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
PublicLandsandWaterManagement(21) No
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
Law,Crime,andFamilyIssues(12) Yes
FarmCreditSystemInsuranceCorporation IND Agriculture(4) NoFarmServiceAgency USDA Agriculture(4) NoFederalAviationAdministration DOT Transportation(10) No
FederalBureauOfInvestigation DOJLaw,Crime,andFamilyIssues(12) Yes
FederalEmergencyManagementAgency DHS
Banking,Finance,andDomesticCommerce(15) No
FederalEmergencyManagementAgency IND
Banking,Finance,andDomesticCommerce(15) No
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
Law,Crime,andFamilyIssues(12) Yes
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
Banking,Finance,andDomesticCommerce(15) No
NationalInstitutesOfHealth HHS Health(3) YesNationalNuclearSecurityAdministration DOE Energy(8) NoNationalOceanicAndAtmosphericAdministration DOC
Banking,Finance,andDomesticCommerce(15) No
NationalParkService DOIPublicLandsandWaterManagement(21) No
NationalScienceFoundation INDSpace,Science,Technology,andCommunications(17) No
NationalTelecommunicationsAndInformationAdministration DOC
Banking,Finance,andDomesticCommerce(15) No
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
CommunityDevelopmentandHousingIssues(14) Yes
OfficeOfElementaryAndSecondaryEducation ED Education(6) YesOfficeOfFairHousingAndEqualOpportunity HUD
CommunityDevelopmentandHousingIssues(14) Yes
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
CommunityDevelopmentandHousingIssues(14) Yes
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
PublicLandsandWaterManagement(21) No
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
PublicLandsandWaterManagement(21) No
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
PublicLandsandWaterManagement(21) No
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.
21
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.
25
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.
26
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.
28
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.
30
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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