DP2013-19

Trade Liberalization and Skill Prem iu m
in C hile *Yoshimichi MURAKAMI
June 3, 2013

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Trade Liberalization and Skill Premium in Chile#
Yoshimichi MURAKAMI ∗Abstract
This study empirically analyzes whether trade liberalization increases wage inequality between
skilled and unskilled workers in Chile during 1974–2007. The findings show that tariff reductions
contributed to increases in wage inequality by causing price reductions of unskilled labor-intensive
goods protected with the highest tariffs prior to trade liberalization. In contrast, we found noevidence that new technologies embodied in capital and intermediate goods caused skill-biased
technological change. In addition, this study shows that an increase in the relative supply of college
equivalents did not contribute to wage equalization, while an increase in the minimum wages
contributed to wage equalization during the period of the democratic governments.

JEL classifications: F16, O15
Key words: Trade liberalization, Skill-biased technological change, Wage inequality, Chile#

I am most grateful to the Microdata Center, Department of Economics, the University of Chile, forproviding me with the data from Encuesta de Ocupación y desocupación en el Gran Santiago. I am also
deeply grateful to the late Prof. Shoji Nishijima, Prof. Nobuaki Hamaguchi, Prof. Takahiro Sato, Prof.
Tomokazu Nomura, and Prof. Tsuyoshi Yasuhara for their insightful comments and suggestions. Needlessto say, I hold sole responsibility for any errors and misinterpretations present in this paper.
∗Part-time Research Associate, RIEB (Research Institute for Economics and Business Administration)
Kobe University, 2-1, Rokkodai, Nada-ku, Kobe 657-8501, Japan. Email: y-murakami@rieb.kobe-u.ac.jp

11. Introduction
Does trade liberalization increase wage inequality between skilled and unskilled workers? If so,through which channel would trade liberalization affect wage inequality? In spite of traditional
theorem inherent in the Heckscher–Ohlin–Samuelson (henceforth, HOS) theory, many empiricalstudies have shown that trade liberalization has increased wage inequality in Latin American and
Caribbean countries (henceforth, LACs) during the last three decades. In this regard, Goldberg andPavcnik (2007) provide two important explanations based on an abundant body of empirical research.
The first line of explanation for these findings focuses on the inconsistency with the assumptions of
the HOS theory. Previous research notes that contrary to expectations, the unskilled labor-intensive
sectors were in fact the ones most protected, prior to trade liberalization, and that they experienced
the largest tariff reductions during the trade liberalization period. Therefore, a rise in wage inequality
is exactly what Stolper–Samuelson would predict. In contrast, the second line of explanation focuses
on within-industry changes. According to past works, one of the main determinants of increases inwage inequality is the increase in demand for more skilled workers within all industries; that is, askill-biased technological change (henceforth, SBTC). This increase is attributable to “defensive
innovation” (Wood, 1995), which indicates that intensified competition from abroad induces firms totake advantage of the existing new technologies that were unavailable prior to trade liberalization.
SBTCs also arise from the incorporation of new technologies embodied in cheaper imported capital
goods, such as machines and office equipment, or intermediate goods that are complementary toskilled workers.
With regard to the evolution of wage inequality between skilled and unskilled workers inChile, the first of the LACs to introduce far-reaching trade liberalization, there seems to be little
room for argument; that is, there is, in general, a common agreement that wage inequality increased
significantly since the mid-1970s, reached its peak in 1987, then decreased substantially up to thefirst half of the 1990s, once again increased in the mid- or late 1990s, and decreased afterward overthe past decade (that is, during 1998–2007). However, the impacts of trade liberalization on theevolution of wage inequality still remain controversial. Beyer et al. (1999) and Gallego (2012), in amore recent study, try to answer the aforementioned questions using exactly the same employment
survey over the same period while employing a similar methodology. However, the findings arequite different. The former study finds that trade liberalization, measured as the volume of trade overGDP (the ratio of exports plus imports to GDP), and the price reductions of unskilled labor-intensive
goods widen the wage premium gap between skilled and unskilled workers during 1960–1996.
Therefore, they conclude that the evolution of wage premiums during this period can be understoodwithin the context of the aforementioned inconsistencies in the assumptions of the HOS theory. In
contrast, Gallego (2012) finds that the wage premium in the United States, which is assumed to be a2

proxy for SBTC in developed countries, increased the relative demand for skilled workers in Chile,
while he finds no statistical significance of the volume of trade over GDP and the prices of unskilledlabor-intensive goods during 1960–2000. Therefore, he concludes that SBTC caused by technology
transfers from developed countries through imported capital is the crucial determinant of theevolution of relative demand for skilled workers.
Furthermore, the impacts of an increase in the supply of skilled workers on wage
inequality are also controversial among previous studies. Theoretically, an increase in the supply ofskilled workers should push the wages of skilled workers down relative to that of unskilled workers,
thereby contributing to wage equalization. Beyer et al. (1999) find that an increase in the proportionof the labor force comprising university graduates reduces wage inequality. However, Robbins(1994) finds that an increase in the relative supply of university graduates does not explain theobserved wage inequality during 1975–1992. Gindling and Robbins (2001) also find that
education-price effects, that is, wage premiums for more educated workers, dominate the effects ofan increase in the supply of education during 1974–1990.
In addition to considering the changes in labor supply, we must take into account otherpossible explanations, such as labor market conditions, when analyzing the evolution of wage
inequality between skilled and unskilled workers (Gindling and Robbins, 2001). Among the labormarket conditions, minimum wages, unemployment, and union bargaining should be considered therelevant factors for the Chilean case. 1 However, the findings of previous studies are also ambiguous.
Gindling and Robbins (2001) find a negative relationship between minimum wages and wage
premiums: an increase in the minimum wages contributes to wage equalization, but the correlations
are not statistically significant at 10%. Gallego (2012) too finds a negative relationship between
minimum wages and the relative demand for skilled workers but these correlations are notstatistically significant at 10%. Gindling and Robbins (2001) find a positive relationship between
unemployment and wage premiums: a decrease in unemployment contributes to wage equalization;
however, the correlations are not statistically significant at 10%. Thus, these previous studies findthat the evolution of wage inequality cannot be explained by changes in labor market conditions,although they do recognize the potential role that they play. In contrast, Reinecke and Valenzuela(2011) argue that the weaknesses of unionization and collective bargaining are the key determinantsof the limited improvement found in wage inequality after 1990.
We assume that there are at least two important reasons for the aforementioned unsettle
debate on the distributional effects of trade liberalization. First, the two studies mentioned above,
Beyer et al. (1999) and Gallego (2012), cover not only the post-1974 trade liberalization period but

1

For more details on the role of labor market conditions, see Bussolo et al. (2002) and Reineckeand Valenzuela (2011). The minimum wage law was established in the immediate aftermath of themilitary coup d’état, in October, 1973.
3

also the pre-1973 import substitution industrialization period. The problem with including thisperiod is that Chile had experienced political and economic turmoil during this period, especiallyfrom 1970 to 1973; that is, Chile passed through the “socialist-populist Chilean experience” (Larraín
and Meller, 1991) era. While various previous studies show that wage inequality was at its lowest
level during this period, the determinants of wage equalization comprise the temporal results of
distributional policies typically inherent in “economic populism” (Dornbusch and Edwards, 1991);
thus, wage equalization should be interpreted as in a separate mechanism from the tradeliberalization perspective. This problem is also applicable in the case of labor market condition
variables. For example, a decrease in wage inequality chronologically coincides with temporary
increases in real minimum wages; this is one of the typical “economic populism” policies. Thus, theaforementioned negative relationship between minimum wages and wage premiums could disappear
if we exclude this period from our study. Therefore, we should only cover the post-1974 period whenanalyzing the impact of trade liberalization on wage inequality; this study extends the analysis to2007.
Further, we argue that all the trade-related variables used by Beyer et al. (1999) andGallego (2012) are not necessarily appropriate measures of trade liberalization. First, the volume oftrade over GDP has flaws as a measure of trade liberalization, as pointed out by Rodriguez andRodrik (2001). In particular, when one considers the specific Chilean context, trade volumes havecrucial flaws as a measure of trade liberalization. This is because exports from Chile still depend
heavily on a limited number of commodities, including copper; thus, trade volumes can beapproximately determined from the price of copper and the real exchange rate, neither of which isrelated to trade liberalization by itself. 2 Second, changes in the prices of unskilled labor-intensive
goods occur not only because of trade liberalization, that is, tariff reductions or elimination of
non-tariff barriers, but also because of other factors, such as decreases in domestic demand.
Therefore, we cannot deny the fact that the prices of unskilled labor-intensive goods are endogenous
variables with respect to wage inequality. Third, the channel through which wage premiums in the
United States would contribute to SBTC in Chile is ambiguous, although wage premiums in the
United States are certainly exogenous variables with respect to wage inequality in Chile. In thisregard, Gallego (2012) argues that the imported capital from developed countries plays a crucial rolein technological transfer. However, he does not show any direct evidence linking technologicaltransfers and imported capital. Therefore, we are rather skeptical of the findings of the two studies2

In fact, during 1974–1996, the correlation coefficient between the volume of trade over GDP and
the real effective exchange rate was very high: 0.89. The data on the ratio of exports plus imports to
GDP are from the International Financial Statistics (IFS) 2009 CD-ROM. The data on the realeffective exchange rate are from the calculations developed by the author on the basis ofFfrench-Davis, Leiva and Madrid (1992: 44) during 1974–1976, Banco Central de Chile (2002:
345-349) during 1977–1985, and the bank’ s web page (http://www.bcentral.cl/) during 1986–2007.
4and therefore, argue that we should use tariffs, which are the most direct measures of tradeliberalization (Rodriguez and Rodrik, 2001). Moreover, tariffs have been not only appropriate
proxies for trade liberalization but also exogenously determined with respect to wage inequality inthe case of Chile, because Chile applied flat tariffs to most goods since 1979. 3
Therefore, the objective of this study is to empirically analyze whether trade liberalization
increases wage inequality between skilled and unskilled workers and identify the channel throughwhich it would affect wage inequality by using tariffs that are the most direct measure of tradeliberalization, and covering only the post-1974 Chilean trade liberalization period. In addition, thispaper tries to identify the impacts of the labor supply of skilled workers and labor market conditions
on wage inequality, which vary in previous studies. With regard to the analytical methodology, thispaper takes advantage of the methodology developed by the relevant labor economics literature, as
will be discussed in greater detail in the following section.
This paper is organized as follows. Section 2 describes a stylized theoretical model toanalyze wage inequality between skilled and unskilled workers and explains how to apply the modelto the Chilean context. Section 3 describes the data and method of estimation. Section 4 provides thefindings of econometric analysis. The conclusions are summarized in Section 5.

2. ModelIn this section, we describe a stylized theoretical model to analyze wage inequality between skilled
and unskilled workers and explain how to apply this model to the Chilean context. This model waspresented by Katz and Murphy (1992) and then developed by Card and Lemieux (2001) and manyother previous studies. This model assumes that wage inequality between skilled and unskilledworkers is attributable to the evolution of the relative supply and relative demand for skilled workers.
While Card and Lemieux (2001) developed a model incorporating imperfect substitutability between
not only different skill groups but also different age groups, this study uses the simplest model
presented by Katz and Murphy (1992),4 because this study uses not wages in themselves but “pure”
skill premiums estimated by controlling for observable workers’ characteristics, as will be discussed
in greater detail in the next section.
Therefore, we assume the following CES production function for aggregate output Y withtwo production factors, skilled workers and unskilled workers:

3

Although the tariffs levied in each industrial sector varied widely, from 0% to 750%, and theaverage tariff was 94% at the end of Allende’s administration, the military government not only
completely eliminated NTBs but also applied a uniform 10% tariff on most goods by 1979. The
center-left coalition government, which took office in 1990, implemented further uniform tariff
reductions (see Figure 1).
4
We also owe our specification to Acemoglu and Autor (2011), a recent labor economics textbook.
5

Yt = [(at Lσ t )

(1)

where

σ −1
σ

+ (bt Lu t )σ −1 σσ σ −1],

Ls t and Lu t are the total quantities of skilled and unskilled workers used in production

during period t;

at and bt are the factor-augmenting technological parameters in period t,

meaning that technological changes cause an increase in the productivity of skilled and unskilledworkers; and σ ∈ [0, ∞) is the time-invariant elasticity of substitution between skilled andunskilled workers.
If we assume that the wage rate of unskilled workers is equal to the marginal product of
unskilled workers and that the wage rate of skilled workers is equal to the marginal product ofskilled workers, we obtain the following wage inequality function as the natural log of the ratio of
the wage rate of skilled workers to that of unskilled workers:

(2)

ln(

Wst
a
Ls
1
s −1
)=(
) ln( t ) − ln( t ) ,
Wu t
bt
s
s Lu twhere Wst and Wu t stand for the wage rate of skilled and unskilled workers, respectively, inperiod t. This equation decomposes the evolution of wage inequality between skilled and unskilledworkers into the first term, which represents the relative demand for skilled workers, ln(

the second term, which represents the relative supply of skilled workers, ln(

at) , andbt

Ls t) . What is
Lu t

relevant from the theoretical assumption regarding the impacts of an increase in the supply of skilledworkers on wage inequality can be given as follows:

Wσt
)
Wu t
1
= − < 0.
Lσσ
∂ ln( t )
Lu t

∂ ln(
(3)

Therefore, this model shows that an increase in the relative supply of skilled workers reduces the6

wage inequality between skilled and unskilled workers.

Since we assume that σ is a time-invariant production parameter, technological change

is skill neutral as long as the productivity of skilled and that of unskilled workers increase at thesame rate. However, this model usually assumes the existence of SBTC, that is,at> 1 . Since atbt

and bt are not directly observable parameters, the literature assumes that there is a log linear
increase in the relative demand for skilled workers over time coming from technology; that is, theevolution of the relative demand for skilled workers can be captured by a linear time trend(Acemoglu and Autor, 2011). Therefore, we make the following assumption on the first term, which
represents the relative demand for skilled workers:(4)aln( t ) = γ 0 + γ 1t ,btwhere t is a linear time trend. If we substitute equation (4) into equation (2), we obtain

(5)

ln(

Wst
Ls
s −1
s −1
1
)=(
)γ 0 + ()γ 1t − ln( t ) .
ss
Wu ts Lu tApparently, this model assumes that SBTC takes place at a constant rate. However, there isno special reason for SBTC to always take place at a constant rate; therefore, we do not have tonecessarily abide by the aforementioned assumption. In fact, Autor et al. (2008) allow the term
representing the relative demand for skilled workers to have more flexible specifications; they
include not only a linear time trend but also a quadric time trend and a cubic time trend. Moreover, it
is natural to assume that SBTC takes place in Chile at a varying rate, because trade liberalization in
Chile is very rapid and comprehensive as compared to other developing countries, including the
LACs. Therefore, we assume that a linear time trend can capture a SBTC that is not necessarily
related to trade liberalization as mentioned in the usual labor economics literature; thus, we include
trade-related variables that can capture the SBTC inherent in trade liberalization instead of a quadricor a cubic time trend. Under this assumption, the term representing the relative demand for skilledworkers can be described as follows:7aln( t ) = γ 0 + γ 1t + γ 2 tradet ,bt(6)

where trade stands for the trade-related variables in period t. We include the average nominal tariff
rates of each year as the measure of trade liberalization, as discussed in the introduction. We also
separately include the ratio of imports of consumption goods, capital goods, and intermediate goods
to each year’s GDP (Consumption M/GDP, Capital M/GDP, and Intermediate M/GDP) to identify
the channel through which trade liberalization would affect SBTC. This is because SBTC is
attributable to both import competition in final goods and the incorporation of new technologies
embodied in capital and intermediate goods, as discussed in the introduction; thus, we need to
identify which channel is relevant to the case of Chile. Therefore, this is the estimation strategy inwhich we apply this model to the Chilean context. If we again introduce equation (6) into equation(2), we obtain

(7)

ln(

Wst
Ls
1
s −1
s −1
s −1
)=(
)γ 0 + ()γ 1t + ()γ 2 tradet − ln( t ) .
Wu t
s
s
s
s Lu tIn addition, we add possible control variables that could have different effects on skilled
and unskilled workers. First, we need to control for labor market conditions, such as minimum
wages and unemployment, as discussed in the introduction. Theoretically, if the wages of unskilledworkers are affected by the minimum wages while the wages of skilled workers are not, an increasein minimum wages can be expected to increase the wages of unskilled workers; thus, it contributes
to a reduction in wage inequality (Gindling and Robbins, 2001). Concerning unemployment, if
unemployment requires some additional qualification, an increase in unemployment would be
expected to lead to an increase in the wages of skilled workers, thus contributing to an increase inwage inequality (Larrañaga, 2001). Second, we consider the export impacts predicted by the HOS
theory: if the non-traditional natural resource-based exports, e.g., fish, fruit, and forestry products,
expanding after trade liberalization comprises unskilled labor-intensive goods, these changes would
favor unskilled labor, thus contributing to a reduction in wage inequality. 5 In our empirical analyses,
we estimate equations (5) and (7) without and with control variables, as will be discussed in greater
detail in the next section.

5

For example, Scott (1996) and Schurman (2002) point out that those non-traditional natural
resource-based export sectors, especially the fruit sector, were successful in creating relatively
unskilled labor-intensive jobs as compared to copper, which is a traditional export sector in Chile.
8

3. Data and estimation method
In this section, we describe the data used in this study and the method of estimation. In theestimation of equations (5) and (7) mentioned above, the evolution of wage inequality between
skilled and unskilled workers, ln(

ln(

Wst
) , and that of the relative supply of skilled workers,
Wu t

Ls t
) , are observable variables. Therefore, the most relevant issue in the estimation method is
Lu t

how to calculate the aforementioned two variables.
The data used for calculating the variables come from the Employment andUnemployment Survey for Greater Santiago (Encuesta de Ocupación y desocupación en el Gran
Santiago), conducted by the University of Chile. This survey covers the Greater Santiago area,
which includes roughly 40% of total Chilean population, and is conducted in June every year. Eachsurvey covers fixed 3600 households, 6 and approximately 10000 and 14000 individuals during
1974–2007. The data are repeated cross-section, and the sample is fully representative of the Greater
Santiago area.
We define “wages” as the remuneration from one’s principal occupation; thus, wages does
not include the income from self-employment, assets, pensions, or other sources. The samples usedin this study comprise the working age (14–65 years) population who report positive income andpositive work hours. The sample includes only salaried workers—that is, white-collar (“empleados”)
and blue-collar (“obreros”) workers—who work on a full-time basis (more than 30 hours a week).
Therefore, self-employed workers—that is, employers and independent workers—are not included.
Domestic servants, unpaid family workers, and military personnel are also excluded from the sample
because their wages are not likely to be determined by market forces. The samples whose variables
are not answered in at least one survey question are also eliminated in advance.
To calculate the evolution of wage inequality between skilled and unskilled workers andthe relative supply of skilled workers, we define skilled workers as college equivalents and unskilledworkers as high-school equivalents, because there is a notable wage gap between people whocompleted university education and those who completed only high school in Chile (Ffrench-Davis,
2010). Therefore, the evolution of wage inequality between skilled and unskilled workers and that ofrelative supply of skilled workers are calculated as the skill premiums between college equivalents
6

Although the number of households fluctuated during 1957–1979, the number has stayed fixedsince 1980.
9and high-school equivalents (wp) and that of the relative supply of college equivalents (relative
supply). Then, we apply the methodology of Autor et al. (2008) to our study to calculate theevolution of wage inequality college equivalents and high-school equivalents. The methodology is asfollows. First, we construct six education categories (workers with up to primary school education,
high-school dropouts, high-school graduates, vocational high-school dropouts and graduates, somecollege, 7 and university graduates) up to 1997, five education categories (workers with up to
primary school education, high-school dropout, high-school graduate, some college, and university
graduates) after 1998,8 and four potential experience categories 9 (0–9, 10–19, 20–29, and 30+
years), and then construct 24 or 20 education × experience groups. Second, we estimate the skill
premiums for high-school graduates and university graduates from the following wage equation,
limiting the sample to males: 10(8)ln(Wi ) = cons + eδucαteg i′α + β1 exp i + β 2 exp i + X i′δ ,2where i indexes an individual; W stands for hourly wages obtained by dividing monthly wages byfour weeks of working hours and are then deflated by the national consumer price index (December
1998 = 1); vector educateg represents the aforementioned education categories; 11 exp is years ofpotential experience; 12 and vector X contains a head of household dummy that is 1 for workers witha head position, a public sector dummy that is 1 for workers employed in the public sector, and 8
industry indicators. 13 Third, using the results of the aforementioned regression, we calculate thepredicted log wages for high-school graduates and university graduates evaluated at the relevant
experience level (5, 15, 25, or 35 years, depending on the aforementioned experience groups) and at
7“Some college” comprises workers who had not completed Centro de Formación Técnica (CFT)or Instituto Profesional (IP)-based education (which are non-university post-high-school education
programs), CFT- or IP-based education graduates, or university dropouts.
8
The classification of education categories has changed since the 1998 survey.
9
The years of potential labor experience is calculated by (age – years of schooling – 6).
10
Following Card and Lemieux (2001), we consider only males when calculating the evolution of
wage inequality between college equivalents and high-school equivalents to avoid potential sample
selection biases, but we consider both males and females when calculating the evolution of therelative supply of college equivalents.
11
Workers with up to primary school education are chosen as the base category.
12
Although Autor et al. (2008) include the interactions of the quadric and Gallego (2012) includes
the interactions of cubic for experience with education categories, we do not include the interaction
terms with education categories because we cannot reject the jointly conducted F test for theinsignificance of the interaction terms at the 10% level for most of the analysis period.
13
The industry indicators are classified as follows: agriculture, mining, construction, manufacturing,commerce, financial services, personal services, community services, and transportation. The
manufacturing sector is chosen as the base category because it holds the largest employment shareamong all the industrial sectors.
10the base categories with regard to all variables in vector X. Finally, we calculate the difference
between the predicted log wage for the group of university graduates, ln(Wˆ s t ) , and high-school
graduates, ln(Wˆ u t ) , using the average share of total monthly hours worked by education ×
experience groups in each year as weights.
To calculate the evolution of the relative supply of college equivalents, we apply themethodology followed by Card and Lemieux (2001). The model assumes that there are only two
kinds of production factors, skilled workers and unskilled workers. Therefore, we need to classify all
male and female workers into two groups—that is, college equivalents and high-school equivalents.
In this regard, we calculate the quantities of high-school equivalents,

Lu t , in period t as follows:

wtUG
− 1)
HSD
wtPS
wtSC
PS
HSD wt
HSG
SC
Lu t ≡ Lt ( HSG ) + Lt ( HSG ) + Lt + Lt (
),
wt
wt
wtSC
wtUG
( HSG − 1) + ( SC − 1)
wt
wt
(

(9)

where

HSD
HSG
SC
LPS
, Lt
, and Lt are the total monthly working hours of workers who havet , Ltcompleted up to primary school education, are high-school dropouts, are high-school graduates, andPS

have attended some college, respectively; wt ,wtHSD , wtHSG , wtSC and wtUG are the average

wages of each year for workers who have completed up to primary school education, are high-school
dropouts, are high-school graduates, have attended some college, and are university graduates,
respectively. Therefore, we assume perfect substitution between the less educated workers up to
high-school graduates, and precisely weigh their working hours according to the ratio of their actual
hourly average wages to those of high-school graduates. We distribute the working hours of theworkers with some college education, which are “split” between college equivalents and high-school
equivalents on the basis of their relative wages, as shown in equation (9). The quantities of college
equivalents,

Ls t , in period t are calculated in exactly the same way:

11

(
(10)

Lu t ≡ L

UG
t

+L (
SC
t

w jtSC
wtHSG

− 1)

wtSC
wtUG
( HSG − 1) + ( SC − 1)
wt
wt

).

Although Gallego (2012) also weighs the working hours of workers according to theireducation level, he applies fixed weights to all years: for example, the working hours of high-school
dropouts are half of those of high-school graduates. However, the relative wages substantiallyfluctuated during his analysis period, rendering his assumption unrealistic and his calculations of therelative supply of skilled workers biased.
Regarding the trade-related variables, the data of average nominal tariff rates (Tariff) arefrom Ffrench-Davis, Leiva and Madrid (1992) for 1974–1989, and Ffrench-Davis (2002) and World
Integrated Trade Solution (WITS) for 1990–2007 (shown in percentage terms). We do not consider
preferential tariffs given the enforcement of numerous preferential free trade agreements that Chile
has actively pursued since the 1990s. The data of the imports of consumption goods, capital goods,
and intermediate goods are from Banco Central de Chile (2002) for 1974–1999 and its web page for
2000–2007 (shown in percentage terms). As for the control variables stated earlier, we use theunemployment rate of males (Unemployment) from our own calculations based on data from theaforementioned employment survey (shown in percentage terms), real minimum wages (Realminwage) from Banco Central de Chile (2002), and the web page of Biblioteca del Congreso
Nacional de Chile 14 deflated by the national consumer price index (December 1998 = 100). As aproxy for non-traditional natural resource-based exports, we use the ratio of non-copper exports to
GDP (Non-copper EX /GDP) from Banco Central de Chile (2002) for 1974–1999 and its web pagefor 2000–2007 (shown in percentage terms).

4. Estimation results
Figure 2 shows the evolution of skill premiums between college equivalents and high-school
equivalents, and figure 3 shows the evolution of the relative supply of college equivalents during
1974–2007. The evolution of skill premiums basically coincides with that seen in the previous
studies: it increased significantly since the mid-1970s, reaching its peak in 1987; it then decreased
substantially up to the first half of the 1990s, and once again increased in the mid-1990s; after that, itslightly decreased, although fluctuating year by year. This is as discussed in the introduction. The
evolution of the relative supply of college equivalents basically shows an increasing trend, although
this too fluctuated year by year.
14

http://www.bcn.cl/ (accessed on December 8, 2011)12

The results of the Augmented Dickey–Fuller (ADF) unit roots tests without and with timetrend are presented in table 1. The lag length is determined by the sequential t test on thehighest-order lag, beginning with higher-order lags and stopping when the coefficient is statisticallysignificant. The results show that we cannot reject the null hypotheses of unit roots at the 5%
significance level in without and with time trend, except for the relative supply of college
equivalents and the ratio of the imports of intermediate goods to GDP, indicating the existence ofunit roots in dependent and all independent variables. Therefore, we perform the Engel–Granger
cointegration tests in the estimation equations.
Table 2 reports the results of estimation equations (5) and (7). The coefficients on therelative supply of college equivalents are not statistically significant at all. Moreover, they show
positive in some models—that is, they do not show theoretically the “correct” signs. Therefore, wecannot reject the perfect substitution between college equivalents and high-school equivalents; wefind that an increase in the relative supply of skilled workers does not contribute to wageequalization during 1974–2007. The findings are quite different from both Gallego (2012), who
rejects the perfect substitution between college equivalents and high-school equivalents during
1960–2000,15 and Beyer et al. (1999), who find that an increase in the proportion of labor force with
university graduates reduces wage inequality during 1960–1996, but agree with Robbins (1994).
Regarding trade-related variables, the coefficients on tariffs are negatively significant inmodels without and with control variables (reported in columns [3], [4] and [8]), indicating that tariff
reductions contribute to increases in wage inequality between skilled and unskilled workers. The
coefficients on the ratio of the imports of consumption goods and capital goods to GDP are positive
but not statistically significant (reported in columns [5] and [6]). The coefficients on the ratio of theimports of intermediate goods to GDP are negatively significant at 10% level (reported in column[7]). However, the significance of the ratio of the imports of intermediate goods to GDP disappears
after controlling for tariffs, while the significance of tariffs is found to persist (reported in column[8]). Therefore, the results show that tariff reductions in themselves affect wage inequality, while theobserved negative relationship between the ratio of the imports of intermediate goods to GDP andwage inequality is lack of robustness; that is, tariffs correlate with both wage inequality and the ratioof the imports of intermediate goods to GDP. In fact, a regression of the ratio of the imports ofintermediate goods to GDP on tariffs yields a positive and statistically significant coefficient of
0.074 (with a T-statistic of 4.46) during 1974–1989.
The null hypotheses of no cointegration are also rejected, as shown in columns [4] and [8];thus, the relationship between wage inequality and tariffs is not spurious. Moreover, the impacts of15

Gallego (2012) estimates the elasticity of substitution between college equivalents andhigh-school equivalents from 1.40 to 1.70; that is, the coefficients on relative supply of college
equivalents are estimated from –0.714 to –0.588. For more details, see footnote 14 of Gallego(2012).
13

tariffs on wage inequality are practically large; a coefficient of –0.0090 shows that a 50% tariff
reduction would lead to a 0.45-point increase in the log wage difference between college equivalents
and high-school equivalents; thus, 67% of the total change of skill premiums from 1974 to 1987 canbe explained by tariff reductions.
A logical examination of these findings would directly show what Beyer et al. (1999)
imply by using the prices of unskilled labor-intensive goods; the unskilled labor-intensive sectorsthat were protected with the highest tariffs prior to trade liberalization experienced the largest tariff
reductions. In fact, Hachette (2000) shows that the two unskilled labor-intensive sectors, textiles, andfootwear and apparel, which were protected with the highest effective tariffs among 23 industrial
sectors in 1975, experienced the largest tariff reductions from 1975 to 1979. 16 Therefore, tariff
reductions contribute to increases in wage inequality through the Stolper–Samuelson effects.
A possible explanation for the insignificance of the ratio of the imports of capital andintermediate goods to GDP is the effect of “imports-de-substitution” (Ffrench-Davis, 2008; 2010)
after trade liberalization in Chile. Ffrench-Davis (2008; 2010) point out that the abandonment of theimport substitution of final consumption goods reduced the imports of intermediate goods andcapital goods, such as machinery and equipment, while it increased the imports of consumption
goods (particularly non-food goods), leading to bankruptcy in the remaining import substitutionsectors, such as the automotive industry. Therefore, the findings show that SBTC is not attributable
to the incorporation of new technologies embodied in capital and intermediate goods in the case of
Chile. Moreover, the findings are unique to Chile in that they are in contrast with some other LACs
like Mexico, which experienced a growing importance of trade in intermediate goods; thus, theimport catalyzed technological change is the main cause for SBTC 17. Therefore, the findings arequite different from both Gallego (2012) and Gindling and Robbins (2001), who conclude thatskill-intensive technologies embodied in imported capital goods caused SBTC in Chile. The
findings can be also interpreted as the experience of a country with a weak manufacturing sector
and comparative advantage in natural resources after rapid and far-reaching trade liberalization, thatis, “premature de-industrialization” (McMillan and Rodrik, 2011).
Another interesting finding is the impact of minimum wages. The coefficients on
minimum wages are negatively significant in cointegrated models (reported in columns [2], [4], [5],[6] and [8]). The finding shows that a decrease in minimum wages certainly contributes to increases
in wage inequality between skilled and unskilled workers, even if we exclude the “socialist-populist
Chilean experience” era from the analysis period; thus, it is quite different from the ambiguous
results of the previous studies. This is a reasonable finding, because the minimum wages were

16

The effective tariffs of the textiles sector decreased from 138% to 16% and those of the footwear
and apparel sector decreased from 164% to 14% from 1975 to 1979.
17
See, for example, Crag and Epelbaum (1996) and Harrison and Hanson (1999).
14

sufficiently low under the military government; it is just the same as the per capita poverty line
income of an urban area in Chile. 18 Thus, the finding coincides with the theoretical assumption that
if the wages of unskilled workers are affected by minimum wages while the wages of skilledworkers are not, an increase in minimum wages would be expected to decrease wage inequality.
Therefore, we conclude that the weak performance of labor markets under the military governmentis also an important determinant of increasing in wage inequality, while the improvement in labormarket performances, especially an increase in the minimum wages, contributed to wageequalization during the period of the democratic governments. On the other hand, the coefficients on
unemployment are positive, except for column [7]; that is, they are the expected signs, but notstatistically significant.
The coefficients on the ratio of non-copper exports to GDP are positive except for incolumns [4]. However, one possible explanation for the unexpected positive sign is that this
employment survey covers only the Greater Santiago area—an urban area that includes part of a
metropolitan region; that is, only about 1% of all the workers surveyed there were employed in the
agriculture, forestry, or fishing sectors, excluding for the natural resource-based manufacturingsectors. Therefore, it is not appropriate to empirically analyze the impacts of natural resource-based
exports on wage inequality using this employment survey.

5. Conclusions
This study empirically analyzed whether trade liberalization increases wage inequality between
skilled and unskilled workers and identifies the channel through which it would affect wage
inequality in Chile. The findings show that tariff reductions contributed to increases in wage
inequality by causing price reductions of unskilled labor-intensive goods protected with the highest
tariffs prior to trade liberalization. In contrast, we found no evidence that new technologies
embodied in capital and intermediate goods caused SBTC. In addition, this study shows that anincrease in the relative supply of college equivalents did not contribute to wage equalization. In
contrast, the minimum wages did matter—that is, a decrease in minimum wages contributed toincreases in wage inequality during the period of the military government, while an increase in theminimum wages contributed to wage equalization during the period of the democratic governments.
Therefore, this study found that under the military government, unskilled workers faced two major
issues. First, the price reductions of unskilled labor-intensive goods after trade liberalization
decreased their relative wages. Second, they were also affected by a squeeze on the real minimum
wage due to the government’s repressive labor policy. In this regard, this study shows quite different
18

For example, the monthly minimum wage was only 10120 pesos, while the per capita poverty lineof monthly income in urban areas was 10158 pesos in 1987 (Banco Central de Chile, 2002).
15

findings from previous studies by using tariffs, which are the most direct measures of tradeliberalization, and by covering only the post-1974 trade liberalization period in Chile.
Further research needs to be done in this area. First, we have not mentioned the reason
why the increase in the relative supply of college equivalents does not contribute to wageequalization. One possible explanation is the quality of education. If new private universities
established after 1980 are not compatible with traditional universities, our calculation of the relative
supply of college equivalents could be biased, although both Gindling and Robbins (2001) andGallego (2012) argue using cohort effects analyses that the difference in the quality of education
between the new universities and the traditional universities does not influence the skill premiums
for college equivalents. Second, if we were to obtain more detailed data on the components ofconsumption, capital, and intermediate goods, we could identify more precisely the channel throughwhich trade liberalization affects wage equality in Chile. For example, if we were to classify
consumption goods into non-food and food goods and capital goods into machinery and equipment
and other goods, we could obtain the precise impact of trade liberalization on SBTCs. However, nosuch empirical analyses regarding these issues were undertaken in this study; this area will be aninteresting subject for future research.

16

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18

Table 1
Results of the ADF unit root tests
Test statistics
without trend-2.772 *-2.4330.390-2.443-2.464-2.055-2.042-0.558-2.213variable
wp
Relative supply
Real minwage
Unemployment
TariffConsumption M/GDP
Capital M/GDP
Intermediate M/GDP
Non-copper EX/GDP

Number of Number of Test statistics
lags
obs
with trend
231-1.930033-4.183 **528-2.301033-2.8341
32-3.127033-2.5230
33-2.096528-4.226 **033-3.157

Number oflags

Number of
obs
2
31
0
33
3
30
0
33
1
32
0
33
0
33
0
33
4
31

** and * indicate significance at the 5% and 10% levels, respectively.

Table 2
Regression results of estimation equations (5) and (7)Relative supply
Trend

equation (5)[1]0.1221(0.1708)0.0049(0.0043)

Real minwage
Unemployment[2]0.1479(0.1707)0.0183 ***(0.0056)-0.0035 ***(0.0012)0.0025(0.0031)

Tariffequation (7)[3]-0.0702(0.1871)0.0045(0.0048)[4][5]-0.1889(0.1466)
0.0237 ***(0.0085)-0.0045 ***(0.0009)0.0057 *(0.0028)-0.0081 ***(0.0021)-0.0048 *(0.0026)

Consumption M/GDP[6]0.0825(0.1370)0.0143(0.0117)-0.0036 *(0.0020)0.0038(0.0055)

0.1463(0.1406)
0.0090(0.0072)-0.0025 **(0.0012)0.0019(0.0076)-0.1652(0.1404)0.0202 ***(0.0036)-0.0046 ***(0.0009)
0.0061 *(0.0034)-0.0090 ***(0.0023)-0.0253 *(0.0129)0.0244 *(0.0140)
1.3568 ***(0.3160)34
0.5181-5.977 ***0.0121(0.0102)0.0064(0.0323)

Intermediate M/GDP
Non-copper EX/GDP

Number of obs
R-squared
ADF-Test[8]0.0142(0.1549)
0.0097(0.0080)-0.0019(0.0012)-0.0006(0.0039)0.0275(0.0426)

Capital M/GDP

Constant[7]

1.3413 ***(0.3138)34
0.2287-3.6471.5894 ***(0.3412)34
0.4583-5.102 ***-0.0041(0.0142)1.2684 ***(0.2446)34
0.6058-6.947 ***1.1345 ***(0.3334)
34
0.2897-3.6930.0142(0.0140)
1.2813 ***(0.2638)34
0.4846-5.409 ***0.0175(0.0105)
1.3692 ***(0.4208)34
0.4794-5.288 ******, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Note: Numbers in parentheses are Newey–West HAC standard errors.

19

1.1792 ***(0.2819)34
0.6115-6.861 ***Figure 1
Evolution of average nominal tariff rates, 1974–2007 (%)

80

70

60

50

40

30

20

10

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

1982

1981

1980

1979

1978

1977

1976

1975

1974

0

Source: Ffrench-Davis, Leiva and Madrid (1992), Ffrench-Davis (2002), and World Integrated Trade
Solution (WITS).

20

Figure 2
Evolution of skill premiums between college equivalents and high-school equivalents inGreater Santiago, 1974–2007
1.8
1.7
1.6
1.5
1.4
1.3
1.2
1.1
1
0.9

2006

2007

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1989

1990

1988

1987

1986

1985

1984

1983

1982

1981

1980

1979

1978

1977

1976

1975

1974

0.8

Figure 3
Evolution of the relative supply of college equivalents in Greater Santiago, 1974–2007
0.4

0.35

0.3

0.25

0.2

0.15

0.1

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

1982

1981

1980

1979

1978

1977

1976

1975

1974

0.05

Source: Author’s calculations based on the data from Encuesta de Ocupación y desocupación en el Gran
Santiago.

21