Trade and Labor Reallocation with Heterogeneous ......3 Allowing for globalization to impact plants...

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TradeandLaborReallocationwithHeterogeneousEnforcementofLaborRegulations

RitaK.Almeida

TheWorldBankandIZA

JenniferP.PooleUniversityofCalifornia,SantaCruz

September2012

PRELIMINARYANDINCOMPLETE

Abstract:Thispaperrevisitsthequestionofhowtradeopennessaffectslabormarketoutcomesinadeveloping country setting.Weexplore the fact thatplants face varyingdegreesof exposure toglobalmarketsand to theenforcementof labormarket regulations, and relyonBrazil’s currencycrisis in 1999 as an exogenous source of variation in industry‐specific real exchange rates andhence, access to foreign markets. Using administrative data on employers matched to theiremployees andon the enforcement of labor regulations at the city level overBrazil’smain crisisperiod,we document that theway trade openness affects labormarket outcomes for plants andworkersdependsonthestringencyofdefactolabormarketregulations.Inparticular,weshowthatafter a trade shockplants facing stricter enforcement of the labor lawdecrease job creation andincrease job destruction by more than plants facing looser enforcement. Consistent with ourpredictions, this effect is strongest among small, labor‐intensive, non‐exporting plants, forwhichlabor regulations are most binding. We also note a stronger impact of enforcement on youngerworkers. Finally, our findings are consistent with the hypothesis that increased regulatoryenforcement limits the plant‐level productivity gains associated with trade openness. The latterimpliesthatincreasingtheflexibilityofdejureregulationswillallowforbroaderaccesstothegainsfromtrade.

Keywords: Globalization; Enforcement; Labor Market Regulations; Linked Employer‐EmployeeData.JEL:F16;J6;J8.

OurspecialthankstoPauloFurtadodeCastroforhelpwiththeRAISandSECEXdataandtotheDepartmentofEconomicsandAcademicComputingServicesattheUniversityofCalifornia,SanDiegoforassistancewithdata access.We also thank theBrazilianMinistry of Labor for providing data on the enforcement of laborregulationsand important informationabouttheprocessofenforcement,especiallyEdgarBrandao,SandraBrandao,andMarceloCampos.Forhelpfulcommentsandsuggestions,wearegratefultoKevinMilliganandPetiaTopalova,aswellasworkshopparticipantsattheFederalReserveBankofSanFrancisco,UCIrvine,andvarious conferences. Financial support from the UCSC Academic Senate Committee on Research isacknowledged.AaronColeandKunLiprovidedexcellentresearchassistance. CorrespondingAuthor: 437Engineering2,DepartmentofEconomics,UniversityofCalifornia,SantaCruz,(831)459‐5397,[email protected].

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1. Introduction

Akeyargumentinfavorofliberalizingtraderelationsisthatfactorscanreallocatetomoreefficient

uses,allowingforenhancedproductivity,incomegrowth,andwelfare(Pavcnik2002;Feyrer2009;

Broda and Weinstein 2006). Early studies in many developing countries, however, found little

impact of trade liberalization on plant‐level employment andwages (Currie and Harrison 1997;

Feliciano 2001). More recent work offers mixed results on the causal link from exporting to

productivityandevidenceofslowlabormarketadjustmenttotradereform.Apotentialexplanation

forthesevariousfindingsarerestrictivelabormarketregulations,whichinhibitthereallocationof

workers, limiting the extent to which plants can benefit from increased openness (Freund and

Bolaky2008;Kaplan2009).

Inthispaper,werevisitthequestionoftheimpactoftradeliberalizationonlaborreallocationina

developing country by exploring the fact that plants vary in the degree of exposure to global

marketsandthatdefactolaborregulationsareheterogeneouswithincountries.Werelyondetailed

administrative data from Brazil covering the currency’s devaluation episode. Ourmain reduced‐

form specification relates exogenous industry‐specific exchange rate shocks to plant andworker

outcomes over time, differentially for plants located in distinct labor market regulatory

environments. Our findings show that more stringent de facto regulations reinforce the

contractionary labor market effects of trade openness for small, labor‐intensive, non‐exporting

plants.Overall,domesticplantsinstrictly‐enforcedareasincreasejobdestructionanddecreasejob

creationbymorethanotherwiseidenticaldomesticplantsinweakly‐enforcedareas.Fromapolicy

standpoint,ourwork thereforecontributes toanunderstandingof jobsecurity inan increasingly

globalizedworld.Increasingtheflexibilityofdejureregulationswillstimulatejobcreationandoffer

broaderaccesstothegainsfromtrade.

Wecontributetoagrowingbodyofworkinseveralways.First,themicro‐dataavailableforBrazil

arerichandappropriatetostudytheeffectsoftradeliberalizationonlaborturnover.Weexploita

matchedemployer‐employeedatabasecoveringtheformal‐sectorlaborforce,incombinationwith

information on the plant’s exposure to global markets. The data allow us to analyze total

employment at the plant level, and also to trace the movement of workers across different

employers in response to the trade shock. Furthermore, it permits the decomposition of labor

turnover into changes along theextensivemargin (the accessionand separationofworkers) and

alongtheintensivemargin(hoursworkedandtemporarycontracts).Theabilitytomatchworkers

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totheiremployersisanintegralpartofouranalysis,asrecentevidencepointstoasortingeffectof

globalization.1For instance,as in themodeldescribed inHelpman, Itskhoki,andRedding(2010),

when there are complementaritiesbetweenplantproductivity andworker ability, plantshave an

incentivetoscreenforworkersbelowagivenabilitylevel.Higherproductivityexportersscreentoa

higher ability threshold and will thus have a workforce of higher average ability than non‐

exporters.2Becauseglobalization increasesplant selection intoexportingas inMelitz (2003)and

theincentivestoscreenforhigh‐qualitymatches,itisimportanttoaccountforheterogeneityinthe

quality of the worker‐plant match in determining the effects of globalization on labor market

outcomes(Woodcock2011;Krishna,Poole,andSenses2011).Inoursetting,anotherwiseidentical

workermayhaveahigherprobabilityofseparationfrom(oralowerprobabilityofaccessionto)a

high productivity plant than from (and to) a low productivity plant, when such worker‐plant

production complementarities exist. This diversity offers disparate predictions for the effects of

trade liberalization onworker turnover at exporting (high productivity) and non‐exporting (low

productivity) plants. Our reduced‐form specification builds on the existing literature in this

dimension. Notably, our preferred specification uses information on worker‐level labor market

outcomes,separatelyforexportinganddomesticplants,andincludesplant‐workermatch‐specific

effects to allow for the possibility ofworker sorting and unobservable plant,worker, andmatch

heterogeneity.

Whilewearenot the first to investigate the impactof tradeby theplant’smodeof globalization

(e.g.,AmitiandDavis(2012)),wearenotawareofanypaperallowingforglobalizationto impact

plantsdifferentlydependingontheirexposuretolabormarketregulatoryenforcement.3Brazilhas

oneof themostrestrictive labormarketregulatoryframeworksintheworld(Botero,Djankov,La

1Verhoogen(2008)documentsaskill‐upgradinginMexicanexportingplantsafterthe1994pesodevaluation.Bustos(2011)looksattheBrazilianreductionintariffsaspartoftheMercosurregionalfreetradeagreementandfindsthatArgentineanplantsabovethemediansizeupgradeskills,whileplantsbelowthemediansizedowngradeskills.2Changesintheskillcompositionoftheworkforceatexportersrelativetonon‐exportersarealsopresentinanumberofotherrecenttrademodels(seeforexample,Yeaple(2005)andDavidson,Matusz,andShevchenko(2008)).3 Allowing for globalization to impact plants differently depending on exposure to regulatory enforcementmayhelptoprovideanexplanationforthemixedresultsintheliteratureonthecausalimpactofexportingonplant‐level productivity. Clerides, Lach, and Tybout (1998) argue that the positive relationship betweenexportingandproductivityisaresultoftheself‐selectionofplantsintoexports.Inmostcases,plantsdonotbecomemoreproductivebyexporting.Bycontrast,usingplant‐leveldatafromSlovenia,DeLoecker(2007),controllingforself‐selection,findsevidencethatexportersbecomemoreproductive.

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Porta,andLopezdeSilanes2004;AlmeidaandCarneiro2012).4However,thesizeoftheinformal

labor force suggests that enforcement isweak in some areas, hinting at a gap between the laws

statedonthebooks(dejureregulations)andtheireffectiveimplementation(defactoregulations).

Therefore, contrary to previous studies which rely on cross‐country or across‐state variation in

existing de jure labor regulations (e.g., Besley and Burgess (2004) and Autor, Kerr, and Kugler

(2007)),we explore the fact thatBrazilian employers are exposed to varyingdegrees ofde facto

labor regulations, via Ministry of Labor inspections. Especially in a developing country context

where enforcement is not homogeneous, we argue exploring time series and within‐country

variationinregulatoryenforcementoffersabettermeasureofaplant’strueflexibilityinadjusting

labortoshocksthanlookingatvariationsindejureregulations.5Wethusinvestigatethedifferential

impactof globalizationonworker turnover amongotherwise identicalplantsandworkers facing

differentdefactoenforcementofthelaborlaw.6

Finally, incontrast tomostof the literature investigating the impactof trade liberalizationon the

real economy using potentially endogenous tariff changes7, we explore the Brazilian currency’s

strong devaluation in January 1999 as a large and unanticipated exogenous shock to both

employersandworkers.8FollowingGoldberg(2004),weconstructtrade‐weightedindustry‐specific

real exchange rates in order to capture changes in industry competitiveness over time. The

economy‐widerealexchangeratedepreciated32%from1996to2001,witha23%dropoccurring

between December 1998 and January 1999 alone (see Figure 3.1; Muendler 2003). However,

though all industries suffered exchange rate declines over this time period, some enduredmore

severe shocks than others, as measured by trade‐weighted real exchange rates. We rely on this 4There isanextensive literature fordevelopingcountriesanalyzing therelationshipbetween labormarketregulations and labor market outcomes (e.g., Kugler (1999), Kugler and Kugler (2009), Ahsan and Pages(2009),PetrinandSivadasan(2006),andseveralotherstudiescitedinHeckmanandPages(2004)).5Todate, fewpapershaveexploredbothwithin‐countryandtimeseriesmeasuresofenforcement.NotableexceptionsincludeCaballero,Cowan,Engel,andMicco(2004)andAlmeidaandCarneiro(2012).6 Currie and Harrison (1997) rule out labor market regulations as an explanation for their insignificantfinding of the effects of trade reform on employment levels, and suggest that despite formal labormarketbarriers there is littleenforcementwhich leavesregulations ineffective.UnlikeCurrieandHarrison(1997),our city‐level data on Ministry of Labor inspections allow us to capture exactly this variation in within‐countrycompliancewithlabormarketregulations.7Politicaleconomyfactorsintariffformationandadjustmenthavebeennotedbyanumberofauthors.See,forexample,OlarreagaandSoloaga(1998)forthecaseofBrazil’sregionalfreetradearea,Mercosur.Infact,asprotectionistpressuresgrewintheaftermathoftheintroductionofanewcurrencyin1994,averagetariffsmarginallyincreasebeginningin1995.SeeSection2.2forfurtherdiscussion.8 Other papers using currency shocks as exogenous sources of variation to investigate international traderelationshipsincludeVerhoogen(2008),whousesMexico’s1994pesodevaluationtoexploretherelationshipbetweentradeandinequality,andBrambilla,Lederman,andPorto(forthcoming),whouseBrazil’scurrencycrisisasashocktoArgentineanexporters.

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industry‐levelvariationinrealexchangeratesovertimetoexogenouslyidentifytheeffectofBrazil’s

increasedglobalizationonemploymentandlaborturnoverattheplantandworkerlevel.

Tosummarize,weareinterestedinanalyzingtheeffectoftradeliberalizationonlaborreallocation

withinBrazil.Weexploreacrossindustryandovertimevariationinrealexchangeratesinorderto

capture changes in industry competitiveness, in combinationwith city and time variation in the

enforcementoflabormarketregulations,facingexportersandnon‐exporters.Animportantconcern

relatestotheexogeneityofthevariationintheenforcementoflaborregulationsacrosscities.Atany

givenpoint in time,enforcementof laborregulationsat thecity level isnot likely toberandomly

distributed.On theonehand,enforcementmaybestronger incitieswithhigherviolationsof the

law.Ontheotherhand,citieswithbetterinstitutionscouldhavestricterenforcement.Althoughitis

unclear how these patterns may impact worker reallocation, a potential bias may still exist. To

minimize this concern, we note that our empiricalmethodology follows the program evaluation

literature and relates exogenous real exchange rate changes to plant andworker outcomes over

time,differentially forplants located invariable labormarketregulatoryenvironments.Ourmain

coefficient of interest is the differential effect of changing enforcement on plants that, all else

constant, are exposed to different exogenous industry‐specific trade shocks. Therefore, our

reduced‐form specification relates annual changes in theprobability of inspection in a given city

(whichweproxyforusingtotallaborinspectionsper100plants)andannualchangesinindustry‐

specific real exchange rates, with annual changes in labor market outcomes. We run this

specificationseparatelyforexportingandnon‐exportingplants.

Onecouldstillquestiontheexogeneityofchangesinenforcementatthecitylevel.Totheextentthat

these changes correlatewith changes in labormarket outcomes, our estimates for the effects of

enforcementmaybebiased.Weemphasize,however,thatourfocusisontheinteractionbetween

exogenouschangesinindustry‐specificrealexchangeratesandchangesinthedegreeofregulatory

enforcement, as is customary in the program evaluation literature. For our main coefficient of

interest tobebiased, itmustbe thatplants in industriesexposed togreaterdepreciationsand in

citiesexposedtogreaterdefactoenforcementalsohavesystematicallydifferentlaborturnover,for

someunobservedreasons.Onepossibilityisthatindustriesareregionally‐concentrated,suchthat

theindustriesexperiencingthemostseveredepreciationsarelocatedinthecitiesexperiencingthe

greatest increases in enforcement (i.e., growing cities that may also have more dynamic labor

markets).Ourreduced‐formestimationincludesstate‐specificyeardummies,whichwearguehelps

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to correct for some of this bias. In ongoingwork,we also include city‐specific year dummies in

ordertocontrolforthepossibilitythatdifferencesinlaborflowsovertimeincertaincitiesmaybe

drivingthedifferencesinlaborturnoverwefind.

Webeginouranalysisattheplant‐level, investigatingthedifferentialimpactoftradeopennesson

plantsizeforplantslocatedinheavily‐inspectedcitiesrelativetoplantslocatedinweakly‐inspected

cities.Wethenconsideramoredisaggregatedworker‐levelanalysis.Thisallowsustodecompose

how plants adjust labor in response to currency devaluations and how enforcement influences

these adjustments—along the extensivemargin (hiring and firing) or along the intensivemargin

(changesinhoursworkedorbetweenfull‐timeandtemporarycontracts).Meanwhile,ourworker‐

levelanalysishelpstoaddressthepossibilityofworkersortinginthelaborreallocationprocess.

We now briefly discuss the main empirical predictions in our reduced‐form model. With a

devaluation of the Brazilian currency (the real), imports into Brazil become more expensive,

improving the competitiveness and enhancing the profitability of Brazilian plants selling in the

domestic market. To the extent that profits and employment growth are correlated at the plant

level, a currency depreciation is expected to increase employment for the average plant in the

country.9Thesamedepreciationdifferentiallyimprovesconditionsforexporters,asBrazil’strading

partnersneedfewercurrencyunits topurchaseBraziliangoods.Weexpectthisenhancedforeign

marketaccess todifferentially increaseemploymentatBrazil’sexportingplants relative toplants

producingonlyforthedomesticmarket.10

Ourhypothesisrelieson theextent towhich laborregulations “bite”; that is,whetherregulations

are enforced. An increase in the enforcement of labor market regulations through more labor

inspectionsisexpectedtodirectlyimpactthecompliancewithlaborregulationsthroughthehiring

andfiringof formal labor.11However,thedirectionoftheeffectofenforcementonemploymentis

9 Revenga (1992) uses the sharp appreciation of the U.S. dollar during the early 1980s to demonstratesignificantemploymentreductionsforimport‐competingindustries.Ribeiro,etal(2004)considerthecaseofBrazil,documentingtheimportanceoftheexchangerateforjobcreation.Interestingly,BurgessandKnetter(1998)evaluateemploymentresponsestoexchangerateshocksattheindustry‐levelacrosstheG‐7countries,andargue that country‐leveldifferences in the response to theexchange rate shockmaybeattributable tovariationinlabormarketregulations.10 Goldberg and Tracy (2003) demonstrate that the employment declines associated with a U.S. dollarappreciationgrowstrongerasindustriesincreaseinexportorientation.11CardosoandLage(2007)showthatinspectionsareprimarilylinkedtostricterenforcementofmandatoryseverancepayments,mandatedhealthandsafetyregulations,andtotheworker’sformalregistration.Bertola,Boeri,andCazes(2000)suggestthatdifferencesinenforcementacrosscountries,relatedforexampletothe

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ambiguous. On the one hand, stricter enforcement of labor regulations raises the cost of formal

workers. As such, plants facing stricter enforcement will have increased difficulties in adjusting

labor.Ontheotherhand,thestricterenforcementoflaborregulationsalsoincreasesjobquality,in

termsofcompliancewithmandatedbenefitsfortheworker.Forthisreason,wemayfindincreases

inemployment inmoreheavily‐enforcedcities, as formalemploymentbecomesamoreattractive

optionandformalworkregistrationincreases.

In this paper,we focus on the differential impact of openness on labormarket outcomes across

plants located in cities with varying degrees of regulatory enforcement. Our main results are

consistentwiththeviewthattheextenttowhichtradeaffectslabormarketoutcomesdependson

thedefactodegreeofstringencyofthelaborregulationsfacedbyplants.Inparticular,plantsfacing

stricter enforcement of the labor laws increase employment by less than plants facing fewer

inspections with the expansionary trade shock. Moreover, conditional on the (unobservable and

time‐invariant) plant‐worker match, and several time‐varying worker, plant, and sector

characteristics,wenotethatopennessisassociatedwithanincreaseintheprobabilityofhiringat

exportersandadecreaseintheprobabilityofhiringatdomestically‐orientedplants,asispredicted

bynewheterogeneousfirmtrademodels.Theresultssuggestthatenforcementinfluencesmainly

non‐exporting plants along the extensive margin, but has little effect on adjustment along the

intensivemargin(ascapturedbyhoursworkedandworkerswithfull‐timecontracts).Wenotealso

that our findings aremainly concentrated among small, labor‐intensive, non‐exportingplants for

whichlaborregulationsarelikelytobemostrestrictive.

Themagnitudesofourestimatesseemtobeplausible.Evaluatingtheeffectonworkersandplants

located in municipalities at themean level of inspections, a 10 percent depreciation of the real

increases job match creation at exporting plants by 4.1% and decreases job creation at non‐

exporters by 7.3%. Meanwhile, the impact for domestic plants varies depending on the level of

enforcementtheplantfaces—fordomesticplants locatedinmunicipalitiestthe10thpercentileof

inspections, a10percentdepreciationof the real decreases theprobabilityofhirebyonly6.0%,

efficiencyofacountry’slegalsystem,areasorevenmoreimportant,thandifferencesindejureregulations.Forexample,Caballero,Cowan,Engel,andMicco(2010)exploreapanelof60countriesaroundtheworldandfindthatlaborregulationshaveadverseeffectsonjobturnoverandplants’speedofadjustmenttoshocks,butonlyincountrieswithastrongruleoflawandgovernmentefficiency(takenasmeasuresofenforcementofregulations). However, as with many cross‐country studies, the limited time series variation in laborregulationsandmeasuresofenforcementposeschallengesforidentification.

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while workers matched with domestic plants located in municipalities in the 90th percentile of

inspectionsexperienceadecreaseinjobmatchcreationofaround9.0%.

Our results strongly suggest that more stringent de facto regulations limit job creation with

enhanced trade openness. Overall, small, labor‐intensive, non‐exporters separate from more

workers and hire fewerwith increased enforcement of regulations.We show that this increased

enforcementof laborregulations isalsoassociatedwith lowerplant‐levelproductivity,asproxied

byplant‐levelaveragewages.Itseemsclearfromourdatathatstrictlabormarketinstitutionslimit

thepossibility forplant‐level productivity andprofitability gains associatedwith tradeopenness.

Fromapolicystandpoint,ourworkthereforecontributestoanunderstandingofjobsecurityinan

increasingly globalized world. Increasing the flexibility of de jure regulations will offer broader

accesstothegainsfromtradeandenhancedjobcreation.

Inadditiontotheworkcitedabove,ourpaperrelateswithanumberofdifferentliteratures.First,

ourresearchiscloselylinkedtoagrowingbodyofstructuralmodelslinkingtradeandlabormarket

policies, such as firing costs. In the model presented by Cosar, Guner, and Tybout (2011), tariff

liberalizationsincreasefirm‐leveljobturnover,andreductionsinfiringcostsreinforcetheimpactof

globalization further increasing job turnover. Kambourov (2009) presents a model in which

liberalizingtradeinarestrictedlabormarketenvironmentisassociatedwithslowerinter‐sectoral

labormarketreallocation, loweroutput,andreducedproductivity.Weseethesestructuralpapers

ascomplementarytoourreduced‐formframeworkdesignedtoidentifythecausal implicationsof

tradeopennessonlaborreallocationinthepresenceofacompletesetoflabormarketregulations.

Second, our research is related to a set of empirical papers on productmarket liberalizations in

different labor market environments. Aghion, Burgess, Redding, and Zilibotti (2008) show that

India’s deregulation of the License Raj (control over entry and production in the manufacturing

industry) led todifferential ratesofgrowthacross industries located instateswithpro‐employer

labormarketinstitutionsrelativetoindustrieslocatedinstateswithpro‐workerlaborinstitutions.

Similarly,Hasan,Mitra,andRamaswamy(2007)alsodistinguishIndia’sstatesbytheextentoflabor

market restrictions, and analyze the impact of India’s 1991 trade reform on labor demand. The

authorsfindsupportiveevidencefortheinteractionoftradereformandlaborregulations;thatis,

theimpactoftradereformonlabordemandislargerinstateswithmoreflexiblelaborinstitutions.

Using the same data, Topalova (2010) demonstrates that India’s trade liberalization negatively

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impacted poverty and per capita expenditures predominantly in states with less flexible labor

markets. Also relevant to our study is Freund andBolaky (2008)who argue that trade can only

improvelivingstandardsinflexibleeconomies.Inparticular,theirfindingsonhiringandfiringcosts

suggest that the positive effects of openness are reducedwhen labor regulations are excessive.12

Thebenefitoflinkedemployer‐employeedataallowsustomovebeyondtheindustrylevelandstate

level, to compute individual‐level accessions and separations, as well as to incorporate plant,

individual,andmatchheterogeneity.Moreover,aswepreviouslymention,exploitingvariationinde

factolaborregulationsoffersamorecompletemeasureoflabormarketflexibilitythanvariationin

dejurelaborregulationsalone.

Thepaperproceedsasfollows.Inthenextsection,weprovidebackgroundinformationonthe1988

Constitutionalreform,whichestablishedthecurrentlabormarketregulatoryframeworkinBrazil,

therecentevolutionintheenforcementoftheselaborlawsconductedbytheMinistryofLabor,and

themainfeaturesofBrazil’srecentglobalization.InSection3,weoutlineourmaindatasourcesand

offersomesimpledescriptivestatistics.Section4discussestheconceptualframeworkbehindour

mainempiricalstrategyandproposesasimpledifference‐in‐differencereduced‐formspecification

fortheempiricalwork.InSection5,wepresentourmainfindings.Section5alsooffersevidenceon

theheterogeneityofourmainfindings,consideringtheindustry’stechnologicalintensity,plantsize,

and theageof theworker,andaggregate implicationsof theenforcementof labor regulationson

plant‐levelproductivity.WeconcludeinSection6byhighlightingthemainpolicyimplications.

2. PolicyBackground

Thelate1980stotheearly2000smarkedaperiodofsubstantialmarket‐orientedreforminBrazil.

Of particular relevance to ourwork are the establishment of a new Constitution in 1988,which

offered increased employment protections for workers, the liberalizing trade policy reforms

beginningin1987,andtheimplementationofanewcurrency,thereal, in1994.Subsequently,the

currencyexperiencedasevereandunanticipateddevaluationinJanuary1999.Eachofthesepolicy

changesmayhavecontributedtochanginglaborcostsandlaborreallocation.

2.1. LabormarketregulationswithinBrazil

12Eslava,Haltiwanger,Kugler,andKugler(2010)considerthecaseofColombia’spro‐marketreformsofthe1990s. The authors find that allowing for frictionless factor adjustment would lead to substantialimprovementsinefficiencyoverthereformperiod.

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The1988Constitutionalreform The Brazilian Federal Constitution of 1988 imposed high

labor costs to plants and was very favorable to workers. First, it reduced themaximumweekly

workingperiodfrom48to44hours.Second,itincreasedtheovertimewagepremiumfrom20%to

50%oftheregularwage.Third,themaximumnumberofhoursforacontinuousworkshiftdropped

from8to6hours.Fourth,maternity leave increasedfrom3to4months.Finally, it increasedone

month’svacationtimepayfrom1to4/3ofamonthlywage.

Followingthe1988changes in the laborcode, thecostof labor toemployers increased.First, the

employer’spayrollcontribution increasedfrom18%to20%.Second, thepenaltyontheplant for

dismissingtheworkerwithoutcauseincreasedfrom10%to40%ofthetotalcontributionstothe

severancefund,FundodeGarantiadoTempodeServiço(FGTS).13EmployersinBrazilmustalsogive

advancenoticetoworkersinordertoterminateemployment.Duringthisinterimperiod,workers

aregranteduptotwohoursperday(25%ofaregularworkingday)tosearchforanewjob.14

Enforcementoflaborregulations Thesede jure labor regulationsare effective throughout the

country. However, as theMinistry of Labor is chargedwith enforcing compliancewith the labor

regulations, there issignificantheterogeneitybothwithincountryandover time in termsofhow

bindingisthelaw.15Giventhegeographicscopeofthecountry,enforcementisfirstdecentralizedto

thestate levelwith themain laboroffices(delegacias) located in thestatecapital.Enforcement is

thenfurtherdecentralizedtothelocallevelwithineachstate,dependingonthesizeofthestate.For

example, in 2001 the state of Sao Paulo had 24 local labor offices (subdelegacias)while smaller

stateslikeAcrehadonlytheoneofficecoincidingwiththedelegaciainthestatecapital.

Throughout the late1990sandearly2000s, labor inspectionsbecamemorefrequentasthe large

publicdeficit led theBraziliangovernment tosearch foralternativeways tocollect taxrevenue.16

13Iftheworkerisdismissedwithoutjustification(withtheexceptionofworkersonaprobationaryperiod),theplantisfinedandhastopaytheworker40%oftheFGTScontributions.14Someplantsvoluntarilychoosetograntworkersthefullmonthlywagewithoutrequiringwork.BarrosandCorseuil(2004)findthattherearelargeproductivitylossesduringthisperiod.15AcomprehensiveexplanationoftheenforcementofthelaborregulationsystemanditsimportanceinBrazilisgiveninCardosoandLage(2007).16Aninspectioncanbetriggeredeitherbyarandomplantaudit,orbyareport(oftenanonymous)ofnon‐compliancewiththelaw.Workers,unions,thepublicprosecutor’soffice,oreventhepolicecanmakereports.Inpractice,almostallofthetargetedplantsareformalplantsbecauseitisdifficulttovisitaplantthatisnotregistered,sincetherearenorecordsofitsactivity.

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Wenotetimevariationinthelocationoflocallaborofficesasnewsubdelegaciasopenovertime.For

instance, in 1996 in addition to the delegacia in the state capital, the state of Bahia had 6

subdelegacias. By 2001, Bahia had 8 subdelegacias. Because of this, the average distance to the

nearestMinistryofLaborofficedecreasedbyabout5%between1996and2001. Inaddition, the

averagenumberofinspectionsinthemanufacturingsectorpermunicipalityincreasedfrom13.2in

1996 to 14.3 in 2001.As inspectors reachedoutwith increased intensity, themediannumber of

inspections also increased, suggesting a leftward shift of the distribution of inspections across

municipalities.

Most of the inspections and subsequent fines for infractions in Brazil are to ensure plants’

compliance with the worker's formal registration in the Ministry of Labor, contributions to the

severancepayfund(FGTS),compliancewiththeminimumwage,andwiththemaximumworking

lengths. Evasion of one of these dimensions accounted formore than 40% of all fines issued in

2001.Themonetaryamountofthefinesiseconomicallysignificantandmaybeissuedperworker

oritmaybeindexedtotheplant’ssize.Forexample,in2001values,aplantisfined216reais(or

approximatelyUSD100)foreachworkerwithoutacarteiradetrabalho,formalworkauthorization.

Considering that, at 2001prices, the federalminimumwagewas222 reais, non‐compliancewith

worker registration is non‐trivial, implying a penalty of approximately one monthly wage per

worker.

Plants weigh the costs and benefits of complying with this strict labor regulation. They decide

whethertohireformally,informally,orformallybutwithoutfullycomplyingwithspecificfeatures

of the labor code (e.g., avoiding the provision of specificmandated benefits, such as health and

safetyconditions,oravoidingpaymentstosocialsecurity).Theexpectedcostofevadingthelawisa

functionofthemonetaryvalueofthepenalties(finesandlossofreputation)andoftheprobability

ofbeingcaught.Inturn,theprobabilityofbeingcaughtdependsontheplant’scharacteristics(such

assize,globalizationstatus,andlegalstatus)andonthedegreeofenforcementofregulationinthe

citywheretheplantislocated.17

2.2. Brazil’sglobalization

17Asinspectorsfaceaperformance‐basedpayscheme,theyoftenlookforcaseswherethepenaltyislikelytobelarge.Assuch,thereisastrongcorrelationbetweenthesizeofthefirm,asaproxyforthevisibilityofthefirm,andthenumberofinspections(CardosoandLage2007).

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Policyreforms Thesecondhalfof the20thcentury inBrazilwascharacterizedby tight import

substitutionindustrializationpoliciesdesignedtoprotectthedomesticmanufacturingsectorfrom

foreigncompetition.Beyondhightariffrates,substantialnon‐tariffbarrierscharacterizedBrazilian

tradepolicyduring this timeperiod.The latterhalfof the1980sand thebeginningof the1990s,

however,witnessedsweepingchangesinBraziliantradepolicy.Thisoccurredintwophases.First,

averageadvaloremfinalgoodstariffratesfellfrom58%in1987to32%in1989.Thesereformshad

little impact on import competition however, as non‐tariff barriers remained highly restrictive.

Second,between1990and1993,thefederalgovernmentabolishedallremainingnon‐tariffbarriers

inheritedfromtheimportsubstitutioneraandannouncedascheduleforthereductionofnominal

tariffsoverthenextfouryears(MoreiraandCorrea1998).Effectiveratesofprotectionfellbyover

70%injustfouryears—fromapproximately48%,onaverage,in1990to14%,onaverage,in1994

(Kume,Piani,andSouza2003).

In1994,afterdecadesofhighinflationandseveralunsuccessfulstabilizationattempts,theBrazilian

government succeeded with a macroeconomic stabilization plan (Plano Real), designed to help

correctalargefiscaldeficitandlastinglyendhyperinflation.Thenewcurrency,thereal,waspegged

totheU.S.dollar,andbeganatparityonJuly1,1994.Officially, therealwasset toacrawlingpeg

whichpermittedthecurrencytodepreciateatacontrolledrateagainsttheU.S.dollar.However,as

the country’s persistent effort to control inflation paid off, the real exchange rate actually

appreciated in its first months. In response, the government partially reversed trade reforms in

1995aftermanufacturingindustrieslostcompetitivenessduetothereal’sappreciation.18

Currencycrisis Despite efforts to control public spending and raise tax revenues, Brazil’s

fiscaldeficitsremainedhighandcontinuedtogrow.Meanwhile,persistentcurrentaccountdeficits

placedsignificantpressureonthepeggedexchangerateandgovernmentreserves,leadinginvestors

towithdraw funds from Brazil. In response to the financial crises in Asia in 1997 and Russia in

1998,theauthoritiesraisedinterestratestoencouragedomesticsavingsandinvestment.However,

as debt service obligations increased, investor panic persisted. Dollar reserves fell from

approximately $58 billion in 1996 to $43 billion in 1998. Inmid‐January 1999, capital outflows

acceleratedfurtherwhentheGovernoroftheStateofMinasGeraisdeclaredamoratoriumonthe

state’s debt payments to thenational government, triggering the government’s announcement of

18Averageadvaloremtariffsclimbedslightlyinsubsequentyears—fromanaverageof12.2%in1994toanaverageof14.4%in2001.

13

the end of the crawling peg, allowing the real to float against the U.S. dollar (Gruben and Kiser

1999).Overnight,thenominalexchangeratedevaluedby9%againsttheU.S.dollar,andbytheend

ofthemonth,therealhaddepreciatedby25%(seeFigure3.1).

3. Data

Our main data are administrative records from Brazil for formal sector workers linked to their

employers. We match these data by the plant’s municipality with city‐level information on the

enforcementoflabormarketregulations,bytheplant’ssectorwithinformationonindustry‐specific

real exchange rates, and by the employer tax identifier to information on exposure to global

markets.ThesampleperiodforanalysiscoversBrazil’smaincurrencycrisisperiod,between1996

and2001.Thisexogenousshocktoplantandworkeroutcomesallowsustouncoverthedifferential

impact of increased exposure to trade on labor reallocation depending on the degree to which

plantsfaceregulatoryenforcement.

Matchedemployer‐employeeadministrativedata Weusedata collectedby theBrazilianLabor

Ministry,whichrequiresbylawthatallregisteredestablishmentsreportontheirformalworkforce

ineachyear.19ThisinformationhasbeencollectedintheadministrativerecordsRelaçãoAnualde

InformaçõesSociais (RAIS) since 1986. For our analysis, however,weuse data fromRAIS for the

years 1996 through 2001, when we also have complementary information on regulatory

enforcement,exchangerates,andtheemployer’sglobalizationstatus.

The main benefit of the RAIS database is that both plants and workers are uniquely identified

allowing us to trace workers over time and across different plants. The data also include the

industryandmunicipalityofeachplant.20Otherrelevantvariablesofinterestincludetheworker’s

monthofaccessiontoandthemonthofseparationfromthejob,weeklyhoursworked,andthetype

19Our analysis is restricted to the effect of trade liberalizationon formal labor reallocation. It is plausible,however, that in citieswithweaker enforcementandhencemore flexible laboradjustment, alongboth theformal and informal margin, plants may be more likely to make adjustments along the informal margin.Therefore, our findings on formal labor adjustment do not capture total labor adjustment. It is not clear,however, how the resultswould change.GoldbergandPavcnik (2003), Paz (2012), andMenezes‐Filho andMuendler(2011)findmixedresultsontheimpactoftradeliberalizationontheinformalsectorinBrazil.Also,totheextentthatenforcementincreasesthecostof labor,wemaynoteshiftsawayfromlabor(bothformalandinformal)towardscapital.20The industrial classification available inRAIS is the4‐digitNationalClassificationofEconomicActivities(CNAE).

14

of employment contract (temporary versus permanent), as well as detailed information on the

worker’shumancapital, includingheroccupation,education, tenureat theplant,gender,andage.

WedefineaworkerashiredtoemployerjduringyeartifRAISreportsanon‐missingvalueforthe

monthofaccession.Wedefineaworkerasfiredfromemployerjduringyeart ifRAISreportsthe

workernolongeremployedatfirmjonDecember31ofyeart.

WerestrictobservationsinRAISasfollows.Wedrawa1%randomsampleofthecompletelistof

workers ever to appear in the national records and retrieve their complete formal sector

employmenthistory.Weincludeonlymanufacturingsector21workersbetweentheagesof15and

64years,withapositiveaveragemonthlywage,andemployedinprivate‐sector jobs.Toensurea

more precise identification of worker andmatch fixed effects, we further restrict the sample to

thoseworkerswhoareinthedataforatleasttwoyears.

Enforcementdata We also explore administrative city‐level data on the enforcementof labor

regulations,alsocollectedbytheBrazilianMinistryofLabor.Dataforthenumberofinspectorvisits

are available by city and 1‐digit sector for the years 1996, 1998, 2000, and 2002. We use the

informationonvisitsby inspectors tomanufacturingplantsonly.Forouranalysis,we interpolate

averagevaluesforthemissingyearsandmatchtheenforcementdatatotheRAISdatabytheplant’s

municipal location. This information identifies plants and workers facing varying degrees of

regulatoryenforcement.

Weproxy thedegreeof regulatoryenforcementwith the intensityof labor inspectionsat thecity

level.Inparticular,ourmainmeasureofenforcement,designedtocapturetheprobabilityofavisit

bylaborinspectorstoplantswithinacity,isthelogarithmofthenumberoflaborinspectionsatthe

city level (plusone)per100plants in thecitybasedonRAIS.Thisscaledmeasureof inspections

helpstocontrolforimportantdifferencesacrosscities.Moreover,theimpactofsuchameasurewill

reflectthedirecteffectofinspections,aswellasplants’perceivedthreatofinspections(eveninthe

absenceofplant‐levelinspections)basedoninspectionsatneighboringplants.

Table3.1 reports thenationwide increase inenforcementof labor regulationsbetween1996and

2001.Theproportionofcitieswithatleast1manufacturinginspectionrosefrom33%in1996to

21ThemanufacturingsectorconsistsofCNAE2‐digitcodes15‐37. In futuredraftsofthepaper,wehopetoincorporateothersectorsforacomparativeanalysis,aswell.

15

52%in2001.Thiscorrespondstoan increase in theaveragenumberof inspectionsacrosscities,

mostnotablybetween1998and2000,whentheaveragenumberofinspectionsincreasedby15%.

Asthenumberofinspectionsatthecityleveliscorrelatedwiththesizeofthecity(i.e.,population,

labor force, andnumberofplants), ourpreferredmeasuredocuments thenumberof inspections

per100registeredplantsasisreportedincolumn(3).Thedatareportincreasesinthenumberof

inspections per plant and per worker, as inspectors intensify the enforcement process to reach

additionalplants,workers,andcities.22

We also note significant heterogeneity in the within‐country variation in the intensity of

enforcementacrosscitiesinBrazil,asisdepictedinFigure3.2.Theleftpanelillustratesthenumber

ofinspectionsperBraziliancityin1998,withdarkershadesportrayinghighernumbers.Theright

paneldepictsthesamestatistictwoyearslaterintheyear2000.Weremarkonthevariationacross

municipalities and over time. First,we observe the darkest areas of themap in the high‐income

SouthernandSoutheasternregionsofthecountry.Wealsonoticeadarkeningofthemapbetween

1998and2000asenforcementspreadstofurtherpartsofthecountry.23Intheanalysisthatfollows,

weincludeinteractivestate‐yeardummiesintoourdifference‐in‐differenceapproachtoaccountfor

thespatialpatternofinspections.

Industry‐specificexchangerates Weconstructtrade‐weightedindustry‐specificrealexchange

ratesbasedonbilateralrealexchangeratedatafromtheInternationalMonetaryFundandbilateral

tradeflowsbycommoditymadeavailablebytheNationalBureauofEconomicResearch(Feenstra,

Lipsey,Deng,Ma,andMo2004).24Wematchtheindustry‐specificrealexchangeratestotheRAIS

data by the plant’s 4‐digit industrial classification, in order to identify plants and workers in

industrieswithdifferentialglobalizationexperiences.25

22Indoingso,thedistributionofinspectionsacrosscitiesshiftsleft.Mostnotably,thecityofSaoPauloreportsthemaximumnumberofinspectionsineveryyear,aswouldbeexpectedgiventhecity’slargepopulationandlabor force size. However, Sao Paulo city also sees its number of inspections reduced by a third over thecourseofthefiveyears.23 The same qualitative patterns hold for the number of inspections and the number of inspections perworker.24Trade flowsareorganizedbyStandardIndustrialTradeClassification(SITC)codes.Wematchthe4‐digitSITC revision 2 codes to the 4‐digit CNAE codes available in RAIS using publicly available concordances(http://www.econ.ucsd.edu/muendler/html/brazil.html#brazsec).25AswediscussinSection2.2,averagetariffrateswererelativelyflatoveroursampleperiod.Variationsintherealexchangerate,therefore,provideamorerealisticmeasureofchangesintradeopennessduringoursampleperiod.

16

Aswaspreviouslynoted,Brazil’saggregaterealexchangeratedevaluedinJanuary1999,increasing

therelativepriceofBrazilianimports.However,theaggregateexchangeratemaybelesseffectiveat

capturing true changes in industry competitiveness, induced by changes in specific bilateral

exchangerates,ifparticulartradingpartnersareofparticularimportanceforparticularindustries.

Thatis,movementsinthedollar/real,peso/real,andeuro/realexchangeratesmayhavedifferent

implicationsfordifferentindustries,dependingontheindustry’stradewiththeU.S.,Argentina,and

Europe, respectively. Therefore, following Goldberg (2004), we calculate the trade‐weighted real

exchangerateasfollows:

. 5 ∗∑

.5 ∗∑

∗

wheretindexestime,kindexesindustry,andcindexescountry,suchthatthebilateralrealexchange

rate, ,denotedintermsofforeigncurrencyunitsperreal,isweightedbyindustry‐specificand

time‐varyingexportshares(∑

)andimportshares(∑

).26

A decrease in the value of this index implies a real depreciation of the Brazilian real in trade‐

weightedtermsforindustryk.Acrossallindustries,theaverageindexdecreasedfrom0.97in1996

to0.62in2001,withthemostdramaticdropofroughly30%occurringbetween1998and1999.As

Figure3.3illustratesintheleftandrightpanels,respectively,thereisalsosubstantialheterogeneity

acrossindustriesinboththelevelofandannualchangesintherealexchangerate.Thoughthemean

exchangerateisvaluedat0.66in1999intheaftermathofthecrisis,themanufactureofotherfood

products has a substantially lower trade‐weighted real exchange rate at 0.54, while the trade‐

weightedrealexchangerateintheindustrywhichmanufacturesstrings,cables,andothercordsis

far higher at 0.80. Similarly,while all sectors experienced sharp exchange rate declines between

1998 and 1999, some sufferedmore than others. Non‐ferrousmetalmanufacturing endured the

steepestannualdepreciationof40percentagepoints,whilesugarmanufacturingfacedamere16

percentagepointdecline.

Exposuretoglobalmarkets We also investigate information on the firm’s degree of global

engagement,ascapturedbytotalexportsales.WerelyoncomplementarydatafromtheBrazilian 26FollowingCampaandGoldberg(2001),welagthetradesharesoneperiodtoavoidissuesofendogeneitybetweentradeandtheexchangerate.

17

CustomsOffice(SECEX)tocreateasingleindicatorforthefirm’sglobalizationstatus.Information

onfirm‐levelexporttransactionsisavailablefromSECEX,whichrecordsalllegally‐registeredfirms

inBrazilwithatleastoneexporttransactioninagivenyear.Wedenoteexporterstobethosefirms

thatexportedanypositivedollaramountat anypointduring the1996 to2001 timeperiod.This

time‐invariant indicator isdesigned tominimizepotential endogeneity concerns surrounding the

exportdecisionpost‐devaluation.

3.1. Descriptivestatistics

We report detailed descriptive statistics in Table 3.2. Column (1) reports statistics for our final

sampleofformal‐sectormanufacturingworkers,aswellastheplants,cities,andindustriesinwhich

theywork.Column(2)reportssummarystatisticsforthesampleofexportingplants,whilecolumn

(3) reports statistics for domestic plants. The final sample has 313,940 worker‐plant‐year

observations,with81,254workersemployed in53,011plantsallowing for122,017worker‐plant

matches,covering2,769municipalitiesand2404‐digitindustriesthroughoutthesampleperiodof

1996to2001.

Approximately29%ofmanufacturingworkerswerehired to anewemployerduringour sample

period,while25%wereseparatedfromtheiremployer.Thedatareportthatonly2%ofworkersare

employedwithtemporarycontracts.Acrossemployers,workersaveraged43.5hoursperweek.The

averageageofaworkeris32years.Themajorityofthemanufacturinglaborforcehaslessthana

highschooleducation,whileabout25%haveatleastahighschooleducation,andonly7%havea

tertiary education. Roughly a third of themanufacturing labor force is employed in skilled blue

collarprofessions,suchasmachineoperators.Another25%areinwhitecollarprofessions—7%in

secretarial and sales positions and 17% in professional,managerial, and technical positions. Ten

percent of the formalmanufacturing sector is employed in unskilled blue collar jobs,most often

foundintheconstructionandservicesectors.Theaverageplantemploys59workers,andpaysan

averageannualwageof2,970reais.Theaveragedurationofaworker‐plantmatchinthesampleis2

years and 9months. The averagemunicipality in our sample faces approximately 35 inspections

duringthe1996to2001sampleperiod.27Theaverageindustryhasatrade‐weightedrealexchange

rateindexof0.62andemploysover18,000workers,ofwhichabout1inevery5areunionized.

27ThisnumberstandsincontrasttotheaveragenumberofinspectionsacrossallBrazilianmunicipalities(seeTable3.1).Our1%randomsamplecoversonlyregisteredfirmswhichare,onaverage,larger.Therefore,this

18

Ofthe53,011plants,roughlyaquarterareexporters.However,these13,891plantsrepresentover

halfofthetotalnumberofobservationsandworker‐plantmatches,largelybecauseexportingplants

employmoreworkersonaverage(at161workersascomparedto27workersfordomesticplants)

and because the average worker‐plant match duration is roughly 4 months longer at exporting

plantsthanatdomesticplants.SimilartoMenezes‐FilhoandMuendler(2011),ourdatareportthat

accession rates are lower at exporters than at non‐exporters—24% as compared to 34%,

respectively. We also find that separation rates are lower at exporting plants for our sample of

workers. Our RAIS matched data sample report the common finding in the literature that, on

average,exportersaremoreskill‐intensiveandpayhigherwages(e.g.,BernardandJensen(1995)).

Almost 40% of the manufacturing labor force at exporters is high‐skilled, as defined by those

workerswithatleastahighschooleducation,whilebycomparison25%oftheworkforceishigh‐

skilledatplantsservingthedomesticmarket.Theaverageannualwagepaidbyexportingplantsis

4,907reais,ascomparedto2,541reaisatdomesticplants.Exportersareonlyrepresentedinabout

53%ofthe2,769municipalitiescoveredbyourformalsectordata.28Combinedwiththeirgreater

visibility due to the higher total employment numbers, the data indicate that on average the

municipalities in which exporters are located are more heavily enforced than those in which

domesticplantsarelocated.Onaverage,exportingplantsface61manufacturinginspectionswhile

domestic plants face, on average, 39 inspections. By contrast, exporters and non‐exporters are

represented across almost all industries in Brazil. For this reason, we see little variation across

plant‐typeintheaveragetrade‐weightedrealexchangerateorinunionizationrates.

4. EmpiricalModel

Our goal in this paper is to uncover how trade openness affects labor market reallocation. We

consider thedevaluationof thereal in1999as themainexogenous tradeshockandargue thata

similar trade shock impacts plants differentially based on their exposure to the enforcement of

laborregulationsandtheirmodeofglobalization.Webeginwiththefollowingframeworkinmind:

(1)

istobeexpected,asthesefirmsarenaturallymoreexposedtoenforcementthansmallerfirms(CardosoandLage2007).28 See Aguayo‐Tellez, Muendler, and Poole (2010) for further information on the spatial distribution ofexportingplants.

19

wherejindexestheplant,kindexestheplant’s4‐digitindustry,andtindexestime.Werelateplant‐

level outcomes ( ), such as total plant employment, to time‐varying, plant characteristics ( )

such as average worker tenure at the plant, and the age, gender, educational, and occupational

compositionoftheplant,andtime‐varying, industrycharacteristics( )suchastheunionization

rate29, industry employment, average worker tenure in the industry, and the age, gender,

educational, and occupational composition of the industry. The specification also includes plant

fixed effects ( to capture time‐invariant factors, such as the plant’s unobserved underlying

productivity, technology, ormanagement style,whichmay influence both a plant’s selection into

exportingandplant‐levellabormarketadjustment,andstate‐specificyeardummies( tocontrol

fortheaverageeffectonlaborturnoverofBrazil’smanypolicyreformsoverthistimeperiod.

Importantly, among the time‐varying, industry‐specific characteristics is the trade‐weighted

industry‐specific real exchange rate ( which serves as an exogenous shock to trade

openness.Ourbasicargumentisbasedonthefactthatwhentherealdepreciates,thepriceofgoods

typically imported into Brazil will rise, improving the competitiveness and increasing profits of

Brazilianplants.Totheextentthatplantprofitsandemploymentgrowtharecorrelated,weexpect

that a devaluation of theBrazilian realwill increase employment for the averageplant. The first

column of Appendix Table A.1 reports results from the estimation of equation (1) where the

dependent variable is the logarithm of plant employment. Consistent with the literature (e.g.,

Revenga (1992)), a depreciation of the trade‐weighted real exchange rate is associated with

increasesinemploymentfortheaverageplant.

Equation (1), however, considers only the industry‐time shock of the exchange rate devaluation.

Brazil’s large informal sector suggests significant evasion of Ministry of Labor regulations. The

implications of increased trade openness, via a real exchange rate depreciation, for formal labor

turnover depend on the degree to which plants are exposed to labor market regulatory

enforcement.Wehypothesizethattwoidenticalplantswillresponddifferentlytochangesintrade

opennessdependingonthedefactoregulationstheyface.Forthisreason,weadaptequation(1)as

follows:

29 As labor turnover may be restricted in heavily‐unionized industries, we control for the time‐varyingunionizationrate,basedonBrazilianhouseholdsurveys(PNAD).

20

∗ (2)

where m now indexes the city (munícipio). represents time‐varying, municipality‐level

enforcement of labor regulations, as captured byMinistry of Labor inspections.We beginwith a

measureofenforcementasthelogarithmofthenumberofinspectionsatthecitylevel(plusone).

, ourmain coefficient of interest, captures thedifferential impact of a trade shockonplants in

strictly‐enforced municipalities relative to weakly‐enforced municipalities. In response to an

expansionarytradeshock,suchasBrazil’scurrencydevaluation,plantswishtoexpandemployment

( 0).However,plantsinheavily‐inspectedcitiesmaybedifferentiallyrestrictedfromadjusting

labor ( 0)—as the cost of a formal worker increases, strictly‐enforced plants will increase

employmentby less thanweakly‐enforcedplants—ormayadjust formal laborby relativelymore

( 0),asformalworkregistrationsincrease.

Onecouldworrythatthelevelofenforcementoflabormarketregulationsisnotexogenoustoplant

outcomes( ).Inparticular,enforcementmaybestricterincitieswhereviolationsofthelabor

lawsaremorefrequentorincitieswhereinstitutionsaremoredeveloped.Moreover,enforcement

may be stronger formore visible (i.e., larger andmore globalized) plants. As violations of labor

laws, better institutions, and plant size and type are likely also correlated with labor market

outcomes, tominimizethisconcern,weemphasizethatequation(2)relateschangesovertimein

the enforcement of labor market regulations to labor turnover. Importantly, as in much of the

programevaluationliterature,ouridentificationstrategyreliesonhowtheexogenoustradeshock

impactsplantturnoverdifferentiallybasedontheplant’sexposuretoregulatoryenforcement.

Column(2)ofAppendixTableA.1reportsresultsfromequation(2).Ourdatalargelyconfirmthese

predictions. The exogenous real exchange rate depreciation increases plant‐level employment.

However, consistent with findings in Almeida and Carneiro (2009), increases in regulatory

enforcementatthecityleveltendtodecreaseformalplantsize,suggestingthatincreasesinthecost

of formal workers dominate any potential impact from increased compliance with mandated

benefits and formal work registrations. In this paper, we are interested in the interaction term

reflecting the differential impact of globalization on plants located in strictly‐enforced

municipalities ( ). Our results report that plants in heavily‐inspected cities are restricted from

expandingemploymentwithadepreciationofthecurrency.

21

In the absence of plant‐level information on inspections, our analysis intends to capture the

probabilitythataplant is inspected.Thisallowsforthedirecteffectof inspections,aswellasthe

indirect effect of a neighboring plant’s inspections. To the extent that large cities have many

inspections,butalsomanyplantstobeinspected, incolumn(3)ofAppendixTableA.1,weadjust

our main enforcement variable to control for the size of the city. Specifically, our preferred

enforcement variable characterizes inspections per 100 plants in the city. Evaluated at the 10th

percentileofinspections,a10percentdepreciationincreasesemploymentby1.6%,whilethesame

devaluationincreasesemploymentbyonly1.0%atplantslocatedincitiesatthe90thpercentileof

inspections. These plant‐level results highlight our main predictions—that strict labor market

institutionslimitplants’laboradjustmentinresponsetoshocks.

4.1. Worker‐levelemploymenttransitions

When facinga tradeshock,expandingplantscanadjustalong theextensivemarginby increasing

hiring, decreasing firing, or both, as well as along the intensivemargin by increasing the hours

worked for existing employees or switching from temporary to permanent contracts. Moreover,

enforcement also likely influences adjustment along each of these margins. In order to better

understandthesemechanisms,ourmainreduced‐formequationfocusesonaworker‐levelanalysis.

Inparticular,weaugmentequation(2)asfollows:

∗

(3)

where i indexes the worker and represents worker‐level labor market outcomes, such as

employmenttransitionsandhoursworkedperweek.Wecharacterizeemploymenttransitionswith

threevariables:anindicatorvariablethattakesthevalueoneifamatchbetweenworkeriandplant

jiscreatedattimet(i.e.,ifthereisaplant‐yearaccession);anindicatorvariablethattakesthevalue

one if amatch between worker i and plant j is destroyed at time t (i.e., if there is a plant‐year

separation);andan indicatorvariablethat takesthevalueonewhenworker i isemployedwitha

full‐timecontractinplantjattimet.Allothervariablesaredefinedaspreviouslydiscussed. are

time‐varyingworker‐levelcharacteristics(suchastheworker’stenureattheplant,theworker’sage

(and age squared), education, and occupation) and is a worker‐plant (time‐invariant) match

effect.

22

Wearguethattime‐invariantworker‐plantmatcheffectsareimportantbecausewhenworker‐plant

productioncomplementaritiesexist(asinnewtrademodels),highproductivityplantswillscreen

forhighabilityworkers.Thismayleadtothesortingofhighabilityworkersintohighproductivity

plants.30Inoursetting,thisimpliesthatfollowingatradeliberalization,otherwiseidenticalworkers

may have a higher probability of separation from (or a lower probability of accession to) a high

productivityplantthanfrom(andto)alowproductivityplant.Forthisreason,wereplacetheplant

fixedeffectsfromequation(2)withworker‐plantmatch‐specificfixedeffects( ),whichallowfor

time‐invariant,unobservablematchquality(associatedwiththepotentialforworkersorting)inthe

laborreallocationprocess.31

Ourmainspecificationinequation(3)relatesexogenouschangesinindustry‐specificrealexchange

rates with match‐specific outcomes, between 1996 and 2001, differentially for worker‐plant

matches in strictly‐enforced areas. In other words, we explore a difference‐in‐difference

methodologytoidentifytheeffectsofopennessonlaborturnover.Themaincoefficientofinterestin

equation (3) is , which captures the differential effect of stricter enforcement for workers

employedinplantsexposedtovaryingrealexchangeratechanges.

Inaddition,wearguethatthe implicationsofarealexchangeratedevaluationareheterogeneous

acrossplanttypesandtherefore,considerequation(3)separatelyforexportinganddomestically‐

orientedplants.Ourprioristhatopennessallowsplantsbestplacedtocompeteabroadtoexpand

and those in import‐competing industries to relatively contract. We hypothesize that the

expansionaryeffect(increaseinhiringanddecreaseinfiring)oftheexchangerateshock( )will

belargerforexportingplantsthanforplantsservingonlythedomesticmarket,asforeignmarket

accessimproves.

Asintheplant‐levelanalysis,thetheoreticalpredictionsfor areambiguous.Ontheonehand,the

stricterenforcementof laborregulationsraises thecostof formalworkers.Assuch,plants facing

strictenforcementwillhaveincreaseddifficultiesinadjustinglabor.Ontheotherhand,thestricter

30Krishna,Poole,andSenses(2011)documenttheimportanceofworker‐firmcomplementarityinthelaborreallocation process post‐liberalization using matched employer‐employee data for Brazil. Their results,controllingforthenon‐randomassignmentofworkerstofirms,suggestsastrongbiasinplant‐levelanalyses,asexportersdifferentiallyincreasematchqualityrelativetonon‐exporterspost‐liberalization.31Asneithertheplantnortheworkervarywithinamatch, thematch‐specificeffectsalsocontrol fortime‐invariant,unobservableplantheterogeneityandtime‐invariant,unobservableworkerheterogeneity.

23

enforcementoflaborregulationsalsoincreasesjobquality,intermsofcompliancewithmandated

benefitsfortheworker.Forthisreason,wemightexpectincreasesinemploymentinmoreheavily‐

enforced cities, as formal employment becomes a more attractive option and formal work

registrationincreases.Weempiricallytestthisambiguitygivenourstrongpredictionsontheimpact

of trade openness on employment for exporters relative to non‐exporters. Moreover, we further

hypothesize that labormarket regulations on formal employment are less binding for exporting

firms,andthusexpecttheeffectsofregulatoryenforcementtobelessimportantforplantsexposed

toglobalmarkets.32

Asmany of the covariates in equation (3) are also dummy variables, we choose to estimate the

equation using a linear probability model. Compared to a probit analysis, the linear probability

model has the advantage of allowing for a straightforward interpretation of the regression

coefficients.33 To take into account theoccurrence of repeated observationsof individualswithin

city‐sectors,weclustertherobuststandarderrorsatthecity‐sectorlevel,thoughourmainresults

arerobusttoclusteringatthematchlevel,aswell.

5. MainResults

Table 5.1 reports our main estimates where the dependent variable is the worker‐plant‐year

accession.Incolumns(1)and(2)ofTable5.1,weestimatevariantsofequation(3)forallworker‐

plant matches in our sample. As discussed, the specification controls for unobservable, time‐

invariant worker‐plant match quality, observable, time‐varying worker, plant, and industry

32CardosoandLage(2007)arguethattheintegrationoffirmsininternationaltradeandtheneedtocomplywithinternationalqualitystandardsimplicitlyforcefirmstocomplywithlaborregulations.ThisisreinforcedinHarrisonandScorse(2003),whoreport thatexportersand foreignfirms in Indonesiaaremore likelytocomplywithlaborregulations.Inaddition,BloomandVanReenen(2010)showthatlabormarketregulationsare negatively correlated with the quality of management practices across countries. At the same time,multinational and exporting firms tend to be better managed across all countries, suggesting the betterinstitutionalenvironmentatexportingplantsoffersenhancedcompliance.33 It is well‐known that in the extreme case of a fully saturated model (i.e., one where all independentvariablesarediscretevariablesformutually‐exhaustivecategories),thelinearprobabilitymodeliscompletelygeneralandthefittedprobabilitiesliewithintheinterval[0,1].Whenlookingataccessions,separations,andfulltimecontracts,ourdependentvariablewillbeadummyvariableandapplyingleastsquareswillnotyieldthemostefficientestimator.Therefore, inongoingwork,weareestimatingourmainmodelsusingaprobitanalysisforrobustness.

24

characteristics, and state‐year dummies.34 The point estimates in columns (1) and (2) are never

statisticallysignificant,butthesignsareinformative.Inparticular,thecoefficientssuggestthatfor

otherwiseidenticalworkers,plants,andmatches,adepreciationoftherealexchangerateincreases

aworker’sprobabilityofhireastheaverageplantexpands.

Weargue,however,thattradeshocksandregulatoryenforcementhavedifferenteffectsdepending

on the plant’s mode of globalization. As real exchange rate depreciations increase the

competitivenessofexportingplantsinforeignmarkets,weanticipateanexpansionofemployment

atexportersrelativetonon‐exporters.InthefinaltwocolumnsofTable5.1,wereportcoefficients

forthesetofexportingplantsandnon‐exportingplants,respectively.Wedenoteexportersasthose

firms thatexportedat anypointduring theperiod1996 to2001.This time‐invariant indicator is

designedtominimizepotentialendogeneityconcernssurroundingtheglobalizationdecisionpost‐

devaluation.35

Aspredictedbynewheterogeneousfirmtrademodels,adepreciationoftherealincreaseshiringat

exporters,anddecreaseshiringatplantsproducingforthedomesticmarket.Moreover,consistent

with the literature, the way in which enforcement impacts hiring is different depending on the

globalizationstatusoftheplant.Notably,enforcementhasnostatisticalimpactonexportingplants,

inlinewithresultsinHarrisonandScorse(2003)forIndonesiathatglobalizedfirmsareinternally‐

enforced and more likely to follow existing labor regulations. By contrast, domestic plants in

strictly‐enforcedmunicipalitiesdecreasehiringbymorethanotherwiseidenticaldomesticplantsin

weakly‐enforcedmunicipalities,asthecostofformalworkersincreasesfortheseplants.Theimpact

of trade openness differs for plantswith the samemode of globalization but varying degrees of

34 In particular,we include at theworker level, theworker’s age (andage squared), tenure at theplant inmonths,education(astwodummyvariables—atleasthighschoolandmorethanhighschoolwherelessthanhighschool istheomittedcategory)andoccupation(asthreedummyvariables—skilledbluecollarworker,unskilledwhitecollarworker,andprofessional/managerialworkerwhereunskilledbluecollarworkeristheomitted category). At the plant level, we include average plant wages, plant employment, average workertenure at the plant, and the age, gender, educational, and occupational composition of the plant.We alsoincludethefollowingindustrycharacteristics:theindustryunionizationrate,industryemployment,averageworkertenureintheindustry,andtheage,gender,educational,andoccupationalcompositionoftheindustry.35 In unreported regressions,we test the robustnessof our results to the endogeneity of the export statusindicator. Specifically,we categorize plants based on the industry’s import penetration. This industry‐levelcategorization of the plant’s globalization status largely confirms ourmain findings. Alternatively, we alsoclassifyplantsbasedonthecity’smarketaccess(intheformofthreeindicators:distancetothestatecapital,distanceto the federalcapital,andan indexof transportationcosts fromthecity tothenearestcapitalcityregardlessofstate).Acrossallmeasures,ourmainfindingsarerobusttoadefinitionofglobalizationbasedonthecity’smarketaccess.

25

exposuretodefactolabormarketregulations.

Themagnitudesofourestimatesseemtobeplausible.Evaluatingtheeffectonworkersandplants

located in municipalities at themean level of inspections, a 10 percent depreciation of the real

increasesthehiringprobabilityatexportingplantsby4.1%anddecreasesthehiringprobabilityat

domestic plants by 7.3%. The impact for domestic plants varies depending on the level of

enforcementtheplantfaces—fordomesticplantslocatedinmunicipalitiesatthe10thpercentileof

inspections, a10percentdepreciationof the realdecreases theprobabilityofhirebyonly6.0%,

while workers matched with domestic plants located in municipalities in the 90th percentile of

inspectionsexperienceadecreaseintheaccessionprobabilityofaround9.0%.

InTable5.2,wereportestimatesforequation(3),bytheplant’smodeofglobalization,wherethe

maindependentvariablerepresentstheworker’sseparationfromtheplant.Wehypothesizethata

depreciationoftherealexchangeratedecreasestheprobabilityofseparationfortheaverageplant,

ascompetitivenessincreases.ThisisconfirmedinTable5.2;thepointestimateon ispositive,but

statisticallyinsignificant.Wealsonotethat,aspredicted,theeffectisdrivenbydecreasesinfiringat

expandingexportingplants.Thoughstatistically insignificant, theresults fornon‐exportingplants

point to an increase in firing relative to exporting plants in response to a real exchange rate

depreciation.

Weremindthereaderthatincreasesininspectionsambiguouslyrelatetoseparations.Ontheone

hand,more enforcement increases the cost of firing and thusmaydecrease firings.On the other

hand,withincreasedenforcement,existinglaborbecomesmoreexpensiveandtheplantmayresort

to firingworkers in the short run to overcome the increased cost. Once again,we anticipate the

impact of regulatory enforcement to be stronger for non‐exporting firms where regulations are

most binding. Our data from Brazil support these claims. Exporters in strictly‐enforced

municipalitiesrespondnodifferentlytoanexpansionarytradeshockthandoidenticalexportersin

weakly‐enforced cities. However, non‐exporting plants in strictly‐enforced municipalities

differentially increase firing as compared to similar non‐exporters facing weaker enforcement.

Domesticplantslocatedincitiesatthe10thpercentileofinspectionscontractbyincreasingfiringby

approximately2.0%inresponsetoa10percentdevaluation,whiletheprobabilitythatamatchis

destroyedatsimilardomesticplantslocatedincitiesatthe90thpercentileofinspectionsincreases

bycloseto5.0%inresponsetothesametradeshock.

26

Takentogether,Tables5.1and5.2suggestthatstrongerenforcementoflaborregulationsinfluence

labor turnover along the extensivemargin for non‐exporting plants through increased firing and

decreased hiring. That is, contracting non‐exporters decrease job creation and increase job

destruction even further due to increases in the cost of formal employment in strictly‐regulated

areas. These results offer important implications for policy. Job security in an increasingly

globalizedworldreceivesconsiderableattentionfromacademics,policymakers,andthemedia.Our

results confirm recent trademodels inwhichnon‐exporting plants contract in response to trade

reform. More importantly, our data imply that labor market regulations reinforce these

contractionaryeffectsoftradereformforsmall,constrainednon‐exportingplants.

Expandingandcontractingplantsmayalsoadjustalong the intensivemargin.For instance,when

facedwitha tradeshock,employersmayadjust thehoursworked forexistingemployeesorshift

workers between full‐time and temporary contracts. Although the labor law limits a continuous

work shift to 6 hours, limits theweeklyworking period to 44 hours, andmandates increases in

overtimepay, theeffectsof the labor lawsonplantswill likelydifferdependingon thedegreeof

enforcement.Similarly, the increasedcostof formal laborassociatedwithstricterenforcementof

laborregulationsmayleadplantstoshifttowardstheuseoftemporarycontractsoverpermanent

contracts.Thelattercouldhelpemployersovercomethelong‐termrelationshipsofmorerestrictive

employmentcontracts.Wenextconsidertheseadjustmentsforallplantsandbytheplant’smodeof

globalization.

Table5.3reportsresultsfromtheestimationofequation(3)wherethedependentvariable isthe

logarithmofhoursworkedperweekforallplantsandbytheplant’sexportstatus.Table5.4reports

coefficientsfortheestimationofequation(3)wherethedependentvariableisanindicatorvariable

ifworkeriisemployedwithafull‐timecontractinplantj intimet.AcrossTables5.3and5.4,our

data suggest little variation in the intensive margin in response to exchange rate shocks and

regulatory enforcement for both exporters and non‐exporters. The point estimates in Table 5.3

providesomesuggestiveevidencefortheideathatexportersdifferentiallyexpandhoursandnon‐

exporters differentially contract hours. In Table 5.4, the evidence points to an increased use of

temporary contracts to help smooth the shock from exchange rate changes, particularly at non‐

exportingplants.However,acrossallplanttypes,thedegreeofregulatoryenforcement,contraryto

predictions,doesnotinfluencehoursworkedorcontracttypes.

27

5.1. HeterogeneityoftheResults

Our main results provide suggestive evidence that non‐exporters facing strict labor market

regulations differentially decrease job creation and differentially increase job destruction in

responsetotradeopenness,ascomparedtosimilarplantslocatedinareaswithweaklabormarket

regulatoryenforcement.Wedonotfindanysignificantimpactofenforcementonadjustmentsalong

the intensive margin (hours and contract type) for either exporters or non‐exporters. In this

section,weconsidertheheterogeneityoftheseresultsbasedonindustry‐leveldifferencessuchas

thesector’stechnologicalintensity,basedonplant‐leveldifferencessuchasemployment,andbased

onworker‐leveldifferencessuchastheworker’sagegroup.Giventhelackofstatisticalevidencein

supportofadjustmentsalongtheintensivemargin,werestricttheanalysisforwardtotheimpacton

jobcreationandjobdestruction.Theresultsforhoursandcontracttypeareavailableuponrequest.

TechnologicalIntensity Ourmainargumentrestsonthefactthatatradeshockwillreallocate

factors of production towards more efficient use. We consider labor as the relevant factor of

production in this paper.However, the same shockmay also influence adjustments in the short‐

term in terms of capital and other physical materials factors of production. To ensure that the

effects of labor market regulatory enforcement we find in Tables 5.1 and 5.2 reflect plants’

constraintsinadjustinglaborintheshort‐run,wesplitourmainsampleintosectorsdependingon

the technological intensity. Our assumption here, consistent with much of the literature, is that

sectorsrelyingontechnologyarerelativelycapital‐intensive.Therefore,lowtechnologysectorsare

assumedtobemorelabor‐intensive.Forthisreason,weanticipatethatourmainfindingsaredriven

byplants in low‐tech(labor‐intensive) industries.Werelyondata fromtheWorldBanktodefine

sectors’technologicalintensity.Examplesofhigh‐techindustriesare:petroleumrefining,chemical

manufacturing, and automobile manufacturing. Examples of low‐tech industries are: food and

beverage,textile,andwoodmanufacturing.

Wereportcoefficientsfromtheestimationofequation(3)bythesector’stechnologicalintensityin

Table5.5.Inthetoppanel,thedependentvariablerepresentsanindicatorforjobcreation,whilein

thebottompanel,weuseajobdestructionindicatorasthedependentvariable.Theresultsmostly

confirm our hypotheses. The result that domestic plants in strictly‐enforcedmunicipalities limit

hiring by more in response to a trade shock is driven by domestic plants in low‐tech (labor‐

28

intensive) industries. Table 5.2’s finding that domestic plants located in strictly‐enforced areas

increase jobdestructionbymore is also largelydrivenbydomesticplants in low‐tech industries.

Themaininteractionparameterofinterestisnegativeandstatisticallysignificantatthe10%level

ofsignificance.

PlantSize AsisemphasizedinCardosoandLage(2007),thereisastrongcorrelationbetweenthe

sizeofthefirm,asaproxyforthevisibilityofthefirm,andthenumberofinspections.Theresultsin

Kugler (2004) reinforce this finding. The author reports Colombian labormarket reforms had a

greaterimpactonworkersinlargerfirms.Moreover,itisnowwell‐establishedintheinternational

economicsliteraturethatexportersandnon‐exportersdiffersubstantiallyintermsofproductivity

andsize,amongotherattributes(BernardandJensen1995).Forthesereasons,wenextexplorethe

heterogeneityofourmainfindingsbythesizeoftheplant.Wedefineatime‐invariantlargeplant

indicatorequaltooneforthoseplantswithaverageemploymentbetween1996and2001greater

than themedian value.36We argue that comparing similarly‐sized plants helps tominimize any

possibleselectionbiasassociatedwiththeplant’sglobalizationstatus.

Table5.6displaysresultsfromtheestimationofequation(3),bythesizeoftheplant,forallplants

andbytheplant’smodeofglobalization,wherethedependentvariableinthetoppanelisaworker‐

plantjobcreation.First,wenotethatthepositive,butinsignificant,interactioncoefficientincolumn

(1)ofTable5.1 isdrivenby the impactonbelow‐mediansizedplants.Furthermore, it is evident

thatthiseffectiswhollyconcentratedamongsmall,non‐exportingplants.Thismakessenseasthese

are the plants for which we argue labor market regulations are most restrictive. Due to their

visibility,asissuggestedbyCardosoandLage(2007),largeplantsaremorelikelyalreadyinspected

andaremorelikelytofollowexistingregulations.Assuch,smallnon‐exporterswhoexperiencedan

increaseinenforcementoveroursampleperioddemonstratesignificantdifferencesinjobcreation

and job destruction in response to trade reform as compared to similar plants facing less labor

marketregulatoryenforcement.

In addition, we note that when comparing large exporters to large non‐exporters, the main

coefficientsonthetrade‐weightedrealexchangerateremainstatisticallysignificantandofthesame

signincomparisontoTable5.1.Consideringsimilarly‐sizedplantshelpstominimizepotentialbias

36Medianemploymentacrossallplants is9employees.Wemaintain this cutoff thresholdacrossallplant‐types.

29

associated with selection into exporting post‐trade reform. The results for worker‐plant job

destructionarereported inthebottompanel.Ourconclusionsare in linewiththose fromthe job

creationresultsinthetoppanel.

WorkerAge Our main findings show that, following a trade shock, labor adjustment (and

particularly at non‐exporting plants) varies depending on the degree of enforcement of labor

marketregulations.However,inadditiontothismaineffect,thecompositionofemploymentisalso

likelytobeaffectedbythestringencyofenforcementof laborregulations.Wehypothesizethatin

environments facing strict enforcement, those already employed are more likely to remain

employed,whilenewentrantsorre‐entrantsintothelaborforce—asislikelythecasewithyounger

workers—are less likely to be hired. An implication of this is thatwithin plants after a currency

depreciation, we expect to observe more older workers in the workforce. Table 5.7 reports

estimates for equation (3), dividing the sample by the age of the worker and the plant’s

globalizationstatus.Wedefineworkersas“older”whentheyareolderthan32(themeanworker

ageinthesample)and“younger”whentheyare32orless.

As predicted, we see that increases in de facto labor regulations decrease the hiring of young

workers at non‐exportingplants. The sign on themain interaction term therefore shows that, in

response to a real exchange rate depreciation, non‐exporting plants in strictly‐enforced

municipalitiesdifferentiallydecreasehiringofyouthworkersinparticular.Similarly,theresultthat

non‐exporters in strictly‐enforcedmunicipalitiesdifferentially increase jobdestruction relative to

non‐exportersinweakly‐enforcedareasisdrivenbytheimpactonyoungworkers.

5.3. AggregateImplications

Akeyargumentinfavoroftradeliberalizingreformsisthatfactorscanreallocatetomoreefficient

uses,allowingforenhancedproductivityandgrowth.However, inthispaperwedemonstratethat

the efficient reallocation of labor in response to trade shocks is inhibitedby strictde facto labor

marketregulations.Inthissection,weinvestigatetheextenttowhichdampenedlaborreallocation

alsorestrictsthewithinplantproductivitygainsassociatedwithtradeopenness.

Thereareafewpotentialchannelslinkingincreasedenforcementtolowerplant‐levelproductivity.

Forinstance,theinabilityofplantstoadjusttochangingconditionsandtoreallocatefromdeclining

30

todynamicsectorsmayreduceplant‐levelproductivity.Inaddition,moreregulationsmayprevent

plants from introducing new goods or investing inmore complexproduction technologieswhich

may have higher value‐added, but also face more volatile demand and thus require greater

adjustments.Finally,giventhehighcostsofdismissalsinareaswithstrictemploymentprotection,

employersmay now be forced to retain unproductiveworkers theywould have otherwise fired.

Also,giventheexpectationofajob‐for‐life,employeesmaynowhavelessincentivetoexerteffort,

thusloweringtheirplantandworkerproductivity.

On theotherhand,wecanalso imagineeffects in theotherdirection; that is, stricter regulations

increasingplant‐levelproductivity.As labormarketregulations increasethecostsassociatedwith

formalemployment,plantsmayalsoraisethebarforthequalityofworkerstheyarewillingtohire,

given the increased costs, and consequently increase plant‐level productivity. Moreover, the

expectationofa long‐termrelationshipmayincrease investments inplant‐specific training,which

neithertheemployernortheworkerwouldbewillingtoincuriftherelationshipwasshort‐term.

Finally, businesses may switch away from hiring workers and use mechanized technologies to

replaceworkers,whichmayraiseproductivityfortheremainingworkers.

Intheabsenceofdirectdataonplant‐levelproductivityandprofitability,werelyoninformationon

plant‐levelaveragewagesundertheassumptionthatincreasesinproductivityandprofitabilitywill

be positively associated with increases in plant‐average wages when plants share rents with

workers.37 In Table 5.8, we present coefficients from the estimation of equation (2), where the

dependentvariableistheplant‐levelaveragewageasaproxyforplant‐levelproductivity.Acrossall

plant‐types,adepreciationsignificantlyincreaseswithin‐plantproductivity(consistentwithstudies

like Pavcnik (2002)). Moreover, globalization increases productivity and profitability bymore at

exporting plants relative to plants serving only the domestic market, also consistent with the

literature (e.g., Trefler (2004)). We also find a statistically significant and negative impact of

increased enforcementonplant‐level productivity, asproxiedbyplant‐averagewages, suggesting

that the first effect pertaining to restricted labor reallocation dominates any potential positive

impact of enforcement on firm productivity via increases in investments in training or physical

capital.

37Forinstance,inamodelliketheoneinAmitiandDavis(2012),whereworkers’wagesaredirectlylinkedtofirmprofitsthrougha“fairwage”mechanism.

31

Our focus, however, is on the interaction term, where our predictions are confirmed. Across all

plant‐types,strictenforcementoflaborregulationslimitspotentialwithin‐plantproductivitygains

(duetotheefficientreallocationofworkers)associatedwithtradeopenness,asproxiedbyplant‐

average wages. Regulatory enforcement inhibits productivity gains for both exporting and non‐

exportingplants.

6. ConclusionsandPolicyImplications

Economists have long debated the effects of trade liberalization on labor market outcomes in

developingcountries.Earlystudieshavefoundlittleimpactonplant‐levelemploymentchanges.We

argueapotentialexplanationrelates tohowrestrictive labormarket regulationsare in inhibiting

the reallocation of workers. In this paper, we revisit the question of the impact of trade

liberalization on labor reallocation using data for Brazil. Brazil is an especially interesting case

studygiventhestringencyofthedejure labormarketregulationsinthecountry(seeBotero,etal

(2004)). Furthermore, the topic is also at the forefront of economic policy discussions as the

countryconsidersnewwaysoffosteringindustrialproductivityandofcreatingamorecompetitive

workforce.38 Finally, the size andgeographicheterogeneity of the country also creates significant

variationwithinthecountryontheenforcementofthelaborlaw.

Weexplorethefactthatwithincountries,plantsvaryinthedegreeofexposuretoglobalmarkets

and in the incidence of de facto labor regulations they face. We use a difference‐in‐difference

methodologytoidentifytheeffectsofopennessonlaborturnover,forfirmswithdifferentdegrees

ofexposuretotradeanddefactolaborregulations.Inparticular,weanalyzetheimpactofincreased

exposuretotrade,followingBrazil’scurrencycrisisin1999,anddiscerntheimpactdependingon

theplant’sexposuretoglobalmarketsandtotheenforcementoflabormarketregulationsbasedon

theplant’smunicipallocation.

Weshowthat,inBrazil,theextenttowhichtradeaffectslabormarketoutcomesdependsonthede

facto degree of stringency of the labor regulations faced by plants. Conditional on the

(unobservable) time‐invariant plant‐worker match, and several time‐varying worker, plant, and

38Forexample,theBraziliangovernmenthasrecentlylaunchedaprogramofincentivestopromoteindustrialgrowthandcompetitiveness.Theprogramproposesanexemptionfroma20%socialsecuritylevyonworkerpayrollsforcertainsectors.Eligiblesectorsincludeautomotives,textiles,footwearandplastics(seeFinancialTimes2012).

32

sectorcharacteristics,wenotethat,asispredictedbynewheterogeneousfirmtrademodels,trade

openness is associated with an increase in the probability of hiring at exporting plants and a

decrease in the probability of hiring at domestically‐oriented plants. Furthermore, we find that

labor inspections largely influence labor adjustment along the extensive margin at small, labor‐

intensive, non‐exporting firms for which labor regulations are likely most binding. This is an

especiallyinterestingfindingforpolicymakers,giventhecurrentchallengeofrevampingindustrial

growth,throughamorecompetitivelaborforce,inthefaceofaglobalizingworld.

Insummary,ourresultsstronglysuggestthatmorestringentdefactoregulationslimitjobcreation

with enhanced trade openness. Overall, stricter labor enforcement increases job destruction and

decreases jobcreationat firmsmost restrictedby labor regulations.Wealsoshowthis increased

enforcementisassociatedwithlowerproductivitygainspost‐tradereform.Ourworkthereforemay

offer an explanation for themixed results in the literature on the causal impact of exporting on

plant‐level productivity. From a policy standpoint, our work also suggests that increasing the

flexibilityofde jure laborregulationswillallowfor increased jobcreationandthusofferbroader

accesstothegainsfromtrade.

33

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Servo, and Andree Souza, 2004. “Trade Liberalization, the Exchange Rate, and Job Flows inBrazil,”TheJournalofPolicyReform,74(4),pp.209‐223.

Topalova,Petia,2010.“Factor ImmobilityandRegional ImpactsofTradeLiberalization: Evidence

onPovertyfromIndia,”AmericanEconomicJournal:AppliedEconomics,2,pp.1‐41.Trefler, Daniel, 2004. “The Long and Short of the Canada‐U.S. Free Trade Agreement,” American

EconomicReview,94,pp.870‐895.Verhoogen, Eric, 2008. “Trade, Quality Upgrading and Wage Inequality in the Mexican

ManufacturingSector,”TheQuarterlyJournalofEconomics,123(2),pp.489‐530.Yeaple,StephenRoss,2005.“ASimpleModelofFirmHeterogeneity,InternationalTradeandWages,”

JournalofInternationalEconomics,65,pp.1‐20.Woodcock,SimonD.,2011.“MatchEffects,”unpublishedmanuscript.

1

Figure3.1:NominalandRealExchangeRateSeriesforBrazil,1994–2001

0.0

0.5

1.0

1.5

2.0

2.5

3.0Jul‐94

Nov‐94

Mar‐95

Jul‐95

Nov‐95

Mar‐96

Jul‐96

Nov‐96

Mar‐97

Jul‐97

Nov‐97

Mar‐98

Jul‐98

Nov‐98

Mar‐99

Jul‐99

Nov‐99

Mar‐00

Jul‐00

Nov‐00

Mar‐01

Jul‐01

Nov‐01

NominalExchangeRate(R/$) RealExchangeRate(R/$)

Source:Muendler(2003).

Figure 3.2: Enforcement Intensity by Municipality, 1998 and 2000

1998 2000

® ®

Source: Authors’ calculations based on administrative data from the Brazilian Ministry of Labor (1996-2001).Note: This figure reports the number of inspections per Brazilian municipality, with darker shades representing higher numbersof inspections. The map on the left is for the year 1998, while the map on the right is for the year 2000.

Figure 3.3: Industry Variation in Trade-Weighted RER (Levels and Changes), 1999

Trade-Weighted RER Changes in Trade-Weighted RER

02

46

810

Den

sity

.55 .6 .65 .7 .75 .8Trade−Weighted Real Exchange Rate

®

05

1015

2025

Den

sity

−.4 −.35 −.3 −.25 −.2 −.15Change in Trade−Weighted RER

®Source: Authors’ calculations based on bilateral real exchange rate data from the IMF and trade flows data from the NBER(1998-1999).Note: This figure illustrates industry-level heterogeneity in the level of the trade-weighted real exchange rate (left panel) andin the annual change of the trade-weighted real exchange rate (right panel).

Table3.1:EnforcementData,1996‐2001

ShareofCitiesInspected

AverageNumberof

InspectionsineachCity

AverageNumberofInspectionsPer100RegisteredPlants

AverageNumberofInspectionsPer10,000RegisteredWorkers

1996 0.33 13.2 2.16 14.831997 0.44 13.0 2.19 16.901998 0.38 12.8 2.39 20.901999 0.50 13.8 2.62 21.552000 0.43 14.8 2.68 22.912001 0.52 14.3 2.28 16.42

Source:Authors'calculationsbasedonadministrativedatafromtheBrazilianMinistryofLabor(1996‐2001).Note: This table reports different statistics at the city level between 1996 and 2001. Column (1) reports theshare of cities that have at least one manufacturing labor inspection. Column (2) reports the average numberof labor inspections in each city. Column (3) reports the average number of inspections per 100 registeredplants in the city and column (4) reports the average number of labor inspections per 1,000 registeredworkers("comcarteira ").

Table3.2:DescriptiveStatistics,1996‐2001

AllPlants ExportersNon‐

Exporters(1) (2) (3)

DependentVariableShareofworkers:Hired 0.29 0.24 0.34Fired 0.25 0.22 0.28TemporaryContract 0.02 0.02 0.02Average:HoursPerWeek 43.5 43.4 43.6

Worker‐levelCovariatesAge 32 32 31Shareofworkers:LessthanHighSchool 0.68 0.62 0.74HighSchool 0.25 0.28 0.22MorethanHighSchool 0.07 0.10 0.03

UnskilledBlueCollar 0.10 0.10 0.10SkilledBlueCollar 0.66 0.64 0.68OtherWhiteCollar 0.07 0.07 0.07ProfessionalorTechnical 0.17 0.19 0.15

Plant‐levelCovariatesEmployment 59 161 27AverageWage(inlogs) 8.00 8.50 7.84AverageMatchDuration(inyears) 2.57 2.73 2.40

Municipality‐levelCovariatesInspections 35.1 60.8 38.5

Industry‐levelCovariatesTrade‐weightedRER 0.62 0.62 0.62Employment 18,328 18,755 18,912UnionizationRate 0.21 0.21 0.21

NumberofObservations 313,940 171,012 142,928NumberofWorkers 81,254 46,181 45,543NumberofPlants 53,011 13,891 39,120NumberofMatches 122,017 62,547 59,470NumberofMunicipalities 2,769 1,466 2,578NumberofIndustries 240 237 236Source:Authors'calculationsbasedonRAIS,MinistryofLaboradministrativedataoninspections,IMFbilateralexchangerates,NBERtradeflows,PNAD,andSECEX(1996‐2001).

Note: This table reports descriptive statistics for the main variables used in our empirical work. We report on

worker‐level variables (averages across workers), plant‐level variables (averages across plants), municipality‐level

variables (averages across municipalities), and industry‐level variables (averages across industries). Column (1)

reports statistics for all plants in our manufacturing sample. Column (2) reports statistics for the set of exporting

plants (defined to be those firms exporting any positive dollar amount at any point during the 1996 to 2001 sample

period)andcolumn(3)reportsstatisticsforthegroupofnon‐exporters.

Table5.1:Trade,Enforcement,andJobCreationDep.Variable:MatchCreation

All All ExportersNon‐

ExportersTRER*Enforcement ‐0.011 ‐0.011 0.042*

(0.015) (0.021) (0.018)Trade‐weightedRER ‐0.020 ‐0.103 ‐0.356** 0.522**

(0.042) (0.080) (0.103) (0.112)NumberofObs. 300,857 300,857 165,176 135,681Plant‐YearControls YES YES YES YESCity‐YearControls YES YES YES YESSector‐YearControls YES YES YES YESWorker‐YearControls YES YES YES YESState‐YearDummies YES YES YES YESMatchFixedEffects YES YES YES YESSource:Authors'calculationsbasedonRAIS,MinistryofLaboradministrativedataoninspections,IMFbilateralrealexchangerates,NBERtradeflows,andSECEX(1996‐2001).

Note: This table reports coefficients from the ordinary least squares estimation of equation (3) in the paper, where the dependentvariable is an indicator variable which takes the value one if a match between worker i and plant j is created in time t , for all plantsand by the plant's export status. ** denotes significance at the 1% level; * denotes significance at the 5% level. Robust standarderrors, clustered at the city‐industry level, are reported in parentheses. All regressions also include the following city controls: log(inspections), log (number of plants), and an interaction between the trade‐weighted real exchange rate and log (number of plants).Unreported covariates at the worker level include the worker’s age (and age squared), tenure at the plant in months, education (astwo dummy variables—at least high school and more than high school where less than high school is the omitted category) andoccupation (as three dummy variables—skilled blue collar worker, unskilled white collar worker, and professional/managerialworker where unskilled blue collar worker is the omitted category). At the plant level, we include average plant wages, plantemployment, average worker tenure at the plant, and the age, gender, educational, and occupational composition of the plant. Wealso include the following industry characteristics: the industry unionization rate, industry employment, average worker tenure intheindustry,andtheage,gender,educational,andoccupationalcompositionoftheindustry.

Table5.2:Trade,Enforcement,andJobDestructionDep.Variable:MatchDestruction


ExportersTRER*Enforcement ‐0.005 0.003 ‐0.041*

(0.012) (0.016) (0.017)Trade‐weightedRER 0.027 0.114 0.194* ‐0.124

(0.036) (0.065) (0.083) (0.094)NumberofObs. 300,857 300,857 165,176 135,681Plant‐YearControls YES YES YES YESCity‐YearControls YES YES YES YESSector‐YearControls YES YES YES YESWorker‐YearControls YES YES YES YESState‐YearDummies YES YES YES YESMatchFixedEffects YES YES YES YESSource:Authors'calculationsbasedonRAIS,MinistryofLaboradministrativedataoninspections,IMFbilateralrealexchangerates,NBERtradeflows,andSECEX(1996‐2001).

Note: This table reports coefficients from the ordinary least squares estimation of equation (3) in the paper, where the dependentvariable is an indicator variable which takes the value one if a match between worker i and plant j is destroyed in time t , for allplants and by the plant's export status. ** denotes significance at the 1% level; * denotes significance at the 5% level. Robuststandard errors, clustered at the city‐industry level, are reported in parentheses. All regressions also include the following citycontrols: log (inspections), log (number of plants), and an interaction between the trade‐weighted real exchange rate and log(number of plants). Unreported covariates at the worker level include the worker’s age (and age squared), tenure at the plant inmonths, education (as two dummy variables—at least high school and more than high school where less than high school is theomitted category) and occupation (as three dummy variables—skilled blue collar worker, unskilled white collar worker, andprofessional/managerial worker where unskilled blue collar worker is the omitted category). At the plant level, we include averageplant wages, plant employment, average worker tenure at the plant, and the age, gender, educational, and occupational compositionof the plant. We also include the following industry characteristics: the industry unionization rate, industry employment, averageworkertenureintheindustry,andtheage,gender,educational,andoccupationalcompositionoftheindustry.

Table5.3:Trade,Enforcement,andHoursDep.Variable:Log(Hours)


ExportersTRER*Enforcement 0.004 0.000 0.007

(0.003) (0.003) (0.005)Trade‐weightedRER ‐0.003 0.013 ‐0.011 0.036

(0.011) (0.014) (0.015) (0.025)NumberofObs. 300,857 300,857 165,176 135,681Plant‐YearControls YES YES YES YESCity‐YearControls YES YES YES YESSector‐YearControls YES YES YES YESWorker‐YearControls YES YES YES YESState‐YearDummies YES YES YES YESMatchFixedEffects YES YES YES YESSource:Authors'calculationsbasedonRAIS,MinistryofLaboradministrativedataoninspections,IMFbilateralrealexchangeratesandNBERtradeflows,andSECEX(1996‐2001).

Note: This table reports coefficients from the ordinary least squares estimation of equation (3) in the paper, where the dependentvariable is the logarithm of hours worked per week for worker i in plant j at time t, for all plants and by the plant's export status. **denotes significance at the 1% level; * denotes significance at the 5% level. Robust standard errors, clustered at the city‐industrylevel, are reported in parentheses. All regressions also include the following city controls: log (inspections), log (number of plants),and an interaction between the trade‐weighted real exchange rate and log (number of plants). Unreported covariates at the workerlevel include the worker’s age (and age squared), tenure at the plant in months, education (as two dummy variables—at least highschool and more than high school where less than high school is the omitted category) and occupation (as three dummyvariables—skilled blue collar worker, unskilled white collar worker, and professional/managerial worker where unskilled bluecollar worker is the omitted category). At the plant level, we include average plant wages, plant employment, average worker tenureat the plant, and the age, gender, educational, and occupational composition of the plant. We also include the following industrycharacteristics: the industry unionization rate, industry employment, average worker tenure in the industry, and the age, gender,educational,andoccupationalcompositionoftheindustry.

Table5.4:Trade,Enforcement,andContractTypeDep.Variable:Full‐TimeContract


ExportersTRER*Enforcement 0.003 ‐0.002 0.009

(0.005) (0.007) (0.006)Trade‐weightedRER 0.022 0.025 ‐0.007 0.060*

(0.012) (0.022) (0.030) (0.028)NumberofObs. 300,857 300,857 165,176 135,681Plant‐YearControls YES YES YES YESCity‐YearControls YES YES YES YESSector‐YearControls YES YES YES YESWorker‐YearControls YES YES YES YESState‐YearDummies YES YES YES YESMatchFixedEffects YES YES YES YESSource:Authors'calculationsbasedonRAIS,MinistryofLaboradministrativedataoninspections,IMFbilateralrealexchangeratesandNBERtradeflows,andSECEX(1996‐2001).

Note: This table reports coefficients from the ordinary least squares estimation of equation (3) in the paper, where the dependentvariable is an indicator variable if worker i is employed with a full‐time contract in plant j at time t, for all plants and by the plant'sexport status. ** denotes significance at the 1% level; * denotes significance at the 5% level. Robust standard errors, clustered at thecity‐industry level, are reported in parentheses. All regressions also include the following city controls: log (inspections), log(number of plants), and an interaction between the trade‐weighted real exchange rate and log (number of plants). Unreportedcovariates at the worker level include the worker’s age (and age squared), tenure at the plant in months, education (as two dummyvariables—at least high school and more than high school where less than high school is the omitted category) and occupation (asthree dummy variables—skilled blue collar worker, unskilled white collar worker, and professional/managerial worker whereunskilled blue collar worker is the omitted category). At the plant level, we include average plant wages, plant employment, averageworker tenure at the plant, and the age, gender, educational, and occupational composition of the plant. We also include thefollowing industry characteristics: the industry unionization rate, industry employment, average worker tenure in the industry, andtheage,gender,educational,andoccupationalcompositionoftheindustry.

Table5.5:Trade,Enforcement,andTurnover,BySectorTech‐Intensity

Dep.Variable:MatchCreation

All ExportersNon‐

ExportersAll Exporters

Non‐Exporters

TRER*Enforcement 0.015 0.020 0.042* ‐0.038 ‐0.028 0.032(0.016) (0.023) (0.021) (0.029) (0.035) (0.036)

Trade‐weightedRER 0.086 ‐0.276 0.547** 0.009 ‐0.117 0.599**(0.101) (0.142) (0.135) (0.125) (0.146) (0.203)

NumberofObs. 208,270 102,615 105,655 92,593 62,567 30,026

Dep.Variable:MatchDestruction

All ExportersNon‐


Non‐Exporters

TRER*Enforcement ‐0.017 ‐0.013 ‐0.036 0.018 0.020 ‐0.046(0.014) (0.018) (0.020) (0.023) (0.027) (0.032)

Trade‐weightedRER 0.099 0.202 ‐0.051 ‐0.088 ‐0.016 ‐0.389*(0.081) (0.110) (0.113) (0.106) (0.130) (0.170)

NumberofObs. 208,270 102,615 105,655 92,593 62,567 30,026Plant‐YearControls YES YES YES YES YES YESCity‐YearControls YES YES YES YES YES YESSector‐YearControls YES YES YES YES YES YESWorker‐YearControls YES YES YES YES YES YESState‐YearDummies YES YES YES YES YES YESMatchFixedEffects YES YES YES YES YES YES

Low‐TechSectors High‐TechSectors

Source:Authors'calculationsbasedonRAIS,MinistryofLaboradministrativedataoninspections,IMFbilateralrealexchangerates,NBERtradeflows,andSECEX(1996‐2001).

Note:Thistablereportscoefficientsfromtheordinaryleastsquaresestimationofequation(3)inthepaper,wherethedependentvariableinthetoppanelisanindicatorvariable

whichtakesthevalueoneifamatchbetweenworkeriandplantjiscreatedintimetandthedependentvariableinthebottompanelisanindicatorvariableequaltooneifamatch

betweenworkeri andplantj isdestroyedintimet ,forallplants,bytheplant'sexportstatus,andbythesector'stech‐intensity.**denotessignificanceatthe1%level;*denotessignificanceatthe5%level.Robuststandarderrors,clusteredatthecity‐industrylevel,arereportedinparentheses.Allregressionsalsoincludethefollowingcitycontrols:log

(inspections),log(numberofplants),andaninteractionbetweenthetrade‐weightedrealexchangerateandlog(numberofplants).Unreportedcovariatesattheworkerlevel

includetheworker’sage(andagesquared),tenureattheplantinmonths,education(astwodummyvariables—atleasthighschoolandmorethanhighschoolwherelessthan

highschoolistheomittedcategory)andoccupation(asthreedummyvariables—skilledbluecollarworker,unskilledwhitecollarworker,andprofessional/managerialworker

whereunskilledbluecollarworkeristheomittedcategory).Attheplantlevel,weincludeaverageplantwages,plantemployment,averageworkertenureattheplant,andtheage,

gender,educational,andoccupationalcompositionoftheplant.Wealsoincludethefollowingindustrycharacteristics:theindustryunionizationrate,industryemployment,

averageworkertenureintheindustry,andtheage,gender,educational,andoccupationalcompositionoftheindustry.

Table5.6:Trade,Enforcement,andTurnover,ByPlantSize


All ExportersNon‐


Non‐Exporters

TRER*Enforcement 0.101** 0.042 0.112** ‐0.007 ‐0.014 0.033(0.032) (0.139) (0.033) (0.016) (0.021) (0.021)

Trade‐weightedRER 0.695** 0.368 0.775** ‐0.107 ‐0.366** 0.532**(0.193) (0.786) (0.201) (0.084) (0.103) (0.129)

NumberofObs. 39,097 2,010 37,087 261,778 163,176 98,602


All ExportersNon‐


Non‐Exporters

TRER*Enforcement ‐0.066* ‐0.038 ‐0.069* ‐0.002 0.006 ‐0.030(0.029) (0.114) (0.030) (0.013) (0.016) (0.020)

Trade‐weightedRER ‐0.059 ‐0.055 ‐0.079 0.101 0.200* ‐0.150(0.171) (0.577) (0.179) (0.069) (0.084) (0.111)


SmallPlants


LargePlants



betweenworkeriandplantjisdestroyedintimet,forallplants,bytheplant'sexportstatus,andbytheplant'ssize.**denotessignificanceatthe1%level;*denotessignificance

atthe5%level.Robuststandarderrors,clusteredatthecity‐industrylevel,arereportedinparentheses.Allregressionsalsoincludethefollowingcitycontrols:log(inspections),

log(numberofplants),andaninteractionbetweenthetrade‐weightedrealexchangerateandlog(numberofplants).Unreportedcovariatesattheworkerlevelincludethe

worker’sage(andagesquared),tenureattheplantinmonths,education(astwodummyvariables—atleasthighschoolandmorethanhighschoolwherelessthanhighschoolis

theomittedcategory)andoccupation(asthreedummyvariables—skilledbluecollarworker,unskilledwhitecollarworker,andprofessional/managerialworkerwhereunskilled

bluecollarworkeristheomittedcategory).Attheplantlevel,weincludeaverageplantwages,plantemployment,averageworkertenureattheplant,andtheage,gender,

educational,andoccupationalcompositionoftheplant.Wealsoincludethefollowingindustrycharacteristics:theindustryunionizationrate,industryemployment,average

workertenureintheindustry,andtheage,gender,educational,andoccupationalcompositionoftheindustry.

Table5.7:Trade,Enforcement,andTurnover,ByWorkerAge


All ExportersNon‐


Non‐Exporters

TRER*Enforcement 0.005 ‐0.009 0.056* ‐0.005 0.003 0.048(0.020) (0.028) (0.025) (0.017) (0.021) (0.025)

Trade‐weightedRER ‐0.058 ‐0.422** 0.477** ‐0.009 ‐0.230* 0.753**(0.110) (0.147) (0.155) (0.089) (0.103) (0.144)

NumberofObs. 173,642 92,230 81,412 127,220 72,951 54,269


All ExportersNon‐


Non‐Exporters

TRER*Enforcement ‐0.018 ‐0.007 ‐0.055* ‐0.003 0.013 ‐0.044(0.017) (0.022) (0.024) (0.014) (0.018) (0.023)

Trade‐weightedRER 0.113 0.229 ‐0.118 0.030 0.115 ‐0.233(0.092) (0.119) (0.133) (0.079) (0.098) (0.128)


YoungWorkers OlderWorkers




betweenworkeriandplantjisdestroyedintimet,forallplants,bytheplant'sexportstatus,andbytheworker'sage.**denotessignificanceatthe1%level;*denotessignificance

atthe5%level.Robuststandarderrors,clusteredatthecity‐industrylevel,arereportedinparentheses.Allregressionsalsoincludethefollowingcitycontrols:log(inspections),

log(numberofplants),andaninteractionbetweenthetrade‐weightedrealexchangerateandlog(numberofplants).Unreportedcovariatesattheworkerlevelincludethe

worker’sage(andagesquared),tenureattheplantinmonths,education(astwodummyvariables—atleasthighschoolandmorethanhighschoolwherelessthanhighschoolis

theomittedcategory)andoccupation(asthreedummyvariables—skilledbluecollarworker,unskilledwhitecollarworker,andprofessional/managerialworkerwhereunskilled

bluecollarworkeristheomittedcategory).Attheplantlevel,weincludeaverageplantwages,plantemployment,averageworkertenureattheplant,andtheage,gender,

educational,andoccupationalcompositionoftheplant.Wealsoincludethefollowingindustrycharacteristics:theindustryunionizationrate,industryemployment,average


Table5.8:Trade,Enforcement,andPlant‐LevelWagesDep.Variable:Log(AverageWage)

All ExportersNon‐

ExportersTRER*Enforcement 0.032** 0.020* 0.032**

(0.006) (0.010) (0.007)Trade‐weightedRER ‐0.123** ‐0.170** ‐0.153**

(0.036) (0.060) (0.043)NumberofObs. 364,555 86,249 278,306Plant‐YearControls YES YES YESCity‐YearControls YES YES YESSector‐YearControls YES YES YESState‐YearDummies YES YES YESPlantFixedEffects YES YES YESSource:Authors calculationsbasedonRAIS,MinistryofLaboradministrativedataoninspections,IMFbilateralrealexchangeratesandNBERtradeflows,andSECEX(1996‐2001).

Note: This table reports coefficients from the ordinary least squares estimation of equation (2) in the paper,

where the dependent variable is the logarithm of plant‐level average wages, for all plants and by the plant's

export status. ** denotes significance at the 1% level; * denotes significance at the 5% level. Robust standard

errors, clustered at the city‐industry level, are reported in parentheses. Unreported covariates at the plant‐level

include average worker tenure at the plant, the age, gender, educational, and occupational composition of the

plant, as well as city‐level enforcement, and industry‐level unionization rate, industry employment, average


TableA.1:Trade,Enforcement,andPlant‐LevelEmployment

Dep.Variable:Log(Employment)

Log(Inspections)

Log(InspectionsPer100Plants)

TRER*Enforcement 0.055** 0.034*(0.005) (0.014)

Trade‐weightedRER ‐0.119** ‐0.430** ‐0.171**(0.044) (0.049) (0.048)

NumberofObs. 367,978 367,978 367,978Plant‐YearControls YES YES YESCity‐YearControls YES YES YESSector‐YearControls YES YES YESState‐YearDummies YES YES YESPlantFixedEffects YES YES YESSource:Authors'calculationsbasedonRAIS,MinistryofLaboradministrativedataoninspections,IMFbilateralrealexchangerates,andNBERtradeflows(1996‐2001).

Note: This table reports coefficients from the ordinary least squares estimation of equations (1) and (2) in the paper,where the dependent variable is the logarithm of plant‐level employment. In columns (1) and (2), enforcement ismeasured as the logarithm of the number of inspections in the city (plus one). In column (3), enforcement is measured asthe logarithm of the number of inspections in the city (plus one) per 100 plants in the city. ** denotes significance at the1% level; * denotes significance at the 5% level. Robust standard errors, clustered at the city‐industry level, are reportedin parentheses. Unreported covariates at the plant‐level include average worker tenure at the plant, the age, gender,educational, and occupational composition of the plant, as well as city‐level enforcement, and industry‐level unionizationrate, industry employment, average worker tenure in the industry, and the age, gender, educational, and occupationalcompositionoftheindustry.

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