Discussion Paper No. 7358 - core.ac.uk · A key argument in favor of liberalizing trade ... , our...

56
econstor www.econstor.eu Der Open-Access-Publikationsserver der ZBW – Leibniz-Informationszentrum Wirtschaft The Open Access Publication Server of the ZBW – Leibniz Information Centre for Economics Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. zbw Leibniz-Informationszentrum Wirtschaft Leibniz Information Centre for Economics Almeida, Rita; Poole, Jennifer P. Working Paper Trade and Labor Reallocation with Heterogeneous Enforcement of Labor Regulations IZA Discussion Paper, No. 7358 Provided in Cooperation with: Institute for the Study of Labor (IZA) Suggested Citation: Almeida, Rita; Poole, Jennifer P. (2013) : Trade and Labor Reallocation with Heterogeneous Enforcement of Labor Regulations, IZA Discussion Paper, No. 7358 This Version is available at: http://hdl.handle.net/10419/80597

Transcript of Discussion Paper No. 7358 - core.ac.uk · A key argument in favor of liberalizing trade ... , our...

econstor www.econstor.eu

Der Open-Access-Publikationsserver der ZBW – Leibniz-Informationszentrum WirtschaftThe Open Access Publication Server of the ZBW – Leibniz Information Centre for Economics

Standard-Nutzungsbedingungen:

Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichenZwecken und zum Privatgebrauch gespeichert und kopiert werden.

Sie dürfen die Dokumente nicht für öffentliche oder kommerzielleZwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglichmachen, vertreiben oder anderweitig nutzen.

Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen(insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten,gelten abweichend von diesen Nutzungsbedingungen die in der dortgenannten Lizenz gewährten Nutzungsrechte.

Terms of use:

Documents in EconStor may be saved and copied for yourpersonal and scholarly purposes.

You are not to copy documents for public or commercialpurposes, to exhibit the documents publicly, to make thempublicly available on the internet, or to distribute or otherwiseuse the documents in public.

If the documents have been made available under an OpenContent Licence (especially Creative Commons Licences), youmay exercise further usage rights as specified in the indicatedlicence.

zbw Leibniz-Informationszentrum WirtschaftLeibniz Information Centre for Economics

Almeida, Rita; Poole, Jennifer P.

Working Paper

Trade and Labor Reallocation with HeterogeneousEnforcement of Labor Regulations

IZA Discussion Paper, No. 7358

Provided in Cooperation with:Institute for the Study of Labor (IZA)

Suggested Citation: Almeida, Rita; Poole, Jennifer P. (2013) : Trade and Labor Reallocationwith Heterogeneous Enforcement of Labor Regulations, IZA Discussion Paper, No. 7358

This Version is available at:http://hdl.handle.net/10419/80597

DI

SC

US

SI

ON

P

AP

ER

S

ER

IE

S

Forschungsinstitut zur Zukunft der ArbeitInstitute for the Study of Labor

Trade and Labor Reallocation with Heterogeneous Enforcement of Labor Regulations

IZA DP No. 7358

April 2013

Rita K. AlmeidaJennifer P. Poole

Trade and Labor Reallocation with

Heterogeneous Enforcement of Labor Regulations

Rita K. Almeida World Bank and IZA

Jennifer P. Poole

University of California, Santa Cruz

Discussion Paper No. 7358 April 2013

IZA

P.O. Box 7240 53072 Bonn

Germany

Phone: +49-228-3894-0 Fax: +49-228-3894-180

E-mail: [email protected]

Any opinions expressed here are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author.

IZA Discussion Paper No. 7358 April 2013

ABSTRACT

Trade and Labor Reallocation with Heterogeneous

Enforcement of Labor Regulations* This paper revisits the question of how trade openness affects labor market outcomes in a developing country setting. We explore the fact that plants face varying degrees of exposure to global markets and to the enforcement of labor market regulations, and rely on Brazil’s currency crisis in 1999 as an exogenous source of variation in access to foreign markets. Using administrative data on employers matched to their employees and on the enforcement of labor regulations at the city level over Brazil’s main crisis period, we document that the way trade openness affects labor market outcomes for plants and workers depends on the stringency of de facto labor market regulations. In particular, we show for Brazil, a country with strict labor market regulations, that after a trade shock, plants facing stricter enforcement of the labor law decrease job creation and increase job destruction by more than plants facing looser enforcement. Consistent with our predictions, this effect is strongest among small, labor‐intensive, non‐exporting plants, for which labor regulations are most binding. These findings are consistent with the hypothesis that, in the context of strict de jure labor market regulations, increased enforcement limits the plant‐level productivity gains associated with increased trade openness. Therefore, increasing the flexibility of de jure regulations may allow for broader access to the gains from trade. JEL Classification: F16, J6, J8 Keywords: globalization, enforcement, labor market regulations, employer‐employee data Corresponding author: Jennifer P. Poole Department of Economics University of California, Santa Cruz 437 Engineering 2 Santa Cruz, CA 95064 USA E-mail: [email protected]

* Our special thanks to Paulo Furtado de Castro for help with the RAIS and SECEX data and to the Department of Economics and Academic Computing Services at the University of California, San Diego for assistance with data access. We also thank the Brazilian Ministry of Labor for providing data on the enforcement of labor regulations and important information about the process of enforcement, especially Edgar Brandão, Sandra Brandão, and Marcelo Campos. For helpful comments and suggestions, we are grateful to JaeBin Ahn, David Atkin, Pedro Carneiro, Kiko Corseuil, Anca Cristea, Lisandra Flach, Penny Goldberg, Jesko Hentschel, Brian Kovak, Kevin Milligan, Devashish Mitra, Marc Muendler, Lourenço Paz, Giovanni Peri, Martin Rama, Priya Ranjan, Dave Richardson, Jose Antonio Rodriguez‐Lopez, Nick Sly, Petia Topalova, Eric Verhoogen, and to workshop participants at Claremont McKenna College, the Federal Reserve Bank of San Francisco, Syracuse University, UC Irvine, UC San Diego, Yale University, and various conferences. Financial support from the UCSC Academic Senate Committee on Research is acknowledged. Aaron Cole and Kun Li provided excellent research assistance.

  2

1. Introduction

Akeyargumentinfavorofliberalizingtraderelationsisthatfactorscanreallocatetomoreefficient

uses, allowing for enhanced productivity, income growth, and consumer welfare (Pavcnik 2002;

Feyrer 2009; Broda andWeinstein 2006). Early studies in many developing countries, however,

found little impact of trade liberalization on plant‐level employment and wages (Currie and

Harrison1997;Feliciano2001).Morerecentworkoffersevidenceofslowlabormarketadjustment

totradereform(Menezes‐FilhoandMuendler2011).Apotentialexplanationforthesefindingsare

restrictivelabormarketregulations,whichinhibitthereallocationofworkers,limitingtheextentto

whichplantscanbenefit fromincreasedopenness(FreundandBolaky2008;Kaplan2009;Hsieh

andKlenow2009).

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 country’s currency devaluation episode. Our main

reduced‐formspecification relates exogenous industry‐specific exchange rate shocks toplant and

worker 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

creationbymore thanotherwise identicaldomesticplants inweakly‐enforcedareas,as they face

increases in the costs of employing workers. We also demonstrate that strict labor market

institutionslimitthepossibilityforplant‐levelproductivityandprofitabilitygainsassociatedwith

tradeopenness.

Froma policy standpoint, ourwork offers anunderstanding of labor turnover in an increasingly

globalizedworld.Thetrade‐offbetweenjobsecurity,ontheonehand,andproductivityandgrowth,

on theotherhand, isoneof themostprominentpublicpolicydebatesworldwide.The long‐term

gainsfromanopenandflexibleeconomymaybeaccompaniedbyshort‐termcostsforworkersin

termsofunemployment.Ourwork shows thatpoliciesdesigned toprotectworkersmay actually

furtherreduceemploymentascoststofirmsincrease.Therefore,increasingtheflexibilityofdejure

regulationswillstimulatejobcreationandofferbroaderaccesstothegainsfromtrade.

  3

Wecontributetoagrowingbodyofworkinseveralways.First,themicro‐dataavailableforBrazil

arerichandappropriatetostudytheeffectsoftradeliberalizationonlaborturnover.Weexploita

matchedemployer‐employeedatabasecoveringtheformal‐sectorlaborforce,incombinationwith

information on the plant’s exposure to globalmarkets. Importantly, the data allow us to analyze

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

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

turnover into changes along theextensivemargin (the accessionand separationofworkers) and

alongtheintensivemargin(hoursworkedandtemporarycontracts).

Ourempiricalstrategyexploitstheabilitytomatchworkerstotheiremployers,whichiscriticalas

pointed out by the recent evidence on the sorting effect of globalization.1 For instance, as in the

model described in Helpman, Itskhoki, and Redding (2010), when there are complementarities

betweenplantproductivityandworkerability,plantshaveanincentivetoscreenforworkersbelow

agivenabilitylevel.Higherproductivityexportersscreentoahigherabilitythresholdandwillthus

have a workforce of higher average ability than non‐exporters.2 Because globalization increases

plant selection into exporting as in Melitz (2003) and the incentives to screen for high‐quality

matches, it is important to account forheterogeneity in thequalityof theworker‐plantmatch in

determiningtheeffectsofglobalizationonlabormarketoutcomes(Woodcock2011;Krishna,Poole,

andSenses2011). Inoursetting,anotherwise identicalworkermayhaveahigherprobabilityof

separationfrom(oralowerprobabilityofaccessionto)ahighproductivityplantthanfrom(andto)

a low productivity plant, when such worker‐plant production complementarities exist. This

diversity offersdisparatepredictions for the effects of trade liberalizationonworker turnover at

exporting (high productivity) and non‐exporting (low productivity) plants. Our reduced‐form

estimationbuildson theexisting literature in thisdimension.Notably,ourpreferredspecification

uses information onworker‐level labormarket outcomes, separately for exporting and domestic

plants,andallowsforthepossibilityofworkersorting.

While we are not the first authors to investigate the impact of trade by the plant’s mode of

globalization (e.g., Amiti and Davis (2012)), we are not aware of any paper allowing for

                                                            1Verhoogen(2008)documentsaskill‐upgradinginMexicanexportingplantsafterthe1994pesodevaluation.Bustos(2011)looksattheBrazilianreductionintariffsaspartoftheMercosurregionalfreetradeagreementandfindsthatArgentineanplantsabovethemediansizeupgradeskills,whileplantsbelowthemediansizedowngradeskills.2Changesintheskillcompositionoftheworkforceatexportersrelativetonon‐exportersarealsopresentinanumberofotherrecenttrademodels(see,forexample,Yeaple(2005)).

  4

globalizationto impactplantsdifferentlydependingontheirexposure to labormarketregulatory

enforcement. Brazil has one of the most restrictive labor market regulatory frameworks in the

world (Botero,etal 2004; Almeida andCarneiro 2012).3However, the size of the informal labor

forcesuggeststhatenforcementisweakinsomeareas,hintingatagapbetweenthelawsstatedon

thebooks(dejureregulations)andtheireffectiveimplementation(defactoregulations).Therefore,

contrarytopreviousstudieswhichrelyoncross‐countryoracross‐statevariationinexistingdejure

laborregulations(e.g.,BesleyandBurgess(2004)andAutor,Kerr,andKugler(2007)),weexplore

the fact thatBrazilianemployersareexposedtovaryingdegreesofde facto laborregulations,via

MinistryofLaborinspections.Especiallyinadevelopingcountrycontextwhereenforcementisnot

homogeneous, we argue exploring time series and within‐country variation in regulatory

enforcementoffers abettermeasureof aplant’s true flexibility in adjusting labor to shocks than

looking at variations in de jure regulations.4 We thus investigate the differential impact of

globalizationonworkerturnoveramongotherwiseidenticalplantsandworkersfacingdifferentde

factoenforcementofthelaborlaw.5

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

real economy using potentially endogenous tariff changes6, we explore the Brazilian currency’s

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

employersandworkers.7FollowingGoldberg(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,                                                            3There isanextensive literature fordevelopingcountriesanalyzing therelationshipbetween labormarketregulations and labor market outcomes (e.g., Kugler (1999), Kugler and Kugler (2009), Ahsan and Pages(2009),PetrinandSivadasan(forthcoming),andseveralotherstudiescitedinHeckmanandPages(2004)).4Todate, fewpapershaveexploredbothwithin‐countryandtimeseriesmeasuresofenforcement.NotableexceptionsincludeCaballero,Cowan,Engel,andMicco(2013)andAlmeidaandCarneiro(2012).5 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.6Politicaleconomyfactorsintariffformationandadjustmenthavebeennotedbyanumberofauthors.See,forexample,OlarreagaandSoloaga(1998)forthecaseofBrazil’sregionalfreetradearea,Mercosur.Infact,asprotectionistpressuresgrewintheaftermathoftheintroductionofanewcurrencyin1994,averagetariffsmarginallyincreasebeginningin1995.SeeSection2.2forfurtherdiscussion.7 Other papers using currency shocks as exogenous sources of variation to investigate international traderelationshipsincludeVerhoogen(2008),whousesMexico’s1994pesodevaluationtoexploretherelationshipbetweentradeandinequality,andBrambilla,Lederman,andPorto(2012),whouseBrazil’scurrencycrisisasashocktoArgentineanexporters.

  5

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

industry‐levelvariationinrealexchangeratesovertimetoexogenouslyidentifytheeffectofBrazil’s

increasedglobalizationonemploymentandlaborturnoverattheplantandworkerlevel.

Tosummarize,weanalyzetheeffectoftradeliberalizationonlaborreallocationwithinBrazil.We

exploreacrossindustryandovertimevariationinrealexchangeratesinordertocapturechanges

inindustrycompetitiveness,incombinationwithcityandtimevariationintheenforcementoflabor

market regulations, facing exporters and non‐exporters. An important concern relates to the

exogeneityofthevariationintheenforcementoflaborregulationsacrosscities.Atanygivenpoint

intime,enforcementoflaborregulationsatthecitylevelisnotlikelytoberandomlydistributed.On

theonehand,enforcementmaybestrongerincitieswithhigherviolationsofthelaw.Ontheother

hand, cities with better institutions could have stricter enforcement. Although it is unclear how

these patternsmay impactworker reallocation, a potential biasmay still exist. Tominimize this

concern, we note that our empiricalmethodology follows the program evaluation literature and

relatesexogenousrealexchangeratechangestoplantandworkeroutcomesovertime,differentially

forplantslocatedinvariablelabormarketregulatoryenvironments.Ourmaincoefficientofinterest

is thedifferential effect of changing enforcementonplants that, all else constant, are exposed to

different exogenous industry‐specific trade shocks. Therefore, our reduced‐form specification

relates annual changes in the probability of inspection in a given city and annual changes in

industry‐specificrealexchangerates,withannualchanges in labormarketoutcomes.Werunthis

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

  6

markets).Ourreduced‐formestimationincludesstate‐specificyeardummies,whichwearguehelps

to correct for some of this bias. We also include interactions between pre‐determined city

conditions and the real exchange rate changes in order to control for the possibility that city‐

specifictrendsmaybedrivingthedifferencesinlaborturnoverwefind.Finally,weshowthatour

mainresultsarerobusttotheinclusionofthelaggedvalueofenforcement.

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.8Thesamedepreciationdifferentiallyimprovesconditionsforexporters,asBrazil’strading

partnersneedfewercurrencyunits topurchaseBraziliangoods.Weexpectthisenhancedforeign

marketaccess todifferentially increaseemploymentatBrazil’sexportingplants relative toplants

producingonlyforthedomesticmarket.9

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

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

inspectionsisexpectedtodirectlyimpactthecompliancewithlaborregulationsthroughthehiring

                                                            8 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.9 Goldberg and Tracy (2003) demonstrate that the employment declines associated with a U.S. dollarappreciationgrowstrongerasindustriesincreaseinexportorientation.

  7

andfiringof formal labor.10However,thedirectionoftheeffectofenforcementonemploymentis

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 several time‐varying

worker,plant, city,andsectorcharacteristics,aswellascontrols forworkersorting,wenote that

openness is associatedwith a decrease in the probability of firing at expanding exporters, and a

decrease in the probability of hiring at relatively contracting domestically‐oriented plants, as is

predictedbynewheterogeneous firm trademodels.The results suggest thatenforcementmainly

influences laboradjustmentalongtheextensivemargin,butalsohassomeeffectonthe intensive

marginascapturedbydifferentialdecreasesintheprobabilityofafull‐timecontract.Wefindlittle

adjustmentalongtheintensivemarginascapturedbyhoursworked.Wenotealsothatourfindings

areconcentratedamongsmall,labor‐intensive,non‐exportingplantsforwhichlaborregulationsare

likely to bemost restrictive, aswell as among youngerworkerswho aremore likely to be labor

market“outsiders”.

Themagnitudesofourestimatesseemtobeplausible.Evaluatingtheeffectonworkersandplants

locatedinmunicipalitiesatthemeanlevelofinspections,a10percentagepointdepreciationofthe

                                                            10CardosoandLage(2007)showthatinspectionsareprimarilylinkedtostricterenforcementofmandatoryseverancepayments,mandatedhealthandsafetyregulations,andtotheworker’sformalregistration.Bertola,Boeri,andCazes(2000)suggestthatdifferencesinenforcementacrosscountries,relatedforexampletotheefficiencyofacountry’slegalsystem,areasorevenmoreimportant,thandifferencesindejureregulations.Forexample,Caballero,Cowan,Engel,andMicco(2013)exploreapanelof60countriesaroundtheworldandfindthatlaborregulationshaveadverseeffectsonjobturnoverandplants’speedofadjustmenttoshocks,butonlyincountrieswithastrongruleoflawandgovernmentefficiency(takenasmeasuresofenforcementofregulations). However, as with many cross‐country studies, the limited time series variation in laborregulationsandmeasuresofenforcementposeschallengesforidentification.

  8

real increases jobmatch creationat exportingplantsby2.3%anddecreases jobcreationatnon‐

exportersby3.6%,asispredictedbyheterogeneousfirmmodelsofinternationaltrade.Meanwhile,

the impact fordomesticplantsvariesdependingon the levelof enforcement theplant faces—for

domesticplantslocatedinmunicipalitiesatthe10thpercentileofinspections,a10percentagepoint

depreciationoftherealdecreasestheprobabilityofhirebyonly2.5%,whileworkersmatchedwith

domesticplantslocatedinmunicipalitiesinthe90thpercentileofinspectionsexperienceadecrease

in jobmatchcreationofaround4.9%.Similarly,a10percentagepointdepreciationdecreases the

probability of job destruction at domestic plants located in cities at the 10th percentile of

inspectionsby0.5%,whilethesameexchangerateshockincreasesthefiringprobabilityatsimilar

domesticplantsincitiesatthe90thpercentileofinspectionsby2.3%.

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 fewer workers with increased enforcement of regulations. We show that this

increasedenforcementoflaborregulationsisalsoassociatedwithlowerplant‐levelproductivity,as

proxiedbyplant‐levelaveragewages.Ourplant‐levelresultsonemploymentalsocorroboratethis

idea.

Inadditiontotheworkcitedabove,ourpaperrelateswithanumberofdifferentliteratures.First,

ourresearchiscloselylinkedtoagrowingbodyofstructuralmodelslinkingtradeandlabormarket

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

liberalizationsincreasefirm‐leveljobturnover,andreductionsinfiringcostsreinforcetheimpactof

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

liberalizingtradeinarestrictedlabormarketenvironmentisassociatedwithslowerinter‐sectoral

labormarket reallocation, lower output, and reduced productivity. Fajgelbaum (2012) notes that

labor market frictions, which increase the costs of hiring workers, reduce firm growth and

productivity,inducinganegativerelationshipbetweenlabormarketrigiditiesandopennessacross

countries. We see these structural papers as complementary to our reduced‐form framework

designedtoidentifythecausalimplicationsoftradeopennessonlaborreallocationinthepresence

ofacompletesetoflabormarketregulations.

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

  9

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.

Hasan,Mitra,andRamaswamy(2007)alsodistinguishIndia’sstatesbytheextentoflabormarket

restrictions,andanalyzetheimpactofIndia’s1991tradereformonlabordemand.Theauthorsfind

supportiveevidencefortheinteractionoftradereformandlaborregulations;thatis,theimpactof

trade reformon labor demand is larger in stateswithmore flexible labor institutions. Using the

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

povertyandpercapitaexpenditurespredominantlyinstateswithlessflexiblelabormarkets.Also

relevant to our study isFreundandBolaky (2008)whoargue that trade canonly improve living

standardsinflexibleeconomies.Inparticular,theirfindingsonhiringandfiringcostssuggestthat

thepositiveeffectsofopennessarereducedwhenlaborregulationsareexcessive.Similarly,Eslava,

Haltiwanger,Kugler,andKugler(2010)considerthecaseofColombia’spro‐marketreformsofthe

1990s.Theauthors find thatallowing for frictionless factoradjustmentwould lead tosubstantial

improvementsinefficiencyoverthereformperiod.Thebenefitof linkedemployer‐employeedata

allowsustomovebeyondtheindustrylevelandstatelevel,tocomputeindividual‐levelaccessions

and separations, as well as to incorporate worker and plant heterogeneity. Moreover, as we

previously mention, exploiting variation in de facto labor regulations offers a more complete

measureoflabormarketflexibilitythanvariationindejurelaborregulationsalone.

Thepaperproceedsasfollows.Inthenextsection,weprovidebackgroundinformationonthe1988

BrazilianConstitutionalreform,whichestablishedthecurrentlabormarketregulatoryframework

inBrazil,therecentevolutionintheenforcementoftheselaborlawsconductedbytheMinistryof

Labor,andthemainfeaturesofBrazil’srecentglobalization.InSection3,weoutlineourmaindata

sourcesandoffersomesimpledescriptivestatistics.Section4discussestheconceptualframework

behind ourmain empirical strategy andproposes a simple difference‐in‐difference reduced‐form

specificationfortheempiricalwork.InSection5,wepresentourmainfindings.Section5alsooffers

evidence for the robustness of ourmain results, aswell as evidence on the heterogeneity of our

main findings. Finally, Section5discusses the aggregate implicationsof theenforcementof labor

regulations onplant‐level productivity.We conclude in Section 6 by highlighting themainpolicy

implications.

  10

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,andtheimplementationofanewcurrencyin1994.Subsequently,thecurrency

experiencedasevereandunanticipateddevaluationinJanuary1999.Eachofthesepolicychanges

mayhavecontributedtochanginglaborcostsandlaborreallocation.

2.1. LabormarketregulationswithinBrazil

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).11EmployersinBrazilmustalsogive

advancenoticetoworkersinordertoterminateemployment.Duringthisinterimperiod,workers

aregranteduptotwohoursperday(25%ofaregularworkingday)tosearchforanewjob.12

Enforcementoflaborregulations Thesede jure labor regulationsare effective throughout the

country. However, as the Ministry of Labor is charged with enforcing compliance with labor

regulations, there issignificantheterogeneitybothwithincountryandover time in termsofhow

bindingisthelaw.13Giventhegeographicscopeofthecountry,enforcementisfirstdecentralizedto

                                                            11Iftheworkerisdismissedwithoutjustification(withtheexceptionofworkersonaprobationaryperiod),theplantisfinedandhastopaytheworker40%oftheFGTScontributions.12Someplantsvoluntarilychoosetograntworkersthefullmonthlywagewithoutrequiringwork.BarrosandCorseuil(2004)findthattherearelargeproductivitylossesduringthisperiod.13AcomprehensiveexplanationoftheenforcementofthelaborregulationsystemanditsimportanceinBrazilisgiveninCardosoandLage(2007).

  11

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

stateshadonlytheoneofficecoincidingwiththedelegaciainthestatecapital.

Throughout the late1990sandearly2000s, labor inspectionsbecamemorefrequentasthe large

publicdeficit led theBraziliangovernment tosearch foralternativeways tocollect taxrevenue.14

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 workers’ formal registration in the Ministry of Labor, contributions to the

severance pay fund (FGTS), minimumwages, andmaximumworking lengths. Evasion of one of

thesedimensionsaccountedformorethan40%ofallfinesissuedin2001.Themonetaryamountof

the fines is economically significant andmay be issued per worker or it may be indexed to the

plant’ssize.Forexample,in2001values,aplantisfined216reais(orapproximately$100)foreach

workerwithoutacarteiradetrabalho,formalworkauthorization.Consideringthat,at2001prices,

the federalminimumwagewas222reais,non‐compliancewithworkerregistration isnon‐trivial,

implyingapenaltyofapproximatelyonemonthlywageperworker.

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

                                                            14Aninspectioncanbetriggeredeitherbyarandomplantaudit,orbyareport(oftenanonymous)ofnon‐compliancewiththelaw.Workers,unions,thepublicprosecutor’soffice,oreventhepolicecanmakereports.Inpractice,almostallofthetargetedplantsareformalplantsbecauseitisdifficulttovisitaplantthatisnotregistered,sincetherearenorecordsofitsactivity.

  12

functionofthemonetaryvalueofthepenalties(finesandlossofreputation)andoftheprobability

ofbeingcaught.Inturn,theprobabilityofbeingcaughtdependsontheplant’scharacteristics(such

assize,globalizationstatus,andlegalstatus)andonthedegreeofenforcementofregulationinthe

citywheretheplantislocated.15

2.2. Brazil’sglobalization

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.16

                                                            15Asinspectorsfaceaperformance‐basedpayscheme,theyoftenlookforcaseswherethepenaltyislikelytobelarge.Assuch,thereisastrongcorrelationbetweenthesizeofthefirm,asaproxyforthevisibilityofthefirm,andthenumberofinspections(CardosoandLage2007).16Averageadvaloremtariffsclimbedslightlyinsubsequentyears—fromanaverageof12.2%in1994toanaverageof14.4%in2001.

  13

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

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

plantsfaceregulatoryenforcementandaccesstoforeignmarkets.

Matchedemployer‐employeeadministrativedata Weusedata collectedby theBrazilianLabor

Ministry,whichrequiresbylawthatallregisteredestablishmentsreportontheirformalworkforce

ineachyear.17ThisinformationhasbeencollectedintheadministrativerecordsRelaçãoAnualde

                                                            17Forthisreason,ouranalysisisrestrictedtotheeffectoftradeliberalizationonformallaborreallocation.Itisplausible,however,thatincitieswithweakerenforcementandhencemoreflexiblelaboradjustment,plantsmaybemorelikelytomakeadjustmentsalongtheinformalmargin.Therefore,ourfindingsonformallaboradjustment do not capture total labor adjustment. It is not clear, however, how the resultswould change.GoldbergandPavcnik(2003),Paz(2012),andMenezes‐FilhoandMuendler(2011)findmixedresultsontheimpactoftradeliberalizationontheinformalsectorinBrazil.Also,totheextentthatenforcementincreasesthecostoflabor,wemaynoteshiftsawayfromlabor(bothformalandinformal)towardscapital.

  14

InformaçõesSociais(RAIS)since1986.Forouranalysis,weusedatafromRAISfortheyears1996

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.18Otherrelevantvariablesofinterestincludetheworker’s

monthofaccessiontoandthemonthofseparationfromthejob,weeklyhoursworked,andthetype

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

worker’shumancapital, includingoccupation,education, tenureat theplant,gender,andage.We

define aworker as hired to employer j during year t if RAIS reports a non‐missing value for the

monthofaccession.Wedefineaworkerasfiredfromemployerjduringyeart ifRAISreportsthe

workernolongeremployedatfirmjonDecember31ofyeart.

WerestrictobservationsinRAISasfollows.Wedrawa1%randomsampleofthecompletelistof

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

employmenthistory.Weincludeonlymanufacturingsector(CNAE2‐digitcodes15‐37)workersin

private‐sectorjobs.

Enforcementdata We explore administrative city‐level data on the enforcement of 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

                                                            18The industrial classification available inRAIS is the4‐digitNationalClassificationofEconomicActivities(CNAE).

  15

helps to control for important size differences across cities (i.e., that Sao Paulo has many

inspections,butalsomanyplantstoinspect).Moreover,theimpactofsuchameasurewillreflectthe

directeffectofinspections,aswellasplants’perceivedthreatofinspections(evenintheabsenceof

plant‐levelinspections)basedoninspectionsatneighboringplants.

Table3.1 reports thenationwide increase inenforcementof labor regulationsbetween1996and

2001.Theproportionofcitieswithatleast1manufacturinginspectionrosefrom33%in1996to

52%in2001.Thiscorrespondstoan increase in theaveragenumberof inspectionsacrosscities,

mostnotablybetween1998and2000,whentheaveragenumberofinspectionsincreasedby15%.

Asthenumberofinspectionsatthecityleveliscorrelatedwiththesizeofthecity(i.e.,population,

labor force, andnumberofplants), ourpreferredmeasuredocuments thenumberof inspections

per100registeredplantsasisreportedincolumn(3).Thedatareportincreasesinthenumberof

inspections per 100 plants, as inspectors intensify the enforcement process to reach additional

plants, workers, and cities. The same patterns hold for the number of inspections per 10,000

workerscomcarteiraincolumn(4).

We also note significantwithin‐country variation in the intensity of enforcement across cities in

Brazil,asisdepictedbytheaveragenumberofinspectionsper100plantsineachcityinFigure3.2.

TheleftpanelillustratestheintensityofenforcementperBraziliancityin1998,withdarkershades

portrayinghighernumbers.The rightpaneldepicts the same statistic twoyears later in theyear

2000.Weremarkonthevariationacrossmunicipalitiesandovertime.First,weobservethedarkest

areas of themap in the high‐income Southern and Southeastern regions of the country.We also

noticeadarkeningofthemapbetween1998and2000asenforcementspreadstofurtherpartsof

the country. Figure 3.3 offers a clearer picture of the across city and over time variation in

regulatoryenforcementbyfocusinginonasinglestate,MatoGrosso.

Werelyonthesedifferentialchangesinenforcementacrosscitiesovertimeinourmainempirical

analysis.Forthisreason,itisimportanttounderstandthedeterminantsofchangesinenforcement

atthecitylevel.Tothisend,Table3.2reportscoefficientsfromanordinaryleastsquaresregression

in first differences for the set of Brazilian municipalities in 2001 for which we have historical

informationfromBrazil’sInstituteforAppliedEconomicResearch(IPEA)@Cidadesdatabase.The

dependentvariable is the change in enforcementbetween1996and2001,whereenforcement is

defined as the logarithm of the number of inspections in the city (plus one) per 100 plants. In

  16

column(1),werelatechangesinenforcementtolaggedchanges(1991‐1996)inthecity’sindustrial

composition(agriculturalGDP,manufacturingGDP,andservicesGDP)andpopulation.Column(2)

alsoincludeslaggedchangesinthecity’surbanizationrate,whilecolumn(3)alsoincludeslagged

changes in the city’spoverty rate.The results report that citieswith growingmanufacturingand

services sectors and increasing urbanization and poverty rates have larger increases in

enforcement. Our main reduced‐form estimation will include interactions between these pre‐

determinedcityconditionsandtheindustry‐specificrealexchangeratechangesinordertocontrol

forthepossibilitythatcity‐specifictrendsmaybedrivingthedifferencesinlaborturnoverwefind.

Industry‐specificexchangerates Weconstructtrade‐weightedindustry‐specificrealexchange

ratesbasedonbilateralrealexchangeratedatafromtheInternationalMonetaryFundandbilateral

tradeflowsbycommoditymadeavailablebytheNationalBureauofEconomicResearch(Feenstra,

etal 2004).19Wematch the industry‐specific real exchange rates to theRAIS data by the plant’s

industrial classification, in order to identify plants and workers in industries with differential

globalizationexperiences.20

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 ∗∑

                                                            19Trade 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).20AswediscussinSection2.2,averagetariffrateswererelativelyflatoveroursampleperiod.Variationsintherealexchangerate,therefore,provideamorerealisticmeasureofchangesintradeopennessduringoursampleperiod.

  17

wheretindexestime,kindexesindustry,andcindexescountry,suchthatthebilateralrealexchange

rate, ,denotedintermsofforeigncurrencyunitsperreal,isweightedbyindustry‐specificand

time‐varying export shares (∑

) and import shares (∑

). Following Campa and Goldberg

(2001),we lag the tradesharesoneperiodtoavoid issuesofendogeneitybetweentradeandthe

exchangerate.

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

Figure3.4illustratesintheleftandrightpanels,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 Finally, we investigate information on the firm’s degree of global

engagement,ascapturedbytotalexportsales.WerelyoncomplementarydatafromtheBrazilian

CustomsOffice(SECEX)tocreateasingleindicatorforthefirm’sglobalizationstatus.Information

onfirm‐levelexporttransactionsisavailablefromSECEX,whichrecordsalllegally‐registeredfirms

inBrazilwithatleastoneexporttransactioninagivenyear.

Wedenoteexporterstobethosefirmsthatexportedapositivedollaramountatanypointduring

the 1996 to 2001 time period. This time‐invariant indicator is designed to minimize potential

endogeneityconcernssurroundingtheexportdecisionpost‐devaluation.Inrobustnesschecks,we

alsocomparesimilarly‐sizedplants,whichwearguehelpstominimizeanypossibleselectionbias

associatedwith the plant’s globalization status. Also, in unreported results,we categorize plants

basedontheindustry’simportpenetrationasanalternativemeasureofglobalization.

3.1. Descriptivestatistics

  18

We report detailed descriptive statistics in Table 3.3. 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 322,614 worker‐plant‐year

observations,with109,086workersemployedin61,462plants,covering2,829municipalitiesand

240industriesthroughoutthesampleperiodof1996to2001.

Approximately33%ofmanufacturingworkerswerehired to anewemployerduringour sample

period,while29%wereseparatedfromtheiremployer.Thedatareportthatonly2%ofworkersare

employedwithtemporarycontracts.Acrossemployers,workersaveraged43.5hoursperweek.The

averageageofaworkeris32years.Themajorityofthemanufacturinglaborforcehaslessthana

highschooleducation,whileabout26%haveatleastahighschooleducation,andonly7%havea

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

collarprofessions,suchasmachineoperators.Another24%areinwhitecollarprofessions—7%in

secretarialandsalespositionsand17%inprofessional,managerial,andtechnicalpositions.Eleven

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

foundintheconstructionandservicesectors.Theaverageplantemploys99workers,andpaysan

average annual wage of 2,909 reais (an average monthly wage of 242 reais). The average

municipality in our sample faces approximately 42 inspections during the 1996 to 2001 sample

period.21Theaverageindustryhasatrade‐weightedrealexchangerateindexof0.81andemploys

over17,000workers,ofwhichabout1inevery5areunionized.

Ofthe61,462plants,roughlyone‐fifthareexporters.However,these13,921plantsrepresentover

halfofthetotalnumberofobservations,largelybecauseexportingplantsemploymoreworkerson

average (at 233workers as compared to 40workers for domestic plants). Our data reports that

accession rates are lower at exporters than at non‐exporters—27% as compared to 39%,

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)).

                                                            21ThisnumberstandsincontrasttotheaveragenumberofinspectionsacrossallBrazilianmunicipalities(seeTable3.1).Our1%randomsamplecoversonlyregisteredfirmswhichare,onaverage,larger.Therefore,thisistobeexpected,asthesefirmsarenaturallymoreexposedtoenforcementthansmallerfirms(CardosoandLage2007).

  19

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

workerswithatleastahighschooleducation,whilebycomparisonabout25%oftheworkforceis

high‐skilled at plants serving the domestic market. The average annual wage paid by exporting

plantsis4,791reais,ascomparedto2,337reaisatdomesticplants.Exportersareonlyrepresented

inabouthalfofthe2,829municipalitiescoveredbyourformalsectordata.22Combinedwiththeir

greatervisibilityduetothehighertotalemploymentnumbers,thedataindicatethatonaveragethe

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

domesticplantsarelocated.Onaverage,exportingplantsface68manufacturinginspectionswhile

domestic plants face, on average, 45 inspections. By contrast, exporters and non‐exporters are

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

plant‐typeintheindustryaveragetrade‐weightedrealexchangerate,employment,orunionization

rates.

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)

where j indexestheplant,k indexes theplant’s industry,andt indexestime.Werelateplant‐level

outcomes( ),suchastotalplantemployment,totime‐varying,plantcharacteristics( )suchas

averageworkertenureattheplant,andtheage,gender,educational,andoccupationalcomposition

oftheplant,andtime‐varying,industrycharacteristics( )suchastheunionizationrate,industry

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

occupationalcompositionoftheindustry.Thespecificationalsoincludesplantfixedeffects( to

capturetime‐invariantfactors,suchastheplant’sunobservedunderlyingproductivity,technology,

ormanagementstyle,whichmay influencebothaplant’s selection intoexportingandplant‐level

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

  20

labormarketadjustment,andstate‐specificyeardummies( tocontrolfortheaverageeffecton

laborturnoverofBrazil’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

thatadevaluationoftheBrazilianrealwillincreaseemploymentfortheaverageplant.

Table4.1,intendedtomotivatetheremainderofthepaper,providesbaselineevidencetothiseffect.

Thefirstcolumnreportsresultsfromtheestimationofequation(1)wherethedependentvariable

is the logarithm of plant employment. Consistent with the literature (e.g., Revenga (1992)), a

depreciation of the trade‐weighted real exchange rate (decrease in by our measure) is

associatedwithincreasesinemploymentfortheaverageplant.

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:

∗ (2)

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

enforcementoflaborregulations,ascapturedbyMinistryofLaborinspections.Allothervariables

areaspreviouslydefined. ,ourmaincoefficientof interest,capturesthedifferential impactofa

tradeshockonplantsinstrictly‐enforcedmunicipalitiesrelativetoweakly‐enforcedmunicipalities.

Inresponse toanexpansionarytradeshock,suchasBrazil’scurrencydevaluation,plantswish to

expand employment ( 0). However, plants in heavily‐inspected cities may be differentially

restrictedfromadjustinglabor( 0)—asthecostofaformalworkerincreases,strictly‐enforced

  21

plantswillincreaseemploymentbylessthanweakly‐enforcedplants—ormayadjustformallabor

byrelativelymore( 0),asformalworkregistrationsincrease.

Wetakethisambiguouspredictiontothedataincolumn(2)ofTable4.1.Enforcementismeasured

by the logarithmof the number of inspections at the city level (plus one). As in column (1), the

exogenousreal exchangeratedepreciation increasesplant‐levelemployment.Consistentwith the

findings in Almeida and Carneiro (2012), the unreported coefficients on city‐level enforcement

demonstratethatincreasesinregulatoryenforcementatthecityleveltendtodecreaseformalplant

size, suggesting that increases in the costof formalworkersdominate anypotential impact from

increasedcompliancewithmandatedbenefitsandformalworkregistrations.

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 confirm that

plantsinheavily‐inspectedcitiesarerestrictedfromexpandingemploymentwithadepreciationof

thecurrencyrelativetoplantsinless‐inspectedcities.

An important concern relates to the exogeneity of the variation in the enforcement of labor

regulationsacrosscities.Inparticular,enforcementmaybestricterincitieswhereviolationsofthe

labor laws are more frequent or in cities where institutions are more developed. Moreover,

enforcementmaybestrongerformorevisible(i.e.,largerandmoreglobalized)plants.Asviolations

oflaborlaws,betterinstitutions,andplantsizeandtypearelikelyalsocorrelatedwithlabormarket

outcomes, tominimize this concern, in column (3)ofTable4.1,weadjustourmain enforcement

variable tocontrol forthesizeof thecity.Specifically,ourpreferredenforcementvariablemoving

forward characterizes inspectionsper100plants in the city. This accounts for the fact that large

citieshavemany inspections,butalsomanyplants tobe inspected. Inaddition, in theabsenceof

plant‐level informationon inspections,ouranalysisaimstocapturetheprobabilitythataplant is

inspected,allowingforthedirecteffectofinspections,aswellastheindirecteffectofaneighboring

plant’sinspections.

Onecouldstillquestiontheexogeneityofchangesinenforcementatthecitylevel.Totheextentthat

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

enforcementmaybebiased.Weemphasize,however,thatourfocusisontheinteractionbetween

exogenouschangesinindustry‐specificrealexchangeratesandchangesinthedegreeofregulatory

  22

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).Wenotethatequation(2)includesstate‐specificyeardummies,whichwearguehelpsto

correctforsomeofthisbias.

Basedontheresultsincolumn(3)ofTable4.1,evaluatedatthe10thpercentileofinspections,a10

percentage point depreciation increases employment by 1.8%, while the same devaluation

increasesemploymentbyonly1.2%atplantslocatedincitiesatthe90thpercentileofinspections.

Theseplant‐levelresultshighlightourmainpredictions—thatstrictlabormarketinstitutionslimit

plants’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

workedforexistingemployeesorswitchingfromtemporarytopermanentcontracts.Enforcement

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

mechanisms,ourmainreduced‐formequationfocusesonaworker‐levelanalysis.Inparticular,we

augmentequation(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 indicatorvariable that takes thevalueonewhenworker i isemployedwitha

  23

full‐timecontractinplantjattimet.Allothervariablesaredefinedaspreviouslydiscussed. are

time‐varyingworker‐levelcharacteristics(suchastheworker’stenureattheplant inmonths,the

worker’s age (and age squared), education23, and occupation24) and is a worker‐plant (time‐

invariant)matcheffect.

Wearguethattime‐invariantworker‐plantmatcheffectsareimportantbecausewhenworker‐plant

productioncomplementaritiesexist(asinnewtrademodels),highproductivityplantswillscreen

forhighabilityworkers.Thismayleadtothesortingofhighabilityworkersintohighproductivity

plants.25Inoursetting,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.26

Aspreviouslynoted,concernsabout theendogeneityofregulatoryenforcementareminimizedas

equation (3) relates changes over time in the enforcement of labor market regulations to labor

marketoutcomes.However,asisdocumentedinTable3.2,citieswithgrowingmanufacturingand

services sectors and increasing urbanization and poverty rates have larger increases in

enforcement.Ourmainspecification,therefore furtheradaptsequation(3)to includeinteractions

betweenthesepre‐determinedcityconditionsandtherealexchangerateshockinordertocontrol

forthedeterminantsofcity‐levelchangesinenforcement,asfollows:

∗ (4)

                                                            23Educationentersas twodummyvariables: completedhighschoolandmore thanhighschool.Less thanhighschoolistheomittedcategory.24 Occupation enters as three dummy variables: skilled blue collar profession, unskilled white collarprofession,andskilledwhitecollarprofession.Unskilledbluecollarprofessionistheomittedcategory.    25Krishna,Poole,andSenses(2011)documenttheimportanceofworker‐firmcomplementarityinthelaborreallocation process post‐liberalization using matched employer‐employee data for Brazil. Their results,controllingforthenon‐randomassignmentofworkerstofirms,suggestsastrongbiasinplant‐levelanalyses,asexportersdifferentiallyincreasematchqualityrelativetonon‐exporterspost‐liberalization.26Asneithertheplantnortheworkervarywithinamatch, thematch‐specificeffectsalsocontrol fortime‐invariant,unobservableplantheterogeneityandtime‐invariant,unobservableworkerheterogeneity.

  24

where are pre‐determined city conditions such as industrial composition (agricultural GDP,

manufacturingGDP,andservicesGDP),population,urbanization,andpoverty.Weinteractthesecity

conditionswiththetrade‐weightedrealexchangeratetocontrolfordifferentialcity‐specifictrends.

Allothervariablesareaspreviouslydefined.

Thespecificationinequation(4)relatesexogenouschangesinindustry‐specificrealexchangerates

withmatch‐specificoutcomes,between1996and2001,differentiallyforworker‐plantmatchesin

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

identifytheeffectsofopennessonlaborturnover.Themaincoefficientofinterestinequation(4)is

, which captures the differential effect of stricter enforcement forworkers employed in plants

exposedtovaryingrealexchangeratechanges.

Inaddition,wearguethatthe implicationsofarealexchangeratedevaluationareheterogeneous

acrossplanttypesandtherefore,considerequation(4)separatelyforexportinganddomestically‐

orientedplants.Ourprioristhatopennessallowsplantsbestplacedtocompeteabroadtoexpand

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

expansionary effect (increase inhiring, decrease in firing, increase inhours, and increase in full‐

timecontracts)of theexchangerateshock( )willbe larger forexportingplants than forplants

servingonlythedomesticmarket,asforeignmarketaccessimproves.

Asintheplant‐levelanalysis,thetheoreticalpredictionsfor areambiguous.Ontheonehand,the

stricterenforcementof laborregulationsraises thecostof formalworkers.Assuch,plants facing

strict enforcement are predicted to relatively decrease hiring, relatively increase firing, relatively

decrease hours, and relatively decrease full‐time contracts. On the other hand, the stricter

enforcementoflaborregulationsalsoincreasesjobquality,intermsofcompliancewithmandated

benefits for theworker.For this reason,wemight findevidenceof increases in turnover inmore

heavily‐enforcedcities,as formalemploymentbecomesamoreattractiveoptionand formalwork

registration increases.We empirically test these ambiguities given our strong predictions on the

impact of trade openness on job creation and job destruction for exporters relative to non‐

exporters.

Moreover, we further hypothesize that labormarket regulations on formal employment are less

binding for exporting firms, and thus expect the effects of regulatory enforcement to be less

  25

important forplantsexposedtoglobalmarkets.Thishypothesishas foundations inthe literature.

CardosoandLage(2007)arguethattheintegrationoffirmsininternationaltradeandtheneedto

complywithinternationalqualitystandardsimplicitlyforcefirmstocomplywithlaborregulations.

This is reinforced inHarrisonandScorse (2003),who report that exporters and foreign firms in

Indonesia aremore likely to complywith labor regulations. In addition, Bloom and Van Reenen

(2010) show that labor market regulations are negatively correlated with the quality of

managementpracticesacrosscountries.Atthesametime,multinationalandexportingfirmstendto

bebettermanagedacrossallcountries,suggestingthebetterinstitutionalenvironmentatexporting

plantsoffersenhancedcompliancewithlaborregulations.

Asmany of the covariates in equation (4) 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.27 To take into account theoccurrence of repeated observationsof individualswithin

city‐sectors,weclustertherobuststandarderrorsatthecity‐sectorlevel,thoughourmainresults

arerobusttoclusteringatthematchlevel,aswell.

5. MainResults

Table 5.1 reports themain results of this paper, as estimated by equation (4). As discussed, the

specification controls for unobservable, time‐invariant worker‐plant match quality, as well as

observable, time‐varyingworker,plant,and industrycharacteristics,andstate‐yeardummies.Our

maindifference‐in‐differenceequationalsoincludespre‐determinedcityconditionsinteractedwith

the exogenous real exchange rate shock to control for the possibility of differential city‐specific

trends.

PanelAreportsestimatesfromequation(4),wherethedependentvariableisaworker‐plant‐year

                                                            27 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,andfull‐timecontracts,ourdependentvariablewillbeadummyvariableandapplyingleastsquareswillnotyieldthemostefficientestimator.However,as65%ofthepredictedprobabilitiesfromourjobcreationestimationlie between0 and1 (60% for jobdestruction, and85% for full‐time contracts),we are confident that anyinconsistencyisminimized.

  26

accession. Across all plants, the effect of a real exchange rate depreciation is not statistically

significant, but the sign is informative. In particular, the coefficient suggests that for otherwise

identical workers and plants, a depreciation of the real exchange rate decreases a worker’s

probabilityofhire.Interestingly,thepointestimateonourmaincoefficientofinterest( ),though

insignificant, is negative, suggesting that hiring differentially increases in strictly‐enforced

municipalitieswithashocktoopenness.

Weargue,however,thattradeshocksandregulatoryenforcementhavedifferenteffectsdepending

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

competitivenessofexportingplantsinforeignmarkets,weanticipateanexpansionofemployment

atexportersrelativetonon‐exporters.Therefore,wenextreportcoefficientsfortheestimationof

equation(4)separately for thesetofexportingplantsandnon‐exportingplants.A time‐invariant

export indicator is designed to minimize potential endogeneity concerns surrounding the

globalizationdecisionpost‐devaluation.28

Aspredictedbynewheterogeneousfirmtrademodels,adepreciationoftherealincreaseshiringat

exporters (insignificantly), and differentially decreases hiring at plants producing solely for the

domesticmarket.Moreover,consistentwiththe literature, theway inwhichenforcement impacts

hiringisdifferentdependingontheglobalizationstatusoftheplant.Notably,enforcementhasno

statistical impactonexportingplants, in linewith the ideas inCardoso andLage (2007)and the

results inHarrison and Scorse (2003) for Indonesia that globalized firms are internally‐enforced

andmorelikelytocomplywithlaborregulations.Bycontrast,domesticplantsinstrictly‐enforced

municipalitiesdecreasehiringbymorethanotherwiseidenticaldomesticplantsinweakly‐enforced

municipalities,asthecostofformalworkersincreasesfortheseplants.

Theresults inPanelAsuggest that the impactof tradeopennessdiffers forplantswith thesame

mode of globalization but varying degrees of exposure tode facto labormarket regulations. The

magnitudes of our estimates seem to be plausible. Evaluating the effect on workers and plants

locatedinmunicipalitiesatthemeanlevelofinspections,a10percentagepointdepreciationofthe

real increases the hiring probability at exporting plants by 2.3% and decreases the hiring

probability at domestic plants by 3.6%, as is predicted by heterogeneous firm models of

                                                            28 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‐levelcategorizationoftheplant’sglobalizationstatuslargelyconfirmsourmainfindings.

  27

international trade.The impact fordomesticplantsvariesdependingon the levelof enforcement

theplantfaces—fordomesticplantslocatedinmunicipalitiesatthe10thpercentileofinspections,a

10percentagepointdepreciationof therealdecreasestheprobabilityofhirebyonly2.5%,while

workersmatchedwithdomesticplantslocatedinmunicipalitiesinthe90thpercentileofinspections

experienceadecreaseintheaccessionprobabilityofaround4.9%.

PanelBofTable5.1reportsestimatesfromequation(4),wherethedependentvariableisaworker‐

plant‐yearseparation,forallplantsandbytheplant’smodeofglobalization.Wehypothesizethata

depreciationoftherealexchangeratedecreasestheprobabilityofseparationfortheaverageplant,

ascompetitivenessincreases.ThisisconfirmedinthefirstcolumnofPanelB,as ispositiveand

statistically significant at the 10% level of significance. We note that, as predicted, the effect is

drivenbydecreasesinfiringatexpandingexportingplants.

Weremindthereaderthatincreasesininspectionsambiguouslyrelatetoseparations.Ontheone

hand,moreenforcementincreasesthecostoffiring(duetothemandatoryseverancebenefits)and

thusmaydecreasefirings.Ontheotherhand,withincreasedenforcement,existinglaborbecomes

more expensive, as there can be less evasion of labor taxes, and the plant may resort to firing

workersintheshortruntoovercometheincreasedlaborcost.Onceagain,weanticipatetheimpact

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

binding. In fact, exporters in strictly‐enforced municipalities respond no differently to an

expansionary trade shock than do identical exporters in weakly‐enforced cities. However, non‐

exporting plants in strictly‐enforced municipalities differentially increase firing as compared to

similarnon‐exporters facingweakerenforcement,pointing to increases in thecostsofemploying

workersasadominantfactor.Domesticplantslocatedincitiesatthe10thpercentileofinspections

contract by increasing firing by approximately 0.5% in response to a 10 percentage point

devaluation,while theprobability thatamatch isdestroyedat similardomesticplants located in

citiesatthe90thpercentileofinspectionsincreasesby2.3%inresponsetothesametradeshock.

Takentogether,PanelsAandBofTable5.1suggestthatstrongerenforcementoflaborregulations

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

firinganddecreasedhiring.That is,contractingnon‐exportersdecrease jobcreationand increase

jobdestructionevenfurtherduetoincreasesinthecostofformalemploymentinstrictly‐regulated

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

  28

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

contractionaryeffectsoftradereformforconstrainednon‐exportingplants.

Expandingandcontractingplantsmayalsoadjustalong the intensivemargin.For instance,when

facedwitha tradeshock,employersmayadjust thehoursworked forexistingemployeesorshift

workersbetweenfull‐timeandtemporarycontracts.AsBrazil’slaborlawlimitsacontinuouswork

shiftto6hours,limitstheweeklyworkingperiodto44hours,andmandatesincreasesinovertime

pay,theeffectsofthelaborlawsonplantswill likelydifferdependingonthedegreeofregulatory

enforcement.Similarly, the increasedcostof formal laborassociatedwithstricterenforcementof

laborregulationsmayleadplantstoshifttowardstheuseoftemporarycontractsoverpermanent

contracts.Thelattercouldhelpemployersovercomethelong‐termrelationshipsofmorerestrictive

employmentcontracts.Wenextconsidertheseadjustmentsforallplantsandbytheplant’smodeof

globalization.

Panel C of Table 5.1 reports results from the estimation of equation (4) where the dependent

variableisthelogarithmofhoursworkedperweekforallplantsandbytheplant’sexportstatus,

while Panel D of Table 5.1 reports coefficients for the estimation of equation (4) where the

dependent variable is an indicator variable equal to one ifworker i is employedwith a full‐time

contractinplantjintimet.Ourdatasuggestlittlevariationinthehoursworkedintensivemarginin

response to exchange rate shocks and regulatory enforcement for both exporters and non‐

exporters.Bycontrast, thedata report thatnon‐exporters facingstrong increases inenforcement

differentially decrease the availability of full‐time contracts when compared to equivalent non‐

exportersinless‐enforcedareas,aspredicted.

5.1. RobustnessoftheResults

ThemainresultsinTable5.1provideevidencethatstrongincreasesinlabormarketenforcement

reinforce the contraction of non‐exporting firms, by decreasing hiring, increasing firing, and

decreasing the probability of full‐time contracts. In this section, we test the robustness of these

results.Asourresults indicatethatmostof the laboradjustment inresponsetotradeshocksand

regulatoryenforcementoccursalongtheextensivemargin,fromthispointforwardinthepaper,we

  29

concentrate our analysis on job creation and job destruction, but results for hours and full‐time

contractsareavailablebyrequest.

LaggedEnforcement Theendogeneityofenforcementisanimportantconcern.Aswehaveargued

untilnow,weminimizethisconcernbyfocusingonwithin‐citychangesinenforcement,byfocusing

ourinterpretationontheinteractiontermwithanexogenoustradeshock,andbyincludingstate‐

specific year dummies and city‐specific trends. In order to further allay concerns about the

endogeneity of changes in enforcement, in Table 5.2 we report results from the estimation of

equation(4)inwhichourmainenforcementmeasureislaggedoneperiod.

Overall,weviewourresultsasbroadlyrobusttotheinclusionoflaggedenforcement.Theresultsin

Table 5.4 are largely consistent with those presented in Table 5.1, though due to the loss of

observations, we lose some statistical significance. In particular, in Panel A, though our main

coefficient of interest ( ) for non‐exporters, representing the differential impact of increased

exposure to enforcement, is statistically insignificant, the signon thepointestimate is consistent

withTable5.1.That is,using laggedvalues forenforcement, thedatapoints to the ideathatnon‐

exportersinstrictly‐enforcedareasdifferentiallydecreasehiringinresponsetoanexogenoustrade

shock,whencomparedtoidenticalnon‐exportingplantsinweakly‐enforcedareas.PanelBreports

resultsforjobdestructionasthedependentvariable.Here,theresultsareentirelyconsistentwith

Table 5.1.Non‐exporting plants facing strong labormarket regulations differentially increase job

destruction,asthecostofemployingworkersincreases.

First‐DifferenceEstimation Our main estimation relies on changes over time in enforcement,

exchange rates, and labormarket outcomes. Econometrically,we rely on fixed effects to consider

thesechangesovertimeforworkersandfirmsproducingindifferentcitiesandindustries.Itiswell

known that the fixed effects estimator is efficient when the errors are serially uncorrelated.

However,Bertrand,Duflo,andMullainathan(2004)remarkthaterrorsmaybeseriallycorrelatedin

difference‐in‐difference estimations like ours. Our results until now have adjusted for this by

clustering thestandarderrorsat the city‐industry level. Inunreportedresults,wealsoshowthat

ourfindingsarerobusttoclusteringthestandarderrorsatthematchlevel.

InTable5.3,wetesttheimportanceofourchoiceofthefixedeffectsestimator.Weinsteadestimate

equation(4)infirst‐differencesandshowthatthemainresultsarerobust.Heavily‐inspectednon‐

  30

exportersdifferentially contract by increasing firing anddecreasinghiring in response to a trade

shockrelativetoidenticalnon‐exportersinless‐inspectedcities.

5.2. 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

regulatory enforcement. In this section,we consider the heterogeneity of these results based on

industry‐level differences such as the sector’s technological intensity, based on plant‐level

differencessuchastotalemployment,andbasedonworker‐leveldifferencessuchastheworker’s

agegroup.Again,giventhe lackofstatisticalevidence insupportofstrongadjustmentsalongthe

intensivemargin,we restrict the analysis to the impact on job creation and job destruction. The

resultsforhoursandcontracttypeareavailableuponrequest.

TechnologicalIntensity Ourmainargumentrestsonthefactthatatradeshockwillreallocate

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

productioninthispaper.However,thesameshockmayalsoinfluenceadjustmentsintheshort‐run

in termsof capitalorotherphysicalmaterials factorsofproduction.Toensure that theeffectsof

labormarket regulatoryenforcementwe find inTable5.1 reflectsplants’ constraints inadjusting

laborintheshort‐run,wesplitourmainsampleintosectorsdependingontechnologicalintensity.

Ourassumptionhere,consistentwithmuchoftheliterature, isthatsectorsrelyingontechnology

are relatively capital‐intensive. Therefore, low technology sectors are assumed to bemore labor‐

intensive. For this reason,we anticipate that ourmain findings are driven by plants in low‐tech

(labor‐intensive)industries.WerelyondatafromtheWorldBank’sInvestmentClimateAssessment

Reportstodefinesectors’technologicalintensity.29Examplesofhigh‐techindustriesare:petroleum

refining,chemicalmanufacturing,andautomobilemanufacturing.Examplesof low‐techindustries

are:foodandbeverage,textile,andwoodmanufacturing.

Wereportcoefficientsfromtheestimationofequation(4)bythesector’stechnologicalintensityin

Table5.4.Inthetoppanel,thedependentvariablerepresentsanindicatorforjobcreation,whilein

                                                            29CNAE2‐digitcodes23‐24and26‐35aredefinedashigh‐techsectors,whileCNAE2‐digitcodes15‐22,25,36‐37aredefinedaslow‐techsectors.

  31

the bottom panel, we use a job destruction indicator as the dependent variable. The results are

mostly in line with our hypotheses. The differential decrease in hiring at strictly‐enforced non‐

exportersappearstoholdacrosslow‐techandhigh‐techindustries,althoughthepointestimateon

ourmaincoefficientofinterestismarginallylargerinmagnitudeforlabor‐intensivenon‐exporters.

In addition, the finding that domestic plants located in strictly‐enforced areas increase job

destruction by more is largely driven by domestic plants in low‐tech industries. The main

interaction parameter of interest is negative and statistically significant at the 10% level of

significance.

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 the median value. We argue that comparing similarly‐sized plants helps to minimize any

possibleselectionbiasassociatedwiththeplant’sglobalizationstatus.

Table5.5displaysresultsfromtheestimationofequation(4),bythesizeoftheplant,forallplants

andbytheplant’smodeofglobalization,wherethedependentvariableinthetoppanelisaworker‐

plantjobcreationandthedependentvariableinthebottompanelisaworker‐plantjobdestruction.

Wenotethatthepositiveinteractioncoefficientforthesetofnon‐exportersinPanelAofTable5.1

and the negative interaction coefficient for the set of non‐exporters in Panel B of Table 5.1 are

whollydrivenby the impactonbelow‐mediansizedplants.Small,non‐exportersare thoseplants

for which labor market regulations are most likely to be binding and restrictive. Due to their

visibility,asissuggestedbyCardosoandLage(2007),largeplantsarealreadylikelytocomplywith

existing regulations, such that increases in inspections have no statistical impact. As such, small

non‐exporterswhoexperiencedan increase in enforcementoverour sampleperioddemonstrate

significantdifferencesinjobcreationandjobdestructioninresponsetotradereformascompared

tosimilarplantsfacinglesslabormarketregulatoryenforcement.

  32

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

coefficientsonthetrade‐weightedrealexchangerateremainstatisticallysignificantandofthesame

signincomparisontoTable5.1.Consideringsimilarly‐sizedplantshelpstominimizepotentialbias

associatedwithselection intoexportingpost‐tradereform. In thisrespect,ourresultsare further

robusttothepotentialfortheendogeneityofexportstatus.

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

(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. Table 5.6 reports estimates for equation (4), dividing the

samplebytheageoftheworkerandtheplant’sglobalizationstatus.Wedefineworkersas“older”

whentheyare31andolder(themedianworkerageinthesample)and“younger”whentheyare30

orless.

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,aswell.

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.

  33

Forinstance,theinabilityofplantstoadjusttochangingconditionsandtoreallocatefromdeclining

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.30 In Table 5.7, we present coefficients from the estimation of equation (2), where the

dependentvariableistheplant‐levelaveragewageasaproxyforplant‐levelproductivity.Acrossall

plants,adepreciationsignificantlyincreaseswithin‐plantproductivity(consistentwithstudieslike

Pavcnik (2002)). We also find a negative impact of increased enforcement on plant‐level

productivity, as proxied by plant‐average wages, suggesting that the first effect pertaining to

restricted labor reallocation dominates any potential positive impact of enforcement on firm

productivityviaincreasesininvestmentsintrainingorphysicalcapital.

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

plants,strictenforcementoflaborregulationslimitspotentialwithin‐plantproductivitygains(due

                                                            30Forinstance,inamodelliketheoneinAmitiandDavis(2012),whereworkers’wagesaredirectlylinkedtofirmprofitsthrougha“fairwage”mechanism.

  34

totheefficientreallocationofworkers)associatedwithtradeopenness.Asbefore,theseeffectsare

wholly concentrated among constrained non‐exporting plants, as strong regulatory enforcement

inhibitsthepotentialforproductivitygains.

6. ConclusionsandPolicyImplications

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

developing countries. Early studies found little impact on plant‐level employment changes. 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.31 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 several time‐

varying worker, plant, city, and sector characteristics, we note that, as is predicted by new

heterogeneous firm trade models, trade openness is associated with an expansion at exporting

                                                            31Forexample,theBraziliangovernmenthasrecentlylaunchedaprogramofincentivestopromoteindustrialgrowthandcompetitiveness.Theprogramproposesanexemptionfroma20%socialsecuritylevyonworkerpayrollsforcertainsectors.Eligiblesectorsincludeautomotives,textiles,footwear,andplastics(seeFinancialTimes2012).

  35

plantsanda relativecontractionatdomestically‐orientedplants.Furthermore,we find that labor

inspectionslargelyinfluencelaboradjustmentalongtheextensivemarginatsmall,labor‐intensive,

non‐exportingfirmsforwhichlaborregulationsaremostbinding.Thisisanespeciallyinteresting

finding for policymakers, given the current challenge of revamping industrial growth, through a

morecompetitivelaborforce,inthefaceofaglobalizingworld.

Ourresultsstronglysuggestthatinasettingofstringentde jureregulations,withenhancedtrade

opennessincreasingenforcement limits jobcreation.Overall,stricter laborenforcementincreases

jobdestructionanddecreases jobcreationatplantsmostrestrictedby laborregulations.Wealso

show this increased enforcement is associated with lower productivity gains post‐trade reform.

Fromapolicystandpoint,ourworkalsosuggeststhatinBrazil increasingtheflexibilityofde jure

labor regulationswill allow for increased job creationand thusofferbroader access to the gains

fromtrade.

  36

ReferencesAghion, Philippe, Robin Burgess, Stephen J. Redding, and Fabrizio Zilibotti. 2008. “The Unequal

Effects of Liberalization: Evidence from Dismantling the License Raj in India,” AmericanEconomicReview,98(4),pp.1397–1412.

Aguayo‐Tellez, Ernesto, Marc‐Andreas Muendler, and Jennifer P. Poole, 2010. “Globalization and

Formal‐SectorMigrationinBrazil,”WorldDevelopment,38(6),pp.840‐856.Ahsan,Ahmad andCarmenPages, 2009. “AreAll LaborRegulationsEqual? Evidence from Indian

Manufacturing,”JournalofComparativeEconomics,37(1),pp.62‐75.Almeida, Rita and Pedro Carneiro, 2012. “Enforcement of Labor Regulation and Informality”

AmericanEconomicJournal:AppliedEconomics,4(3),pp.64‐89.Amiti,MaryandDonaldR.Davis,2012.“Trade,Firms,andWages:TheoryandEvidence,”Reviewof

EconomicStudies,79,pp.1‐36.Autor, David, William R. Kerr, and Adriana Kugler, 2007. “Does Employment Protection Reduce

Productivity?EvidencefromUSStates,”TheEconomicJournal,117,pp.189‐217.Barros, Ricardo P. and Carlos H. Corseuil, 2004. “The Impact of Regulations on Brazilian Labor

Market Performance,” in James J. Heckman and Carmen Pages, Eds., Law andEmployment:LessonsfromLatinAmericaandtheCaribbean,Chicago:TheUniversityofChicagoPress.

Bernard,AndrewB.andJ.BradfordJensen,1995.“Exporters,JobsandWagesinU.S.Manufacturing,

1976‐87,”BrookingsPapersonEconomicActivity:Microeconomics,pp.67‐112.Bertola,Giuseppe,TitoBoeri,andSandrineCazes,2000.“EmploymentProtectioninIndustrialized

Countries:TheCaseforNewIndicators,”InternationalLabourReview,139(1),pp.57‐72.Bertrand,Marianne, Esther Duflo, and SendhilMullainathan, 2004. “HowMuch ShouldWe Trust

Difference‐in‐DifferencesEstimates?,”QuarterlyJournalofEconomics,119(1),pp.249‐275.

Besley,TimothyandRobinBurgess,2004. “CanLaborRegulationHinderEconomicPerformance?EvidencefromIndia,”TheQuarterlyJournalofEconomics,CXIX,pp.91‐134.

Bloom,Nicholasand JohnVanReenen,2010. “WhyDoManagementPracticesDifferacrossFirms

andCountries?”JournalofEconomicPerspectives,24(1),pp.203‐224.Botero, Juan C., Simeon Djankov, Rafael La Porta, and Florencio Lopez de Silanes, 2004. “The

RegulationofLabor,”TheQuarterlyJournalofEconomics,119(4),pp.1339‐1382.Brambilla, Irene, Daniel Lederman, and Guido Porto, 2012. “Exports, Export Destinations, and

Skills,”AmericanEconomicReview,102(7),pp.3406‐3438.Broda, Christian and David E. Weinstein, 2006. “Globalization and the Gains from Variety," The

QuarterlyJournalofEconomics,121(2),pp.541‐585.

  37

Burgess, Simon M. and Michael M. Knetter, 1998. “An International Comparison of EmploymentAdjustment toExchangeRateFluctuations,”Reviewof InternationalEconomics,6 (1),pp.151‐163.

Bustos, Paula, 2011. “The Impact of Trade Liberalization on Skill Upgrading: Evidence from

Argentina,”unpublishedmanuscript.Caballero,RicardoJ.,KevinN.Cowan,EduardoM.R.A.Engel,andAlejandroMicco,2013.“Effective

Labor Regulation andMicroeconomic Flexibility,” Journal ofDevelopmentEconomics, 101, pp.92‐104.

Campa, JoseManuelandLindaS.Goldberg,2001. “EmploymentversusWageAdjustmentand the

U.S.Dollar,”TheReviewofEconomicsandStatistics,83(3),pp.477‐489.Cardoso, Adalberto and Telma Lage, 2007.AsNormas e os Fatos, Editora FGV, Rio de Janeiro,

Brasil.Coşar,A.Kerem,NezihGuner,andJamesR.Tybout,2011.“FirmDynamics,JobTurnover,andWage

DistributionsinanOpenEconomy,”unpublishedmanuscript.Currie,JanetandAnnE.Harrison,1997.“SharingtheCosts:TheImpactofTradeReformonCapital

andLaborinMorocco,”JournalofLaborEconomics,15(3),pp.44‐71.Eslava,Marcela, JohnHaltiwanger,AdrianaKugler,andMauriceKugler,2010.“FactorAdjustments

after Deregulation: Panel Evidence from Colombian Plants,” The Review of Economics andStatistics,92(2),pp.378‐391.

Fajgelbaum, Pablo D., 2012. “Labor Market Frictions, Firm Growth, and International Trade,”

unpublishedmanuscript.Feenstra,RobertC.,RobertE.Lipsey,HaiyanDeng,AlysonC.Ma,andHengyongMo,2004.“World

TradeFlows:1962‐2000,”NBERWorkingPaperNo.11040.Feliciano, Zadia M., 2001. “Workers and Trade Liberalization: The Impact of Trade Reforms in

MexicoonWagesandEmployment,”IndustrialandLaborRelationsReview,55(1),pp.95‐115.Feyrer, James, 2009. “Trade and Income—Exploiting Time Series in Geography,” unpublished

manuscript.TheFinancialTimes,2012.“BrazilUnveilsMeasurestoBoostIndustry,”April3,2012.Freund, Caroline and Bineswaree Bolaky, 2008. “Trade, regulations, and income,” Journal of

DevelopmentEconomics,87(2),pp.309‐321.Goldberg,LindaS.,2004.“Industry‐specificexchangerates fortheUnitedStates,”EconomicPolicy

Review,FederalReserveBankofNewYork,May,pp.1‐16.Goldberg,LindaS.andJosephTracy,2003.“ExchangeRatesandLocalLaborMarkets,”inRobertC.

Feenstra, Ed., The Impact of International Trade onWages, Chicago: The University ofChicagoPress.

  38

Goldberg, Pinelopi and Nina Pavcnik, 2003. “The Response of the Informal Sector to Trade

Liberalization,”JournalofDevelopmentEconomics,72,pp.463‐496.Gonzaga, Gustavo,NaercioAquinoMenezes‐Filho, and Cristina Terra, 2006. “Trade Liberalization

andtheEvolutionofSkillEarningsinBrazil,”JournalofInternationalEconomics,68(2),pp.345‐367.

Gruben,WilliamC.andSherryKiser,1999. “Brazil: TheFirstFinancialCrisisof1999,”Southwest

Economy,pp.13‐14.Harrison, Ann and Jason Scorse, 2003. “Globalization’s Impact on Compliance with Labor

Standards,”BrookingsTradeForum,pp.45‐96.Hasan,Rana,DevashishMitra,andK.VRamaswamy,2007.“TradeReforms,LaborRegulations,and

Labor‐Demand Elasticities: Empirical Evidence from India," The Review of Economics andStatistics,89(3),pp.466‐481.

Heckman,JamesJ.andCarmenPages,2004.LawandEmployment:LessonsfromLatinAmerica

andtheCaribbean,Chicago:TheUniversityofChicagoPress.Helpman,Elhanan,OlegItskhoki,andStephenRedding,2010.“InequalityandUnemploymentina

GlobalEconomy,”Econometrica,78(4),pp.1239‐1283.Hsieh, Chang‐Tai and Peter J. Klenow, 2009. “Misallocation andManufacturing TFP in China and

India,”TheQuarterlyJournalofEconomics,124,pp.1403‐1448.Kambourov,Gueorgui,2009.“LaborMarketRegulationsandtheSectoralReallocationofWorkers:

TheCaseofTradeReforms,”TheReviewofEconomicStudies,76(4),pp.1321‐1358.Kaplan,DavidS.,2009. “JobCreationandLaborReforminLatinAmerica,” JournalofComparative

Economics,37,pp.91‐105.Krishna, Pravin, Jennifer P. Poole, andMine Zeynep Senses, 2011. “WageEffects of TradeReform

withEndogenousWorkerMobility,”NBERWorkingPaperNo.17256.Kugler,Adriana,1999.“TheImpactofFiringCostsonTurnoverandUnemployment:Evidencefrom

theColombianLaborMarketReform,”InternationalTaxandPublicFinanceJournal,6(3).Kugler,Adriana,2004.“TheEffectofJobSecurityRegulationsonLaborMarketFlexibility:Evidence

fromtheColombianLaborMarketReform,”inJamesJ.HeckmanandCarmenPages,Eds.,LawandEmployment:LessonsfromLatinAmericaandtheCaribbean,Chicago:TheUniversityofChicagoPress.

Kugler, Adriana andMaurice Kugler, 2009. “LaborMarket Effects of Payroll Taxes in Developing

Countries: Evidence from Colombia,”EconomicDevelopment and Cultural Change, 57 (2), pp.335‐358.

Kume, Honorio, Guida Piani, and Carlos Frederico Braz de Souza, 2003. “A Polıtica Brasileira de

Importacao no Perıodo 1987‐98: Descricao e Avaliacao,” in Carlos Henrique Corseuil and

  39

Honorio Kume, eds., A abertura comercial brasileira nos anos 1990: Impactos sobreempregoesalários,RiodeJaneiro:MTEandIPEA,chapter1,9–37.

Melitz,MarcJ.,2003.“TheImpactofTradeonIntra‐IndustryReallocationsandAggregateIndustryProductivity,”Econometrica,71(6),pp.1695‐1725.

Menezes‐Filho,NaercioAquinoandMarc‐AndreasMuendler,2011.“LaborAllocationinResponseto

TradeReform,”unpublishedmanuscript.Moreira,MauricioMesquitaandCorrea,PauloGuilherme,1998."Afirstlookattheimpactsoftrade

liberalizationonBrazilianmanufacturingindustry,"WorldDevelopment,26(10),pp.1859‐1874.

Muendler,Marc‐Andreas,2003.“NominalandRealExchangeRateSeriesforBrazil,1986‐2001,”

unpublishedmanuscript.Olarreaga,MarceloandIsidroSoloaga,1998.“EndogenousTariffFormation:TheCaseofMercosur,”

WorldBankEconomicReview,12(2),pp.297‐320.Pavcnik, Nina, 2002. “Trade Liberalization, Exit, and Productivity Improvements: Evidence from

ChileanPlants,”TheReviewofEconomicStudies,69,pp.245‐276.Paz, Lourenço, 2012. “The Impacts of Trade Liberalization on Informal Labor Markets: A

TheoreticalandEmpiricalEvaluationoftheBrazilianCase,”unpublishedmanuscript.Petrin, Amil and Jagadeesh Sivadasan, forthcoming. “Estimating Lost Output from Allocative

Inefficiency, with an Application to Chile and Firing Costs”, The Review of Economics andStatistics.

Revenga, Ana L., 1992. “Exporting Jobs?: The Impact of Import Competition on Employment and

WagesinU.S.Manufacturing,”TheQuarterlyJournalofEconomics,107(1),pp.255‐284.Ribeiro,EduardoPontual,CarlosH.Conseuil,DanielSantos,PauloFurtado,BrunuAmorim,Luciana

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.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).

40

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 average number of inspections per 100 plants by Brazilian municipality, with darker shadesrepresenting higher numbers of inspections. The map on the left is for the year 1998, while the map on the right is for the year2000.

Figure 3.3: Enforcement Intensity by Municipality, Mato Grosso, 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 average number of inspections per 100 plants by Brazilian municipality for the state of MatoGrosso, with darker shades representing higher numbers of inspections. The map on the left is for the year 1998, while themap on the right is for the year 2000.

41

Figure 3.4: 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).

42

Table3.1:EnforcementData,1996‐2001

ShareofCitiesInspected

AverageNumberof

InspectionsineachCity

AverageNumberofInspectionsPer100RegisteredPlants

AverageNumberofInspectionsPer10,000RegisteredWorkers

1996 0.33 13.2 1.92 13.171997 0.44 13.0 2.01 16.041998 0.38 12.8 2.33 20.351999 0.50 13.8 2.56 21.062000 0.43 14.8 2.62 22.382001 0.52 14.3 2.26 16.23Source:Authors'calculationsbasedonadministrativedatafromtheBrazilianMinistryofLabor(1996‐2001).Note: This table reports different statistics at the city level between 1996 and 2001. Column (1) reportsthe share of cities that have at least one manufacturing labor inspection. Column (2) reports the averagenumber of labor inspections in each city. Column (3) reports the average number of inspections per 100registered plants in the city and column (4) reports the average number of labor inspections per 10,000registeredworkers("comcarteira ").

43

Table3.2:ChangesinEnforcement,1996‐2001Dep.Variable:ΔEnforcementm2001‐1996

(1) (2) (3)

ΔLog(AgriculturalGDP)m1996‐1991 ‐0.040*** ‐0.026* ‐0.011(0.014) (0.014) (0.015)

ΔLog(ManufacturingGDP)m1996‐1991 ‐0.004 0.013* 0.048***(0.007) (0.008) (0.008)

ΔLog(ServicesGDP)m1996‐1991 0.171*** 0.124*** 0.121***(0.018) (0.020) (0.020)

ΔLog(Population)m1996‐1991 ‐0.026 ‐0.005 ‐0.030(0.025) (0.025) (0.026)

ΔUrbanizationRatem1996‐1991 0.665*** 0.508***(0.135) (0.135)

ΔPovertyRatem1996‐1991 1.830***(0.193)

NumberofObs. 5,502 5,502 5,502Source:Authors'calculationsbasedonadministrativedatafromtheBrazilianMinistryofLabor(1996‐2001)andIPEA @Cidades(1991‐1996).

Note: This table reports coefficients from a city‐level ordinary least squares regression in first‐differences, where the dependent

variable is the change in enforcement between 1996 and 2001. Enforcement is measured as the logarithm of the number of

inspections in the city (plus one) per 100 plants in the city. *** denotes significance at the 1% level; ** denotes significance at the

5%level;*denotessignificanceatthe10%level.Robuststandarderrorsarereportedinparentheses.

44

Table3.3:DescriptiveStatistics,1996‐2001

AllPlants Exporters Non‐Exporters

Shareofworkers:Hired 0.33 0.27 0.39Fired 0.29 0.26 0.33TemporaryContract 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 0.26 0.28 0.23MorethanHighSchool 0.07 0.10 0.03

UnskilledBlueCollar 0.11 0.10 0.11SkilledBlueCollar 0.65 0.64 0.67OtherWhiteCollar 0.07 0.07 0.07ProfessionalorTechnical 0.17 0.19 0.15

Plant‐levelCovariatesEmployment 99 233 40AverageWage(inlogs) 7.98 8.47 7.76

Municipality‐levelCovariatesInspections 41.6 68.3 44.7

Industry‐levelCovariatesTrade‐weightedRER 0.81 0.81 0.81Employment 17,390 17,717 17,983UnionizationRate 0.22 0.22 0.22NumberofObservations 322,614 169,890 152,724NumberofWorkers 109,086 55,207 63,060NumberofPlants 61,462 13,921 47,541NumberofMunicipalities 2,829 1,407 2,698NumberofIndustries 240 237 239Source:Authors'calculationsbasedonRAIS,administrativedatafromtheBrazilianMinistryofLabor,IMFbilateralrealexchangerates,NBERtradeflows,andSECEX(1996‐2001).

Note: This table reports descriptive statistics for the main variables used in our empirical work, across all plants and bythe plant's mode of globalization. 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(averagesacrossindustries).

45

Table4.1:Trade,Enforcement,andPlant‐LevelEmploymentDep.Variable:Log(Employment)jmkt

(1) (2) (3)

TRERkt*Enforcementmt 0.047*** 0.034**(0.006) (0.015)

Trade‐weightedRERkt ‐0.137*** ‐0.394*** ‐0.192***(0.046) (0.052) (0.051)

NumberofObs. 269,422 269,422 269,422Plant‐YearControls YES YES YESSector‐YearControls YES YES YESState‐YearDummies YES YES YESPlantFixedEffects YES YES YESSource:Authors'calculationsbasedonRAIS,administrativedatafromtheBrazilianMinistryofLabor,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; * denotes significance at the 10% level. Robust standard errors,clustered at the city‐industry level, are reported in parentheses. All regressions also include city‐level enforcement.Unreported covariates at the plant‐level include average worker tenure at the plant, the age, gender, educational, andoccupational composition of the plant. Unreported industry‐level covariates include the unionization rate, industryemployment, average worker tenure in the industry, and the age, gender, educational, and occupational composition oftheindustry.

46

Table5.1:Trade,Enforcement,andLaborAdjustment

All Exporters Non‐Exporters

TRERkt*Enforcementmt ‐0.024 ‐0.017 0.034*(0.015) (0.020) (0.019)

Trade‐weightedRERkt 0.051 ‐0.143 0.185**(0.069) (0.089) (0.093)

TRERkt*Enforcementmt ‐0.003 ‐0.001 ‐0.039**(0.013) (0.016) (0.018)

Trade‐weightedRERkt 0.101* 0.146** 0.115(0.053) (0.069) (0.073)

TRERkt*Enforcementmt 0.003 ‐0.001 0.007(0.003) (0.003) (0.005)

Trade‐weightedRERkt ‐0.012 ‐0.002 ‐0.018(0.015) (0.022) (0.022)

TRERkt*Enforcementmt 0.002 ‐0.006 0.012*(0.007) (0.011) (0.007)

Trade‐weightedRERkt 0.010 0.018 ‐0.009(0.018) (0.022) (0.027)

NumberofObs. 322,614 169,890 152,724City‐YearControls YES YES YESWorker‐YearControls YES YES YESPlant‐YearControls YES YES YESSector‐YearControls YES YES YESState‐YearDummies YES YES YESMatchFixedEffects YES YES YES

Note:Thistablereportscoefficientsfromtheordinaryleastsquaresestimationofequation(4)inthepaper,wherethedependentvariableinPanelAisanindicatorvariablewhichtakesthevalueoneifamatchbetweenworker i andplantjiscreatedintimet ,thedependentvariableinPanelBisanindicatorvariablewhichtakesthevalueoneifamatchbetweenworkeri andplantj isdestroyedintimet ,thedependentvariableinPanelCisthelogarithmofhoursworkedforworker iemployedatplantj intimet ,andthedependentvariableinPanelDisanindicatorvariablewhichtakesthevalueoneifworkeri isemployedinplantj attimet withafull‐timecontract,forallplantsandbytheplant'sexportstatus.***denotessignificanceatthe1%level;**denotessignificanceatthe5%level;*denotessignificanceatthe10%level.Robuststandarderrors,clusteredatthecity‐industrylevel,arereportedinparentheses.Enforcementismeasuredasthelogarithmofthenumberofinspectionsinthecity(plusone)per100plantsinthecity.Unreportedcovariatesatthecity‐levelincludeenforcementandinitialcityconditions(industrialcomposition,population,urbanization,andpovertyrates)interactedwiththetrade‐weightedrealexchangerate.Unreportedcovariatesattheworkerlevelincludetheworker’sage(andagesquared),tenureattheplantinmonths,education(astwodummyvariables—atleasthighschoolandmorethanhighschoolwherelessthanhighschoolistheomittedcategory)andoccupation(asthreedummyvariables—skilledbluecollarworker,unskilledwhitecollarworker,andprofessional/managerialworkerwhereunskilledbluecollarworkeristheomittedcategory).Attheplantlevel,weincludeaverageplantwages,plantemployment,averageworkertenureattheplant,andtheage,gender,educational,andoccupationalcompositionoftheplant.Wealsoincludethefollowingindustrycharacteristics:theindustryunionizationrate,industryemployment,averageworkertenureintheindustry,andtheage,gender,educational,andoccupationalcompositionoftheindustry.

PANELA:JobCreation

PANELC:Log(Hours)

PANELB:JobDestruction

PANELD:Full‐TimeContract

Source:Authors'calculationsbasedonRAIS,MinistryofLaboradministrativedataoninspections,IMFbilateralrealexchangerates,NBERtradeflows,andSECEX(1996‐2001)andIPEA@Cidades (1991‐1996).

47

Table5.2:Trade,LaggedEnforcement,andLaborAdjustment

All Exporters Non‐Exporters

TRERkt*Enforcementmt‐1 0.004 ‐0.002 0.014(0.006) (0.007) (0.010)

Trade‐weightedRERkt ‐0.023 ‐0.032 ‐0.016(0.026) (0.030) (0.046)

TRERkt*Enforcementmt‐1 ‐0.020* ‐0.015 ‐0.042**(0.012) (0.015) (0.020)

Trade‐weightedRERkt ‐0.014 0.040 ‐0.047(0.061) (0.080) (0.087)

NumberofObs. 170,169 96,428 73,741City‐YearControls YES YES YESWorker‐YearControls YES YES YESPlant‐YearControls YES YES YESSector‐YearControls YES YES YESState‐YearDummies YES YES YESMatchFixedEffects YES YES YES

PANELA:JobCreation

PANELB:JobDestruction

Source:Authors'calculationsbasedonRAIS,MinistryofLaboradministrativedataoninspections,IMFbilateralrealexchangerates,NBERtradeflows,andSECEX(1996‐2001)andIPEA@Cidades (1991‐1996).

Note:Thistablereportscoefficientsfromtheordinaryleastsquaresestimationofequation(4)inthepaper,wherethedependentvariableinPanelAisanindicatorvariablewhichtakesthevalueoneifamatchbetweenworker i andplantjiscreatedintimet andthedependentvariableinPanelBisanindicatorvariablewhichtakesthevalueoneifamatchbetweenworkeri andplantj isdestroyedintimet ,forallplantsandbytheplant'sexportstatus.***denotessignificanceatthe1%level;**denotessignificanceatthe5%level;*denotessignificanceatthe10%level.Robuststandarderrors,clusteredatthecity‐industrylevel,arereportedinparentheses.Enforcementismeasuredasthelaggedvalueofthelogarithmofthenumberofinspectionsinthecity(plusone)per100plantsinthecity.Unreportedcovariatesatthecity‐levelincludelaggedenforcementandinitialcityconditions(industrialcomposition,population,urbanization,andpovertyrates)interactedwiththetrade‐weightedrealexchangerate.Unreportedcovariatesattheworkerlevelincludetheworker’sage(andagesquared),tenureattheplantinmonths,education(astwodummyvariables—atleasthighschoolandmorethanhighschoolwherelessthanhighschoolistheomittedcategory)andoccupation(asthreedummyvariables—skilledbluecollarworker,unskilledwhitecollarworker,andprofessional/managerialworkerwhereunskilledbluecollarworkeristheomittedcategory).Attheplantlevel,weincludeaverageplantwages,plantemployment,averageworkertenureattheplant,andtheage,gender,educational,andoccupationalcompositionoftheplant.Wealsoincludethefollowingindustrycharacteristics:theindustryunionizationrate,industryemployment,averageworkertenureintheindustry,andtheage,gender,educational,andoccupationalcompositionoftheindustry.

48

Table5.3:Trade,Enforcement,andLaborAdjustment,First‐Difference

All Exporters Non‐Exporters

ΔTRERkt*Enforcementmt ‐0.017 ‐0.024 0.051**(0.017) (0.023) (0.022)

ΔTrade‐weightedRERkt 0.045 ‐0.146 0.164(0.079) (0.104) (0.101)

ΔTRERkt*Enforcementmt ‐0.005 0.011 ‐0.048**(0.015) (0.019) (0.021)

ΔTrade‐weightedRERkt 0.159** 0.115 0.267***(0.063) (0.083) (0.088)

NumberofObs. 169,264 96,098 73,166City‐YearControls YES YES YESWorker‐YearControls YES YES YESPlant‐YearControls YES YES YESSector‐YearControls YES YES YESState‐YearDummies YES YES YESMatchFixedEffects YES YES YES

PANELA:JobCreation

PANELB:JobDestruction

Source:Authors'calculationsbasedonRAIS,MinistryofLaboradministrativedataoninspections,IMFbilateralrealexchangerates,NBERtradeflows,andSECEX(1996‐2001)andIPEA@Cidades (1991‐1996).

Note:Thistablereportscoefficientsfromtheordinaryleastsquaresestimationofequation(4)inthepaperinfirstdifferences,wherethedependentvariableinPanelAisanindicatorvariablewhichtakesthevalueoneifamatchbetweenworkeri andplantj iscreatedintimet andthedependentvariableinPanelBisanindicatorvariablewhichtakesthevalueoneifamatchbetweenworker i andplantj isdestroyedintimet ,forallplantsandbytheplant'sexportstatus.***denotessignificanceatthe1%level;**denotessignificanceatthe5%level;*denotessignificanceatthe10%level.Robuststandarderrors,clusteredatthecity‐industrylevel,arereportedinparentheses.Enforcementismeasuredasthelogarithmofthenumberofinspectionsinthecity(plusone)per100plantsinthecity.Unreportedcovariatesatthecity‐levelincludeenforcementandinitialcityconditions(industrialcomposition,population,urbanization,andpovertyrates)interactedwiththetrade‐weightedrealexchangerate.Unreportedcovariatesattheworkerlevelincludetheworker’sage(andagesquared),tenureattheplantinmonths,education(astwodummyvariables—atleasthighschoolandmorethanhighschoolwherelessthanhighschoolistheomittedcategory)andoccupation(asthreedummyvariables—skilledbluecollarworker,unskilledwhitecollarworker,andprofessional/managerialworkerwhereunskilledbluecollarworkeristheomittedcategory).Attheplantlevel,weincludeaverageplantwages,plantemployment,averageworkertenureattheplant,andtheage,gender,educational,andoccupationalcompositionoftheplant.Wealsoincludethefollowingindustrycharacteristics:theindustryunionizationrate,industryemployment,averageworkertenureintheindustry,andtheage,gender,educational,andoccupationalcompositionoftheindustry.

49

Table5.4:Trade,Enforcement,andLaborAdjustment,BySectorTech‐Intensity

All Exporters Non‐Exporters All Exporters Non‐Exporters

TRERkt*Enforcementmt ‐0.027 ‐0.023 0.034 0.002 0.011 0.038(0.026) (0.032) (0.033) (0.018) (0.025) (0.024)

Trade‐weightedRERkt 0.191* 0.086 0.074 0.081 ‐0.214** 0.241**(0.110) (0.129) (0.157) (0.079) (0.108) (0.114)

TRERkt*Enforcementmt 0.006 0.007 ‐0.037 ‐0.020 ‐0.021 ‐0.043*(0.021) (0.026) (0.030) (0.015) (0.019) (0.023)

Trade‐weightedRERkt ‐0.075 ‐0.037 ‐0.016 0.121* 0.139 0.183**(0.084) (0.104) (0.128) (0.064) (0.088) (0.090)

NumberofObs. 109,889 67,707 42,182 212,725 102,183 110,542City‐YearControls YES YES YES YES YES YESWorker‐YearControls YES YES YES YES YES YESPlant‐YearControls YES YES YES YES YES YESSector‐YearControls YES YES YES YES YES YESState‐YearDummies YES YES YES YES YES YESMatchFixedEffects YES YES YES YES YES YES

Note:Thistablereportscoefficientsfromtheordinaryleastsquaresestimationofequation(4)inthepaper,wherethedependentvariableinPanelAisanindicatorvariablewhichtakesthevalueoneifamatchbetweenworkeri andplantj iscreatedintimet andthedependentvariableinPanelBisanindicatorvariablewhichtakesthevalueoneifamatchbetweenworkeri andplantj isdestroyedintimet ,bythesector'stech‐intensityandexportstatus.***denotessignificanceatthe1%level;**denotessignificanceatthe5%level;*denotessignificanceatthe10%level.Robuststandarderrors,clusteredatthecity‐industrylevel,arereportedinparentheses.Enforcementismeasuredasthelogarithmofthenumberofinspectionsinthecity(plusone)per100plantsinthecity.Unreportedcovariatesatthecity‐levelincludeenforcementandinitialcityconditions(industrialcomposition,population,urbanization,andpovertyrates)interactedwiththetrade‐weightedrealexchangerate.Unreportedcovariatesattheworkerlevelincludetheworker’sage(andagesquared),tenureattheplantinmonths,education(astwodummyvariables—atleasthighschoolandmorethanhighschoolwherelessthanhighschoolistheomittedcategory)andoccupation(asthreedummyvariables—skilledbluecollarworker,unskilledwhitecollarworker,andprofessional/managerialworkerwhereunskilledbluecollarworkeristheomittedcategory).Attheplantlevel,weincludeaverageplantwages,plantemployment,averageworkertenureattheplant,andtheage,gender,educational,andoccupationalcompositionoftheplant.Wealsoincludethefollowingindustrycharacteristics:theindustryunionizationrate,industryemployment,averageworkertenureintheindustry,andtheage,gender,educational,andoccupationalcompositionoftheindustry.

High‐Tech Low‐Tech

PANELA:JobCreation

PANELB:JobDestruction

Source:Authors'calculationsbasedonRAIS,MinistryofLaboradministrativedataoninspections,IMFbilateralrealexchangerates,NBERtradeflows,andSECEX(1996‐2001),IPEA@Cidades (1991‐1996),andWorldBankInvestmentClimateAssessmentReports.

50

Table5.5:Trade,Enforcement,andLaborAdjustment,ByPlantSize

All Exporters Non‐Exporters All Exporters Non‐Exporters

TRERkt*Enforcementmt ‐0.018 ‐0.017 0.023 0.118*** 0.021 0.137***(0.016) (0.020) (0.022) (0.037) (0.160) (0.038)

Trade‐weightedRERkt 0.017 ‐0.148* 0.205* 0.106 0.327 0.104(0.072) (0.089) (0.106) (0.166) (0.875) (0.170)

TRERkt*Enforcementmt 0.000 ‐0.002 ‐0.019 ‐0.098*** 0.178 ‐0.113***(0.013) (0.016) (0.021) (0.033) (0.145) (0.034)

Trade‐weightedRERkt 0.097* 0.155** 0.062 0.260* ‐0.873 0.295**(0.056) (0.069) (0.083) (0.141) (0.758) (0.144)

NumberofObs. 284,482 168,201 116,281 38,132 1,689 36,443City‐YearControls YES YES YES YES YES YESWorker‐YearControls YES YES YES YES YES YESPlant‐YearControls YES YES YES YES YES YESSector‐YearControls YES YES YES YES YES YESState‐YearDummies YES YES YES YES YES YESMatchFixedEffects YES YES YES YES YES YES

LargePlants SmallPlants

Source:Authors'calculationsbasedonRAIS,MinistryofLaboradministrativedataoninspections,IMFbilateralrealexchangerates,NBERtradeflows,andSECEX(1996‐2001)andIPEA@Cidades(1991‐1996).

Note:Thistablereportscoefficientsfromtheordinaryleastsquaresestimationofequation(4)inthepaper,wherethedependentvariableinPanelAisanindicatorvariablewhichtakesthevalueoneifamatchbetweenworkeri andplantj iscreatedintimet andthedependentvariableinPanelBisanindicatorvariablewhichtakesthevalueoneifamatchbetweenworkeri andplantj isdestroyedintimet ,bytheplant'ssizeandexportstatus.***denotessignificanceatthe1%level;**denotessignificanceatthe5%level;*denotessignificanceatthe10%level.Robuststandarderrors,clusteredatthecity‐industrylevel,arereportedinparentheses.Enforcementismeasuredasthelogarithmofthenumberofinspectionsinthecity(plusone)per100plantsinthecity.Unreportedcovariatesatthecity‐levelincludeenforcementandinitialcityconditions(industrialcomposition,population,urbanization,andpovertyrates)interactedwiththetrade‐weightedrealexchangerate.Unreportedcovariatesattheworkerlevelincludetheworker’sage(andagesquared),tenureattheplantinmonths,education(astwodummyvariables—atleasthighschoolandmorethanhighschoolwherelessthanhighschoolistheomittedcategory)andoccupation(asthreedummyvariables—skilledbluecollarworker,unskilledwhitecollarworker,andprofessional/managerialworkerwhereunskilledbluecollarworkeristheomittedcategory).Attheplantlevel,weincludeaverageplantwages,plantemployment,averageworkertenureattheplant,andtheage,gender,educational,andoccupationalcompositionoftheplant.Wealsoincludethefollowingindustrycharacteristics:theindustryunionizationrate,industryemployment,averageworkertenureintheindustry,andtheage,gender,educational,andoccupationalcompositionoftheindustry.

PANELA:JobCreation

PANELB:JobDestruction

51

Table5.6:Trade,Enforcement,andLaborAdjustment,ByWorkerAge

All Exporters Non‐Exporters All Exporters Non‐Exporters

TRERkt*Enforcementmt ‐0.021 ‐0.002 0.021 ‐0.006 ‐0.010 0.050*(0.016) (0.020) (0.024) (0.021) (0.030) (0.028)

Trade‐weightedRERkt 0.056 ‐0.124 0.268*** 0.045 ‐0.185 0.114(0.070) (0.084) (0.102) (0.100) (0.138) (0.138)

TRERkt*Enforcementmt ‐0.005 ‐0.003 ‐0.031 ‐0.014 ‐0.009 ‐0.056**(0.014) (0.018) (0.023) (0.018) (0.023) (0.026)

Trade‐weightedRERkt 0.082 0.119* 0.058 0.107 0.165 0.159(0.059) (0.072) (0.091) (0.079) (0.109) (0.108)

NumberofObs. 158,447 87,998 70,449 164,167 81,892 82,275City‐YearControls YES YES YES YES YES YESWorker‐YearControls YES YES YES YES YES YESPlant‐YearControls YES YES YES YES YES YESSector‐YearControls YES YES YES YES YES YESState‐YearDummies YES YES YES YES YES YESMatchFixedEffects YES YES YES YES YES YES

Note:Thistablereportscoefficientsfromtheordinaryleastsquaresestimationofequation(4)inthepaper,wherethedependentvariableinPanelAisanindicatorvariablewhichtakesthevalueoneifamatchbetweenworkeri andplantj iscreatedintimet andthedependentvariableinPanelBisanindicatorvariablewhichtakesthevalueoneifamatchbetweenworkeri andplantj isdestroyedintimet ,bytheworker'sageandexportstatus.***denotessignificanceatthe1%level;**denotessignificanceatthe5%level;*denotessignificanceatthe10%level.Robuststandarderrors,clusteredatthecity‐industrylevel,arereportedinparentheses.Enforcementismeasuredasthelogarithmofthenumberofinspectionsinthecity(plusone)per100plantsinthecity.Unreportedcovariatesatthecity‐levelincludeenforcementandinitialcityconditions(industrialcomposition,population,urbanization,andpovertyrates)interactedwiththetrade‐weightedrealexchangerate.Unreportedcovariatesattheworkerlevelincludetheworker’sage(andagesquared),tenureattheplantinmonths,education(astwodummyvariables—atleasthighschoolandmorethanhighschoolwherelessthanhighschoolistheomittedcategory)andoccupation(asthreedummyvariables—skilledbluecollarworker,unskilledwhitecollarworker,andprofessional/managerialworkerwhereunskilledbluecollarworkeristheomittedcategory).Attheplantlevel,weincludeaverageplantwages,plantemployment,averageworkertenureattheplant,andtheage,gender,educational,andoccupationalcompositionoftheplant.Wealsoincludethefollowingindustrycharacteristics:theindustryunionizationrate,industryemployment,averageworkertenureintheindustry,andtheage,gender,educational,andoccupationalcompositionoftheindustry.

OlderWorkers YoungerWorkers

PANELA:JobCreation

PANELB:JobDestruction

Source:Authors'calculationsbasedonRAIS,MinistryofLaboradministrativedataoninspections,IMFbilateralrealexchangerates,NBERtradeflows,andSECEX(1996‐2001)andIPEA@Cidades(1991‐1996).

52

Table5.7:Trade,Enforcement,andPlant‐LevelWagesDep.Variable:Log(AverageWage)jmkt

All Exporters Non‐Exporters

TRERkt*Enforcementmt 0.029* ‐0.004 0.037**(0.016) (0.031) (0.018)

Trade‐weightedRERkt ‐0.130** ‐0.140 ‐0.131**(0.063) (0.146) (0.064)

NumberofObs. 269,422 70,128 199,294Plant‐YearControls YES YES YESSector‐YearControls YES YES YESState‐YearDummies YES YES YESPlantFixedEffects YES YES YESSource:Authors'calculationsbasedonRAIS,administrativedatafromtheBrazilianMinistryofLabor,IMFbilateralrealexchangerates,NBERtradeflows,andSECEX(1996‐2001).

Note: This table reports coefficients from the ordinary least squares estimation of equation (2) in the paper, where thedependent variable is the logarithm of plant‐level average wages. Enforcement is measured as the logarithm of thenumber of inspections in the city (plus one) per 100 plants in the city. *** denotes significance at the 1% level; ** denotessignificance at the 5% level; * denotes significance at the 10% level. Robust standard errors, clustered at the city‐industrylevel, are reported in parentheses. All regressions also include city‐level enforcement. Unreported covariates at the plant‐level include average worker tenure at the plant, the age, gender, educational, and occupational composition of the plant.Unreported industry‐level covariates include the unionization rate, industry employment, average worker tenure in theindustry,andtheage,gender,educational,andoccupationalcompositionoftheindustry.

53