Wage Inequality and Return to Education in Chile,
1974–2007
February 28, 2013


Yoshimichi MURAKAMI

Abstract
This study analyzes the validity of using return to higher education, an education-based
measure of skill premium, as a measure of wage inequality, when taking sample selection bias into
consideration; it uses data drawn from Chile’s trade liberalization period. The findings show that thereturn to higher education estimated by both ordinary least squares (OLS) and Heckman two-stepprocedures (Heckit), as well as other measures of wage inequality such as the Gini of wages, tended
to move precisely in the same direction up to the early 1990s; this was true, especially in theaftermath of trade liberalization, in spite of persistent highly fluctuations in the unemployment rateduring the mid-1970s and the 1980s. However, the return to higher education estimated by Heckit issubstantially larger than that by OLS in more recent, post-2002 periods, although OLS and Heckit
estimates of return to higher education still tended to move in a similar direction in this period.
Therefore, we find that the use of the return to higher education as estimated by OLS, while limiting
the sample to full-time salaried male workers, as a measure of wage inequality should not be overly
problematic, although we need to be more cautious about the presence of selection bias in more
recent periods.

JEL classification: J31, O15
Key words: Chile, Skill premium, Return to education, Sample selection bias

1. Introduction
Chile is the first of Latin American and Caribbean countries (henceforth, LACs) to convert fromimport substitution industrialization (henceforth ISI) to far-reaching trade liberalization, following
the military coup d’état that overthrew Allende’s government in 1973. Thus, among LACs, Chile has
the longest history of external reforms. Moreover, the external reform-experiences in Chile are notThis paper is a revised version of Murakami (2009), originally written in Japanese.
The author is a Ph.D. candidate at the Graduate School of Economics, Kobe University.
1uniform, but have at least three sub-periods (Ffrench-Davis, 2005; 2010). In this regard, Chile
represents unique example not only because of the long history of economic liberalization but also
because the country had reacted to global economic change by applying pragmatic modifications to
the original pure neo-liberal reforms, which might have had relevant distributional impacts.
Therefore, the analysis of changes in wage inequality in Chile has been very interesting for other
LACs that liberalized their trade regimes later in the 1980s and 1990s and had implemented the firststage of external reforms (i.e., import liberalization) or where the second or third stage of external
reforms (i.e., export promotion or reciprocal trade liberalization) have just begun; through suchanalysis, we can see whether different periods of external reforms produced different trends ofinequality in Chile.
In this regard, this study engages in a detailed analysis of changes in wage inequality inChile after the post-1974 trade liberalization period, by using the narrowest measure ofinequality—namely, skill premium, or returns to skill—as a measure of wage inequality. Goldberg
and Pavcnik (2007) point out that this narrowest measure of inequality has been used widely in theliterature on globalization and inequality, because the increase in inequality documented in many
developing countries has been associated with an increase in skill premiums. Moreover, theymention that in several countries, changes in skill premiums seem to chronologically coincide withtrade reforms. In that sense, skill premiums constitute a very suitable measure of inequality if we
focus on an analysis on wage-inequality change following trade liberalization. In addition to theadvantages this measure beers as an indicator, when information on an individual’s education
attainment is available, we can easily use return to higher education as a measure of skill premium(Goldberg and Pavcnik, 2007).
However, we need to be cautious when using return to higher education as a measure ofwage inequality; this is because, as a considerable volume of labor economics literature points out,
estimates of return to education are biased in the presence of violations of OLS assumptions, such asmeasurement error, endogeneity, and sample selection bias. In the case of Chile, endogeneity
problems caused by omitted and unobservable individual-level characteristics, Contreras et al.
(1999) show that the extent of overestimation of the rate of an additional year of education isapproximately only 1%, by using the education attainments of parents as a proxy of unobservable
individual characteristics. On the other hand, most previous studies that analyze return to educationin Chile—such as Montenegro (1998), Mideplan (2000) and Contreras (2002)—limit the sample tofull-time salaried male workers. Thus, they yield biased estimates as long as the decision to be afull-time labor force participant does not affect the determinants of wage.
If we review the process by which external reforms occurred in Chile, it chronologicallycoincides with the many other reforms (e.g., stabilization programs, macroeconomic adjustments,2

wide-ranging privatization, financial sector reforms, tax reforms, and labor market reforms). Thosereforms incurred huge adjustment costs, such as persistently high unemployment, squeeze on real
minimum wages, especially in the first and second subperiods of the external reforms.1 As discussed
in greater detail in section 3-2, the smaller the probability of labor-force participation—that is, thehigher the unemployment rate is, the larger sample selection bias will become. Moreover, theunemployment rate fluctuated greatly during the period under analysis; it sometimes jumped after an
economic crisis (e.g., between 1974 and 1976; after the shock stabilization program, between 1981
and 1982; after the debt crisis, between 1998 and 1999; and after the contagion of the Asian financial
crisis [see Figure 1]). Therefore, we must assume that the impact of sample selection bias cannot beignored during 1974–2007 in Chile, especially in the period during which the unemployment rate
fluctuated greatly.
Therefore, the objective of this study is to analyze the validity of return to higher
education as a measure of wage inequality, when taking sample selection bias into consideration andby using data from the trade liberalization period in Chile. If we find that the changes in the return tohigher education as estimated by OLS, while limiting the sample to full-time male workers, thereturn to higher education as estimated by Heckman selection bias correction procedures—as well asother measures of wage inequality, such as the Gini of wages—tend to move in the same direction,
we can confirm the validity of the return to higher education as a measure of wage inequality in thepresence of sample selection bias.
This paper is organized as follows. Section 2 describes the data used herein and presents
descriptive statistics concerning the changes in wage inequality, education attainment and theindustrial sector’s share in the total employment. Section 3 details the specifications of wage
equation, as well as the estimation method for Heckman selection bias correction procedures.
Section 4 presents the findings of econometric analysis, while Section 5 provides concluding
remarks.

1

The real indices of minimum wage in 1989 (1989=100) were squeezed to levels lower than thosein 1970 (108.9), but they have drastically increased since 1990 becoming 220.3 in 2007
(Ffrench-Davis, 2010: 181).
3

Figure 1. Changes in the unemployment rate of males in Greater Santiago, 1974–2007 (%)
30

25

(%)

20

15

10

5

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

2. Data and Descriptive Statistics
This study uses data from the Employment and Unemployment Survey for Greater Santiago(Encuesta de Ocupación y desocupación en el Gran Santiago), conducted by the University of Chile.
This survey, conducted in June every year, covers the Greater Santiago area, which comprises
roughly 40% of Chile’s total population, and is conducted in June every year. Each survey covers afixed 3600 households2 and approximately 10,000 and 14,000 individuals during 1974–2007. The
data are repeated cross-section, and the sample is fully representative of the Greater Santiago area.
First, changes in two frequently used wage inequality indicators—that is, the Gini ofwages and the variance of the log hourly wages of full-time salaried male workers— are presented in
Figure 2. I define “wages” as income from paid employment; thus, wages do not include income
from self-employment, assets, pensions, or other sources. The sample used in this study comprises
the individuals of working age (14–65 years) who report positive income and a positive number of2

Although the number of households fluctuated during 1957–1979, the number has stayed fixedsince 1980.
4

work hours. Like previous studies, the sample includes only salaried workers—that is, white-collar
workers (empleados), blue-collar workers (obreros), and domestic servants who work on a full-timebasis (i.e., more than 30 hours per week).3 Therefore, self-employed—that is, employers andindependent workers—are not included. 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 or missing in at least one survey question are also
eliminated in advance.4 Females are also eliminated from the sample, because it is natural to assume
that labor market of females are segmented from that of males.5
The data trends can be summarized as follows, with the two aforementioned indicators
showing very similar trends. Both increased significantly in the aftermath of tradeliberalization—that is, the import liberalization period—and reached its peak in 1987, amidst the“pragmatic neo-liberalism with a regressive bias” (Ffrench-Davis, 2005; 2010) period. On the other
hand, they substantially decreased during the early 1990s, when “reforms to the neoliberal reforms”(Ffrench-Davis, 2005; 2010) were carried out, although, they temporarily increased in the mid or
late-1990s and mid-2000s.6 However, they still have not reached the levels of wage inequality seenat the beginning of trade liberalization. Therefore, the findings show that different periods ofexternal reforms certainly produced different trends vis-à-vis wage inequality in Chile. Moreover,changes in the aforementioned wage-based measures of inequality indicate trends very similar thoseof income-based measures of inequality (cf. Larrañaga, 2001); the Gini of incomes increased
significantly since the mid-1970s, reached its peak in 1987, then decreased substantially in the early
1990s. Therefore, the findings show that limiting the components of income to wages and the sample
to full-time salaried male workers would not lead to bias in measurements of inequality, although theabsolute levels of the Gini coefficients in themselves are stably undermeasured during the full period
under analysis.

3

Almost all domestic servants are female; thus, they are de facto excluded when limiting the sample
to male workers.
4
I also exclude the samples whose pre-1997 education attainments are “special schools.” Althoughsome “special schools” seem to be secondary schools—especially in the 1990s— they constitute an
ambiguous and inconsistent category during the entire analysis period.
5
However, I find that the return to an additional year of education has not been statistically different
between males and females in the recent years.
6
The worsening of wage inequality between 1996 and 1999 can be attributable to the contagion of
external negative shocks derived from the Asian financial crisis (Ffrench-Davis, 2010).
5

Figure 2. Changes in the Gini of wages and the variance of the log hourly wages: full-time salaried
male workers in Greater Santiago, 1974–2007

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

0.4

1986

0.3

1985

0.5

1984

0.35

1983

0.6

1982

0.4

1981

0.7

1980

0.45

1979

0.8

1978

0.5

1977

0.9

1976

0.55

1975

1

1974

0.6

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

Second, changes in education attainments within the aforementioned samples arepresented in Figure 3. The figure shows stable improvements in education attainment during
1974–2007. The share of workers who had completed only up to primary school education decreased
from 63.8% in 1974 to 16.6% in 2007, while the share of workers who had attended up to secondaryand higher education was an increasing trend.7 However, changes in secondary and higher school
education showed another characteristic: the share of workers who had attended up to secondary
school education substantially increased until the mid-1980s and after that time, the share remained
relatively stable (about 50%); meanwhile, the share of workers who had attended up to higher
education increased mainly after the mid-1980s.

7The secondary and higher education categories include both dropouts and graduates. Higher
education not only includes university education but also Centro de Formación Técnica (CFT)- or
Instituto Profesional (IP)-based education, which was established by the 1980 higher education
reforms in Chile.
6

Gini
Variance

Figure 3. Changes in education attainment: full- time salaried male workers in Greater Santiago,
1974–2007
0.7

0.6

0.5

0.4

Primary
SecondaryHigher

0.3

0.2

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

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

Finally, the descriptive statistics of full-time salaried male workers in the selected years arepresented in Table 1. We can again confirm stable improvements in education attainments with thenumber of years of schooling increasing from 7.68 in 1974 to 12.00 in 2007. Changes in theindustrial sector’s share of total employment are also presented, to show the transformation of
employment structure after trade liberalization. The original classification typology features 9
sectors, i.e., agriculture, mining, manufacturing, construction, commerce, financial services, personal
services, community services, and transportation. I also break out the manufacturing sector into
naturalresource-based

manufacturing(henceforth,

NRBM)

and

the

non-resource-based

manufacturing sector.8 An especially striking finding in this table is that the employment share of
the manufacturing sector—especially that of non-resource-based manufacturing—substantiallydecreased: 38.0% of full- time salaried male workers were employed in the manufacturing sector at
the beginning of trade liberalization, but the ratio persistently decreased, in a trend that became moreevident after the 1990s; less than 20% (i.e., 17.3%) of workers were employed by the manufacturing
8

I classify the manufacture of foods, beverages, and tobacco; the manufacture of wood and wood
products; and the manufacture of paper and paper products as part of NRBM sector.
7

sector in 2007. Even the employment share of the NRBM sector had been stable or slightly
decreased in the Greater Santiago area. On the other hand, the employment share of service
sectors—such as commerce and financial service sectors—was persistently increasing. Thosesituations reflect the de-industrialization of Chile, following trade liberalization.9

Table 1. Descriptive statistics of full-time salaried male workers in Greater Santiago, 1974–2007

Note: Numbers in parentheses are standard deviations.
Source: Author’s calculations based on data from Encuesta de Ocupación y desocupación en el Gran
Santiago.

3. Estimation Method
3-1. Specifications of Wage Equation
We usually estimate return to education based on the Mincerian wage equation, as follows:(1)

ln wi  cons  1 schooli   2 exp i   3 exp i2  X i  ei ,9Chile has succeeded in expanding of non-traditional natural resource-based export sectors since
the mid-1980s, while the ISI sectors have been pretty much destroyed. However, this employment
survey covers only the Greater Santiago area—an urban area that is a part of a metropolitan region;
thus, only about 1% of all the workers surveyed there were employed in the agriculture, forestry, or
fishing sectors, as shown Table 1. Therefore, this employment survey does not represent theemployment structure of the whole of Chile, especially with respect to natural resource related ones.
8

where w is hourly wage, school is completed years of schooling, exp is years of potential experiencein the labor market (age – school – 6), and vector X is the observable worker characteristics.  1shows the change in wage given a one-unit increase in the number of education years; thus, it can beinterpreted as the rate of return to an additional year of education. However, under this specification,
the rate of return to an additional year of education is constant across all education attainments.
Therefore, I use a spline function, which allows the return to educations to differ among education
attainments. This specification is similar to that seen in Contreras et al. (1999).

(2)

ln wi  cons  1 schooli   2 exp i   3 exp i2   4 * d 8 * (school  8)   5 * d12 * (school  12)
 X i  ei
where the dummy variable d8 is equal to 1 if an individual has more than 8 completed years ofschooling, otherwise; equal to 0. The dummy variable d12 is equal to 1 if an individual has morethan 12 completed years of schooling, otherwise; equal to 0.10 In the current study, vector X contains
a head of the household dummy, which takes a value of 1 for workers with a position, and 0
otherwise; public sector dummy, which takes a value of 1 for workers employed in the public sector,
and 0 otherwise; and 9 industry indicators, which are detailed in the previous section.11 In thisspecification,  1 , 1   4 , and 1   4   5 show the return to primary, secondary, and higher
education, respectively. Previous studies, such as those of Mideplan (2000) and Contreras (2002),
estimate equation (2) by OLS.

3-2. The Estimation Method for the Heckman Model
As mentioned in the introduction, the estimates will be biased as long as full-time labor force
participation does not affect the determinants of wage. In this regard, the Heckman selection
correction procedure might be useful. To simplify the notation, I rewrite as x the set of containing
all the independent variables of the wage equation (2). z is also a set of independent variables of10

Chile’s school system had an 8-4 structure after 1965 and a 6-6 structure before that year. This
employment survey converts the years of schooling from the older system into those of the current
system. For example, an individual who received up to the second year of schooling in the older
system of secondary school is treated as having 8 years of schooling.
11
The industry indicators are classified as follows: agriculture, mining, NRBM, nonresource-based
manufacturing, construction, commerce, financial services, personal services, community services,and transportation. Construction is chosen as a base category because it holds a relatively large
employment share among non-trade sectors; its share is stable for the whole of the analysis period,
as seen in Table 1.
9participation equation, and it determines whether or not an individual i is a full-time wage worker.

ln wi*  xi 0  ei

(3)wi  wi* if z i 0  ui  0  d i  1=0

if z i 0  ui  0  d i  0 ,*where wi shows individual i’s latent offered wage including unobserved samples. d i is a dummy
variable that takes a value of 1 or 0 when individual i is a full-time salaried worker or not,respectively. Thus, individual i’s wage becomes observable—that is, he decides to be in the sample
of workers ( d i  1 ) only when his offered wage exceeds his reservation wage ( z i 0  ui  0 ). We
assume that ei and ui have a bivariate normal distribution with zero means, a known covariancematrix and correlation coefficient  :12(4)

  0    e2
 ei   ～ N   ,   0   
 ui 
 u e

 e u   .
 u2  

Therefore, nonsalaried workers (including part-time workers and unpaid workers)—that is,
those outside the sample of the wage equation—consist of those not at all employed. Thus, workingage (14–65 years) unemployed male, male part-time workers, and male unpaid workers are now
included in the sample for the participation equation. However, self-employed workers—i.e.,
employers and independent workers—remain eliminated from the participation equation,13 because
it is very difficult to find a variable that determines whether an individual is a salaried worker or
self-employed. In this regard, it is very feasible to argue that an individual will make the sequential
decision to work or not work (including part-time work and unpaid work), and given the decision towork whether as a salaried or self-employed workers (Schafgans, 2000). Alternatively, we canassume that the decision of the individual is to be a salaried worker, self-employed, or not to be
We normally assume   0 , because a greater likelihood of labor force participation willassociated with higher wages (Milanovic, 2006). We can relax strong assumptions vis-à-vis the
distribution of error terms by applying a semiparametric estimation technique. See, for example,
Schafgans (2000).
13
The samples whose variables used in the wage equation and participation equation (3) are notanswered or missing in at least one survey question are also eliminated in advance.
1210

employed at all. However, from the view point of this study’s objective of pinpointing a precise
estimate of return to education—that is, estimating the relationship between education and wage—it
does not seem appropriate to include the self-employed in the same manner as salaried workers. First,
the “wages” of self-employed individuals are in question, as they need to be separated precisely fromasset income (Montenegro, 1998). Moreover, the “wages” of self-employed are determined by virtueof many other factors in addition to the education they have received (Mideplan, 2000).14 Therefore,
I eliminate self-employment individuals in advance from the participation equation in the sample
selection correction model.
For identification, at least one variable needs to be included in z that also does not enterx (Schafgans, 2000; Wooldridge, 2003).15 As Schafgans (2000) and Milanovic (2006) point out, an
individual with large non-employment income tends to reduce his or her labor force participation
because he or she will be less likely to work for wages, given the higher opportunity cost of being
employed.16 Like those previous studies, I too use the sum of non-employment income—sum of
income from assets, pensions, and income from other sources in each household—as theaforementioned identifying variable.17 In addition to non-employment income, I add the number ofchildren under the age of 14 years in each household to the identifying variable, because a man
heading a household with a large number of children is more likely to be working full-time in orderto earn more money.
I estimate equation (3) by the Heckman two- step procedure (henceforth, Heckit). We canobtain the conditional expectation of hourly wages of individuals within the sample, as follow:(5)

E (ln wi | xi , z i , d i  1)  xi 0  

 u z i 0
(
),
e u

14

What is relevant from this issue, Repetto (2005) points out, is that Chile is not a purely
meritocratic country; the existing evidence shows that not only education but also socioeconomic
origin plays an important role as determinants of wages. This situation is more likely to applly to
self-employed workers, especially rich employers.
15

If all variables of z are equal to those of x , ̂ i can highly correlated with the variables ofx i ; thus, such multicollinearity can lead to very high standard errors among estimates of ˆ 0
(Wooldridge, 2003).
16
However, Milanovic (2006) does recognize the endogeneity of non-employment income, because
high current non-employment income might have resulted from previous labor force participationand high wages.
17
For example, in this employment survey, house-rent income and estimated rent from the value ofroyalties (regalía) is a component of income from assets. However, I do not include imputed rent asincome from assets, because the imputed rent of each household highly correlates with the wages of
its members; thus it is apparently an endogenous variable with regard to wages.

11wherez ( i 0 )z u( i 0 ) z 
u( i 0 )u—that is, an inverse Mills ratio.

It is clear that we will obtain biased estimates of  0 if we perform OLS while using only the
samples whose wages are observable—that is, those with in the sample for the wage equation.
Equation (5) also shows that the larger the correlation is between the error terms of the wage
equation and those of the participation equation—or the smaller the probability of full-time labor
force participation is—the larger the sample selection bias becomes, because we assume ρ ispositive and the inverse Mills ratio  () is a monotonic decreasing function of the probability thatan observation is selected.
We can obtain a reduced form to estimate from equation (2-5) as follows:

ln wi | xi , z i , d i  1  xi 0  (6)

 u z i 0()  vi .
e u

We can assume E (vi | xi , zi , di  1)  0 . Thus, we can obtain the unbiased estimatesof  0 from equation (2-6). Therefore, the Heckit estimation method is as follows. First, we estimate

ˆ 0
through the probit of d i on z i , using the entire sample, and compute ̂ i . Second, using
usample for the wage equation, we regress wi on x i and ̂ i ; then, we can obtain the estimates of 0 and u. In the Heckit procedures, estimates of  0 are unbiased.
eEquation (6) shows also that the sample selection bias can be considered omitted variablebias. If we perform OLS using only the samples whose wages are observable—that is, if we drop thezi 0() from equation (6) —the term is included in the error term. If
inverse Mills ratio term
u
we assume  0 and number of years of schooling positively correlates with the probability of

12full-time labor force participation (with the omitted variablez i 0u), the number of years of schooling negatively correlatesz 
 ( i 0 ) , because  () is a monotonic decreasing function.
u

Therefore, the omission of the inverse Mills ratio term causes an underestimation of the return toeducation. This is a highly relevant issue, because “actual” skill premiums as measured by return toeducation are larger than the frequently used results that limit the sample to full-time salaried maleworkers.

4. Estimation Results
First, we can see in Figure 4 changes in return to education in terms of various attainments, among
full-time salaried male workers during 1974–2007. The return to higher and secondary education
apparently increased after the post-1974 trade liberalization period. However, the return to secondary
education has persistently decreased since the mid-1980s and is now less than 10%; indeed, it isnoteworthy that it decreased from 19.5% in 1985 to 7.5% in 2007. Thus, the decreasing trend of the
return to secondary education in Chile, which was founded by each of in Contreras et al (1999) andContreras (2002), has persisted since 1997. On the other hand, the return to higher education
continued to increase following trade liberalization, reaching its peak in 1987, but slightly decreased
thereafter; thus, changes in the return to higher education and the Gini of wages have seemed tomove in the same direction. Overall, the return to primary education fluctuated by about 5%.

13

Figure 4. Changes in return to education by various attainments: full-time salaried male workers inGreater Santiago, 1974–2007
0.35

0.3

0.25

0.2
Return to Primary
Return to Secondary
Return to Higher
0.15

0.1

0.05

1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007

0

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

Second, I compare the two measurements of return to education: that estimated by OLS
using only full-time male salaried workers and that by Heckit using the full male sample including
the unemployed, part-time workers and unpaid workers. The estimation results of Heckit arepresented in Table A-1. Among the coefficients of the identifying variables, non-employment income
is negative and statistically significant in most years, while the coefficients of the number of children
under the age of 14 years in each household are not statistically significant in most years. The signs
lack of consistency: they are positive is some years, but negative in others. Thus, we consider thatthe latter is not an appropriate identifying variable. Therefore, the low significance of those
identifying variables in some years create high standard errors among the variables of the wage
equation as estimated by Heckit, compared to that estimated by OLS. The coefficients of the inverse
Mills ratio ̂ i are positive in most years, except 1975 and 1982; they do have the expected signs, butthey are not statistically significant at the 10% level, especially in the 1990s. Moreover, thecoefficients of the years of schooling are positive and statistically significant in all the analyzing
years; thus, more-educated individuals are actually more likely to be part of the full-time labor force.

14

Therefore, those two findings—that the coefficients of the inverse Mills ratio are positive and thatthe coefficients of the years of schooling in the participation equation are positive—and the featurethat the inverse Mills ratio is a monotonic decreasing function, lead to a larger return to education
when estimated by Heckit than when estimated by OLS, except in 1975 and 1982.
Changes in the return to education in terms of various education attainments during
1974–2007, as estimated by both OLS and Heckit are presented in Figure 5. One of the most striking
findings there is that differences between OLS and Heckit estimates are as small as 2% and are very
stable until 1990, in spite of persistently high fluctuations in the unemployment rate during the 1970s
and the 1980s; in comparison, differences are substantially large and greatly fluctuate in the
mid-1990s, in spite of there being during the 1990s very low and stable unemployment rates (seeFigure 1).18 However, the coefficients of years of schooling in most study years and the coefficientsof the inverse Mills ratio in the 1990s are not statistically significant. Therefore, we must reject theassertion that there were sample selection biases during the aforementioned periods; thus, this issue
can be ignored. On the other hand, the differences between OLS and Heckit estimates after 2002 are
not practically small; there, the coefficients of years of schooling and of the inverse Mills ratio are
statistically significant: when considering selection bias, the return to higher education increased, at
most, by 3.2% in 2005. The large differences between OLS and Hekit estimates after 2002 can be
attributed to relatively large coefficients of the inverse Mills ratio—that is, there was higher
correlation between the error terms of the wage equation and the participation equation during that
period (see Table A-1) and a relatively high unemployment rate after 1999 (see Figure 1).
In summary, OLS and Heckit estimates of the return to higher education tend to moveprecisely in the same direction up to 1990, and the differences between them in the mid-1990s are
ignorable. However, the differences between OLS and Heckit estimates after 2002 are robust and notignorable.

18

The ratios of the censored portions of the sample to the entire samples are larger thanunemployment ratios, because not only unemployed but also part-time and unpaid workers areincluded in the sample for the participation equation, as explained in Section 3-2. However, they
moved in precisely the same direction during 1974–2007.
15

Figure 5. Changes in return to education, by various attainments estimated by both OLS and Heckit
in Greater Santiago, 1974–2007
0.3

0.25

0.2

0.15

Return to Primary (OLS)
Return to Secondary (OLS)
Return to Higher (OLS)
Return to Primary (Hekit)
Return to Secondary (Hekit)
Return to Higher (Hekit)

0.1

0.05

1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007

0

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

Third, I compare changes in the Gini of wages and the return to higher education, as
estimated by OLS and Heckit. Changes in the Gini of wages and the return to higher education as
estimated by OLS, in both of which we have limited the samples to full-time salaried male workers
during 1974–2007, are presented in Figure 6, while changes in the Gini of wages among full-timesalaried male workers and the return to higher education estimated by Heckit are presented in Figure
7. The findings show that changes in the Gini of wages and the return to higher education estimatedby both OLS and Heckit move precisely in the same direction up to the early 1990s, especially in theaftermath of trade liberalization. However, changes in the return to higher education as estimated byboth OLS and Heckit tend to deviate from those of Gini of wages since the mid-1990s; especially,
the decreasing trend of the Gini of wages after the 1990s is not evident in the Heckit estimates.
However, the Heckit estimates in the 1990s lack statistical robustness, as mentioned, and we can see
again that changes in Gini of wages and the return to higher education as estimated by Heckit tend tomove in a similar direction after 2003, when the Heckit estimates are statistically significant.
Therefore, it should not be overly problematic to use return to higher education as a measure of wage

16

inequality during the 1974–2007 period, although the decreasing trend of the Gini of wages after the
1990s is not as evident in the case of the return to higher education, in absolute terms.

Figure 6. Changes in the Gini of wages and return to higher education estimated by OLS: full-timesalaried male workers in Greater Santiago, 1974–2007
0.3

0.6

0.55
0.25

0.5

Return to higher (OLS)
Gini

0.2
0.45

0.4

1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007

0.15

Figure 7. Changes in the Gini of wages (full-time salaried male workers) and the return to higher
education estimated by Heckit in Greater Santiago, 1974–2007
0.6

0.3

0.55

0.25

0.5

0.2

0.45

0.15

0.4

Return to higher (Hekit)
Gini

1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007

0.35

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

17

5. Conclusion
This study analyzed the validity of the return to higher education, an education-based measure ofskill premium, as a measure of wage inequality, while taking into consideration sample selection
bias; it uses data drawn from Chile during its trade liberalization period. The main findings are as
follows. First, the return to higher education as estimated by OLS limiting the sample to full-timesalaried male workers, and Heckit estimates tended to move precisely in the same direction up to1990, especially in the aftermath of the trade liberalization, in spite of persistently and highly
fluctuating unemployment rates during the 1970s and the 1980s. However, the return to higher
education as estimated by Heckit was actually larger than those by OLS—at most 3%—astheoretically expected, in more recent periods after 2002. Second, changes in the return to higher
education, as estimated by both OLS and Heckit, as well as other measures of wage inequality suchas the Gini of wages, also tended to move in the same direction up to the early 1990s—that is, they
increased significantly in the aftermath of the trade liberalization period, but substantially decreased
during the early 1990s when trade liberalization had been sustained for some time and “reforms to
the neoliberal reforms” were carried out. Thus, we confirmed—by using the return to higher
education as a measure—that different periods of external reforms certainly produced different
trends in wage inequality in Chile.
Therefore, I assert that the use of return to higher education as estimated by OLS—where
the sample is limited to full-time salaried male workers—as a measure of wage inequality should not
be problematic among data up to the early 1990s, especially in the aftermath of trade liberalization,although we need to be more cautious about the existence of selection bias in post-2002 data.
Further research needs to be done in this area. First, the number of children under than ageof 14 years does not seem to be an appropriate identifying variable when analyzing male sample
selection bias. Therefore, we involve a severe identification problem when the other identifying
variable is not statistically significant in the participation equation. If I were to obtain an appropriate
identifying variable that determines male labor force participation other than non-employment
income, I could more precisely estimate the return to higher education, while taking into
consideration sample selection bias. Second, this study cannot analyze why the return to higher
education as estimated by Heckit has, since the 1990s, deviated from those estimated by OLS. The
relatively large differences between OLS and Heckit estimates after 2002 can be attributed to thehigher correlation between the error terms of the wage equation and those of the participation
equation; this is because the differences in the periods are larger than in 1983, when the18unemployment rate was the highest.19 Therefore, the higher correlation between error terms of thewage equation and those of participation equation in recent years may reflect some kinds of changes
within the Chilean labor market. One possible explanation is that the spread of higher education in adeveloping country is often associated with a general trend wherein an individual is more likely toparticipate in labor market, after controlling for the individual’s education attainment. However, nosuch empirical analyses were undertaken in this study; this area will be an interesting subject for
future research.

Acknowledgements
I am most grateful to the Microdata Center of the Department of Economics at the University ofChile, for providing data from Encuesta de Ocupación y desocupación en el Gran Santiago and to
Prof. Esteban Puentes for answering my questions about interpreting that data. I am deeply gratefulto Prof. Nobuaki Hamaguchi (Kobe University), Prof. Takahiro Sato (Kobe University), Prof.
Matsushita Yukitoshi (Tsukuba University), Prof. Toru Yanagihara (Takushoku University), and Prof.
Yoshiaki Hisamatsu (Toyo University) for providing insightful comments and suggestions.

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19

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20

Appendix
Table A-1.Estimation Results of equation (3) by Heckman two-step procedure, 1974-2007.

21

22

***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Note: Numbers in parenthesis are standard errors.

23