gendering the financial crisis
TRANSCRIPT
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Genderingthe2008FinancialCrisis:
AReexaminationofIntrahouseholdBargainingPowerAnalytics
JohnnyHuynh
PomonaCollege
May6,2012
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PartI:Introduction
Neoclassicalmicroeconomicthoughtconsidersthehouseholdtobetheprimary
decision-makingagentwithasetofindividualpreferences.However,manyeconomists
haveidentifiedthathouseholdssometimesdemonstrateinternalconflictandinequity
(Phipps,Burton1995).Impactstohouseholdsarealsodistributedunevenlybetween
spouses,children.ThesefindingsunderminetheParetoefficiencyassumptionwithin
households,whichhascompoundingeffectsonmarketandmacroproductivity(Doss
1996).Tofullyunderstandhouseholdsandtheirroleintheeconomyrequiresananalysis
ofintra-householddynamics.
Iexaminehowcyclicalunemploymentandcreditcrunchesfollowingafinancial
crisisaffecthouseholddecisionsandthewelfareofhouseholdmembers.Particularly,Ilook
atthedisparateeffectsofthe2008financialcrisisonmenandwomen,andreexaminepast
frameworksthatexploreddecision-makingandrisk-bearingwithinthehousehold.This
paperutilizesempiricsfromthePanelStudyofIncomeDynamics(PSID)totestpast
modelsofintra-householdrelations.Becauseofthesubstantialrolethatmortgageshadin
the2008crisis,Iemphasizehousingassets.HousingassetsarealsonoteworthyinNew
HouseholdEconomicsbecausetheyaccruereturnsforboththebreadwinnerand
homemaker.
The2008financialcrisishasbeendescribedtohavetwoseparableeffectsonthe
household.Thefirstisareductionofaggregatedemand,whichledtohighunemployment
andadecreaseinincomeandwealth.Thesecondistheshutdownofmanycreditmarkets
thatledtoinaccessibilityofcredit(Walby2009).Ifocusonbothimpactsindividuallybut
acknowledgetheinteractionofincomeandcreditaccessibility1.
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Bustsinthebusinesscyclehavebeenshowntohaveunequaleffectsonmenand
womenemployment,aswellasunequaleffectsinthecreditmarket2.Theseoutcomesare
likelytoalterbargainingpowerswithinthehousehold.Exploringchangesinbargaining
powerhelpstoexplainbehavioralchangesinhouseholddecisionsduetoexogenousshocks
inthebusinesscycle.
Reviewingcurrentliterature,partIIassessesfinancialcrisesdisparateeffecton
menandwomen.Emphasizediscomparativeanalysisbetweenthe1997Asiancrisisand
the2008crisis.PartIIIsummarizestheexistinganalyticalframeworkofhouseholdsand
crises,andlooksattheoreticalmodelpredictions.PartIVteststhepredictionsusingPSID
empirics.PartVconcludeswithrevisionstothemodelsandimplications.
PartII:Empiricsaboutthe2008Crisis
Aftertheearly2000srecessionanduntilthe2008financialcrisis,U.S.GDPgrew
consistentlyattwotothreepercentandpeakedat3.9percentin2003.From2002to2006,
householdsincreaseddurableconsumptionandleveragedhousingmortgagestofund
expenditures(Mian,Sufi2009).Realestatespeculationandanationalhousingbubble
boostedperceivedwealth.Afterthepeakin2003,growthslowlydeclinedeachyearuntil
2008,whenGDPgrowthbecamenegative(BLS2012).The2008recessionprimarily
impactedconstruction,manufacturing,retailtrade,financeandinsurance,andrealestate
industrieswherecorporateprofitsdroppedtotheirlowestlevelsinthedecade(BLS2012).
Theeffectsofthe2008financialcrisiscanbedividedintotwomacroeconomic
effects:higherunemploymentandcreditinaccessibility.Likemosteconomiccrises,the
2008financialcrisiswitnessedasharpdeclineinprivateinvestments,suchastechnology
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spending,employment,andcapitalexpenditures.Firmswerealsounabletoborrowin
traditionalcreditmarketsandfundinvestmentopportunities(Campello,etal2010).
DespitetheFederalReservetargetinglowfederalfundsrates,banksmaintainedhigh
reserveratiosandwerehesitanttomakeloans.
Effectstothemacroeconomyalsospreadtohouseholds.Highunemployment,lossof
assetwealth,andpooreconomicoutlookscausedhouseholdstoreducespending(Hurd,
Rohwedder2010).Additionally,householdslackedaccesstofunctioningcreditmarkets,
despitecreditbeingespeciallyimportantduringfinancialcrises(Sullivan2005).
Householdstypicallysubstituteincomewithcredit,usesavings,andreduceconsumption
tomakeupthedifferential.AverageU.S.householdsreducedexpendituresbymorethan
fourpercent,despiteaverageincomeonlyfallinglessthantwopercentfrom2008to2010.
Householdreceiptsdeclinedinallareasexcepthealthcareandat-homefoodexpenditures3
(BLS2010).
Oftheseeconomicimpacts,onecanexplorethedisparitiesincostsincurredamong
menandwomen4.Whilehistoricallymosteconomiccrisesdisproportionatelyaffectfemale
employment5,theinitialeffectsof2008financialcrisiswereconcentratedinconstruction,
financialservice,andautomotivesectors,whicharemale-dominated.Rather,women
occupyjobslesssensitivetothebusinesscycle,suchaseducationandhealthservices.After
thecrisis,femaleunemploymentwasonaveragetwopercentagepointsbelowmale
unemploymentintheUnitedStates.Menwerealsomorelikelytodropoutofthelabor
forceinthe2008crisis(Hartman2009).Thesetrendsareillustratedinthe2003to2009
PSIDhouseholddatainTable1.
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Theeconomicrecovery,however,disproportionatelybenefitedmaleemployment
relativetofemaleemployment.TheBureauofLaborStatistics(2012)notesthatfemale
unemploymentrateshaveremainedconstantwhereasmaleunemploymentrateshave
substantiallydecreased,almostreachingparitywithfemaleunemploymentratesby2012.
Thereisnotyetadefinitiveexplanationoftherobustnessofthemaleemployment
recovery,althoughsomecitegovernmentstimulusofconstructionandautomobile
industries,aswellasbudgetcutsinstateandlocalgovernments,whicharefemale-
concentrated(Ruggieri2010).
Themajorityofliteratureonfinancialcrisisandhouseholdsdrawonevidencefrom
the1997Asianfinancialcrisis.Thedifferencesbetweenthe1997Asiancrisisandthe2008
crisisarenotable.Priorto1997,EastAsiaexperiencedsubstantialfinancialliberalization,
dissolvinginterestratecontrolsandderegulatingfinancialmarkets.InmanyAsian
countries,financialrepressionoftenprecededliberalizationpolicies.Newcapitalmarkets
spurredforeignownership,andEastAsiaexperiencedcapitalinflows.Somehave
suggestedthattheinflowofcapitalledtoaspeculativebubbleinrealestateandequity,
sincedemandforclaimsoutpacedtheirproductivecapabilities(Dymski1999).New
financialinstitutionsinEastAsiaalsoexpandedcreditaccesstohouseholdsthatotherwise
reliedonpersonalrelationsforloans.
Asecondaryeffectwasthatforeigninvestmentscreatedmoreopportunitiesfor
marketproduction.Employmentprospectsandhighersalariesgeneratedmoreandsmaller
households,sincehouseholdsweremorewillingandabletoincurthecostsofnottaking
advantageofeconomiesofscale.Greaterjobprospectsandhigherrealwagesthat
accompaniedforeigninvestmentsalsoincreasedlaborforceparticipationamongwomen.
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Althoughmaleemploymentalsorose,thedisparityinmale-to-femalemarketemployment
proportionsconverged.
PartIII:ExaminingBargainingPowerTheory
Ahusbandandwifeexhibitcooperativeconflict.Althoughtheyvaluetheothers
wellbeing,theyalsostruggletooptimizehouseholdandmarketproductionunderbudget
andtimeconstraints(M).
ai+aj+X=M (1)
Empiricalliteratureidentifiesthathusbandsandwiveshavedifferenttastes
concerninghouseholdexpenditures.Obviously,husbandsandwivesplacemoreworthto
assetsthataccrueprivatereturns(ai)totheirrespectiveselves.Theyalsovalue
consumptionofpublicgoods(X)inthehousehold(Lam1988;LunderbergandPollak
2008).Dependingonthealtruisticbehaviorofthehusbandorwife,theyderivesomeutility
fromtheirspousesconsumption.
Ui=Ui(ai,X)+iUj(aj,X) (2)
Becausetheutilityprofilesofhusbandsandwivesgenerallydiffer,theoptimalbundleof
privateassetsandpublicgoodsforhusbandsandwivesconflict.Eachspousevalues
differentproportionsofhisorherassetsandtheothersassets.
argmaxUi=BiargmaxUj=Bj (3) ai,aj,X ai,aj,X
Theequilibriumbundleofhusbandsassetsandwifesassetsisultimatelydeterminedby
theirrespectivebargainingpowers().Relativelymorebargainingpowerimpliesan
equilibriumbundlemorecloselyrelatedtohisorherpreference.
B=f( i,j)[Bi,Bj] (4)
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Bargainingpowermodelsarecontinuingtobereshaped,andthevariablesthat
influencethemarestillbeingdiscussed.Pollak(2005)maintainsthatbargainingpowerisa
resultofhusbandsandwifesrespectivemarketandhouseholdproductivity.Hecitesthat
wagerates,nottotalearnings,determinehouseholddecisions.Otherpapersnotethat
respectivecontributionstopooledhouseholdresourcesaresignificantvariables.The
distributionofbargainingpoweralsovariesatdifferentlevelsofproductivity(Bittman,et
al2001).Moreover,bargainingtheorymaintainsthathavingoutsideoptionsincreases
bargainingpowerbecausetheopportunitycostofnottransactingislowerfortheactor
withoptionsthanfortheactorwithoutoptions(Muthoo2000).Costsarealsolowerforthe
spousewhocansurviveandthriveoutsidethehousehold(Agarwal1997).
Thismodelsupposesthathouseholdproduction(w)canbeeasilysubstitutedby
marketproduction(h),whereastheinverseisnottrue.Thus,inahouseholdwherethe
breadwinningandhomemakerareequallyproductive,thebreadwinnerwillhavemore
bargainingpowerbecausehehasoutsideoptions.
i=f(wi,wj,hi,hj) (5)
!!!
!!! !!!
!!! 0 !!!
!!! !!!
!!! 0 (6)
Intheirseminalpaper,FinancialCrisis,Gender,andPower:AnAnalyticalFramework
(2000),FloroandDymski(whoseanalyticsIwillrefertoasthebenchmark)explorethe
effectsoffinancialliberalizationandcrisisonintrahouseholddecision-making.Mostly
usingempiricalaccountsfromthe1997Asianfinancialcrisis,theirpaperprovidesa
narrativeattemptingtomodelthebehavioralchangesinriskandassetaccumulation
amongmenandwomen.Thebenchmarkmodelpositsthatthederegulationoffinancial
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institutionsandoutwardorientationduringtheonsetofthe1990shadtwonotableeffects
onhouseholds.
First,financialliberalizationincreasesaccesstocredit,especiallytowives,spurring
householdleveraging.Householdsgaincredit-financedpurchasingpoweranduseitto
mitigatebudgetconstraints.ThisisillustratedbyanincreaseinMinequation(1),which
resultsinmorehusbandassets,wifeassets,and/orpublicgoods.Higherincomesand
wealthtriggermoreconsumptionofmarketsubstitutesofhouseholdproduction.Thisfrees
timefornon-productiveactivities,suchasleisure,butalsoincreasesmanyhouseholds
financialfragilitythelikelihoodthatahouseholdwillbeunabletorepayitsdebtifthere
isanexogenousreductionintheflowofincomeoremployment.
Second,themodelconjecturesthatfemaleparticipationintheformalsector
underminespatriarchalnorms.Wivescontributemoretohouseholdborrowingabilityand
therepaymentofdebtobligationsduetoincreasedformalsectorincome.Thisis
demonstratedbyasubstitutionofhouseholdproductionwithmarketproductionin
equation(5),assumingthatproductivecapabilitiesremainconstant.Additionaloutside
optionsprovidewomenmorebargainingpowerinhouseholddecisions.
Thebenchmarkoffersagraphicalanalysisoftheinteractionbetweenbargaining
power,creditdeterminants,andleverage6.Theysuggestthatfemalevoice()wives
bargainingpowerandcreditfactors(C)determineloan-marketleverage(H).
H=f(,C) [0,1],C>0 (7) Themodelsupposesthatfemalesaremorerisk-aversethanmalesbecausefemales
areatgreaterriskofincurringthecostsoffinancialdistress7.Themodelalsoindicatesthat
thefunctionalformisconcave.
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!!!
!!
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amountthathouseholdsleveragedbecausehighermortgagesimplysmallerdown
payments,ceterisparibus.Moreover,householdcouldborrowmoneyontheirrealestateby
takingoutasecondmortgage,increasingfinancialfragility.Usingthismetric,however,
limitsthescopeofthedatatohouseholdsthatcurrentlyhaveorhavehadatleastone
mortgage.
Thedatademonstratethatforeachyearfrom2003to2009,theproportionof
householdsthathaveremainingmortgagesincreased.In2003,theproportionwas73.2
percentandby2009,itincreasedto74.4percent.Themeanvalueofthisvariablehasalso
increasedforeachyearaswell.Thelargestjumpsoccurredbetween2003and2007,when
meanmortgageincreased17,000pertwoyears.Theseresultsindicatethathouseholds
increasedabsoluteleveragingduringthebuilduptothecrisis.Sincetheproportionand
meanvalueofmortgagesdidnotfallafter2008,thedatasetdidnotyetreflectsignificant
mortgagedefaultsthatoccurredpost-crisis.
Thoughweconcludethatabsolutehouseholdleveragingincreasedfrom2003to
2008,Ialsolookathouseholdleveragingrelativetotime-varianthouseholdcharacteristics,
suchasincomeandhousingvalue.IuseanOLSregressionwithremainingprincipal
mortgageasthedependentvariable.Sincetheonsetofthe2008crisisprimarilyimpacted
certainindustries,Ialsousethesamemodeladjustedforemploymentbyindustry.
MORTGi=0i+1iXi+2iYi+3iZi+4i*INCMi+5i*HVALUEi+i (9)
whereXisatime-invariantcharacteristicsvector,Yisatime-variantcharacteristicsvector
(excludingincomeandhousingvalue),andZisahusband/wifedifferentialvector.Ilookat
theintercepttermandthecoefficients4and5tomeasurehouseholdleveraging.
4i=!!"#$%!
!!"#$!
5i=!!"#$%!
!!"#$%&!
(10)
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Anincreaseintheintercepttermorthecoefficientsimpliesthathouseholdsaremorerisk
tolerantbecauseremainingprincipalmortgagesincreaseexogenouslyormortgages
increasemorethanincomeorhousingvalueincreases,respectively.
ThedataforEquation(9)areillustratedinTable2,andTable3adjustedfor
industryemployment.Thelatteronlyincludeshouseholdswhoseheadsareemployedin
construction,manufacturing,retailtrade,financeandinsurance,orrealestate.
Table2:Determinantsofhouseholdleveragingintwo-personhouseholdswithmortgage:OLSmodel;
DependentVariable:REMAININGPRINCIPALMORTGAGE
Variable 2003 2005 2007 2009
AGE(Head) -1582.42 -1628.68 -1948.25 -1923.37 (139.87)*** (146.05)*** (179.05)*** (189.87)***
EDU(Head) 1322.36 1979.94 2910.63 4000.78
(977.52) (1141.7)* (1811.7) (2347.6)*
INCM(Head) 0.25088 0.44419 0.24273 0.15119
(0.0614)*** (0.1115)*** (0.1168)** (.13381)
AGEDIFF 937.524 490.363 117.363 119.630
(276.32)*** (333.27) (62.478)* (37.790)***
EMPLDIFF 6534.38 7496.46 6484.94 4774.47
(3988.0) (4176.2)* (5799.9) (5142.4)
EDUDIFF -27.7840 11.9301 -1496.40 -1728.46
(771.00) (905.16) (1212.28) (1475.9)
INCMDIFF -0.20185 -0.03907 -0.24100 -0.14626
(0.0565)*** (0.1089)*** (0.1288)* (0.1358)
HVALUE 0.37471 0.29559 0.29380 0.32447
(0.0339)*** (0.0247)*** (0.0436)*** (0.0543)***
INTRCPT 78666.5 86927.4 103588 95661.0
(13065)*** (15893)*** (21554)*** (27041)***
N 2099 2122 2133 2147
R2 0.6467 0.5811 0.5226 0.4879NoteRobuststandarderrorsareinparentheses *Significantatthe10%level;**Significantatthe5%level;***Significantatthe1%level
Table3:Determinantsofhouseholdleveragingintwo-personhouseholdswithmortgage;adjustedfor
industry:OLSmodel;DependentVariable:REMAININGPRINCIPALMORTGAGE
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Variable 2003 2005 2007 2009
AGE(Head) -1476.61 -1757.06 -2205.49 -2095.70
(162.33)*** (205.42)*** (200.05)*** (243.49)***
EDU(Head) 206.859 303.965 2419.93 -77.2690
(974.48) (1391.6) (1409.1)* (1681.8)
INCM(Head) 0.36831 0.43089 0.20432 0.18434 (0.0911)*** (0.1530)*** (0.1202)* (0.1767)
AGEDIFF 608.675 106.786 825.738 68.0674
(425.04) (441.66) (581.13) (15.872)***
EMPLDIFF 7093.37 8643.63 6286.54 1458.11
(5325.4) (6647.9) (7266.6) (7906.7)
EDUDIFF 958.171 747.966 -396.553 918.883
(977.08) (1104.1) (1274.2) (1546.4)
INCMDIFF -0.31392 -0.39634 -0.19916 -0.21584
(0.09059)*** (0.1236)*** (0.1233) (0.1496)
HVALUE 0.40666 0.34411 0.35560 0.44906
(0.0269)*** (0.0316)*** (0.0220)*** (0.0429)***
INTRCPT 81104.4 103450 103493 128878
(13984)*** (20489)*** (20552)*** (24139)***
N 1010 1102 1119 1062
R2 0.6272 0.6001 0.5637 0.5506NoteRobuststandarderrorsareinparentheses *Significantatthe10%level;**Significantatthe5%level;***Significantatthe1%level
Therearelittledifferencesbetweentheresultsforbothsamples.Inbothregressions,the
intercepttermincreasesbienniallyfrom2003to2007.Itcontinuesincreasingforthe
unadjustedgroupandslightlydecreasesfortheadjustedgroupbetween2007and2009.
Thereisastatisticallysignificantandpositiveoveralltrendoftheinterceptterm.However,
thecoefficientsforhousingvalueandincomearelessconclusive.Thehousingvalue
coefficientissignificantatonepercentsizeforallyears,butitsvaluedoesnotchange.The
incomecoefficientpeaksin2005anddecreasesforcontinuingyears.Itissignificantatone
percentsizein2003and2005,issignificantatfivetotenpercentsizein2007andisnot
significantin2009forbothsamples.ThesetrendsareillustratedinFigure1.
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Theriseintheintercepttermsuggeststhathouseholdsexogenouslyleveraged
duringtheonsetofthefinancialcrisis.Thecontrolvariablescouldnotexplainthisincrease
inremainingprincipalmortgages,whichcouldimplythatthemodelomitsanuncorrelated
variablethatinfluenceshouseholdleveraging,suchasinterestratesorrisktolerance.Since
thetrendsinthehousingvalueandincomecoefficientsareindeterminate,thispaper
neitheracceptsnorrejectsthehypothesisthatperceivedhigherincomesorrisinghousing
pricesinfluencedleveragingbehavior.Thisconclusionisconsistentwiththebenchmark
predictionthathouseholdleveragingwouldincreaseduringthestartofafinancialcrisis.
Itestthesecondbenchmarkpredictionthatwivesaremoreriskadversethan
husbandsusingtwomethods.First,Iexaminebargainingpowermetrics,suchas
differentialsinincome,employment,age,andeducationbetweenhusbandandwife,and
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theirinfluenceonhouseholdleveraging.Second,Icomparetheriskprofilesofsinglemale-
headedandsinglefemale-headedhouseholdstoseewhetherthereareinherentrisk
aversiondifferentialsbygender.
Thefirstmethodusesthesamemodelinequation(9)butassessestheinfluenceof
thehusband/wifedifferentialvectorZ.Alldifferentialvariablesarecalculatedby
subtractingthefemalecharacteristicvaluefromthemalecharacteristicvalue.Thus,a
positivedifferentialvalueindicatesthatthehusbandhasahigherincome,age,or
education,orisemployedwhereasthewifedoesnotparticipateintheformallabormarket.
Thesevariablesareproxiesforintrahouseholdbargainingpowerbecauserelativelevelsof
productivityandhavingoutsideoptionsdeterminebargainingpowers.Apositive
differentialsuggeststhathusbandshavemorebargainingpower.Thecoefficientsofvector
Zmeasurebargainingpowerseffectsonremainingprincipalmortgage.Thatis,ifwivesare
relativelymoreriskaverse,thenapositiveincreaseinadifferentialvariablewillincrease
householdleveraging,ceterisparibus.
TheresultsaredemonstratedinTable2andTable3,thoughthesamplesprovide
similarconclusions.
Table2:Determinantsofhouseholdleveragingintwo-personhouseholdswithmortgage:OLSmodel;
DependentVariable:REMAININGPRINCIPALMORTGAGE
Variable 2003 2005 2007 2009
AGE(Head) -1582.42 -1628.68 -1948.25 -1923.37
(139.87)*** (146.05)*** (179.05)*** (189.87)***
EDU(Head) 1322.36 1979.94 2910.63 4000.78
(977.52) (1141.7)* (1811.7) (2347.6)*
INCM(Head) 0.25088 0.44419 0.24273 0.15119
(0.0614)*** (0.1115)*** (0.1168)** (.13381)
AGEDIFF 937.524 490.363 117.363 119.630
(276.32)*** (333.27) (62.478)* (37.790)***
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EMPLDIFF 6534.38 7496.46 6484.94 4774.47
(3988.0) (4176.2)* (5799.9) (5142.4)
EDUDIFF -27.7840 11.9301 -1496.40 -1728.46
(771.00) (905.16) (1212.28) (1475.9)
INCMDIFF -0.20185 -0.03907 -0.24100 -0.14626
(0.0565)*** (0.1089)*** (0.1288)* (0.1358)
HVALUE 0.37471 0.29559 0.29380 0.32447
(0.0339)*** (0.0247)*** (0.0436)*** (0.0543)***
INTRCPT 78666.5 86927.4 103588 95661.0
(13065)*** (15893)*** (21554)*** (27041)***
N 2099 2122 2133 2147
R2 0.6467 0.5811 0.5226 0.4879NoteRobuststandarderrorsareinparentheses *Significantatthe10%level;**Significantatthe5%level;***Significantatthe1%level
Table3:Determinantsofhouseholdleveragingintwo-personhouseholdswithmortgage;adjustedfor
industry:OLSmodel;DependentVariable:REMAININGPRINCIPALMORTGAGE
Variable 2003 2005 2007 2009
AGE(Head) -1476.61 -1757.06 -2205.49 -2095.70
(162.33)*** (205.42)*** (200.05)*** (243.49)***
EDU(Head) 206.859 303.965 2419.93 -77.2690
(974.48) (1391.6) (1409.1)* (1681.8)
INCM(Head) 0.36831 0.43089 0.20432 0.18434 (0.0911)*** (0.1530)*** (0.1202)* (0.1767)
AGEDIFF 608.675 106.786 825.738 68.0674
(425.04) (441.66) (581.13) (15.872)***
EMPLDIFF 7093.37 8643.63 6286.54 1458.11
(5325.4) (6647.9) (7266.6) (7906.7)
EDUDIFF 958.171 747.966 -396.553 918.883
(977.08) (1104.1) (1274.2) (1546.4)
INCMDIFF -0.31392 -0.39634 -0.19916 -0.21584 (0.09059)*** (0.1236)*** (0.1233) (0.1496)
HVALUE 0.40666 0.34411 0.35560 0.44906
(0.0269)*** (0.0316)*** (0.0220)*** (0.0429)***
INTRCPT 81104.4 103450 103493 128878
(13984)*** (20489)*** (20552)*** (24139)***
N 1010 1102 1119 1062
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R2 0.6272 0.6001 0.5637 0.5506NoteRobuststandarderrorsareinparentheses *Significantatthe10%level;**Significantatthe5%level;***Significantatthe1%level
Theeducationandemploymentdifferentialvariablesarenotstatisticallysignificantforany
years,sothispaperdisregardsthemasviablemeasuresofbargainingpowerinthis
regression.However,theageandincomedifferentialsaresignificant,butatdifferentyears.
SinceItestifgenderedriskaversionexistsratherthanlookingattrendsinriskaversion
overtime,significanceforallyearsislessessential.Thedatademonstratethattheage
differentialcoefficientispositiveforallyearsandforbothsamples.However,theincome
differentialcoefficientisnegativeforallyearsandforbothsamples.
Thattheagedifferentialcoefficientispositivesupportsthebenchmarkprediction
thatwivesaremoreriskaversethanhusbands.Ifagesuggestsmorebargainingpower,
thenwiveswhoarelessyoungorareolderthantheirhusbandshaverelativelymore
bargainingpower.Therefore,householddecisionsmorestronglyreflectwivestastes.The
positivecoefficientoftheagedifferentialimpliesthathouseholdswithweakerwifesvoice
willleveragemore.However,theincomedifferentialcoefficientrejectsthebenchmark
prediction.Sincetheincomedifferentialcoefficientisnegative,itsuggeststhathouseholds
withstrongerwifesvoicewillleveragemore.Thatis,wiveshavestrongertastesforrisk.
ThesecondmethodoftestingthesecondpredictionusesasimilarOLSregressionas
equation(9)butexcludesthehusband/wifedifferentialvectorZbecausethedataonlyuses
singleperson-headedhouseholds.
MORTGi=0i+1iXi+2iYi+3i*INCMi+4i*HVALUEi+i (11)
Althoughcomparingcohabitingandsingleperson-headedhouseholdsignoreshow
thedistributionofcostsaffectsriskbehaviorthespousewhoincurslesscostcould
exhibitamoralhazarditdoesprovideinsightonhowgenderitselfaffectsrisktolerance.
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Liketheprevioustest,Iusetheintercepttermandthecoefficientsforincomeandhousing
valueasleveragingmetrics.Agreaterinterceptorcoefficientimpliesmoreexogenous
leveragingorleveraginggivenasetincomeorhousingvalue,respectively.Theresultsfor
2003and2009aredemonstratedinTable4.
Table4:Householdleveragingamongsinglemenandwomen:OLSmodel;DependentVariable:
REMAININGPRINCIPALMORTGAGE
2003 2009
Variable Male Female Male Female
AGE -1045.72 -711.786 -1438.34 -1789.36
(203.56)*** (157.91)*** (401.71)*** (253.72)***
EDU 2177.17 -249.786 4597.05 471.003 (1239.3)* (1002.4) (2421.8)* (1327.6)
INCM 0.01746 0.29913 -0.00331 0.30600
(0.0986) (0.1168)** (0.1941) (0.1664)*
EMPLMT 2581.32 8282.05 -868.814 -12433.0
(8565.2) (6025.9) (17554) (11462)
HVALUE 0.45602 0.31251 0.39206 0.41516
(0.0484)*** (0.0529)*** (0.0600)*** (0.0740)***
INTRCPT 32241.1 56853.9 45485.5 121290
(17031)* (15481)*** (47134) (23716)***
N 256 461 232 538
R2 0.5955 0.4924 0.4200 0.5108NoteRobuststandarderrorsareinparentheses *Significantatthe10%level;**Significantatthe5%level;***Significantatthe1%level
Forsinglefemale-headedhouseholds,theintercept,income,andhousingvaluecoefficients
arestatisticallysignificant,whereasonlythehousingvaluecoefficientissignificantfor
singlemale-headedhouseholds.Thefemaleintercepttermisgreaterforbothyears,andin
2009,thefemaleinterceptisgreaterthanthemaleinterceptplustwostandarddeviations.
Likewise,thefemalecoefficientforincomeisconsistentlygreaterthanthemalecoefficient.
In2003,themalehousingvaluecoefficientisgreaterthanthefemalecoefficient,butby
2009,thefemalecoefficientsurpassesthemalecoefficient.
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Theinterceptsandincomecoefficientssuggestthatin2003and2009,femaleswere
lessriskaversethanmales.Femalesdemonstratedsignificantlyhigherinterceptsand
incomecoefficients.Femalesleveragedmoreonrealestateexogenouslyandrelativeto
theirincomes.However,thehousingvaluecoefficientsdemonstratethatmalesleveraged
morerelativetohousingvaluein2003,butby2009,themalesdecreasedtheirleverage
andfemalesincreasedtheirleverage.
Theresultsofthetwomethodsdonotsupportfromthebenchmarkpredictionthat
wivesaremoreriskaversethanhusbands.Theonlyvariablethatsupportsthebenchmark
istheagedifferentialcoefficient.Nonetheless,apositiveincomedifferentialcoefficientand
positivefemaleinterceptandcoefficientssuggestthatfemalesareactuallylessriskaverse
thanmales.
Thethirdbenchmarkpredictsthatincreasedfemaleemploymentintheformallabor
marketincreaseswivesbargainingpower.Iusechildcareasaproxyforfemalebargaining
power.Phillips,etal.(1997)arguethattraditionalgendernormsstillconsiderchildcareto
bethewivesdutydespitechildrenbeingapublicgood.Greaterproportionsofwives
incomepayformarket-substitutedchildcare.Thus,householdswherewiveshavemore
bargainingpoweraremorelikelytopurchasepaidchildcare.
P(DAYCARE=1|!!"#$)P(DAYCARE=1|!!!
!"#$)>0 !!"#$>!!!!"#$ (12) Althoughtheabsoluteproportionofhouseholdsthathadpaidchildcaredecreased
duringtheonsetofthecrisis,Iexploretheisolatedeffectsofbargainingpowertrendson
paidchildcare.Itreatthefinancialcrisisasanaturalexperimentonhouseholdexpenditure
behavior.Sincedisparitiesbetweenhusbandandwifesincomeandemployment
convergedin2009,bargainingpowermodelsshouldpredictthatwivesgainedrelative
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influenceduringthecrisis.Householdexpendituresshouldhavemorestronglyreflected
wivespreferences,namelypaidchildcare.Itestthispredictionusingalogitregression
withthedummyvariableDAYCARE,whetherthehouseholdhaspaidchildcare,asthe
dependentvariable.
P(DAYCAREi=1|X,Y,Z)=0i+1iXi+2iYi+3iZi+i (13)
Ifthereisnobargainingpowerdifference,thenafallineithermaleorfemaleemployment
ceterisparibusshoulddecreasechildcareequallybecausetheunemployedspousewill
substitutemarketproductionforhouseholdproduction.Positivecoefficientsforthe
husband/wifedifferentialvectorsuggestthatchildcareisinthehusbandsdomain,
whereasnegativecoefficientssuggestthatchildcareisinthewifesdomain.
TheresultsareillustratedinTable5andTable6.Iusesimilaradjustmentsfor
industryemploymentinthelattertable.
Table5:Probabilityoftwo-personandchildhouseholdwithchildinnon-householddaycare:Logitmodel
DependentVariable:DAYCARE
Variable 2003 2005 2007 2009
AGE(Head) -0.0912 -0.0740 -0.0642 -0.0734
(0.0122)*** (0.0127)*** (0.0143)*** (0.0154)***
EDU(Head) 0.1743 0.0873 0.1403 0.1939
(0.0501)** (0.0489)* (0.0530)*** (0.0575)***
DEBTx10-6 1.3645 3.5496 4.6849 3.3038
(4.0063) (3.6327) (3.4109) (2.995)
HEQUTYx10-6 -2.2124 -0.0763 -1.7900 -0.0468
(1.0446)** (0.5597) (0.8636)** (0.6518)
INCMx10-6 5.0147 3.2095 3.3245 0.1608
(2.2859)** (2.1472) (1.6081)** (1.9976)
INCMDIFFx10-6 -8.2887 -4.2782 -3.5281 -3.6092
(2.3644)*** (2.0934)** (1.6081)** (2.2145)*
EMPLDIFF -0.9427 -0.7236 -0.9992 -0.4166
(0.2376)*** (0.2386)*** (0.3011)*** (0.2579)
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EDUDIFF -0.1046 -0.0359 -0.1368 -0.1464
(0.0484)** (0.0506) (0.0578)** (0.0603)**
INTRCPT 0.0495 0.3163 -0.8045 -1.6790
(0.7605) (0.7719) (0.8664) (0.9265)
N 990 934 826 759PseudoR2 0.1228 0.0712 0.0886 0.0745NoteStandarderrorsareinparentheses *Significantatthe10%level;**Significantatthe5%level;***Significantatthe1%level
Table6:Probabilityoftwo-personandchildhouseholdwithchildinnon-householddaycare;adjustedfor
industry:Logitmodel
DependentVariable:DAYCARE
Variable 2003 2005 2007 2009
AGE(Head) -0.0641 -0.0470 -0.0526 -0.0498
(0.0122)*** (0.0106)*** (0.0118)*** (0.0110)***
EDU(Head) 0.1306 0.0700 0.1764 0.1704
(0.0613)** (0.0547) (0.0568)*** (0.0551)***
DEBTx10-6 7.4530 14.3315 3.2875 6.7646
(6.5850) (5.2736)** (3.4820) (3.1985)**
HEQUTYx10-6 -2.6112 0.1456 -0.8859 -0.8059
(1.5175)* (0.7145) (0.8733) (0.9309)
INCMx10-6 13.6091 5.5764 13.4790 0.7889
(5.8967)** (5.1235) (4.1473)*** (4.1786)
INCMDIFFx10-6 -15.9895 -5.3082 -14.6487 -5.7108
(5.0292)*** (4.4213) (4.1943)*** (3.7596)
EMPLDIFF -1.5514 -1.7372 -1.4255 -1.0128
(0.3652)*** (0.3817)*** (0.3814)*** (0.2897)***
EDUDIFF -0.1024 -0.0548 -0.1185 -0.1770
(0.0558)* (0.0501) (0.0522)** (0.0502)***
INTRCPT -0.4817 -0.4229 -1.7297 -1.5425
(0.8496) (0.7584) (0.8079)** (0.7767)**
N 782 874 876 842
PseudoR2 0.1172 0.0790 0.0956 0.0818NoteStandarderrorsareinparentheses *Significantatthe10%level;**Significantatthe5%level;***Significantatthe1%level
Theresultsshownegativecoefficientsforalldifferentialvariablesineveryyearfrom2003
to2009:educationdifference,incomedifference,andemploymentdifference,whichaffirm
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pastempiricalstudiesthatconsiderchildcaretobethewifesrole.Iinvestigatethetrends
inthemagnitudesofthesevariablestomeasurechangesinbargainingpower.Areduction
inmagnitude(anincreaseinvalue)ofthecoefficientsindicatesthateitherculturalnorms
areerasingdistinctmaleandfemalerolesinchildcare,whichseemsdoubtfulinthispapers
six-yeartimeframe,orthatthehusband/wifedifferentialisbecominglessunlikelytoaffect
ahouseholdspaidchildcare.
Theunadjustedandadjustededucationdifferentialcoefficients,thoughconsistently
negative,donotofferconclusiveinterpretationssincetheirtrendsareirregularand
unsubstantial.Thevariableisalsonotidealbecauseitistime-invariant.Ithusexcludethe
educationaldifferentialvariablesinceitdoesnotappeartobeausefulmetricfor
bargainingpower.Theunadjustedincomedifferentialcoefficient,however,demonstratesa
negativetrend,especiallyfrom2003to2005whenitsmagnitudehalved.Itsmagnitude
continuestodecreaseandstaysrelativelyconstantduringthecrisis.Theadjustedincome
differentialcoefficientisirregular,largelypeakingin2003and2007;itprovideslittle
informationaboutthetrends.Theunadjustedandadjustedemploymentdifferential
coefficientsdemonstratefluctuatingdeclinesintheirmagnitudesforallyears.Between
2007and2009,thecoefficientsdeclinethemost.Fortheadjustedsample,thecoefficient
dropsfromapproximately-1.4to-1.0,andtheunadjustedsampleisonlystatistically
significantattenpercentsize,suggestingthereisonlyweakevidencethatthevalueis
negative.
Theresultsoftheincomeandemploymentdifferentialcoefficientsagreewiththe
benchmarkpredictions.WhiletheEastAsiancrisismodelssupposedthatfemale
bargainingpowerdecreasedinAsia,thedisparateeffectofthe2008crisisonmalesalaries
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andemploymentshouldposittheinverse.Theevidencesuggeststhatwivesbargaining
powerdidincrease,thoughithadbeengraduallyincreasingpriortothecrisis,whichcould
beexplainedbythenarrowinginhusband/wifesalariesandemploymentinallyearsfrom
2003to2008.Theemploymentdifferentialcoefficientsofferthemostconclusiveevidence.
Thelossofstatisticalsignificanceintheunadjustedemploymentcoefficientpointsto
robustgainsinfemalebargainingpowerafter2007.Likewise,thesubstantialdeclineinthe
adjustedemploymentcoefficientsmagnitudeimpliesstrongerfemalevoiceinthe
household.
PartV:Conclusion
Whilethe2008financialcrisisgeneratedtremendouseconomiccoststowhich
orthodoxindicesofeconomicwellbeing,suchasconsumption,GDP,andcorporateprofits,
havepointed,theimpactshavealsoundulatedtodifferentpartsoftheeconomyinless
detectableways.Exogenousshocksofthecrisisdecliningaggregatedemandandcredit
inaccessibilityalsoaffectedintrahouseholdbehavior.Sincethecrisisinitiallyimpacted
male-concentratedindustries,householdsexperiencedshiftsinbargainingpowers,budget
decisions,andriskaversion.Thispaperexploresthegenderedeffectsofthe2008crisison
householdsbyexaminingthreehypothesesputforthbymodelsthatlookedatthe1997
EastAsiancrisis.Ituses2003to2009PSIDdatatoempiricallytestthem.
Firstisthathouseholdsignificantlyleveragedduringtheonsetofthecrisis.Data
fromapanelsampleofabout2100UShouseholdsillustratesthathouseholdsdid
exogenouslyleveragerealestatebyincreasingtheamountandnumberofmortgages,even
whencontrolledforincomeandhousingprices.Thisimpliesthathouseholdsleverageddue
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touncontrolledfactors,suchaschangingrisktoleranceorinterestrates.Secondisthat
wivesaremoreriskaversethanhusbandsare.Usingtwomethodologiestotestthe
hypothesis,Icomparerisktoleranceamongsinglewomenandsinglemen,andlooksat
bargainingpowervariablesinmortgageregressions.Bothtestsdisagreewiththe
hypothesis,andinterestinglyoffersomeevidencethathusbandswererelativelymorerisk
averse.Thirdisthatconvergingincomesandemploymentofwivestohusbandsduringthe
crisisincreasedfemalebargainingpower.Usingpaidchildcareasameasureoffemale
bargainingpower,Iinvestigatetheinfluenceofincomeandemploymentdifferentials
betweenhusbandsandwivesonprobabilitiesofhavingpaidchildcare.Theresultsindicate
thatwivesbargainingpowerhadbeenincreasingfrom2003to2007,butsignificantly
jumpedfrom2007to2009.
SinceIemploymodelscreatedusingevidencefromthe1997EastAsiancrisis,the
benchmarkpredictionsandtheempiricalresultsmaydisagreebecauseofdrawbacksof
extendingtheEastAsiantheoryassumptionstotheUnitedStates.Theempirical
conclusionsofthispaperendorsearevisitationoftheintersectionofhouseholdbargaining
powerandfinancialcrises.Italsoillustratesthenecessityoffurtherresearchinto
householdbargainingmodelsfordevelopednations.Especiallyforpolicymakersand
economistswhoattempttorealizethefullcostsoffinancialcrisis,anunderstandingthat
economiccrisesalsoechointohouseholds,andthatunemploymentandcreditshocks
disparatelyaffectmenandwomenisessential.
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Notes:
1Theirendogeneity,however,islesspronouncedfordevelopedcountries,suchasthe
UnitedStates,sincetheelasticitybetweenincomeandcreditisweakerthanfordeveloping
countries(Knell,Stix2005).
2Thisisespeciallytrueindevelopingcountrieswherewomenreceivemostoftheircredit
frominformalcreditmarkets.
3Iarguethatisbecausehealthcareisrelativelyinelastictowealtheffects.In-homefood,however,isaninferiorgood,sinceout-of-homefoodexpendituredecreased.
4Thispaperconsidersthetermsmenasmarketproducers,andwomenashousehold
producers.Iacknowledgethatmanyhouseholdsetupsexist,suchasfemale-headed,single-
parent,andsame-sexhouseholds.Drago,etal(2005);Blau,etal(2005);Black,etal(2007)exploretheeconomicsofnontraditionalhouseholdstructures,andsomeanalyticsoverlap.
5Recentlyindevelopedcountries,unemploymentratesofmenarebecomingmoreelastic
tothebusinesscycle,whereasfemaleunemploymentratesarelesselastic.Indeveloping
countries,however,femalestypicallybearhighercostsofeconomicbusts.6Althoughtheirpaperdoesnotspecifyafunctionalform,Iproposetwopossibleformsthat
agreewiththeiranalysis.
H=!!!
!orH= !(1 !) [0,1],C>0
IprovidethefunctionalformtotestwhetherempiricsconfirmFloroandDymskis
graphicalanalysis.
7Althoughsignificantliteraturecitethatwomenaremoreriskaversethanmen,thesestudiesfocusondevelopingcountriesandsinglewomen.SeeKaber(2002),and
Jianakoplosetal.(1998).
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Table1:SummarystatisticsMeanswithstandarddeviationsinparentheses
Variable 2003 2005 2007 2009
DAYCARE 0.2129 0.1917 0.1727 0.1764
(0.4095) (0.3938) (0.3782) (0.3813)
AGE(Head) 40.351 41.160 42.078 43.082
(8.2242) (8.0741) (7.9522) (7.9021)
EDU(Head) 13.328 13.310 13.261 13.204
(2.6415) (2.6782) (2.6892) (2.7457)
EDU(Wife) 13.442 13.473 13.483 13.488
(2.6329) (2.6173) (2.6126) (2.6685)
EDUDIFF -0.1263 -0.1863 -0.2514 -0.3016
(2.1248) (2.0867) (2.0780) (2.0825)
DEBT 8410.6 9545.1 12016 14251
(19116) (20104) (24369) (35005)
HEQUTY 68556 98932 126810 91473
(113877) (162503) (199726) (149953)
INCM(Head) 24243 23968 29751 26745
(69937) (41273) (110814) (45795)
INCM(Wife) 8979.9 9681.3 11505 12255
(21617) (23601) (27631) (27327)
INCMDIFF 15346 14504 18271 14715 (69890) (45231) (109342) (49890)
EMPL(Head) 0.9727 0.9634 0.9575 0.9491
(0.1629) (0.1877) (0.2017) (0.2198)
EMPL(Wife) 0.7989 0.8019 0.8035 0.8051
(0.4009) (0.3986) (0.3975) (0.3962)
EMPLDIFF 0.1737 0.1614 0.1539 0.1439
(0.4216) (0.4307) (0.433) (0.4389)
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