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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
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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
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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.
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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)).
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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.
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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
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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.
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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
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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).
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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.
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