FUNDA C AO GET ULIO VARGAS ESCOLA DE P OS-GRADUA C AO EM ECONOMIA - Ignacio Crespo Rey Trade Liberalization and Regional Impacts on Residual ...
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FUNDAÇÃO GETÚLIO VARGAS
ESCOLA DE PÓS-GRADUAÇÃO EM
ECONOMIA
Ignacio Crespo Rey
Trade Liberalization and Regional Impacts on
Residual Wage Inequality: Evidence from Brazil
Rio de Janeiro
2013Ignacio Crespo Rey
Trade Liberalization and Regional Impacts on
Residual Wage Inequality: Evidence from Brazil
Dissertação submetida a Escola de Pós-
Graduação em Economia como requesito par-
cial para a obtenção do grau de Mestre em
Economia.
Área de Concentração: Comércio Internacional
Orientador: Germán Pablo Pupato
Rio de Janeiro
2013Rey, Ignacio Crespo
Trade liberalization and regional impacts on residual wage inequality : evidence
from Brazil / Ignacio Crespo Rey. – 2013.
60 f.
Dissertação (mestrado) - Fundação Getulio Vargas, Escola de Pós-Graduação
em Economia.
Orientador: German Pablo Pupato.
Inclui bibliografia.
1. Comércio internacional. 2. Política comercial. 3. Salários. I. Pupato, German
Pablo. II. Fundação Getulio Vargas. Escola de Pós- Graduação em Economia. III.
Título.
CDD – 382.3Resumo O objetivo deste trabalho é entender mais sobre o papel da liberalização sobre a desigualdade salarial, mais precisamente, sobre a desigualdade residual dos salários. Usando a abertura comercial brasileira, a extensa redução tarifária que ocorreu en- tre 1987 e 1995, é investigado empiricamente se os diferentes nı́veis de exposição ao comércio entre os estados contribuı́ram para os diferentes movimentos da desigual- dade. Os resultados indicam que estados mais expostos à liberalização comercial experimentaram um aumento relativo da desigualdade residual dos salários ou, de forma equivalente, uma menor redução. Estes resultados enriquecem a discussão dos efeitos da abertura comercial sobre a desigualdade. PALAVRAS-CHAVE: liberalização comercial, desigualdade residual dos salários, impactos regionais
Abstract The aim of this paper is to understand more about the role of trade liberalization on wage inequality, more precisely, on the residual wage inequality. Using the Brazil- ian trade openness, the large tariff cuts that occurred between 1987 and 1995, it is empirically investigated whether different levels of exposure to trade across states contributed to the different inequality movements. Results indicate that states more exposed to trade liberalization experienced a relative increase in residual wage in- equality or, equivalently, a smaller decrease. These findings enrich the discussion on the effects of trade openness on inequality. KEYWORDS: trade liberalization, residual wage inequality, regional impacts
Contents 1 Introduction 6 2 Brazilian Trade Liberalization 10 2.1 Nationwide Impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2 Regional Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3 Data 16 4 Methodology 18 4.1 Residual Wage Inequality . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.2 Regional Tariffs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.3 Local Labor Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.4 Econometric Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 5 Results 30 6 Conclusion 35 References 36 A Appendix 40 A.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 A.2 Composition effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 A.3 Exogeneity of the trade policy . . . . . . . . . . . . . . . . . . . . . . . . . 48 A.4 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
1 Introduction
During the 1980s and early 1990s, trade liberalization took place in several devel-
oping countries, including Mexico, Colombia, Argentina, Brazil, Chile, India and Hong
Kong. However, at least three reasons make the Brazilian case an event worth study-
ing. First, the Brazilian economy moved from an extremely protected to opened one in
a short period of time1 . Second, Brazilian wage inequality is one of the highest in the
world2 . Third, and more surprisingly, there is evidence suggesting that earnings inequal-
ity decreased in Brazil during the liberalization period, although evidences from most
developing countries show the contrary3 .
On the one hand, it is astonishing to see so much evidence in favor of increasing in-
equality during liberalization periods within most developing economies4 , since standard
trade models, as the Hecksher-Ohlin, suggest the opposite. Thus, other wage distribu-
tional channels were playing some role, variables not taken into account were indeed very
important or even strong assumptions were not reasonable in this model. On the other
hand, due to most empirical evidences, it is also surprising to see that effects in Brazil
were different and, to some extent, in line with theoretical predictions. Gonzaga et al.
(2006) document that from 1988 to 1995 average wages of more educated workers in Brazil
declined 15.5% in relation to less educated workers. A natural question arises: what hap-
pened to wage inequality between workers with the same observable characteristics?
Many alternative stories were told and models built in order to understand why so
many countries did not experience the expected effect between trade liberalization and
inequality. More recently, theories based on the interaction of heterogeneous firms and
workers are in vogue. A clear example is Helpman et al. (2010). This latter paper is
especially important since it reconciles different empirical evidences and concluded that a
given change in trade openness can either raise or reduce wage inequality, depending on
the initial level of openness. In addition, it calls the attention to the fact that both within
and between-group inequality can experience these effects as a result of the opening to
trade. Although there are many links between trade and inequality, the one suggested
by Helpman et al. (2010) appears to be convincing not only from the theoretical but also
from the empirical perspective. The latter is better discussed in Helpman et al. (2012)
and further details are presented in the Appendix.
1
Kume et al. (2003) and Gonzaga et al. (2006).
2
Gasparini (2003) and Li et al. (1998).
3
Sources of information on specific countries are: Argentina: Gasparini (2003); Brazil: Gonzaga et al.
(2006); Chile: Beyer et al. (1999), Robbins (1996), Ferreira and Litchfield (1999); Mexico: Hanson and
Harrison (1999), Robertson (2004), Hanson (2003); Colombia: Attanasio et al. (2003); India: Kijima
(2006), Topalova (2010), Deaton and Dreze (2002); Hong Kong: Hsieh and Woo (2005)
4
Usually labeled as unskilled labor abundant, when one considers as production factors of the economy
only skilled and unskilled labor.
6In order to clarify the discussion it is interesting to understand the mechanism
through which trade openness impacts inequality. Helpman et al. (2010) develops a the-
oretical framework for examining the channels through which wage inequality among ex-
ante homogeneous workers is affected by trade openness. They introduce standard search
and matching frictions into a Melitz (2003) model together with ex-post match-specific
heterogeneity in worker’s ability. Since worker’s ability and firm productivity are com-
plementary, the higher the productivity of the firm, the higher the incentives to screen
workers and improve the composition of the workforce5 . At the end, more productive
firms screen more intensively and have workforces of higher average ability compared to
less productive firms. Wage inequality between ex-ante homogeneous workers that are
employed by different firms derives from the fact that higher-ability workforces are more
costly to replace due to the screening costs. Employees in a high productivity firm have a
higher bargaining power than employees in a low productivity firm, and, thus, firms with
a higher productivity level will pay higher wages to their employees.
As it is the case in Melitz (2003), when the economy opens to trade, only the
more productive firms export and this increases their revenue relatively to less productive
firms, further increasing incentives to screen workers more intensively and, consequently,
increasing inequality. The other way around is also true: less productive firms have
less incentives to screen workers and their workforce, easier to replace, will be paid less,
increasing even more the wage gap. One of the most interesting results in Helpman et al.
(2010) is that the relationship between wage inequality and trade openness is at first
increasing and later decreasing, depending on the initial level of openness.
Regarding this paper, I contribute with the literature studying further the impacts
of the Brazilian trade liberalization on homogeneous workers, since they are still underex-
plored. Before going into details, it should be clear which measures of trade and inequality
are being used, since there are several ways one can measure these variables. In contrast to
other empirical studies, this paper focuses on tariffs (nominal and effective) as a measure
of trade policy, rather than outcome variables. This is clearly an advantage as discussed
in Goldberg and Pavcnik (2007). Measuring trade liberalization indirectly, through data
on trade volumes or any kind of outcome variable, might reflect not only changes in trade
policy, but also some endogenous variables such as transport costs, technology, demand
and factor prices of each country. Furthermore, since in the Brazilian case nontariff bar-
riers were not playing substantial role during the main period considered, tariffs seem to
be a reliable measure of exposure to trade.
As this paper was inspired to a large extent by Helpman et al. (2010), I use a specific
measure of inequality. Helpman et al. (2012) report that the residual wage inequality is at
5
Have in mind that, ex-ante, workers are homogeneous, however, after they are matched with firms
their productivity level will possibly be different.
7least as important as worker observables in explaining the overall level and growth of the
wage inequality in Brazil, from 1986 to 1995. This is also consistent with findings using
U.S. data, including Autor et al. (2008) and Lemieux (2006). In other words, the residual
wage inequality is measuring the wage variance that is not explained by the observable
characteristics. This is a poorly explored measure of inequality in the literature and makes
this study different from many others that concentrate on the effects of trade on the return
to particular worker characteristics or industry wage premiums.
In addition, it should be clear that this paper does not intend to investigate the
overall effect of trade liberalization on inequality. Answering this ambitious question re-
quires a general equilibrium model of trade. For instance, Porto (2006) tried to examine
the implications of the Argentinian trade reform on the distribution of household wel-
fare. Although there is a clear advantage of this approach, since it yields an answer of
the overall impact within a country, predictions of the model depend on the estimates of
unknown parameters including the wage-price elasticities and the degree of pass-through
from trade policy changes to product prices. Since these parameters are difficult to esti-
mate consistently and there are other drawbacks in adopting this general approach6 , this
paper adopts the differential exposure approach.
I investigate whether Brazilian states that were more exposed to trade liberaliza-
tion experienced smaller or bigger changes in inequality in comparison to less exposed
states. Note that different impacts on inequality are expected and regional characteristics
seem to be important. Few papers, such as Topalova (2010) and McCaig (2011), took
this differential exposure approach. Furthermore, they typically focus on poverty rather
than inequality. Regarding inequality, Verhoogen (2008) is an interesting example which
exploits the differential exposure approach and highlights that the proposed link between
trade and inequality cannot explain the overall trend in Mexican wage inequality. He
continues by saying that many other factors not taken into account may have contributed
to changes in aggregate inequality. This is not the focus of the study.
In a slightly different context, Topalova (2010) exploits the differential exposure of
Indian districts to trade reform to identify the effects of trade on poverty. Results indicate
that districts that where more exposed to trade experienced a smaller decrease in poverty,
although poverty has sharply declined over this period in India. As discussed earlier, this
approach can identify the role of trade in explaining district-specific deviations from the
aggregate trend, but it cannot identify the role of trade in explaining the trend itself. For
all studies that follow this line of work, a crucial premise is that labor is not perfectly
mobile across regions. Otherwise, factors would reallocate in order to equalize the returns
of all skills and the heterogeneous shocks would be immediately dissipated.
6
For further details see Goldberg and Pavcnik (2007).
8In sum, this paper studies the relation between trade policy and residual wage in-
equality. More than that, it investigates whether differences in the exposure to trade
across states were important to explain the differences in inequality movements. As a
secondary objective, this paper looks for local labor market effects of the trade liberal-
ization. These latter tests intend to support the importance and existence of regional
markets within Brazil.
Results suggest that states that experienced the largest drops in tariffs, from 1987 to
1995, had the smallest declines in residual wage inequality. This result is even more inter-
esting considering the findings in Gonzaga et al. (2006) and it highlights the importance
of considering the residual wage inequality vis-a-vis other inequality measures. Moreover,
limitations on mobility of workers both geographical and across industries appear to be
relevant, yet the first one is even more important. Finally, I conclude that considering the
impacts of a macroeconomic shock with different regions, even within the same country,
is not only interesting but also relevant for policy analysis.
This paper is organized as follows. Section 2 presents and contextualizes the Brazil-
ian trade liberalization. Section 3 details the data used. Section 4 presents the econometric
methodology while Section 5 analyzes the results obtained. Finally, Section 6 concludes.
Further details and figures are presented in the Appendix.
92 Brazilian Trade Liberalization
2.1 Nationwide Impact
Up to the end of the 1980s Brazil’s growth strategy was based on the import sub-
stitution industrialization (ISI). The government only allowed imports of goods that were
not produced domestically, and some other important goods to supply the excessive de-
mand that could not be matched by the Brazilian industry. The government managed
to restrict trade by imposing many non-tariff barriers (NTB) and some tariffs as well.
According to Kume (1990), the tariff structure dates back from 1957, and most tariffs
were redundant.
Kume et al. (2003) point out that although the ISI strategy enabled the existence
of diversified industries, many of them were not efficient and had survived only due to
the exaggerated government protectionism. Therefore, that strategy was distorting the
competitiveness of Brazilian firms and it became evident that otherwise, many of them
would not be able to survive to the trade openness.
Indeed, with the evolution of the international market, Brazil could not remain stuck
with its closed economy and started to react. The first steps towards a more free economy
were taken still during José Sarney’s term as President. Kume et al. (2003) recalls that
in 1988-89 the government started to reduce some redundant tariffs, not causing great
effect on the quantity imported. It was only in 1990, when President Fernando Collor de
Mello took office that structural changes happened and the Brazilian trade policy started
to take a different direction. In March of that year the government extinguished the
most important NTB. Nevertheless, that was only the first stage of what was to come:
in 1991 the government announced and started to implement a gradual reduction of the
tariffs that would go through until the end of 1993. Kume et al. (2003) explains that at
that point there were no more artificial barriers to trade. The timetable had been fully
implemented and Brazilian imports were only controlled by tariffs, whose levels were then
in line with other developing economies.
Kume et al. (2003) add that after the institution of the Real Plan in 1994, Brazilian
imports suffered further liberalization. This time with the inner motivation to control the
price of imported products and avoid inflation, and an external requirement to accom-
plish with the common external tariff (CET) stipulated in the Southern Common Market
(Mercosur). Goldberg and Pavcnik (2007) highlight that changes in trade policy implied
by this regional agreement were substantially smaller than the declines in trade barriers
observed during the unilateral trade reform, yet added more strength to the liberalization.
At the end of these years, the Brazilian economy had reached a different stage, with much
more opened market and liberalized trade.
10As a matter of fact, in 1987 the average industrial nominal tariff was 58.2% with
a standard deviation of 20.6%, while in 1989 these figures fell to 32.1% and 15.5%, re-
spectively. In 1994 they reached as low as 11.2% and 5.8%, representing an enormous
change in the restriction of imports. Note that not only the average tariff is important
to analyze the liberalization, but also the standard deviation. It indicates how distortive
the trade policies are, showing another facet of the protectionism in Brazil. In order to
better understand this process, Figure 1 shows the percentual variation in effective tariffs
across industries from 1987 to 1995.7 Further statistics on the evolution of these tariffs
from 1987 to 1995 can be seen through Table 4 and Table 5 in the Appendix.
Figure 1: Effective tariff changes across industries, 1987 - 1995
In order to have a clearer picture of the liberalization, Figure 2 shows that industries
whose tariff levels were higher in 1987 have had the larger tariff cuts from 1987 to 1995.
The correlation in both graphs is greater than 0.9 and as it is discussed latter, this suggests
that the Brazilian trade liberalization was not subject to political pressures. Otherwise,
the initial tariff levels would not be determinant to the subsequent tariff drops. Gonzaga
et al. (2006) highlight another interesting point: Brazilian tariff protection pattern in
1988 did not have a significative correlation with skill intensity. The authors argue that
7
Figure 8 in the Appendix presents the nominal tariff changes.
11trade barriers were raised to face macroeconomic problems and not to protect sectors in
which Brazil had no comparative advantage. Thus, it suggests that political pressures
were not relevant to explain the initial pattern of protection across industries. The above
arguments intend to support that either tariff changes or the initial pattern of protection
were not driven by other variables. Later I discuss further the exogeneity issue of the
trade variables.
Figure 2: Initial levels and changes in industrial tariffs
2.2 Regional Impacts
Despite the nationwide tariff cuts, states with different workforce compositions ex-
perienced different impacts. For instance, the composition of the labor force across all
manufacturing industries in Sao Paulo (SP), Rio de Janeiro (RJ) and Bahia (BA) do
vary substantially and this fact can be seen in Figure 3. One can verify that even Sao
Paulo and Rio de Janeiro, the richer states in Brazil, have different workforce composi-
tions. Therefore, it is not a surprise that states from other parts of Brazil, as those of
the north and northeast regions, tend to have an even more different composition of their
workforces. This can be seen in the state of Bahia (BA), for example.
12As it is explained later, it is possible to compute a state tariff through industrial
tariffs and the workforce composition8 and allows one to analyze the exposure to trade of
each state. Figure 4 shows that at the state level there is a dramatic decline in tariffs from
1987 to 1995, yet states with the highest tariffs before the liberalization period had the
highest tariff cuts (in p.p.). It is not surprising to see that the Brazilian trade liberalization
had different impacts depending on the composition of the workforce in each state since
other papers, including Topalova (2010), McCaig (2011) and Autor et al. (2013b), call the
attention to the regional effects due to macroeconomic shocks. In addition, Figure 9 in
the Appendix presents a much more aggregate information, yet still important.
Figure 3: Industrial Composition of the Workforce, 1987
As a matter of fact, some states were initially almost twice as closed as others,
suggesting that the trade reform was far from being a homogeneous shock to Brazilian
states. The more protected states in 1987 were Rio de Janeiro (RJ) and Sao Paulo (SP),
while the less protected were Amapa (AP), Mato Grosso (MT) and Maranhao (MA).
Since the standard deviation sharply declined from 1987 to 1995, it suggests that the
earlier states experienced a larger shock compared to the latter ones. Hypothetically, if
the standard deviation of 1995 was zero, some states would have experienced a shock
8
Topalova (2010).
13Figure 4: Initial levels and changes in state tariffs
twice as strong as others.
Figure 5 shows a different representation of the same fact. Since tariff changes
represent the trade openness shock, it is clear that the trade reform cannot be seen as a
macroeconomic shock that impacted all states equally. For instance, the state of Rio de
Janeiro (RJ) experienced an effective tariff drop of 59.1 p.p., while other states experienced
a less dramatic tariff cut as it is the case of Amapa (AP), whose effective tariff drop by
36.5 p.p.9 .
Regarding the macroeconomic context, it is important to highlight that other eco-
nomic reforms occurred during the analyzed period in Brazil and at some extent could
influence the results. For instance, in 1994 an anti-inflation programme succeeded for the
first time in decades. In addition, privatizations for public utilities started in 1991 and ac-
celerated during the mid-1990s, while capital-account constraints were relaxed. However,
and more importantly, trade openness played a dominant role for labor-market outcomes.
This claim is also defended by Muendler (2008), who examined the Brazilian trade liber-
alization period and controlled for variables related to simultaneous reforms, confirming
9
Similar conclusions can be drawn from the analysis of the nominal tariffs in Figure 16 in the Ap-
pendix.
14Figure 5: Effective Tariff Changes at the State Level
the overwhelming predictive power of the trade openness in comparison to other shocks.
Thus, there is evidence supporting that coincident reforms were not substantially relevant
for examining the impacts on inequality vis-a-vis the trade reform. In addition, Goldberg
and Pavcnik (2007) defend that, in contrast to many other developing countries, in Brazil
there are less concerns regarding the bias that other concomitant economic reforms could
bring.
153 Data
Two types of data were used in this paper: microdata at household level and tariff
data at industry level. Both data sets were combined in order to address the different
responses of inequality to trade, across regions in Brazil. As already said, this study
considers the period from 1987 to 1995 since it comprises the trade liberalization.
The earlier data set is sourced from the Pesquisa Nacional por Amostra de Domicı́lios
(PNAD). The survey is held on an annual basis by the Brazilian Census Bureau (Insti-
tuto Brasileiro de Geografia e Estatı́stica (IBGE)). It gathers general information from
the population, such as age, gender, education, occupation, experience, wage, location,
industry affiliation, etc. Following the literature, I use data from full-time10 employees
receiving nonzero wage income. Moreover, I consider the hourly wage, measured in the
currency prevailing at the time of each survey. Note that different currencies measuring
the hourly wage do not jeopardize this study because the PNAD data is used in cross-
section estimations that include a constant. In addition, I use data on gender, age (where
10-21, 22-27, 28-33, 34-39, 40-56, 57+ are the categories considered), education (where
less than 1 year, 1-4, 5-8, 9-11, 12+ are the categories considered) and sector affiliation
(where manufacture, agriculture and services are the categories considered) in order to
explain the hourly wage. The criterion used in order to establish the categories is simple
and similar to the one used in Helpman et al. (2012).
The later type of data, tariff data at the Nivel 50 industrial level (similar to 2-digit
SIC), gently provided by Honorio Kume11 and Pedro Miranda12 , is used together with
worker data in order to generate a variable that indicates the regional exposure to trade.
I use both nominal and effective tariffs. The first one, refers to the import tax rate fixed
by the Brazilian legislation as explained in Kume et al. (2003). The second one takes into
account all inputs that were required to produce the final goods and I took it from the
same source. Since the latter one seems to be a more reliable measure it is preferred, but
results are also obtained using the nominal rates of protection. In order to compute the
measure of state exposure to trade, one should weight the industry tariffs by the number
of workers employed in the corresponding industry of each state13 as it is explained in the
methodological section. In order to utilize data sets of different years it was necessary
to construct a common industry classification, detailed in Table 8 in the Appendix. The
final classification consists of 22 industries, including agricultural and nontrade goods.
10
Defined as working more than 20 hours per week.
11
Associate Professor at the Faculty of Economics from the Rio de Janeiro State University (FCE-
UERJ).
12
Assistant for International Cooperation at the Institute for Applied Economic Research (IPEA).
13
This strategy was used by some papers including Topalova (2010); McCaig (2011); Kovak (2011);
McLaren and Hakobyan (2010) and Autor et al. (2011)
16Since one Brazilian state was split within the period studied, it was necessary to
recode data into the original state, before division. The state of Goias was split, in 1988,
in two parts: the north one became a new state called Tocantins, while the south part
remained called Goias. Due to this reason, it was necessary to recode data into the original
state, before division, and this paper considers 26 Brazilian states. This strategy was also
applied by McCaig (2011), since three Vietnamese provinces were split during the period
studied. In contrast to Topalova (2010), where smaller geographic units are considered,
this paper analyzes states since the PNAD only reports state-level geographic information.
However, providing that the workforce composition differ across states, this fact does not
represent a drawback. Hasan et al. (2006), Besley and Burgess (2004) and even McCaig
(2011) also concentrate on more aggregated geographical units.
174 Methodology
4.1 Residual Wage Inequality
This section presents a method to compute the wage inequality measures used in
this paper. Taking into account the observable characteristics of individuals enables the
computation of the variability of wages that is not explained by the variability of those
covariates. This is exactly the residual wage inequality and it is possible to compute it at
the state level if one runs the following regression separately for each state:
6
X 11
X 13
X
g e a p
ωit = α1 + α2 I + αk I[ed=k−1] + αk I[age=k−5] + αk I[pr=k−10] + it (1)
k=3 k=7 k=12
where ωit is the log of the hourly wage of individual i at period t, I g is an indicator
e
variable for gender (where the male category is excluded), Ied is an indicator variable for
a
education (where ed = 1, ..., 5 and the less educated group, ed = 1, is excluded), Iage is
an indicator variable for age (where age = 1, ..., 6 and the younger group, age = 1, is
p
excluded) and finally, Ipr is an indicator variable for production sector affiliation (where
pr = 1, 2, 3 and the agricultural sector, pr = 1, is excluded). At the end, I estimate 13
coefficients in order to control for the observable characteristics.
The categories considered in equation 1 can be easily modified and variables can be
excluded or included in order to explain the wage variability across individuals. However,
I chose a simple specification since results do not vary substantially by rearranging the
observable categories.14 Regressions results are not reported since there is no special
interest on the observable returns across states, yet it is important to highlight that the
returns to the observable characteristics do vary a lot across states.
In 1987, if one considers a single labor market and run regression 1 for Brazil as a
whole, workers in the highest education category (12 years or more) earned, on average,
207 percent more than workers in the less educated category (less than 1 year). However,
this education premium shows substantial heterogeneity across states. In Ceara (CE), the
more educated workers earned on average 250 percent more than the less educated ones,
but only 170 percent more in Sao Paulo (SP). The standard deviation across states was
20.6 percent.
In 1995, the education premia continued to show substantial dispersion across states.
14
Helpman et al. (2012) consider an even simpler specification in order to compute the residual wage
inequality.
18Using the same educational categories, the more educated workers earned on average
143 percent more than the less educated when the national labor market is considered.
However, in Mato Grosso (MT) the more educated workers earned on average 153 percent
more than the less educated workers, but only 107 percent more in Roraima (RR). The
standard deviation across states was still high, 14 percent.
In addition, it also important to mention that a big portion of the wage variability is
still unexplained or, in other words, the residual wage inequality component still accounts
for a big portion of the overall wage inequality. Actually, on average, only 43% of the
overall wage variability in Brazil is explained by the observable characteristics, when both
1987 and 1995 are considered. At the state level, I obtained similar numbers. Although
small, these figures are in line with the labor literature and seem reasonable.
Using equation 1, it is straightforward to compute the residual wage inequality for
each state, s. This can be seen if decomposing the overall wage variability into two
components:
0
V ar (ωits ) = V ar ψits αˆts + V ar (ˆits ) (2)
0
where V ar (ωits ) denotes the overall wage variability for state s; V ar ψits αˆts denotes the
0
explained portion of the overall inequality, as ψit is the vector that contains all the ob-
servable characteristics already mentioned; V ar (ˆits ) denotes the residual wage inequality
for state s; and the hat denotes the estimated values from regression 1.
Examining the residual wage inequality might be tricky since there are at least three
reasons why workers with the same observable characteristics may report different wages.
In other words, there are at least three sources of bias that are important to consider.
Lemieux (2006) find that these sources were indeed very important to explain the increase
in inequality in the U.S. during the last three decades, yet the bias due to the composition
effect was clearly the most important one. This discussion is explained in details in the
Appendix.
The composition effect is related to the dispersion in unobserved skills. Consider an
economy where the population is getting older and more educated over time. If unobserved
skills are more dispersed among older and more educated workers, the residual wage
inequality could increase simply because of the composition change of the population. If
this is the case, any causal effect on inequality attributed to the tariff decline might be
incorrect, since the only source of change in inequality could have nothing to do with
trade.
19Figure 6: Residual wage inequality across states
An earlier paper that controls for composition effects is DiNardo et al. (1996). They
show that a third of the growth in residual inequality from 1979 to 1988 in the U.S. is
due to the composition effect. For the Brazilian case it is also important to control for
the potential role of this effect to understand the impact of trade openness on residual
wage inequality. In order to have a clearer picture of what happened in Brazil during this
period, Figure 6 presents the residual wage inequality across states for 1987 and 1995,
also considering the role of the composition effect. One can see that the more unequal
states in 1987 were: Roraima (RR), in the north; Piaui (PI), Paraiba (PB), Maranhao
(MA) and Ceara (CE), in the northeast. The Appendix present additional graphs.
The analysis of this paper considers two measures: one residual wage inequality
∗
that takes into account the potential role of the composition effect, denoted by V arst , and
another one that ignores it, denoted by V arst . Both measures are used in the empirical
section and it is expected that the less important the composition effect, the more similar
results will be. Further details on the composition effect and how these different measures
are calculated are available in the Appendix.
At last, it is worth emphasizing that the inequality measure constructed has the ad-
20vantage of including the general equilibrium effect of trade opening within a geographical
unit. Indeed, this is the case since all workers within states are consider in equation 1 and
no distinction between workers in tradable or nontradable sectors is done. As highlighted
by Topalova (2010), manufacturing workers typically represent a very small fraction of
the population, though often a large share of income. The strategy used in this paper
captures not only the effect of trade openness on manufacturing and agricultural workers,
but also on their dependents and individuals in related and unrelated sectors.
214.2 Regional Tariffs
The strategy used to compute the state tariffs was proposed in Topalova (2010) and
also used in other papers, including McCaig (2011). They can be calculated as follows:
X
τst = ηzs τzt (3)
z
where τst is the effective tariff for state s at time t; ηzs = LLzss is the fraction of the workforce
of state s that is employed by industry z at the initial year; and τzt is the tariff measure for
industry z at time t. It is important to highlight that Ls accounts for the total workforce
employed by the tradable industries since they were directly impacted. Instead of using
the effective tariffs, τst , one could use the nominal tariffs, τ st .
The state tariffs are based on employment weighted average of the industry tariffs
using time-invariant pre liberalization employment data. In other words, when the period
1987-1995 is considered, the employment structure of 1987 is used and kept fixed until
1995. Since some robustness checks are done, it was also necessary to compute the state
tariffs for a placebo period (without trade liberalization): 1996-2004. In this case, the
employment data was based on 1996.
Fixing the employment distribution at the initial year intends to control for what
would have been the evolution of the residual wage inequality across states in the absence
of trade liberalization reform. Hence, changes in employment that result from this episode
should not be included in the calculation of state tariffs. The use of pre-reform time-
invariant weights is common in many empirical papers including DiNardo et al. (1996).
4.3 Local Labor Markets
Two complementary tests intend to investigate the local labor market hypothesis and
verify if geographical barriers are indeed important in the Brazilian case. The first one
examines if the returns to the observable characteristics are very different across states.
Although this strategy may seem naive, important papers as Bernard and Jensen (2000),
for instance, also empoly it. As it was said earlier, through equation 1, returns to the
observable characteristics do vary a lot across states in Brazil. The second, as in McLaren
and Hakobyan (2010), tests whether heterogeneous trade shocks across regions impact
wages differently. This would suggest that regional shocks do not propagate immediately
across states and, consequently, factors of production do not reallocate promptly. In sum,
I expect to find that labor is, at least partially, an immobile factor of production.
22Some objections might arise regarding the earlier test15 . Azzoni and Servo (2002)
in their study for Brazil account not only for the differences regarding the individual
characteristics but also for the different costs of living among the ten largest metropolitan
areas in the 1990s. Not surprisingly, they still find that there are huge regional wage
differences. Despite all the labor market imperfections that also play their role, there
is evidence supporting regional labor markets even in advanced economies where those
factors tend to be less important16 . This latter fact suggests that even if it was possible
to account for all differences across regions, substantial wage differentials would still be
observed in Brazil.
In addition, Robertson (2000) studies the labor market integration between U.S.
and Mexico and finds that the two markets are closely integrated, and that they respond
simultaneously to common shocks, despite a large wage differential between them. Thus,
it suggests that merely using wage differentials to indicate market integration might not
always enlighten the question whether markets are integrated or not. That is exactly why
the methodology used in McLaren and Hakobyan (2010) is interesting, it explores the
response of wages to heterogeneous shocks. The idea is to test if limitations on mobility
of workers, both geographical and across industries, are important to determine their
wages. Finding that workers are at some extent immobile across Brazilian states would
strengthen the hypothesis on regional labor markets.
The empirical approach requires measures of protection by industry and also by
state. In order to test whether geographical limitations on mobility of workers matter for
the wage determination, consider the following specification:
29
X 33
X
e e
ωijst = βXijst + I1995 + λk I[ed=k−24] I1995 ∆τj + λk I[ed=k−28] I1995 ∆τs + υijst (4)
k=26 k=30
where ωijst is the log of the hourly wage of individual i in industry j in state s at period
t; Xijst is the vector of observable characteristics17 ; I1995 is an indicator variable for the
e
second year; Ied is an indicator variable for education (where the less educated group
15
For instance, the variability of returns across states might reflect the quality of education, differences
in the composition of the labor force, amenity levels in different states, market imperfections such as lack
of information on wage differences, costs of migration and even cost of living.
16
Weiler (2000) document differences in unemployment across U.S. counties.
17
The characteristics included are almost the same of equation 1. Gender, age and educational cate-
gories are maintained. Since equation 4 is considering all the 22 industries, the sector affiliation variable
considered in equation 1 was not considered here. In order to avoid perfect multicolinearity, the agri-
cultural industry is excluded. In addition, the vector Xijst contains the interaction of the educational
P5 e
categories with an indicator variable for the second year, k=2 λk Ied=k I1995 . Intuitively, equation 4
allows for different time trends depending on the educational category. Finally, the vector Xijst contains
P30 s
indicator variables for states, k=6 λk I[k=s] , where Rio de Janeiro (RJ) is excluded.
23(ed = 1) is excluded); I1995 is an indicator variable for the second year, 1995; τj is the
tariff of industry j and τs is the tariff of state s; and υijst is an error term. Note also that
t refers to 1987 or to 1995, since the analysis requires one year before and another one
after the trade reform.
Equation 4 allows for two kinds of limitations on mobility: across industries and
across states. I expect to find that workers would be at least partially geographically
immobile i.e., that the state where the worker lives is indeed an important variable to
explain her wage. Note that equation 4 allows for a homogeneous impact of trade on
every state and this is captured by the indicator variable, I1995 . Running this regression,
I expect to find that the trade liberalization not only had a nationwide impact but also a
specific impact depending on each region’s exposure to trade. Moreover, the interactions
between education and tariffs allow for different impacts across educational levels. This
flexibility is important since workers with different educational levels have potentially
different degrees of mobility across states and industries. Equation 4 is the most complete
specification used, yet slightly different models are tested.
4.4 Econometric Framework
In order to examine the different impacts that trade liberalization played on state
inequality, the following analysis consider two years: one before, 1987, and another after
the trade reform, 1995. This strategy is similar to the one followed in Topalova (2010)
and McCaig (2011) and it is also inspired on Verhoogen (2008). It starts by considering
the following model:
ρst = θ0 + θ1 It + αs + βΓs It + υst (5)
where ρst is the residual wage inequality of state s at time t; It is a time dummy for the
second year; αs is a time invariant unobserved state effect; Γs is the state tariff at the
initial year, 1987 (Γs ≡ τs,1987 ); and υst is an error term.
This simple specification controls for macroeconomic shocks and trends that have
equally impacted all states in Brazil. As previously discussed during the Brazilian Lib-
eralization section, in the specific case of Brazil there are fewer concerns, in contrast to
other developing countries, regarding other important economic reforms that might have
been coincident with the trade one. However, deregulation of markets and privatizations
occurred during this period and their potential effect should be considered.
In addition, there are important state time invariant characteristics such as labor
24regulation and even culture and geography that should be considered and are represented
by αs . These state time invariant variables are often thought as institutional characteris-
tics since they are typically stable over time. Although difficult to observe and measure,
ignoring them might cause the omitted variable problem and this is precisely why a simple
and naive causality test between current tariffs and inequality, without considering other
variables, could lead to erroneous conclusions18 .
Figure 7 presents the correlation between nominal tariffs and residual wage inequal-
ity19 for 1987 and 1995. As can be seen, there is a small correlation between those variables
and, further to that, if one run an OLS regression the slope will not be statistically dif-
ferent from zero for each case. Since the relationship between those variables cannot be
considered as a causal one, it is simply an attempt to elucidate what would have been the
effect captured by a naive test.
Figure 7: Correlation between tariffs and residual wage inequality
Equation 5 seeks to measure the short-term effects of trade liberalization by compar-
ing more exposed states to less exposed states. As it was discussed earlier, this strategy
18
These state time invariant characteristics need to be taken into account since they can be correlated
to trade policy variables (in this particular case, Γs ).
19
Without considering the composition effect.
25does not intends to identify the overall impact of trade on inequality, but measures whether
some states suffered more than others. Topalova (2010) calls the attention to the fact that
an advantage of this identification strategy is that it includes the general equilibrium ef-
fect of trade within a region. Instead of focusing on the effect of trade liberalization on
manufacturing workers (who typically represent a small fraction of the population), this
approach captures also the effect on workers in related and unrelated sectors.
Some additional comments related to equation 5 need to be done. One possible way
of estimating the parameter β would be pooling both years and applying the OLS method.
Indeed, one could pool all annual data between a pre and a post liberalization year as was
done in a similar way in Arbache and Corseuil (2004). However, Bertrand et al. (2004)
points out that using data from many years of pre and post liberalization may understate
the standard errors on the coefficient estimates. Indeed, the authors claim that this
latter problem was found in most analyzed papers using the difference-in-difference model
and that it has been poorly addressed to date. Thus, I select just one year of data pre
liberalization and one year post liberalization in order to eliminate this potential pitfall.
Bertrand et al. (2004) refers to this strategy as ”ignoring time-series information”. Since
time invariant unobserved state characteristics, αs , could be correlated to trade policy
variables, Γs , it is important to include state dummy variables to the model although
there is no special interest on their interpretations.
An analogous approach that allows for a potential correlation between tariffs and
time invariant unobserved state characteristics, yet avoids estimating the latter ones con-
sists on differentiating equation 5 over time. This strategy leads to the following frame-
work:
∆ρs = θ1 + βΓs + ∆υs (6)
where ∆ represents the time difference.
Note that if there were only two kinds of initial tariffs, high-Γ and low-Γ, then this
strategy would amount to the typical diff-in-diff strategy. By adopting this new specifica-
tion it is possible to verify if the initial level of tariffs explains the inequality movement.
However, this model will be correctly specified only if no confounding trends are present
and driving the results. More precisely, it regards the possibility of assigning erroneously
to tariffs an effect due to underlying trends at the state level. For instance, suppose that
states with the highest tariff levels (consequently, those that experienced the largest tariff
decreases) were the ones that had the more rapid inequality decrease, as it would have
been predicted by the standard Hecksher-Ohlin model. Indeed, the effect attributed to
26trade might be incorrectly assigned if, in the absence of the trade liberalization, those
states would have experienced the same inequality decrease. Of course, it is hard to test
whether this happened or not, yet some falsification tests can be done as an attempt to
control for this drawback.
As stated in McCaig (2011), note that the simple existence of underlying trends
does not directly cause a problem for identification. If cuts in state tariffs were randomly
assigned to states then one could be confident that these cuts would be uncorrelated with
any underlying trend in state residual wage inequality. However, McCaig (2011) highlights
that since state tariffs were calculated from the industry tariffs, these underlying trends
might be correlated to the initial composition of the workforce. In sum, one would like to
avoid a high correlation between tariffs and any kind of state initial condition that could
be promoting an unobserved trend in inequality.
Suppose hypothetically that those states that had initially the highest tariff levels
(or experienced the largest tariff cuts) were exactly the ones that had, previous to the
trade reform, governors (or any kind of political authority at the state level) who put
in practice policies to decrease inequality. This example, although is very unlikely, can
clarify the discussion and show how difficult can be the search for possible state initial
conditions that might be promoting underlying trends. Back to the example, these state
policies might have had their effect felt during the subsequent years, rising doubts on the
reliability of the causal results obtained by running the simplest form of equation 6.
A very important variable which has not been considered yet is related to the size
of the nontradable sector of each state. It is reasonable to expect that states with a
larger nontradable sector would have experienced a smaller shock due to the liberalization.
Recall that the size of the nontradable sector is not taken into account when computing the
state tariffs (equation 3). Thus, including the size of the nontradable sector into equation
6, before the liberalization occurred, is the first attempt to control for underlying trends
correlated with state tariffs.
Additional falsification tests are also adopted. The first test, inspired by Topalova
(2010), consists in verifying whether changes in inequality between two years (both pos-
terior to the trade liberalization episode), from 1996 to 2004, are explained by the initial
tariff levels. Running separately the same regression for each period intends to examine
whether the initial tariff level is indeed important to explain the inequality changes or
it is simply a spurious correlation. In other words, comparing the estimated coefficients,
β̂1987−1995 and β̂1996−2004 (equation 6 separately run for each period) is an interesting way
of checking whether the effect captured during 1987-1995 was indeed explained by the
trade reform. The new measure for the residual wage inequality is called Vg arst and is
27computed using data from 1996 to 200420 . It would be discouraging to capture a simi-
lar and significative effect of tariffs on both inequality changes: from 1987 to 1995 and
from 1996 to 2004. If this is the case, other variables not taken into account would be
impacting inequality and, although erroneously captured by the trade measure, there
would be evidence suggesting that trade has nothing to do with the differential inequality
movements.
The second test intends to verify whether the estimated effect is indeed statistically
different across periods of time. Similar to the one proposed by Verhoogen (2008) and
McCaig (2011), it considers two periods of time and assumes that these potential under-
lying trends are constant across them. The first period considered is 1995-1987 (period
0) while the second period (the placebo period) is 2004-1996 (period 1). The following
specification can be obtained by differentiating equation 6 over both periods:
∆ρ1s − ∆ρ0s = ψ + βΓs + (∆υs1 − ∆υs0 ) (7)
where ψ ≡ θ11 − θ10 and β ≡ β 1 − β 0 .
The estimated coefficient linked to the initial tariff level in equation 7 is expected
to be statistically different from zero, since this would allow one to infer that the effect
of tariffs on inequality during the liberalization period was indeed correctly assigned.
If equation 5 was the typical case of the diff-in-diff model whose treatment is a binary
variable, then equation 7 would amount to a triple-differences strategy: β̂ 0 would reflect
the difference in differences between high-Γ and low-Γ states from 1987 to 1995, β̂ 1 the
difference in differences from 1996 to 2004, and β̂ 1 − β̂ 0 the difference in difference in
differences.
Note that in contrast to the strategy that includes some initial conditions at the state
level to equation 6, this strategy vanishes away all those initial (and potentially important)
state conditions. As it is hard to account for all variables promoting underlying trends
in inequality, it is reasonable to adopt the latter specification, despite the identification
assumption that no other factors are generating different trends.
Finally, an important assumption in this type of research needs to be discussed: the
exogeneity of the trade policy. Indeed, it is vital to have an exogenous shock in order
to infer a causality relation from tariffs cuts towards inequality movements. Luckily, the
exogeneity can be endorsed in the Brazilian case and there is strong evidence provided in
Kovak (2011) and Goldberg and Pavcnik (2007) supporting this claim. It is reasonable to
∗
20
If the composition effect is taken into account, the new inequality measure will be called Vg
arst .
28consider trade policy an exogenous variable since labor unions, political power or any kind
of pressure did not influence the tariff decline. Further details regarding this discussion
are presented in the Appendix.
295 Results
Before examining whether trade had different impacts on regional wage inequality,
it is important to verify if geography is indeed a relevant variable to determine wages.
Table 1 presents the results of regression 4 considering the effective tariffs.21 In column
1, the coefficient of interest, I1995 ∆τs , captures only whether workers are perfectly mobile
across states. Intuitively, if workers were perfectly mobile, regional trade shocks would not
impact wages differently as factors of productions would be free to reallocate. Moreover,
column 2 allows for different impacts due to the educational level (where the less educated
group is excluded) when considering the regional shocks, while column 3 also includes the
industrial shocks.
In sum, Table 1 suggests that limitations on mobility of workers are indeed im-
portant, adding more strength to the hypothesis of local labor markets in Brazil. It is
interesting to note that workers with different educational levels have indeed different
degrees of mobility. Evidence suggests that workers with the highest educational level are
the less mobile. Since column 3 includes the industrial trade shocks, results show that
workers are indeed impacted by both types of shocks, yet the effects due to the regional
shocks are larger. Results are again in line with what was expected: workers do not
reallocate immediately across states in order to equalize wages. In addition, it is also
important to highlight that all the estimated coefficients of the individual characteristics
had the expected sign.
Since local labor market effects were found, the next step consists on testing whether
the trade reform had heterogeneous impacts on inequality across regions. Results of
regression 6 are reported in Table 2. Despite columns 6 and 12, the remaining estimated
coefficients allow one to infer that tariff changes were important to explain the different
inequality movements across states. As it was stated before, the aim of this paper is not
to measure the level effect of trade liberalization on inequality across Brazil as a whole.
Rather than that, it measures the relative effect of liberalization on states that were more
or less exposed to trade. More protected states before the trade reform (those with highest
tariff levels), were exactly the ones that experienced the largest shocks and, therefore, it is
expected to find a different impact on inequality if compared to states that were initially
less protected.
The results reported in Table 2 suggest that having a higher tariff level before the
trade reform led states to a relative increase in inequality (a smaller decrease in inequality).
It is also interesting to note that using the effective tariffs instead of the nominal ones
produce an effect of slightly smaller magnitude. An additional comment regarding the
21
Table 6, in the Appendix, presents the results considering the nominal tariffs.
30Table 1: Regression 4 - effective tariffs
(1) (2) (3)
ωijst ωijst ωijst
I1995 ∆τs 0.00664***
(13.55)
I2e I1995 ∆τs 0.00677*** 0.00668***
(9.59) (9.45)
I3e I1995 ∆τs 0.00504*** 0.00456***
(7.21) (6.50)
I4e I1995 ∆τs 0.00328*** 0.00271***
(4.11) (3.38)
I5e I1995 ∆τs 0.00984*** 0.00921***
(8.61) (8.01)
I2e I1995 ∆τj 0.000183
(1.08)
I3e I1995 ∆τj 0.00130***
(8.50)
I4e I1995 ∆τj 0.00172***
(8.23)
I5e I1995 ∆τj 0.00218***
(6.35)
I1995 -1.965*** -2.280*** -2.279***
(-78.00) (-225.66) (-225.36)
Cons 1.980*** 1.973*** 1.972***
(200.88) (200.76) (200.64)
State indicators Yes Yes Yes
Other characteristics Yes Yes Yes
N 179293 179293 179293
Notes: 107.274 observations for 1987 and 72.019 observations for 1995.
Other individual characteristics correspond to the vector Xijst .
Heteroskedasticity-consistent standard errors are calculated.
t statistics in parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
basic results is that the estimated coefficients of column 1 and column 7 (and of column
2 and column 8) are very similar. This latter fact suggests that taking into account the
composition effect does not change substantially the results. In sum, the results found do
not change substantially when nominal tariffs and the composition effect are considered.
In addition, it can be seen through Table 2 that the inclusion of control variables (both
the ones related to the GDP and the nontradable size) does not change the estimated
coefficients significantly.
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