DP2020-11

Trade Liberalization and Wage
Inequality: Evidence from Chile
Yoshimichi MURAKAMI

Revised July 15, 2020

Revised on July 15, 2020

Trade Liberalization and Wage Inequality: Evidence from Chile
Yoshimichi Murakami*
RIEB (Research Institute for Economics and Business Administration), Kobe University,
Kobe, Japan*corresponding author*Assistant Professor, Research Institute for Economics and Business Administration(RIEB), Kobe University, 2-1, Rokkodai, Nada-ku, Kobe 657-8501, Japan.
Email: y-murakami@rieb.kobe-u.ac.jp

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Trade Liberalization and Wage Inequality: Evidence from Chile
This study analyzes the impacts of further tariff reductions resulting from theproliferation of regional trade agreements on wage inequality between skilled andunskilled workers in Chile in the 2000s. Thus, we use data on effective tariff rates
instead of uniform most-favored-nation rates to measure trade liberalization. We
match panel data on industry characteristics, including effective tariff rates, topooled individual cross-section data from national household surveys at theindustry level. We find that the reductions in effective tariffs on final goods
increase industry wage premiums, thus suggesting that liberalization-induced
productivity improvements lead to higher wages. However, considering thedifferential impacts on different skill groups, we find that the reductions
significantly increase industry wage premiums only for skilled workers, thereby
increasing wage inequality. Moreover, the impact is larger in skilled workers
employed in large-sized firms. The results are robust to the inclusion of otherindustry characteristics, including input tariffs, the share of foreign-owned
capital, and payments to foreign technology. The results are also robust to theinclusion of industry productivity, which is likely to affect the effective tariffs
and industry wage premiums simultaneously, as well as control for the potential
endogeneity of trade policy.
Keywords: regional trade agreements; effective tariffs; industry wage premiums;
industry skill premiums; productivity
JEL classification codes: F16; F61; J31; O15; O54

1. Introduction
Over the past four decades, developing countries have implemented far-reaching tradeliberalization and increasingly integrated them into the global economy, which
undoubtedly has affected income distribution through several channels (Goldberg andPavcnik 2007; Murakami 2018; Pavcnik 2017). The distributional aspect of
globalization is particularly relevant for Latin American countries (LACs), not only
because the region has the highest level of income inequality in the world, but also theregion’s inequality is often associated with poor economic growth performance due to2political instability, including distributional conflicts, and various constraints on humancapital accumulation (Bértola and Ocampo 2012). Interestingly, LACs experienced
some improvement in income distribution in the 2000s after a sharp increase in income
inequality in the 1980s and 1990s. Studies such as Messina and Silva (2019) andSzékely and Sámano-Robles (2014) analyzed the factors underlying the recent evolutionof inequality in LACs, revealing that trade liberalization is continuously one of theimportant factors.
Chile, considered as one of the most successful LACs in terms of economic
growth and far-reaching economic reforms, is a particularly interesting case foranalyzing the relationships between trade liberalization and wage inequality. In addition
to continued unilateral liberalization, as typically evidenced by the application of a
uniform tariff of 10% to all products except for automobiles and a few agricultural
products in 1979, Chile has actively pursued regional trade agreements (RTAs) since
the early 1990s (Ffrench-Davis 2010; Kuwayama 2003; Macario 2000; Zechner 2002).
As a result of the enforcement of the RTAs, effective tariffs levied in each industrial
sector have diverged from the uniform most-favored-nation (MFN) rates due to thepreferential margins granted by the RTA scheme. This further trade liberalization,measured by effective tariffs, became especially evident in the 2000s, when free trade
agreements (FTAs) with major trading partners, which have faster and comprehensive
tariff elimination, including FTAs with the European Union (2003), the United States ofAmerica (2004), the Republic of Korea (2004), China (2006), and Japan (2007), cameinto effect.1 Indeed, effective tariffs in some industries have been nearly equal to 0%
since the mid-2000s.
Some studies have tried to analyze the impacts of trade liberalization on wage
inequality in Chile based on time series data and mainly focused on the period of3

unilateral liberalization in the 1970s and 1980s. However, the findings are stillinconclusive and controversial. Beyer, Rojas, and Vergara (1999) find that trade
openness (the ratio of exports plus imports to GDP) widened the wage inequality
between skilled and unskilled workers. Murakami (2014) also finds that tariff
reductions widened wage inequality. By contrast, Gallego (2012) finds thattechnological progress in developed counties widen wage inequality, while trade-related
variables are largely insignificant. Although those studies used relatively long-termtime-series data that are sourced from household surveys covering only the metropolitanarea, they do not analyze the impacts of tariff reductions through RTAs in the 2000s.
Therefore, this study aims to analyze the impacts of further tariff reductions
resulting from the proliferation of RTAs on wage inequality in Chile in the 2000s. This
study is the first, to the best of my knowledge, to analyze the impacts of tradeliberalization on wage inequality in Chile in the 2000s using panel data on industrylevel effective tariff rates and matching them to individual-level microdata fromnationally representative household surveys. Tariffs have an advantage as a measure of
trade liberalization because they are the most direct measures and distinguished fromother indirect measures, such as trade openness, which are not necessarily consequencesof trade policy changes (Rodriguez and Rodrik 2001). However, since Chile has
maintained uniform MFN tariff rates, as long as they are utilized, only time series
analysis using country-level data is allowed. This study overcomes this limitation byusing effective tariff rates.
Several studies have analyzed the relationships between trade liberalization andwage inequality using industry-level tariffs in other LACs, including Attanasio,
Goldberg, and Pavcnik (2004) for Colombia, Feliciano (2001) for Mexico, Galiani andPorto (2010) for Argentina, Goldberg and Pavcnik (2005) for Colombia, Pavcnik et al.

4(2004) for Brazil, and Paz (2014) for Colombia; they are discussed in greater detail in
Section 2. However, those studies ignored the possibility that other possible channels
might also affect wage inequality. Note that survey articles on the distributional aspectsof globalization in developing countries such as Goldberg and Pavcnik (2007), Messina
and Silva (2019), Murakami (2018), and Pavcnik (2017) revealed that there are several
channels, some of which are not captured by tariffs on final goods (i.e., output tariffs).
They pointed out that improved access to better foreign technologies, which usually
measured by tariffs on intermediate goods (i.e., input tariffs) or more directly by theusage of foreign technologies, as well shifting some segments of production processes
to third countries (i.e., within-industry offshoring), which is usually measured byforeign direct investment (FDI), are particularly important. Thus, this study confirmsthat our findings are robust to the inclusion of variables representing those channels
such as input tariffs, FDI, and payments to foreign technology. Therefore, this study
constructs industry-level panel data on those variables by the author’s own calculations
from plant-level microdata. Furthermore, this study also checks the robustness of thefindings by the inclusion of industry productivity, which may affect both effective
tariffs and workers’ wages, as well as control for the potential endogeneity of effective
tariffs due to a political-economic factor.
The rest of this article is organized as follows. Section 2 reviews the theoreticalframeworks of globalization and wage inequality, as well as the empirical findings.
Section 3 presents the empirical specifications. Section 4 explains the data employed in
the analysis and presents descriptive statistics. Section 5 presents the estimation results
and discusses the robustness of findings. The final section concludes the paper.

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2. Theoretical frameworks and literature review
This section reviews theoretical frameworks that predict the impacts of tradeliberalization on wage inequality, summarizing the empirical findings in developing
countries, particularly in LACs. Specifically, this study is interested in the impacts oftrade liberalization through the characteristics of industries with which workers areaffiliated. The wage differentials, attributable to workers’ industry affiliations after
controlling for other observable workers’ characteristics, including educational
attainments, are referred to as industry wage premiums (Galiani and Porto 2010;
Goldberg and Pavcnik 2007; Pavcnik et al. 2004). Further, industry wage premiums forskilled workers (i.e., industry wage premiums that only skilled workers employed in theindustry receive in addition to the base industry wage premium) are referred to asindustry skill premiums (Galiani and Porto 2010; Pavcnik et al. 2004). This section
reviews the theories and empirical findings regarding the impacts of trade liberalization
on industry wage premiums and industry skill premiums.
Note that the standard Heckscher-Ohlin (H-O) model and its companion
Stolper–Samuelson theorem predicts the impacts of tariffs on economy-wide returns
rather than industry-specific returns. Thus, the standard theory that predicts theassociation between industry tariffs and industry wages can be the specific factors
model (Attanasio, Goldberg, and Pavcnik 2004; Galiani and Porto 2010). The model
assumes specific factors to be immobile across industries in the short to medium run.
Thus, if the model assumes the imperfect mobility of workers employed in eachindustry because of the need for industry-specific skills or labor market rigidities, it
predicts that workers employed in industries with larger tariff reductions will experiencea larger decrease in their wages (Attanasio, Goldberg, and Pavcnik 2004; Pavcnik et al.
2004). Therefore, the model predicts a positive association between tariffs and industry
6

wage premiums.2 Furthermore, if the model additionally assumes that unskilled workers
are especially immobile across industries, it predicts that tariff reductions lead to a
proportional decrease in the industry wage premiums for unskilled workers (i.e., anincrease in industry skill premiums), thereby increasing wage inequality between skilled
and unskilled workers (Pavcnik et al. 2004; Galiani and Porto 2010).
Empirical studies using household data have reported mixed results. On the one
hand, some studies find the expected positive association between tariffs and industry
wage premiums (e.g., Attanasio, Goldberg, and Pavcnik 2004 for Colombia; Dutta 2007
for India; Falcone and Galeano 2017 for Argentina; Ferreira, Leite, and Wai-Poi 2007
for Brazil; Galiani and Porto 2006, 2010 for Argentina; Goldberg and Pavcnik 2005 forColombia; Kovak 2013 for Brazil;3 Paz 2014 for Colombia; and Revenga 1997 forMexico). On the other hand, other studies find no significant association (Feliciano
2001 for Mexico;4 Pavcnik et al. 2004 for Brazil; Hasan and Jandoc 2010 for thePhilippines). Moreover, others unexpectedly find a negative association (Kumar and
Mishra 2008 for India). Additionally, several studies have analyzed the association
between tariffs and industry skill premiums. Galiani and Porto (2006, 2010) expectedly
find the negative association between tariffs and industry skill premiums in Argentina.
However, Pavcnik et al. (2004) and Ferreira, Leite, and Wai-Poi (2007) find no
significant association in Brazil.5
By contrast, growing literature based on models of international trade withheterogeneous firms pioneered by Melitz (2003) emphasizes that trade liberalization
reallocates resources towards more productive large firms, thereby improving aggregate
productivity in a given industry. Since such firms tend to utilize more advanced
technologies and workers with higher skills and provide them with higher wages, trade7liberalization induces an increase in industry wage premiums (Bernard, Redding, andSchott 2007; Yeaple 2005).
Note that the original model by Melitz (2003) defines trade liberalization as
symmetric reductions in trade costs of exporting and importing. Modifying the seminalmodel by Melitz (2003), models by Demidova and Rodríguez-Clare (2009, 2013) andFelbermay, Jung, and Larch (2013) predict that even unilateral liberalization (i.e.,
unilateral reductions in import cost in a small economy) will also induce theproductivity improvements in a given industry by forcing low productive firms to exit
due to intensified import competitions and reallocating resources towards moreproductive firms. It is worth noting that several empirical studies (e.g., Eslava et al.
2013 for Colombia; Fernandes 2007 also for Colombia; Nataraj 2011 for India; Schor2004 for Brazil) support the prediction that unilateral tariff reductions are indeed
associated with improving industry level productivity. In addition to the importcompetition effect, Amiti and Konings (2007) and Topalova and Khandelwal (2011)
find that improved access to better foreign technologies due to lower input tariffs also
contributed to the productivity improvements. In the case of Chile, Tybout, de Melo,and Corbo (1991), Pavcnik (2002), and Bergoeing, Hernando, and Repetto (2010) findthat unilateral tariff reductions increased industry productivity mainly through importcompetition. As discussed in the introduction, since Chile has maintained uniform
tariffs across industries, even the use of data on effective tariffs do not provide
sufficient cross-industry variance before 2000.6 Thus, using alternative measures oftrade liberalization such as transportation costs, Bas and Ledezma (2010) and Blyde andIberti (2012) find that reductions in import barriers are associated with industry-levelproductivity improvements in Chile.

8However, only a few studies using firm-level data (e.g., Amiti and Davis 2011
for India; and Krishna, Poole, and Senses 2012 for Brazil) find that workers that
employed industries with tariff reductions experienced an increase in their wages indeveloping countries. Moreover, they find that the increasing impacts on wages arelimited to workers employed in exporting firms.
Although the original model of Melitz (2003) does not predict distributional
aspects of trade liberalization, subsequent studies have developed theoretical models
that incorporate the wage inequality issue into the framework of heterogeneous firms.
For example, by introducing the fair wage model (Egger and Kreickemeier 2009; Amiti
and Davis 2012) or search and matching frictions (Helpman, Itskhoki, and Redding
2010; Coşar, Guner, and Tybout 2016), they show that trade liberalization will increase
between-firm wage inequality (i.e., wage inequality between otherwise similar workers
across heterogeneous firms) because it intensifies the effect that workers employed at
high-productivity large firms receive higher wages than otherwise similar workers
employed at low-productivity small firms. Further, Coşar, Guner, and Tybout (2016)
show that the case of unilateral tariff reductions yields the same results because theformer firms, which take advantage of cheaper intermediate inputs, generate additional
rents to share with their workers and pay higher wages, while the latter firms, whichface greater import competition, pay lower wages. Indeed, using matched employeremployee dataset in Brazil, Krishna, Poole, and Senses (2012) empirically show thattariff reductions are associated with increasing between-firm wage inequality.
Extending their previous 2009 model, Egger and Kreickemeier (2012) show that tradeliberalization will increase not only between-firm wage inequality among workers withidentical skills but also within-firm wage inequality among workers with different
skills, thereby increasing industry skill premiums.

9Empirically, Bustos (2011) for Argentina and Linarello (2018) and Namini and
López (2013) for Chile find that reductions in trading partners’ tariffs expectedly
increased within-firm wage inequality between skilled and unskilled workers. However,there is little evidence on the expected association between a country’s own tariff
reductions and within-firm wage inequality in developing countries. Some studies findno significant association between output tariffs and within-firm wage inequality (Amiti
and Cameron 2012 for Indonesia; Bustos 2011 for Argentina; Caselli 2014 for Mexico;
Harrison and Hanson 1999 for Mexico). Moreover, some of them (Amiti and Cameron2012; Caselli 2014) find a positive association between input tariffs and within-firmwage inequality.7 Indeed, few empirical studies using firm (or plant) level panel data
have reported that within-firm wage inequality is negatively associated with output
tariffs (Furuta 2016 for India) and input tariffs (Giovannetti and Menezes-Filho 2006
for Brazil).
In summary, there are two opposite theoretical predictions: the specific factors
model and the heterogeneous firm trade models. The former predicts a positive
association between tariffs and industry wage premiums, while the latter predicts anegative association. Empirically, abundant literature has supported the former
prediction in LACs. However, there is little supportive evidence for the latter,irrespective of the robust evidence that the import liberalization-induced productivity
improvements indeed occurred at the industry level in LACs. By contrast, both models
can predict that tariffs reductions increase wage inequality between skilled andunskilled workers. Although several (not many) empirical studies using household data
have provided the supportive evidence in LACs, studies using firm-level data have notprovided it in LACs. Additionally, the latter studies have revealed that output and input
tariffs have different effects on wage inequality.

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3. Empirical specifications
3.1. Trade liberalization and industry wage premiums
The specifications of this study closely follow those proposed by Galiani and Porto(2006, 2010). The approach is to pool individual wages over time and regress them on avector of individual characteristics (including skill levels) and a vector of industry-level
characteristics (including industry tariffs) directly in one stage, which Galiani and Porto(2006) call “stronger identification strategy.” Alternatively, we can estimate industry
wage premiums from the wage equation separately for each year in the first stage. We
then pool the industry wage premiums over time and regress them on a vector ofindustry-level characteristics in the second stage. Examples of the two-stage estimation
strategy are Attanasio, Goldberg, and Pavcnik (2004), Feliciano (2001), Dutta (2007),
Goldberg and Pavcnik (2005), Kovak (2013), Kumar and Mishra (2008), Paz (2014),
and Pavcnik et al. (2004). However, the two-stage estimation strategy does not allow
the inclusion of interaction terms between industry and individual characteristics in thesecond stage. Indeed, the heterogeneous firm literature reveals that the impacts of tradeliberalization on wages can be different among the size of firms where the workers are
employed (Helpman, Itskhoki, and Redding 2010). Moreover, the two-stage estimation
strategy is likely to suffer from low statistical significance due to the limited sample
size.8 Thus, this study employs the one-stage estimation strategy. However, to doublecheck the robustness of our results, the supplemental file presents the estimation resultsthat employ the two-stage estimation strategy.
Although previous studies such as Amiti and Cameron (2012), Furuta (2016),
and Giovannetti and Menezes-Filho (2006) find that output and input tariffs havedifferent impacts on wage inequality, as discussed in Section 2, Galiani and Porto(2010) and the above-mentioned empirical studies employing the two-stage estimation11

strategy ignored the role of input tariffs. Thus, we also estimate the specification that
includes input tariffs. Additionally, as discussed in the introduction, previous studies
reveal that within-industry offshoring, usually measured by FDI (Feenstra and Hanson1997),9 and trade-induced technological changes (Acemoglu 2003; Berman and Machin
2000) also affects wage inequality in developing countries. Note that the literature
measured the trade-induced technological changes as a share of imported machinery andequipment (Acosta and Gasparini 2007; Caselli 2012; Harrison and Hanson 1999) and(or) payments to foreign technology (Harrison and Hanson 1999; Namini and López
2013). We measure the technological changes by the latter type variable. Thus, we also
estimate the specification that includes the industry-level share of foreign-owned capitaland payments to licenses and foreign technical assistance.
Therefore, the empirical specification for estimating the impacts of tradeliberation on industry wage premiums is as follows:(1)ln( wijt )  Xit β1  Sit β 2  3 ln(output tariff jt )   4 ln(input tariff jt )  Zjt 1β5  I j  Yt   ijt

where i, j, and t index individual, industry, and time, respectively; w is hourly wage
(deflated by the national consumer price index [December 2008 = 1]); vector X
represents control variables at the individual level, which includes years of potentiallabor market experience (age − years of schooling − 6), its squared term, demographic
dummies (a dummy each for the head of the household and married workers), informal
dummy (a dummy for workers working without any kind of contract), large sized firm
dummy (a dummy for workers working at firms with more than 200 people), and region
dummies; vector S includes dummies for two types of skilled workers (a dummy forsemiskilled works (semiskilled) consisting of workers with secondary education

12

graduates and dropouts and a dummy for skilled workers (skilled) consisting of college
and non-college higher education graduates and dropouts); output tariff represents
effective tariff rates on final goods; input tariff represents effective tariff rates onintermediate inputs; vector Z represents other industry characteristics lagged by one
year to address potential endogeneity, which includes the share of foreign owned capital(FDI) and the ratio of expenditure on licenses and foreign technical assistance (foreigntechnology);10 I is a vector of industry effects; Y is a vector of year effects; and ε is theerror term.
Since previous studies find that the returns to skills captured by the vector β 2
decreased substantially in the 2000s (Murakami and Nomura 2020; Parro and Reyes
2017), this study allows for the returns to skills to vary across periods; thus, interaction
terms between the skill dummies and year effects are included. Additionally, following
Galiani and Porto (2010), the returns to years of potential labor market experience andits squared term are also allowed to vary across periods; hence, their interactions withyear effects are also included. Finally, since both the theoretical and empirical literaturefind a firm size-productivity premium (Berlingieri, Calligaris, and Criscuolo, 2018;
Chang and van Marrewijk 2013; Fernandes 2007) and firm size-wage premium(Berlingieri, Calligaris, and Criscuolo, 2018; Helpman, Itskhoki, and Redding 2010),
trade liberalization is likely to intensify the effect that large-sized firms pay higher
wages. Therefore, following the estimation strategy of Fernendez (2007), this study also
estimates the specification that includes an interaction term between output tariff andthe large-sized firm dummy in addition to the FDI and foreign technology.
To address potential heteroscedasticity and serial correlation at the industry levelin the error term, this study estimates robust standard errors clustered at the three-digit
industry level. Sample weights are also used for all estimations in this study. We

13assume that the effects of common shocks or changes across all industries, such aslabor-reform, exchange rates, and other macroeconomic shocks, can be captured by year
effects, while time-invariant industry characteristics, which are likely to correlate withboth effective tariffs and workers’ wages, can be captured by the industry effects.

3.2. Trade liberalization and industry skill premiums
As explained in Section 2, in addition to the base industry wage premiums, skilledworkers are likely to receive additional wage premiums according to their industry
affiliation. Thus, we estimate the impacts of trade liberalization on industry skill
premiums by adding interaction terms between the effective tariffs (output tariff andinput tariff) and the skill dummies S, as well as interaction terms between industry
characteristics Z, consisting of FDI and foreign technology, and the skill dummies S, toequation (1). The empirical specification is as follows:(2)ln( wijt )  Xit β1  Sit β 2   3 ln( output tariff jt )   4 ln(input tariff jt )  ln( output tariff jt )  Sit β 5 ln(input tariff jt )  Sit β 6  Zjt 1β 7  Z jt 1 * Sit β 8  I j  Yt   ijt

where variables are the same as in equation (1). We also estimate the specification that
includes the interaction terms between the output tariffs, interacted with the skill
dummies, and large-sized firm dummy. Other variables and estimation strategies are thesame as equation (1).

4. Data and descriptive statistics
4.1. Individual-level data
The data on wages and individual characteristics are sourced from Encuesta de

14

Caracterización Socioeconómica Nacional (Socio-economic Characterization Survey,
CASEN) for 2000, 2003, 2006, and 2009.11 CASEN is a nationally representative crosssectional household survey conducted every two or three years by the Ministry of Social
Development and Family of Chile (former the Ministry of Social Development and theMinistry of Planning and Cooperation). CASEN provides detailed information on
demographic characteristics, education, health, housing, employment, and various
sources of income. Further, a favorable feature of this survey for this study is that thesurveys report a worker’s industry affiliation at the four-digit level of international
standard industrial classification (ISIC). Thus, this study pools the individual data fromthe surveys and matches them with panel data on industry characteristics at the ISIC
classification. This study limits the sample to male workers who are employed full-time(more than 40 hours per week) in manufacturing sectors and are aged between 14 to 65
years, excluding self-employed workers and military personnel.
Table 1 reports the descriptive statistics. We find that the share of semiskilledworkers slightly increased from 2000 to 2009, while the shares of unskilled (defined as
those having, at most, a complete primary education) and skilled workers slightly
decreased in this period.
[Insert Table 1 near here]

4.2. Industry-level data
Although this study uses effective tariff rates as the measure of trade liberalization,
there are some difficulties in calculating them. This difficulty is because effective tariff
rates are not necessarily immediately equal to 0 on all products after the enforcement of
RTAs due to the exceptions to tariff reductions and eliminations.12 However, someattempts have certainly been made. Bergoeing, Hernando, and Repetto (2006)15

calculated import-weighted average effective tariffs in Chile for the period from 1980 to
2001. Becerra (2006) calculated them at the industry-level for the period from 2000 to
2005. Thus, subsequent studies such as Cavallo (2008), Valdés and Jara (2008),
Bergoeing, Hernando, and Repetto (2010), and Álvarez and Fuentes (2018) used thesecalculations for their empirical analysis. Regarding the period after 2006, the frequently
used database of the World Integrated Trade Solution (WITS) also provides reliable
data.13 Thus, following the estimation strategy of those studies, this study sources thedata for the output tariffs from Becerra (2006) for the years 2000 and 2003, and the
WITS database for the years 2006 and 2009. We also need to note that Becerra (2006)
do not classify all industries by the three-digit level of ISIC (revision 2); some sectors
are classified according to the two-digit level, and others are classified according to theaggregated three-digit level (i.e., some three-digit level sectors are aggregated into one
sector). Thus, although this study maintains the three-digit level classification for all
periods under analysis, we consider effective tariff rates of such aggregated sectors as
identical at the three-digit level.14
Following Amiti and Cameron (2012), we construct the input tariffs byweighting the output tariffs by the industry’s input cost shares, as follows:(3)

input tariff jt  k  jk  output tariffkt ,where  jk is the share of input of industry k purchased by industry j. The share of input,
based on the total input purchases, including domestic and imported inputs, is calculated
by coefficients of the input-output table of Chile in 2003.15
Table 1 also shows the industry-related descriptive statistics. The average
effective tariff rates in manufacturing sectors decreased from 7.8% in 2000, which isslightly below the MFN tariff rates (9%), to 3.4% in 2003, 2.3% in 2006, and 1.6% in162009, which are substantially below the MFN tariff rates (6%) in those years. The
reduction and divergence from the MFN tariff rates reflect that major FTAs with fasterand comprehensive tariff elimination came into effect in the period, and the tariffelimination periods of major Economic Complementation Agreements (ECAs) (usuallyaround 10 years) expired.
The other two industry characteristics (FDI and foreign technology) arecalculated from plant-level data from Encuesta Nacional Industrial Anual (Annual
Survey of Manufacturing Industries, ENIA).16 The survey, which is carried out by the
National Institute of Statistics of Chile, covers all manufacturing plants with more than
10 employees and provides plant-level detailed information on sales, employment,
wages, input material and service expenditures, and fixed assets (see the Appendix).
Following the FDI studies (e.g., Javorcik 2004), we measure the industry-level FDI by
the share of foreign-owned capital averaged over all plants, weighted by each plant’s
share in sectoral sales. Since the share of foreign capital has significant regionalvariation in Chile, the sectoral sales are calculated at the region-industry level.
Similarly, we measure industry-level foreign technology by the ratio of expenditure on
licenses and foreign technical assistance to total sales averaged over all plants, weighted
by each plant’s share in the industry sales. We find that the share of foreign capital andthe ratio of expenditure on licenses and foreign technical assistance slightly decreased
from 2000 to 2009.

5. Estimation results
5.1. Trade liberalization and industry wage premiums
Table 2 reports the estimation results of equation (1). We find that, in all specifications,the coefficient of output tariffs is negative and significant, indicating that reductions in
17

output tariffs are associated with increasing industry wage premiums. The coefficientsof foreign technology are positive and significant, as expected. In contrast, thecoefficients of input tariffs and FDI are insignificant, and their inclusion does not affectthe sign, magnitude, and significance of output tariffs. The coefficient of output tariffs
interacted with a large-sized firm dummy is expectedly negative but insignificant.
Therefore, the findings indicate that productivity improvements induced through foreigncompetitions lead to higher workers’ wages in Chile. Although the findings are
consistent with Kumar and Mishra (2008) for India, they are in contrast with previous
studies analyzing other LACs, such as Attanasio, Goldberg, and Pavcnik (2004) for
Colombia, Galiani, and Porto (2010) for Argentina, Goldberg and Pavcnik (2005) for
Colombia, Kovak (2013) for Brazil, and Paz (2014) for Colombia, who found a positive
association between tariffs and industry wage premiums and, thus, supported theprediction of specific factors model, as discussed in Section 2. Thus, this study provides
new evidence for the impacts of trade liberalization on industry wage. We also find thatthe coefficients of the interaction terms between skilled workers dummies and year
effects, especially year effect of 2009, are negative, confirming a decrease in economywide returns to skilled workers in the 2000s, as Murakami and Nomura (2020) andParro and Reyes (2017) also find.
[Insert Table 2 near here]

5.2. Trade liberalization and industry skill premiums
Table 3 reports the estimation results of equation (2). Note that we do not repeat theestimation results of the coefficients of skilled dummies (including their interactions
with year effects) in the subsequent tables. We find the higher the skill category, thelarger (in absolute value) coefficient of output tariffs; moreover, the coefficient of18

output tariffs is significant only for the interaction with semiskilled and skilled workers.
Therefore, we find that reductions in output tariffs lead to increases in industry skill
premiums and, thus, wage inequality between skilled and unskilled workers. The
finding is consistent with Galiani and Porto (2006; 2010) for Argentina and Krishna,
Poole, and Senses (2012) for Brazil. Interestingly, this study also finds that thecoefficient of output tariffs is significantly larger (in absolute value) for skilled workers
employed in large-sized firms. Therefore, the findings are precisely consistent with thepredictions of heterogeneous firm trade models: productivity improvements due tointensified foreign competitions are associated with increasing industry wage premiums;
however, the increasing effects on wages are biased towards favoring skilled workers,
especially those employed in large-sized firms.
We also find that the coefficient of FDI is positive and significant only for theinteraction with skilled workers, indicating that an increase in FDI leads to increasing
industry skill premiums and wage inequality between skilled and unskilled workers.
The finding is consistent with recent empirical studies analyzing LACs, including
Herzer et al. (2014), Kristjanpoller and Contreras (2017), Murakami and Hamaguchi(2017), and Suanes (2016). By contrast, the coefficients of input tariffs and foreigntechnology, including their interactions with skilled dummies, are insignificant, thus
suggesting that foreign technologies may not be skill-biased in this period in Chile.
Importantly, we confirm that the inclusion of input tariffs, FDI, and foreign technology,
including their interactions with skilled dummies, do not affect the sign, magnitude, andsignificance of output tariffs.
[Insert Table 3 near here]195.3. Robustness check
A possible concern with the findings is the potential endogeneity of effective tariffs towages. Thus, to ameliorate the endogeneity problem, many control variables such as thetime-varying returns to skills and labor market experience, the detailed individual
characteristics, the industry effects, and the year effects are included in our empirical
specification, as discussed in Sections 3 and 4. However, to check the robustness of thefindings, we perform two additional exercises.
First, the heterogeneous firm trade models predict that the key factor affecting
workers’ wages is the productivity of the firm or industry in which they are employed.
However, productivity is also likely to be correlated with tariffs (Schor 2004; Paz2014). This likelihood is because tariff reductions are considered to improve industrylevel productivity, as discussed in Section 2. Moreover, this negative correlation
between industry-level tariffs and productivity can also be derived from a politicaleconomic factor because low productivity industries are likely to pressure thegovernment for higher tariffs to maintain the protection from foreign competitions(Schor 2004). This factor is especially crucial for this study because effective tariff
rates, which are our measure of trade liberalization instead of nominal MFN tariff rates,
are determined by the exceptions to tariff reductions and eliminations in RTAs, as wellas the choice of partners with which the country has RTAs. Thus, this process is likely
to allow much room for political lobbying. Therefore, for those two reasons, effective
tariffs are considered to be negatively associated with productivity.
Figure 1 shows the relationships between effective tariff rates and productivity
at the three-digit ISIC level in 2000. We estimate the plant-level Levinsohn–Petrin totalfactor productivity (TFP) (Levinsohn and Petrin 2003) using the data from ENIA (fordetails, see the Appendix). We calculate the industry-level weighted average of TFP
20

using each plant’s share in the industry sales as the weights. Interestingly, we find the
expected negative relationships between effective tariffs and TFP at the industry-level;that is, low productivity industries indeed tend to have higher effective tariffs. Thus,
since industry-level productivity is considered to be positively correlated with workers’
wages, the omission of productivity results in the overestimation of the coefficient of
output tariffs in absolute value.
[Insert Figure 1 near here]
Therefore, following Paz (2014), we estimate equations (1) and (2) by adding
industry-level TFP. Tables 4 and 5 show the estimation results. As expected, we findthat the coefficient of TFP is positive (though it is weakly significant). Thus, thecoefficient of output tariffs was indeed slightly overestimated in absolute value in theprevious estimation without the inclusion of TFP: the previous estimation shows that a
1% reduction in output tariffs leads to a 0.134% increase in industry wage premium(Column (1) of Table 2), while the re-estimation with the inclusion of TFP shows that it
leads to a 0.131% increase in industry wage premium (Column (1) of Table 4).17
However, the inclusion of TFP does not affect the baseline results: reductions in output
tariffs lead to an increase in average industry wage premiums; however, considering thedifferential impacts of reductions in output tariffs on different skill groups, they lead toan increase in industry wage premiums for only skilled workers, and the effect issignificantly larger for those employed in large-sized firms. Moreover, the findings arerobust when controlling for input tariffs, FDI, and foreign technology.
[Insert Tables 4 and 5 near here]
However, unobservable time-varying industry characteristics may be stillcorrelated to both workers’ wages and effective tariffs after controlling for industry-

21

level productivity. For example, industries with powerful unions, which are more likely
to lobby the government for protection, are considered to have higher tariffs. Moreover,workers employed in such industries are likely to receive higher wages because thoseindustries can extract some industry rents associated with protection (Galiani and Porto2010). Therefore, the omission of such time-varying political characteristics results inthe underestimation of the coefficient of output tariffs in absolute value.
To address this concern, following Attanasio, Goldberg, and Pavcnik (2004),
Kumar and Mishra (2008), and Pavcnik et al. (2004), we apply an instrumental
variables strategy. An ideal instrument should be highly correlated with effective tariffs
but uncorrelated to the industry-specific time-varying unobservable component ofwages (Kumar and Mishra 2008). Although it is difficult to find such an ideal
instrument, this study argues that the MFN tariff rate in each year interacted with initial
applied tariff levels (in 2000) is an appropriate instrument. This argument is because theMFN tariff rate in each year is completely flat across industries in Chile; thus, it isexogenously determined. Moreover, both applied tariffs and MFN tariffs show adecreasing trend from 2000 to 2009. Furthermore, the initial tariff levels are closely
related to the cross-industry patterns of the subsequent tariff levels. Note that Attanasio,
Goldberg, and Pavcnik (2004), Goldberg and Pavcnik (2005), and Pavcnik et al. (2004)
used the exchange rate and coffee price in each year interacted with initial tariff levels
as the instruments. However, the evolution of the exchange rate and coffee prices acrossthe years do not necessarily link to that of tariff levels. Since the MFN tariff levels
always link to applied tariff levels, our proposed instrument is obviously more
appropriate in the case of Chile. Regarding the interaction terms, we use the original
instrument interacted with the explanatory variables in question (e.g., we use theoriginal instrument interacted with skilled dummies as the instrument for the output

22

tariffs interacted with skilled dummies). Note that we do not include input tariffs in theestimation using the instruments because the variable is already found to be
insignificant.
Table 6 presents the estimation results using instrumental variables. The DurbinWu-Hausman test strongly rejects the null hypothesis of exogeneity of output tariffs.
Given that the Kleibergen–Paap rank F statistics are larger than the Stock-Yogo weak
ID test critical value (see specifications (1) to (3) of Table 6), we reject the null
hypothesis that the instrument is a weak instrument. Interestingly, if we estimate theresidual from the first stage regression, which captures the endogenous components ofeffective tariffs, we find the expected positive relationships between effective tariff
rates and the residual (see Figure 2). Thus, we find that the coefficient of output tariffs
was substantially underestimated in absolute value in the estimation without using the
instrument: the estimation shows that a 1% reduction in output tariffs leads to a 0.131%
increase in industry wage premiums (Column (1) of Table 4), while the estimation using
the instrument shows that it leads to a 0.450% increase in industry wage premiums(Column (1) of Table 6). Therefore, we find that the impacts of output tariffs on wage
inequality are more important and practically large when we appropriately address theendogeneity of effective tariffs. Note that in the estimation using the instrument, we findthat the coefficients of output tariffs are also significant for unskilled workers.
However, skilled workers still receive additional industry wage premiums for them. For
example, Column (5) of Table 6 shows that a 1% reduction in output tariffs leads to a
0.222% increase in industry wage premiums for unskilled workers, while it leads to a
0.439% increase in those for skilled workers. That is, reductions in output tariffs lead toa larger increase in industry wage premiums for skilled workers than unskilled workers,
thereby increasing wage inequality between them. Thus, we find that the baseline

23

results remain unchanged when using the instrument. We again confirm that theinclusion of FDI and foreign technology does not affect the sign, magnitude, andsignificance of output tariffs.
[Insert Figure 2 near here][Insert Table 6 near here]

6. Concluding remarks
This study analyzed the impacts of further tariff reductions resulting from theproliferation of RTAs on the wage inequality between skilled and unskilled workers inChile in the 2000s. For this purpose, we used data on effective tariff rates instead of
uniform most-favored-nation rates as the trade liberalization measure. Furthermore, tocontrol for other possible channels through which globalization would affect wage
inequality, we constructed industry-level panel data on the variables representing thosechannels such as FDI, payments to foreign technology, and productivity based on theauthor’s own calculations from plant-level microdata. Matching those panel data topooled individual cross-section data from nationally representative household surveys at
the industry level, this study estimated the impacts of trade liberalization on workers’
wages directly in one stage. To the best of my knowledge, this is the first study toanalyze the impacts of effective tariffs on wage inequality in Chile using detailed
household and plant-level data.
We found that the reductions in output tariffs increased industry wage
premiums, thus suggesting that liberalization-induced productivity improvements led tohigher industry wages. However, considering the differential impacts of output tariffs
on different skill groups, we found that the reductions significantly increased industry
wage premiums for only skilled workers, thereby increasing wage inequality between

24skilled and unskilled workers. Moreover, the impact of output tariffs on industry skill
premiums is significantly larger for skilled workers employed in large-sized firms.
Therefore, the findings are exactly consistent with the predictions of heterogeneous firm
trade models: productivity improvements due to intensified foreign competitions areassociated with increasing industry wage premiums; however, the increasing effects on
wages are biased towards favoring skilled workers, especially those employed in largesized firms. By contrast, the findings of previous studies analyzing LACs largely
supported the predictions of the specific factors model: tariff reductions decreased
industry wage premiums, while they increased wage inequality between skilled andunskilled workers. Therefore, the findings provide new evidence related to tradeliberalization and inequality in LACs, which is a novel contribution of this study to theliterature.
This study found that FDI had similar impacts on wage inequality in Chile: FDI
was associated with increasing industry wage premiums, while it was particularly
associated with those for skilled workers, thereby increasing wage inequality, which
supports the findings of previous studies analyzing LACs. By contrast, we found that
input tariffs and payments to foreign technology, which are variables related to theimproved access to foreign technology and its usage, did not affect wage inequality
between skilled and unskilled workers.
This study also made a novel contribution to the literature by carefully showing
the robustness of the impacts of output tariffs on wage inequality. First, we showed thatthe impacts were robust to the inclusion of input tariffs, FDI, and the payments toforeign technology, which representative studies analyzing the links between tariffs andwage inequality in LACs such as Attanasio, Goldberg, and Pavcnik (2004), Galiani andPorto (2010), Goldberg and Pavcnik (2005), Pavcnik et al. (2004), and Paz (2014)25

ignored. Second, following Paz (2014), we showed that impacts were also robust to theinclusion of industry productivity, which affected effective tariffs and industry wage
premiums simultaneously, thereby finding that its omission led to biased estimates ofeffective tariffs. Finally, using instrumental variables, we verified the robustness of theresults by controlling for the potential endogeneity of effective tariffs due to a politicaleconomic factor: although the baseline results remain unchanged, the impacts of output
tariffs on wage inequality became more important if appropriately addressing theendogeneity of effective tariffs.
The finding that trade liberalization still operated in the direction of increasing
wage inequality in Chile in the 2000s may require further research. First, considering
the observed decrease in wage inequality in this period, the finding may be puzzling.
However, this decrease can be attributed to supply-side factors such as an increase in
more educated workers due to the expansion of higher education, as pointed out by
Murakami and Nomura (2020). Thus, further research on the economic factors
explaining the decrease in wage inequality in this period, considering both demand andsupply factors, may be required. Second, to reveal the distributional effects of tradeliberalization entirely, we require a detailed analysis of the effects on household welfare
across different income groups, especially based on the estimation of tariff pass-throughon domestic prices (for example, see Casabianca 2016 for Paraguay; Porto 2006 forArgentina; Finot, LaFleur and Durán 2011 for Chile). Such an analysis is beyond thescope of this study; however, especially from a policy perspective, it may be aninteresting subject for future research.

Acknowledgments
The author is grateful to Manabu Furuta, Yasuhiro Gintani, Nobuaki Hamaguchi, Isao Kamata,Hiroyuki Nishiyama, and Takahiro Sato for their insightful comments and constructive

26

suggestions. The author is also grateful to Seiji Horii, Masahiro Ikeda, Kouta Ito, Hiroyuki
Mizuno for assisting the research. Note that some ideas and methodologies of this article arebased upon the author’s discussion paper “Trade policy and wage inequality in Chile since the1990s,” (Documentos de Proyectos 518, The United Nations Economic Commission for Latin
America and the Caribbean (ECLAC)). Thus, the author’s thanks also go to José Durán,
Sebastián Faúndez, Hernán Frigollett, Atsushi Fukumi, Yoshiaki Hisamatsu, Ekaterina
Krivonos, Kyosuke Kurita, Mikio Kuwayama, Xavier Mancero, Yukitoshi Matsushita, the late
Shoji Nishijima, Tomokazu Nomura, Andrea Pellandra, and Setsuko Yukawa for their insightfulcomments and constructive suggestions in this stage. This study was substantially developed
from the discussion paper, incorporating new theoretical frameworks and literature review,
dataset, and methodologies. Any remaining errors are the author’s own.

Declaration of interest statement
There are no potential conflicts of interest to declare.

Funding
This work was supported by the [JSPS KAKENHI] under Grant [Number 17K17877].

27

Notes
1. Most of the RTAs that came into effect during the 1990s fall into a category known as
Economic Complementation Agreements (ECAs), which focus on the elimination of tariffs andnon-tariff barriers for goods. The representative examples are ECAs with Mexico (1992),
Venezuela (1993), Bolivia (1993), Colombia (1994), Ecuador (1995), and Peru (1998). By
contrast, FTAs not only have faster and more comprehensive tariff elimination but also include
those areas not addressed by ECAs, such as investment, trade in services, competition policy,
government procurement, and intellectual property rights. For more details, see Kuwayama(2003).
2. Note that the model that incorporates non-competitive wage-setting mechanisms such aslabor union into simple the H-O model also provides a positive association between tariffs andindustry wage premiums because tariff reductions are likely to decrease the industry rents. For
more details, see Galiani and Porto (2010).
3. Kovak (2013) estimates wage premiums at the region instead of the industry level.
4. Feliciano (2001) finds a positive association between import licenses and industry wage
premiums.
5. Using export shares instead of tariffs, Brambilla et al. (2011) find that the variable increased
industry skill premiums in 16 LACs.
6. Thus, Pavcnik (2002) assumes that the impacts of uniform tariffs can be captured by the year
indicators.
7. Amiti and Cameron (2012) argue that the finding is not surprising because reductions in
input tariffs induced firms to switch from producing high-skilled intensive intermediate inputs
to importing them, thereby reducing relative demand for skilled workers.
8. Even classified according to the three-digit ISIC level, the number of industries is only 29.
9. Since offshoring usually involves FDI, except for the case of foreign outsourcing, which
is very difficult to measure, the impacts of offshoring are usually assessed by using FDI(Khalifa and Mengova 2010).
10. Due to the data availability, the variables are lagged by two years in the year
2009. Due to the classification change of industry affiliation from ISIC revision 2 to 3 in 2008,
ENIA for 2007 provides the last available data classified at ISIC revision 2.
11. We sourced the data from http://observatorio.ministeriodesarrollosocial.gob.cl/casenmultidimensional/casen/basedatos.php (accessed on June 6, 2018).
12. For the list of the exceptions to tariff reductions in RTAs, see Agosin (1999) andTable Annex 1-A of Schuschny et al. (2007: 101).
13. Although the share of imports from countries with which Chile has RTAs to the total

28

imports was exceeded in 1996 and continuously increased since then (Dúran 2008), theeffective tariff rates until 2003 that WITS provides are almost homogenous and identical to
MFN tariff rates. Thus, the data in this period, apparently, do not reflect the realities and are notreliable.
14. For example, since Becerra (2006) aggregate ISIC 381, 383, and 385 into one sector, weconsider effective tariff rates of those sectors as identical.
15. Thus, we assume that the share of input is constant over the period under the analysis. We
sourced the data from https://si3.bcentral.cl/estadisticas/Principal1/Excel/CCNN/cdr/excel.html(accessed on January 27, 2019).
16. We sourced the data fromhttp://historico.ine.cl/canales/chile_estadistico/estadisticas_economicas/industria/series_estadist
icas/series_estadisticas_enia.php (accessed on April 14, 2016).
17. However, the overestimation is substantially smaller than that of Paz (2014) for Colombia,
who finds that the inclusion of TFP decreases the tariff coefficient by almost 40%.

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Valdés, A., and E. Jara. 2008. Chile. In Distortions to agricultural incentives in Latin
America, ed. Kym Anderson and Alberto Valdés, 119–58, Washington DC: The
World Bank.
Yeaple, S.R. 2005. A simple model of firm heterogeneity, international trade, andwages. Journal of International Economics 65: 1–20.
Zechner, C. 2002. Expanding NAFTA: Economic effects on Chile of free trade with theUnited States. Hamburg: Lit.

35

Appendix
For the estimation of the plant-level TFP, we construct an unbalanced panel data for the
1995–2007 period. We estimate the production function separately for each three-digit
industry. Note that, based on the Becerra’s (2006) classification for effective tariff rates,
we aggregate some industries with small observations at the three-digit level into one
sector. Table A.1 reports the estimation results. The details of the variables employed
are described below.
Output is measured by real sales. The deflator is the wholesale price index(originally, índice de precios al por mayor) [June 1992 = 1] in industrial sectors. The
data for 1999 are sourced from Banco Central de Chile,1 while those for the 2000–2007
period are sourced from INE.2
Skilled and unskilled labor are measured by the total annual working hours ofworkers in the following occupational categories: skilled labor consists of owners,
managers, specialized production workers, administrative personnel, and commissioned
employees; unskilled labor consists of workers directly or indirectly involved in the
production process and services workers.
Materials are the sum of the real domestic and imported materials. The deflators
are the wholesale price index [June 1992 = 1] of domestic and imported intermediate
inputs, respectively. For 1999, we calculate them based on the weighted average of thewholesale price index, whose basket and weights are given by INE. For the 2000–2007
period, we source them directly from INE.
The energy inputs are the sum of the real net purchased value of electricity,
other combustibles, and water. The deflator is the implicit deflator of electricity, gas,

1
2

https://si3.bcentral.cl/Siete/secure/cuadros/home.aspx, accessed on February 8, 2018
http://www.ine.cl/estadisticas/precios/ipm, accessed on February 8, 201836

and water sectors [1992 = 1], sourced from the Statistical Yearbook for Latin America
and the Caribbean of ECLAC.3 Service is measured by the sum of real expenditure on
services such as advertising and promotion, commission payments, communications,insurance, legal and technical consulting, license and foreign technical assistance,
maintenance and repair payments, rental and leasing payments, transport and storage,
and other services. Payments to subcontracts are also included in this category. The
deflator is the implicit deflator of service sectors [1992 = 1], sourced from the Statistical
Yearbook for Latin America and the Caribbean of ECLAC.
Capital is proxied by the real values of tangible fixed assets, consisting ofbuilding, land, machinery and equipment, and vehicles. The deflators are the wholesale
price index [June 1992 = 1] of domestic capital goods. For the year 1999, we calculatethem based on the weighted average of the wholesale price index, whose basket andweights are given by INE. For the 2000–2007 period, we source them directly fromINE.

3

http://interwp.cepal.org/anuario_estadistico/anuario_2015/en/index.asp, accessed on
February 8, 201837

Table A.1 Estimation results of the production function
ISIC
Log of skilled laborLog of unskilled laborLog of capitalLog of services
Log of materials
Log of electricity
Observations

ISIC
Log of skilled laborLog of unskilled laborLog of capitalLog of services
Log of materials
Log of electricity
Observations

Dependent variable: log of output
311, 312
313, 314
0.0128**0.0342(0.00599)(0.0328)0.111***
0.0219(0.00611)(0.0226)
0.109***
0.112***(0.0302)(0.0396)0.0947***0.127**(0.0291)(0.0603)
0.551***
0.538***(0.0560)(0.0792)0.401***-0.0461(0.0572)(0.0495)
14,3981,493

321
0.0958***(0.0193)
0.0989***(0.0168)
0.0636***(0.0183)
0.212***(0.0192)
0.373***(0.0353)-0.0270(0.0173)
2,089

322
0.0909***(0.0162)0.0888***(0.0133)
0.0429**(0.0216)0.181***(0.0289)
0.521***(0.0449)
0.188***(0.0225)
1,531

323, 324
0.0884***(0.0209)
0.0418**(0.0178)
0.0568***(0.0151)0.145***(0.0269)
0.607***(0.0211)0.167***(0.0225)1,142

3310.0807***(0.0217)0.147***(0.0227)
0.0761***(0.0281)0.190***(0.0439)0.452***(0.0335)
0.381***(0.0539)2,442

332
0.0613***(0.0223)
0.0411**(0.0174)
0.0796***(0.0268)
0.182***(0.0335)
0.593***(0.0433)
0.117***(0.0356)
818

Dependent variable: log of output
341
342
0.106***
0.0905***(0.0199)(0.0217)0.01500.0478***(0.0220)(0.0154)0.03210.0108(0.0289)(0.0214)0.189***
0.139***(0.0468)(0.0301)
0.468***
0.513***

351
0.0568(0.0480)0.0398(0.0308)
0.148***(0.0414)0.198***(0.0411)0.454***

352
0.140***(0.0256)0.0842***(0.0205)
0.0251(0.0224)
0.151***(0.0308)
0.468***

353, 354, 355
0.0621*(0.0353)
0.0669***(0.0231)0.0189(0.0292)
0.0875**(0.0371)0.648***

356
0.0980***(0.0184)
0.0595***(0.0130)
0.0606***(0.0209)
0.132***(0.0254)0.480***

361, 362
0.102***(0.0321)0.238***(0.0411)
0.0971***(0.0334)
0.121**(0.0517)0.450***(0.0600)
0.310***(0.0430)524(0.0327)0.0891***(0.0245)1,374(0.0319)0.406***(0.0246)645(0.0435)
0.137***(0.0284)2,126(0.0486)
0.0567(0.0467)437(0.0635)
0.398***(0.0632)875(0.0531)-0.0805***(0.0277)
1,682

38

ISIC
Log of skilled laborLog of unskilled laborLog of capitalLog of services
Log of materials
Log of electricity
Observations

Dependent variable: log of output
369
371, 372
0.0490**0.0204(0.0240)(0.0350)
0.0411**
0.0420(0.0197)(0.0258)
0.00479
0.0321(0.0176)(0.0444)0.204***
0.249***(0.0240)(0.0556)
0.570***
0.432***(0.0383)(0.0652)
0.0667*1.193***(0.0349)(0.0766)
1,077
989

381, 383, 385
0.135***(0.0128)0.112***(0.00992)
0.0569***(0.0146)0.145***(0.0158)
0.479***(0.0326)
0.0792***(0.0197)
6,345

382
0.126***(0.0205)
0.0885***(0.0135)
0.0357(0.0251)0.202***(0.0312)0.396***(0.0429)0.289***(0.0339)1,665

384
0.161***(0.0278)
0.0830***(0.0201)
0.0571(0.0428)0.251***(0.0603)0.300**(0.127)-0.0575(0.0509)758

390
0.114***(0.0403)0.183***(0.0415)0.0733**(0.0318)
0.198***(0.0645)0.307***(0.0494)
0.527***(0.0377)439

Note: ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. Numbers in parentheses represent standard errors.

39

Table 1. Descriptive statistics of individual and industry characteristics
Variable

2000

2003

2006

2009

Observations

4,268

4,375

5,055

3,334

Log hourly wage

7.134

7.097

7.174

7.308

Years of potential labor market experience

19.07919.55719.81521.529Head of the household

0.710

0.640

0.588

0.617

Married

0.631

0.570

0.520

0.528

Informal

0.110

0.114

0.134

0.097

Large-sized firm

0.361

0.354

0.356

0.389

Unskilled workers

0.223

0.211

0.196

0.191

Semiskilled workers

0.560

0.600

0.606

0.619

Skilled workers

0.218

0.189

0.197

0.190

Effective tariffs on final goods

0.078

0.034

0.022

0.016

Effective tariffs on intermediate goods

0.047

0.019

0.012

0.012

Share of foreign owned capital

0.166

0.145

0.149

0.150

Ratio of expenditure on licenses and foreign technicalassistance

0.002

0.002

0.002

0.001

Individual characteristics

Industry characteristics

Note: The number of observations is limited to the target profile described in Section 4 andadditionally excludes any samples with missing values for the variables used.
Source: Author’s calculations based on data from CASEN (2000, 2003, 2006, and 2009) for theindividual characteristic, and data from ENIA (1999, 2002, 2005, and 2007), Becerra (2006), and
WITS for the industry characteristics.

40

Table 2. Estimation results of the impacts of trade liberalization on industry wage
premiums
Log output tariff(1)-0.134***(0.040)Log input tariff(2)-0.134***(0.044)-0.001(0.055)

FDI
Foreign technology(3)-0.130***(0.043)(4)-0.116**(0.044)0.124(0.081)12.731**(5.687)0.122(0.080)12.807**(5.690)0.275***(0.035)-0.018(0.035)-0.022(0.035)-0.081(0.048)1.141***(0.080)-0.140(0.092)-0.219**(0.091)-0.375***(0.083)0.159***(0.023)5.796***(0.137)yes
yes
17,0320.428-0.030(0.026)
0.278***(0.037)-0.021(0.037)-0.027(0.036)-0.089*(0.051)
1.149***(0.080)-0.148(0.091)-0.231**(0.089)-0.391***(0.080)0.055(0.107)5.833***(0.146)yes
yes
17,0320.429

Log output tariff × large-sizedfirms
Semiskilled
Semiskilled × year2003
Semiskilled × year2006
Semiskilled × year2009
SkilledSkilled × year2003
Skilled × year2006
Skilled × year2009
Large-sized firms
Constant
Industry effects
Year effects
Observations
R-squared

0.279***(0.036)-0.023(0.036)-0.029(0.036)-0.084*(0.048)
1.153***(0.084)-0.150(0.092)-0.237**(0.097)-0.378***(0.086)0.161***(0.022)5.824***(0.138)yes
yes
17,0320.426

0.280***(0.036)-0.023(0.036)-0.029(0.036)-0.084*(0.049)1.153***(0.084)-0.150(0.091)-0.237**(0.097)-0.379***(0.086)0.161***(0.022)5.821***(0.188)yes
yes
17,0320.426

Note: **, and * indicate significance at 1%, 5%, and 10% levels, respectively. Numbers inparentheses represent robust-standard errors clustered at the three-digit industry level. Years ofpotential labor market experience and its squared term including their interactions with year
effects, demographic dummies, informal dummy, and region dummies are also included.

41

Table 3. Estimation results of impacts of trade liberalization on industry skill premiums(1)-0.039(0.035)-0.097**(0.045)-0.186***(0.067)Log output tariff
Log output tariff × semiskilled
Log output tariff × skilled
Log input tariff
Log input tariff × semiskilled
Log input tariff × skilled(2)-0.045(0.040)-0.070(0.047)-0.220**(0.102)0.011(0.060)-0.056(0.040)0.075(0.134)

FDI
FDI × semiskilled
FDI × skilled
Foreign technology
Foreign technology × semiskilled
Foreign technology × skilled(3)-0.033(0.037)-0.098**(0.047)-0.167**(0.067)(4)-0.035(0.040)-0.089*(0.048)-0.134*(0.066)-0.017(0.109)0.049(0.096)
0.466**(0.178)13.402(9.415)-6.367(7.420)6.123(13.913)-0.008(0.111)
0.043(0.099)0.444**(0.179)13.140(9.520)-6.111(7.641)
6.709(14.141)-0.005(0.030)-0.013*(0.007)-0.037**(0.015)0.084(0.109)6.062***(0.138)yes
yes
17,0320.433

Log output tariff × large-sized firms
Log output tariff × semiskilled × large-sized firms
Log output tariff × skilled × large-sized firms
Large-sized firms

0.160***(0.022)
6.053***(0.128)yes
yes
17,032
0.427

Constant
Industry effects
Year effects
Observations
R-squared

0.160***(0.022)
6.073***(0.177)yes
yes
17,0320.427

0.158***(0.022)
6.053***(0.129)yes
yes
17,0320.432

Note: ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. Numbers inparentheses represent robust-standard errors clustered at the three-digit industry level. Skilleddummies and their interactions with year effects, years of potential labor market experience andits squared term, including their interactions with year effects, demographic dummies, informal
dummy, and region dummies are also included.

42

Table 4. Estimation results of the impacts of trade liberalization on industry wage
premiums with industry-level TFP
TFP
Log output tariff

(1)
0.030
(0.019)-0.131***(0.040)Log input tariff

(2)
0.030
(0.019)-0.130***(0.045)-0.004(0.051)

FDI
Foreign technology(3)0.032**(0.015)-0.126***(0.042)(4)0.034**(0.015)-0.111**(0.044)0.135(0.082)13.015**(5.739)0.133(0.082)13.089**(5.759)-0.033(0.026)0.043(0.109)5.771***(0.141)yes
yes
17,0150.429

Log output tariff × large-sized firms
Large-sized firms
Constant
Industry effects
Year effects
Observations
R-squared

0.160***(0.022)5.767***(0.130)yes
yes
17,0150.426

0.160***(0.022)5.759***(0.174)yes
yes
17,0150.426

0.158***(0.023)5.732***(0.132)yes
yes
17,0150.429

Note: ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. Numbers inparentheses represent robust-standard errors clustered at the three-digit industry level. Skilleddummies and their interactions with year effects, years of potential labor market experience andits squared term, including their interactions with year effects, demographic dummies, informal
dummy, and region dummies are also included. The smaller number of observations than theestimation without industry-level TFP is due to the missing samples of ISIC 353 in ENIA 2005.

43

Table 5. Estimation results of the impacts of trade liberalization on industry skill
premiums with industry-level TFP
TFP
Log output tariff
Log output tariff × semiskilled
Log output tariff × skilled(1)0.032*(0.018)-0.033(0.035)-0.098**(0.044)-0.193***(0.065)

Log input tariff
Log input tariff × semiskilled
Log input tariff × skilled(2)0.032*(0.018)-0.040(0.042)-0.070(0.046)-0.222**(0.103)0.012(0.057)-0.058(0.040)0.064(0.136)

FDI
FDI × semiskilled
FDI × skilled
Foreign technology
Foreign technology × semiskilled
Foreign technology × skilled(3)0.031**(0.015)-0.027(0.038)-0.099**(0.047)-0.176**(0.064)(4)0.032**(0.015)-0.028(0.041)-0.089*(0.047)-0.142**(0.063)-0.002(0.109)0.044(0.096)
0.464**(0.178)13.719(9.196)-6.228(7.405)6.004(13.830)

0.007(0.111)
0.036(0.099)0.442**(0.180)13.421(9.336)-5.953(7.639)6.542(14.045)-0.007(0.030)Log output tariff × large-sized firms
Log output tariff × semiskilled × large-sized
firms-0.014**(0.007)Log output tariff × skilled × large-sizedfirms
Large-sized firms
Constant
Industry effects
Year effects
Observations
R-squared

0.160***(0.022)
5.998***(0.126)yes
yes
17,0150.427

0.160***(0.022)6.019***(0.173)yes
yes
17,0150.427

0.158***(0.023)
5.995***(0.128)yes
yes
17,0150.433-0.037**(0.015)0.075(0.111)6.004***(0.137)yes
yes
17,0150.434

Note: ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. Numbers inparentheses represent robust-standard errors clustered at the three-digit industry level. Skilleddummies and their interactions with year effects, years of potential labor market experience andits squared term, including their interactions with year effects, demographic dummies, informal
dummy, and region dummies are also included.

44

Table 6. Estimation results of the impacts of trade liberalization on industry wage
premiums and industry skill premiums using instrumental variable
Technique
TFP
Log output tariff

(1)
2SLS
0.015
(0.023)-0.450***(0.112)

(2)
2SLS
0.018
(0.016)-0.412***(0.084)

(3)
2SLS
0.019
(0.016)-0.405***(0.086)0.101(0.073)0.099(0.072)13.388***(4.660)13.468***(4.669)Log output tariff × semiskilled
Log output tariff × skilled
FDI

(4)
2SLS
0.016
(0.023)-0.262**(0.114)-0.232***(0.053)-0.204**(0.097)

FDI × semiskilled
FDI × skilled
Foreign technology
Foreign technology ×
semiskilled
Foreign technology × skilled
Log output tariff × large-sized
firms

(5)
2SLS
0.016
(0.016)-0.222**(0.091)-0.251***(0.057)-0.217**(0.098)-0.020(0.096)0.027(0.101)0.434***(0.167)15.603**(6.910)

(6)
2SLS
0.016
(0.017)-0.232***(0.089)-0.246***(0.055)-0.207**(0.099)-0.012(0.097)0.021(0.104)0.412**(0.168)15.401**(6.969)-8.064(8.275)4.051(13.132)-7.861(8.449)4.402(13.309)-0.031(0.035)0.001(0.045)Log output tariff × semiskilled
× large-sized firms-0.012*(0.007)Log output tariff × skilled ×large-sized firms
Large-sized firms
Constant
Industry effects
Year effects
Kleibergen–Paap rank F statistic
Stock-Yogo weak ID test
critical value at 10%
Durbin-Wu-Hausman test
Observations
R-squared

0.148***(0.022)5.026***(0.240)
yes
yes
33.97

0.148***(0.023)
5.077***(0.183)
yes
yes
33.99

0.039
(0.138)5.096***(0.187)
yes
yes
17.14

0.149***(0.023)5.486***(0.251)
yes
yes
11.37

0.147***(0.023)5.568***(0.206)
yes
yes
11.39

-0.034**(0.016)
0.099(0.166)5.554***(0.201)
yes
yes
5.78

16.38
10.33***[0.0033]
17,0150.412

16.38
10.39***[0.0032]
17,0150.417

7.03
5.51***[0.0096]
17,0150.417

7.96***[0.0005]
17,0150.413

8.42***[0.0004]
17,0150.420

6.76***[0.0002]
17,0150.420

Note: ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. Numbers inparentheses represent robust-standard errors clustered at the three-digit industry level. Numbersin brackets represent p-values. 2SLS indicates that the two-stage least squares estimation is used.
The Stock-Yogo critical values are available up to a maximum of two endogenous regressors (seeTable 5.2 in Stock and Yogo 2005, 101). Skilled dummies and their interactions with year effects,
years of potential labor market experience and its squared term, including their interactions withyear effects, demographic dummies, informal dummy, and region dummies are also included.

45

0.12
312

311

0.1

322

0.08

Effective tariff rate

390

323

324

321

355 385
381

356

384

331
354
361

0.06

332

353

383

352

362
369

314

351

382

313
371

341

372

0.04
342

0.02

0
0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

TFP

Figure 1.
Source: Author’s elaboration.
Note: The numbers show three-digit international standard industrial classification (ISIC Rev.2)
codes.

46

0.12

311

312

0.1

355
356

324
323

390

381

383
0.08

354

353

384

Effective tariff rate

322

321

382

385

314
313

362
369
361

352 0.06

351
372

331

332

341

371

0.04

342
0.02

0

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Residual from the first stage regression

Figure 2.
Source: Author’s elaboration.
Note: The numbers show three-digit international standard industrial classification (ISIC Rev.2)
codes.

47

Figure Captions
Figure 1. Effective tariff rate and TFP in 2000
Figure 2. Effective tariff rate and the residual from the first stage regression in 2000

48