Returns to education and wage structure in the Philippines:
a quantile regression perspective
By Jan Carlo B. Punongbayan1
Abstract
In the Philippines, education is seen as a crucial pathway in reducing poverty and inequality. However,
education can prove to be inequality-increasing if returns to education are found to be larger for
higher-income workers than for lower-income workers. In this study we go beyond usual OLS
earnings equations and use quantile regressions on the Philippine Labor Force Survey (LFS) to
estimate returns to education across the conditional wage distribution. We find that returns to
education (especially at higher levels) actually decrease with quantiles (i.e., are higher for low-wage
individuals). To our mind this supports efforts to expand access to formal education. At the same time,
our findings challenge the merits of promoting the vocational school paradigm, which may limit the
ability of low-ability individuals to reap the rewards of investments in higher education. More
generally, our results uncover trends in the country’s wage structure which would otherwise not be
captured by OLS analysis. We argue that a quantile regression perspective offers a fuller
characterization of the country’s wage structure, thereby offering more depth in the analysis and
formulation of various labor market policies.
Keywords: returns to education; quantile regression; income inequality; Philippine Labor Force Survey
JEL Classification: I24, J31 C31
1
Introduction
Since the seminal work of Becker [1974] on human capital theory, there has been an explosion of
theoretical and empirical work on the demand for and returns to education. The primary workhorse for
the empirical estimation of such returns to education was pioneered by Mincer [1974], where earnings is
expressed as a function of schooling, experience, and other variables assumed to affect earnings. Not
only can the coefficient for schooling be interpreted as the private financial return to schooling, but it
can also be understood as the proportionate effect of wages to an additional unit of schooling [Harmon
et al. 2003].
For earnings equations estimated using ordinary least squares (OLS), such coefficient captures the effect
of education on earnings for someone on the mean wage. Thus far, a great majority of the existing
literature on returns to education has relied on OLS, pointing to the (intuitive) stylized fact that extra
schooling results in higher wages, on average.
OLS, however, ignores the possibility that the returns to schooling may vary for individuals found along
different parts of the conditional earnings distribution. That is, it may be incorrect to assume that the
schooling-related increment to earnings is constant across the wage distribution — an assumption that
OLS makes. For instance, an extra year of schooling may have (significantly) different coefficients
1
The author is presently a Research Assistant at the World Bank’s Poverty Reduction and Economic Management Unit
(PREM). He is a summa cum laude graduate of the University of the Philippines School of Economics in 2009, holds a
master’s degree from the same school, and served as Economist for the Securities and Exchange Commission from 2012 to
2013. This paper benefited from helpful clarifications on the LFS by Louie Limkin.
1
between individuals with low wages and those with high wages. Since OLS hides these differential
effects across the conditional earnings distribution, an alternative econometric approach to the returns to
education puzzle can provide a more holistic understanding of the determinants of earnings.
This approach is especially important in understanding the effects of education on earnings inequality.
For instance, if ability and education are treated as complements, a finding that schooling has a greater
positive effect on high-wage individuals (versus low-wage individuals) may lead to the implication that
educational investments should be concentrated among high-ability individuals given the superior
returns on their wages. Education, therefore, turns out to be inequality-increasing. On the other hand, if
schooling has a greater positive effect on low-wage individuals (versus high-wage individuals), one
might conclude that education is inequality-reducing.
An empirical understanding of the differential effects of schooling on the conditional earnings
distribution, can, therefore, have important public policy implications in terms of education and
redistribution (among others). In the Philippines, we are unaware of any previous study which has
explored education returns from the perspective of quantile regression. This paper hopes to contribute to
the discussion, especially in light of the fact that education remains to be regarded as one of the most
surefire pathways out of poverty in the country.
Our findings suggest that returns to education actually decrease with quantiles, and education indeed is
an inequality-reducing mechanism that justifies the expansion of formal education especially for those
with low-ability. These results thereby challenge the policy of segregating students into vocational
school and higher education (e.g., the K to 12 program), since this may limit low-ability individuals’
chances of reaping the financial rewards of investing in higher education. More generally, our results
also uncover other trends in the wage structure (e.g., pertaining to the gender wage gap and regional
wage “penalties”) which would otherwise not be captured by the usual OLS analysis.
This paper proceeds as follows. Section 2 provides a quick overview of quantile regression as has been
used to study returns to education. Section 3 goes through the basics of quantile regression model and
the data and empirical strategy used in this study. Section 4 summarizes and discusses the results and
Section 5 explores possible implications. Section 6 concludes.
2
Review of literature
The stylized fact that earnings increase with education is well-established [Card 1994], almost to the
point of becoming self-evident. However, recent studies suggest that the gap between returns to primary
schooling and tertiary schooling has been increasing over time. That is, the returns to post-primary
education (i.e., secondary and tertiary school) are increasing relative to the returns to primary education
[Fasih et al. 2012; Jimenez and Patrinos 2008]. This would suggest that the usual goal of universal
primary education may no longer be adequate in lifting the poor out of poverty, since the poor will have
to progress to higher and higher levels of education to enjoy high financial returns to their schooling
investment.
However, not everyone may benefit from additional education in the same way. For instance, even if
individuals enjoyed greater returns from tertiary schooling on average, some individuals may enjoy
much greater (or much less) returns than the average individual. This poses a problem if certain
2
individuals (such as high-skilled people) systematically enjoy higher returns to further schooling, since
this implies that tertiary schooling is, after all, most beneficial to non-poor individuals than poor
individuals. Owing to the implications of heterogeneous returns to schooling on inequality and public
policy, there has since been a move to go beyond usual estimations of returns to schooling on average.
Among the first to recognize the value of examining the heterogeneous returns to education was
Buchinsky [1994]. His research came at a time when competing explanations were used to explain the
dramatic changes in wage inequality seen in the US during the 1980s. For instance, while wage
inequality within racial groups seemed to have declined in the 1960s, such inequality seems to have
risen in the 1970s and even accelerated in the 1980s. Returns to education and experience were seen as
crucial in the widening wage gap.
Buchinsky [1994] found that while returns to education in the US are higher at the higher quantiles,
returns to experience are higher at the lower quantiles. Also, the overall increase in returns to education
from the 1960s to the 1980s in the US was primarily due to the increase in the return to college
education for new entrants and experienced workers. The study was particularly path-breaking in tracing
the returns to education at extreme quantiles of the wage distribution, owing to the availability of
household survey data from 1964 to 1988.
The stylized fact that returns to education are higher for those at the top of the earnings distribution
seems to be a consistent finding among studies of developed countries. Martins and Pereira [2004],
drawing evidence from household surveys in 16 countries in North America and Europe, found that the
additional earnings associated with schooling is higher for those whose unobservable characteristics
place them on the top of the conditional wage distribution. Hence, schooling likely contributes to withingroup inequality.
Martins and Pereira [2004] cite possible reasons behind this finding, including over-education (where
those with much schooling take on low-paying jobs) and differences in school quality. But one possible
explanation, which has since emerged to be among the most prominent in the literature, is the possible
interaction between schooling and ability in which those with high ability benefit the most from
schooling. At the very least, there seems to be an interaction between schooling and some factors which
affect the pay gap and are heterogeneously distributed among workers at any level of education.
The evidence from developing countries is more mixed. Patrinos et al. [2009] find that while returns are
higher for high-wage individuals in Latin American countries (similar to the trend in North American
and European countries), returns are lower for high-wage individuals in East Asian countries. By
distinguishing between private and public sector employment, however, it was found that the
“equalizing” effect of schooling was found in the public sector, and not so much in the private sector.
Hence, this implies that the extent of public sector employment may be another important factor in
determining the structure of returns to schooling.
In the Philippines, studies on the returns to education are surprisingly few and far in between. Gerochi
[2002] estimated a 14% return to college education relative to secondary education, and a larger 17.1%
return to elementary education relative to no education. While this study found that returns to education
in the Philippines were at par with developed countries, this was contrary to the findings of an earlier
3
study [Tan and Paqueo 1989], which found that educational investments yield lower returns than in the
average developing country.
One of the latest studies on returns to education in the Philippines is Luo and Terada [2009] who use in
their analysis the Labor Force Survey pooled between 2003 to 2007. Aside from finding a monotonic
increase in returns for workers with elementary, secondary, and tertiary education (relative to those with
no education), they also find that education is the single most important factor contributing to wage
differentials (as much as 30% of wage differentials at the national level).
To our knowledge, no study has yet looked at returns to education in the Philippines from a quantile
regression perspective. In this paper we conduct such an analysis, seeing that returns to education on
average may provide a limited view of the wage structure of the Philippines. Our use of quantile
regression is also supported by the apparent importance of education in explaining a large part of wage
inequality in the Philippines [Luo and Terada 2009].
3
Estimation methodology
This section briefly introduces the method of quantile regression; the nature of the datasets used; and the
estimation strategy for the empirical analysis.
3.1 Quantile regression
Since the pioneering work of Buchinsky [1994], quantile regression or QR has been the predominant
empirical methodology used in examining heterogeneous returns to education. While alternative
methodologies such as instrumental variable (IV) and marginal treatment effects (MTE) techniques have
also been used, it is perhaps because of the relatively more advanced development of QR theory and its
practical implementation (in various statistical software) that gave rise to QR’s being the predominant
method of choice.2
QR stems from the seminal work of Koenker and Bassett [1978]. Whereas OLS summarizes the
relationship between regressors and an outcome variable based on the conditional mean function
𝐸(𝑦|𝑥), quantile regression considers such relationship using the conditional quantile function 𝑄𝑞 (𝑦|𝑥)
wherein the quantile 𝑞 ∈ (0,1) represents how 𝑦 splits the data into proportions 𝑞 below and (1 − 𝑞)
above.
Consider for instance the case of median regression (or least absolute deviations regression), which is a
special case of the quantile regression model where 𝑞 = 0.5. Whereas OLS minimizes the sum of
squared residuals, or ∑𝑖 𝑒𝑖2 , median regression minimizes the sum of absolute residuals instead, or
∑𝑖|𝑒𝑖 |.
Specifically, for the median case or q=0.5, define 𝑆(𝑦, 𝑏) = ∑𝑖 |𝑦𝑖 − 𝑏| . The first order condition is
given by:
Fasih et al. [2012] comment that the IV technique works best under very strict assumptions on the instrument’s
correspondence with the policy, while the MTE technique usually faces severe data limitations.
2
4
𝜕𝑆(𝑦, 𝑏)
= ∑[𝐼(𝑢𝑖 > 0)(+1) + 𝐼(𝑢𝑖 < 0)(−1)]
𝜕𝑏
𝑖
where 𝑢𝑖 = 𝑦𝑖 − 𝑏 and 𝐼(. ) is an indicator function. Hence, a median regression minimizes the sum of
residuals such that there are as many 𝑦𝑖 higher than 𝑏 as there are 𝑦𝑖 smaller than 𝑏.
Note, however, that the absolute-error loss function is symmetric in that the signs of prediction errors are
irrelevant. Hence, for quantiles q other than the median, there is an “asymmetric penalty”. More
generally, the quantile regression estimator for quantile q minimizes the weighted sum of absolute
residuals such that:
argmin𝑏 [ ∑ 𝑞|𝑦𝑖 − 𝑥𝑖′ 𝑏| + ∑ (1 − 𝑞)|𝑦𝑖 − 𝑥𝑖′ 𝑏|]
𝑖:𝑌𝑖 ≥𝑥𝑖′ 𝑏
𝑖:𝑌𝑖 <𝑥𝑖′ 𝑏
or, equivalently:
argmin𝑏 ∑ 𝜌𝑞 (𝑦𝑖 − 𝑏)
𝑖
where the weight function is specified by 𝜌𝑞 (𝑢) = 𝑢[𝜏 − 𝐼(𝑢 < 0)]. As such, QR minimizes a sum that
gives asymmetrical penalties (1 − 𝑞)|𝑦𝑖 − 𝑏| for overprediction and 𝑞|𝑦𝑖 − 𝑏| for underprediction. This
function can be minimized via the simplex method, and a solution is guaranteed to exist in a finite
number of iterations.
Unlike OLS, QR is more robust to non-normal errors and outliers, and is also invariant to monotonic
transformations such as log(.) [Baum 2013]. Hence, the presence of heteroskedasticity (as detected by,
say, the Breusch-Pagan test) is particularly useful in justifying the use of QR vis-à-vis OLS.
Also, QR can be thought of as a summary of all quantile effects, so that ∫ 𝑄(𝑞|𝑥)𝑑𝑞 = 𝐸[𝑦|𝑥] where
𝑄(𝑞|𝑥) = 𝑎(𝜏) + 𝑥𝑏(𝜏) is the qth conditional quantile on 𝑥 [Bassett Jr. et al 2002]. The QR parameter
𝑏𝑗 (𝑞) can be thought of as the effect on the dependent variable due to a unit change in regressor 𝑥𝑗 at a
specified quantile q, hence making coefficient interpretations similar to OLS. It is in this sense that QR
allows for a richer characterization of the data at hand, since it allows one to uncover the impact of
regressors on the entire conditional distribution, and not just on its mean, as what OLS is limited to.
3.2 Data and empirical strategy
For our analysis we use the October rounds of the Philippine Labor Force Survey (LFS) from 2001 to
2010. The LFS is a household-level survey conducted quarterly nationwide for the primary purpose of
monitoring changes in the employment status of the working age population for a specified period of
time [BLES 2011]. The sample size for any given period is around 51,000 households, and considering
only individuals for which data on wages, education, and province are non-missing, the 10-year sample
is reduced to 341,400 observations (an average of 34,140 per year).
5
Our primary interest in using the LFS is its collection of data on basic pay per day for individuals 15
years old and above. Collected starting 2001, the basic pay variable (also known as basic wage) is
defined as “the pay for normal time, prior to deductions of social security contributions, withholding
taxes, etc. It excludes allowances, bonuses, commissions, overtime pay, benefits in kind, etc.” [NSO
2010]. This wage variable (deflated using provincial CPI data with 2006 as the base year3), along with
data on highest educational attainment and experience (which takes on the usual Mincerian form of age
minus years of schooling minus 6), form the basis for our estimations of the wage equation per survey
year.
We also create dummies by sector for manufacturing and services (with agriculture as the reference
category); by occupation for officials/managers/executives and professionals (with workers/laborers as
the reference category); and by regional group for Luzon, Visayas, and Mindanao (with the National
Capital Region as the reference category).
Table 1. Descriptive statistics by LFS survey year
Log real daily wage
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Observations
36,148
33,276
37,794
35,192
32,497
32,069
32,903
32,987
33,749
34,785
Percent
of total
obs.
10.59
9.75
11.07
10.31
9.52
9.39
9.64
9.66
9.89
10.19
Mean
age
34.74
35.01
33.97
34.35
34.61
34.67
35.03
35.16
35.18
35.41
Mean
years
of
educ.
12.47
12.57
12.24
12.27
12.54
12.57
12.64
12.62
12.67
12.71
Mean
years of
experience
16.27
16.44
15.72
16.09
16.07
16.10
16.39
16.54
16.50
16.70
Mean
log
daily
wage
5.39
5.41
5.31
5.30
5.28
5.28
5.31
5.26
5.27
5.27
10th
percentile
4.43
4.46
4.38
4.35
4.35
4.34
4.38
4.41
4.38
4.36
50th
percentile
5.43
5.46
5.36
5.35
5.33
5.29
5.34
5.29
5.31
5.30
90th
percentile
6.33
6.34
6.26
6.23
6.22
6.20
6.18
6.18
6.23
6.25
Note: All LFS survey years refer to the October round. Years of schooling based on assigned estimated years based on
highest educational attainment. Experience=age–years of schooling–6.
Table 1 above provides an overview of the yearly LFS data used in terms of years of education,
experience, and log of daily wage. Note that while there has been a very slight change in years of
education (approximately corresponding to the same category as high school graduate), there has been
an 11% decrease in average real daily wages over the same period (average nominal wages, on the other
hand, increased by 31% over the same period). What is more, the decrease in average real daily wages is
higher for the 50th percentile than for the 10th and 90th percentiles (12% versus 8% and 8%,
respectively).
3
Note that wages were not deflated for Zamboanga Sibugay, Compostela Valley, and the City of Isabela (amounting to 1.02%
of total observations) due to the absence of such CPI data.
6
4
Results
In this section we go through the differences between OLS and QR results for the main covariates used
in the regression, namely education, experience, sex, sector and occupation, and region. In between we
also analyze apparent changes in the estimated returns over time.
But before we proceed, a technical note on the interpretation of coefficients. We recognize the
commonly overlooked mistake of interpreting coefficients of dummy variables in semilogarithmic
regression models as if they were coefficients for continuous variables. However, as pointed out by
Halvorsen and Palmquist [1980], the proper representation of the impact 𝑝𝑗 of a zero-one dummy
variable on the dependent variable with coefficient 𝑐𝑗 is 𝑝𝑗 = 100[exp(𝑐𝑗 ) − 1], not 𝑝𝑗 = 100𝑐𝑗 . Hence,
in all our results below which use dummy variables, reported effects on the log of wages use this more
proper interpretation.
4.1 Education
Figure 1 below shows that, as might be expected, returns to college education (relative to no education)
are highest (an average of 183% by 2010), followed by returns to high school (50% by 2010) and returns
to elementary education (14% by 2010). It also appears that from 2001 to 2010 average returns to
college seem to have risen the most (45.8 percentage points), relative to returns to high school and
elementary education (just 6.5 and 1.2 percentage points, respectively).
At this point it is worth noting that our OLS results are largely consistent with earlier findings in the
Philippine literature. Luo and Terada [2009] also find a 12% return to elementary education (versus no
education), a 49% relative return to high school education, and a 175% return to college education.
Similarly, Limkin et al. [2013], in their study of the determinants of the rural-urban wage gap, also find
a 49% return to high school education, but a much higher 253% return to college education.4
While returns to college education have increased on average (a 45.8 percentage point difference as
shown by the OLS column in Table 2), returns look very differently across the conditional wage
quantiles. In particular, returns have actually increased more for those at the higher end of the wage
distribution (a 58.5 percentage point increase among those at q=0.90) than those at the lower end (a 27.3
percentage point increase among those at q=0.10). This has led to the trend that, while returns to college
education are higher for low-wage workers than high-wage workers in 2010, the gap between them has
diminished since 2001.
Furthermore, OLS is unable to uncover these underlying changes in the return to college education
across the quantiles, as signified by the asterisks in Table 2 and the trends shown in Figure 2.5
Note that these previous studies’ results were already adjusted to account for the proper interpretation of dummy variables in
semilog regression models. Hence, it is possible that previously published returns to education in the Philippines may be
understated by the non-adjustment of coefficients as outlined by Halvorsen and Palmquist [1980].
5 Our use of QR is further justified by the finding that all OLS regressions from 2001 to 2010 (using whether education
dummies or years of education) are characterized by heteroskedasticity as found by implementing the Breusch-Pagan test.
4
7
Figure 1. OLS versus QR results — returns to education over time*
200.0%
180.0%
Percent returns
160.0%
College
140.0%
120.0%
100.0%
80.0%
High school
60.0%
40.0%
Elementary
20.0%
0.0%
2001
2002
2003
2004
2005
2006
2007
2008
Elem.-OLS
Elem.-Q10
Elem.-Q90
HS-OLS
HS-Q90
College-OLS
College-Q10
College-Q90
2009
2010
HS-Q10
*Note: Reference category is “no education”.
Table 2. OLS versus QR results — returns to college
OLS
Q10
2001
137%
155%
2002
140%
155%
2003
134%
143%
2004
138%
149%
2005
171%
182%
2006
165%
172%
2007
154%
152%
2008
162%
161%
2009
173%
173%
2010
183%
183%
Difference (2001 to 2010)
46%
27%
Q90
*
*
*
*
*
110%
126%
125%
118%
148%
150%
148%
153%
161%
169%
59%
*QR estimate significantly different from OLS estimate (i.e., they fall outside the 95% OLS
estimate C.I.). All estimates significant at α=1%.
8
*
*
*
*
*
*
*
*
*
Figure 2. OLS versus QR results across quantiles — returns to college
200%
190%
2010
2010
2010
Percent returns
180%
2010
2010
2001
2010
2010
170%
2010
2010
2010
2003
160%
2001
150%
2001
2004
2001
2005
2001
140%
130%
2006
2001
2001
2007
120%
2001
110%
2008
2001
2001
100%
OLS
q10
q20
2002
q30
q40
q50
q60
q70
q80
2009
2001
q90
2010
Quantiles
As an alternative perspective to the returns to education results, we have also estimated OLS and QR
results using years of education instead of the dummy variables for educational attainment. What we get
is a similar result in that returns to an extra year of education do seem to increase over time (from 2001
to 2010) but that OLS has a difficult time capturing changes in trends across wage quantiles, especially
for those at the higher portions of the wage distribution.
Breaking down returns by quantile as in Figure 3 below, we see that while OLS estimates do capture
returns for the lower quantiles (e.g., q=0.10), they do not fully capture changes in return trends in other
quantiles, especially for the highest quantiles (e.g., q=0.60 to 0.90). In particular, returns to education for
these higher wage quantiles consistently fall below and outside the confidence range of the OLS
estimates. In 2005 for instance, while OLS captured the increase in returns to education among lowwage individuals, OLS failed to show that returns for high-wage individuals actually did not increase as
much.
Figure 3. OLS versus QR results across time — returns to years of education
10.00%
OLS
OLS
9.50%
OLS
9.00%
OLS
q10
OLS
q10
Percent returns
8.50%
8.00%
OLS
q10
6.50%
6.00%
q90
q10
q90 q10
OLS
q90
q10
7.00%
q90
q80
q10
q10
q10q90
OLS
OLS
7.50%
OLS
q90
q80
q80
q90
q80
q90
q70
q80
q80
OLS
q80
q10
OLS-C1
q90 q10q90
q80
q80
OLS-C2
5.00%
2002
2003
2004
q80
q90
q80
5.50%
2001
q60
2005
2006
2007
9
2008
2009
2010
4.2 Experience
To derive returns to experience, we need to evaluate it at certain levels of experience using the formula
𝛽2 + 2𝛽3 𝐸 (where the betas are the coefficients for experience E and its square). We choose 5 and 15
years for those with low experience and high experience, respectively.
Figure 4 below shows that returns per extra year of experience is higher among the less experienced than
the experienced (as is expected). Also, among the less experienced, while returns have increased among
low-wage workers (those in q=0.10), returns among the high-wage workers have actually declined
considerably (by almost half a percentage point over the 10-year period). Among those with more
experience, the high-wage workers have also experienced a notable decline in returns to experience than
those with lower wages (although the decline is muted relative to their relatively less experienced
counterparts).
It is possible that this trend of decreasing returns to experience for high-wage individuals comes from
greater job mobility among the high-wage workers (whether less or more experienced), and that moving
from one job to the other is becoming less and less of a liability in terms of career movements. Hence,
an additional year of experience does not count as much as other factors affecting wages.
Figure 4. OLS versus QR results across time — returns to experience (5 yrs. and 15 yrs.)
2.4%
2.2%
OLS - 5y
Percent returns
2.0%
q10 - 5y
q90 - 5y
1.8% q90 - 5y
q90 - 5y q90 - 5y
q50 - 5y
q90 - 5y
q90 - 5y
q90 - 5y
1.6%
q90 - 5y
OLS - 15y
q90 - 5y
1.4%
q10 - 15y
q90 - 5y
q90 - 5y
q90 - 15y
1.2% q90 - 15yq90 - 15y
q90 - 15y
q90 - 15y
q90 - 15y
q90 - 15yq90 - 15y
1.0%
q90 - 15y
q50 - 15y
q90 - 15y
q90 - 15y
0.8%
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
4.3 Sex
OLS tells us that males earn higher wages than females on average, but the QR estimates qualify that
this is more the case among low-wage earnings than high-wage earners. As shown in Figure 5, the
quantile returns to being male are significantly higher for those at q=0.10 than those at q=0.90.
Moreover, such returns fall outside the estimates derived from OLS, especially for the extreme ends.
10
Lastly, it appears that such wage gap has increased over time across all quantiles, but especially among
those in the lower wage quantiles.6
Figure 5. OLS versus QR results across quantiles — sex dummy
70.0%
2005
2005
60.0%
2005
Percent returns
50.0%
2005
2010
2010
2010
40.0%
30.0%
2010
2001
2005
2010
2001
2001
2005
2010
2001
20.0%
2001
2005
2005
2005
2010
2001
2001
2005
2010
2001
2010
2001
2010
2001
q70
q80
q90
10.0%
0.0%
OLS
q10
q20
q30
q40
q50
q60
Quantiles
4.4 Sector and occupation
Returns to employment in the manufacturing sector (vis-à-vis the agricultural sector) are higher
compared to the relative return on the services sector. But across both manufacturing and services, there
seems to be a concave pattern of quantile returns, in that returns increase from the low-wage to midwage earners, and then begin to decline until one reaches the highest-wage earners. In other words,
returns to working in these sectors increase as one’s wages increases, but at a diminishing rate across
quantiles.
Also, with respect to occupation, it appears that the wage premium for officials, managers, and
executives (vis-à-vis workers and laborers) increase along the wage distribution. But as for professionals
(including those in academia) returns are particularly lower for those at the higher end of the wage
distribution. A further breakdown will reveal that much of this trend comes from public sector
employment among professionals, rather than private sector employment. These results conform with
the earlier findings of Patrinos et al. [2004] where it was found that public sector employment may
actually comprise a different wage structure than the private sector.
4.5 Region
In a sense, the regional dummy variables can be thought of as wage “penalties” for those not situated in
the National Capital Region. As one might expect, such “penalty” is lower for Luzon than in the Visayas
and Mindanao.7
We leave for future study the implementation of Heckman’s sample selection model to account for possible sample selection
bias. Note that at present variables required to execute such model are not available in the LFS datasets alone.
6
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But over time, the OLS estimates suggest that the penalty is growing across the regions, and also across
all quantiles according to the QR results (albeit at varying degrees). For instance, while in 2001 the
minimum penalty is 11% (for the highest wage decile in Luzon) and the maximum is 35% (for the 4th
decile in Mindanao), in 2010 the minimum is 23% (for the highest wage decile in Luzon) and the
maximum is 50% (for the lowest wage decile in Mindanao).
While the overall average trends given by OLS may suggest the worsening primacy of wages in the
NCR, the QR results further characterize which segments of the wage distribution in each region face
the greatest increases in such wage penalties over time. Figure 6 below elaborates on the QR returns for
Visayas, for example: over the years returns for the lowest-wage earners (q=0.10) have gone down the
most relative to other parts of the wage distribution — a trend that OLS fails to reflect. Similar trends
are also apparent for the Luzon and Mindanao dummies, but the quantile trends are not as striking as
those for the Visayas dummy.
Figure 6. OLS versus QR results across quantiles — Visayas dummy
-20.0%
2001
Percent returns
-25.0%
2001
2001
2001
2001
2001
2001
2001
2001
2001
-30.0%
-35.0%
2010
2010
2010
-40.0%
2010
2010
2010
-45.0%
2010
2010
-50.0%
OLS
2010
q10
q20
2010
q30
q40
q50
q60
q70
q80
q90
Quantiles
5
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Implications
Our results indicate that OLS has difficulty capturing the returns to education especially for the higher
wage quantiles. Also, much of the discrepancy between OLS and QR comes in the college dummy. This
might provide an indication that current returns to college education may be overestimated for those
with higher wages, than those with lower wages. But this is not to say that the gap between both ends of
the wage spectrum has not improved: as seen in Figure 2, returns to college for high-wage earners seems
to have caught up over time to the returns enjoyed by low-wage earners.
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These results also conform with the findings of Limkin et al. [2013], who find that the gap between rural and urban wages is
around 33%. However we only consider in the paper NCR vis-à-vis the rest of the country, which is a more aggregated
treatment of the rural-urban wage gap.
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Thus, in accordance with findings in the literature (particularly Patrinos et al. [2009]), the Philippine
labor market seems to display an “equalizing” effect of education on financial returns, in that those in
the higher end of the wage distribution do not enjoy significantly greater returns than those at the lower
end. In fact, quite the opposite is true. In this sense, if ability is taken to be a complement to education
(as is widely proposed in the literature), education (particularly higher education) remains a viable
investment even for those with low ability. In other words, this suggests that (higher) education is indeed
inequality-reducing in the Philippines, justifying increased educational investments across the board.
This result therefore also puts into question the merits of programs (such as the K to 12 Basic Education
Program of the Department of Education) which promote vocational education for those with low
ability, in that segregating high school graduates into those taking up vocational school and higher
education may limit the ability of low-ability individuals to reap the financial rewards of investments in
higher education.
Aside from returns to education, the estimated QR effects tend to behave similarly and consistently
across quantiles, whether we use education dummies or years of education: The gender wage gap seems
to be higher among low-wage earners; returns to manufacturing and service industry employment both
have concave trends across quantiles; the wage premia for officials and executives increase across
quantiles but decrease among professionals; and the wage “penalties” for those in Luzon, Visayas, and
Mindanao are particularly high for the lowest-wage earners, and low for the highest-wage earners.
Furthermore, our analysis examines how such quantile effects have changed over time. In particular, our
findings uncover a possibly increasing gender wage gap across quantiles (especially for the lowest wage
quantiles), as well as a possibly increasing regional wage penalties (also for the lowest wage quantiles).
These bring to light issues which might require policy intervention but are otherwise not captured by
standard OLS studies.
Although the possible interventions may prove to be complex and interrelated (e.g., policies which
promote employment growth in the regions and at the same time promote greater gender wage equality),
our findings point to the usefulness of using a quantile perspective in order to look at wages and uncover
a fuller characterization of the wage structure and its correlates. From a policy viewpoint, the QR
methodology arguably offers a superior description of the overall wage structure, thereby offering more
power and depth in the analysis and formulation of labor market policies.
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Conclusion
Our study used the method of quantile regression to examine the wage structure of the Philippine labor
market, focusing on the returns to education. Our findings are consistent with findings from other Asian
countries in that education tends to “equalize” returns across the conditional wage distribution, in that, if
ability and schooling are taken to be complements, high-ability individuals do not enjoy superior returns
to education vis-à-vis their low-ability counterparts. In fact, quite the reverse is true.
On the one hand, this finding supports efforts toward greater educational access and investments across
the board, since education (particularly higher education) remains a viable investment even for those
with low ability. On the other hand, our results also put into question the merits of programs such as K
to 12 Basic Education Program which segregate students into vocational school and higher education,
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thereby possibly limiting the ability of low-skilled individuals to reap the rewards of investments in
higher education.
Our results also uncover a number of trends over time, such as a growing gender wage gap especially for
low-wage individuals, and a growing wage penalty for those in the regions especially for low-wage
individuals. We leave for future study a full exploration of possible reasons for these emergent trends,
but our results do underscore the merits of studying the overall wage structure from a quantile regression
perspective, which moves away from the limited focus on “average” returns.
References
Bassett Jr., G., M. Tam and K. Knight [2002] “Quantile models and estimators for data analysis”,
Metrika 55(1-2): 17-26.
Baum, C. [2013] “Quantile regression”, lecture notes for Applied Econometrics, Boston College, Spring
2013.
Buchinsky, M. [1994] “Changes in the US wage structure 1963-1987: application of quantile
regression”, Econometrica 62: 405-458.
Bureau of Labor Statistics and Employment [2011] “Session on Labor Force Survey”, LearnStat:
Learning Statistics the Easy Way. Manila: Department of Labor and Employment.
<http://www.bles.dole.gov.ph/learnstat/9_Labor%20Force%20Survey%20(LFS).pdf> Accessed 3
October 2013.
Card, D. [1994] “Earnings, schooling, and ability revisited”, National Bureau of Economic Research,
No. w4832.
Fasih, T. et al. [2012] “Heterogeneous returns to education in the labor market”, World Bank Policy
Research Working Paper 6170.
Gerochi, H. [2002] “Rate of return to education in the Philippines”, University of the Philippines School
of Economics.
Halvorsen, R. and R. Palmquist [1980] “The interpretation of dummy variables in semilogarithmic
equations”, American Economic Review 70(3): 474-75.
Harmon, C., H. Oosterbeek, and I. Walker [2003] “The returns to education: microeconomics”, The
Journal of Economic Surveys 17(2).
Jimenez, E., and H. Patrinos [2008] “Can cost-benefit analysis guide education policy in developing
countries?” World Bank Policy Research Working Paper Series.
Koenker, R. and G. Bassett Jr. [1978] “Regression quantiles”, Econometrica: 33-50.
Limkin, L., K. K. Chua, J. Nye, J. Williamson [2013] “Exploring regional inequity: determinants of the
rural-urban wage gap”, World Bank working paper.
Luo, X. and T. Terada, [2009] “Education and wage differentials in the Philippines”, World Bank Policy
Research Working Paper.
Martins, P. S. and P. T. Pereira [2004] “Does education reduce wage inequality? quantile regression
evidence from 16 countries”, Labor Economics 11(3), 355-371.
National Statistics Office [2010] “2008 LFS Data Dictionary”, NSO Data Archive.
<https://census.gov.ph/nsoda/index.php/catalog/62/datafile/F8>
Patrinos, H., C. Ridao-Cano, and C. Sakellariou [2009] “A note on schooling and wage inequality in the
public and private sector”, Empirical Economics.
Tan, J. P. and V. B. Paqueo [1989] “The economic returns to education in the Philippines”, International
Journal of Educational Development 9(3): 243-250.
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