Micro Determinants of Labor Force Status Among Older Americans

Micro Determinantsof LaborForceStatusAmongOlderAmericans†

HugoBenıtez-Silva‡

SUNY at Stony Brook

First version:May 1998Thisversion:September30,2000

Abstract

Thispaperusesthefirst threewavesof theHealthandRetirementSurvey (HRS)to investigatethedeter-minantsof laborforcestatusamongolderAmericans.Usingtransitionsat two-yearintervalswefind thatafterbeingretiredor unemployed,thosewhoareactively searchingfor a job haveahigherprobabilityofreturningto work. We alsofind thatbeingin goodphysicalandmentalhealth—measuredby objectiveandsubjective variables—increasesthe chancesof becomingemployed,asdoeshaving worked in thelast twelve months.Thosewho arereceiving disability paymentsarelesslikely to make this transition.If we focuson thosewho aremarried,we find a preferencefor joint leisurethroughthe influenceofthe labor force status,healthandageof the respondent’s partneron the transitiondecisions. We in-vestigatetransitionsin andout of employmentandself-employment,andfor subsamplesof malesandfemales.Usingmonthlyemploymentdummiesfor theperiod1989-97,we analyzemonthly, quarterly,semi-annualandannualtransitionsandfind thatmostof our conclusionsareindependentof theperiod-icity but thattheeffectsof thevariablesvaryacrossspecifications.

Keywords: LaborSupply, LaborForceTransitions,RetirementDecision,HealthandRetirementSurvey.

JEL classification: J14,J2

† I am grateful to JohnRust,MosheBuchinsky, Hiu Man ChanandSofiaCheidvasserfor their help, encouragementand

comments,andalsofor allowing meto usethedatasetwe areworkingwith. I would like to thankDonaldW.K. Andrews,Michael

Boozer, Agar Brugiavini, Kevin Foster, Eric French,Ann Huff-Stevens,Jenny Hunt, JohnKnowles, David Margolis, Massimo

Massa,Paul Mishkin, Olivia S. Mitchell, Giorgio Pauletto,T. Paul Schultz,Chris Sims, Anna Vellve-Torras, the participants

of the Yale SummerMicroeconomicsSeminar, the Joint GraduateStudentSeminarat the University of Pennsylvania, the Yale

MicroeconomicsWorkshopon LaborandPopulation,theNorth AmericanMeetingsof theEconometricSocietyin Madison,the

NBERAging SummerInstitute,theLatin AmericanMeetingsof theEconometricSocietyin Cancun,andtheESEM/EEAmeetings

in Santiagofor insightful commentsand discussions.I am also grateful for the financial supportof the “la Caixa Fellowship

Program,” theCowlesFoundationfor Researchin Economicsthrougha Carl Arvid AndersonDissertationFellowship,andto the

JohnPerryMiller Fundfor additionalfinancialsupport.All remainingerrorsaremy own.‡ EconomicsDepartment,StateUniversityof New York at Stony Brook. Stony Brook,New York 11794–4384,phone:(631)

632-7551,fax: (631)632-7516,e-mail:[email protected]

1 Intr oduction

This paperpresentsan empiricalanalysisof the determinantsof labor force statusin the HealthandRe-

tirementStudy(HRS).The HRS is a large, longitudinalsurvey that follows a cohort,ages51 to 61 asof

1992/93,andtheir spouses,from pre-retirementinto retirement.It is speciallydesignedto track the labor

market behavior of olderAmericansandtheirhealthstatus.

Laborforcetransitionshave beentheobjectof aconsiderablenumberof studies,andthey will continue

to beasbetterdatasetsbecomeavailableandinterestincreasesin theevolutionof thelabormarketbehavior

of olderworkers,in largepartdueto its policy implicationsin thecurrentdebateonthefutureandthereform

of theAmericanPensionandSocialSecuritySystems.

A significantnumberof studieshave usedthe RetirementandHistory Survey (RHS) asthe basisfor

their empiricalwork. TheRHSis a longitudinalsurvey of menandsinglewomenages58 to 63 asof 1969

thatfollowedrespondentsupto 1979.Rust(1990),Blau(1994)andRustandPhelan(1997)focusonmales,

Blau (1997and1998)studiesmarriedcouplesandPozzebonandMitchell (1989)restricttheir attentionto

marriedwomen.PeracchiandWelch(1994)usetheCurrentPopulationSurvey (CPS)to studylabor force

transitionsof malesandfemalesfrom 1968to 1991,andHurd (1990a)usesthe New BeneficiarySurvey

(NBS) to analyzethejoint retirementdecisionof husbandsandwives.

Theavailability of theHRShasfostereda new wave of empiricalwork in this area,respondingto the

needto updatethesourcesof datain a time of changein policy andattitudetowardsthe importanceof the

labor market behavior of older Americans.Also, the fact thata majority of HRS respondentsarefemales

hasallowed someresearchersto incorporatethe analysisof labor market behavior of older womeninto

their research.Hurd andMcGarry (1993),Gustman,Mitchell, andSteinmeier(1995),Blau andGilleskie

(1997),Blau,Gilleskie,andSlusher(1997),Friedberg (1997),Karoly andRogowski (1997),Rogowski and

Karoly (1997),Quinn(1998),Coile (1999),Costa(1999),andCoileandGruber(1999)areexamplesof this

research.Quinn (1998)is the only studythat usesthe threeavailablewavesof the HRS aswe do in this

paper, but concentratesonly on thoseemployedasof thefirst wave of interviews. BlauandRiphahn(1998)

usetheGermanSocioeconomicPanelto studymarriedcouplesin Germany, andit is theonly otherstudy

alongwith thisonethatmakesuseof monthlyemploymentindicators.1

Thispapercontributesto theliteraturein severalways:

� This is oneof thefirst papersto useall threewavesof theHRSto analyzethelatestevidenceontrends

1 Hurd (1990b),Lumsdaine(1995),LumsdaineandMitchell (1998),andCurrieandMadrian(1998)areexcellentsurveys oftheliteratureon retirementbehavior andrelatedissues.

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in retirementandlaborsupplyamongtheAmericanelderly.

� This is thefirst panelstudy, usingAmericandata,to trackemploymentdown to themonthlylevel.

� Theuseof awiderangeof health,demographicandsocio-economicvariablesprovidesamoredetailed

andupdatedpictureof themicro determinantsof laborforcetransitions.

� This papertakesa closerlook at thosemakinga reversetransitiondecision,to exploremorein depth

thanprevious literaturetheir characteristicsandlabor forcedecisions.TheEconomicsof Aging has

focusedmuchmoreontheretirementdecisionbut changesin demographicandsocio-economictrends

(higherlongevity, improvementsin healthoutcomes,technologicalprogressthatcanchangethenature

of work, etc.) justify that we take a closerlook at the importantvariablesbehindthe decisionsof

comingbackto work aftera job displacementspellor retirement.

� This is oneof the first papersthat analyzesmarriedpeopleseparatelyusingthe HRS, allowing for

someof thespouse’s variablesto have aneffecton therespondents’decisions.

Someaggregatevariablesseemto show that in the last two or threeyears,as the economyhasgrown

steadilyandunemploymentrateshave gonedown without triggeringinflation, olderAmericansout of the

labormarket arefinding it easierto returnif they sochoose.2

Figures1 and2 show theevolution of the labor forceparticipationrateamongmalesandfemalesages

55 to 64, from January1988to August2000basedon monthly datafrom the Bureauof Labor Statistics

(BLS). For bothgroups,wecanseeanincreasein this ratein thelast3 or 4 yearsthatis sharperfor women,

confirmingahistoricaltrend,but it is alsopresentfor men,implying areversalin atrendthatstarteddecades

ago.This hasto beconsideredalongwith decreasingunemploymentratesamongthesamepopulation.We

canconcludethat peoplein theseagerangewho want to work, have a positionalmostassured,given the

currenteconomicconditions.3 For those65andover theparticipationratesaremuchlowerandmorestable

in thelast10years,but ashasbeennoticedrecentlyin themedia,thereis someevidencebasedonBLS data

of a changein thetrendin thelastyearsfor thisagegroupaswell.4

2 Theliteraturehasalreadyreferredto thepossibilityof this trend.Hurd (1990b,p.631)considerstheoverall rationalitybehindapossibleincreasein laborforceparticipationof theelderly: “I canwell imaginethatasthelaborpoolof youngerworkersshrinks,employment opportunitiesfor older workerswill expand,and that many will increasetheir work lives in responseto the betterjobs.” Schulz(1995,p.282)arguesthat: “Without dramaticchangesin the ability of countriesto moderatebusinesscyclesandkeepunemployment low over the long run, theremay be little changein currentprovisionsthat encourageretirementandearlyretirement.” Ruhm(1996,p.100)arguesthat: “...changesin theSocialSecuritySystemwill encouragelongerwork livesasbenefitsbecomelessgenerousfor futureretirees,theageof “normal” retirementis raised,andthepenaltyfor earlyretirementis increased.”

3 Ruhm(1996)mentionsthe possibility that official unemployment statisticsunderstatethe labor market problemsof olderworkers,whoaremorelikely to bediscouragedanddropoutof theworkforce.

4 “The New Facesof Retirement”.TheNew York Times,January3, 1999.

2

How canwe explain this changein trend?Onepossibleexplanationis that theSocialSecurityamend-

mentsof 1983andothermorerecentefforts to make work moreappealingat theendof the life cycle are

having animpacton behavior amongtheelderly. This canalsohappenasanindirectresultof thechangein

attitudetowardswork asAmericansreachtheretirementagein betterhealthandwith longerlife expectan-

cies,andasfirms seetheir workforcesagingasthebabyboomersgrow older. An alternative would be to

arguethatthis is atemporaryphenomenondueto thetight labormarketandtheeconomicexpansionthathas

forcedfirms to resortto lesstraditionalsourcesof workers,includingtheelderly.5 Wecancomplementthis

lastexplanationwith thefact thatthecurrenttechnologicalrevolution canhelpmake work moreaccessible

to older workers. Self-employment, part-timework usingthe internet,and telecommutingare just some

examplesof the radicalchangesin theconceptof employmentthatwe will seein the future,changesthat

couldfavor theincreasein employmentamongolderAmericans.

Thispapercannotfully addresstheseissues,but with themin mind,weareableto shedlight onwhether

themicro determinantsof labor forcestatusamongtheAmericanelderlyhave changedin the lastdecade.

The changein trendobserved in Figure1 justifiesour interestin learningmoreaboutwhat affects labor

market decisionsnow thatwe have accessto very rich dataon this population.Thereducedform natureof

ouranalysismakesit anexploratoryeffort contributing to theunderstandingof laborforcetransitionsof this

populationwith a richerandupdateddataset.

Our empirical resultsshow that amongthe non-employed, thosewho have healthinsuranceare less

likely to returnto work, revealinga “spike” at age62 anda 65+ effect, especiallyamongmen.6 Among

the employed, thosewith accessto retireehealthinsurancearemore likely to leave the workforce,with

theageeffectspeakingat age63-64.7 Theseresultsareconsistentwith Blau andGilleskie(1997)who use

thesamedatasetbut restrictattentionto malerespondentsin thefirst two wavesof thesurvey, andusea

somewhat controversial characterizationof the transitionperiods;andwith Karoly andRogowski (1997)

thatconcentrateon menandonly on theretirementdecision.Theresultscanalsobereconciledwith those

of RustandPhelan(1997), limited by the fact that they build a structuralmodel incorporatingthe Social

Securityincentives,anapproachbeyondthescopeof this study.

Our resultson theeffectsof healthstatusarefairly standard;thatis, thosewith healthproblemsareless

5 TheNationalCouncilon Aging launchedduring1998the“100,000jobsCampaign”thataimsat placingthatmany workersage50 or olderduringthenext five years.Many majorAmericanemployersareparticipantsin thecampaign.This canhelpmakethis increasein demanda lesstemporaryone.

6 We have groupedherethoserespondentsage65 with thoseover 65 becauseof the small numberof respondentsin eithergroup. We caninterpretthis 65+ effect asreflectingthe fact that individualsstartreceiving S.S.benefitsandbecomeeligible forpublichealthinsurancethroughMedicareafterreachingage65.

7 Theseeffectsprobablyincludetheinfluenceof theCOBRA legislationthatrequiresfirms of a certainsizeto provide contin-uationof healthbenefitsfor amaximumperiodof 18monthsonceemployeesleave thefirm.

3

likely to returnto work andmorelikely to leave work. We usea wide varietyof objective andsubjectives

measures,measuresof activities of daily living (ADLs), instrumentalactivities of daily living (IADLs), and

cognitionvariables.Theeffect of marriageis alsoconsistentwith the resultsof Blau (1994)andPeracchi

andWelch(1994),showing significantdifferencesbetweenmalesandfemales.Marriedmenaremorelikely

to returnto work andlesslikely to leave theworkforce,while theoppositeis truefor marriedwomen.We

find thateducationvariableshave a smalleffect andarerarelysignificantin the transitiondecisions.This

differsfrom theresultsof PeracchiandWelch(1994),but is consistentwith thesmalleffectsfoundby Blau

(1994). This is not surprisingsincewe arecontrolling for a largerarrayof variablesin which educationis

left with almostno explanatorypower.

An importantandoriginal result is the relevanceof the job searchbehavior of the respondentin the

re-entrydecision.Thosewho weresearchingfor a job in themonthprior to theinterview weremuchmore

likely to besuccessful.This resultcontrastswith Rust(1990),who did not find a significanteffect of the

searchingdecisionontheprobabilityof findinga job afterwards.Wewill show thatthis resultis notmerely

spuriousandtesttheexogeneityof this variablein oursampleof non-employedrespondents.

We alsoanalyzemarriedpeopleseparatelyandfind that the influenceof thespouse’s laborstatus,age

andhealthontherespondents’decisionscanbeinterpretedeitherasacomplementarityof timeeffect,or for

thosenon-employed,asa preferencefor joint leisure.This resultis consistentwith PozzebonandMitchell

(1989),Hurd (1990a),Blau (1998),andBlauandRiphahn(1998).

An importantcontribution of thispaperis theestimationof shorttransitionsusingmonthlyemployment

dummies. We calculatemonthly, quarterly, semi-annualand annualtransitionsin addition to the more

commonsurvey to survey transitions.Thisgivesusasenseof someof thedynamiceffectsof theexplanatory

variablesandcanbe interpretedasa testof our resultsusingthe traditionalbiennial transitions.Most of

thesignificantvariableshave strongereffectsfor the shortertransitions,implying that theestimatesusing

longertimeperiodscouldrepresenta lower boundfor thetrueeffectof somevariables.

In thenext sectionwe presentthedatausedin this studyanddiscussits limitationsandpotentialprob-

lems,aswell asits strengthsfor thepurposesof our research.In sectionthreewe presentandanalyzethe

empiricalresults.Sectionfour concludesandofferssomesuggestionsfor furtherresearch.

2 Data and Summary Statistics

Thedatausedin this paperis from theHealthandRetirementStudy(HRS).TheHRSis anationallyrepre-

sentative longitudinalsurvey of 7,700householdsheadedby anindividual ages51 to 61 asof wave 1. The

4

primarypurposeof theHRSis to studythelaborforcetransitionsbetweenwork andretirementwith partic-

ularemphasisonsourcesof retirementincomeandhealthcareneeds.It is asurvey conductedby theSurvey

ResearchCenter(SRC)at theUniversityof Michiganandfundedby theNationalInstituteonAging.8 Up to

now dataof thefirst four wavesof thesurvey areavailable,andin thispaperwewill considerthefirst three.

Wework with 12,652respondentsfrom wave 1 of thedatacollectedin 1992and1993,11,596respondents

from wave2 collectedmainly in 1994,and10,970respondentsfrom wave3, collectedduring1996andJan-

uaryof 1997.Thelasttwo waveswereconductedby phoneusingthecomputerassistedtechnology(CATI)

whichallows for muchbettercontrolof theskip patternsandreducesrecallerrors.

Deathandattrition have reducedthenumberof participantsin thesurvey. This cancreatesomeselec-

tivity issuesthatwe assumeaway in ourestimations.Additional individuals,who have beenincluded,have

enteredthesurvey lateronmainlyasspousesof previousrespondents.Wewill restrictourattentionto those

age50 andover giventheobjectivesof this paper. Thevariablesusedin this studyaretheresultof several

programsthatcalculatethehoursof work, earnings,employmenthistory, wealth,income,healthstatusand

severalotherrelevantvariablesfor therespondentof thethreewaves.Thehardesttaskhasbeento construct

theemploymenthistoriesof therespondents.Fortunately, thesurvey wasdesignedwith this in mind,but the

complexity of theskip patternandthemultiplicity of differentemploymenthistoriesover the threewaves

hasmadeour work a time consumingone. In theemploymenthistoriesof therespondentswe definethree

states:

� Employed: works for someoneelseandreceivesa salaryor wage. In general,this category hasthe

highestproportionof respondents.

� Self-employed: worksfor him/herselfreceiving asalary, profitsor evennothing.Theseparateanalysis

of theserespondentsis fairly new in theliterature.We have decidedto differentiatethemfrom those

employed for a third partybecausethosewho areself-employed have fairly differentcharacteristics

from thoseworking for a company, as shown below, and we conjecturedthat this could result in

obscuringsomeof theestimationresults.9

� Non-employed: doesnot do any work for pay. We includein this category peoplewho areout of the

laborforce,in thesensethatthey arenotnecessarilylooking for a job.10

8 SeeJusterandSuzman(1995),alsoGustman,Mitchell andSteinmeier(1994and1995)or theHRSwebpage.9 Wewill seein thenext sectionthatsomevariableshavedifferenteffectsfor employedandself-employedindividuals,justifying

thesplittingof thesample.Also theempiricaltransitionprobabilitiesfrom oneemploymentstateto anothershow differentpatternsfor employeesandself-employedindividuals.

10 This labor forcestatusis comparableto theoneusedin the literature. However, PeracchiandWelch (1994)includein this

5

We have flaggedthe respondentsappropriatelyto know their trajectoriesup to wave 3 or their last survey

interview. Although we usethe threewaves of data,we only have two decisionmaking periods. Our

datawill assumethat the respondentsmake labor force decisionsat eachinterview, given their statusand

characteristicsat that time. Theresultsof thesedecisionscanthenbemeasuredin thenext interview. We

canidentify thisapproximatelytwo-yearperiodasaproblem,sincearespondent’s statusin thenext wave is

somehow aresultof hisdecisionsandcharacteristicstwo yearsago,but it alsodependsonmany otherevents

thathaveoccurredin themeantime.To avoid thisproblemwehavealsorunmonthly, quarterly, semi-annual

andannualtransitionsfrom thedateof theinterview. Wewill presenttheresultsandwe will comparethem

with thesurvey to survey transitions.

Thedatafor therespondentsweremergedfromwave3backwardsto waves2and1,andweconstructeda

setof consistentvariablesondifferentsourcesof income,wealth,healthandsocio-economiccharacteristics

that were assignedto eachdecisionmaker appropriately. We work with a sampleof more than 20,000

person/periodobservations.We assumethateachindividual makesat mostonedecisionin wave 1 andone

in wave 2. Therefore,eachrespondentcanhave a maximumof two decisions,dependingon whetherwe

have beenableto follow him or herthroughthesubsequentwaves.

For ourestimationpurposeswe have brokendown this sampleinto sub-samples.For example,thesub-

sampleof thosenon-employed in any of the two waveshas7,117observations,that of the self-employed

has2,393observationsandthatof theemployed10,259observations.We dividedour sub-samplesfurther,

constructingsmallerdatasetsfor malesandfemalesfor eachsub-sample.This allows us to analyzesepa-

ratelymalesandfemalesto uncover relevantdifferencesandsimilarities.It is interestingto emphasizethat

femalesoutnumberedmalesin thesamplesof non-employedandemployed,andmenweremorenumerous

amongthe self-employed. We alsocreatesub-samplesof marriedrespondentsto analyzethat population

separately. Table1, panelsA to C offer meansandstandarddeviationsof mostof thevariablesusedfor the

threemainsub-samples.

Thesampleof non-employedcomparedwith thewholesamplehasrespondentswith lower educational

level, lower family andpersonalincomelevelsin theyearbeforetheinterview, a higherproportionof them

receivesHealthInsurancecoveragefrom the government,anda lower proportionfrom pastemployersor

their’sspousesemployers.They arein worsehealthby any measure,andthey haveworkedmuchlessduring

theyearprior to the interview. If we concentrateon the reversetransitioners(not shown) we seethat they

areyounger, they have higher incomelevels, and fewer of themreceive disability paymentsor consider

categoryonly thoseoutof thelaborforce,andincludethoseunemployedin thefull-time andpart-timecategorydependingontheirsearchingdecision.Thisallows themto introducea variablethatcontrolsfor unemploymentstatus.

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themselvesretired. A muchhigherproportionof thosethat returnto work wassearchingfor a job in the

monthbeforetheinterview, anda higherproportionleft their last job becausethey quit or werefired. They

arealsoin betterhealththantheaveragenon-employed.

The sub-sampleof self-employed is white, male,married,andmoreeducatedthanthe whole sample.

They have higherincomeandwealthlevels. They worked morein theyearbeforethe interview andthey

arein betterhealth. Among the sub-sampleof employed, it is worth concentratingon the transitionersto

non-employment(not shown) who tendto beolder, in worsehealth,with lower incomelevelsanda lower

proportionof themexpectto work afterreachingage62.

3 Estimation Results

In this sectionwe first presentempiricaltransitionprobabilitiesto comparelaborforcestatusof therespon-

dentsin differentperiods.Wethenreportthemultinomiallogit estimatesof thesurvey to survey transitions

out of the different labor force statusand also the binomial logit resultsof the shortertransitions. We

will presentonly our preferredspecificationsandonly summarytablesof theshortertransitions,but other

specificationsandthebinomiallogits areavailableuponrequest.

3.1 Empirical Transition Probabilities

The transitionprobability for movementfrom stateA to stateB is the numberof respondentsthatmoved

from thefirst stateto thesecondbetweenthetwo periodsof analysisdividedby thetotal numberin stateA

in thefirst period.Hereweareonly consideringthoserespondentsthatappearin bothwaves.In oursample

we canlook at transitionprobabilitiesfrom wave 1 to wave 2 andfrom wave 2 to wave 3. In Table2 we

show thesematricesfor transitionsbetweensurvey waves.

FromTable2 thefirst importantissueto consideris thatcomingbackto work is fairly commonamong

this populationthereis, regardlessof thesampleandperiod,morethan11%probability that if a personis

non-employed in thepreviouswave he/shewill have a job in thenext wave,with a muchhigherprobability

for transitionsinto employmentthaninto self-employment.

PanelsA andB show theempiricaltransitionprobabilitiesfor menandwomen,respectively, from wave

1 to wave 2. PanelsC andD show thesamefor transitionsbetweenwave 2 andwave 3. Thecomparison

acrossperiodsrevealsvery similar transitionsprobabilities,with a slightly higherprobabilityof becoming

employedafterthesecondwave for men.

ComparingpanelsA and B we seethat the probability for self-employed womenof becomingnon-

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employed is clearly larger thanfor menwho have a muchhigherprobabilityof keepworking at their busi-

nesses.The situationreverseswhenit comesto leave employment: womenhave a higherprobability of

keepingtheir jobsanda lower probabilityof becomingself-employed. Again, this differencepatternjusti-

fieshaving splittedthesamplebetweenemployeesandself-employedrespondents.Theprobabilitiesoutof

non-employmentarequitesimilar, with slightly higherprobabilityfor womenof stayingoutof work.

ComparingpanelsC andD wecanseeagainthatfemaleshadamuchhigherprobabilityof leaving work

if they wereself-employed—the probability increases12 percentagepoints,a very significantdifference.

On theotherhand,in thecaseof transitionsout of employment,womenhave transitionprobabilitiesvery

similar to thoseof men. Thehigherpersistenceof non-employmentamongwomenis maintainedin these

panels.

Anotherinterestingresultthat is truebothfor menandwomenis that it is morelikely to becomeself-

employed if you werenon-employed in theprevious interview thanif you wereandemployee,suggesting

that being out of work might foster, other things equaland comparatively to being and employee, that

individualsdecideto setup their own business.

3.2 Transitions from non-employment to self-employment and employment

We presentherethe estimationresultsfor the non-employed, trying to uncover the importantvariables

affectingthedecisionto returnto work eitherasemployeeor asself-employed. This is thespecificationthat

we will considermorein detailasbeingtheonelessstudiedin theliterature.

Table3 presentstheregressioncoefficientsof theMultinomial logit modelusingMaximumLikelihood

for the transitionsfrom non-employment to self-employment andemployment, and their standarderrors

adjustingfor heterocedasticity(White’s SE).Thebasecaseis remainingnon-employed. We alsoincludeat

thebottomof eachcolumntheaveragelog likelihoodandthenumberof observationsfor thatspecification.

Thistablehassix setsof resultssincewereportthreedifferentspecifications,andthecoefficientsfor thetwo

transitionchoicesareincluded.Thefirst sectionof thetablerefersto thetransitionsfrom non-employment

to self-employment,andthesecondrefersto thetransitionsto employment.

We include many independentvariables,up to 34, obtaininga good fit. A high proportionof them

aresignificant,themajority at the5% level or better. We have includedagerangedummiesto capturethe

differentageeffects(includingdummiesfor ages62and65+to capturetheearlyretirement“spike” thathave

beenstudiedin theliteratureandtheS.S.andMedicareeffectfor those65yearsof ageor older),andtheones

presentedarewith respectto thedummyfor thoseunder55. Wehavealsoincludedinteractiontermsbetween

the agedummiesand a dummy that takes the value 1 if respondentshave health insurancefrom a past

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employer or their spouse’s employer. Herewe canseethatbeing62 or 65 andinsuredclearlydecreasesthe

probabilityof makingareversetransition,especiallyinto employment.Wealsoincludeadummythattakes

the value1 if the respondentshave governmenthealthinsurancecoverage,this decreasesthe probability

of making a transition,especiallyinto self-employment, as a matterof fact it is not significant for the

transitionsto employment,supportingour ideathat thedeterminantsof the transitionsto self-employment

andemploymentcanbedifferent.

Therearerelatively few respondentsin the62 to 65 agerangeamongthereversetransitioners,around

20%of thespellsbackto work comefrom theserespondents,but consideringthataround16%of oursample

is in thatagerangeindicatessomeunconditionalpropensityamongthis group. Most of themhave access

to retireehealthinsuranceaswell. Basedon analyzingthe characteristicsof the transitioners(not on the

estimationresults)thosethat returnat 65 or over tendto be marriedandmalein a lower proportionwith

respectto thosethatstaynon-employed in thesameagerange,but in generalareolder thantheir spouses,

13 yearsolder versus11 for the non-transitioners,andreceive retireehealthinsurance(especiallyhealth

insurancepaidby theemployer) andgovernmenthealthinsurancein a lower proportion.Thosethat return

to work at age62 tendto be male,marriedandwhite in a higherproportionthannon-transitionersat the

sameage.They alsotendto beolderthantheir spousesby around2 years,comparewith around9 months

for thosethat stay non-employed. The biggestdifferencebetweentransitionersand non-transitionersof

age62 is that thosethatchangelabor forcestatusreceive healthinsurancefrom thegovernmentin a much

lower proportion.Only around5% of transitionersreceive this kind of insuranceversusabout30%of the

non-transitioners.

The analysisof the effectsof healthinsuranceon labor force decisionsof the elderly is an important

issue,andour resultsareconsistentwith thoseof Blau andGilleskie(1997)who alsousetheHRSbut only

thefirst twowavesof thesurvey andconcentrateonly onmalesage51to 61asof wave1. Ourresultsarealso

consistentwith RustandPhelan(1997)who identify a groupof “health insuranceconstrained”individuals

that needto wait until they have accessto governmenthealthinsuranceto retire. Thoseindividualsseem

alsolesslikely to returnto work.11

A dummyvariablethat is 1 if the decisionis madein wave 2 is negatively relatedto the decisionof

changinglaborforcestatus,meaningthatdecidingin thesecondwave,1994/95,impliesa lowerprobability

of returningto work, proxyingfor somebusinesscycle effect. However, this dummyis only significantfor

thetransitionsto self-employmentandonly for women.

11 For a review of the literatureon theeffectsof healthinsuranceon thelabormarket outcomesof olderpeopleseeCurrieandMadrian(1998)andthediscussionin Lumsdaine(1995).

9

On the other hand, the effect of marriageis consistentwith Blau (1994) and Peracchiand Welch

(1994).12 Married menaremorelikely to returnto work asemployeesbut marriedwomenhave a lower

probabilityof makingthistransition.Thismightbereflectingthetraditionalroleof womenin thehousehold

amongthispopulation.Thiseffect doesnotappearin thetransitionsto self-employment.

Anothervery importantvariablein this estimation,andin fact acrossall specifications,is Searchj, a

dummythattakesthevalue1 for thosewhoweresearchingfor a job duringthemonthbeforetheinterview.

Thecoefficient is very largeandsignificantfor thetransitionsto employment,meaningthat theprobability

of returningto work increasessharplyif the individual is actuallysearchingfor a job. In the transitions

to self-employment the variableis lessimportant,andonly significantfor males. This is plausibleif we

believe thatestablishingone’s own businessis a decisionthat dependslesson one’s willingnessto search

for a job. This factalsoreinforcesour decisionto split thesampleof working respondentsinto employees

andself-employed individuals.

This resultmight seemobvious, after all if a personis not searchingfor a job the chancesof finding

onearevery slim. However, we have to considerthat this variablecanbemakingthedistinctionbetween

unemployedandoutof thelaborforceindividuals,andalsofor thebusinesscycleeffect, in thesensethatif

apersonis searchingandtheeconomyis doingrelatively well, thechancesof actuallyfindinga job increase

sharply. Unemploymentratesareat recordlows amongthis population,which meansthat almostanyone

whowantsto work findsa job. Thiscontrastsin aninterestingwaywith thedevelopmentsin Europewhere

giventhecurrenteconomicconditions,weconjecturethatasearchvariableof thetypeusedherewouldhave

amuchsmallereffect.

As canbeseenfrom thetable,thisvariableis veryrobustacrosssamplesandspecificationsandtherefore

it is a very goodpredictorof the decisionsto return to work, even after controlling for a wide rangeof

variables. We interpretour finding of the importanceof the searchvariableamongthis populationas a

confirmationof the main result in Flinn andHeckman(1983), that is, that unemploymentandout of the

labor force arebehaviorally distinct labor force states. They obtain this result using a small dataset of

youngmen. We find that thedistinctionbetweenthesetwo statesis alsomeaningfulfor older individuals,

in the sensethat the searchdecision(the variablethat we chooseto ultimately distinguishbetweenthese

two states)hasanlargeeffect on theprobabilityof finding a job. This resultis somewhatdifferentfrom the

resultsreportedby Rust(1990)usingtheDynamicProgrammingmodel,wheretheintentionto work hada

verysmallex-postprobabilityof becomingtrue.TheseresultswereobtainedusingRHSdataonly for males

12 However, theseauthorsonly considertransitionsinto part-timeor full-time jobs, not making the distinctionbetweenself-employmentandemployment,this therefore,qualifiesourcomparisons.Also, Blau (1994)concentratesonly onmen.

10

andlessdirectsearchvariables,whichmaybesubjectto highermeasurementerror.13

However, we have to acknowledgethe possibleendogeneityof Searchj. The respondent’s searching

decisioncanbecorrelatedwith unobservablesthatmakethatpersonmorelikely to find ajob, resultingin an

upwardbiasof ourestimatedueto thisspuriouscorrelation.Wearguethatthisbiasis nottoolargegiventhat

wearealreadycontrollingfor acomprehensive arrayof characteristicsof therespondents.Furthermore,we

testtheexogeneityof thesearchingdecision,following Heckman(1978),by usinga bivariateprobit model

of the decisionto changelabor force statusand the decisionto searchfor a job. In this set up, testing

for exogeneityreducesto testingthe independenceof the probit equations.The LagrangeMultiplier test

statisticis 0.01699,andsinceit follows a χ2 distribution with one degreeof freedom,we cannotreject

the null hypothesisof independenceof the probit equationsor equivalently the exogeneityof our search

decision.14 This importantresultdeservesfurtherattentionanda morepreciselook at thedeterminantsof

thesearchingactivity. This is partof ourongoingresearch.15

Receiving disability paymentsdecreasesthe probability of makinga reversetransition. This result is

only significantfor femalesandreflectsthefact that thosereceiving disability have majorhealthproblems

andalsothefactthatthey canonly work duringa trial period,otherwisethey losetheir benefits.

Personalincomeis positively and significantly relatedwith the decisionof returningto work as an

employee.Thiscouldseemapuzzle,but it mightmeanthatthosepeoplewhowereearningmorefrom their

previous jobshave morepressureto find a job, sincetheir familieshave grown accustomedto their living

standards.Rust(1989)considersincome(andwealth)asaproxy for unobservedjob skills whichmaymake

the worker moreemployable. This could alsomeanthat individualsworking continuouslybeforeexiting

work, andthereforeearningmorethanthosedisplacedfor longerperiods,have higherchancesof finding a

job if they needone. That is why we introducethevariableMonthp, that reflectstheproportionof months

worked in the twelve monthsprior to the interview. This variableis significantfor both the transitions

into self-employmentandthetransitionsinto employment,andthecoefficient is higherfor thosebecoming

employees. This is consistentacrossall specificationsfor the respondentsbecomingemployed, andalso

consistentwith theliteratureon thepersistenteffectsof job displacement.

Receiving pensionsisnegatively relatedwith becomingself-employedoremployed,somethingwecould

expect. Net wealth is positively relatedto the decisionof becomingself-employed, but the coefficient

arenot significant. However, it is negatively relatedandsignificantfor the decisionof employment,and

13 Rustusestwo variables:theself-reportedplannedhoursof work in theyearfollowing thesurvey andtheactualhoursworkedin theyearfollowing thesurvey.

14 AppendixA explainsin detail theconstructionof thetest,showing alsotheprobit results.15 SeeBenıtez-Silva (2000).

11

robustacrossmostspecifications.16 This is consistentwith Blau (1994)who foundthathigherassetswere

associatedwith adecreasedre-entryrate.Also consistentwith Blau(1994)wefind thatwhitesarelesslikely

to make reversetransitions.

Healthvariablesarealsoquiteimportantandin generalnegatively relatedwith thedecisionto returnto

work. This agreeswith theliterature,Rust(1990),Blau (1994),RustandPhelan(1997),Blau et al. (1997)

andQuinn(1998)find comparableeffects.17 Themostimportanthealthvariablesfor thedecisionto become

self-employedarethedummiesfor having ahealthlimitation thatpreventswork,having highbloodpressure

andpsychologicalproblems.18 For thedecisionto becomeemployed,having difficulty readinga maphasa

negative effect, andhaving a healthlimitation thatpreventswork doesaswell. We alsoincludea cognition

variable,constructedfrom the resultsof a memorytest. This variableis a numberbetween0 and20 and

reflectsthenumberof wordsthatrespondentscouldrememberout of a list readto them20 minutesearlier.

Thisvariableis positive andsignificantfor thetransitionto employment.

Anotherinterestingspecificationis shown in Table4, which usesa sub-sampleof marriedrespondents,

includinga dummyvariablethat is 1 if therespondent’s spousewasnon-employedat thetime of theinter-

view. We alsoincludethe differencebetweenthe spouses’ages,a variablethat wasfound to be relevant

amongmarriedwomenin PozzebonandMitchell (1989),andthehealthstatusof thespouse,avariableused

in theliterature.19

Thesevariablescouldpotentiallycapturesomeissuesof preferencefor joint leisureor thecomplemen-

tarity of time spentin andout of the labor market. The fact that the respondent’s spouseor partnerwas

non-employedsignificantlydecreases(moreclearlyfor returningasanemployee)theprobabilityof return-

ing to work. This agreeswith BlauandRiphahn(1998)andBlau (1998).

If a respondent’s spousehasa healthlimitation that limits work, he or sheis morelikely to returnto

work asself-employed, which is only significantfor womenandit is not significantfor the transitioninto

16 This is aninterestingresultgiventhatthis revealsaninterestingeffect of animportantvariablelike currentnetwealthon thetypeof employmentanindividual takes.Giving usa bettersensewhy is it worthsplitting thewaywe have thesampleof worker.

17 Theeffect of healthvariableson labormarket decisionshasbeen,andstill is, at thecenterof acontroversialdebateontheuseof self-reportedandobjectivevariablesin behavioral models.Thepossibleendogeneityof theregressorscoupledwith measurementerrorcould leadto substantialbiasesin thecoefficientsof interest.Most of thedebatehasconcentratedon theeffect of healthontheretirementdecision,but theextensionof theargumentsto thedecisionof returningto work is straightforward. We have testedtheexogeneityof theself-reportedhealthmeasureswith respectto thedecisionof returningto work with thesamemodelwe testedtheexogeneityhypothesisfor thesearchdecisionandagainfoundnoevidenceof endogeneityof any of thesemeasures.Theeffectof healthvariablesis importantbut doesnotseemto beoverstateddueto asupposetendency of therespondentsto rationalizetheirproblemsin thelabormarket usingtheirhealthcondition.

18 Thepsychologicalproblemdummyhasthecorrectsign in mostspecificationsin this andin theotherestimationsusingtheothertwo sub-samples.Theseresultssupportprevious literaturemorefocusedon thespecificeffectsof thesekindsof problems.SeeCurrieandMadrian(1998,p.11)for a survey.

19 Thedifferencebetweenthespouses’agesis calculateddifferentlyhere.PozzebonandMitchell subtractthewife’s agefromthehusband’s. Wesubtractthespouse’s or partner’s agefrom therespondent’s, regardlessof thesex.

12

employment.This is in generalagreementwith Blau andRiphahn(1998),consideringthatthey controlfor

theinteractionbetweenthehealthandlaborstatusof thespouse.

The differenceof agesis positively and significantly relatedwith the transitionto employment, and

giventhedifferentcharacterizationof thisvariable,it is consistentwith theresultsin PozzebonandMitchell

(1989). They find, usinga sub-sampleof marriedwomen,thatwhenthewife is youngerthanthehusband

shetendsto retireearlier, againmaybeproxyingfor somesortof joint laborforceparticipationdecision.In

our case,high agedifferencereflectsthattherespondentis older, andin this caseis morelikely to returnto

work.

Theseresultsareimportantandconsistentwith Hurd (1990a)andprobablyproxy for thefactthatolder

peopleseemto make joint labor forceparticipationdecisions,with a biastowardspreferringdecisionsthat

allow them to spendtime together, or that take into accountthe spouse’s characteristicsin the decision

makingprocess.We estimatethis very samemodelwith a sub-sampleof youngerrespondentstrying to

seeif this signcould reverseamongthem. We find that thevariablebecameinsignificantandthat for that

populationtheothercovariatesdid a goodenoughjob explainingthedecisions.A dynamicgamein amore

theoreticallychargedmodelthattriesto take into accountthedifferentvalueof onemoreperiodof work for

bothpartnerswouldbeneededto analyzethisquestionin full andis definitelyhigh in our researchagenda.

Thedirectcomparisonbetweenmalesandfemalesrevealsthat the importantvariablesremainsimilar,

with theclearimportanceof agedummieswheninteractedwith insurancestatus.Thesearchvariableand

having ahealthlimitation have thesamesignsmentionedabove. Theproportionof workingmonthsis more

importantfor womenandreceiving pensionshasa strongernegative effect for womenin the decisionto

returnasself-employed.

It is worth mentioningthe little significanceacrosstheboardof educationvariables.Oncewe control

for a largenumberof characteristicsthey areleft with relatively little explanatorypower. Thiscontrastswith

PeracchiandWelch(1994)who find thateducationis a goodpredictorof transitionsbut is moreagreement

with Blau (1994)whofindsvery smalleffectsof educationvariables.

3.3 Transitions from non-employment to employment: shorter transitions

Oneof the importantcontributionsof this paperis the useof monthly employment indicatorsto estimate

shortertransitions. Most of the literatureusesthe survey to survey transitions,but several authorshave

emphasizedthattheidealsituationwouldbeto have finer time periods.Blau (1994)is ableto usequarterly

data,and recentlyBlau and Gilleskie (1997) and Blau et al. (1997) useone year transitions. And as

mentionedbefore,Blau andRiphahn(1998)usemonthlyGermandata.

13

We run binomial logit modelsfor non-employed respondentswith the dependentvariablebeing the

employmentdummyvariableat the different time intervals. We run monthly, quarterly, semi-annualand

annualtransitions,andalsothe usualbiennial transition. In this casewe cannotdistinguishbetweenem-

ploymentandself-employmentandwe pool theminto onecategory of employed if they hada onein the

correspondingdummyvariable.We run all theseestimationsfor thesub-sampleof non-employedandalso

for malesandfemales.We presenta summaryof resultsin Table5, only reportinga subsetof variablesbut

thespecificationsareavailablefrom theauthoruponrequest.

Table5 presentsthreepanels.PanelA usesthefull sub-sampleof non-employed respondents50 years

or older. We report in eachpanelthe averageprobability, andthe marginal effectsasa proportionof the

averageprobabilityfor eachestimation.20

The first importantthing to notice is that the marginal effects vary dependingon the time periodof

our estimation. The effect of having a limitation that preventswork and the effect of beingmarriedare

the morestableones. The importanceof theSearchj variableon the probability of becomingemployed

seemsto be strongerfor shortertransitions,sinceit reflectssearchbehavior in a shortperiodbeforethe

interview. However, it hasa very large positive effect for the transitionto employment regardlessof the

periodicity. This is consistentwith our previousresults,andimpliesthatour coefficient usingthesurvey to

survey transitionscouldbea lower boundif we believe that therelevant transitionshappenin shortertime

periods.

Theeffectof thepercentageof monthsworkedin theyearprior to theinterview followsasimilarpattern

andis alsoconsistent,sincethepastattachmentto thelabor forceshouldhave a decreasinginfluenceonce

thepersonis outof thelabormarket for longerperiodsof time. Theeffectsof otherimportantvariables,such

asreceiving disabilitypaymentsor being62 andhealthinsured,arealsostrongerfor theshortertransitions.

PanelsB andC reporttheresultsfor malesandfemales.Theeffectsarevery similar, but it is important

to notice the strongereffect for womenof the searchvariableand the labor force attachmentvariables,

especiallyfor the shortertransitions. And it is amongthem that we seelarger differencesbetweenthe

effectsof the variablesusing the shortertransitionsand the two year results,implying that if we do not

considerthesefiner time intervalswecouldbeunderestimatingtheeffectsof thesevariablesfor women.

The conclusionfrom theseshorterestimationsis that the variableswe identified as importantin the

survey to survey transitionsremainthe relevantones.However, theeffectsvary significantly. We mustbe

20 For continuousvariablesthemarginal effectsarecomputedastheaveragederivative of theestimatedchoiceprobabilitywithrespectto the variablein question.For dummyvariablesthey arethe averageof the differencebetweenthe probability with thebinaryvariablesetto 1 andtheprobabilitywith thebinaryvariablesetto 0.

14

awareof this if we think that the relevant transitionsare the shorterones. However, it is not clearwhat

theappropriatetime interval to look at is if we weretrying to reacha sharperconclusionon theeffectsof

differentvariables. Onemight think that monthly transitionsarepotentiallyvery noisy, sincewe do not

considerthatmaybeanindividual hasaerraticlaborforcetrajectory. But again,thesamecanbesaidabout

thesurvey to survey transitionssincerespondentsareinterviewedat anarbitrarypoint in time.

It is importantto emphasizethatour resultsareconsistentregardlessof thetime periodchosen,but we

acknowledgetheneedfor adynamicperspective to analyzethelaborforcetrajectoriesof olderAmericans.

Thanksto the monthly employment indicators,we will be ableto characterizedifferentpatternsof labor

force trajectoriesover the1989-1997period,flaggingevery respondentdependingon his or herpatternof

attachmentto thelaborforce.Thiswill beanimportantcontribution to thestudyof olderworkersandtheir

retirementandlabormarket decisions.21

3.4 Transitions from self-employment to non-employment and employment

In Table6 we presentthe resultsfor the transitionsfrom self-employment to the other two states.These

areresultsrarelyreportedin theliterature,exceptasasubsetof thoseemployed.However, aswe have seen

above it seemsto bejustifiedto split thesamplein this waybecausetheeffectsof theexplanatoryvariables

canbedifferentfor thetwo typesof workers.Giventhatwe have fewer observations,thefit is not asgood

andtherearenotasmany significantvariablesacrossspecifications.

Theresultsfor thefull sampleshow thatfor transitionsfrom self-employmentto employmentonly afew

variablesaresignificant.Here,having highernetwealthhasa negative effect on switchingto employment,

which probablymeansthat,otherthingsequal,if a person’s businessis goingwell they have no incentive

to change.The effect of this variableis alsonegative for transitionsto non-employment. Having higher

wealthprobablymeansthat the businessis doing betterand thereforethe person’s chancesof becoming

non-employeddecreaseconsiderably. Wefind few significanthealthvariables.Having difficulty stoopingis

positively correlatedwith thetransitionto employmentandmight beproxyingfor activities morecommon

if thepersonrunshis or herown business.Having ahealthlimitation thatinterfereswith theperson’s work

hasthe correctsignsbut it is only significantfor the transitionsto non-employment. Cognitionvariables

alsohave thecorrectsigns:beingin bettermentalhealthmeasuredby memoryandcognitive testsdecreases

theprobabilityof becomingnon-employed.

21 Initial resultsshow that a high proportionof respondentsexhibit “normal” patterns,eithercontinuouswork or continuousnon-employmentalongwith differentpatternsof transitionsinto retirement.We alsoidentify animportantnumberof respondentswith lessconventionallaborforceparticipationpatternsandtheir analysisis partour researchagenda.

15

A very importantvariablefor the decisionto becomean employee is the proportionof monthsthat

respondents’workedduring thepreviousyear. Themorethey worked, the lesslikely they wereto switch.

This is againintuitive, if we believe that working a lot in their own businessesmeansthat they aredoing

well andwill probablynot join anothercompany. Total hoursworked in the last year is alsonegatively

relatedto exiting work, which is plausibleaswell. Having psychologicalproblemssuchasstressincreases

thechancesof leaving work, andsodoesbeingage62.

The higherthe self assessedprobabilityof working full time whenthey reach62 or 65 the lesslikely

they areto make a transition,proxying for the expectationof continuingwith their businesses.The male

indicatorhasa negative andsignificanteffect for transitionsto non-employment, as wasexpectedgiven

theempiricaltransitionprobabilities,wherewomenhada muchhighertransitionprobabilityof becoming

non-employed.

Comparingtheestimationresultsfor menandwomen,thedifferencebetweenthe two groupsin refer-

enceto the dummyfor marital statusis interesting. For women,beingmarriedis positively relatedwith

goingoutof work, andnegatively with joining acompany, while for menexactly theoppositeis true.Being

white is significantandnegative for malesexiting work, andpositive for becomingemployed, while for

womenraceis insignificant. Theexpectationsvariablecomesout significantfor menbut insignificantfor

women.

If werestrictourattentionto marriedrespondents,Table7,andintroduceadummythatis 1 if therespon-

dent’s spouseor partneris self-employedat thetime of theinterview, theprobabilityof makinga transition

outof self-employmentdecreases,aresultthatcanbeinterpretedassupportingthecomplementarityof time

hypothesis.This variable,however, is only significantfor women. We alsointroducea dummythat is 1

if theperson’s spousehasa healthlimitation that limits work. This variableis positive andsignificantfor

womenfor bothtransitions,but negative andinsignificantfor men.

3.5 Transitions from employment to non-employment and self-employment

In Table8 we presentthe resultsfor the transitionsfrom employment to the other two states.Given that

this is themostnumeroussub-sample,thenumberof significantvariablesis quitehighacrossspecifications,

especiallyfor thetransitionsto non-employment.

The effect of having accessto retireehealthinsuranceis positive, but only significantfor males,on

the probability of exiting work, a resultconsistentwith Karoly andRogowski (1997). However, by also

consideringin the analysisrespondentsolder than 61 we are able to captureinterestingeffects for this

agegroup,somethingnot presentin mostof the literature. The effectsof agepeakat 63-64,something

16

consistentwith the fact that oncerespondentsreachthat agethey will not losetheir healthcoverage(for

example)if they chooseto leave the work force given the currentCOBRA laws, andthe fact that by the

next interview they will probablyreachthenormalretirementageandthereforehave accessto Medicareor

Medicaid.22 Themaindifferencesbetweenthoseage62 at thetime of the interview thatmake a transition

and thosethat stayemployed, are that the transitionersreceive healthinsurancefrom their employersor

pastemployersin a lowerproportion,especiallyinsurancepaidby theemployer, andin ahigherproportion

receive governmenthealthinsuranceanddisability payments.In a sense,thesecharacteristicsreflect less

attachmentto their employersandhealthconditionsthatresultin changesin laborforcestatus.If we focus

on those65 and over, the transitionersare males,married,and white in a higher proportion. A higher

proportionof themreceivesgovernmenthealthinsuranceor disability payments,andin a lower proportion

have accessto paid retireehealthinsurance.They alsohave, comparatively, a lower agedifferencewith

their spousesor partners.Theserespondentscanagainbe identifiedasthe “health insuranceconstrained”

describedby RustandPhelan(1997).

Also, receiving pensions,having a healthlimitation, psychologicalproblemsor difficultiesperforming

activities of daily living have theexpectedpositive impacton thetransitionoutof work. On theotherhand,

having workedmorein thelastyearseemsto benegatively relatedwith this decisionandsodoesreporting

beingin excellentor goodphysicalhealthor mentalhealth. Theseresultsarein generalagreementwith

previousliterature.Theself-assessedprobabilityof workingfull timeby age65or overhasanegativeeffect

on this transition,proxyingfor generalexpectationsaboutthelaborforceattachmentof therespondent.

Thetransitionto self-employmentpresentslesssignificantvariablesbut somevery clearresults.Being

white,male,wealthyandwith ahealthlimitation significantlyincreasesthelikelihoodof startingabusiness,

andsodoesexpectingto beworking by age65. However, having difficulty walking multiple blocks,self-

reportingfair health,or having workeda lot in thelastyeararenegatively relatedto thisdecision.

Looking separatelyat menandwomen,the importantvariablesarethesameones.Someof theresults

sharpenwhen looking at thesesub-samples,like the strongeffect of being62 or 63-64andwith retiree

healthinsuranceon thetransitionout of work, or thenegative effect of theexpectationsto work after65 on

thesametransition.

The specificationconsideringonly marriedemployees,Table9, is againof interest. We introducea

dummythat is 1 if the respondent’s spousewasanemployeeat the time of the interview. In this case,the

respondentis lesslikely to exit the employmentstatus,consistentwith our previous resultson the com-

22 In alternative specificationswe interactedtheagedummieswith thehealthinsurancevariableandobtaineda similar patternof coefficientsthatleadto theverysameinterpretation.

17

plementarityof spouses’time. Anotherdummythat takesthe value1 if the person’s spousehasa health

limitation that limits work is positive andsignificantfor womenexiting work, but insignificantfor men.

This resultdiffersfrom thefindingsin PozzebonandMitchell (1989)andBlau (1998).They reportthatfor

women,thespouse’s healthconditionhasa negative effect on theexit rate,meaningthat respondentstend

to delayretirement.Both groupsof researchersusetheRHSwherewomenareunderrepresented,but more

importantly, they do not control for healthinsurancestatus,and in fact justify the resultasmeaningthat

couplesdependon thewife’s insuranceif thehusbandis in poorhealth,forcing thewife to stayemployed.

Oncethis is controlledfor, thesignschange.

Theexpectationsvariableis alsoveryimportantfor thissub-sample,decreasingtheprobabilityof exiting

work and increasingthe transitionto self-employment, probablycharacterizingpeoplethat feel they can

continueworking for a long timeafterthey reachage65.

3.6 Transitions from employment to non-employment: shorter transitions

Here we turn againto analyzethe shortertransitionsusing binomial logits for the decisionto leave the

employment status. In Table10 we presentthe summaryresultsalong threepanels. We report in each

tablethe averageprobability, andthe marginal effectsasa proportionof the averageprobability for each

estimation.PanelA usesthe full sub-sampleof employed respondents50 yearsor older. Our resultsare

consistentwith thesurvey to survey transitionestimates,but now someof thecoefficientsarenotsignificant

soweseesomeswingsin themarginal effectsof theimportantvariables.

We canagainseethat mostvariableshave a strongereffect for the shortertransitions,except for the

dummythat interactsbeing63-64andhaving accessto retireehealthinsurance,this effect is higherfor the

annualandtwo yeartransitions,resultdrivenby men. This couldreflectthefact thatoncetherespondents

reachage63-64(62 for our femalesub-sample)they retireat differenttime intervals,but by age65,a high

proportionof thosethatwantedto retirewill have doneso.Wemustemphasizethestabilityof themarginal

effectof theexpectationsvariable:theself-assessedprobabilityof workingfull timeby age65 is negatively

andsignificantly relatedto becomingnon-employed, and its effect is very similar regardlessof the time

periodof the transitionwe pick. This is expectedif we believe this variableis picking up unobservable

characteristicsof therespondentsthataremoreor lessstableover time.

18

4 Conclusions

In this paperwe usethe first threewavesof the HRS to addsomeinsight to the literatureon labor force

transitions,usingamorecurrentandcompletesourceof data.This is oneof thefirst attemptsto analyzethe

trendsin retirementandlaborsupplyamongthispopulationusingall threewavesof thesurvey. This is also

thefirst panelstudyto trackemploymentto themonthlylevel with Americandata

We have paidspecialattentionto reversetransitions,somethinglessstudiedthantransitionsinto retire-

ment. Oneof our initial concernswas to study thesereversetransitionsin depthto verify whetherthey

have increaseddueto the healthystateof the economy, and to learnmoreaboutthesedeterminantsin a

time that moreof this kind of transitionscanbe expectedfor individuals55 andolder. Several important

variablesin our specificationcanbe interpretedassupportingthis trend,andthe evidencefrom the labor

forceparticipationratesusingBLS dataseemto point in thatdirection. We emphasize,however, that this

studyconcentrateson themicrodeterminantsof laborforcestatusanddoesnotaddressthemacroeconomic

issuesthatarealsoimportantin explainingthis trend.

Wealsoestimatethelaborforcedecisionsof marriedrespondents,includingdummiesfor thelaborforce

statusof the spouse,their agesandtheir health,andthe resultsshow a preferencefor joint leisureand/or

complementarityof time betweenspouses.Theseresultsareconsistentwith previous literatureusingdata

from the1970’s andearly80’s. Theanalysisof joint retirementbehavior deservesspecialattentionin future

research.

It is alsoworth emphasizingthatwe usea slightly differentcharacterizationof labor forcestatusfrom

the oneusein the literature,sincewe distinguishbetweenbeingself-employed andbeingemployed for

someoneelse.This makessomeof our resultslesscomparablewith previousresearch,but at thesametime

we investigatethis issuein muchmoredepththanpreviousstudiesdo, andwe find that thanksto splitting

thesamplein thisfashionwecaptureddifferenteffectsof theexplanatoryvariablesonthevarioustransitions

probabilitiesandavoid possibleoffsettingeffectsof somevariables.

We usea multinomial anda binomial logit model for estimatingthe decisionsto changelabor force

statusat eachinterview dateasanapproximationto a moreinvolvedmodelof thetransitionbehavior. The

reducedform natureof our analysisuncoversinterestingrelationshipsthat,however, cannotbeinterpreted

in a causalway. We run separateestimationsfor the threesub-samplesof non-employed, employed and

self-employedrespondents,andalsofor morenarrowly definedsub-samplessuchasmalesandfemales.

In the decisionsto returnto work the interactionof agedummieswith healthinsurancestatusreveals

a “spike” at 62 anda 65+ effect, anda negative effect on the probability of returningto work. Among

19

the employed, we find that accessto retireehealth insurancehasa positive effect on the probability of

becomingnon-employed (especiallyfor men). Theseresultsareconsistentwith the importanteffectsof

healthinsurancein labormarket outcomesthathave beenfoundin theliterature.

Theeffect of marriageis quiteinterestingandconsistentwith previousresearch.Marriedmenaremore

likely to returnto work asemployeesandlesslikely to leave theworkforce.Theoppositeis truefor married

women.Healthandcognitionvariablesarealsosignificantandhavetheexpectedsignsacrossspecifications,

wherebeingin worsehealthstatus,by a wide varietyof measures,hasa negative effect on theprobability

of returningto work andapositive effecton becomingnon-employed.

Oneof themostimportantandsignificantvariablespositively affecting the decisionto returnto work

(especiallyasa paidemployee)is therespondent’s job searchdecision.We interpretthis asa variablethat

allows us to distinguishbetweenthoseout of the labor forceandthosestill active in the labormarket, and

for somesortof implied businesscycle effect, sincethewillingnessto find a job hassucha strongpositive

effecton theprobabilityof finding it, whichmeansthatthelabormarket is healthyenoughto accommodate

mostof thereversetransitioners.Theseresultscontrastwith previousresultsin theliteraturethatdid notfind

asignificanteffectof thesearchingdecisionontheprobabilityof findinga job afterwards.Weacknowledge

thepossibleendogeneityof this variable,andtesttheexogeneityof thesearchingdecisionusingabivariate

probitmodel.Wedonot rejectits exogeneityatany standardlevel of significance.

Incomein theyearbeforetheinterview is positively relatedto returningto work. This couldmeanthat

thosethatwereearningmorearemorelikely to re-entry. This possibility is confirmedwhenwe introduce

a variablethat shows the proportionsof monthsworked during the yearprior to the interview. This is a

significantvariablefor the transitionsto employment, consistentwith the literatureon the persistenceof

job displacement.The longertheperiodthat thepersonspendsnon-employed the lesslikely he or sheis

to return to work. Also, morework in the yearprior to the decisionreducesthe likelihoodof makinga

transitionfrom employmentor from self-employment.

Finally, an importantcontribution of this paperis the estimationof shortertransitionsusingmonthly

employmentindicators.We run monthly, quarterly, semi-annualandannualtransitionsfor thedecisionsto

exit theemploymentstatusor to returnto it. Our resultsareconsistentwith thefindingsusingthesurvey

to survey intervals, but most of the importantvariableshave strongereffects for the shortertransitions,

implying that the estimatesusinglongertime periodscould be a lower boundfor the true effect of some

variables.Wearguethata dynamicstudyof laborforcetrajectoriesusingthesemonthlydummieswill bea

relevantcontribution to theliterature,andit is partof ourundergoingresearch.

We considerthat theseresultsmake a contribution to the literature.However, many issuesarestill not

20

addressed,for example,therole of smallercompaniesandnon-profitorganizationsin hiring olderworkers

sincethey could be cheaper, becausetheseworkers might needlower investmentsin pensionandhealth

benefits,or training costs. Thosecompaniescan usethe savings and the experiencedeliveredby older

workers. Also, we might think that theservicesectoris theonepreferredby theelderlybecauseit allows

for moreflexibility andconvenience.

Anotherimportantissueis the incidenceof part-timework in thesetransitions,which onemight think

would be higher amongthe elderly. This is somethingthat could be includedin the model as another

employmentstate.

Many of thesestudiesfocusonolderworkers,but a lot of insightcouldalsobeobtainedby studyingim-

migrants,temporaryworkers,low incomeindividualsor minorities.23 An interestingareaof futureresearch

is to integratetheseanalysesto comparehow public policy andthebusinesscycle is affectingthedecisions

of thesepopulations.

It is a fact thatthelabormarket is changing,andthelaborsupplyanddemandof andfor olderworkers

is not an exception. We interpretsomeof our resultsassupportingthe increasingtrendof longercareers

andsecondcareersamongolder individuals,in a healthyandgrowing labormarket. This hassomepolicy

implicationsbecauseit cangive usa differentperspective on the issueof thefuturesolvency of theSocial

SecurityandRetirementsystem.In ahealthyeconomymaybeit will notmatterthatmuchthattheproportion

of older peopleincreaseswith respectto previous decades,becausetheir working liveswill last longeras

life expectancy increasesanda majority of jobs requiremoreknowledgeandlessphysicalstrength.This

alsomeansthattheelderlyin thenearfuturewill have to keepupwith thetechnologicalchanges,otherwise

they will find themselveswith thejobsthatothersdo notwantor will decideto stayoutof thelabormarket

increasingtheinflationarypressuresin astrongeconomicenvironment.24

23 Evencombinationsof thosecharacteristicsareworthstudyingasthelaborsupplydecisionsof particulargroupscanbestronglyinfluencingtheentiremarket. SeeFlippenandTienda(1996)for anstudyonHispanicolderworkersin theUS laborforce.

24 SeeFriedberg (1999a)for a discussionof theimpactof technologicalchangeon olderworkers,andBenıtez-Silva (2000)fora modelthattriesto shedlight onhumancapitalinvestmentdecisionsat theendof thelife cycle.

21

Table1. PanelA: Meansand Standard Deviations

Variable Full Non-Employed Self-Employed EmployedSample Sub-Sample Sub-Sample Sub-Sample

N 19,769 7,117 2,393 10,259

Age 57.63 59.21 57.48 56.58(0.06) (0.15) (0.12) (0.05)

White 73.54 68.92 82.76 74.69(0.30) (0.53) (0.74) (0.43)

Male 45.28 38.68 61.54 50.05(0.34) (0.56) (0.95) (0.49)

Married 80.76 79.17 87.29 78.19(0.27) (0.47) (0.65) (0.41)

Divorced 16.44 17.65 10.71 18.77(0.25) (0.44) (0.61) (0.39)

BachelorDegree 20.74 12.96 26.60 24.17(0.28) (0.39) (0.87) (0.42)

ProfessionalDegree 7.31 3.78 9.87 9.09(0.18) (0.22) (0.58) (0.28)

Family Income 60,350 49,119 84,086 61,591(1,618) (2,976) (3,886) (2,416)

Resp.Income 22,229 5,357 46,187 28,533(652) (841) (4,717) (371)

Incomefrom Pensions 4,712 6,415 2,616 4,372(785) (987) (256) (1,484)

Net Worth 236,249 215,813 528,982 182,027(3,372) (4,850) (18,146) (3,401)

HousingWealth 73,054 69,548 102,565 69,674(899) (1,217) (4316) (1,208)

HoursWorked 1,441 327 2,061 1,937(7) (9) (22) (7)

% MonthsWorkedlastyear 0.66 0.10 0.97 0.98(0.00) (0.00) (0.00) (0.00)

GovernmentHealthIn. 0.18 0.34 0.10 0.09(0.00) (0.01) (0.01) (0.00)

EmployerHealth-In. 0.71 0.54 0.55 0.85(0.00) (0.01) (0.01) (0.00)

PrivateHealth-In. 0.15 0.15 0.27 0.12(0.00) (0.00) (0.01) (0.00)

Receiving Disability 0.06 0.18 0.00 0.00(0.00) (0.00) (0.00) (0.00)

Retired 0.16 0.38 0.09 0.03(0.00) (0.01) (0.01) (0.00)

22

Table1. PanelB: Meansand Standard Deviations


N 19,769 7,117 2,393 10,259

Non-elegibleSSI 0.92 0.86 0.94 0.95(0.00) (0.00) (0.00) (0.00)

Pr.Working Full Time62+ 44.28 22.55 55.86 45.28(0.33) (0.86) (0.84) (0.41)

Pr.Working Full Time65+ 28.35 23.15 43.77 25.70(0.31) (1.03) (0.81) (0.36)

Pr.Living to 75 64.44 60.40 69.41 66.11(0.21) (0.38) (0.57) (0.28)

Pr.Living to 85 44.10 42.29 47.33 44.37(0.22) (0.40) (0.64) (0.32)

HealthLimitation 22.20 45.48 13.90 8.80for work (0.28) (0.57) (0.68) (0.28)

HealthLimitation 10.56 29.79 0.42 0.18preventswork (0.21) (0.52) (0.13) (0.04)

AgeSpouse 55.24 56.98 54.30 54.96(0.08) (0.16) (0.13) (0.12)

AgeDifference 1.29 1.44 2.11 1.67(0.05) (0.09) (0.14) (0.06)

SpouseN-Employed 0.37 0.47 0.30 0.31(0.00) (0.01) (0.01) (0.01)

SpouseEmployed 0.46 0.37 0.38 0.53(0.00) (0.01) (0.01) (0.01)

SpouseS-Employed 0.11 0.09 0.26 0.09(0.00) (0.01) (0.01) (0.01)

SpousehasHlimwk. 20.66 24.88 7.73 18.75(0.22) (0.44) (0.81) (0.44)

SpousehasHlimpw. 9.24 12.71 5.71 7.87(0.22) (0.44) (0.49) (0.31)

23

Table1 PanelC: Meansand Standard Deviations


N 19,769 7,117 2,393 10,259Objective HealthMeasures:

HospitalStays 0.23 0.42 0.12 0.14(0.01) (0.02) (0.01) (0.01)

DoctorVisits 5.26 7.44 3.63 4.17(0.06) (0.13) (0.11) (0.06)

High Blood pressure 22.18 25.19 18.58 21.66(0.28) (0.50) (0.76) (0.41)

Diabetes 6.76 9.72 4.72 5.37(0.17) (0.34) (0.42) (0.22)

Cancer 2.39 3.24 1.61 2.10(0.10) (0.20) (0.25) (0.14)

Stroke 1.03 2.45 0.19 0.30(0.07) (0.18) (0.09) (0.05)

PsychologicalProblems 6.83 10.67 4.49 4.52(0.17) (0.35) (0.41) (0.21)

BackProblems 32.12 39.87 27.95 27.98(0.32) (0.56) (0.88) (0.44)

FeetProblems 33.03 43.75 25.60 27.88(0.32) (0.57) (0.86) (0.44)

NursingHomeStays 0.57 1.38 0.15 0.14(0.05) (0.13) (0.08) (0.04)

MemoryTest1 7.72 7.27 7.92 7.88(0.02) (0.04) (0.06) (0.03)

MemoryTest2 5.78 5.34 6.00 5.91(0.02) (0.04) (0.06) (0.03)

Cognitive Test 6.20 5.72 6.61 6.35(0.02) (0.04) (0.06) (0.03)

Subjective HealthMeasuresandADL:ExcellentHealth 20.85 13.21 28.10 23.60

(0.28) (0.39) (0.88) (0.42)Fair Health 14.36 21.45 10.29 10.81

(0.24) (0.47) (0.60) (0.31)PoorHealth 7.05 16.73 2.00 1.93

(0.17) (0.43) (0.27) (0.14)WorseHealth 2.98 3.09 2.92 2.93

(0.00) (0.01) (0.01) (0.01)ExcellentCognition 17.12 13.91 2.84 17.77

(0.26) (0.40) (0.82) (0.38)PoorCognition 2.95 6.28 0.73 1.28

(0.11) (0.28) (0.17) (0.11)Diff. Wlk. M.Blocks 14.35 27.39 8.09 7.49

(0.24) (0.51) (0.54) (0.26)Diff. ClimbingStairs 9.48 19.63 3.49 4.23

(0.20) (0.46) (0.36) (0.20)Diff. Stooping 27.43 41.24 8.51 20.70

(0.30) (0.57) (0.76) (0.40)

24

Table2: Empirical Transition Probabilities

Panel A. Transitionsfrom wave1 to wave2. Males50 andover.EmployedWave 2 S-EmployedWave 2 N-EmployedWave 2 Observations

EmployedWave 1 0.8065 0.0367 0.1568 2,832S-EmployedWave 1 0.0782 0.8142 0.1076 818N-EmployedWave 1 0.0842 0.0454 0.8704 1,389

Observations 2,465 833 1,741 5,039

Panel B. Transitionsfrom wave1 to wave2. Females50andover.EmployedWave 2 S-EmployedWave 2 N-EmployedWave 2 Observations


Observations 2,417 415 2,315 5,147

Panel C. Transitionsfrom wave 2 to wave3. Males50 andover.EmployedWave 3 S-EmployedWave 3 N-EmployedWave 3 Observations


Observations 2,031 731 1,798 4,560

Panel D. Transitionsfrom wave2 to wave3. Females50 andover.EmployedWave 3 S-EmployedWave 3 N-EmployedWave 3 Observations


Observations 2,138 382 2,503 5,023

25

Tabl

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26

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etire

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316

27

Table 5: AverageProbabilities and Mar ginal Effects for the Non-Employment toEmployment Transitions. Binomial Logits25

Panel A. Sub-Sampleof thoseage50 andover1 month 3 months 6 months 12 months 24 months

AverageProbability 0.033 0.040 0.053 0.065 0.126

HealthLimitation Preventswork -0.315 -0.209 -0.675 -0.628 -0.627% of Monthsworkedlastyear 1.791 1.705 1.277 1.191 1.309Searchedfor a job lastmonth 2.361 2.847 2.628 2.479 1.307Insuredand62 -0.678 -0.569 -0.491 -0.718 -0.608Receiving DI -0.675 -0.584 -0.317 -0.141 -0.226Married -0.341 -0.334 -0.309 -0.020 -0.1819

Panel B. Sub-Sampleof malesage50andover1 month 3 months 6 months 12 months 24 months


HealthLimitation Preventswork -0.401 -0.460 -0.865 -0.861 -0.609% of Monthsworkedlastyear 1.353 1.551 0.919 0.780 0.945Searchedfor a job lastmonth 2.146 2.128 2.408 2.512 1.574Insuredand62 -0.489 -0.492 -0.052 -0.731 -0.817Receiving DI -0.479 -0.423 0.061 0.3425 0.083Married -0.080 -0.064 -0.353 0.065 0.149

Panel C. Sub-Sampleof Femalesage50 andover1 month 3 months 6 months 12 months 24 months


HealthLimitation Preventswork -0.508 -0.044 -0.5535 -0.480 -0.667% of Monthsworkedlastyear 2.221 1.672 1.193 1.363 1.628Searchedfor a job lastmonth 2.655 3.483 3.070 2.430 0.978Insuredand62 -0.870 -0.851 -0.890 -0.720 -0.179Receiving DI — -0.755 -0.726 -0.565 -0.564Married -0.501 -0.4794 -0.183 -0.025 -0.425

25SelectedVariables. Marginal Effectsare in timesthe AverageProbability. All regressionsalso includedummiesfor beingmale,white, andhaving madethedecisionin wave 2. Also educationlevels,agesinteractedwith healthinsurancestatus,income,wealth,self reportedhealthvariables,ADLs, objectivehealthmeasuresandcognitionvariables.All themarginaleffectscomefromsignificantcoefficientsat the5%or 10%level, exceptfor thosereportedin italics.

28

Tabl

e6:

Est

imat

ions

for

Sel

f-E

mpl

oyed

Tra

nsiti

oner

sto

Non

-Em

ploy

men

tand

Em

ploy

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t

Sel

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mpl

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iabl

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ales

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sam

ple

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stan

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29

Tabl

e7:

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for

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32

Table10: AverageProbabilities and Mar ginal Effects for the Employment toNon-EmploymentTransitions. Binomial Logits26

Panel A. Sub-Sampleof thoseage50 andover1 month 3 months 6 months 12 months 24months


HealthLimitation for work 0.943 0.296 0.411 0.202 0.360% Monthsworkedlastyear -1.046 -1.331 -2.024 -1.128 -1.06755-57with RetireeH-I. 0.238 0.240 -0.121 0.327 0.22163-64with RetireeH-I. — 0.674 1.959 2.919 2.255No High SchoolDiploma 0.314 0.643 0.126 0.188 0.293Prob. Working FT by age65 -0.495 -0.293 -0.594 -0.671 -0.792

Panel B. Sub-Sampleof malesage50andover1 month 3 months 6 months 12 months 24months


HealthLimitation for work 2.252 0.384 0.297 0.070 0.505% Monthsworkedlastyear -1.193 -1.826 -2.175 -1.457 -1.18355-57with RetireeH-I. 0.306 0.108 0.017 0.261 0.24463-64with RetireeH-I. — — 1.661 1.983 2.871No High SchoolDiploma 0.526 0.377 0.007 0.138 0.234Prob. Working FT by age65 -1.149 -0.711 -0.729 -0.923 -1.014

Panel C. Sub-Sampleof Femalesage50 andover1 month 3 months 6 months 12 months 24months


HealthLimitation for work 0.173 0.141 0.613 0.351 0.234% Monthsworkedlastyear -1.005 -0.978 -1.925 -0.873 -1.03055-57with RetireeH-I. 0.246 0.381 0.285 0.360 0.19962with RetireeH-I. 0.567 2.817 1.724 1.612 1.539No High SchoolDiploma 0.185 0.822 0.258 0.218 0.311Prob. Working FT by age65 -0.137 0.113 -0.470 -0.597 -0.804

26Marginal Effectsarein timestheAverageProbability. All regressionsalsoincludedummiesfor beingmale,married,white,non-eligiblefor disability insuranceandhaving madethe decisionin wave 2. Also educationlevels, agesinteractedwith healthinsurancestatus,income,wealth,self reportedhealthvariables,ADLs, objective healthmeasuresandcognitionvariables.All themarginaleffectscomefrom significantcoefficientsat the5% or 10%level, exceptfor thosereportedin italics.

33

Figure1: Labor Forceparticipation. Males 55 to 64

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 63

64

65

66

67

68

69

Labo

r For

ce P

artic

ipatio

n

Figure2: Labor Forceparticipation. Females55 to 64

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 42

43

44

45

46

47

48

49

50

51

52

53

Labo

r For

ce P

artic

ipatio

n

34

Appendix A

In this Appendixwe borrow from Heckman(1978),Kiefer (1982)andGreene(1993)to show how we testfor theexogeneityof thesearchingdecisionin ourestimations.27

Considera two equationsystem:

y�1i� X1iβ1

�diα1

�y�2iγ1

�U1i (1)

y�2i� X2iβ2

�diα2

�y�1iγ2

�U2i (2)

wherethedummyvariabledi is definedby: di� 1 iff y

�2i � 0, anddi

� 0 otherwise.It is importantto emphasizethat thesystemabove representstwo continuouslatentvariablesthatgen-

erateobservablediscretedummyvariables(y1i andy2i) whenthey reacha threshold.This modelis flexibleenough,asHeckman(1978)shows, to includea numberof importantspecifications.Thecasethatwe areinterestedin is onewhereequation(1) representsthestructuralequationof interest,in ourcasethedecisionto becomeemployed in thenext period(employmentfor a third partyandself-employmentarenot distin-guishfor thepurposeof this test)and(2) representsthe searchingdecisionthatmight be endogenous.Inequations(1) and(2), X1i andX2i, arerespectively, 1 � K1 and1 � K2 row vectorsof boundedexogenousvariables.Thejoint densityof continuousrandomvariablesU1i � U2i is g � U1i � U2i � which is assumedto beabivariatenormaldensity, with meannormalizedto be0 andthe2 � 2 covariancewith variancesnormalizedto 1, andcorrelationcoefficient ρ �� 1 � 1� .

Our objective is to test exogeneityof the searchingdecisionwith respectto the structuraldecision.Without lostof generalitywecanconsiderasimplecharacterizationof thesystemunderthenull hypothesisof exogeneityof thesearchvariable,by settingα2

� 0 � γ1� 0 � and γ2

� 0. This modelthenreducesto astandardbivariateprobit model,wherethe test for independenceof the probit equationsis equivalent forus to a test of exogeneityof the searchingdecision. If we cannotreject that ρ � 0 then we can safelyassumeexogeneityin ourestimations.If werejectthenull hypothesiswewill have to estimatethestructuralparametersthroughabivariateprobitwith astructuralshift.

Therefore,we test the independenceof the probit equationsresultingfrom (1) and(2). The simplestmethodto constructthetestof thehypothesisthatρ � 0 follows Kiefer (1982)andGreene(1993).Thecon-structionof theLagrangeMultiplier (LM) testonly requirestheestimationof thetwo independentprobits:

LM � f 2

h(3)

where f andh arecalculatedasfollows:

f � ∑i

q1iq2iφ � w1i � φ � w2i �Φ � w1i � Φ � w2i � (4)

h � ∑i

φ � w1i � φ � w2i �� 2

Φ � w1i � Φ �� w1i � Φ � w2i � Φ �� w2i � (5)

where,

q1i� 2y1i � 1 � and q2i

� 2y2i � 1 (6)

w1i� q1iβ �1X1i � and w2i

� q2iβ �2X2i (7)

27 Benıtez-Silva et al. (2000)alsousethis approachin their studyof theSocialSecurityDisability awardprocess.

35

TableA1 presentstheestimatesof theindependentprobitsfor theemploymentandsearchdecisions.TheLM teststatisticfor thisspecificationis 0.0169andfollowsaχ2

1, deliveringaP-valueof 0.896,sowecannotrejectthenull hypothesisof exogeneityof thesearchdecision.TheLM teststatisticis simplerto calculatethantheWaldstatisticandtheLikelihoodRatiothatrequiretheestimationof thebivariateprobit,andgiventhatwe have morethan4,000observationswe believe our resultsarenot sensitive to theteststatisticused.

TableA.1: Probit Estimatesof the Employment and Search decisions

Not-Employedto Employed SearchingDecision

No. Variable Estimate StandardError Estimate StandardError1 Constant -0.785 0.178 -0.285 0.1822 Male 0.1664 0.065 0.448 0.0793 Married -0.199 0.082 -0.428 0.0884 White -0.079 0.072 -0.429 0.0795 No Diploma 0.080 0.061 0.156 0.0756 VocationalTraining 0.024 0.066 0.016 0.0827 Wave2-Ind -0.144 0.096 -0.228 0.1228 Age55-59 -0.137 0.111 0.210 0.1259 Age60-61 -0.182 0.164 -0.094 0.17810 Age62 -0.269 0.204 -0.449 0.23211 Age63-64 -0.375 0.214 -0.073 0.23612 Age65 + -0.312 0.178 -0.138 0.22613 Insured55-59 -0.169 0.116 -0.463 0.12614 Insured60-61 -0.395 0.180 -0.484 0.19515 Insured62 -0.537 0.241 -0.180 0.26616 Insured63-64 -0.121 0.233 -0.793 0.27617 Insured65 + -0.190 0.188 -0.809 0.26918 GovernmentInsurance -0.288 0.087 -0.491 0.11619 OwnHealthInsurance 0.035 0.096 -0.440 0.10420 Income($1,000) 0.003 0.002 -0.000 0.00321 Pentot.($1,000) -0.005 0.004 -0.020 0.00622 Nworth($105) -0.026 0.016 -0.062 0.02423 MonthP 0.736 0.093 0.953 0.09624 Receiving DI -0.093 0.129 -0.264 0.15225 Hbloodp -0.066 0.085 -0.002 0.10326 Pshyc -0.040 0.124 0.273 0.12827 MemoryTest 0.014 0.009 0.014 0.01028 Dif-Wkmb -0.201 0.091 -0.038 0.11229 Dif-rmap -0.137 0.080 -0.058 0.09430 Hlimpw -0.454 0.110 -0.621 0.12131 Ex-Health 0.110 0.083 0.141 0.09732 Vg-Health 0.016 0.073 0.094 0.08833 Fair-Health -0.024 0.092 0.070 0.10534 Poor-Health -0.093 0.138 -0.270 0.17635 Searchj 0.816 0.084 — —

Avg. Log L/Obs. -0.2991 4,175 -0.2088 4,175

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Micro Determinants of Labor Force Status Among Older Americans

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