The Impact of Education on Wage Inequality in the Philippines: A Two-Stage Quantile Regression Approach

The Impact of Education on Wage Inequality in the Philippines:

A Two-Stage Quantile Regression Approach

An Undergraduate Thesis Presented to

The Faculty of the School of Economics

De La Salle University- Manila

In Partial Completion of the Requirements for the Degree of

Bachelor of Science in Applied Economics

Submitted by:

Caluban, Paul Terrence T.

Chan, Sharmaine S.

Gonzales, Erika Louise R.

Gustilo, Robert Paul P.

Advisers:

Christopher Cabuay

Tereso Tullao, Jr., Ph. D.

Winfred Villlamil. Ph. D.

August 2016

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ABSTRACT

Literature has always pointed towards the causal relationship between education and wage

inequality, and further studies have only gone as so far as to determine the characteristics

of an individual that correlate to and/or further aggravate the said inequality. Utilizing a 2-

stage quantile regression on the merged 2012 Labor Force Survey and Family Income and

Expenditure Survey, this study aims to pinpoint the gravity of the returns on education on

the several income deciles, as well as on its other causal variables such as educational

attainment, individual, and household characteristics. The results show that returns to

education increases across deciles, meaning education exacerbates wage inequality. This

prompts individuals belonging to the lower deciles to work instead of pursue higher

education because the short-term returns of employment are greater than the long-term

returns of education.

JEL Classification: I21, J24, C31, C36.

Keywords: education, wage, inequality, 2-stage quantile regression, employment

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ACKNOWLEDGEMENTS

We would like to thank our family and loved ones for their eternal support, without

them we would not have found the determination and love to accomplish whatever we put

our hearts to. To Dr. Tereso Tullao, Jr., Dr. Winfred Vilamil, Prof. Christopher Cabuay,

and Dr. Mitzie Conchada, our panelists, for their patience, wisdom, and guidance; we shall

forever be grateful.

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TABLE OF CONTENTS

INTRODUCTION 4

Statement of the Problem 6

Objectives of the Study 7

Significance of the Study 8

Scope and Limitations 9

REVIEW OF RELATED LITERATURE 11

THEORETICAL FRAMEWORK 24

Conceptual Framework 28

Operational Framework 31

METHODOLOGY 34

Empirical Procedures 35

RESULTS AND DISCUSSION 46

Estimation Results 50

CONCLUSION AND RECOMMENDATIONS 77

REFERENCES 81

APPENDIX 86

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I. INTRODUCTION

Decades worth of literature have shown that individuals who possess or exhibit more

skills shall be given more reward, as they contribute a greater heft to the community. In

the ancient civilizations, this was seen in the ranks of the generals and leaders of society,

which were determined by their skill on the symposium or in the battlefield. Today,

however, even if we observe the same trend, the rewards are no longer fame, glory, and

fortune. We could simplify this in its most common trend: that skill is to be proportionally

rewarded with wages. Skill, however, is an arbitrary variable that cannot simply be

quantified for measure. Therefore, several employers look toward the only prima facie

evidence available to them: educational attainment and background.

According to the Philippine Statistics Authority (2015), the highest paying

occupation as of July 2014 were accountants and bookkeeping clerks who worked in

electricity, gas, steam and air conditioning supply companies. On the other hand, unskilled

workers who mainly perform routine manual tasks, require little or no experience and

minimum amount of training received wages less than half of what accountants and

bookkeeping clerks would during the same period. We could already see a prevalent bias

towards specialization and knowledge/skills only learned/available given a post-secondary

education. This shows that wage inequality is prevalent in the Philippines, especially

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among high-skilled and low-skilled workers. Albert et. al (2015) have gathered that for

2009 alone, the poorest 20% of the population only earned 14,022 Php while the richest

20% of 176,863 Php. While the former shared a little over 4% of the national income, the

latter accounted for over 76%. Furthermore, considering that most, if not all, of these jobs

require high levels of technical skill and/or education, one may contemplate on the

indirect effects of the education system and the wage disparity found within the labor

market.

The Philippines’ educational system and unemployment rates are undoubtedly

classified among the prevalent and pressing problems the country currently faces.

Inadequate opportunities in education, as well as its discrimination towards different

sectors of society, are the biggest factors why many Filipinos are not able to experience

the same quality of education that the fortunate few get. According to the Commission on

Higher Education (n.d.), only three out of every sixteen college enrollees every year are

able to complete tertiary education, and out of these three, two of them are from private

institutions which imply that they are well-off.

Given these statistics, it is undeniable that several workers in the Philippines were

not able to achieve the recommended minimum of at least fourteen years of education.

Because of lack of ample knowledge, skills, and trainings of many laborers, it becomes

very difficult for them to get employed and/or have permanent jobs. This is also the reason

many people choose to settle to having low-paying jobs rather than none at all.

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Upon realizing how alarming these statistics are, we have decided that we should

conduct a study on how we can decrease wage inequality in the Philippines with the aid of

education. Though there already exist a plethora of literature about this issue, our group

would utilize a new methodology called the Two-Stage Quantile Regression to acquire

more accurate and unbiased results regarding the impact of education on wages in the

Philippines.

1.1 Statement of the Problem

As companies increase their standards in terms of educational attainment, more

citizens have enrolled in public and private colleges and universities throughout the

country. According to the Commission on Higher Education (n.d.), the number of

enrollees from S.Y. 2007-2008 to S.Y. 2011-2012 increased by 14%. However, after the

average years of a program (usually four years), less than 20% of the enrollees graduate.

This shows that most of the undergraduate students either stop studying or dropout of

school. It means that students who were able to obtain undergraduate diplomas tend to

have higher advantage in terms of specialization and wage offering as compared to those

who were not able to finish tertiary or any post-secondary education. Thus, the problem

that this paper aims to address is: Is education an effective tool to decrease wage

inequality in the Philippines?

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1.2 Objectives of the Study

The study aims to determine the gravity of the effects of educational attainment to

the wage one earns and/or receives. Determinants of wage inequality and its correlation to

education may be inferred from the results, as to be tabulated or graphically represented.

Throughout the course of the study, this paper aims to achieve the following:

To assess which model best explains the effects of educational attainment on wage

inequality in the Philippine setting and to measure how this differs from the model

that past studies conducted.

To determine the returns to education across different deciles on aggregate level

and across sub-samples, specifically gender and urbanity

To determine whether pursuing higher education may be a more feasible decision

amidst the wage inequality.

The application of such knowledge and the answers to these questions shall then be

properly utilized for future policy recommendations for the increased accessibility of

higher education and the mitigation, if not elimination, of wage inequality.

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1.3 Significance of the Study

As a growing developing country, the Philippines suffers from a labor market

consisting of mostly secondary-level educated masses with an economy that demands

from the highly-skilled labor force. With such a large disparity in the supply and demand

for labor, there are corresponding consequences that are later reflected in the balance of

society, just as in the case of wage inequality. Thus, through this study, the researchers

aim to determine the impact of one’s educational attainment to his wage. Also, due to the

lack of recent empirical studies regarding this topic in the Philippines, the study will give

an update on the changes, if there are, about the correlation of educational attainment and

wage inequality in the Philippines. Furthermore, using different models, specifically

ordinary least squares (OLS), 2-staged least squares (2SLS), quantile regression (QR), and

2-staged quantile regression (2SQR) the researchers will identify which model is more

appropriate in terms of assessing the impact of educational attainment to wage inequality.

After which the researchers will provide policy recommendations on how the government

can alleviate poverty using various educational programs.

1.4 Scope and Limitations

The scope of this study would be Filipino workers and households as sampled and

documented in the merged datasets of the 2012 Labour Force Survey (LFS) and the

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Family Income and Expenditure Survey (FIES). It would tackle topics and variables

directly related to wage determination and educational accessibility. Aside from the

identification of the determinants and factors of wage inequality, its impact shall also be

analyzed in comparison given the different income deciles of the Filipinos. However, the

static nature of our study (due to the limitations in the data) does not allow us to properly

determine and ascertain any structural changes on the impact of education over time.

Since this study would go into depth on wage inequality, individuals who are not

earning wages, and families that have no wage-earning members have been dropped. Non-

family household members (such as non-relatives, boarders, and domestic helpers) were

also dropped. In addition, all wage-earning individuals who are considered child labor

(below 16) and past retirement (65 and above) were dropped from the data as well. Single

household heads were dropped as well because literature has provided that the education

of the head (and his/her spouse) shall serve as an influence on the child’s education. A

greater consideration was given to the marital status due to the prevalence of other studies

identifying the importance of parental and spouse’s education in explaining the influence

of the parent’s characteristics onto their child.

To facilitate an ease in the generation of certain variables, it was assumed that the

standard time for elementary is 6 years (no grade 7), 4 years for both high school and

bachelor’s, and 2-4 years for all postgraduate studies (Masters and PhD level were merged

in the formation of the dataset and could not be separated). Vocational studies were 2

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years and counted as post-secondary education substitutes. Preschool was also allocated at

2 years for those who have expressed that they undergone pre-school education;

otherwise, it was assumed they went straight to Grade 1. For those who reported that they

were unable to complete a level of education, an across the board assumption was

formulated that they were able to receive half the year's’ worth of education (i.e. 3 years

for elementary, 2 years for high school, and so forth). This general rule of thumb would

only be followed for individuals who have only stated their highest educational

attainment, but not their highest grade completed, as the latter provides more specificity.

For example, in the generation of the parent’s individual education, f_educ and m_educ,

we were only able to extract years of education and not their highest grade completed.

Therefore, certain assumptions were made on which levels of education they were able to

achieve based on the years of formal education they have taken.

The remainder of the paper is structured as follows. The next section discusses

various results of previous studies. Section III provides the theories that explain the

relationship between education and wage. Section IV explains the model and data used to

assess the impact of educational attainment to wage inequality. Section V discusses the

results and analyses, and lastly, section VI concludes this paper, as well as provide

recommendations based on our findings and inferences.

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II. REVIEW OF RELATED LITERATURE

It was not a very recent idea to correlate rewards to increased skill; the concept of

education and aspects of human capital influencing the returns to labor (or wage) is, if

anything, a most often touched about topic in the fields of labor and demographic

economics. There have been several studies performed on this subject, and though most of

them are theoretical in nature, the results are consistent and have found themselves in

lectures, studies, and textbooks. During the late 1900s, inequality was increasing in almost

every aspect of the word, and any study performed to find the root cause led to symptoms

being ubiquitous. Studies then started pointing at every single direction as factors of

inequality, going on about education, technological advancement, or even racial traits.

The Role of Education

The aspect of education covers two dimensions: (i) observable skill and (ii)

unobservable skill. Matters such as specialization and diplomas are observable skills

because they are utilizing a skillset, method, or work ethic that may be exclusively found

within higher levels of education. Unobservable skills, however, are more difficult to

determine. While some point towards soft skills as unobservable, others look towards the

holistic aspect of education.

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One of the principal arguments for education as a source of income inequality is its

ability to supply different kinds of labor as there is a technological change in society that

requires or demands more technical and specialized skillsets. It could be inherently seen in

the study of The Hidden Increase in Wage Inequality (Michaelsen, 2011) which argues

that the primary reason of wage inequality was skill-biased technological change. It stated

that the shift in demand of jobs from low-skilled to high-skilled was induced by

technological progress; wages have increased for the high-skilled jobs but remained

stagnant for the low-skilled ones. People were challenged to pursue higher education to

quickly adapt to advancements in technology, specifically, the pursuit of professional

specialization. This enabled them to qualify for high-skilled jobs that provided higher

wages. Using a panel regression model, the results showed that wage inequality might

continue to increase if this trend in the skill-biased technological change continues.

Juhn, Murphy, and Pierce (1993) can attest to this, having long proved that

inequality was growing over time mostly due to the increased demand in skill. It was then

postulated that there must be a distinction between the observed and unobserved

dimensions of skill; a different approach compared to the usual dichotomy of hard and soft

dimensions of skill. The most common observed dimension of skill was educational

attainment while unobserved dimensions of skill were the ones that cannot be measured.

With a quantile approach, they showed that the increase in the premium of the dimensions

of skill, both observed and unobserved, contribute to the trend of greater wage inequality.

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In the research of Lindley and Machin (2011), using the United States and Great

Britain, they presented new evidence on how rising wage inequality was due to changes in

the educational systems. They documented that the number of people who qualify for

postgraduate degrees have been increasing over time. Furthermore, they also showed that

the wages of the workers with postgraduate qualifications have risen strongly relative to

the other workers with only undergraduate degrees.

In addition, one study using multivariate regression somehow supports findings of

Estudillo. Results from the study of Checchi (2001) on Education, Inequality, and Income

Inequality state that education can actually reduce income inequality if and only if these

following two conditions are met by society: (i) "the initial level of educational attainment

must be sufficiently low", and (ii) "the average educational attainment must be raised

sufficiently rapidly." In other words, when the number of educated people who enter the

labor market rapidly increases, more jobs would be created together with technological

innovation. With this, more people will earn higher wages which further leads to a decline

of income inequality.

Gerochi (2002) conducted a study regarding the returns on education in the

Philippines. In this study, she estimated a 17.1% return to elementary graduation relative

to no education, a 12.8% return on high school education relative to an elementary

education, and a 14% return on college graduation as compared to high school education.

This shows that elementary education leads to the highest return to an individual as

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compared to high school and college education. These findings support the attempts of the

government to provide access to basic education in the country.

As for the income inequality in the Philippines from 1960s to early 1990s, in the

study of Estudillo (1997), he examined four factors that caused changes in the household

income inequality. One of those factors is the increasing number of highly educated

individuals. This suggests that the relationship between higher education and wage

inequality has already been evident for the past six decades. However, unlike the previous

studies presented, this study stated that as the number of household heads who completed

college education increases, wage inequality decreases. According to Estudillo, this

relationship resulted from the shift of the household head population to become more

educated, especially those who are in the lower quintiles of income distribution.

Moreover, compared to age distribution changes and differences in the nature of

employment which also cause changes in household income, education of the household

head appears to be the most significant factor that affects wage inequality.

Worker Characteristics

Determination of inequality is not solely founded on education, though, seeing as

there are several factors that could prompt a difference in wages. A common characteristic

that employers do tend to base their judgment on (sans education) are demographic

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characteristics of the individual such as the gender, income bracket, and so forth. We may

also say that the geographical location of the employment (whether regional or

international in scale) plays a considerable part as there are differences in minimum wage

determination, as well as foreign exchange rates. It is important to note the changes per

income bracket since the primary objective of the study is to analyze the prevalence of

inequality per quantile.

Harmon, Oosterbeek, and Walker (2000) studied the returns of education on wages

on an international scale. Using meta-analysis on the data compiled by the Public Funding

and Private Returns to Education (PURE), results showed that the average return of

schooling was 6.5 percent across various countries. However, there were some exceptions.

Lower rate of return was confirmed in the Nordic countries, while higher than average

returns were exhibited by the UK and Ireland. The quantile regression done showed

evidences that suggest that those at the top decile of the income distribution have higher

returns to education than those in the bottom decile. Recent years may have caused this

inequality to increase. Complementarity of education and ability is their explanation for

this occurrence.

Further approaches using statistical methods such as quantile regression have

shown results that point towards post-secondary education. Lemieux’s study in 2006

showed that post-secondary education from the era of the 1970s until the early 2000s pose

a dramatic increase of wage inequality. The model with heterogeneous returns also helped

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explain why both the relative wages and the within-group dispersion among highly-

educated workers have increased in tandem over time. These findings add to the growing

evidence that, far from being ubiquitous, changes in wage inequality are increasingly

concentrated in the very top end of the wage distribution. These are further supported by

Piketty (2013) who demonstrated that the relative wage gains are disproportionately

concentrated in the very top of the earnings distribution. In addition, any changes in

residual inequality also appear to be concentrated at the top end.

During 2002, De Gregorio and Lee’s panel regression models showed that there

was a large difference of income inequality across countries from different regions.

Furthermore, the factor that affected these findings the most are educational factors,

particularly inequality in education. Results showed that as the average educational

attainment in a region increased, its Gini coefficient increased as well, which suggested

that income equality will intensify.

Using quantile regression on the 2005 India Human Development Survey, Agrawal

(2011) showed that the returns to education of primary, secondary, and tertiary levels are

higher in urban sectors than in rural areas. Furthermore, results showed that returns to

education increases across deciles both in rural and urban areas, yet the rates of return in

urban areas are at least 4% higher than that of the results in rural sectors. The author stated

that this may be attributed to the rural-urban migration done by educated individuals who

choose to transfer to industrialized areas where more employment opportunities are

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present. However, if rural-urban migration continues to persist, the author argues that the

rates of return, may in fact, decrease in the future due to the high supply and low demand.

Therefore, not only are there differing levels of income inequality among sexes

and household characteristics, but the geographical location of the individual also plays a

powerful role.

Age and Experience

All of these previous studies and literature point in the same direction that whichever

the cause of the inequality, the return to post-secondary education increases sharply all

throughout while returns to lower levels of education (ie. primary, secondary) remained

relatively unchanged. These follow the hypotheses and conclusions that Mincer (1974)

have initially proposed and presented. However, it is important to note one final variable

that Mincer and the other authors have considered, which may play a more important role

than the other two factors: experience. They do say that with age comes wisdom, and that

could be reflected by the worker’s growth and evolving skillset caused by prolonged

exposure to different work environments. It can safely be said that this has a near

exponential effect to the wage one may receive since a longer time spent in the working

environment would also allow for the work background to compensate for a lack in

education such as the employment providing the worker a form of specialization. Having

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received wages over a long period of time may also change the demographic

characteristics of an individual, making them wealthier (changing income bracket) or

moving to a neighborhood of higher standards of living.

Gender Gap

Using panel data from Argentine Permanent Household Survey in the study of

Marcos (2016), the author tested whether or not there was gender differential in the returns

to education in the years 2005, 2010, and 2015. With the Mincerian equation as her base

equation, she used dummy variables to indicate whether an individual has completed

primary, secondary, and tertiary education to represent the variable for education.

Considering the entire sample population in the study, Marcos found that returns to

education were increasing across quantiles, such that those with higher wages enjoy

greater educational returns than those with relatively lower wages. Furthermore, not only

do males have higher mean hourly wages than females, but results also show that they

have relatively higher returns to education than women across the quantiles and

throughout the period of 2005 to 2015.

On the other hand, a study of Olvera (2013) in Mexico showed different results.

Olvera also used quantile regression with Mincerian equation as her basis, but she added

marital status of the individual as one of the explanatory variables because it exogenously

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affects wages as well. Though there was still a gender difference in the returns to

education, it appeared that females have higher returns than males. Through the period of

24 years from 1988 to 2011, the same trend was apparent between males and females,

even across the wage distribution.

In the study of De Silva (2011) in which she used two-stage least squares regression

approach and data from the 1983-2003 Bicol Multipurpose Survey, she found that women

in the said rural area of the Philippines have around 20% returns to schooling while men

only have 12%. To account for the endogeneity problem of education, De Silva used birth

order, number of children in the family, percentage of male siblings, and parental

education as instruments for education. Based on the result, De Silva concluded that

“practice of educational homogamy has facilitated the attainment of gender parity in

schooling by encouraging parents to invest in the schooling of girls regardless of whether

or not they expect their daughters to eventually enter the labor force.”

According to Maluccio (1998), the possible reason as to why education is

endogenous in nature is because of “unobserved determinants of education that [also]

influence wages”. With that, an individual’s decision to acquire certain level of education

could depend on labor market conditions such as labor demand by firms, wage

legislations, or labor mobility. Moreover, within-household factors like preferences and

household resource constraints can also affect one’s schooling decision. Card (2001) states

that individual heterogeneity comes from two sources: differences in the marginal costs of

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schooling, and economic benefits of pursuing higher education. He further expounds that

the distribution of marginal returns to schooling is endogenous for the whole labor market.

Meaning, as more workers with high educational attainment enter the labor marker, this

would affect the decisions of future entrants whether to pursue higher education or not.

Figure 2.1: Poverty Incidence in the Philippines (millions of Filipinos) Source: Philippine Statistics Authority (2015)

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Figure 2.2: Child Labor in the Philippines (millions of Filipinos) Source: ILO-NSO Survey (2013), Philippine Statistics Authority (2015)

In the case of the Philippines, there exists a looming presence of poverty and child

labour. For those households in the lower income deciles of the wage distribution, the lack

of resources provides a greater opportunity cost for families to pursue education against

immediate employment (such as child labour). The evident expansion of both child labour

and poverty incidence would undoubtedly increase the number of households who would

forego the long-term investment of education to find immediate employment and income-

sourcing, in turn mitigating the relatively lower welfare to which they suffer from.

Research Gap

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There have been decades worth of research on the topic and related fields, however,

there is a limited amount of studies that were conducted in the Philippines. Any pre-

existing research are outdated by at least half a decade and are simply iterations of the

existing bodies of work, only contributing updated data and observations. In terms of

quantitative analysis, most if not all Philippine research utilize 2SLS estimation method

and Instrumental Variable (IV) approach. Any works using QR, and Instrumental Variable

Quantile Regression (IVQR) or 2SQR approaches were performed overseas, therefore a

lack of literature on the localization of the quantile method.

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Figure 2.3 Literature Map

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III. THEORETICAL FRAMEWORK

Human Capital Theory

When it was designed in the early to mid-60s, the Human Capital Theory has

become a framework and foundation upon which most Western education pattern their

government policies. The theorem itself finds its basis in Adam Smith’s The Wealth of

Nations (1776) where it would explain the wage disparities found among employments. In

its simplest form, human capital arises out of any activity able to raise an individual or

worker’s productive capacity. A common example would be dedication to full-time

education, as the correlation is primarily evident. For workers, investment in human

capital involves both direct costs, and costs in foregone earnings. Workers making the

investment decisions compare the attractiveness of alternative future income and

consumption streams, some of which offer enhanced future income, in exchange for

higher present training costs and deferred consumption. Returns on societal investment in

human capital may in principle be calculated in an analogous way.

Leading economists such as Gary Becker and Jacob Mincer would later come across

Smith’s work. They have both claimed that costs of learning the job are a very important

component of net advantage, and other things being equal, personal incomes vary

according to the amount of investment in human capital; that is, the education and training

undertaken by individuals or groups of workers.

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According to Acemoglu and Autor’s (n.d.) work, there are some alternative ways of

viewing the human capital theory for the purpose of deeply understanding the concept of

it. From the view of Becker, he believes that human capital directly increases a worker’s

productivity in any production process, and that human capital is unidimensional which

can be represented by a set of knowledge or skills. Somehow contrary to that is the view

of Howard Gardener. He argued that human capital is composed of different types of

skills, thus, making it not unidimensional. Just considering the main difference of mental

and physical abilities, Gardener stated that Becker must be wrong in implying that human

capital is unidimensional. From the Nelson-Phelps view, it is said that human capital

measures the capacity of a worker to adapt in changing environment. This is supported by

the Bowles-Gintis view stating that human capital is one’s capacity to adapt to a hierarchy

or organizations in general.

The human capital theory is one of the major theoretical basis of this study because

this research aims to show the impact of an additional year of education to an individual’s

wage, and as stated in the theory, education is considered as a long-term investment in

human capital.

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Mincer Equation

Inspired by Smith’s theory on human capital, Jacob Mincer (1974), later to be

known as the father of modern labor economics, came up with a single function model

that explains returns to investment and experience. Specifically, the model explains

earnings as a function of schooling and experience, and it would be known as the Mincer

equation, or Mincer earnings function, and it would be as follows:

(1)

The natural logarithm of earnings is modelled as the sum of years of education and a

quadratic formulation of years of experience. The variable w would denote wages, while S

would be years of schooling, EXP would be years of potential labor market experience,

EXP2 is experience squared, and 1coefficient is the percentage change in wages for every

additional year of schooling.

This classic model has become the workhorse of earning determination and

empirical research, making it one of the most widely-used models. Its popularity may be

attributed to two factors: the first being that the equation is based on a formal model of

investment in human capital, and the second being the introduction of experience as a

standard regressor in earnings functions. Prior to this model, earnings grew as a concave

function of age, but this was later revised to accommodate a different rate of return that

was evident among the age groups.

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The study will use the Mincer equation as the model used to estimate returns to

education. Wages will be explained as a function of an individual’s years of educational

attainment and his experience.

Urban Bias Theory

Initiated by Michael Lipton in 1977, urban bias theory has two major propositions:

(i) development of third world countries is systematically biased towards the urban

classes, and (ii) this bias is actually caused by the political structure of the country. To

make it much simpler, because of urban bias, rural areas become economically poor due to

its political powerlessness. As Lipton once said, the most important conflict that the

government in poor countries should have more concern for is the one existing between

the rural and urban classes; and this was caused by the fact that most of the industrial and

transformational development are for the benefit of the urban citizens. Another argument

presented by Lipton as to why this bias exists is because urban citizens have more

influence over the government to make more taxation and economic decisions in favor of

them (Lipton, 1977; Varshney, 1993).

By creating sub-samples and differentiating the observations to rural and urban

residents, we want to determine if urban bias exists in the Philippines. If individuals who

28

live in urban areas have higher returns to education than those in rural areas, then urban

bias theory is applicable in the country.

3.1 Conceptual Framework

Sibling-Resource Dilution

The sibling-resource dilution theory has three assumptions: (i) that household

resources are finite; (ii) that as the number of children in the family increases, the

resources accrued by any one child necessarily decline; (iii) and that each sibling receives

an equal share of time, attention, and resources. Downey (2001) argues that siblings act

and serve as competitors for both parents' time, energy, and financial assets. Therefore,

fewer siblings would equate to larger partitions of allocated resources per child. Larger

families would mean that the resources, time, attention, et cetera must be further divided,

giving smaller allocations and portions to each child. This allocation would not only go

into basic welfare such as the support in terms of food or clothing, but the child’s

education as well.

Because the researchers cannot extract the exact number of siblings in a family, we

used the total number of household members as an instrumental variable. The expectation

29

is that as the number of household members increase, the investments allotted for a family

member decreases, and vice versa.

Parental Socioeconomic Status

The socioeconomic classification or status of the parents are often a reflection of

how the household shall be treated under his/her care, considering that they are the

principal decision maker and fund manager of all the household and its constituents’

investments. Lareau (2003) dictates that differing socioeconomic tiers, whether in income

or educational attainment, serve as a basis in which we may determine the exclusive

opportunities that may exist only in said tiers. Those with a greater pool of capital, they

are more inclined towards larger investments towards their children, and could easily

afford such a discourse. The lack of capital, however, means that the children in a

household will grow up with only sub-par investments into their education and other

aspects of life. Boushey and Weller (2005) continue this by showing that these higher

levels in return are associated with better economic and psychological outcomes (ie: more

income, more control, and greater social support and networking).

Thus, using the parents’ educational attainment as indicators of a household’s

socioeconomic status, we would want to use these variables as instruments to lessen the

endogeneity bias inherent on a person’s choice to pursue higher education or not.

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Socialization Theory and Economic Theory

In the study of Garasky (1995), two theories were discussed that relates family

structure to the educational attainment of a child. First, in the socialization theory, it states

that a child's educational attainment depends on the parent's ability to provide support and

skills necessary for schooling. This theory suggests that disruptions in family structures

weaken the relationship of the parent to children, which may have negative effects on the

children's attitude towards education. Next is the economic theory. This theory is more

focused on financial investments of the parent to his children wherein it claims that

lacking investments on education by the parent, both in terms of time and wealth, would

lead to poor intellectual capacity of the children. Considering some families are raised by

a single parent or stepparent, economic theory also suggests that marital dissolution would

create a detrimental effect on children's educational attainment. Thus, the researchers will

use household wealth as a measure of the parents’ capabilities to support their children’s

educational endeavors, and as an instrumental variable to reduce the effects of

endogeneity bias on the results. A higher index for the wealth of the family shows that the

household has adequate resources to increase the welfare of its family members, thus, an

offspring of a family with a high wealth index is expected to have higher level of

educational attainment.

31

3.2 Operational Framework

Variable Label Description

Regressand

Wage lnwage This variable is measured as the basic pay per day of a

respondent.

Regressors

Education i_yreduc A variable representing the total years of education

respondents received.

Experience i_exp Because the actual working experience is available in our

dataset, we use the potential working experience which will

be computed by using the formula:

i_exp = age - years of schooling - 4

where 4 is the age when Filipinos may begin attending pre-

school education. The equation was originally minus 6 (for

primary education), however, the statement of certain

respondents to attending pre-school education has forced us

to create decrease the starting age as to not create a bias

against certain respondents who have gone to attend more

formal education.

Gender i_sex A dummy variable indicating the gender of the respondent:

0 – male

1 – female

This variable represents how much impact gender

discrimination has in the Philippine market.

Urban urban A dummy variable indicating the residential area

classification of the respondent:

0 – urban

1 – rural

32

This variable represents how prevalent rural-urban disparity

is in the Philippines.

Instrumental Variables

Number of

members in

the

household

members A quantitative variable that indicates the number of

members within the household.

Parent’s

education

f_educ

m_educ

This variable quantifies the education received by each

parent, as it is extracted for the father and mother,

specifically and respectively.

Household

Wealth

hh_wi The household’s wealth derived from an index of household

infrastructure, contents, and characteristics that may

represent the family’s general welfare.

Table 3.1. List of Variables

Variable A-priori

expectation

Intuition

Regressors

i_yreduc + The higher the educational attainment a worker achieved, the

higher the wage he would have due to an advanced skillset or

specialization. This was supported by Lindley and Machin

(2011) whose study shows that the increase of postgraduate

workers in the labor market is widening income inequality in

the United States and Great Britain.

i_exp + It is said that prolonged exposure in a working environment

would compensate for any lack in education or provide a form

of specialization.

i_sex + Gender would determine any gaps or differences in the

33

opportunities and stigmas provided to the respondent. In

Pakistan, women have higher returns to education; however,

men receive higher wages in the labor market (Aslam, 2007).

This suggests gender wage gap is present in Pakistan. The a-

priori expectation can either be positive or negative because

gender gap, whether it is favorable for males or females

depends on the culture of the study. In the case of the

Philippines, de Silva and Bakhtiar (2011) shows that females

in the Bicol region receive 5.8% higher returns to education

than men.

urban - Even though the returns to education in India shows

increasing trends across quantiles both in the rural and urban

sectors, the returns to education in the urban sector is

generally higher than that in the rural sector (Agrawal, 2011).


f_educ

m_educ

+ After conducting three regressions, Al-Samarrai and Peasgood

(1997) shows that in Tanzania, children are ensured of

obtaining higher levels of education if they belong to

educated households.

members - A study conducted in 20 sub-Saharan countries showed that

the number of siblings or dependents has negative and

significant impact on the educational attainment of children

belonging to families with at least 4 or 5 children (Kuepie and

Tenikue, 2012).

hh_wi + Households with higher levels of income would be able to

sustain a higher standard of living, thus, the households’

children are given more access to better education. This, in

turn, causes greater wage inequality because educational

attainment and success are linked to labor market success

(Campbell, et.al., 2005).

Table 3.2. Hypothesized Relationship

34

IV. METHODOLOGY

4.1 Data

In our study, we utilize the data gathered by the Philippine Statistics Authority

(PSA) in 2012 of the merged LFS and FIES surveys. The LFS is a national survey that

aims to provide the country’s, and different regions’ trends of employment through the

collection of individual worker’s responses. It contains information which is gathered

through interviewing members of the households. Furthermore, the survey has a multi-

stage sampling design that involves selecting specific barangays, sample enumeration

areas, and sample households. By systematically selecting the sample in accordance to the

probability which is proportional to the size, the accuracy of the provincial-level estimates

obtained from household surveys is improved (Philippine Statistics Authority, 2012). On

the other hand, FIES is a nationwide survey conducted every three years which provides

information regarding income and expenditure patterns of households belonging to

different income groups. The enumeration of this survey was conducted twice wherein the

first visit was done in July, and the second visit was during January of the succeeding

year. During both visits, the same questions were asked to accommodate any changes

during the reference periods (Philippine Statistics Authority, 2014).

For the 2012 merged LFS and FIES dataset, a total of 42, 397 households were

interviewed encompassing 201, 652 individuals. However, after restricting our study to

35

the working age population, and dropping non-nuclear relatives, and parents to prevent

redundancy when using the instruments parental education, the number of observations

was reduced to 835 individuals. Using the 835 respondents, we will conduct tests and

produce results using various models.

4.2 Empirical Procedures

4.2.1 Mean Approach

Ordinary Least Squares (OLS)

Considered as one of the most common estimator used for regression analysis

because of its attractive statistical properties, the ordinary least squares (OLS) estimator

focuses on the conditional mean effect on the regressand (Gujarati and Porter, 2009). This

will be used only for comparative purposes because according to Binder and Coad (2010)

solely focusing on the mean may produce under- or overestimated results. Furthermore,

another weakness of using OLS is the possible presence of a correlation between the

regressand or one of the endogenous variables and the error term of the model. This, in

return, could produce misleading results. However, for comparative purposes, we will still

use OLS as presented below:

36

(2)

where is the dependent variable, while the variables on the right hand side

are all the explanatory variables explaining the factors that affect a person's wage. Since

there is a self-selection bias present in the equation, the results will be unreliable.

Fortunately, the problem can be addressed through using the two-stage least squares

(2SLS) model (Gujarati and Porter, 2009).

2-Stage Least Squares (2SLS)

The 2-stage least squares was developed by Henri Theil and Robert Basmann. The

main reason for the use of this method is to eliminate the possible correlation between the

dependent variable and the stochastic disturbance term. This is achieved by including two

successive applications of OLS.

The first stage involves regressing the endogenous independent variable with

respect to an instrument and the independent variables. The reduced-form equation is

shown below:

(3)

37

The difference lies on the second stage. The second equation is exactly the same as

equation (2), but unlike OLS, 2SLS involves regressing the dependent variable using the

estimates of the first regression in which endogeneity has already been accounted for.

represents the instrumental variables, specifically and

used to proxy the unexplained factors related to the endogenous variable.

(4)

As a result of the simultaneous equations, the estimators that are obtained are

consistent and they converge to their true values as the sample size increases (Gujarati &

Porter, 2009). However, according to Card (2001), both the ordinary least squares and

instrumental variables produce upward-biased results. However, using 2SLS produce

more upward-biased results because of unobserved differences and heterogeneity in

returns to education.

This method would not be the main econometric model we would use and would

also only be used for comparison. Even though the method reduces the endogeneity bias

found in OLS, it would not produce results that are in line with the objective of the study

which is to determine the returns to education at different wage levels; thus, a quantile

regression is more appropriate as 2SLS only gives estimates at the mean.

38

4.2.2 Distributional Approach

Quantile Regression (QR)

Quantile regression gives a more comprehensive depiction of the effects of the

explanatory variables to the dependent variable than that of OLS. While this estimation

technique does not account for the endogeneity problem present in the study, it does

address the heterogeneity of the variable . Also, this method shows the

relationship between the independent variables and certain percentiles or quantiles of the

dependent variable. Through this method, it is possible to observe the differences in the

impact of certain variables on the model across percentiles rather than just the mean.

According to Beltrano, Leong and Co (2013), quantile regression is ideal if the

conditional distribution of Y given X exhibits the following characteristics: (i) thick tails,

(ii) asymmetry, and (iii) non-unimodal. According to Koenker and Bassett (1978) quantile

regression have the following properties: robust to presence of outliers, insensitive to

heteroscedasticity, robust to distributional assumption of normality which makes estimates

reliable even if the errors are not normal, nonparametric method as it does not change too

much when the dataset is unusual and because of this we can mitigate small sample bias,

and lastly, it is best used for focused intervention policies because we can concentrate on

the effects of the explanatory variables to the different quantiles.

39

However just like OLS, quantile regression would produce misleading results

because, as previously mentioned, it does not address the self-selection bias of the variable

; however, this method will still be used in order to show how the results will

change if we account for endogeneity. The equation used in this model is presented below:

(5)

where is the return to X , the set of explanatory variables excluding education,

at the quantile, and is the return to education at the quantile. By comparing

equations (2) and (3), we can see that in the QR method, returns to education is now

allowed to vary across .

Two-Stage Quantile Regression (2SQR)

To overcome the endogeneity problem brought about by the conventional quantile

regression, the IVQR estimation method was developed by Chernozhukov and Hansen

(2006, 2008, 2013). While QR can account for heterogenity bias, IVQR, on the other

hand, can solve the endogeneity and heterogeneity biases at the same time. Hence, our

study’s main concern, which is to validate whether or not education is an equalizing tool

that the government can utilize to alleviate poverty and decrease wage inequality, can be

answered through the use of this method. This method was also used by Balesta and

40

Gellner (2013) to learn who benefits the most with higher educational attainment. Using

the QR method, the results showed that the returns to education increased over the wage

distribution, meaning people belonging to the higher quantiles have receive higher

incomes when they have higher educational attainment. However, when the authors

accounted for endogeneity through the use of IVQR, results showed that returns to

education decrease over the wage distribution. This means that people belonging the to the

bottom deciles receive more benefit when they attain higher educational attainment. Thus,

by accounting for the self-selection bias present in the equation, specifically the variable

which represents the educational attainment of a respondent, the estimates of

the IVQR method become more consistent. However, using IVQR means that only one

instrumental variable is used to explain the endogenous variable. This may still produce

inaccurate results because the single IV used may not be enough to truly capture the

effects of the endogenous variable on the regressand; thus, the researchers decided to use

two IVs to capture the effects of the variable on the regressand . With

the use of more than one IV, the name of the method changes to Two-Stage Quantile

Regression (2SQR) instead of IVQR. However, two-stage quantile regression is generally

considered as an over-identified model, which means that it may produce overestimated

results. Since we were not able to take into account skills or ability in this study that might

offset the upward bias of using multiple instrumental variables, results of returns to

education may really be overestimated.

41

The 2SQR has three conditions. The first condition basically allows for

inconsistency in the first-stage estimation of the model by “imposing that the bias terms

are bounded and restricted to the elements corresponding to the exogenous variables in the

structural equation” (Kim & Muller, 2012). The second condition, on the other hand,

ensures if 2SQR is the relevant and correct method to be used. If the orthogonality

condition is satisfied, which means that there exists no endogenous variable in the

equation, then the conditional quantile regression would already be sufficient to be used.

However, if this orthogonality condition is not satisfied and there exists at least one

endogenous variable, then we need to use the 2SQR approach. Finally, the third condition

for the two-stage quantile regression is a test whether or not the instrumental variables

used for the model are exogenous.

2SQR, just like 2SLS, includes two successive processes of quantile regression.

For the first stage, the endogenous independent variable would be regressed using OLS

with respect to the chosen instruments and exogenous independent variables as presented

below:

It can be noted that the reduced-form equation in the first stage is similar to

equation (3). The reason behind this is that both methods account for endogeneity bias,

and since our study will be using a set of IVs and explanatory variables to determine how

42

the endogenous variable, will be explained, both methods’ reduced-form

equations will be the same.

On the second stage, the quantile regression would be applied to the dependent

variable using the estimates from the first regression as explanatory variables (Chen and

Kashiwagi, 2014). The second equation presented below is adopted from Baletsra and

Gellner (2013):

0 arg min [ (ln ) ' ( ) ( )( _ ) ( , )]i if F

E wage X i yreduc f X Z

(6)

where similar to Balestra and Gellner (2013) we restrict F, the class of measurable

functions of , to the values of . Furthermore, Z is the set of IVs used in the study.

Through the use of this method, we hope to attain the same results as those of Balestra and

Gellner.

4.2.3 Instrumental Variables

To correct the correlation between the endogenous variable in our models, OLS

and QR, and the error term, an estimation technique called Instrumental Variable will be

used. It was first introduced by Philip G. Wright in his 1928 publication called The Tariff

on Animal and Vegetable Oil (Moffatt, 2015). The two primary conditions for an

43

instrumental variable to be considered valid are: (i) it should be exogenous to the

dependent variable, and (ii) it should be correlated with the endogenous variable.

A. Test for Endogeneity

Before even employing IVs, we must first verify whether endogeneity exists in the

model. By using the Durbin-Wu-Hausman Endogeneity Test, we can determine if

endogenous variable/s is/are present in the model.

Durbin-Wu-Hausman Endogeneity Test

Since OLS does not capture endogeneity nor simultaneity bias, there is a need to

check if the model used in estimation ̶ 2SLS, exhibits simultaneity bias. Specifically, the

Durbin-Wu-Hausman Endogeneity test is used to determine if the equations used display

simultaneity bias or not. First, we generate the reduced-form equation/s wherein the first

endogenous variable is estimated with respect to all the exogenous regressors present in

the system. Next, the second endogenous variable will be estimated with respect to the

first endogenous variable along with the residuals obtained from the first step. Lastly, we

determine if simultaneity bias is present or not. If the coefficient of the residuals is

insignificant, simultaneity bias is present, hence, OLS is inappropriate. Otherwise, OLS

can be used if the results show that there is no simultaneity bias. Also, if the p-value of the

tests are greater than 0.05, it means that we accept the null hypothesis that there is no

44

endogenous variable. However, if the p-value is lower than 0.05, there is at least one

endogenous variable.

B. Test for Identification and Validity

According to Gujarati and Porter (2009), the test for identification refers to

whether the estimates of the coefficients of the equations in the system can be derived

from the estimates of the reduced-form equation or not. The equations can either be

identified, under identified, unidentified, or over identified.

Order Condition

This condition is a necessary but insufficient condition which states that a system

is considered as identified if the number of exogenous variables from the equation is at

least one less than the number of endogenous variables in the system. The summary of the

order condition is presented below:

Under-identified

Identified

Over-identified

Table 4.2.3.1. Summary of the Order Condition

Rank Condition

In the rank condition, which is a necessary and sufficient condition, “an equation is

considered as identified if and only if there exists at least one non-singular

45

sub-matrix formed out of the coefficients of the variables excluded

from the equation” (Gujarati and Porter, 2009).

Through the use of the Sargan-Basmann test, we can test the validity of the

instruments used in the study. If the p-values are greater than 0.05, it means that the IVs

are exogenous to the regressand. While a p-value lower than 0.05 means that the

instruments are invalid and correlated to the regresand.

C. Test for Instrument Relevance

Partial F-test

Carrying out the partial F-test of the joint significance of instruments and

calculating its partial R2 gives us verification on whether or not an instrument is weak.

According to Staiger and Stock (1997) an F-statistic below 10 means that the model

contains a weak instrument. After conducting the test; thus, if the F-statistic is greater than

10, it means that model is using strong instruments.

Through the multiple tests mentioned above, the researchers will also use these to

determine whether the instruments used are applicable and valid for the study.

Other than estimating the returns to education on all remaining observations, we

also estimated the impact of an additional year of education to one’s wage in more specific

manners, specifically based on urbanity and gender. Additionally, we further subdivided

46

the sample by comparing the returns to education on females who reside in rural areas and

females who reside in urban areas, males who live in rural areas and males who live in

urban areas, females and males who reside in rural areas, and females and males who live

in urban areas. By doing this, we are able to narrow down the scope of our study and to

compare specific characteristics of the respondents.

Estimation procedures will be carried out using Stata 13.

V. RESULTS AND DISCUSSION

5.1 Descriptive Statistics

Variable Name Label Mean Std. Dev. Min Max

Regressand

Individual’s basic

pay per day

(i_wage)

lnwage

(172.5437)

5.010608

(93.39644)

0.5400618

(20)

2.995732

(552)

6.313548

Regressors

Individual’s

experience

i_exp 7.839521 4.977226 0 29

Individual’s highest

educational

attainment

i_yreduc 9.643114 2.935283 0 16


47

Father’s education f_educ 8.220359 3.085406 0 16

Mother’s education m_educ 8.48982 2.77421 0 15

Household size members 6.874251 2.213918 3 15

Household Wealth hh_wi -0.3600747 2.239547 -4.42819 1.552443

Total Observations: 835

Table 5.1.1. Summary Statistics

Variable Frequency Percentage (%)

i_educ No diploma received 176 21.08

Elementary graduates 323 38.68

High school graduates 323 38.68

College graduates 13 1.56

Post-baccalaureate graduates 0 0

f_educ No diploma received 160 19.16





m_educ No diploma received 108 12.93



48



i_sex Male 630 75.45

Female 205 24.55

urban Urban 292 34.97

Rural 543 65.03

Table 5.1.2. Frequencies of Selected Variables

From the data, we can ascertain that most of the respondents are primary and

secondary education graduates, most of whom have parents who are only elementary

graduates. It can be noted that there are more parents who did not receive as much

education as their children, though the difference is not noticeably large.

An explanation offered by Gang and Zimmermann (2000) provides that the

parent’s educational background and improvement in welfare over the course of his/her

life adds to the inherent nature of children to be given education of at least par with their

parents. In turn, the added residual effect makes the child more educationally adept and

learned compared to their parents. Another possible explanation behind such a

phenomenon is that as globalization increases technological advancements and eases

rural-urban migration, companies are increasing their minimum requirement for

employment positions; and given that more companies have been geared towards hiring

49

high-skilled workers, parents have been investing more of their resources to the education

of their children.

It is also worth noting that none of these individuals have achieved post-

baccalaureate or post-graduate studies. In addition, majority of the respondents also

happen to be male or coming from a rural area. The descriptive statistics also show that

the highest educational attainment is a mere 16 years. While at first it may be difficult to

ascertain up to what grade completed that shows, a generated dummy variable, gradpost,

shows that there are none of the individuals nor their parents who were able to finish post-

graduate studies. Considering the data limitations and qualifications as aforementioned, it

is also a noteworthy find the least one makes in a day is Php 20, while the most is Php 552

(for these respondents). Even for the wealthiest (in terms of salary), the pay grade is still

unremarkably low and close to the designated minimum wage of Php 466 (Department of

Labor and Employment, 2016). Furthermore, the minimum number of members in a

household in which extended family members are excluded is three, while the maximum

is fifteen. This means that apart from parents, children can be as few as one to as many as

thirteen. Given that the sibling resource dilution is present in the Philippines, as previously

shown by (Orbeta Jr., 2005), it can be said that the poorest households, which commonly

have the highest number of members experience the largest negative impact compared to

households belonging to other socioeconomic groups.

50

5.2 Estimation Results

We formalize our instruments by conducting several tests to verify the validity of

different variables that can be used as instruments as provided by the available dataset and

literature. Results of the 2SLS regression may be found in Appendix A. Using the results

of the 2SLS regression, tests were conducted to verify if the model indeed has endogeneity

bias, and to examine the validity of the instruments used to mitigate the potential problem,

tests for endogeneity, and overidentification, and report of the first stage statistics were

conducted.

Tests of endogeneity

H0: variables are exogenous

Durbin (score) chi2(1) = 22.1926 (p = 0.0000)

Wu-Hausman F(1,830) = 22.662 (p = 0.0000)

Table 5.2.1. Tests of endogeneity

Because the p-values of the Durbin and Wu-Hausman statistics are 0.00, which is

below 0.05, we reject the null hypothesis that the variables are exogenous. This means that

at least one variable in the system is endogenous.

51

Tests of Overidentifying Restrictions

H0: instruments are exogenous to the regressand

Sargan (score) chi2(1) = 5.85724 (p = 0.1188)

Basmann chi2(1) = 5.84917 (p = 0.1192)

Table 5.2.2. Tests of Overidentifying Restrictions

The Sargan and Basmann tests verify the validity of the instruments used in the

study, and check if these instruments are exogenous to the regressand. The p-values of

Sargan and Basmann being 0.1188, and 0.1192, respectively. These values are greater than

0.05, which means that we accept the null hypothesis that the instruments are exogenous

to the regressand. They also show that the instruments we used are valid.

Report for First Stage Statistics

Variable R2 Adjusted R

2 Partial R

2 F(4,828) Prob > F

educ 0.4155 0.4113 0.2058 53.6302 0.0000

Minimum eigenvalue statistic = 53.6302

Critical Values # of endogenous regressors: 1

H0: Instruments are weak # of excluded instruments: 4

5% 10% 20% 30%

2SLS relative bias 16.85 10.27 6.71 5.34

10% 15% 20% 25%

52

2SLS Size of nominal 5% Wald

test

19.93 11.59 8.75 7.25

LIML Size of nominal 5% Wald

test

8.68 5.33 4.42 3.92

Table 5.2.3. Report of First Stage Regression Statistics

Through this test, we can determine if the instruments are strong enough to explain

the endogenous variable. First, the partial R2 is checked to verify the correlation of the

instruments used to the endogenous variable. Even though the partial R2 of 0.2058 is quite

low if one considers it, it is still appropriate to say that the instruments are correlated to

the endogenous variable. Next, we check the partial F-statistic. If the F-statistic is greater

than 10, then we reject the null hypothesis that the instruments are weak. With our F-

statistic having a value of 53.6302, which is greater than any of the critical values, we can

conclude that the instruments used in the model are not weak. The instruments to be used

in the 2SLS will be also used in the 2SQR because both models account for endogeneity.

With the endogeneity problem and instrumental variables verified, we can now

compare the results from OLS and 2SLS. Below are the tabulated estimates of both

models using aggregate data.

53

Variable OLS 2SLS

Returns to education 0.0371***

(0.000)

0.0969***

(0.000)

Experience 0.0390***

(0.0010)

0.0713***

(0.0000)

Experience2 -0.0004

(0.4633)

-0.0011

(0.0749)

Constant 4.1826***

(0.0000)

3.3545***

(0.0000) *Indicates significance at the 10% level

**Indicates significance at the 5% level

***Indicates significance at the 1% level

Table 5.2.4. OLS vs 2SLS Results on Aggregate

The results show that with OLS, an additional year to schooling increases an

individual’s wage by 5.77%, On the other hand, after accounting for endogeneity, an

additional year to schooling significantly increases one’s wage by 12.32%, approximately

7% higher than the results produced via OLS. An extra year of working experience raises

salary by 7.13%. Results for both OLS and 2SLS show that experience squared is

insignificant with p-values of 0.4633, and 0.0749, respectively. The comparison shows

that the results of OLS is underestimated because it does not address, or at least mitigate

the endogeneity bias present in the model. This means that 2SLS can capture the “truer”

returns to education by accounting for endogeneity. However, as previously mentioned,

2SLS can produce upward-biased results because it fails to address heterogeneity and

unobserved factors. Also, years of schooling and experience are both positive and

significant with p-values being 0.00 (except for experience in OLS which has a p-value of

0.001). To further expound, years of schooling is in line with the a-priori expectation that

54

as an individual increases his years of educational attainment, his wage also increases.

Likewise, experience also satisfies the a-priori expectation established in Section III.

Results using OLS showed that an additional year of experience will increase an

individual’s wage by 3.90%, while the 2SLS regression results show that as a person

increases his experience by one year, his wage will increase by 7.13%. On the other hand,

even though experience squared satisfies the a-priori expectation mentioned before, the

variable is insignificant for both OLS, and 2SLS.

However, the focus of our study is to determine which socio-economic stratum is

the most affected by the wage inequality present in the Philippines. Thus, we would focus

more on the results of QR and 2SQR.

Variable Percentile QR 2SQR

Returns to

education

10th

0.0484***

(0.025)

0.0943***

(0.0006)

30th

0.0421***

(0.000)

0.1401***

(0.0000)

50th

0.0489***

(0.002)

0.1277***

(0.0000)

70th

0.0690***

(0.000)

0.1533***

(0.0000)

90th

0.0726***

(0.000)

0.2046***




Table 5.2.5. QR vs 2SQR Results on Aggregate

55

Figure 5.2.1. Coefficients of the Returns to Education Variable Across Deciles

The results show that the returns to education increases across deciles. In other

words, wealthier individuals receive a proportionally greater increase in wages due to

investment in education compared to relatively poorer individuals and/or households. In

the QR, the returns to education in the lowest decile is 4.84%, on the other hand, in the

2SQR, result in the same decile shows a return of 9.43%. This change in estimates is not

only obtained in the first decile but also in the upper deciles. Furthermore, this shows that

education exacerbates wage inequality, and that wage inequality is more prevalent in the

lower deciles. Thus, individuals belonging to the lower deciles may opt to work instead of

pursue higher education because the effect of the returns to education takes a longer time

to take place. If the person decides to immediately enter the work force, then he can

receive wages, albeit lower, as soon as possible which he can use to acquire basic

56

necessities. However, contrary to the results of Ballestra and Gellner (2013) in

Switzerland who likewise used IVQR, their results showed that when QR was used, the

returns to education across deciles were increasing, but upon employing IVQR, the linear

trend of the regression reversed and portrayed a downward slope. Unlike our study, their

study used the compulsory education policy imposed by the government as their

instrumental variable. They also used a longitudinal dataset procured by the Swiss

government to evaluate if the policy was effective by comparing the results during pre-

and post-implementation of the said law. By being able to account for the structural

change that occurred in the country’s educational system, they were able to obtain

distinctive results from just using QR and IVQR. Wang (2011) in China, and Girma and

Kedir (2003) in Ethiopia, on the other hand, showed that when QR and IVQR were used

on their countries’ datasets, the returns were decreasing across deciles. However, upon

accounting for endogeneity, our results show that individuals in the higher deciles have

higher estimated returns to education as compared to those who belong in lower deciles. In

other words, the literature proves otherwise, stating that the poorer individuals and

households must portray and exhibit greater returns to investments in education as

opposed to their wealthier counterparts. In verifying if the results were in line with the a-

priori expectations discussed in Section III, similar to the results of OLS and 2SLS, an

additional year of schooling across deciles significantly and positively affect wages for

both QR and 2SQR.

57

The results of our study may have strayed from the pattern literature has laid out

due to statistical anomalies and weaknesses, in turn undermining the integrity of the

regression. Some of these weaknesses may have been caused by the lack of differing time

elements. This means that without considering structural changes such as economic and

educational reforms like the case of Switzerland (Ballestra & Gellner, 2013) and China

(Wang, 2011), this static study exhibits only patterns present within the year; thus,

impacts, whether positive or negative caused by reforms cannot be assessed. Furthermore,

we do not have any ways of verifying if and how quality of education improved over time.

Also, due to shortfall of our dataset in terms of instrumental variables available,

specifically parents’ educational attainment, we resorted to extracting the years of

education while removing the parents to prevent redundancy. This significantly reduced

our observations from approximately 30,000 to only 835. While Girma and Kedir (2003)

also utilized parental education, unlike in the Philippines, Ethiopia’s Urban Household

Survey clearly indicates parents’ total educational attainment; thus, they did not need to

remove observations that could otherwise have been part of the regression analysis,

allowing them to use a sample that is representative of their population.

Therefore, there is still room for improvement for future studies on this topic

which will be expounded on Section VI. Nonetheless, with the objective of identifying

which model best explains the effects of educational attainment on wage inequality in the

58

Philippine setting, we determine that 2SQR is still the best model among the four because

the conditions of the 2SQR discussed in Section IV were met.

Aside from the regression as stated in our framework, other variables were also

tested as to closely examine against the regression results for possibly a clearer

explanation of the unidentified characteristics at play. These metaphorical ‘lenses’ which

we use to magnify our analyses of the data are the geographical area of the household

(urban or rural) and their sex (male or female).

First, we look at the comparison between the returns to education of individuals

in rural and urban sectors.

Variable Rural Urban

OLS 2SLS OLS 2SLS

Returns to

education

0.0371***

(0.000)

0.0969***

(0.000)

0.0824***

(0.000)

0.1396***




Table 5.2.6. Returns to Education using OLS and 2SLS of Residents in Rural and Urban

Sectors

Based on both the OLS and 2SLS results, individuals who reside in urban areas

have higher returns to education as compared to those who live in rural areas. This means

that for every additional year of educational attainment of a person residing in an urban

area will increase his wage by 8.24% and 13.96%, respectively. This is in accordance to

the urban bias theory because, as stated previously, more opportunities such as higher

paying jobs, and better schools can be found in urban areas.

59

Table 5.2.7. Returns to Education using QR and 2SQR of Residents in Rural and Urban

Sectors

Figure 5.2.2. QR Results of Rural vs Urban on Aggregate

Variable Percentile Rural Urban

QR 2SQR QR 2SQR

Returns to

education

10th

0.0206

(0.284)

0.1076***

(0.0063)

0.1147***

(0.001)

0.1668***

(0.0001)

30th

0.0294***

(0.002)

0.1281***

(0.0000)

0.0490**

(0.049)

0.2285***

(0.0000)

50th

0.0148***

(0.297)

0.0984***

(0.0004)

0.0801***

(0.000)

0.1395***

(0.0000)

70th

0.0441***

(0.000)

0.1039***

(0.0005)

0.0885***

(0.000)

0.2278***

(0.0000)

90th

0.0623***

(0.000)

0.0903**

(0.0168)

0.0683***

(0.000)

0.1047***




60

Figure 5.2.3. 2SQR Results of Rural vs Urban on Aggregate

Both the QR and 2SQR results also show that an additional year of education has

greater impact on urban residents across deciles as compared to those who reside in rural

areas. Furthermore, the QR results of individuals living in rural areas show a generally

upward trend. This means that education is a factor that can lower wage inequality in the

rural sectors. However, based on Table 5.2.7. an additional year of education is

insignificant for those who belong in the lowest decile. This may be attributed to the low-

skilled type of jobs such as agriculture and fishing typically seen in the rural sectors.

Because these are skill-based occupations, education will have little to no impact on the

wage of an individual. On the other hand, returns to education of urban residents show a

downward then slightly upward trend across deciles. This means that individuals

61

belonging in the 1st decile have higher rates of return; thus, they should invest in their

education to increase their wages in the long-run.

Using the 2SQR, results show that urban residents belonging to the 9th

decile has

the lowest returns to education. This does not necessarily mean that these individuals

should not pursue higher education anymore, rather, the low returns may be attributed to

factors which we were not able to account for such as family connections, and abilities.

On the other hand, it can be noted that the rates of return of rural residents increased after

accounting for endogeneity and heterogeneity. Moreover, from a generally upward trend

(based on QR), results now show a downward trend. This means that the impact of one

additional year of education on the wage of individuals in the 1st decile is almost at par as

those in the 9th

decile.

Next, we compare the returns to education of females and males based on the

sample.

Variable Female Male

OLS 2SLS OLS 2SLS

Returns to

education

0.0810***

(0.000)

0.2333***

(0.000)

0.0586***

(0.000)

0.0912***




Table 5.2.8. Returns to Education using OLS and 2SLS of Females and Males

Based on the results on Table 5.2.8., women have higher rates of return for both

OLS and 2SLS. Furthermore, women’s wages increase by 23.33% (using 2SLS) for every

62

increase in the years of educational attainment which is 14.21% than that of men. This is

also in line with the a-priori expectation that women have higher returns to education than

men in the Philippines.

Variable Percentile Female Male

QR 2SQR QR 2SQR

Returns to

education

10th

0.0288

(0.527)

0.3033***

(0.0004)

0.0591***

(0.000)

0.0631**

(0.0305)

30th

0.0679***

(0.001)

0.2016***

(0.0002)

0.0491***

(0.000)

0.0835***

(0.0002)

50th

0.0754***

(0.000)

0.2129***

(0.0000)

0.0518***

(0.000)

0.1019***

(0.0000)

70th

0.1017***

(0.000)

0.3019***

(0.0000)

0.0604***

(0.000)

0.1427***

(0.0000)

90th

0.1071***

(0.000)

0.2972***

(0.0011)

0.0590***

(0.000)

0.1188***




Table 5.2.9. Returns to Education using QR and 2SQR of Females and Males

63

Figure 5.2.4. QR Results of Female vs Male on Aggregate

Figure 5.2.5. 2SQR Results of Female vs Male on Aggregate

64

Results using the QR method show that returns to education of women belonging

to the 1st decile is insignificant, while the other deciles’ rates of return show an increasing

trend. It means that women with the lowest wages may have jobs that are more skill-based

rather than academic-based which means that their work may be leaning towards low-

skilled jobs. On the other hand, results for males show that education does not aggravate

wage inequality because the difference on the rates of return between the lowest and

highest deciles is only 0.01%. Thus, other factors such as quality of schools attended,

social skills, and other unobserved abilities may have larger impact on the existing wage

inequality in the country. On the other hand, the returns to education of men across deciles

show a slight downward trend, then slight upward trend. Based on Table 5.2.9., there is

only 0.01% difference on the impact of an additional year of education on an individual

belonging to the 1st and 9

th deciles. This implies that pursuing higher education can lower

wage inequality.

After accounting for endogeneity, the returns to education of women who have

the lowest wages became significant, and they have higher returns than those who have

the highest wages. This means that women who have low wages should give more

considerations in pursuing higher education. Meanwhile, the returns to education of men

show an increasing trend across deciles, as opposed to the results using the QR method.

The results show that, like China (Wang, 2011), a large degree of within-group

heterogeneity exists in the Philippine market. This is also in line with Dougherty’s (2005)

65

discussion regarding double effect. The double effect states that education can increase

both men and women’s productivity, at the same time, it can also decrease gender

discrimination and preferences which mostly concerns to women. This also shows that

education can be utilized as a tool to reduce gender gap.

Then, we further narrow down our view by comparing the returns to education

across deciles of females who reside in rural and urban sectors.


OLS 2SLS OLS 2SLS

Returns to

education

0.0658***

(0.002)

0.1911***

(0.000)

0.0930***

(0.002)

0.2171***




Table 5.2.10. Returns to Education using OLS and 2SLS of Females Who Reside in Rural

and Urban Areas

Similar to the previous results, living in urban areas provides women with higher

returns to education as compared to those who reside in rural areas. This hold true for the

OLS and 2SLS methods. In OLS, urban residents have 2.72% higher rates of return, while

in 2SLS, individuals living in urban areas have 2.6% higher returns for every additional

year of educational attainment. The results are still in line with the our a-priori

expectation, and the urban bias theory.

66


QR 2SQR QR 2SQR

Returns to

education

10th

-0.0140

(0.525)

-0.0644

(0.4368)

0.1393**

(0.043)

0.0812

(0.3693)

30th

0.0794***

(0.002)

0.3082***

(0.0004)

0.0722**

(0.033)

0.1982***

(0.0089)

50th

0.0859***

(0.005)

0.2304***

(0.0010)

0.0498

(0.175)

0.2979***

(0.0005)

70th

0.0966***

(0.000)

0.1685***

(0.0109)

0.1133***

(0.000)

0.2547***

(0.0017)

90th

0.0649**

(0.053)

0.2934**

(0.0184)

0.1092***

(0.013)

0.2962**




Table 5.2.11. Returns to Education using QR and 2SQR of Females Who Reside in Rural

and Urban Areas

Figure 5.2.6. QR Results of Females in Rural Areas vs Females in Urban Areas

67

Figure 5.2.7. QR Results of Females in Rural Areas vs Females in Urban Areas

Based on Table 5.2.11., for both QR and 2SQR, rural female residents belonging

to the 1st decile have negative returns to education. This is counter-intuitive because

according to the human capital theory, educational attainment increases productive

capacity and in effect, earnings. We believe that results became negative because among

the female rural residents, three of them were able to attain 12 years of education yet they

still received low wages. This may be due to the occupation that the three women chose.

They may have opted to work as low-skilled workers to take care of their children, or they

may have chosen to open small businesses that are near their homes. Based on the results

of the QR method, from the 3rd

decile onwards, the returns to education slightly increases

then decreases on the 9th

decile. Given that the 7th

decile’s and 3rd

decile’s returns to

68

education is higher by 3.17% and 1.45%, respectively, than the 9th

decile, we can say that

in this scenario, education is not a factor that causes inequality. On the other hand, female

urban residents who have the lowest wages experience the highest returns to education.

For every additional year of educational attainment, their wages increase by 13.93% as

compared to those who are in the 9th

decile whose returns to education is only 10.92%. It

can also be noted that the 3rd

decile’s rate of return is lowest among the five deciles;

however, additional year of education is insignificant for those who are lower-middle

income earners.

Meanwhile, after accounting for endogeneity and heterogeneity, returns to

education across deciles in the rural sector (other than the 1st decile) increase by at least

twofold. On the urban sector, there is a steady increase in the returns to education, which

as previously mentioned, may show that education exacerbates wage inequality.

Next, we compare the returns to education of males who reside in rural and urban

areas.


OLS 2SLS OLS 2SLS

Returns to

education

0.0377***

(0.000)

0.0674***

(0.003)

0.0872***

(0.000)

0.1140***




Table 5.2.12. Returns to Education using OLS and 2SLS of Males Who Reside in Rural

and Urban Areas

69

In the case of males, those who reside in urban areas also have higher returns to

education that those who live in rural sectors. This means that on the average, the effect of

an additional year of educational attainment of a male rural resident will be 4.95% lower

than that of an urban resident. However, if we use instruments to lessen the problem of

endogeneity, then an urban male respondent will receive 4.66% higher wages for every

additional year of education compared to a rural male respondent.


QR 2SQR QR 2SQR

Returns to

education

10th

0.0454**

(0.022)

0.0295

(0.4509)

0.1298***

(0.002)

0.0553

(0.2333)

30th

0.0416***

(0.000)

0.0646**

(0.0331)

0.0942***

(0.000)

0.2204***

(0.000)

50th

-2.38e-16

(1.000)

3.747e-16

(1.000)

0.0805***

(0.000)

0.1416***

(0.0000)

70th

0.0413***

(0.001)

0.1048***

(0.001)

0.0736***

(0.000)

0.1688***

(0.000)

90th

0.0594***

(0.001)

0.0881**

(0.0282)

0.0639***

(0.000)

0.0811*




Table 5.2.13. Returns to Education using QR and 2SQR of Males Who Reside in Rural

and Urban Areas

70

Figure 5.2.8. QR Results of Males in Rural Areas vs Males in Urban Areas

Figure 5.2.9. 2SQR Results of Males in Rural Areas vs Males in Urban Areas

71

From Figures 5.2.8., we can see that in the comparison of male individuals in our

sample size, there exist two patterns in their returns to education. First is the divergence of

both urban and rural returns to education as their income decile increases. The second

pattern is that after the initial divergence, as the decile increases, the returns to education

converges, removing disparity among the two. In general, however, individuals living in

an urban setting exhibit higher returns. This may be attributed to the relatively greater

opportunities found within an urban setting. The general difference we may attribute to the

2SQR approach which was not visible in the original QR approach is the now parabolic

shape of the urban male, as opposed to the previous downwards sloping line it exhibited. It

is also worth noting that both rural males have a generally unchanging pattern throughout

the two methodological discourses. This shows that there is large within-group

heterogeneity. In particular, the difference in the estimated returns to education between

males living in rural and urban areas at the 30th

percentile is as large as 15.58%.

After that, we compare the returns to education of females and males who reside

in rural areas.


OLS 2SLS OLS 2SLS

Returns to

education

0.0658***

(0.002)

0.1911***

(0.000)

0.0377***

(0.000)

0.0674***




Table 5.2.14. Returns to Education using OLS and 2SLS of Females and Males Who

Reside in Rural Areas

72

Similar to the results in the aggregate female and male groups, estimated returns

to education of women residing in rural areas is higher than that of men who reside in

same areas. This holds true for both OLS and 2SLS.

Variable Percentile Female Male

QR 2SQR QR 2SQR

Returns to

education

10th

-0.0140

(0.525)

-0.0644

(0.4368)

0.0454**

(0.022)

0.0295

(0.4509)

30th

0.0794***

(0.002)

0.3082***

(0.0004)

0.0416***

(0.000)

0.0646**

0.0331)

50th

0.0859***

(0.005)

0.2304***

(0.0010)

-2.38e-16

(1.000)

3.747e-16

(1.000)

70th

0.0966***

(0.000)

0.1685***

(0.0109)

0.0413***

(0.001)

0.1048***

(0.001)

90th

0.0649**

(0.053)

0.2934**

(0.0184)

0.0594***

(0.001)

0.0881**




Table 5.2.15. Returns to Education using QR and 2SQR of Females and Males Who

Reside in Rural Areas

73

Figure 5.2.10. QR Results of Females in Rural Areas vs Males in Rural Areas

Figure 5.2.11. 2SQR Results of Females in Rural Areas vs Males in Rural Areas

74

As previously mentioned, counter-intuitive results are obtained for the estimated

returns to education of female rural residents who belong to the 1st decile. It can also be

noted that in this case, the estimated returns to education of males earning the average

wage are insignificant and counter-intuitive as well. Furthermore, the results also show a

large within-group heterogeneity present in the rural sector. The differences between the

estimated impact of an additional year to wage is as high as 20.53% (2SQR) on the 90th

percentile. However, results also show that after using QR, the estimated returns to

education of both women and men living in rural areas are almost the same, with only a

0.55% gap.

Lastly, we compare the differences of the returns of education of females and

males who live in urban areas.


OLS 2SLS OLS 2SLS

Returns to

education

0.0930***

(0.002)

0.2171***

(0.000)

0.0872***

(0.000)

0.1140***




Table 5.2.16. Returns to Education using OLS and 2SLS of Females and Males Who

Reside in Urban Areas

Regardless of where females reside, their estimated returns to education is higher in

all cases compared to males. Thus, as previously mentioned, education can be used as a

tool to reduce gender gap.

75

Variable Decile Female Male

QR 2SQR QR 2SQR

Returns to

education

10th

0.1393**

(0.043)

0.0812

(0.3693)

0.1298***

(0.002)

0.0553

(0.2333)

30th

0.0722**

(0.033)

0.1982***

(0.0089)

0.0942***

(0.000)

0.2204***

(0.000)

50th

0.0498

(0.175)

0.2979***

(0.0005)

0.0805***

(0.000)

0.1416***

(0.0000)

70th

0.1133***

(0.000)

0.2547***

(0.0017)

0.0736***

(0.000)

0.1688***

(0.000)

90th

0.1092***

(0.013)

0.2962**

(0.0201)

0.0639***

(0.000)

0.0811*




Table 5.2.17. Returns to Education using QR and 2SQR of Females and Males Who

Reside in Urban Areas

Figure 5.2.12. QR Results of Females in Urban Areas vs Males in Urban Areas

76

Figure 5.2.13. 2SQR Results of Females in Urban Areas vs Males in Urban Areas

From Tables 5.2.16 and 5.2.17, we may see that similar to the previous urban-rural

and sex classifications and comparisons, there exists a trend of higher returns of education

derived from the 2SQR approach against the QR model. It may be noted that the first

decile is highly insignificant for both men and women living in urban areas. When

presented with the graphical tabulation (Figures 5.2.12 and 5.2.13), the QR model

regression generally presents an generally downwards sloping trend. On the other hand,

when a 2-stage approach is utilized, the trend inverts towards a generally upwards sloping

nature. With the 2SQR approach, we see that both genders diverge after a certain

socioeconomic strata. Upon said divergence, the general trend which has been observed in

77

the previous comparisons once again appears, that females exhibit dramatically higher

returns to education than men.

VI. CONCLUSION AND RECOMMENDATIONS

In general, we find that returns to education increase as one’s socioeconomic strata

increases. Females, in general, also exhibit higher returns to education than males

regardless of their classification of settlement. However, considering that the observations

have been trimmed down considerably from the original amount to satisfy the

prerequisites and conditions required for the study, the sample may not appropriately

represent the actual population. This entailed that our sample suffered from narrow

variations in certain variables (such as educational attainment), in turn mitigating the

statistical accuracy of the sample size depicted.

In addition, the consideration for instrumental variables has solved the previous

problem of the results becoming underestimated. However, the lack of an explanatory

variable for experience, and opting to use a formulated proxy for it, has aggravated a form

of overestimation, which was not methodologically remedied. While the 2-stage quantile

regression model was slightly faulty as so far as to require additional treating, it was the

best among the selected appropriate models for the study in determining and estimating

the results given the pre-requisites as mentioned previously.

78

One may, however, completely shun the fact that this paper contradicts the

literature, given that this is the first Philippine study to conduct an Instrumental Variable

Quantile Regression on this subject matter. There could be certain ethnic, cultural, social,

or psychological effects at play which cannot be identified now, but would play an

important role in determining the difference of results of this country against another’s. Of

course, there have been statistical discrepancies which the study was not able to address

yet are aware of countermeasures of which will be discussed later in the recommendations

for future research in this matter.

Given such statistical inaccuracies and prevalent problems that arose during the

construction of the dataset all the way to the statistical processes, one cannot easily

ascertain the exact confidence with which recommendations can be planted upon.

However, one thing has been made clear through the statistics, and that is that women

observe dramatically higher returns to education than men. A policy recommendation one

may produce from this are incentivized education to employment programs for females,

especially those who are out of school or those have stopped pursuing formal education,

especially household wives and low-qualification blue and pink-collar workers. Aside

from the exhibited higher returns to education of females regardless of their urban-rural

classification against their male counterparts, the statistical descriptions also show that

despite the higher returns, the female sample are mostly of low-earning wages and/or

relatively lack years of formal education. It is almost as if females exhibit a greater

79

potential reward if education is pursued, however, they do not pursue it as much as males

do. We may pursue this policy proposal through an expansion of the Pantawid Pamilyang

Pilipino Program (4Ps) or the creation of a sister program similar to it, but aimed towards

women who are aiming to pursue or have stopped pursuing formal education.

Given the static nature of the study (considering data from 2012 alone), this

hampers the study’s ability to be of a greater value or confident accuracy. Research on this

field of study could greatly improve if the Philippines would be able to collect data while

the new K-12 program (Kindergarten + 12 years of formal education) is still developing

up to when it is already on its maturity stage. We suggest constructing a longitudinal

dataset in order to observe the results of the program more accurately. This would require

a great discipline on behalf of the PSA, who still revise their questionnaires every 3 years,

making it difficult to perform a time series research. Other, more specific, surveys could

also be drafted to simply accomplish this goal, rather than be forced to find a dataset (or

merged datasets, in this matter) that would possess the variables necessary for the study.

While many of the proxy variables (such as experience) have no easily quantifiable trait,

other variables such as wealth could be better recorded through other quantitative

variables over time, such as asset worth. Other data, though extractable, would be much

preferred if it were stated in the questionnaire (such as parents’ education; or father’s and

mother’s, respectively) as to remove any assumptions that would have to be made in the

data manipulation. With a better dataset, future studies on this matter would be able to

80

generate results based on tens of thousands, if not hundreds of thousands, of respondents

which would create accurate results and inferences (especially compared to the hindrances

of this dataset, only having 835 observations, after extracting from a pool of over 200,000

respondents). On the other side, the results of this paper may easily be shunned due to the

econometric and statistical difficulties it has encountered in producing said regressions;

difficulties that may have invalidated or caused heavy biases towards the study, in turn

weakening the mathematical arguments of this paper.

Considering the recent implementation of the K-12 program, a structural reform

shall be brought to the educational system of the Philippines. Previously discussed

literature, such as those in Sweden and China, have found benefits overall for the general

public. In addition, the across-the-board increase in education has given the government

and academe a stronger eye for policy implementation that would target different

socioeconomic strata and household characteristics. The same benefits and research

opportunities lay before the Philippines as it has just started to implement the K-12

program. During both the infancy and at the maturity stage of K-12, we strongly

recommend conducting more studies to estimate the effects of investing in education in

the country.

81

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APPENDIX

Appendix A - 2SLS Results

First-stage regressions -----------------------

Number of obs = 835 F( 6, 828) = 98.10 Prob > F = 0.0000 R-squared = 0.4155 Adj R-squared = 0.4113 Root MSE = 2.2522

------------------------------------------------------------------------------ i_yreduc | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- i_exp | -.3929737 .049812 -7.89 0.000 -.4907463 -.2952011 i_exp2 | .0082766 .0023927 3.46 0.001 .0035802 .012973 f_educ | .2134583 .0305579 6.99 0.000 .1534782 .2734384 m_educ | .2118903 .0344848 6.14 0.000 .1442023 .2795782 hh_wi | .0144233 .0350661 0.41 0.681 -.0544056 .0832522 members | -.1676189 .0357879 -4.68 0.000 -.2378646 -.0973732 _cons | 9.61422 .4341101 22.15 0.000 8.762134 10.46631 ------------------------------------------------------------------------------

Instrumental variables (2SLS) regression Number of obs = 835 Wald chi2(3) = 70.63 Prob > chi2 = 0.0000 R-squared = . Root MSE = .54082

------------------------------------------------------------------------------ lnwage | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- i_yreduc | .1232884 .0163949 7.52 0.000 .0911549 .1554219 i_exp | .0712621 .0142861 4.99 0.000 .0432618 .0992624 i_exp2 | -.0010605 .0005953 -1.78 0.075 -.0022273 .0001064 _cons | 3.354476 .2135051 15.71 0.000 2.936014 3.772938 ------------------------------------------------------------------------------ Instrumented: i_yreduc Instruments: i_exp i_exp2 f_educ m_educ hh_wi members

87

Appendix B - Quantile Regression Results

Simultaneous quantile regression Number of obs = 835 bootstrap(20) SEs .10 Pseudo R2 = 0.0355 .30 Pseudo R2 = 0.0388 .50 Pseudo R2 = 0.0170 .70 Pseudo R2 = 0.0510 .90 Pseudo R2 = 0.0751

------------------------------------------------------------------------------ | Bootstrap lnwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- q10 | i_yreduc | .0483938 .0224531 2.16 0.031 .0043224 .0924651 i_exp | .057807 .0203405 2.84 0.005 .0178821 .0977319 i_exp2 | -.0008403 .0007664 -1.10 0.273 -.0023445 .000664 _cons | 3.526529 .2710916 13.01 0.000 2.994425 4.058634 -------------+---------------------------------------------------------------- q30 | i_yreduc | .0420856 .0076173 5.52 0.000 .0271342 .0570371 i_exp | .051585 .016091 3.21 0.001 .0200012 .0831687 i_exp2 | -.0011423 .0007424 -1.54 0.124 -.0025996 .0003149 _cons | 4.0497 .1157313 34.99 0.000 3.82254 4.27686 -------------+---------------------------------------------------------------- q50 | i_yreduc | .0489356 .0104049 4.70 0.000 .0285127 .0693586 i_exp | .026484 .0179868 1.47 0.141 -.0088209 .0617888 i_exp2 | -.000126 .0009088 -0.14 0.890 -.0019099 .0016579 _cons | 4.370944 .1580965 27.65 0.000 4.060629 4.681259 -------------+---------------------------------------------------------------- q70 | i_yreduc | .0690098 .0099894 6.91 0.000 .0494024 .0886173 i_exp | .0239652 .0218051 1.10 0.272 -.0188344 .0667647 i_exp2 | .0004877 .0012052 0.40 0.686 -.0018779 .0028532 _cons | 4.404924 .1502169 29.32 0.000 4.110075 4.699773 -------------+---------------------------------------------------------------- q90 | i_yreduc | .0726403 .0105391 6.89 0.000 .0519538 .0933268 i_exp | .0425465 .0176846 2.41 0.016 .0078347 .0772583 i_exp2 | -.0005189 .000832 -0.62 0.533 -.0021519 .0011142 _cons | 4.670215 .1516209 30.80 0.000 4.37261 4.96782 ------------------------------------------------------------------------------

88

Appendix C – 2SQR Results (Aggregate) ------------------------------------------------------------------------------- Variable | q10 q30 q50 q70 q90 -------------+----------------------------------------------------------------- i_yreduc | .09431653 .14006454 .12767252 .1532656 .20458865 | .027471 .0222674 .02068075 .0226812 .03275992 | 0.0006 0.0000 0.0000 0.0000 0.0000 i_exp | .07390125 .10072991 .08712746 .07215841 .12562359 | .02393754 .01940325 .01802069 .01976383 .02854617 | 0.0020 0.0000 0.0000 0.0003 0.0000 i_exp2 | -.00150045 -.00254695 -.0019457 -.00062171 -.00221612 | .00099755 .00080859 .00075098 .00082362 .00118961 | 0.1325 0.0016 0.0096 0.4503 0.0625 _cons | 2.8877354 2.8224999 3.36171 3.3612208 3.0975529 | .35774443 .28997993 .26931762 .29536872 .42662007 | 0.0000 0.0000 0.0000 0.0000 0.0000 -------------------------------------------------------------------------------

Appendix D - Quantile Results if Female in Rural Area


------------------------------------------------------------------------------ | Bootstrap lnwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- q10 | i_yreduc | -.0139805 .0219295 -0.64 0.525 -.0574107 .0294497 i_exp | -.0222873 .0552123 -0.40 0.687 -.1316324 .0870578 i_exp2 | .0019121 .0027835 0.69 0.493 -.0036004 .0074247 _cons | 4.144677 .3725682 11.12 0.000 3.406825 4.882528 -------------+---------------------------------------------------------------- q30 | i_yreduc | .0794462 .0253932 3.13 0.002 .0291562 .1297362 i_exp | .0262459 .0738044 0.36 0.723 -.1199198 .1724116 i_exp2 | .0000394 .0038136 0.01 0.992 -.0075133 .007592 _cons | 3.492924 .4530897 7.71 0.000 2.595603 4.390244 -------------+---------------------------------------------------------------- q50 | i_yreduc | .0858752 .0302646 2.84 0.005 .0259377 .1458127 i_exp | .0257554 .0615902 0.42 0.677 -.0962207 .1477315 i_exp2 | -.0005702 .0035254 -0.16 0.872 -.0075521 .0064116 _cons | 3.714429 .4688867 7.92 0.000 2.785824 4.643035 -------------+---------------------------------------------------------------- q70 | i_yreduc | .0965672 .0262075 3.68 0.000 .0446646 .1484698

89

i_exp | .0274297 .0720936 0.38 0.704 -.1153479 .1702074 i_exp2 | -.0000303 .0054672 -0.01 0.996 -.0108577 .0107972 _cons | 3.82443 .3870527 9.88 0.000 3.057892 4.590967 -------------+---------------------------------------------------------------- q90 | i_yreduc | .0648776 .0332239 1.95 0.053 -.0009207 .1306759 i_exp | .0584072 .0687065 0.85 0.397 -.0776625 .1944769 i_exp2 | -.0013766 .0051424 -0.27 0.789 -.011561 .0088077 _cons | 4.462756 .3649193 12.23 0.000 3.740052 5.18546 ------------------------------------------------------------------------------

Appendix E – 2SQR Results if Female in Rural Area

------------------------------------------------------------------------------- Variable | q10 q30 q50 q70 q90 -------------+----------------------------------------------------------------- i_yreduc | -.06435464 .30820766 .23035678 .16846883 .29340431 | .08275156 .08781597 .07013934 .0661816 .12445682 | 0.4368 0.0004 0.0010 0.0109 0.0184 i_exp | -.07119523 .03243267 .07004657 .05274431 .07174781 | .0691308 .07336163 .05859454 .05528823 .10397146 | 0.3031 0.6584 0.2319 0.3401 0.4901 i_exp2 | .00373957 .00206882 -.00007476 .0000688 .00673783 | .00361486 .00383609 .00306392 .00289103 .00543669 | 0.3009 0.5897 0.9805 0.9810 0.2152 _cons | 4.3165072 .76618728 2.005096 3.124058 1.945402 | 1.055861 1.1204799 .89493657 .84443806 1.5879955 | 0.0000 0.4941 0.0251 0.0002 0.2205 ------------------------------------------------------------------------------- legend: b/se/p

Appendix F - Quantile Results if Male in Rural Area

Simultaneous quantile regression Number of obs = 422 bootstrap(20) SEs .10 Pseudo R2 = 0.0176 .30 Pseudo R2 = 0.0208 .50 Pseudo R2 = -0.0000 .70 Pseudo R2 = 0.0341 .90 Pseudo R2 = 0.0653

------------------------------------------------------------------------------ | Bootstrap lnwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- q10 | i_yreduc | .0453629 .0197505 2.30 0.022 .0065403 .0841856 i_exp | -.0053477 .0351272 -0.15 0.879 -.0743956 .0637002 i_exp2 | .0010193 .001394 0.73 0.465 -.0017207 .0037594 _cons | 4.007479 .2684352 14.93 0.000 3.479828 4.53513 -------------+----------------------------------------------------------------

90

q30 | i_yreduc | .0416193 .0116784 3.56 0.000 .0186636 .064575 i_exp | .0052197 .0250909 0.21 0.835 -.0441003 .0545396 i_exp2 | .0005837 .0010136 0.58 0.565 -.0014086 .0025761 _cons | 4.372227 .1914284 22.84 0.000 3.995945 4.748509 -------------+---------------------------------------------------------------- q50 | i_yreduc | -2.38e-16 .0177194 -0.00 1.000 -.0348303 .0348303 i_exp | -2.22e-16 .0108426 -0.00 1.000 -.0213129 .0213129 i_exp2 | 6.33e-18 .0006508 0.00 1.000 -.0012793 .0012793 _cons | 5.010635 .1869637 26.80 0.000 4.643129 5.378142 -------------+---------------------------------------------------------------- q70 | i_yreduc | .0413022 .0121696 3.39 0.001 .0173809 .0652235 i_exp | -.0049511 .0189572 -0.26 0.794 -.0422144 .0323123 i_exp2 | .0012378 .0009777 1.27 0.206 -.000684 .0031595 _cons | 4.806405 .1626743 29.55 0.000 4.486643 5.126166 -------------+---------------------------------------------------------------- q90 | i_yreduc | .0593836 .0175418 3.39 0.001 .0249024 .0938648 i_exp | .000367 .0331167 0.01 0.991 -.064729 .0654629 i_exp2 | .0011479 .0016135 0.71 0.477 -.0020236 .0043195 _cons | 4.866726 .2344041 20.76 0.000 4.405968 5.327484 ------------------------------------------------------------------------------

Appendix G – 2SQR Results if Male in Rural Area

------------------------------------------------------------------------------- Variable | q10 q30 q50 q70 q90 -------------+----------------------------------------------------------------- i_yreduc | .02950228 .06455562 3.747e-16 .1048106 .08807648 | .03913375 .03028683 .0291938 .03195765 .04012699 | 0.4509 0.0331 1.0000 0.0010 0.0282 i_exp | -.00910855 .00696127 -1.886e-16 .04799855 .02233249 | .02946851 .0228066 .02198352 .02406476 .03021644 | 0.7572 0.7602 1.0000 0.0461 0.4599 i_exp2 | .00135744 .0008185 1.975e-17 -.00039508 .00057986 | .00110395 .00085438 .00082354 .00090151 .00113196 | 0.2188 0.3381 1.0000 0.6612 0.6085 _cons | 3.8462329 4.1353883 5.0106354 3.9338721 4.5356785 | .49233706 .38103512 .36728374 .4020555 .5048329 | 0.0000 0.0000 0.0000 0.0000 0.0000 ------------------------------------------------------------------------------- legend: b/se/p

91

Appendix H - Quantile Results if Female in Urban Area


------------------------------------------------------------------------------ | Bootstrap lnwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------+---------------------------------------------------------------- q10 | i_yreduc | .1393486 .0677624 2.06 0.043 .0044971 .2742 i_exp | .1411032 .0709131 1.99 0.050 -.0000184 .2822247 i_exp2 | -.0060986 .0043676 -1.40 0.166 -.0147905 .0025932 _cons | 2.104836 .8919917 2.36 0.021 .3297158 3.879956 -------------+---------------------------------------------------------------- q30 | i_yreduc | .0722027 .0332644 2.17 0.033 .0060043 .138401 i_exp | .1548723 .0673271 2.30 0.024 .0208871 .2888575 i_exp2 | -.0069601 .0038869 -1.79 0.077 -.0146952 .000775 _cons | 3.305893 .4948294 6.68 0.000 2.321151 4.290635 -------------+---------------------------------------------------------------- q50 | i_yreduc | .0498361 .0363872 1.37 0.175 -.0225766 .1222489 i_exp | .058108 .1141041 0.51 0.612 -.1689665 .2851824

i_exp2 | -.0029834 .0069281 -0.43 0.668 -.0167706 .0108039 _cons | 4.227903 .6706442 6.30 0.000 2.893279 5.562528 -------------+---------------------------------------------------------------- q70 | i_yreduc | .1133285 .0270621 4.19 0.000 .0594731 .1671838 i_exp | .0673766 .0910548 0.74 0.461 -.1138283 .2485814 i_exp2 | -.0030393 .0051039 -0.60 0.553 -.0131965 .0071179 _cons | 3.938376 .4791464 8.22 0.000 2.984844 4.891908 -------------+---------------------------------------------------------------- q90 | i_yreduc | .1091993 .0430301 2.54 0.013 .0235667 .194832 i_exp | .0316631 .1294301 0.24 0.807 -.225911 .2892371 i_exp2 | -.0001589 .0073994 -0.02 0.983 -.014884 .0145663 _cons | 4.399983 .7474198 5.89 0.000 2.912571 5.887396 ------------------------------------------------------------------------------

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Appendix I – 2SQR Results if Female in Urban Area ------------------------------------------------------------------------------- Variable | q10 q30 q50 q70 q90 -------------+----------------------------------------------------------------- i_yreduc | .08115658 .19820328 .2979427 .25466886 .29616696 | .09040415 .07580658 .08606653 .08103741 .12743385 | 0.3693 0.0089 0.0005 0.0017 0.0201 i_exp | .1932807 .25793234 .20849509 .171204 .16046912 | .09500105 .07966121 .09044286 .08515802 .13391364 | 0.0419 0.0012 0.0212 0.0444 0.2308 i_exp2 | -.00814008 -.01295896 -.01056327 -.00879854 -.00569455 | .00545878 .00457735 .00519686 .00489319 .0076947 | 0.1359 0.0046 0.0421 0.0722 0.4593 _cons | 3.078548 2.2155508 .75820619 2.2142654 1.4652435 | 1.1837797 .99263461 1.1269815 1.0611287 1.6686579 | 0.0093 0.0256 0.5011 0.0369 0.3799 ------------------------------------------------------------------------------- legend: b/se/p

Appendix J - Quantile Results if Male in Urban Area


------------------------------------------------------------------------------ | Bootstrap lnwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- q10 | i_yreduc | .1297611 .0403774 3.21 0.002 .0501505 .2093716 i_exp | .0587848 .0369575 1.59 0.113 -.0140829 .1316524 i_exp2 | -.0004198 .0014738 -0.28 0.776 -.0033255 .002486 _cons | 2.705233 .4526326 5.98 0.000 1.812795 3.597671 -------------+---------------------------------------------------------------- q30 | i_yreduc | .0942437 .0157824 5.97 0.000 .0631261 .1253613 i_exp | .1461924 .0442255 3.31 0.001 .0589948 .2333901 i_exp2 | -.0046052 .0018155 -2.54 0.012 -.0081847 -.0010257 _cons | 3.23617 .2504069 12.92 0.000 2.742452 3.729887 -------------+---------------------------------------------------------------- q50 | i_yreduc | .080501 .0111167 7.24 0.000 .0585826 .1024193 i_exp | .0962947 .0353458 2.72 0.007 .0266047 .1659847 i_exp2 | -.0028232 .0016925 -1.67 0.097 -.0061603 .0005139 _cons | 3.856172 .2072447 18.61 0.000 3.447556 4.264789 -------------+---------------------------------------------------------------- q70 | i_yreduc | .073621 .0159306 4.62 0.000 .0422112 .1050309

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i_exp | .0737933 .0438277 1.68 0.094 -.01262 .1602067 i_exp2 | -.0017424 .0018792 -0.93 0.355 -.0054476 .0019628 _cons | 4.342814 .2821488 15.39 0.000 3.786512 4.899116 -------------+---------------------------------------------------------------- q90 | i_yreduc | .0639333 .0080483 7.94 0.000 .0480648 .0798018 i_exp | .0516157 .021369 2.42 0.017 .0094832 .0937482 i_exp2 | -.0011688 .0010538 -1.11 0.269 -.0032465 .0009089 _cons | 4.856189 .1602136 30.31 0.000 4.540302 5.172075 ------------------------------------------------------------------------------

Appendix K – 2SQR Results if Male in Urban Area

------------------------------------------------------------------------------- Variable | q10 q30 q50 q70 q90 -------------+----------------------------------------------------------------- i_yreduc | .05525857 .22044162 .14163399 .1688073 .08110172 | .0463655 .04184807 .03454801 .03762977 .04520781 | 0.2333 0.0000 0.0000 0.0000 0.0728 i_exp | .04440698 .12669095 .15965058 .10732383 .0707153 | .05313411 .04795721 .03959146 .04312311 .05180742 | 0.4033 0.0082 0.0001 0.0128 0.1723 i_exp2 | -.00017092 -.00376915 -.00471595 -.00241977 -.00200254 | .00224293 .0020244 .00167126 .00182034 .00218693 | 0.9393 0.0626 0.0048 0.1838 0.3598 _cons | 3.6758549 2.5416221 3.1399988 3.3124621 4.292652 | .64480321 .58197945 .48045783 .52331578 .62870333 | 0.0000 0.0000 0.0000 0.0000 0.0000 ------------------------------------------------------------------------------- legend: b/se/p

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The Impact of Education on Wage Inequality in the Philippines: A Two-Stage Quantile Regression Approach