofFemaleLeadershiponFinalizedRuleImpact

Ifaleaderoverseestheinitiationofanimportantproposedruleinapolicyareathat

heorsheisparticularlypassionateabout,itisnotunreasonabletoconceivethattheleader

willwanttoseethatsamerulethroughtofinalization.Anadditionalimplicationofour

theoryisthatwomenleaderswillfinalizeimportantrulesatagreaterratewhentheyserve

inagenciesthatfocusonwomen’spolicyissuesthanwhentheyserveintraditionallymale-

dominatedpolicyareas.

ThemodelsinTableC1combinetheapproachesofthefirsttwohypothesistestsin

ordertotestthisimplication.Specifically,weonceagainusetheImpactdependentvariable

andOrdinaryLeastSquaresanalysis.However,ratherthanincludingallrulesinthe

analysis,welookonlyatthesubsetof5,318rulesthattheleaderbothinitiatedandsaw

throughtofinalization.Thisallowsustoexplorewhethertherulesfinalizedbywomen

leadersinwomen’sissueareasaremoreimpactfulthanarefoundinotherconfigurations

ofleadersandissueareas.

Consistentwithexpectations,weonceagainfindsupportforthehypothesis.Inthis

case,therearenostatisticallysignificantdifferencesbetweenmaleandfemaleleaders

generally,norbetweenwomen’sissueareasandotherissues.However,womenasleaders

inregulatingwomen’sissueareasisapotentcombination,producingimpactfulrulesthat

theyseethroughtofinalization.Thecoefficientof0.024ontheinteractioncharacterizing

womenleadersofwomen’sissueagenciesissizablerelativetothevarianceofthe

dependentvariable.Specifically,itis22%ofastandarddeviationofImpact.FigureC1

13

illustratestheseresults,againdemonstratingasimilarpatterntotheresultsfromthe

figuresinthepaper.

TableC1:EffectsofFemaleLeadershiponFinalizedRuleImpact

(1) (2) FinalizedImpact FinalizedImpact Women'sIssue 0.008 0.000 (0.011) (0.011)Female -0.002 -0.008* (0.005) (0.004)Women'sIssue×Female 0.024** (0.011)Minority -0.005 -0.007 (0.007) (0.007)Bachelors -0.025 -0.028 (0.035) (0.034)Masters -0.009 -0.011 (0.035) (0.035)PhD -0.005 -0.008 (0.035) (0.035)WorkedinAnotherDepartment 0.013 0.009 (0.014) (0.014)BureauExperience 0.003 0.002 (0.008) (0.008)PreviousPublicMgmtExperience -0.025*** -0.023** (0.009) (0.010)Senate-ConfirmedPosition -0.002 -0.002 (0.011) (0.011)LeaderTenure -0.000 -0.001 (0.002) (0.002)LeaderTenureSquared -0.000 -0.000 (0.000) (0.000)Constant 0.153*** 0.159*** (0.037) (0.037)Observations 5,318 5,318Numberofagencies 134 134AgencyRandomEffects YES YESYearFixedEffects YES YESAdjustedR2 0.09 0.09Notes:OrdinaryLeastSquaresmodels.Robuststandarderrorsclusteredontheagencyareinparentheses.Two-tailedtests:*p<0.05,**p<0.01,***p<0.001.

FigureC1:MarginalEffectofLeadingaWomen’sIssueAgencyonFinalizedImpact

Note:ResultsfromModel2inTableC1.Barsindicate95%confidenceintervals.

Male Leader

Female Leader

.16 .18 .2 .22

Women's Issues Agency Other Issues Agency

15

C2.ModelsofLeaderIdeologyonRulemakingOutcomes Numerousstudiesshowthat,intheaggregate,femaleelitestendtobemore

ideologicallyliberalthanmaleelites(see,e.g.,AnziaandBerry2011,Poggione2004,

ThomsenandSwers2017,Welch1985);giventhatrelationship,ourgendermeasurecould

potentiallybeproxyingforideology,suggestingthatourfindingsshowthatmoreliberal

leaders—ratherthanfemaleleadersperse—aremoresuccessfulrulemakers.

Inordertoteaseoutwhethergenderorideologyisdoingthework,weneeda

measureoftheideologyofindividualagencyleaders.Fortunately,weareabletorelyon

Bonicaetal.’s(2015)“CFscores,”ameasureoftheidealpointsofbureaucraticleaders

basedoncampaignfinancedataandwhogivestowhom.AsBonicaetal.(2015)explain,

thismethod“recover[s]idealpointsforcandidatesandcontributorsusingajoint

estimationprocedureanalogoustothewidelyusedmethodstoscalerollcalldata.”

Specifically,inTableC2weusethesescorestoreestimatethemodelsinTable2,

substitutingtheCFscoreinlieuofthegendermeasure.(Wekeepleadergenderasa

controlvariable,however.)SinceCFscoresareavailableonlyfortheClintonandGeorge

W.Bushadministrations,oursamplesizeisnecessarilylimited.Ifideologyisthe

motivatingforcebehindourresults,weexpecttoseethatwhenliberalleadersareputinto

leadershippositionsinwomen’sissuesareastheyoverseetheproductionofhigherimpact

proposedrulesandfinalizethematagreaterratethanconservativeleadersatthosesame

agencies.Theresultsinsteadshowthatthereisnodifferencebetweenconservativeand

liberalleadersintermsofruleImpact,generally(Model1)orinwomen’sissueagencies

(Model2).Ifthealternatehypothesisweresupported,wewouldhaveexpectedtoseemore

liberalleaderswritingmoreimpactfulruleswhentheyleadwomen’sagencies.

16

WithrespecttoFinalization,weseethatmoreconservativeleadersfinalizerulesata

greaterrate(Model3),andthiseffectismostexaggeratedinagenciesthatdealwithissues

otherthanwomen’spolicyareas(Model4).Thisisasurprisingresult;generallyspeaking,

conservativesoverseethefinalizationofrulesatahigherratethanliberals.Thispattern

holdsacrossallagencytypes,includingwomen’sissueagencies.Whilethisisaninteresting

findingworthyoffutureresearch,itsuggeststhattheremaybeanevenhigherbarfor

women,whotendtobemoreliberalthanmen,tosucceedintherulemakingcontext.

Importantly,forthepurposesherein,itisnotconsistentwiththealternatehypothesis

beingtested.

17

TableC2:EffectsofLeaderIdeologyonRulemakingOutcomes

(1) (2) (3) (4) Impact Impact Finalization Finalization Women'sIssue -0.002 -0.001 0.170 0.109 (0.012) (0.012) (0.303) (0.300)CFScore -0.0001 -0.002 0.551** 0.666** (0.007) (0.007) (0.253) (0.273)Women'sIssue×CFScore 0.006 -0.429*** (0.006) (0.143)Female 0.010 0.011 0.105 0.022 (0.007) (0.008) (0.191) (0.197)Minority -0.021** -0.021** -0.191 -0.161 (0.010) (0.010) (0.352) (0.366)Bachelors 0.016 0.010 0.244 0.720 (0.048) (0.049) (0.518) (0.475)Masters 0.019 0.012 0.711** 1.206*** (0.049) (0.051) (0.328) (0.315)PhD 0.026 0.019 0.534* 1.008*** (0.048) (0.050) (0.296) (0.339)WorkedinAnotherDepartment -0.010 -0.011 0.239 0.342 (0.015) (0.015) (0.294) (0.343)BureauExperience -0.024* -0.025* 0.403 0.474 (0.013) (0.013) (0.394) (0.436)PreviousPublicMgmtExperience 0.013 0.013 -0.304 -0.325 (0.013) (0.013) (0.321) (0.352)Senate-ConfirmedPosition -0.021 -0.020 -0.276 -0.307 (0.016) (0.016) (0.439) (0.405)LeaderTenure -0.008 -0.008 -0.552*** -0.533*** (0.007) (0.007) (0.096) (0.097)LeaderTenureSquared 0.001 0.001 0.029** 0.022* (0.001) (0.001) (0.012) (0.012)Constant 0.149*** 0.156*** 1.859*** 1.308** (0.050) (0.051) (0.669) (0.655)Observations 4,745 4,745 4,658 4,658Numberofagencies 111 111 111 111AgencyRandomEffects YES YES YES YESYearFixedEffects YES YES YES YESAdjustedR2/PseudoR2 0.05 0.05 0.17 0.18Notes:OrdinaryLeastSquaresanalysesforModels1and2;logitanalysesforModels3and4.Robuststandarderrorsclusteredontheagencyareinparentheses.Two-tailedtests:*p<0.05,**p<0.01,***p<0.001.

18

C3.ModelswithoutControls

AsnotedinAppendixB,wewereunabletocollectdataforallofthebiographical

characteristicsoftheleadersinourdataset.However,wewereabletoobtainthegender

ofeachleader.InTableC3,weconsidermoreparsimoniousmodelsthatfocusonlyonthe

keytheoreticalvariablesandincludealloftheleadersweidentified.Importantly,the

resultsareunaffectedbyfocusingonthislargersetofagencyleaders.

TableC3:ModelswithoutControls

(1) (2) Impact Finalization Women'sIssue -0.004 -0.283 (0.010) (0.199)Female -0.008** -0.194 (0.004) (0.159)Women'sIssue×Female 0.027*** 0.504* (0.008) (0..267)Constant 0.143*** 0.969*** (0.009) (0.239)Observations 9,258 9,121Numberofagencies 145 145AgencyRandomEffects YES YESYearFixedEffects YES YESAdjustedR2/PseudoR2 0.05 0.09

Notes:Ordinary Least Squares analysis for Model 1; logit analysis for Model 2. Robuststandarderrorsclusteredontheagencyareinparentheses.Two-tailedtests:*p<0.05,**p<0.01,***p<0.001.

19

C4.ModelsofRuleImportanceusingPriorityMeasure

Inthepaper,werelyonPotter’s(2017)measureofarule’sImpactasthedependent

variableintestingourhypotheses.InTableC4below,weemployadifferentmeasureofa

rule’simportance.Priorityisa5-categoryclassificationofarule’simportanceaccordingto

theUnifiedAgenda.Itiscodedasfollows:1=economicallysignificant;2=othersignificant;

3=substantive,non-significant;4=routineandfrequent;and5=

informational/administrative/other.Whileausefulrobustnesscheck,thismeasureis

necessarilylessnuancedthanImpactandalsoisself-reportedbyagencies,somaybe

subjecttosystematicbiases.Readersshouldalsonotethatthisvariableisreversecoded

(i.e.,weshouldexpectanegativecoefficientontheinteractionterm,ratherthanthe

positive-termexpectedwiththeImpactmeasure).Nonetheless,theresultsinTableC4,

whichshoworderedprobitmodelswithPriorityasthedependentvariable,produce

substantivelysimilarresultstothosereportedinTable2.

20

TableC4:ModelsofRuleImportanceusingPriorityMeasure

(1) (2) Priority PriorityWomen'sIssue -0.462*** -0.443*** (0.124) (0.131)Female 0.075 0.078 (0.088) (0.078)Women'sIssue×Female -0.296*** -0.281** (0.112) (0.110)Minority 0.035 (0.085)Bachelors 0.071 (0.381)Masters 0.051 (0.378)PhD 0.007 (0.377)WorkedinAnotherDepartment 0.075 (0.147)PriorExperienceinBureau 0.074 (0.124)PreviousPublicMgmtExperience -0.147 (0.126)Senate-ConfirmedPosition -0.125 (0.138)LeaderTenure -0.001 (0.025)LeaderTenureSquared -0.001 (0.003)Cut1 -2.266*** -2.392*** (0.153) (0.417)Cut2 -0.818*** -0.940** (0.118) (0.415)Cut3 1.600*** 1.492*** (0.208) (0.445)Cut4 1.906*** 1.801*** (0.203) (0.435)Observations 9,258 9,148Numberofagencies 145 144AgencyRandomEffects YES YESYearFixedEffects YES YESχ2 136.83 212.31Notes:Ordered probit analyses. Robust standard errors clustered on the agency are inparentheses.Two-tailedtests:*p<0.05,**p<0.01,***p<0.001.

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C5.ModelswithAlternateRuleFinalizationAssumptions

InModel2inTable2,weassumedthatrulesthatwerecensored(i.e.,notfinalized

duringtheperiodunderourstudy)werenotfinalizedduringthetenureoftheleaderin

question(i.e.,wegaveFinalizationavalueofzero).InTableC5,werelaxthisassumption

andfocusonlyonrulesthathadadefinitiveoutcomeduringtheperiodunderstudy.That

is,wefocusonlyonrulesthatwerefinalizedorwithdrawnduringthestudy’speriod.This

reducesoursamplesize,butdoesnotaffectthesubstantiveinterpretationofourresults.

22

TableC5:ModelswithAlternateRuleFinalizationAssumptions

(1) (2) Finalization FinalizationWomen'sIssue -0.140 -0.116 (0.238) (0.328)Female -0.231 -0.363 (0.233) (0.352)Women'sIssue×Female 0.386 1.143** (0.345) (0.538)Minority -0.108 (0.255)Bachelors -1.574* (0.883)Masters -0.737 (0.838)PhD -0.709 (0.858)WorkedinAnotherDepartment -0.138 (0.343)PriorExperienceinBureau 0.415 (0.292)PreviousPublicMgmtExperience -0.081 (0.300)Senate-ConfirmedPosition -0.552* (0.324)LeaderTenure -0.819*** (0.144)LeaderTenureSquared 0.038*** (0.013)Constant 1.807*** 4.748*** (0.347) (0.969)Observations 7,505 7,413Numberofagencies 140 139AgencyRandomEffects YES YESYearFixedEffects YES YESPseudoR2 0.15 0.29

Notes:Logitanalyses.Robuststandarderrorsclusteredontheagencyare inparentheses.Two-tailedtests:*p<0.05,**p<0.01,***p<0.001.

23

C6.ModelswithAdditionalBureau-LevelControls

Lewis(2007)studiesleaderattributes,specificallytheeffectofwhetheraleaderisa

politicalappointeeoracareerist—onagencyperformanceoutcomes.Wemodelthecoding

ofeachleader’sbiographicalcharacteristicsonLewis’scoding,becausehisisthemost

prominentstudyoftheroleofagencyleadersonagencyperformance.Inadditiontothe

leader-specificcovariatesthatweincludeinourmodels,Lewisfindsstatisticallysignificant

effectsforthreeadditionalvariables:FixedTerm:whethertheleaderservedforafixed

term;CreatedUnderDividedGovernment:whethertheagencywascreatedunderdivided

government;andCreatedUnderDemocraticPresident:whethertheagencywascreatedbya

Democraticpresident.InTableC6weincorporatethesethreevariablesintothemodels

fromTable2.Importantly,thisreducesthesizeofoursamplebynearlyhalf,sinceitwas

notpossibletocollectsuchdataformanyofthesmallerbureausandofficesinourdataset.

Nevertheless,theresultsinTableC6comportreasonablywellwiththosewereportinthe

bodyofthemanuscript.

24

TableC6:ModelswithAdditionalBureau-LevelControls (1) (2) Impact FinalizationWomen'sIssue 0.005 -1.127*** (0.015) (0.404)Female -0.010* -0.500* (0.005) (0.275)Women'sIssue×Female 0.015 1.729*** (0.010) (0.500)Minority -0.010 -0.315* (0.006) (0.188)Bachelors 0.028 -1.012*** (0.030) (0.375)Masters 0.036 0.145 (0.031) (0.349)PhD 0.037 0.149 (0.031) (0.412)WorkedinAnotherDepartment 0.007 -0.128 (0.014) (0.364)PriorExperienceinBureau -0.003 0.432 (0.010) (0.297)PreviousPublicMgmtExperience -0.023** -0.207 (0.010) (0.308)Senate-ConfirmedPosition 0.021 0.161 (0.022) (0.448)LeaderTenure -0.009** -0.673*** (0.004) (0.144)LeaderTenureSquared 0.001** 0.027* (0.001) (0.015)FixedTerm 0.022 1.166** (0.029) (0.534)CreatedUnderDividedGovernment -0.002 -0.837* (0.020) (0.490)CreatedUnderDemocraticPresident -0.025 -0.254 (0.020) (0.409)Constant 0.134*** 2.907*** (0.038) (0.618)Observations 5,142 5,101Numberofagencies 45 45AgencyRandomEffects YES YESYearFixedEffects YES YESAdjustedR2/PseudoR2 0.05 0.27

Notes:Ordinary Least Squares analysis for Model 1; logit analysis for Model 2. Robuststandarderrorsclusteredontheagencyareinparentheses.Two-tailedtests:*p<0.05,**p<0.01,***p<0.001.

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C7.HierarchicalModels

Theunitofanalysisinourstudyisaproposedrule.Throughoutthemanuscript,we

includerandomeffectsattheagencyleveltoaccountforthefactthattheremaybeagency-

specificfactorsrelatedtohowagenciesproducerules.However,thevastmajorityofthe

agenciesinoursamplearesub-unitsofalargerdepartment(e.g.,theOccupationalSafety

andHealthAdministrationwithintheDepartmentofLabor)andourdesigndoesnottake

thishierarchyexplicitlyintoaccount.InTableC7,weaddressthisstructure(i.e.,rules

nestedwithinbureausnestedwithindepartments)usingamulti-levelmodelingapproach

(GelmanandHill2006).Althoughthebureau-anddepartment-levelcoefficientssuggest

thereisimportantvariationthatoccursatbothoftheselevels,importantly,accountingfor

thisstructuredoesnotaffectoursubstantivefindingsabouttheenhancedeffectivenessof

womenleadersinwomen’sissueagencies.

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TableC7:HierarchicalLinearModels

(1) (2) Impact FinalizationWomen'sIssue -0.009 -0.447 (0.015) (0.350)Female -0.008*** -0.363*** (0.003) (0.080)Women'sIssue×Female 0.025*** 1.139*** (0.007) (0.147)Minority -0.008* -0.198** (0.005) (0.086)Bachelors 0.012 -0.468 (0.012) (0.586)Masters 0.013 0.155 (0.013) (0.586)PhD 0.016 -0.009 (0.013) (0.584)WorkedinAnotherDepartment 0.009 -0.007 (0.011) (0.150)PriorExperienceinBureau -0.003 0.279** (0.004) (0.117)PreviousPublicMgmtExperience -0.014** -0.167 (0.006) (0.126)Senate-ConfirmedPosition 0.008 -0.545** (0.010) (0.245)LeaderTenure -0.001 -0.565*** (0.001) (0.041)LeaderTenureSquared -0.000 0.028*** (0.000) (0.006)Department-levelRandomEffects 0.023*** 0.601*** (0.004) (0.183)Bureau-levelRandomEffects 0.039*** 0.803*** (0.003) (0.091)Constant 0.137*** 2.712*** (0.017) (0.649)Observations 9,148 9,016Numberofdepartments 27 27YearFixedEffects YES YES

Notes:Robuststandarderrorsclusteredonthedepartmentareinparentheses.Two-tailedtests:*p<0.05,**p<0.01,***p<0.001.

27

C8.ModelsofImpact,byWomen’sIssueAreasandOtherIssueAreas ThisanalysisdisaggregatestheresultsforImpactfromTable2,bywomen’sissue

areasandotherissueareas.Theleftmostcolumnrunstheanalysisonthesixissueareas

thatVolden,Wiseman,andWittmer(2016)empiricallyidentifyas“women’sissues”in

Congress(i.e.,wherefemalelegislatorsintroducemorebillsonaveragethandomale

legislators).Theseinclude:civilrights;health;labor;education;law,crime,andfamily;and

housing.Thesecondcolumnincludesallotherissueareas(accordingtothePolicyAgendas

coding).Thethirdcolumnincludestwoadditionalissueareaswherethereisgeneral

scholarlyconsensusthattheissueare“women’sissues”(i.e.,theenvironmentandsocial

welfare);seeVolden,Wiseman,andWittmer(2016)foradditionaldiscussion.Thefourth

columnincludestheremainingotherissues.Thefindingsfromthesesubsettedanalyses

matchthosefoundinthemainmanuscript.

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TableC8:ModelsofImpact,byWomen’sIssueAreasandOtherIssueAreas

(1) (2) (3) (4) Impact Impact Impact Impact Women’s

Issues(All6)

AllOtherIssues

Women’sIssuesOnly(All8)

AllOtherIssues

Female 0.044*** -0.006 0.035** -0.008** (0.015) (0.004) (0.014) (0.004)Minority -0.015 -0.004 -0.013 -0.002 (0.016) (0.006) (0.012) (0.007)Bachelors -0.035 0.029*** -0.030 0.023*** (0.042) (0.008) (0.041) (0.007)Masters -0.008 0.025*** -0.006 0.022*** (0.049) (0.008) (0.042) (0.008)PhD 0.011 0.038*** 0.011 0.028*** (0.043) (0.009) (0.041) (0.007)WorkedinAnotherDepartment 0.014 0.016* 0.019 0.009 (0.032) (0.009) (0.039) (0.009)BureauExperience -0.008 0.003 -0.010 0.002 (0.022) (0.006) (0.024) (0.006)PreviousPublicMgmtExperience -0.036 -0.021** -0.033 -0.013 (0.026) (0.008) (0.025) (0.010)Senate-ConfirmedPosition 0.038** -0.001 0.040** 0.000 (0.019) (0.012) (0.018) (0.012)LeaderTenure -0.016** -0.000 -0.013** -0.002 (0.008) (0.002) (0.006) (0.002)LeaderTenureSquared 0.001 -0.000 0.001 -0.000 (0.001) (0.000) (0.001) (0.000)Constant 0.176*** 0.119*** 0.149*** 0.125*** (0.049) (0.014) (0.045) (0.014)Observations 1,265 7,883 2,329 6,819Numberofagency 55 89 58 86AgencyRE YES YES YES YESYearFE YES YES YES YESAdjustedR2 0.14 0.04 0.07 0.06Notes:OrdinaryLeastSquaresmodels.Robuststandarderrorsclusteredontheagencyareinparentheses.Two-tailedtests:*p<0.05,**p<0.01,***p<0.001.

29

C9.ModelsofFinalization,byWomen’sIssueAreasandOtherIssueAreas ThisanalysisdisaggregatestheresultsforFinalizationfromTable2,bywomen’s

issueareasandotherissueareas.Theleftmostcolumnrunstheanalysisonthesixissue

areasthatVolden,Wiseman,andWittmer(2016)empiricallyidentifyas“women’sissues”

inCongress(i.e.,wherefemalelegislatorsintroducemorebillsonaveragethandomale

legislators).Theseinclude:civilrights;health;labor;education;law,crime,andfamily;and

housing.Thesecondcolumnincludesallotherissueareas(accordingtothePolicyAgendas

coding).Thethirdcolumnincludestwoadditionalissueareaswherethereisgeneral

scholarlyconsensusthattheissueare“women’sissues”(i.e.,theenvironmentandsocial

welfare);seeVolden,Wiseman,andWittmer(2016)foradditionaldiscussion.Thefourth

columnincludestheremainingotherissues.Thefindingsfromthesesubsettedanalyses

matchthosefoundinthemainmanuscript.

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TableC9:ModelsofFinalization,byWomen’sIssueAreasandOtherIssueAreas

(1) (2) (3) (4) Finalization

Women’sIssues(All6)

FinalizationAll

OtherIssues

FinalizationWomen’sIssues(All8)

FinalizationAll

OtherIssues

Female 1.193*** -0.158 1.021*** -0.284 (0.260) (0.192) (0.226) (0.223)Minority -0.284 0.008 -0.444* 0.050 (0.270) (0.113) (0.250) (0.163)Bachelors -0.416 -1.546*** -1.345** -1.622*** (0.471) (0.297) (0.655) (0.358)Masters 0.532 -1.066*** 1.026* -1.391*** (0.409) (0.346) (0.578) (0.401)PhD -0.114 -0.938** 0.188 -1.530*** (0.430) (0.443) (0.527) (0.392)WorkedinAnotherDepartment 0.881** 0.047 1.378** -0.188 (0.360) (0.302) (0.542) (0.299)BureauExperience 0.039 0.438* 0.106 0.421* (0.337) (0.262) (0.443) (0.229)PreviousPublicMgmtExperience -0.637* -0.236 -0.750 -0.165 (0.384) (0.280) (0.485) (0.252)Senate-ConfirmedPosition -0.451 -0.426 -0.424 -0.429 (0.558) (0.297) (0.737) (0.315)LeaderTenure -0.383** -0.605*** -0.368** -0.685*** (0.166) (0.116) (0.150) (0.104)LeaderTenureSquared 0.006 0.032*** 0.009 0.034*** (0.025) (0.012) (0.018) (0.012)Constant 2.317*** 3.376*** 2.118** 3.817*** (0.735) (0.562) (0.886) (0.642)Observations 1,243 7,773 2,310 6,706Numberofagency 55 89 58 86AgencyRE YES YES YES YESYearFE YES YES YES YESPseudoR2 0.21 0.22 0.26 0.24Notes:Logitanalyses.Robuststandarderrorsclusteredontheagencyare inparentheses.Two-tailedtests:*p<0.05,**p<0.01,***p<0.001.

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