metadata: authors: 0: author: Krishna, Pravin
1: author: Poole, Jennifer P.
2: author: Senses, Mine Zeynep
count: World
docna: 0: docna: Trade liberalization, firm heterogeneity,
            and wages : new evidence from matched employer-employee data
docty: Policy Research Working Paper
subtopic: Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Labor Markets,Trade Policy,International Trade and Trade Rules,Business Cycles and Stabilization Policies,Trade and Multilateral Issues
teratopic: Macroeconomics and Economic Growth,Social Protections and Labor,International Economics and Trade,Finance and Financial Sector Development
abstracts: cdata!: In this paper, the authors use a linked
            employer-employee database from Brazil to examine the impact
            of trade reform on the wages of workers employed at
            heterogeneous firms. The analysis of the data at the
            firm-level confirms earlier findings of a differential
            positive effect of trade liberalization on the average wages
            at exporting firms relative to non-exporting firms. However,
            this analysis of average firm-level wages is incomplete
            along several dimensions. First, it cannot fully account for
            the impact of a change in trade barriers on workforce
            composition especially in terms of unobservable
            (time-invariant) characteristics of workers (innate ability)
            and any additional productivity that obtains in the context
            of employment in the specific firm (match specific ability).
            Furthermore, the firm-level analysis is undertaken under the
            assumption that the assignment of workers to firms is
            random. This ignores the sorting of worker into firms and
            leads to a bias in estimates of the differential impact of
            trade on workers at exporting firms relative to
            non-exporting firms. Using detailed information on worker
            and firm characteristics to control for compositional
            effects and using firm-worker match specific effects to
            account for the endogenous mobility of workers, the authors
            find the differential effect of trade openness on wages in
            exporting firms relative to domestic firms to be
            insignificant. Consistent with the models of Helpman,
            Itskhoki, and Redding (2010) and Davidson, Matusz and
            Schevchenko (2008), they also find that the workforce
            composition improves systematically in exporting firms in
            terms of innate (time invariant) worker ability and in terms
            the quality of the worker-firm matches.
repnme: repnme: Trade liberalization, firm heterogeneity,
            and wages : new evidence from matched employer-employee data
display_title: Trade liberalization, firm
            heterogeneity, and wages : new evidence from matched
            employer-employee data
listing_relative_url: /research/2011/06/14482241/trade-liberalization-firm-heterogeneity-wages-new-evidence-matched-employer-employee-data
topicv3: Impact of Trade m1326020 1192,Manufacturing Sectors m1326521 1281,Trade m1326924 698,Trade Policy and Integration m1327407 775,Jobs m1503931 2670
publishtoextweb_dt: 2011-06-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Social Protections and Labor,International Economics and Trade,Finance and Financial Sector Development
no_of_pages: 49
model_extractions: WPS5711 Policy Research Working Paper 5711 Trade Liberalization, Firm Heterogneity, and Wages New Evidence from Matched Employer-Employee Data _Pravin Krishna Jennifer P. Poole Mine Zeynep. Senses_ The World Bank Development Research Group Trade and Integration Team June 2011
Policy Research Working Paper 5711

# **Abstract**

In this paper, the authors use a linked employeremployee database from Brazil to examine the impact of trade reform on the wages of workers employed at heterogeneous firms. The analysis of the data at the firmlevel confirms earlier findings of a differential positive effect of trade liberalization on the average wages at exporting firms relative to non-exporting firms. However, this analysis of average firm-level wages is incomplete along several dimensions. First, it cannot fully account for the impact of a change in trade barriers on workforce composition especially in terms of unobservable (timeinvariant) characteristics of workers (innate ability) and any additional productivity that obtains in the context of employment in the specific firm (match specific ability). Furthermore, the firm-level analysis is undertaken under the assumption that the assignment of workers to firms is random. This ignores the sorting of worker into firms and leads to a bias in estimates of the differential impact of trade on workers at exporting firms relative to nonexporting firms. Using detailed information on worker and firm characteristics to control for compositional effects and using firm-worker match specific effects to account for the endogenous mobility of workers, the authors find the differential effect of trade openness on wages in exporting firms relative to domestic firms to be insignificant. Consistent with the models of Helpman, Itskhoki, and Redding (2010) and Davidson, Matusz and Schevchenko (2008), they also find that the workforce composition improves systematically in exporting firms in terms of innate (time invariant) worker ability and in terms the quality of the worker-firm matches.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at Pravin_Krishna@jhu.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Trade   Liberalization,   Firm   Heterogeneity,   and   Wages: New   Evidence   from   Matched   Employer–Employee   Data**<sup>*****</sup>

Pravin  Krishna _Johns  Hopkins  University  and  NBER_ Jennifer  P.  Poole _University  of  California,  Santa  Cruz_ Mine  Zeynep  Senses _Johns  Hopkins  University_ June  2011

**Abstract**

In  this  paper,  we  use  a  linked  employer–employee  database  from  Brazil  to  examine  the impact  of  trade  reform  on  the  wages  of  workers  employed  at  heterogeneous  firms.   Our analysis  of  the  data  at  the  firm-­‐level  confirms  earlier  findings  of  a  differential  positive  effect of  trade  liberalization  on  the  average  wages  at  exporting  firms  relative  to  non-­‐exporting firms.  However,  this  analysis  of  average  firm-­‐level  wages  is  incomplete  along  several dimensions.  First,  it  cannot  fully  account  for  the  impact  of  a  change  in  trade  barriers  on workforce  composition  especially  in  terms  of  unobservable  (time-­‐invariant)  characteristics of  workers  (innate  ability)  and  any  additional  productivity  that  obtains  in  the  context  of employment  in  the  specific  firm  (match  specific  ability).   Furthermore,  the  firm-­‐level analysis  is  undertaken  under  the  assumption  that  the  assignment  of  workers  to  firms  is random.  This  ignores  the  sorting  of  worker  into  firms  and  leads  to  a  bias  in  estimates  of  the differential  impact  of  trade  on  workers  at  exporting  firms  relative  to  non-­‐exporting  firms. Using  detailed  information  on  worker  and  firm  characteristics  to  control  for  compositional effects  and  using  firm-­‐worker  match  specific  effects  to  account  for  the  endogenous  mobility of  workers,  we  find  the  differential  effect  of  trade  openness  on  wages  in  exporting  firms relative  to  domestic  firms  to  be  insignificant.  Consistent  with  the  models  of  Helpman, Itskhoki,  and  Redding  (2010)  and  Davidson,  Matusz  and  Schevchenko  (2008),  we  also  find that  the  workforce  composition  improves  systematically  in  exporting  firms  in  terms  of innate  (time  invariant)  worker  ability  and  in  terms  the  quality  of  the  worker-­‐firm  matches.

**Keywords:** Trade   liberalization,   firm   heterogeneity,   linked   employer-­‐employee   data. **JEL   Classification:** F16

> *   Our   special   thanks   to   Paulo   Furtado   de   Castro   for   help   with   the   RAIS   and   SECEX   data,   to   Marc-­‐ Andreas   Muendler   for   sharing   three-­‐firm   random   aggregates   from   the   PIA   data,   and   to   the Department   of   Economics   and   Academic   Computing   Services   at   the   University   of   California,   San Diego   for   assistance   with   data   access.   Pravin   Krishna   and   Jennifer   Poole   were   visiting   scholars   in   the International   Trade   Division   of   the   Development   Research   Group   of   the   World   Bank   while   work   on this   paper   was   conducted   and   acknowledge   the   funding   support   of   the   World   Bank   executed   Multi-­‐ Donor   Trust   Fund   on   Trade.

1
# **1. Introduction**

It  is  a  well-­‐established  empirical  regularity  in  the  international  economics  literature  that even  within  narrowly-­‐defined  industries,  globally-­‐engaged  firms  and  domestic  firms  differ in  terms  of  their  productivity,  size,  employment  composition,  and  wages  (Bernard  and Jensen  (1995,  1997)  and  Bernard,  Jensen,  and  Schott  (2009)).<sup>1</sup> In  the  presence  of  such heterogeneity,  trade  liberalization  will  induce  inter-­‐firm  reallocations  within  an  industry  as the  more  productive,  exporting,  firms  expand  and  the  least  productive  firms  shrink  or  exit the  industry  (Melitz,  2003).  In  an  environment  with  labor  market  frictions,  this  within-­‐ industry  reallocation  can  have  distributional  consequences  for  workers  employed  in  firms with  differing  levels  of  global  engagement.

The  international  trade  literature  has  discussed  the  putative  impact  of  trade  liberalization on  wages  at  different  levels  of  aggregation,  examining  this  question  alternately  at  the  level of  the  firm  and  at  the  level  of  the  individual  worker.   There  are  various  channels  through which  a  decline  in  trade  protection  could  result  in  differential  changes  in  average  wages  at exporting  firms  relative  to  firms  selling  only  to  the  domestic  market.  For  instance,  if liberalization  is  associated  with  a  change  in  relative  returns  to  worker  characteristics,<sup>2</sup> differences  in  work  force  composition  will  imply  that  average  firm-­‐level  wages  in  exporting firms  will  change  differentially  relative  to  domestic  firms  following  liberalization.  If  trade liberalization  does  not  affect  the  returns  to  worker  characteristics,  a  differential  wage  effect may,  nevertheless,  be  observed  if  changes  in  trade  policy  induce  compositional  changes  in the  workforce  of  exporting  firms  that  are  different  from  those  in  non-­‐exporting  firms.<sup>3</sup> 1  Recent  work  extends  the  findings  of  Bernard  and  Jensen  (1995)  using  matched  employer-­‐employee data  for  Germany  (Schank,  Schnabel,  and  Wagner  (2007)  and  Klein,  Moser,  and  Urban  (2010)), Denmark  (Munch  and  Skaksen  2008),  and  Mexico  (Frías,  Kaplan,  and  Verhoogen  2009).  See  also Hummels,  Jorgensen,  Munch  and  Xiang  (2010)  for  analysis  of  the  impact  of  outsourcing  on  wages  and employment  using  matched  employer-­‐employee  data  from  Denmark.

2  A  vast  empirical  literature  has  examined  the  effects  of  globalization  on  the  wage  outcomes  of workers  in  the  domestic  economy  with  a  particular  focus  on  the  important  question  of  how  trade affects  the  average  wages  of  workers  with  different  levels  of  skill.  Classic  papers  in  this  literature include  Lawrence  and  Slaughter  (1993),  Leamer  (1996),  and  Feenstra  and  Hanson  (1999).  Revenga (1997),  Currie  and  Harrison  (1997),  and  Trefler  (2004)  analyze  the  effect  of  trade  liberalization  on firm-­‐level  wages  in  Mexico,  Morocco,  and  Canada,  respectively,  with  mixed  results.  Feenstra  and Hanson  (2002)  and  Goldberg  and  Pavcnik  (2007)  provide  excellent  survey  treatments.

3  An  expansion  in  exports  could  result  in  differential  labor  quality  upgrading  in  exporting  firms,  for example,  by  inducing  these  firms  to  adopt  technologies  favoring  highly  skilled  workers,  as  in  Yeaple (2005)  and  Bustos  (2011),  or  by  inducing  these  firms  to  upgrade  product  and  hence  labor  quality  as in  Verhoogen  (2008)  and  Kugler  and  Verhoogen  (2008).

2
Furthermore,  in  the  presence  of  labor  market  frictions,  changes  in  trade  protection  may result  in  differential  changes,  in  exporting  firms  relative  to  non-­‐exporting  firms,  in  the wages  of  ex-­‐ante  identical  workers.  For  instance,  if  firms  engage  in  some  form  of  rent sharing  with  their  workers,  the  wages  of  workers  employed  in  exporting  firms,  which experience  a  relative  improvement  in  their  profits  or  market  share  after  a  decline  in protection,  will  correspondingly  be  better  compared  to  workers  employed  in  firms  serving only  the  domestic  market  (Egger  and  Kreickemeier  (2009)  and  Amiti  and  Davis (forthcoming)).   Equally,  if  the  labor  allocation  process  is  subject  to  search  and  matching frictions,  exporting  firms  may  screen  (ex-­‐ante  identical)  workers  more  intensively,  employ workers  of  higher  match-­‐specific-­‐ability,  and  pay  higher  wages  relative  to  non-­‐exporting firms  as  Helpman,  Itskhoki,  and  Redding  (2010)  have  shown  in  their  comprehensive theoretical  analysis  of  the  links  between  trade  and  labor  markets  in  this  context.  Here, opening  the  economy  to  trade  is  predicted  to  alter  this  wage  gap  between  exporting  and non-­‐exporting  firms  and  to  affect  wage  inequality.<sup>4</sup> Does  trade  liberalization,  in  fact,  affect  differently  workers  employed  in  exporting  firms relative  to  non-­‐exporting  firms?  We  explore  this  issue  empirically  using  a  detailed  dataset with  matched  employer-­‐employee  data  on  workers  in  Brazil  for  the  years  1990-­‐1998.  The dataset  traces  individually  identifiable  workers  across  employers  over  time  and  contains detailed  information  on  worker  characteristics  such  as  age,  gender,  education,  occupation, and  tenure  at  the  firm  which  allows  us  to  suitably  account  for  the  role  of  unobservable worker  and  firm  characteristics  in  determining  wages.  We  complement  this  worker-­‐level information  with  firm-­‐level  data  on  exporter  status  from  the  Brazilian  Customs  Office,  and industry-­‐level  information  on  trade  protection  levels  to  capture  Brazil’s  main  trade  policy

> 4   More   generally,   the   various   channels   linking   trade   protection   to   wages   that   we   describe   are   each potentially   associated   with   changes   in   within-­‐group   inequality   (inequality   between   workers   with identical   observable   characteristics),   in   changes   in   between-­‐group   inequality   (inequality   between workers   with   different   characteristics,   such   as,   levels   of   education)   and   possibly   even   no   change   in inequality   at   all.      For   instance,   under   the   rent-­‐sharing   mechanism,   within-­‐group   inequality   will change   following   trade   liberalization   if   otherwise   identical   workers   are   employed   at   firms   which experience   differential   profit   changes.   Alternatively,   if   trade   liberalization   changes   the   returns   to worker   characteristics,   this   will   result   in   a   change   in   between-­‐group   inequality.   Finally,   if   workers are   paid   competitive   wages   and   the   only   impact   of   trade   liberalization   is   to   reallocate   workers   across firms   without   any   changes   in   the   returns   to   worker   characteristics,   there   will   be   no   change   in   either within-­‐group   or   between-­‐group   wage   inequality.

3
# reforms.

We  begin  our  analysis  by  exploring  the  behavior  of  average  firm-­‐level  wages  and  find  that  a decline  in  trade  protection  is  associated  with  an  increase  in  average  wages  in  exporting firms  relative  to  domestic  firms,  consistent  with  the  findings  of  Amiti  and  Davis (forthcoming)  for  the  liberalization  period  in  Indonesia<sup>5</sup> .   However,  we  argue  that  the analysis  of  average  firm-­‐level  wages,  although  informative,  is  incomplete  along  several dimensions.  First,  it  cannot  fully  account  for  the  impact  of  a  change  in  trade  barriers  on workforce  composition  in  terms  of  both  observable  worker  characteristics  (not  available  in firm-­‐level  datasets),  as  well  as  factors  that  are  observable  to  the  managers  of  the  firm  and hence  impact  wages  but  are  unobservable  in  the  data,  such  as  the  innate  ability  of  the worker  and  any  additional  productivity  (ability)  that  obtains  in  the  context  of  employment in  the  specific  firm  (match  specific  ability).   Furthermore,  the  firm-­‐level  analysis  is undertaken  under  the  assumption  that  the  assignment  of  workers  to  firms  is  random  and ignores  the  sorting  of  worker  into  firms  and  the  resulting  change  in  the  distribution  of match-­‐specific  ability  in  different  firms.  Using  matched  employer-­‐employee  data,  we  test whether  wage  behavior  at  the  worker  level  confirms  the  maintained  assumption  of exogenous  worker  mobility,  as  in  Abowd,  McKinney  and  Schmutte  (2010).  Consistent  with theoretical  models  emphasizing  search  dynamics  or  other  frictions  as  important determinants  of  the  relationship  between  trade  liberalization  and  wages  such  as  Helpman, Itskhoki,  and  Redding  (2010)  and  Davidson,  Matusz  and  Schevchenko  (2008),  we  find  that the  data  decisively  reject  the  assumption  of  exogenous  job  mobility.

Importantly,  using  detailed  information  on  worker  and  firm  characteristics  to  control  for compositional  effects  and  using  firm-­‐worker  match  specific  effects  to  account  for  the endogenous  mobility  of  workers,  we  find  the  differential  effect  of  trade  openness  on  wages 5  To  our  knowledge  Amiti  and  Davis  (forthcoming)  is  the  first  paper  to  incorporate  firm-­‐level heterogeneity  in  empirically  studying  the  impact  of  trade  liberalization  on  wages.  They  introduce  a general  equilibrium  model,  which  combines  firm  heterogeneity  in  exporting  patterns,  trade  in intermediate  inputs,  and  firm-­‐specific  wages.  The  latter  is  incorporated  into  the  model  by  assuming  a fair-­‐wage  specification  that  results  in  a  direct  link  between  firm  wages  and  firm  profitability.  The model  predicts  that  a  decline  in  final  goods  tariffs  reduces  the  wages  of  workers  at  firms  that  sell only  in  the  domestic  market,  but  raises  the  wages  of  workers  at  firms  that  export.  Consistent  with  the model,  they  find  differential  changes  in  average  firm-­‐level  wages  across  Indonesian  firms  with different  levels  of  global  engagement  following  liberalization,  which  they  attribute  wholly  to  firm performance.

4
in  exporting  firms  relative  to  domestic  firms  to  be  insignificant.  Thus,  our  findings  using matched  employer  employee  data  suggest  a  quite  different  picture  of  the  links  between trade  liberalization  and  wages  than  is  obtained  by  analyzing  the  data  at  a  more  aggregate (firm)  level.  Consistent  with  the  models  of  Helpman,  Itskhoki,  and  Redding  (2010)  and Davidson,  Matusz  and  Schevchenko  (2008),  we  find  that  the  workforce  composition improves  systematically  in  exporting  firms  in  terms  of  innate  (time  invariant)  ability  and  in terms  the  quality  of  the  worker-­‐firm  matches.   This  finding  also  serves  to  explain  the difference  between  the  results  at  the  firm  level  and  those  at  the  worker  level.  If  average worker  ability  improves  systematically  in  exporting  firms  following  trade  liberalization,  and this  change  in  not  taken  into  account,  it  will  appear  that  trade  liberalization  leads  to  a differential  wage  improvement  for  workers  at  exporting  firms  even  when  this  is  not  the case.

The  remainder  of  this  paper  is  organized  as  follows.  In  Section  2,  we  present  a  background discussion  on  Brazil’s  trade  policy  reforms  and  describe  the  data.  We  present  the  empirical methodology  and  estimation  results  for  the  aggregate  (firm-­‐level)  analysis  in  Section  3  and for  the  analysis  at  the  worker  level  in  Section  4.  Section  5  concludes.

# **2. Data   and   Policy   Background**

Our  main  data  are  administrative  records  from  Brazil  for  formal-­‐sector  workers  linked  to their  employers.  We  combine  this  worker-­‐level  information  with  complementary  data sources  on  firm-­‐level  exporter  status  and  information  on  industry-­‐level  trade  protection during  Brazil’s  main  trade  policy  reform  period.

# **2.1.   Brazil’s   Policy   Reforms**

The  1990s  were  a  period  of  dramatic  policy  reform  in  Brazil,  providing  a  particularly appropriate  setting  in  which  to  study  the  impact  of  trade  liberalization  on  wages.  As compared  to  the  gradual  process  of  globalization  in  many  developed  countries,  Brazil’s trade  reform  occurred  over  a  relatively  short  period  of  time,  and  with  substantial  cross-­‐ industry  variation.  Furthermore,  many  of  the  policy  reforms  were  arguably  unanticipated 5
and  could  be  viewed  as  exogenous  to  changes  in  wages  at  the  firm  and  worker  level.<sup>6</sup> The  second  half  of  the  20th  century  in  Brazil  was  characterized  by  tight  import  substitution industrialization  policies  designed  to  protect  the  domestic  manufacturing  sector  from foreign  competition.  Special  import  regimes  and  discretionary  import  controls  like  the  “law of  similars”,  under  which  goods  were  banned  if  they  too  closely  resembled  a  Brazilian product,  were  commonplace.  Coverage  of  these  quantitative  restrictions  remained  close  to 100  percent  throughout  this  period,  leaving  Brazilian  manufacturers  highly  protected.

The  1990s,  however,  witnessed  sweeping  changes  in  Brazilian  trade  policy.  Beginning  with the  Collor  de  Melo  administration  and  continuing  with  President  Cardoso,  Brazil  began extensive  policies  of  trade  liberalization  in  1988,  which  paved  the  way  for  the  multilateral free  trade  area, _Mercosul_ ,  with  its  Southern  Cone  neighbors  (Argentina,  Paraguay,  and Uruguay)  in  1991.  Average  ad  valorem  final  goods  tariff  rates  fell  from  41%  to  18% between  1988  and  1989.<sup>7</sup> The  federal  government  abolished  all  remaining  non-­‐tariff barriers  inherited  from  the  import  substitution  era  and  brought  nominal  tariffs  further down  in  1990.  Effective  rates  of  protection  fell  by  over  70%  in  just  four  years—from approximately  42%,  on  average,  in  1988  to  12%,  on  average,  in  1994  (Kume,  Piani,  and Souza  2003).

After  decades  of  high  inflation  and  several  unsuccessful  stabilization  attempts,  the  Brazilian government  succeeded  with  its  macroeconomic  stabilization  plan  ( _Plano  Real_ )  in  1994  and lastingly  ended  hyperinflation.  The  new  currency,  the _real_ ,  began  officially  at  parity  with  the U.S.  dollar  on  July  1,  1994,  trading  freely  on  international  markets  and  appreciating  in  its first  months.  In  response,  the  government  partially  reversed  trade  reforms  in  1995  after manufacturing  industries  lost  competitiveness  due  to  the _real_ ’s  appreciation;<sup>8</sup> the  effective rate  of  protection  climbed  slightly  in  subsequent  years  from  an  average  of  12%  in  1994  to

> 6   The   same   argument   could   not   be   made   if   our   analysis   focused   on   industry-­‐level   wages,   given   the substantial   evidence   for   political   economy   factors   influencing   trade   policy   (see,   for   example, Grossman   and   Helpman   (1994)   and   Olarreaga   and   Soloaga   (1998)   for   evidence   from   Brazil’s   customs union, _Mercosul_ ).

> 7   These   reforms   had   little   impact   on   import   competition   however,   as   non-­‐tariff   barriers   remained highly   restrictive.

> 8   Prior   to   1994   and   the   implementation   of   the   new   currency,   controls   on   Brazil’s   former   currency,   the _cruzado_ ,   had   served   as   yet   another   form   of   implicit   import   protection.   In   our   empirical   analysis,   we allow   for   any   differential   impacts   Brazil’s   exchange   rate   may   have   on   firms   with   differing   trade exposure.

6
an  average  of  18%  in  1998.<sup>9</sup>

# **2.2.   Worker   data**

The  linked  employer–employee  data  are  from  the  Brazilian  Labor  Ministry,  which  requires by  law  that  all  formally-­‐registered  firms  report  to  the  ministry  on  all  workers  in  every year.<sup>10</sup> These  administrative  records  have  been  collected  in  the _Relação  Anual  de Informações  Sociais_ (RAIS)  database  since  1986.  In  this  paper,  we  use  information  from RAIS  for  the  years  1990  through  1998,  when  we  also  have  complementary  data  on  the export  status  of  firms  and  industry-­‐level  tariff  rates.

The  main  benefit  of  the  RAIS  database  is  the  ability  to  trace  individually-­‐identifiable workers  over  time  and  across  firms.  The  data  include  a  unique  worker-­‐identification number  (which  remains  with  the  worker  throughout  his  work  history),  the  tax  number  of the  worker’s  firm,  the  industrial  classification  of  the  worker’s  firm<sup>11</sup> ,  and  the  municipality  of the  worker’s  firm.  These  data  are  particularly  valuable  as  they  offer  variables  beyond  the available  information  in  many  other  firm-­‐level  databases,  often  used  in  studies  like  ours.  In particular,  the  data  contain  detailed  information  on  workers’  skill-­‐levels  (as  defined  by occupation<sup>12</sup> and  education),  which  are  paramount  to  this  analysis.  Other  variables  of 9  Trade  policy  reforms  coincided  with  gradual  foreign  investment  liberalizations  and  the privatization  of  state-­‐owned  companies,  both  of  which  contributed  to  attracting  substantial  capital inflows  over  this  time  period.  Meanwhile,  the  government’s  regional  development  plans  also included  export  promotion  policies  as  explicit  elements,  helping  to  boost  exports  beginning  in  1995. In  each  specification,  we  include  region-­‐specific  year  dummies  to  capture  the  impact  of  these  and other  general  macroeconomic  trends  on  wages.

10  The  process  for  firms  to  report  on  their  workers  is  extensive  and  costly.  However,  this  information is  used  to  administer  payments  of  the  annual  public  wage  supplements  to  every  formally-­‐employed worker,  thus  creating  a  strong  incentive  for  workers  to  urge  their  employers  to  report  accurately.  In practice,  however,  only _formally_ -­‐employed  workers  will  be  properly  recorded.  Goldberg  and  Pavcnik (2003)  estimate  informal  workers  represent  approximately  16%  of  Brazil’s  manufacturing  labor force  over  our  sample  period.  The  literature  on  the  impact  of  trade  reform  on  the  informal  sector offers  mixed  results  (see  Goldberg  and  Pavcnik  (2003),  Menezes-­‐Filho  and  Muendler  (2007),  and  Paz (2009)).

> 11   The   sector   classification   used   in   this   paper   comes   from   Brazil’s   statistical   office   ( _Instituto   Brasileiro de   Geografia   e   Estatística_ ,   IBGE)   and   is   roughly   comparable   to   a   3-­‐digit   NAICS   classification (Muendler   2002).

12  Muendler,  Poole,  Ramey  and  Wajnberg  (2004)  map  the  Brazilian  classification  of  occupations ( _Classificação  Brasileira  de  Ocupações_ (CBO))  to  the _International  Standard  Classification  of Occupations_ (ISCO).  The  CBO  is  a  detailed,  task-­‐oriented  classification  system,  while  ISCO  reflects  a less-­‐detailed  and  more  skill-­‐oriented  classification  system.  The  skill  classification  is  intended  to incorporate  on-­‐the-­‐job  experience,  informal  training,  and  the  technological  skill  content  of  the 7
interest  are  the  worker’s  annual  real  wages  in  Brazilian _reais_<sup>13</sup> ,  tenure  at  the  firm  in  months, gender,  and  age.

We  restrict  observations  as  follows.  First,  we  draw  a  one  percent  random  sample  from  the complete  list  of  workers  ever  to  appear  in  the  national  records.  We  match  the  sampled workers  back  to  the  population  data  to  find  all  firms  in  which  these  workers  were  ever employed  over  time  to  create  a  complete  employment  history  of  this  one  percent  random sample  of  the  population  of  the  Brazilian  formal-­‐sector  labor  force.<sup>14</sup> Next,  we  keep  only workers  with  valid  worker  identification  numbers  to  ensure  that  we  can  track  individuals over  time.  As  is  standard  in  the  literature,  we  include  only  prime-­‐age  workers  between  the ages  of  15  and  64  years,  workers  with  a  positive  average  monthly  wage,  and  workers  in private-­‐sector  jobs.  Finally,  for  workers  with  multiple  jobs  in  a  given  year,  only  the  most recent  job  is  included  in  the  sample.  If  a  worker  has  multiple  current  jobs,  only  the  highest paying  job  is  included.

# **2.3.   Complementary   data**

**Export   Status** Brazilian   firms’   tax   identification   numbers   are   common   across   many databases,   allowing   us   to   match   the   RAIS   data   to   complementary   firm-­‐level   data   sources. Information   on   firm-­‐level   export   transactions   is   available   from   the   Brazilian   Customs   Office ( _Secretaria   de   Comércio   Exterior_ ,   SECEX).   SECEX   records   all   legally-­‐registered   firms   in   Brazil with   at   least   one   export   transaction   in   a   given   year.   We   match   the   SECEX   firm-­‐level   exporter status   data   to   our   RAIS   worker   data   by   the   firm’s   tax   identification   code   to   identify   workers at   exporting   and   non-­‐exporting   firms.   We   define   an   indicator   variable   equal   to   one   if   a worker   holds   a   job   at   a   firm   with   a   positive   dollar   value   of   free-­‐on-­‐board   exports   in   a   given year   and   zero   otherwise.

> occupation   (Elias   and   Birch   1994).   ISCO   occupations   can   be   grouped   into   four   broad   occupational categories   following   Abowd,   Kramarz,   Margolis,   and   Troske   (2001)   to   reflect   the   skill-­‐intensity   of   the occupation.

> 13   RAIS   reports   an   average   monthly   wage   (in   multiples   of   the   current   minimum   wage)   for   each   job   in which   a   worker   is   employed   in   each   year.   In   combination   with   information   on   the   number   of   months a   worker   was   employed   during   the   year   and   deflated   minimum   wage   information   in _reais_ from   the Brazilian   Central   Bank,   we   calculate   an   annual   real   wage   for   each   worker.

> 14   Note   that   any   firm-­‐level   aggregates   we   use   in   our   analysis,   however,   are   based   on   the   complete population   of   workers.

8
As  would  be  predicted  by  new  heterogeneous  firm  models  of  international  trade,  Brazil’s trade  liberalization  in  the  early  1990s  was  associated  with  significant  firm-­‐level  entry  into exporting.  The  share  of  exporting  firms  increased  over  50%—from  only  8.5%  in  1990  to 12.9%  in  1994  before  leveling  off.

**Tariffs** In   our   analysis   of   Brazil’s   trade   policy,   we   concentrate   on   two   trade   protection measures:   the   final   goods   output   tariff   and   the   effective   rate   of   protection   (ERP).   The effective   rate   of   protection   allows   us   to   incorporate   changes   in   tariffs   placed   on   inputs   into   a firm’s   production   process.   Our   data   on   final   goods   tariffs   are   from   Muendler   (2003),   who reports   monthly   nominal   final   goods   output   tariffs   at   the _Nível   80_ Brazilian   industrial classification   level.   We   access   monthly   ERP   at   the _Nível   80_ industrial   classification   level from   Kume,   Piani,   and   Souza   (2003).<sup>15</sup> We   match   the   December   tariffs   from   1990   to   1998 with   our   annual   RAIS   worker   data   by   the   2-­‐digit   IBGE   subsector<sup>16</sup> to   identify   workers   and firms   in   industries   with   differential   rates   of   protection   and   liberalization   experiences.

Figure  2.1  displays  both  the  mean  and  median  values  of  the  effective  rate  of  protection  in the  manufacturing  sector  during  the  1990  to  1998  period.  The  early  1990s  experienced sharp  declines  in  the  effective  rate  of  protection.  Mean  rates  fell  from  approximately  60 percent  to  20  percent,  while  median  rates  fell  from  40  percent  to  20  percent  in  a  half-­‐ decade.  The  slight  aforementioned  protectionist  response  to  the  appreciation  of  the _real_ is also  evident.  Most  strongly  in  the  early  part  of  the  decade,  median  ERP  was  smaller  than average  ERP,  suggesting  that  the  distribution  of  the  effective  rate  of  protection  is  skewed  to the  right.  Over  time,  as  the  sectoral  variation  narrows,  the  mean  ERP  and  median  ERP converge.

This  substantial  cross-­‐industry  variation  in  both  levels  and  changes  in  the  ERP  is

> 15   Kume,   Piani,   and   Souza   (2003)   formally   measure   ERP   for   sector _k_ as   the   increase   in   the   value-­‐ added   due   to   the   structure   of   tariffs   relative   to   value-­‐added   at   free   trade   prices,   as   follows: _ERPk_ = τ _k_ − 1∑− ∑ _amka_ τ _mkk_ ,   where _amk_ =<sup>_amk_</sup> _d_ 1(+τ1+τ _m_ _<u>k</u>_ <u>)</u> is   the   input-­‐output   matrix   at   free-­‐trade international   prices,   final   goods   and   intermediate   inputs   tariffs,   respectively. _amkd_ is   the   input-­‐output   matrix   at   distorted   domestic   prices,   and τ _k_ and      are   the   τ _m_

> 16   We   follow   industry   concordances   available   at _http://econ.ucsd.edu/muendler/brazil_ to   concord   the _Nível   80_ classification   to   the   2-­‐digit   IBGE   subsector   classification   used   in   RAIS.   This   constitutes   the IBGE   subsectors   2   through   13.

9
documented  in  more  detail  in  Figure  2.2  where  we  present  the  distribution  of  tariffs  across industries  in  1990  and  1998,  and  the  average  annual  change  in  ERP  during  this  period.  Note that  compared  to  1990,  the  distribution  of  the  effective  rate  of  protection  across  industries at  the  end  of  our  sample  is  much  more  compressed  around  a  lower  mean.  We  also  note substantial  variation  across  sectors  in  the  average  annual  changes  in  protection  rates.

# **2.4.   Descriptive   statistics**

The  complete  matched  data  include  3,932,297  worker-­‐firm-­‐year  observations,  with 657,572  workers  in  490,884  firms.  Our  final  sample  of  the  manufacturing  sector,  however, has  504,660  worker-­‐firm-­‐year  observations,  with  114,042  workers  in  58,578  firms.  We report  detailed  descriptive  statistics  in  Table  2.1.  Roughly  three-­‐quarters  of  the  formal-­‐ sector  labor  force  have  at  most  a  primary  school  education.  An  additional  18%  of manufacturing  sector  workers  are  high  school  educated,  and  only  7%  of  workers  have  a college  degree.  The  majority  of  Brazil’s  labor  force  is  employed  in  skilled  blue-­‐collar occupations,  like  machine  operators  and  assemblers.  Almost  20%  of  the  manufacturing workforce  is  in  professional  or  managerial  positions,  with  11%  in  unskilled  blue-­‐collar  jobs. Other  white-­‐collar  workers  (for  example,  those  in  secretarial  and  office  assistant occupations)  represent  only  8%  of  the  formal  sector  manufacturing  labor  force.  The  average number  of  employees  in  manufacturing  firms  is  relatively  small  at  73.

There  are  64,212  workers  working  in  11,143  exporting  firms,  and  80,895  workers  working in  53,537  domestic  firms  over  the  sample  period.  Exporters  pay  a  substantially  higher average  wage  than  do  non-­‐exporters.  Exporters  employ  a  higher  share  of  skilled  workers, on  average,  compared  to  non-­‐exporting  firms,  consistent  with  the  existing  literature.  Almost 10%  of  workers  at  exporting  firms  are  college  educated  and  21%  are  high-­‐school  graduates. In  comparison,  in  non-­‐exporting  firms  the  share  of  college  educated  and  high-­‐school educated  workers  are  4%  and  17%,  respectively.<sup>17</sup> Exporters  also  employ  a  higher  share  of workers  in  professional,  managerial,  and  technical  occupations.  Exporters  are  significantly larger  in  terms  of  their  average  employment  compared  to  non-­‐exporters.  The  average exporter  employs  346  employees,  while  the  average  non-­‐exporter  has  only  37  employees.

17  Note  that  in  a  country  like  Brazil,  the  share  of  workers  with  a  high-­‐school  education  is  a  more meaningful  representation  of  skill  (Gonzaga,  Menezes-­‐Filho,  and  Terra  2006).

10
The  set  of  firm  level  variables  available  in  our  dataset  allow  us  to  appropriately  control  for these  differences  between  exporters  and  domestic  firms  in  identifying  the  heterogeneous impact  of  trade  policy  on  wages  of  these  firms.

# **3.   Firm-­‐Level   Analysis**

We  begin  our  analysis  at  the  firm  level  to  ensure  the  comparability  of  our  results  with  those of  the  existing  literature  based  on  firm-­‐level  data  and  to  highlight  the  importance  of introducing  worker  and  match  heterogeneity  into  the  analysis.  To  this  end,  we  aggregate the  matched  employer-­‐employee  data  to  the  firm  level  and  estimate  the  following specification:

> where   the   dependent   variable, !"# _!_ $ ,   is   the   logarithm   of   average   wages   at   the   firm   level _"#_ for   firm _j_ at   time _t. !"!_ denotes   the   level   of   protection   in   sector _k_ in   which   the   firm   operates, and _!"#_ is   an   indicator   variable   equal   to   one   if   firm _j_ reports   a   positive   dollar   value   of _$%_ exports   at   time _t_ and   zero   otherwise.   The   level   of   protection   at   the   sector-­‐level   is   measured by   both   tariffs   and   the   effective   rate   of   protection   (ERP).   We   use   the   latter   measure   in   our main   specifications,   since   in   an   environment   in   which   Brazilian   firms   face   declines   in   both final   goods   and   intermediate   input   tariffs,   the   ERP   is   a   more   appropriate   measure   of protection   faced   by   domestic   firms.   In   each   specification,   we   include   an   interaction   term between _!"!_ and _!"# $%_ to   allow   for   changes   in   protection   to   have   differential   effects   on exporters   and   firms   serving   only   the   domestic   market.

As  we  noted  earlier,  the  post-­‐liberalization  period  in  Brazil  coincided  with  a  period  of  an appreciation  of  the  currency,  the _real_ ,  making  Brazilian  goods  less  competitive  on international  markets,  while  making  imported  goods  cheaper  in _real_ terms.  Failing  to incorporate  such  fluctuations  in  exchange  rates  into  our  analysis  could  bias  the  estimated effect  of  liberalization  on  wages.  Henceforth,  in  each  specification,  we  also  include  an 11
interaction  of  Brazil’s  real  exchange  rate  (RER)<sup>18</sup> and  the  firm’s  export  status.<sup>19</sup> The  time-­‐varying,  firm-­‐level  controls, _! "#_ ,  include  variables  available  in  standard  firm-­‐level data  sets  such  as  log  employment,  and  the  occupational  skill  composition<sup>20</sup> of  the  firm  in addition  to  average  worker  tenure  at  the  firm,  and  controls  for  the  age<sup>21</sup> ,  gender,  and educational  skill  composition<sup>22</sup> of  the  firm.<sup>23</sup> Each  specification  also  includes  firm  fixed effects, ! _!_ ,  accounting  for  time-­‐invariant,  firm  characteristics  and  interactive  region-­‐year fixed  effects, ! _!"_ ,  capturing  the  average  effect  of  policy  changes  that  may  differentially impact  wages  of  firms  in  different  regions  of  Brazil.<sup>24</sup> Here, ! is  an  error  term  that  is _!"_ assumed  to  exhibit  no  serial  correlation,  and  to  be  orthogonal  to  all  regressors.  In  each specification,  the  standard  errors  are  clustered  at  the  industry-­‐year  level  to  account  for  the possibility  of  within-­‐industry,  across-­‐firm  correlation  in  errors  following  Moulton  (1990).

In  interpreting  our  estimates  from  specification  (1)  we  focus  specifically  on  the  magnitude of  the  differential  change  in  average  firm-­‐level  wages  at  exporters  relative  to  non-­‐exporters (!! )  as  well  as  the  overall  wage  impact  of  a  decline  in  protection  for  exporters  (!! +!" )  and non-­‐exporters  (!! ),  separately.  The  responsiveness  of  average  wages  in  firms  serving  only 18  The  real  exchange  rate  series  for  Brazil  is  constructed  in  Muendler  (2003)  and  is  available  at _<u>http://www.econ.ucsd.edu/muendler/html/brazil.html#brazdata.</u>_ 19  Since  the  overall  impact  of  the  time-­‐varying,  economy-­‐wide  RER  is  absorbed  by  the  region-­‐specific year  effects  ( _relative_ to  domestic  firms.  We  also  conduct  robustness  checks  using  industry-­‐specific  exchange  rates.  δ _tr_ ),  we  can  only  separately  identify  the  effect  of  the  RER  changes  on  exporting  firms 20  We  define  the  firm’s  occupational  skill  composition  as  the  share  of  the  firm’s  workforce  in  four occupational  categories:  unskilled  blue  collar,  skilled  blue  collar,  other  white  collar,  and  professional and  managerial  workers.  Unskilled  blue-­‐collar  workers  are  the  omitted  category.

21  We  define  the  firm’s  age  composition  as  the  share  of  the  firm’s  workforce  in  six  age  categories: youth  (15-­‐17),  adolescent  (18-­‐24),  nascent  career  (25-­‐29),  early  career  (30-­‐39),  peak  career  (40-­‐49), and  late  career  (50-­‐64).  Youth  workers  are  the  omitted  category.

22  We  define  the  firm’s  educational  skill  composition  as  the  share  of  the  firm  in  three  education categories:  less  than  high  school,  at  least  high  school,  and  more  than  high  school.  Less  than  high school  is  the  omitted  category.

23  By  including  both  the  educational  and  the  occupational  skill  composition  of  the  firm,  we  are  able  to allow  for  the  possibility  that  firms  use  increasingly  higher-­‐skilled  individuals  (as  defined  by education)  in  lower-­‐skilled  occupations.  See  Muendler  (2008)  for  evidence  on  the  skill  upgrading  of occupations  in  response  to  trade  reform  in  Brazil.

24  We  consider  Brazil’s  five  main  geographic  regions:  the  North,  Northeast,  Center-­‐West,  Southeast, and  South.

12
the  domestic  market  to  changes  in  protection  is  reflected  in  the  coefficient  !! .  A  positive  !! would  suggest  that  a  decline  in  protection  is  associated  with  a  decrease  in  average  wages  in firms  serving  solely  the  domestic  market.  Note  that  when  ERP  is  the  measure  of  protection (instead  of  tariffs),  !! would  reflect  a  combined  effect  of  the  positive  impact  of  a  reduction in  input  tariffs  through  prices  and  access  to  enhanced  variety  and  quality  on  firm  profits<sup>25</sup> , as  well  as  any  negative  impact  of  increased  import  competition  due  to  a  decline  in  output tariffs.  Hence,  we  expect  the  coefficient  to  be  smaller  in  magnitude  when  the  measure  of protection  is  ERP  compared  to  the  estimated  coefficient  when  protection  is  measured  by (output)  tariffs.

The  coefficient  on  the  interaction  term, !! ,  reflects  the  differential  effect  of  trade  policy changes  on  average  wages  in  exporting  firms  relative  to  firms  serving  only  the  domestic market.  If  a  decline  in  protection  results  in  a  differential  increase  in  firm-­‐level  average wages  in  exporting  firms,  we  expect !! < " .  There  are  various  reasons  a  decline  in protection  could  result  in  a  differential  increase  in  firm-­‐level  average  wages  of  exporting firms.  For  example,  if  firms  engage  in  some  form  of  rent-­‐sharing  with  their  workers, workers  employed  in  exporting  firms,  whose  prospects  improve  as  a  result  of  a  decline  in protection,  could  experience  an  increase  in  their  wages.  Similarly,  if  liberalization  is associated  with  a  change  in  the  relative  returns  to  skill,  and  if  exporting  firms  differ  in  terms of  their  labor  force  composition,  average  firm-­‐level  wages  in  exporting  firms  will  increase relative  to  domestic  firms.  Also,  the  relative  increase  in  wages  in  exporting  firms  could  be  a reflection  of  a  change  on  workforce  composition  in  terms  of  factors  that  are  observable  to the  managers  of  the  firm  and  hence  impact  wages,  but  are  unobservable  in  the  data  (such  as innate  worker  ability  or  match-­‐specific  ability).

# **3.1   Estimation   Results**

> 25   See   Arkolakis,   Demidova,   Klenow,   and   Rodriguez-­‐Clare   (2008)   and   Goldberg,   Khandelwal,   Pavcnik, and   Topalova   (2008)   for   evidence   on   improved   input   variety,   and   Kugler   and   Verhoogen   (2008), Amiti   and   Konings   (2007),   Csillag   and   Koren   (2009),   and   Halpern,   Koren   and   Szeidl   (2009)   for evidence   on   improved   input   quality.

13
Estimation  results  from  equation  (1)  with  tariffs<sup>26</sup> as  the  measure  of  protection  are reported  in  Table  3.1.  The  results  suggest  that  a  decline  in  tariffs  is  associated  with  a  decline in  average  wages  at  non-­‐exporting  firms,  consistent  with  a  negative  impact  of  an  increase  in foreign  competition  on  these  firms.  We  find  that  a  ten  percentage  point  decrease  in  tariffs leads  to  a  decrease  in  average  firm-­‐level  wages  by  1.7%  for  these  firms.  The  negative  and significant  coefficient  on  the  interaction  term  between  tariffs  and  export  status  suggests that  the  wages  in  exporting  firms  increase  in  response  to  a  decline  in  tariffs  relative  to  firms serving  only  the  domestic  market.  We  find  that  a  change  in  tariffs  has  no  statistical  impact on  average  wages  at  the  firm-­‐level  for  exporting  firms.<sup>27</sup> The  RER-­‐exporter  interaction  term suggests  that  a  depreciation  in  the  RER  (a  decrease  in  the  RER  as  is  it  defined  in  our  data) increases  the  wages  in  exporting  firms  relative  to  non-­‐exporting  firms,  as  expected.  All  the firm-­‐level  controls  are  statistically  significant  and  enter  with  the  expected  signs  and magnitudes.  Average  firm-­‐level  wages  are  increasing  with  the  average  tenure  and  age-­‐ profile  at  the  firm,  while  they  are  decreasing  in  the  share  of  female  workers  employed  at  the firm.  Wages  are  increasing  in  both  the  educational  and  occupational  skill  composition  of  the firm  and  the  size  if  the  firm.

Next,  we  test  whether  the  differential  impact  of  tariffs  we  document  on  exporting firms  holds  equally  for  firms  operating  in  all  industries.  More  specifically,  we  allow  for  the effect  of  a  change  in  tariffs  to  be  different  for  firms  operating  in  Brazil’s  comparative advantage  sectors.  We  expect  that  following  liberalization,  exporters  in  these  sectors  will experience  a  more  pronounced  increase  in  profitability  relative  to  exporters  in  low comparative  advantage  sectors  and  hence,  expect  a  stronger  impact  on  average  wages  at these  firms.  We  divide  our  sample  into  high  and  low  comparative  advantage  sectors  using data  on  each  industry’s  Balassa  (1965)  comparative  advantage.<sup>28</sup> The  next  two  columns  in Table  3.1  report  our  results  by  the  comparative  advantage  of  the  firm’s  sector  where  high (low)  comparative  advantage  sectors  are  those  with  an  above  (below)  median  value  of  the

> 26   Our   estimation   results   are   essentially   the   same   when   we   use   tariffs   weighted   by   the   value   added   of the   industry   instead   of   un-­‐weighted   tariffs.

> 27   The   absolute   effect   on   exporters,   the _F-­‐statistic_ and   corresponding _p-­‐value_ on   the   joint   significance of   the   coefficients   are   reported   at   the   bottom   of   Table   3.1.

> 28   Industry _k_ ’s   Balassa   (1965)   comparative   advantage   in   year _t_ is   constructed   in   Muendler   (2007)   as follows:

14
Balassa  (1965)  comparative  advantage  measure  across  all  merchandise  trade  sectors  in 1986.<sup>2930</sup> Interestingly,  we  find  the  differential  impact  of  trade  liberalization  on  exporters to  be  prevalent  only  for  firms  operating  in  high  comparative  advantage  sectors.  Firms  in  low comparative  advantage  sectors  experience  a  decline  in  average  wages  regardless  of  their export  status.

During  the  liberalization  period,  Brazilian  firms  faced  declines  in  both  final  goods  and  in intermediate  input  tariffs.  While  a  decrease  in  final  goods  tariffs  could  decrease  the profitability  of  domestic  firms  by  increasing  the  foreign  competition  that  they  face,  a decrease  in  input  tariffs  is  likely  to  have  the  opposite  effect  by  decreasing  the  price  of inputs,  and  improving  the  variety  and  possibly  quality  of  inputs  that  the  firm  has  access  to. If  the  industries  that  experienced  a  decline  in  final  goods  tariffs  also  experienced  a  decline in  input  tariffs,  the  product  tariff  is  likely  to  overestimate  the  actual  decrease  in  protection that  the  industry  has  experienced.  To  address  this  issue,  we  repeat  the  previous  analysis with  the  effective  rate  of  protection  (ERP)  instead  of  (output)  tariffs  as  the  measure  of protection.  The  estimation  results  suggest  that  a  decline  in  ERP  has  no  significant  impact  on average  wages  at  non-­‐exporting  firms.  Improved  access  to  imported  intermediates  from abroad  could  explain  the  difference  between  these  results  and  those  reported  in  first  half  of Table  3.1.  Note  that  this  coefficient  now  reflects  a  combined  effect  of  the  positive  impact  of  a reduction  in  input  tariffs  through  prices  and  access  to  variety  and  quality,  as  well  as  any negative  impact  of  increased  import  competition  due  to  a  decline  in  output  tariffs.  The negative  and  significant  coefficient  on  the  interaction  term  suggests  that  the  wages  in exporting  firms  increase  in  response  to  a  decline  in  the  ERP.   A  ten  percentage  point decrease  in  the  ERP  increases  average  wages  by  1%  at  exporting  firms.  Similar  to  the results  for  output  tariffs,  in  the  high  comparative  advantage  sector,  we  find  that  a  decline  in ERP  is  associated  with  a  differential  increase  in  wages  in  exporting  firms  relative  to  their non-­‐exporting  counterparts.  The  increase  in  wages  associated  with  a  ten  percentage  point

> 29   We   choose   the   pre-­‐reform   year   of   1986   to   avoid   any   possible   endogeneity   between   the comparative   advantage   measure   based   on   exports   and   tariff   reforms   which   began   in   1988.   We   also experimented   with   generating   the   comparative   advantage   measure   across   only   manufacturing sectors   and   using   a   post-­‐reform   year   with   no   difference   in   the   results.   Brazil’s   Balassa   (1965) revealed   comparative   advantage   is   largely   time-­‐invariant.

> 30   The   following   sectors   are   defined   as   high   comparative   advantage   sectors:      Manufacture   of   non-­‐ metallic   mineral   products,   Manufacture   of   metallic   products,   Manufacture   of   transport   equipment, Manufacture   of   wood   products   and   furniture,   Manufacture   of   footwear,   and   the   Manufacture   of   food, beverages,   and   ethyl   alcohol.

15
decrease  in  ERP  is  2.9%  for  exporters  while  it  is  0.8%  (and  significant  only  at  10%  level)  for firms  serving  the  domestic  market.  The  impact  of  trade  liberalization  on  average  firm  wages is  not  statistically  significant  for  domestic  firms  or  exporters  in  low  comparative  advantage sectors.  Our  results  also  indicate  that  the  exporter  premium  is  slightly  higher  for  exporters in  the  high  comparative  advantage  sector.

A  potential  concern  for  the  results  reported  in  Table  3.1  is  the  selection  bias  introduced  by the  heterogeneity  of  firms  exiting  the  sample  following  trade  liberalization.  If  a  decline  in ERP  results  in  the  exit  of  low  productivity  firms,  the  negative  effect  of  liberalization  on domestic  firms  will  be  underestimated,  as  the  remaining  firms  in  the  sample  would  have high  productivity  and  pay  high  wages.  To  evaluate  the  relevance  of  this  possibility  in explaining  the  differential  impact  we  find  on  exporting  firms,  we  repeat  our  analysis  for  a balanced  panel  of  firms,  which  were  under  operation  during  the  entire  sample  period. Results  reported  in  the  first  column  of  Table  3.2  suggest  that  restricting  our  sample  to  those firms,  which  do  not  enter  or  exit,  does  not  alter  the  magnitude  or  the  significance  of  our main  coefficient  of  interest,  suggesting  that  our  results  are  not  driven  by  sample-­‐selection.<sup>31</sup> We  provide  further  robustness  checks  in  the  remaining  columns  of  Table  3.2.  As  we  address in  the  data  section,  our  main  firm-­‐level  regressions  draw  on  the  complete  employment history  of  a  1%  random  sample  of  the  population  of  Brazil’s  formal-­‐sector  labor  force.  To ensure  this  data  is  representative  of  Brazilian  firms  and  industries,  in  the  second  column  of Table  3.2,  we  repeat  the  analysis  drawing  on  the  complete  employment  history  of  a  5% random  sample  of  formal-­‐sector  males  living  in  metropolitan  areas.  In  the  third  column,  we restrict  the  data  to  Brazil’s  main  trade  reform  period  (1990-­‐1994)  when  average  protection levels  consistently  declined.  In  both  cases,  our  main  coefficient  of  interest  remains significant  with  little  change  in  its  magnitude.  The  results  reported  in  the  next  column suggest  that  our  findings  are  robust  to  replacing  the  economy-­‐wide  real  exchange  rate  with industry-­‐specific  real  exchange  rates  in  order  to  capture  differences  in  the  relative

> 31   Similarly,   if   the   least   productive   exporters   switch   out   of   exporting   following   liberalization,   the magnitude   of   the   differential   effect   of   a   decline   in   ERP   on   exporters   will   be   biased   upwards.   In unreported   regressions,   available   by   request,   we   include   log   total   factor   productivity   as   an   additional explanatory   variable   based   on   estimates   from   Muendler   (2004).   We   find   the   effect   of   log   TFP   on average   firm   wages   to   be   insignificant.   Importantly,   the   inclusion   of   log   TFP   does   not   alter   the   main coefficient   of   interest.

16
importance  of  trading  partners  across  industries.<sup>32</sup> We  also  test  whether  our  results  are  sensitive  to  the  exporting  thresholds  we  use  to  assign the  indicator  variable  denoting  a  firm’s  export  status.  In  the  main  firm-­‐level  specifications,  a firm  is  defined  as  an  exporter  if  it  exported  any  positive  dollar  amount  that  year.  Instead,  in columns  4  and  5,  we  only  consider  firms  with  an  export  value  more  than  the  5th  percentile (cutoff  5)  in  that  year,  and  more  than  the  10th  percentile  ( _cutoff  10_ )  as  exporting  firms, respectively.  Next,  we  replace  the  exporter  status  dummy  in  our  main  specification  with two  time-­‐invariant  measures  of  export  status. _Export90_ takes  the  value  1  if  the  firm  was  an exporter  as  of  the  beginning  of  our  sample,  and  zero  otherwise. _Export_Once_ takes  value  1  if the  firm  exported  for  at  least  one  year  during  the  sample  period.  Our  estimation  results continue  to  suggest  a  differential  positive  impact  of  liberalization  on  wages  at  exporting firms  relative  to  non-­‐exporters.  In  the  last  column,  we  report  results  from  a  specification  in which  we  include  the  logarithm  of  the  value  of  exports  for  firms  with  positive  exports.  Our results  indicate  that  conditional  on  exporting,  there  is  a  differential  increase  in  wages  at firms  with  higher  values  of  exports  following  liberalization.

Next,  we  test  whether  the  differential  impact  on  exporters  we  document  could  simply  be attributed  to  compositional  differences  between  exporters  and  non-­‐exporters  in  an environment  in  which  the  returns  to  observable  worker  characteristics  are  changing  as  a result  of  liberalization.  Specifically,  we  allow  for  changes  in  the  premium  paid  by  firms  with different  workforce  skill  compositions,  by  interacting  ERP  with  measures  of  labor  force composition  at  the  firm  level  in  equation  (1)  and  find  no  impact  on  our  main  coefficient  of interest.  This  finding  suggests  that  the  differential  effect  that  we  find  cannot  be  explained  by changes  in  the  relative  returns  to  observable  characteristics  following  liberalization.<sup>33</sup> Finally,  we  consider  the  possibility  that  an  omission  of  the  importer  status  of  the  firm  could 32  We  construct  industry-­‐specific  real  exchange  rates  using  time-­‐varying  trade  weights,  as  in Goldberg  (2004).  More  specifically,  we  calculate rates  for  trading  partner _c_ of  Brazil, _X tkc_ and _M tkc_ are  exports  and  imports  in  industry _k_ to  or  from country _c_ at  time _t._ Note  that _RERt_<sup>_c_</sup> is  also  included  in  this  specification. 33  These  results  are  not  reported  to  conserve  space  and  are  available  upon  request.

17
bias  our  results  if  improved  access  to  foreign  intermediate  inputs  increases  wages  at  the firm  level  and  if  the  firm’s  export  status  is  correlated  with  its  import  status.  Amiti  and  Davis (forthcoming)  find  this  effect  to  be  important  in  Indonesia:  following  a  decline  in  input tariffs,  average  wages  in  importing  firms  increase  relative  to  firms  that  do  not  import.  Our estimation  results  based  on  a  sub-­‐sample  of  firms  for  which  data  on  import  status  is available  suggest  no  such  differential  impact  on  importing  firms  in  Brazil.  Moreover,  the differential  impact  of  liberalization  on  average  wages  in  exporting  firms  is  robust  to  various specifications  controlling  for  the  import  status  of  the  firms.  Appendix  includes  a  more detailed  description  of  these  results.

# **3.2   Discussion   of   Firm-­‐Level   Analysis**

Our  firm-­‐level  analysis  confirms  findings  in  earlier  studies  regarding  the  differential  impact of  trade  reform  on  average  wages  at  firms  with  differing  degrees  of  trade  exposure, especially  in  high  comparative  advantage  sectors.  However,  this  analysis  of  average  firm-­‐ level  wages,  although  informative,  is  not  well  suited  to  examine  the  differential  impact  of liberalization  on  workers  in  heterogeneous  firms  for  a  number  of  inter-­‐related  reasons.  In general,  we  would  expect  that  in  addition  to  observable  worker  and  firm  characteristics,  the allocation  of  workers  to  firms  is  a  function  of  the  worker  characteristics  that  are unobservable  in  the  but  the  managers  can  observe  and  reward,  such  as  the  innate  (time-­‐ invariant)  ability  of  the  worker  and  any  additional  productivity  (ability)  that  obtains  in  the context  of  employment  in  the  specific  firm  (match-­‐specific  ability).   Recent  theoretical models  (including  Mortensen  (2003),  Postel-­‐Vinay  and  Robin  (2002),  Lentz  (2010),  Shimer (2005))  and  Gibbons  et  al.  (2005))  variously  describe  the  central  role  played  by  ex-­‐ante unobservable  worker  characteristics  in  determining  the  equilibrium  assignment  of  workers to  firms.  In  the  context  of  international  trade  and  labor  markets,  Helpman,  Itskhoki,  and Redding  (2010),  who  model  the  labor  allocation  process  with  heterogeneous  firms  as  being subject  to  search  and  matching  frictions,  find  that  the  more  productive,  exporting,  firms  will screen  workers  more  intensively  and  employ  workers  of  higher  match-­‐specific  ability,  i.e., the  equilibrium  assignment  of  workers  is  again  a  function  of  unobservable  match  specific worker  ability  and  is  thus  non-­‐random.

Importantly,  in  an  environment  in  which  firms  are  changing  the  composition  and  quality  of 18
their  labor  force  in  response  to  liberalization,<sup>34</sup> analysis  conducted  at  the  firm  level  faces  at least  two  problems.  If  exporting  firms  respond  to  liberalization  by  changing  the composition  of  their  workforce  systematically,  (for  example,  towards  workers  with  higher innate  ability  or  match-­‐specific  ability)  our  estimates  will  be  biased.  This  is  because  part  of the  differential  effect  we  find  for  exporters  at  the  firm-­‐level  could  be  due  to  compositional differences  between  firms  with  different  trade  orientation  and  not  because  otherwise identical  workers  are  being  paid  different  wages  in  different  firms.  Furthermore,  when  the job  mobility  of  workers  is  at  least  partly  determined  by  their  unobservable  characteristics (endogenous  mobility),  estimates  of  the  parameters  in  (1)  will  be  biased.  This  is  because non-­‐random  job  assignment  implies  a  correlation  between  the  error  term ε _jt_ (which subsumes  the  un-­‐observables  associated  with  workers  in  firm _j_ at  time _t_ )  and  the  firm’s characteristics  represented  by  the  right  hand  side  variables, Ψ _j_ and _Z jt_ ,  and  thus  a  failure of  the  maintained  assumption, _E_ (ε _jt_ | Ψ _j_ , _Z jt_ ) = 0 ,  underlying  the  estimation.

In  the  following  section,  we  describe  the  worker-­‐level  analysis  we  undertake  using  matched employer-­‐employee  data  to  account  for  the  issues  of  unobservable  worker  ability differences  and  endogenous  job  mobility  that  we  have  just  described.  We  also  describe  in much  greater  detail  the  issue  of  endogenous  job  mobility  which  persists,  in  principle,  with worker-­‐level  analysis,  but  can  be  dealt  with  using  firm-­‐worker  match  effects  in  the  wage specification.

# **4. Worker-­‐Level   Analysis**

We  begin  our  worker-­‐level  analysis  by  considering  the  basic  specification  of  Abowd, Kramarz,  and  Margolis  (1999)  in  which  a  worker’s  wages  can  be  decomposed  as  follows:

> 34   In   unreported   results   available   upon   request,   we   provide   some   evidence   of   differential   skill upgrading   at   exporting   firms   relative   to   non-­‐exporting   firms   with   trade   liberalization.   Specifically,   we re-­‐estimate   equation   (1)   with   the   share   of   workers   with   different   levels   of   education   as   the dependent   variable.   While   a   change   in   the   ERP   has   no   significant   impact   on   the   skill   composition   of workers   in   non-­‐exporting   firms,   in   exporting   firms   a   decline   in   the   ERP   is   associated   with   an   increase in   the   share   of   college-­‐educated   workers   and   a   decrease   in   the   share   of   workers   with   less   than   high school   education.

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(2) where _i_ indexes  the  individual, _j_ indexes  the  firm, _t_ indexes  time,  and ln _yijt_ denotes individual-­‐level  log  wages.  The  panel  of  linked  worker-­‐firm  data  allows  us  to  control  for  a rich  array  of  fixed  factors  that  may  influence  a  worker’s  wages,  in  addition  to  time-­‐varying, observable,  firm  characteristics  ( _! "#_ )  and  worker  characteristics  ( _!"#_ )  including  indicator variables  for  the  worker’s  occupation,  age,  and  education,  as  well  as  the  worker’s  tenure  at the  current  firm.  The  model  also  includes  individual  fixed  effects,  ,  which  allow  us  to  α _i_ control  for  any  are  time-­‐invariant  unobservable  worker  characteristics,  and  firm  fixed effects, ! _j_ ( _i_ , _t_ ) ,  for  firm _j_ at  which  worker _i_ is  employed  at  time _t_ ,  representing  firm heterogeneity.   Equation  (2)  could  then  be  augmented  to  include  sector  level  measures  of protection  interacted  with  the  firm’s  export  status  in  order  to  test  the  contribution  of unobservable  differences  across  workers  on  the  differential  response  to  trade  reform.

# **4.1   Endogenous   Worker   Mobility**

It  is  important  to  note  that  the  identifying  assumption  for  equation  (2)  is  that  the idiosyncratic  disturbance  term  in  each  period  is  independent  of  observable  worker  and  firm characteristics  as  well  as  firm  and  worker  fixed-­‐effects.

Often  referred  to  in  the  literature  as  the  assumption  of  “conditional  exogenous  mobility” (see,  for  instance,  Abowd,  Kramarz,  and  Margolis  (1999),  Woodcock  (2008)  and  Soerensen and  Vejlin  (2009)),  the  assumption  implies  that  employment  mobility  could  depend  on time-­‐varying  observable  worker  or  firm  characteristics  and  firm  and  worker  fixed  effects, but  not ! _it_ .  This  assumption  is  at  odds  with  many  well-­‐known  models  of  the  labor  market with  directed  search,  learning,  or  coordination  frictions.  For  example,  as  we  have  discussed before,  in  Helpman,  Itskhoki,  and  Redding  (2010),  workers  are _ex  ante_ identical  and  job 20
allocation  is  determined  on  the  basis  of  match  specific  ability  that  is  heterogenous _ex  post._ Furthermore,  high  productivity  firms  (exporters)  screen  more  intensively  resulting  in higher  quality  firm-­‐worker  matches.  In  this  case  the  estimates  in  our  firm-­‐level  analyses will  be  biased,  due  to  an  omitted  worker-­‐firm  match  effect.  Similarly,  if  workers  with  certain observable  characteristics  are  more  successful  at  generating  good  matches  than  others  (for example,  because  the  return  from  search  is  higher  or  due  to  learning)  and  hence  higher wages,  omitted  match  effects  could  also  bias  the  estimated  returns  to  observable characteristics  in  equation  (2).

We  test  the  validity  of  the  exogenous  mobility  assumption  using  the  “match  effects  test” introduced  by  Abowd,  McKinney,  and  Schmutte  (2010).  The  test  statistic  is  based  on estimated  match  effects  that  are  computed  from  the  average  (over  time)  residual  for  a person _i_ at  a  firm _j_ .  The  test  rests  on  the  logic  that  the  match  effect,  under  the  null  of exogenous  mobility,  should  not  predict  the  transitions  of  workers  between  firms. Specifically,  under  exogenous  mobility,  an  individual’s  average  residual, ! _ijt_ !1 (within quintiles  of  the  residual  distribution)  from  the  most  recently  completed  job  should  not predict  the  transition  across  firms  with  heterogeneous  values  of ! _j_ ( _i_ , _t_ ) (say  from  a particular  quintile  of  the ! _j_ ( _i_ , _t_ ) distribution  to  another).  The  test  is  implemented  as  follows. First,  equation  (2)  is  estimated  for  the  full  sample  of  workers.  Then,  for  workers  who switched  employers  between  time _t_ and _t-­‐1_ ,  the  average  residual  within  person  and  firm (! _ijt_ !1 )  is  calculated  across  all  years  throughout  the  duration  of  the  match  (i.e.,  until _t-­‐1_ ); ! _ijt_ !1 represents  the  match  effect  for  the  firm  and  worker  pair.  Under  the  null  hypothesis, the  transition  rates  across  quintiles  of  the  firm  effects  distribution,  from  the ! _j_ ( _i_ , _t_ "1) quintile to  the ! _j_ ( _i_ , _t_ ) quintile,  are  independent  of ! _ijt_ !1 .  Importantly,  if  the  null  is  rejected  for  our data,  this  would  suggest  that  the  estimation  results  from  equation  (2)  are  biased  and  that firm-­‐worker  match  effects  should  be  included  in  the  specification  to  account  for  the endogenous  mobility  of  workers.

In  constructing  the  test  statistic,  we  restrict  the  sample  to  those  workers  with  at  least  two years  of  data  and  to  firms  in  the  manufacturing  sector.  Since  estimates  of  firm  fixed  effects for  firms  with  only  few  movers  are  likely  to  be  imprecise,  Abowd,  Kramarz  and  Margolis 21
(1999)  suggests  grouping  small  firms  together  and  estimating  one ! _j_ ( _i_ , _t_ ) for  these  firms with  few  movers.  We  calculate  the  test  statistic  both  by  grouping  small  firms  with  less  than two  movers  into  one  firm  and  by  excluding  these  small  firms  from  the  sample.  In  both  cases, we  conduct  the  test  on  the  largest  mobility  group,  since  the  firm  fixed  effects  estimated  for unconnected  groups  are  not  directly  comparable  with  each  other.<sup>35</sup> Consistent  with  the Abowd,  McKinney,  and  Schmutte  (2010)  finding  for  the  U.S.,  the  test  statistic  strongly rejects  the  null  hypothesis  of  exogenous  mobility  for  the  sample  of  job  switchers  in  our  data. The  match  effects  test  statistic  has  a  value !<sup>2</sup> = 25,000 and  is  distributed  chi-­‐sq  with  496 of  degrees  of  freedom  (and  thus  a  p-­‐value  of  0.000).<sup>36</sup> This  finding  confirms  the  relevance  of models  of  labor  allocation  involving  search  dynamics  or  other  frictions  (such  as  Helpman, Itskhoki  and  Redding  (2010)  and  Davidson,  Matusz  and  Schevchenko  (2008))  and highlights  the  importance  of  allowing  for  the  possibility  of  firm-­‐worker  match heterogeneity  in  wage  determination  to  account  for  the  endogenous  mobility  of  workers.

To  further  emphasize  this  point,  we  plot,  in  Figure  4.1,  the  conditional  distribution  of quintiles  of  the  firm  fixed  effects  for  the  previous  job, ! _j_ ( _i_ , _t_ "1) ,  given  the  quintiles  of  the individual’s  average  residual  from  the  most  recently  completed  job,  for  the  sample  of  job changers.  Under  the  assumption  of  exogenous  mobility,  the  distribution  of ! _j_ ( _i_ , _t_ "1) should not  show  any  variation  across  quintiles  of  the  average  residual.  That  is  to  say,  the estimation  strategy  requires  that  the  quality  of  the  firm-­‐worker  match  in  the  previous  job should  not  contain  any  information  about  the  estimated  firm-­‐fixed  effects  for  that  job. Figure  4.1  clearly  demonstrates  that  this  is  not  the  case  in  our  data.  For  example,  while  the job  changers  in  the  first  quintile  of  the  match  effect  (residual)  distribution  mostly  originate from  the  second  quintile  of  the ! _j_ ( _i_ , _t_ "1) distribution,  most  switchers  in  the  second  quintile of  the  match  effect  originate  from  the  first  quintile  of  the ! _j_ ( _i_ , _t_ "1) distribution.

35  A  connected  group  includes  all  the  workers  who  have  ever  worked  for  any  of  the  firms  in  that group  as  well  as  all  the  firms  at  which  any  of  these  workers  were  employed  at  during  the  sample period.  Since  within  each  group  the  mean-­‐deviated  firm  fixed  effects  sum  to  zero,  the  estimates  of ! _!_ ! _"_ " _#_ # are  not  directly  comparable  across  unconnected  groups.  As  a  possible  solution,  one  can normalize  the  fixed  effects  so  they  have  the  same  mean  across  groups  (Abowd,  Creecy,  and  Kramarz (2002)).  Our  results  are  robust  to  using  the  full  sample  of  workers  and  correcting  the  fixed  effects  for the  unconnected  groups  in  this  fashion.

> 36   The   degrees   of   freedom   are   calculated   as: !" _!_ !! _"_ #$" _!_ !" _#_ ! _"_ % _$_ !&##$" _!_ !" _#_ ! _"_ % _$_ ##!&#$" _!_ !# _"$_ !&#!&# =(5*5*5-­‐ 1)*(5-­‐1)   where   #Q   denotes   the   number   of   quintiles.

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In  Figure  4.2  we  plot  the  transition  rates  from  a  job  in  a  given ! _j_ ( _i_ , _t_ "1) quintile  to  a  job  in ! _j_ ( _i_ , _t_ ) quintile,  again  for  the  sample  of  job  changers.  Here,  we  find  strong  evidence  that  job transitions  are  not  random;  most  workers  move  between  jobs  within  the  same  employer-­‐ effect  quintile,  which  is  evident  from  the  rightward  movement  of  the  peak  of  the ! _j_ ( _i_ , _t_ ) distribution  with  higher  quintiles  of  the  original  job.  Moreover,  Figure  4.3  clearly  illustrates that  these  transition  probabilities  vary  considerably  by  different  quintiles  of  the  match effect  distribution.  Figures  4.3a,  4.3b,  and  4.3c  plot  the  transition  probabilities  for  the  first, third  and  fifth  quintiles  respectively,  of  the  residual  distribution.  For  example,  while  job switchers  are  much  more  likely  to  improve  their  employer  effect  in  the  middle  and  (to  a lesser  extent)  in  the  higher  end  of  the  residual  distribution,  we  find  this  not  to  be  the  case  in the  first  quintile  of  the  match  effect  distribution.   This  illustrates  the  failure  of  the exogenous  mobility  assumption  as  the  estimated  match  effects  clearly  contain  information on  job-­‐to-­‐job  transitions  that  take  place  in  the  data.

Our  findings  on  endogenous  worker  mobility  have  interesting  implications  for  the  analysis of  trade  and  labor  markets,  especially  since  the  workings  of  the  labor  market  have  been modeled  in  a  number  of  different  ways  in  the  recent  literature  on  international  trade  with heterogeneous  firms.  In  the  well-­‐known  paper  of  Melitz  (2003),  heterogeneous monopolistically  competitive  firms  pay  their  (homogeneous)  workers  an  identical  wage, with  the  assignment  of  particular  workers  to  firms  being  (effectively)  random.  Similarly,  in Egger  and  Kreickemeier  (2009)  and  Amiti  and  Davis  (forthcoming),  the  assignment  of workers  to  firms  is  random,  even  if  rent-­‐sharing  behavior  of  firms  implies  that  identical workers  may  be  paid  different  wages,  based  on  the  profits  of  the  firms  in  which  they  are employed.  The  decisive  rejection  of  the  assumption  of  exogenous  worker  mobility  in  our data  supports  a  picture  of  the  labor  market  that  is  closer  in  line  with  the  framework  of Helpman,  Itskhoki,  and  Redding  (2010),  where,  as  we  have  previously  discussed,  the  labor allocation  process  is  subject  to  search  and  matching  frictions  and  the  equilibrium assignment  of  workers  to  firms  is  clearly  non-­‐random.

# **4.2   Estimation   with   Match   Effects**

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If  match-­‐specific  effects  are  important  in  wage  determination,  the  Abowd,  Kramarz,  and Margolis  (1999)  specification  in  equation  (2)  including  only  worker  and  firm  fixed  effects will  result  in  both  biased  estimates  of  these  fixed  effects,  as  well  as  biased  estimates  of  the returns  to  observable  worker  and  firm  characteristics  (Woodcock  (2008)).  For  example,  if more  experienced  workers  are  likely  to  draw  better  matches,  omission  of  the  match  effect will  result  in  an  over  estimation  of  the  returns  to  experience.  In  the  context  of  the international  trade  literature,  if  the  labor  market  functions  in  the  manner  described  by Helpman,  Itskhoki  and  Redding  (2010),  the  screening  thresholds  for  match-­‐specific  ability will  be  different  in  the  resulting  equilibrium  after  liberalization,  shifting  the  distribution  of worker  abilities  (i.e.,  the  quality  of  matches)  within  each  firm.  Given  that  this  shift  varies systematically  with  the  export  status  of  the  firm,  not  controlling  for  match  quality  will  result in  biased  estimates  of  the  differential  effect  of  trade  liberalization  on  wages  in  exporting firms.

To  account  for  the  fact  that  a  worker’s  employment  history  may  not  be  independent  of  the idiosyncratic  part  of  the  residual  in  equation  (2),  we  now  consider  a  more  elaborate specification  of  wages,  in  which  worker-­‐firm  match  fixed  effects  (or  spell  fixed  effects),  Mij, which  denote  a  given  worker _i_ ’s  employment  at  a  given  firm _j_ ,  are  included  on  the  right hand  side: Note  that  since  for  each  spell  of  a  worker  within  a  firm  neither  the  worker  nor  the  firm  fixed effect  varies,  inclusion  of  match  fixed  obviates  the  need  for  separate  inclusion  of  worker  and firm  fixed  effects.

# **4.3   Estimation   Results**

Table  4.1  includes  estimation  results  from  equation  (3)  with  spell  fixed  effects  with  both tariffs  and  ERP  as  the  measure  of  protection.  Column  1  presents  results  with  tariffs  taken  as the  measure  of  trade  protection  in  the  sector.  Our  results  suggest  that  the  inclusion  of  spell fixed  effects  results  in  insignificant  estimates  of  both  !!" and !!" .  The  insignificant  effect  of  a decline  in  protection  on  both  exporters  and  firms  serving  the  domestic  market  also  holds 24
separately  for  the  high  and  low  comparative  advantage  sectors,  although  the  interaction term  is  more  than  three  times  higher  in  magnitude  for  the  high  comparative  advantage sector.  These  results  continue  to  hold  when  we  use  effective  rate  of  protection  in  the  sector as  our  measure  of  trade  protection.

In  Table  4.2  we  present  a  wide  array  of  robustness  checks  we  have  conducted.  The  first  two columns  report  estimation  results  from  specification  (3)  for  various  alternative  samples.  In column  1,  we  use  a  5%  random  sample  of  males  in  metropolitan  areas  instead  of  a  1% random  sample  of  the  full  population.  In  column  2  we  restrict  our  analysis  to  only  the liberalization  period  (1990-­‐1994)  during  which  average  ERP  was  decreasing.  Our  results are  robust  to  these  alternative  samples  and  to  using  an  industry-­‐specific  real  exchange  rate measure  as  in  column  3,  instead  of  an  economy-­‐wide  measure.  Next,  we  test  whether  our result  of  insignificant  interaction  coefficient  is  sensitive  to  the  exporting  thresholds  we  use to  assign  the  indicator  variable  denoting  a  firm’s  export  status.  In  the  baseline  specifications a  firm  is  defined  as  an  exporter  if  it  exported  a  positive  dollar  amount  that  year.  Instead,  in columns  5  and  6,  we  only  consider  firms  with  an  export  value  more  than  the  5th  percentile (cutoff  5),  and  more  than  the  10th  percentile  (cutoff  10)  as  exporting  firms,  respectively. While  the  magnitude  of  the  interaction  term  increases  slightly  as  we  increase  the  cut  off threshold,  the  coefficient  remains  statistically  insignificantly  different  than  zero.  In  the  next two  columns  we  consider  two  time-­‐invariant  measures  of  export  status.  In  column  7,  a  firm is  defined  as  an  exporter  if  it  exported  a  positive  dollar  value  at  the  beginning  of  our  sample at  1990  (Export90).  The  export  status  variable  in  column  8  takes  the  value  1  if  the  firm reported  positive  exports  during  every  year  between  90  and  98.  Finally,  when  we  use  the magnitude  of  exports  for  the  sub-­‐sample  of  exporting  firms  to  represent  the  relevance  of exports  to  the  firm  instead,  we  obtain  yet  again  estimates  of !!" and !!" that  are insignificantly  different  from  zero.<sup>37</sup> Our  findings  using  worker  level  data  and  taking  endogenous  worker  mobility  into  account, suggests  an  insignificant  differential  effect  of  trade  policy  on  the  wages  of  workers

> 37   Other   robustness   checks   we   have   conducted   but   we   do   not   reported   to   conserve   space   include defining   a   firm   as   exporter   if   the   firm   exported   only   to   non-­‐Mercosur   countries   (and   omitting exporters   to   Mercosur   countries   from   the   sample)   and   using   a   more   detailed   industry   classification available   only   for   the   1994-­‐1998   period,   which   allows   us   to   identify   changes   in   industry   level   ERP   at a   more   disaggregated   level.   We   find   our   main   conclusions   to   be   robust.

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employed  in  exporting  firms.  This  finding  stands  in  sharp  contrast  to  results  obtained  using average  firm  level  wages  instead.  One  reason  for  this  difference  is  simply  that  the  use  of detailed  worker  level  data  allows  us  to  take  into  full  account  any  changes  in  the  composition of  the  workforce  (by  controlling  for  both  observable  and  time-­‐invariant  unobservable worker  characteristics)  following  trade  policy.  Furthermore,  by  taking  worker-­‐firm  match effects  into  account,  we  are  able  to  control  for  changes  in  the  composition  of  firms’  match quality  following  trade  policy  changes.  If,  following  trade  liberalization,  exporting  firms improve  their  average  match  quality  by  hiring  workers  with  better  match  quality,  the estimates  of  the  coefficient !!" without  controlling  for  match  effects  would  mistakenly  be estimated  as  significant  even  in  the  absence  of  any  true  effect.

Table  4.3,  which  compares  the  changes  in  the  distribution  of  estimated  match  effects  in exporting  firms  relative  to  non-­‐exporting  firms,  confirms  this  point.<sup>38</sup> Comparing  the  period 1998  to  1990,  we  see  an  increase  in  both  the  mean  and  the  median  match  effect  in  exporting firms,  while  the  mean  and  median  match  effect  both  fall  in  non-­‐exporting  firms.  As  we  have noted  earlier,  match  fixed  effects  in  equation  (3)  absorb  both  worker  and  firm  fixed  effects in  addition  to  time-­‐invariant  match  quality  of  a  given  employment  spell.  Consequently,  our finding  of  an  improvement  in  average  match  effect  in  exporting  firms  between  1990  and 1998  summarizes  the  combined  effect  of  a  change  in  workforce  composition  in  these  firms in  terms  of  improvement  in  worker  quality  in  time  invariant  worker  specific  characteristics, such  as  innate  ability  (captured  by  worker  fixed  effects)  and  improvement  in  the  quality  of the  worker-­‐firm  matches  (captured  by  match-­‐fixed  effects).<sup>39</sup> The  improvement  in  the distribution  of  match-­‐specific  ability  in  exporting  firms  is  consistent  with  the  Helpman, Itskhoki,  Redding  (2010)  model  which  predicts  that  with  trade  liberalization,   exporting firms  will  screen  more  intensively  and  set  a  higher  ability  threshold  for  employment.  It  is also  roughly  in  line  with  the  prediction  of  Davidson,  Matusz,  and  Shevchenko  (2008)  that greater  openness  leads  to  better  labor  market  sorting  of  higher  ability  workers  into  higher technology  firms.  This  finding  serves  to  explain  the  difference  between  the  results  at  the firm  level  and  those  at  the  worker  level:  If  average  quality  of  the  workforce  (in  terms  of match  specific  ability  or  innate  (time-­‐invariant)  ability)  improves  systematically  in

> 38   Table   4.3   is   for   a   group   of   firms,   which   were   included   in   our   sample   in   both   1990   and   1998   and   did not   switch   export   status   during   this   period. 39   Note   that   firm-­‐fixed   effect   for   a   given   firm   is   constant   across   time   and   cannot   account   for   the improvement   in   match   quality   between   1990   and   1998.

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exporting  firms  following  trade  liberalization,  not  controlling  for  match  effects  (as  is  the case  in  firm-­‐level  analysis),  will  incorrectly  suggest  that  trade  liberalization  leads  to  a differential  wage  improvement  for  workers  at  exporting  firms  even  when  this  is  not  the case.

A  comparison  of  estimates  obtained  from  specifications  with  and  without  spell  fixed  effects suggests  that  match  effects  matter  both  qualitatively  and  quantitatively.  Table  4.4  compares estimates  obtained  from  alternate  specifications  with  only  firm  fixed  effects,  with  both worker  and  firm  fixed  effects  included,  and  finally  with  match  fixed  effects  included.  Note that  this  comparison  can  only  be  made  for  the  sample  of  workers  who  switch  jobs  during this  period,  as  worker  effects  cannot  be  separately  identified  from  match  effects  for  those workers  who  do  not.  As  expected,  the  inclusion  of  worker  fixed  effects  lowers  (absolute value  of)  the  magnitude  of !!" from  0.242  to  0.151  for  tariffs,  and  from  0.086  to  0.050  for ERP  as  the  measure  of  protection.  The  inclusion  of  match  effects  lowers  the  coefficient further  from  0.151  to  0.104  and  0.050  to  0.018,  when  the  measure  of  protection  is  tariffs and  ERP,  respectively.

Finally,  Table  4.5  reports  estimates  of !!" and !!" when  workers  with  different  levels  of human  capital  (grouped  into  two  educational  categories  –  high  education  and  low education)  are  examined  separately.  Interestingly,  there  are  heterogeneous  effects  across the  two  educational  groups.  We  find  that  the  relative  wages  of  workers  with  higher  levels  of education  employed  in  exporting  firms  improve  with  liberalization  compared  to  similar workers  employed  in  firms  serving  the  domestic  market.  One  explanation  for  this  finding  is that  the  rent  sharing  mechanism  we  have  discussed  earlier,  whereby  workers  share  a fraction  of  the  profits  made  by  the  firms,  may  be  relevant  in  the  case  of  high  education workers  even  it  remains  insignificant  in  workers  with  lower  levels  of  education. Alternatively,  the  improvement  in  the  quality  of  the  labor  force  in  exporting  firms,  could result  in  positive  productivity  spillovers  on  more  educated  workers  and  hence  higher wages.

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# **5.   Conclusions**

In  this  paper,  we  use  a  linked  employer–employee  database  from  Brazil  to  examine  the impact  of  trade  reform  on  the  wages  of  workers  employed  at  heterogeneous  firms.   Our analysis  of  the  data  at  the  firm-­‐level  confirms  earlier  findings  of  a  differential  positive  effect of  trade  liberalization  on  the  average  wages  at  exporting  firms  relative  to  non-­‐exporting firms.  However,  this  analysis  of  average  firm-­‐level  wages  is  incomplete  along  several dimensions.  First,  it  cannot  fully  account  for  the  impact  of  a  change  in  trade  barriers  on workforce  composition  especially  in  terms  of  unobservable  (time-­‐invariant)  characteristics of  workers  (innate  ability)  and  any  additional  productivity  that  obtains  in  the  context  of employment  in  the  specific  firm  (match  specific  ability).  Furthermore,  the  firm-­‐level analysis  is  undertaken  under  the  assumption  that  the  assignment  of  workers  to  firms  is random.  This  ignores  the  sorting  of  worker  into  firms  and  leads  to  a  bias  in  estimates  of  the differential  impact  of  trade  on  workers  at  exporting  firms  relative  to  non-­‐exporting  firms. Using  detailed  information  on  worker  and  firm  characteristics  to  control  for  compositional effects  and  using  firm-­‐worker  match  specific  effects  to  account  for  the  endogenous  mobility of  workers,  we  find  the  differential  effect  of  trade  openness  on  wages  in  exporting  firms relative  to  domestic  firms  to  be  insignificant.  Consistent  with  the  models  of  Helpman, Itskhoki,  and  Redding  (2010)  and  Davidson,  Matusz  and  Schevchenko  (2008),  we  also  find that  the  workforce  composition  improves  systematically  in  exporting  firms  in  terms  of innate  (time  invariant)  worker  ability  and  in  terms  the  quality  of  the  worker-­‐firm  matches.

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