UNIVERSITY OF SANTO TOMAS
Labor Mismatch in the Philippines: Analysis of the Impact of
Education-Occupation Mismatch on Wage
and Analysis of the Beveridge Curve
An Undergraduate Thesis
Presented to the
Economics Department
Faculty of Arts and Letters
University of Santo Tomas
In Partial Fulfillment of the
Requirements for the Degree
Bachelor of Arts Major in Economics
By
Jasa, Mary Del A.
Jasa, Mary Ann A.
Corpuz, Edralyn L.
February 2013
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APPROVAL SHEET
This thesis entitled: Labor Mismatch in the Philippines: Analysis of
the Impact of Education-Occupation Mismatch on Wage and Analysis of
the Beveridge Curve, prepared and submitted by Mary Del A. Jasa, Mary Ann
A. Jasa and Edralyn L. Corpuz has been approved and accepted in partial
fulfillment of the requirements for the degree, BACHELOR OF ARTS IN
ECONOMICS.
_________________________
Emmanuel Lopez, Ph.D.
Adviser
PANEL OF EXAMINEES
Approved by the
of____________.
Committee
on
Oral
Examination
with
the
grade
_________________________
Emmanuel Lopez, Ph.D.
Chairman
__________________
Alvin Ang, Ph.D.
Panel Member
_______________________
Carlos Manapat, Ph.D.
Panel Member
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ACKNOWLEDGEMENT
The researchers- Maan, Madel & Ral would like to express their sincerest
appreciation and gratitude to the following persons who have greatly helped in the
completion of this research:
Dr. Alvin Ang for patiently helping them in accomplishing the initial works and first
draft of their thesis.
Dr. Emmanuel Lopez, their adviser, for his valuable suggestions and guidance.
Ms. Rosario Abragan, Administrative Assistant II- Databank and Information
Services Division of the National Statistics Office, for patiently helping them in
acquiring and analyzing the data from NSO.
Kuya RJ Angeles, Vice President and COO at Romar Commodities, Inc.,
Associate Director at Standard Chartered Bank and teaching fellow at University of
the Philippines, for the assistance in suggesting data sources and methodology for
their thesis.
Kuya Jeck Samson and Ate Rona Jasa for unconditionally sharing their time in
imparting their knowledge and experience in doing their undergraduate thesis at
the University of the Philippines; for supervising them in the completion of their
thesis and inspiring them not to give up in the course of doing the research.
Tita Mary Ann Mendoza, for giving them recommendations about their study.
Mang Eseng for being their driver from University of Sto. Tomas to NSO, to
NSCB, to University of the Philippines, to Makati and from UST to Bulacan in the
course of doing the entire research.
Mr. and Mrs. Edgardo Corpuz (Papa and Mama) for the moral and financial
support as well as for helping them to coordinate with NSO.
Engr. and Mrs. Rodel Jasa (Daddy and Mommy) for the financial support and for
motivating them to believe in themselves and to be the best of who they can be.
Their family, siblings, relatives, classmates and friends from 4ECO1 & 4ECO2
for all the prayers, love and sacrifices for the success of the thesis.
And above all, to God Almighty for giving them strength, patience, knowledge and
understanding for the very start up until the end and for making all of these
success. To God be the glory!
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ABSTRACT
Title: Labor Mismatch in the Philippines: Analysis of the Impact of EducationOccupation Mismatch on Wage and Analysis of the Beveridge Curve
Researchers:
Jasa, Mary Del A.
Jasa, Mary Ann A.
Corpuz, Edralyn L.
School: University of Santo Tomas
Degree: Bachelor of Arts
Major: Economics
Year: Fourth Year
Adviser: Dr. Emmanuel Lopez
In analyzing the education-occupation mismatch on wage, the
researchers run a regression of the dummy variable: MATCH variable relating
to the college graduates that were employed either in matched or unmatched
occupations against the log of hourly wages. A matched individual is the one
having the primary occupation in line with his/her field of study. An unmatched
individual is the one having the primary occupation not in line with his/ her field
of study. The college graduates under these 3 fields of study -Education, Social
Sciences, Business and Law & Services who pursue with the occupation
related to their field of study earn higher wages rather than having occupation
not related to their field of study (lower wages). While, the college graduates
under these 5 Fields of Study (Humanities and Art, Science, Engineering,
Manufacturing and Construction, Agriculture, Health and Welfare) who pursue
with the occupation related to their field of study (matched), earn lower wages;
while if they pursue with the occupation not related to his/her field of study
(unmatched), they could higher wages. Also, the Beveridge Curve showed a
positive correlation (0.768721874) between the unemployment rate and job
vacancy rate. From this point, the researchers conclude the existence of the
education-occupation mismatch in the Philippines.
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CHAPTER 1: INTRODUCTION
I.
Background of the Study
It is evident that the distribution of income in an economy is related to the
amount of education people have accumulated. The educational attainment of
a person will greatly reflect on the career and the job he will pursue that will
significantly amount the income fitted for his labor.
Mismatch exists in the labor market in the form of educational or skills
mismatch, education-job mismatches. According to Allen & van der Velden
(2001), these are reported to have serious effects on wages and associated
with negative labor market outcomes. The basic idea is that, although higher
education raises productivity in general, the actual level of productivity realized
is also determined by the match between educational and job level.
There are different kinds of mismatches. Spatial mismatch refers to the
disparity between where people who need jobs live and where jobs are
available. An example by Buchan & Calman (2004) is when health workers
typically prefer to live and work in larger cities that offer greater job
opportunities and infrastructure resulting in greater shortage in rural settings
and unemployment or underemployment in urban settings.
Skills mismatch refers to the situation where the workers‟ skills and
education are not adequate for the demands of jobs in the current economy.
There is a mismatch between the skills workers possess and what jobs require,
what economists call an imbalance between the supply of and demand for
human capital. Handel (2003) said that skills mismatch can describe situations
in which workers‟ skills exceed or lag behind those employers seek. An
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example of this in the Philippine setting is the oversupply of nurses and lack of
demand for them. The Filipino nurses may have difficulty entering the US labor
market until 2020 since the shortage of nurses in America ended in 2010 and
now, they have ample supply of US-educated nurses. The government is
pushing for new legislation that would establish a special local jobs plan for idle
Filipino nurses, now estimated at more than 300,000.
In line with these, the focus of this research is to specifically analyze the
education-occupation mismatch and its impact on wage. This research calls for
a need to look on the issues of mismatches since it affects the labor market. As
in the case of the Philippines, there is indeed a need to look and review labor
mismatch because this causes high unemployment rate. The evident reason
observed, is that college graduates‟ skills do not match with the available job
vacancies and the specific fields they should be placed on is already occupied,
therefore they will be left unemployed. The Department of Labor and
Employment said that many of the graduates do not satisfy what the economy
needs. They are either not ready for the jobs or they don‟t possess the needed
skills or knowledge needed for the work they applied for.
Mismatch can also be noticed when workers and jobs are randomly
assigned to labor markets. Each labor market clears at each instant but some
have more workers than jobs, hence unemployment, and some have more jobs
than workers, hence vacancies. According to Shimer (2005), as workers and
jobs move between labor markets, some unemployed workers find vacant jobs
and some employed workers lose or leave their job and become unemployed.
Thus, this research will focus also analyze the Beveridge Curve (ratio of
vacant jobs and unemployed workers) that will be important in understanding
the existence of mismatch on the Philippine labor market.
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II.
Objectives of the Study
The objectives of the research are:

To be able to analyze the situation of the Philippine labor market
specifically the presence of the education-occupation mismatch and its
effect to wage

To be able to see the importance of a balanced labor market where job
vacancies can accommodate workers available for work

To provide possible information that would be important for the
government in solving certain issues in the labor market
III.
Statement of the Problem
The research is intended to answer the following questions:
1. Does labor mismatch exist in the Philippines? To what extent?
2. How does education- occupation mismatch affect wage?
3. Which fields of study reflect the existence of the labor mismatch in the
Philippines?
4. What are the negative effects of the labor mismatch in the labor market?
5. Does the Beveridge Curve (unemployment rate and job vacancy rate
ratio) exist in the Philippines?
6. Does the Beveridge Curve reflect the positive relationship between the
unemployment rate and job vacancy rate signifying the occurrence of
labor mismatch?
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IV.
Significance of the Study
The analysis of educational-occupational mismatch and the existence of
the Beveridge Curve in the Philippine labor market would be significant to the
following:
a. The Government- Mismatch worsens the employment circulation of
the labor market and brings negative effects to the workers in the
labor market who face occupational downgrading in their careers as
in the case of underemployment in which workers that are highly
skilled work in low paying and low-skilled jobs. The research could
help the government in addressing these problems.

The Department of Labor and Employment (DOLE) - The study
would be helpful for the DOLE to find possible solutions to address
the negative effects of the existence of labor mismatch such as
providing job opportunities and skills training that would augment the
mismatch experienced by the mismatched individuals in their line of
work.

Department of Education (DepEd), Commission on Higher Education
(CHED) and Technical Education and Skills Development Authority
(TESDA) - The study would be helpful to the following education
sectors as through the study, they can have knowledge on what field
of study they should give more focus and attention as according to
what the labor market demands in order to solve the problem of
underemployment and unemployment.
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b. The Students- The study would be beneficial to the high school students
as the study would provide information that can help them on their
choice of the field of study (course) that they would be taking in their
college. The study can also help the graduating college students in
choosing their path of careers.
c. Economy- The research would be significant for it can provide the basis
of the government in taking actions to properly utilize the labor force in
order for them to be productive and therefore be instruments in creating
a better economy.
d. The researchers- The research would be significant to the researchers
for this paper will enable them to apply what they have learned in the 4
years of study and this would enable them to learn and discover new
findings relevant to their field of study, Economics.
V.
Scope and Limitation
The study will only cover the following parameters. The research
analyzed the impact of education- occupation mismatch on wage in the
Philippines through the use of the data from the National Statistics Office‟s
Labor Force Survey (LFS) October 2011 and the Philippine Standard
Occupational Classification (PSOC).The researchers used the CSPro or
Census and Survey Processing System which is a public domain statistical
package provided by NSO to obtain the necessary data to be used in the study.
The research does not deal on individual survey data.
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In making an empirical analysis of the existence of the Beveridge Curve
(unemployment-vacancies ratio), the researchers used the data of job vacancy
rates obtained from Bureau of Labor and Employment Statistics and the data of
unemployment rates
was obtained from Philippine National Statistical
Coordination Board only for the years 1999 up to 2009.
VI.
Hypotheses
There is no significant increase in wage when the education and occupation of
a college graduate is matched.
There is a significant increase in wage when the education and occupation of a
college graduate is matched.
This hypothesis will be tested in each of the 8 Major Classifications of the Field
of Study taken by the college graduates:
1. Education
2. Humanities and Arts
3. Social Sciences, Business and Law
4. Science
5. Engineering, Manufacturing and Construction
6. Agriculture
7. Health and Welfare
8. Services
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VII.
Definition of Terms
a. Conceptual Definition

Education- is a powerful driver of development and one of the strongest
instruments for reducing poverty and improving health, gender equality,
peace, and stability (World Bank).

Occupation- refers to an activity in which one engages; the principal
business of one's life (Merriam Webster Dictionary)

Mismatch- refers to the situation where two objects or people do not go
together

Matched- refers to one that closely resembles or harmonizes with
another

Unmatched- refers to one that does not closely resembles or
harmonizes with another

Wage- refers to an employee's base pay is the pay they will receive at a
minimum, while extra forms of pay may or may increase the total pay
above this level (Investopedia)

Beveridge Curve- refers to the unemployment-vacancy ratio wherein the
negative/inverse relationship depicts a balanced cycle in the labor
market implying that the job matching process in the labor market is
functioning well while the positive relationship implies that there is a
mismatch in the labor market.
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b. Operational Definition

Education- refers to the field of study taken by the college graduates

Occupation- refers to the primary occupations provided by the National
Statistics Office‟s (NSO) regularly conducted Labor Force Survey‟s
(LFS) October 2011 (4th Quarter of 2011) public user file with wage data

Mismatch- refers to the situation when education (field of study taken by
college graduates) do not match with his/her occupation taken after
graduation

Matched- refers to the situation when an individual‟s occupation is in line
with his/her field of study

Unmatched- refers to the situation when an individual‟s occupation is not
in line with his/her field of study

Wage- refers to the log of hourly wages which can be obtained by
dividing the basic pay per day by normal working hours per day for the
past week

Beveridge Curve- refers to the unemployment-vacancy ratio wherein the
negative/inverse relationship depicts a balanced cycle in the labor
market implying that the job matching process in the labor market is
functioning well while the positive relationship implies that there is a
mismatch in the labor market.
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CHAPTER 2: REVIEW OF RELATED LITERATURE
The review of the related literature for this study focuses on the different studies
concerning the impact of education-occupation mismatch on wage differentials
and the Beveridge Curve.
I.
Impact of Education-Occupation Mismatch on Wage
Education and Income Distribution
Tilak (1989) presented the following studies explaining the relation
between education and income distribution. Simon Kuznets (1955) predicted
that income distribution in capitalist countries would become more equal as the
labor force becomes more educated. As Knight and Sabot (1983) observed, the
change in educational composition of the labor force itself has an effect on
inequality. Whether it raises or lowers inequality, assuming all other factors are
held constant, depends on the relative sizes of different educational categories,
their relative mean wages, and their relative wage dispersions. The process of
education effecting income distribution can be simply explained as follows:
education creates a more skilled labor force. This will produce a shift from low
paid, unskilled employment to high paid, skilled employment. This shift
produces higher labor incomes, a reduction in skill differentials and an increase
in the share of wages in total output.
The Impact of Education and Mismatch on Wages
In the research study of Muysken and Hoppe (2002), The Impact of
Education and Mismatch on Wages: Germany, it is cited that in the study taken
in the Netherlands, Muysken and Ruholl traced that personal characteristics
which entails education and experience, and job characteristics which entails
the skills required are the two important determinants used to explain the wage
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differentials since the changes in personal characteristics explains about half of
the variation of wages and job characteristics explains at least thirty percent of
the variation of wages. This assumption was supported by the established
similar results in the United States.
In the estimation results of the study, it is figured out that variables used
in personal characteristics which pertains to age, working experience,
education received and number of hours worked, and job characteristics which
pertains to size of the firm and level of skills required, are significant in defining
the variation of wages, in fact as the job requires a higher level of skills the
earning wages yields higher. The experiences attained by a worker also
generate a positive impact towards defining wage. Job characteristics and
experience plays an important part in determining the wage differences of the
educational category of workers and the educational level is the one to define
the remaining part of it.
Education and Occupation Mismatch in the Labor Market
The educational market and the labor market are the two market
systems that facilitate the matching of education and occupations. Both are
systems of controlling demand and supply, and systems of evaluation and
allocation of positions and agents. As a rule, education qualifies mainly for the
labor market, not for the work or occupation itself (Masuda, T. & Muta, H.
1996).
Ahola et al. (1991) concluded that there are two dimensions of matching.
First is the level of education which is considered to be the primary dimension
of matching process that is related and connected to the segmentation of
structural classes and to the reproduction of social positions. And the
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secondary dimension is the field of study wherein it performs its act within the
different levels of education (Ahola, S. 1991)
The study, “The Matching of Educational and Occupational Structures in
Sweden and Finland” explains that apart from having a strong connection of
education and occupation in the professional fields, individuals who possess
different educational backgrounds can also have an easy way to get matched
up with various occupations in a relatively elastic way (Ahola et al. 1991).
Occupational domains are narrowing or sometimes widening in certain
fields. Narrowing occupational profiles can be found in the fields where
vocational/professional educational programmes have been developed to meet
the needs of specialized occupational tasks (Ahola et al. 1999).
Vocational Schooling as an Advantage Tool in the Labor Market
The study of Vocational Schooling Occupational Matching and Labor
Market Earnings in Israel concluded that vocational schooling is more costeffective than the general academic training in Israel. The students who came
from vocational programs and seek out for work that are related to their field of
study had earned more. In fact, their wages are generally higher by up to ten
percent a month than those who studied academic secondary schools but
found employment in occupations not related to their field (Neuman, S. &
Ziderman, A. 1991).
Productivity in Matched Occupation-Education
In the study of Patrick van Eijs and Hans Heijke (1996), The Relation
between the Wage, Job-related Training and the Quality of the Match between
Occupations and Types of Education, it is stated that efficiency and wages
depend on the matching of the demanded and attained abilities and therefore
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there will be a higher wages if there will be a match on these two that causes a
higher productiveness. This concludes that through the matching of the
laborer‟s specific education skills with the occupational job characteristics
which yields efficiency, the human capital of the labor force is being utilized well
and this paves a way to achieve the right and deserved wage for the rendered
skill.
Income Penalty of Mismatch
But in the labor market there are existing problems that hinders the right
allocation of deserved wages and this is due to the education-occupation
mismatch that creates an income penalty to the workers. A study supporting
this conclusion is the pioneering paper of Robst which had shown that in the
data wherein US college graduates who do not matched their occupation to the
major course they have taken had almost 11 percent lower annual income as to
be compared to the graduates who had a matched one.
Also in the study done by the 2006 Survey of Labour and Income
Dynamics (SLID) of Canada it is concluded in the survey results that those 58
percent workers reported who had matched their attained education closely
related to their present work and those 19 percent reported who had matched
their attained education somewhat related to their present work have a 35
percent higher wage given a $27 mean wage rate than those 23 percent
workers reported who did not found their attained education to be related to
their present work given $20 mean wage rate.
Reasons for having Occupations not related to the Field of Study
The general personal reasons for choosing the occupation even if it is not
related to the field of study are job security wherein the worker had found the
secureness, assurance and continuity of gainful employment to the said
occupation even if it is not inclined to his taken course, professional growth in
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which they had achieved the
sense of
fulfillment and usefulness of their
personal drive on the said occupation they have taken even if it is unmatched
to their field of study, pay, which pertains to monetary reason of earning a
higher compensation to the unmatched occupation than to the related one, and
quality of work life that refers to the benefits, environment and way of life that
the worker is attaining which provides him the sense of belongingness and
satisfaction (Edgewise.ph, 2010). Prejudiced situations is depicted in the
condition of the nurses who are under the health and welfare field of study in
way that nurses fail to pursue their careers not only because of financial
incapability of hospitals to give them a just compensation but worst is they
themselves are paying just to gain experience and after they will still not be
hired (Filipino Nurses Blog). And in the case of the agricultural workers they
had a greater tendency to prefer not to be in lined to their field of study because
of the existence of non-competitive salaries and incentives due to inefficient
utilization of our agricultural sector; therefore attaining an occupation
unmatched in this field would provide them a higher wage (Rwanda Skills
Survey 2012 Agriculture Sector Report).
II.
Beveridge Curve
The Cyclical Behavior of Equilibrium Unemployment and Vacancies
This journal explains that with an increase in labor productivity
(interpreted as a technology or supply shock in one-sector model and referred
as the preference or demand shock changing the relative price of goods in
multi-sector model) in relation to the value of nonmarket activity (referred as
leisure) and to the cost of advertising a job vacancy makes unemployment
relatively expensive and vacancies relatively cheap. The market substitutes
toward vacancies, and the increased job-finding rate pulls down the
unemployment rate, resulting to a downward sloping Beveridge curve
(vacancy-unemployment ratio) (Shimer, 2005).
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This study presented that the increase in hiring, shortens unemployment
duration, increases workers‟ threat in wage bargaining, and increases also the
expected present value of wages in new jobs. Then, it can be said that higher
wages absorb most of the productivity increase, thus reducing the incentive for
the creation of vacancies. Therefore, the labor productivity shock results
primarily in higher wages, with little impact on the unemployment, vacancy, and
job-finding rates (Shimer, 2005).
This study presented that an increase in separations results to
decreasing employment duration, increasing unemployment rate and so
therefore increasing vacancies. As a result, fluctuations in the separation rate
or separation shocks generate an increase in both unemployment and
vacancies (Shimer, 2005).
According to search theory, unemployed workers have left their old job
and are actively searching for a new employer. It is a theory of former steel
workers moving to a new city to look for positions as nurses (Lucas and
Prescott 1974). In contrast, this study emphasizes the mismatch theory in
which unemployed workers are attached to an occupation and a geographic
location in which jobs are presently scarce. In here, it is a theory of former steel
workers choosing to remain near a closed plant in the hope that it reopens
(Shimer, 2005). These two theories are complementary and it is reasonable to
think that mismatch may be as important as search in understanding
equilibrium unemployment.
The Beveridge curve is part of the study since it reflects the efficiency of
the job matching process through depicting the state of the labor market using
the shifts along the curve that illustrates occurring regular changes in the
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demand and supply of labor. In the study of skills mismatches and labor
mobility of the European Union, they concluded that having an upturn shift in
curve reflects that there is a higher vacancies which shows that there is a
higher supply of jobs then definitely exemplifies lower unemployment and this
greatly explains that it indicates labor shortages while on the other hand the
downturn shift in curve reflects that there is a lower vacancies which shows that
there is a lower supply of jobs then definitely exemplifies lower unemployment
and this greatly explains that it indicates labor surplus (Shimer, 2005).
William Beveridge identified and implied the negative relationship
between unemployment and job vacancies that depicts a balanced cycle in the
labor market because a negative relationship between the two reflects that the
job matching process in the labor market is functioning well. Therefore if there
will be a positive relationship between them wherein a direct relationship
between them exists, mismatch in the labor market occurs (Shimer, 2005).
The conclusions made in this study was significantly discussed and
proven in the said research through the evaluation of the major skills challenge
of Europe wherein high levels of unemployment is still existing even if job
vacancies also started to increase which illustrates that mismatch in their labor
market exists which may be indicated by first, qualification mismatch that
pertains to the mismatch between educational qualifications a worker
possesses and the prerequisites of the job (Shimer, 2005). Situations involved
in this mismatch are over-education wherein a worker possesses more required
educational qualifications and under-education in which a worker possesses
fewer educational qualifications. Second is the structural unemployment that
pertains to the mismatch between labor demand and skills and the location of
the potential workers. Third, skills mismatch that pertains to incompatibility
between the skills possessed by the worker and the demanded skills of the job,
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and situation involves are skill deficit wherein the worker‟s skill didn‟t fit into the
job prerequisites and skill underutilization in which worker‟s skill went beyond
the job prerequisites. Fourth, Regional and sectoral mismatch which exists
when job openings in the locations and sectors are open but not well matched
with potential workers.
Labor Market Mismatches
Problems of matching between labor supply and labor demand in
Belgium are visible from the Beveridge curve, which shows the relationship
between the unemployment rate and the job vacancy rate. Belgium has both a
significant pool of unfilled job vacancies and persistent unemployment implying
that both unemployment rate and job vacancy rates move in the same
direction. This observation rises to the problem of labor mismatch or to the
question of how labor supply matches up with labor demand. The reasons for a
mismatch between the two can be cyclical, frictional or structural, for example
when the educational level of job-seekers does not correspond with the profiles
sought on the labor market, or when there is a lack of geographic mobility
(Zimmer, 2012).
2009-2010 Bureau of Labor and Employment Statistics (BLES)
Integrated Survey (BITS)
The 2009/2010 BITS results showed that from January 2009 to January
2010 the total number of job vacancies are at 276,940 while job applicants is
roughly 6 times higher at 1,969,976 for all occupations. Manufacturing, real
estate, renting and business activities, and education industries had the most
number of hard-to-fill vacancies. The most common problems in filling up
vacant positions include lack of competency, high expectations in wage/salary,
and lack of work experience.
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III.
THEORETICAL FRAMEWORK
A. Impact of Education- Occupation Mismatch on Wage
Recent studies of education and wage determination are almost always
embedded in the framework of Mincer's (1974) human capital earnings function
(HCEF).
According to this model, the log of individual earnings (y) in a given time period
can be decomposed into an additive function of a linear education term:
𝑙𝑜𝑔𝑦 = 𝑎 + 𝑏𝑆 + 𝑐𝑋 + 𝑑𝑋 2 + 𝑒
where S represents years of completed education, X represents the number of
years an individual has worked since completing schooling, and e is a statistical
residual. In the absence of direct information on experience Mincer proposed
the use of "potential experience": the number of years an individual of age A
could have worked, assuming he started school at age 6, finished S years of
schooling in exactly S years, and began working immediately thereafter: X =A S - 6.
B. Beveridge Curve
1. Skills Mismatches and Labor Mobility
A study done in the Member States of the European Union showed that the
unfilled job vacancies co-exist with high levels of unemployment. The so-called
Beveridge curve, which relates unemployment rates to job vacancies, typically
shows
a
negative
relationship
between
unemployment rate (Shimer, 2005).
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the
vacancy
rate
and
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The fact that unemployment was still rising when vacancies started to
increase reflects problems in the job matching process, which may be related
to mismatches in skills/educational qualifications required for a certain job and
regional/sectoral mismatches.
The Beveridge curve reflects the efficiency of the job matching
process.In the study, the curves of Member States are analyzed to help us
understand whether these structural changes are taking place.
- Shifts along the curve represent cyclical changes in the demand for labor
(higher vacancies and lower unemployment in upturns indicating labor
shortages; lower vacancies and higher unemployment in downturns indicating
an excess of labor)
- Shifts of the curve towards the left or right are indicative of structural changes.
Increases in long-term unemployment will push the curve away from the
starting position, pointing to potential mismatches in the labour market.
2. Mismatch by Shimer
The study conducted in the mismatch model of Shimer significantly showed
the negative correlation between unemployment and vacancy that pertains to
Beveridge curve and the positive correlation between rate wherein the
unemployed workers are able to discern jobs and the vacancy-unemployment
ratio.
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Theory
The unemployment and vacancy rates are dependent
on the exogenous
number of workers per market M and the endogenous number of jobs per
market N.
The vacancy-unemployment (v-u) ratio is V (N)/U(N) and the unemployment
and vacancy rates are u(N) =U(N)/M and v(N) =V (N)/N.
Therefore unemployment and vacancies are being affected by productivity
shocks due to the unemployment and vacancies‟ impact on the number of jobs
per market.
The following proposition demonstrates how:
The unemployment rate u is increasing in the number of workers per labor
market M and decreasing in the number of jobs per labor market N.
The vacancy rate v is decreasing in the number of workers per labor market M
and increasing in the number of jobs per labor market N.
In this, there are a lot of implications. First, a higher productivity encourages
firms to produce more jobs, and therefore this would raise the number of jobs
per labor market N, and hence diminishes unemployment rate and upsurges
vacancy rate. Thus it presents to us that the impact of productivity shocks
cause a downward-sloping vacancy-un employment (v-u) locus movement.
Second, the unemployment and vacancy rates both decreases whenever there
is a proportional increase in both M and N.
Doubling M and N is equivalent to merging randomly selected pairs of labor
markets. If both markets have unemployment, this merger does not affect the
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UNIVERSITY OF SANTO TOMAS
unemployment or vacancy rates, and similarly if both markets have vacancies.
But merging a market with unemployment and a market with vacancies reduces
the unemployment and vacancy rate in both.
Measurement
In the United States, the Bureau of Labor Statistics (BLS) has measured job
vacancies using the JOLTS. The said measurement is the most reliable time
series for vacancies in the U.S.. In accordance to the prerequisites of job
openings, the BLS specified that job opening entails first, there is a specific slot
of position that occurs, second, within 30 days, the work could start, and third,
to fill in the vacant position, the employer is enthusiastically hiring outside the
institution itself. Wherein, active recruiting pertains to the commitment of the
institution in rendering contemporary efforts in fulfilling the opening through
advertisement and other methods of publicity. Also the time preferential such
as full-time, part-time, permanent, temporary, and short-term openings are
incorporated.
The vacancy rate is measured as the ratio of vacancies to vacancies plus
employment.
In order to measure the unemployment rate of each month, The Bureau of
Labor Statistics (BLS) uses the Current Population Survey (CPS) wherein it
entails a measurement procedure of using household questionnaire.
The unemployment rate is measured as the ratio of unemployment to the sum
of unemployment and employment.
The strong negative correlation between unemployment and vacancies over
this time period is shown by the empirical Beveridge curve.
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UNIVERSITY OF SANTO TOMAS
IV.
CONCEPTUAL FRAMEWORK
A. The Model used by the Researchers in the Analysis of EducationOccupation Mismatch effect on wage
This section provides a framework for the regression model to be estimated:
no formal model of wage determination is presented; the researchers draw
from the previous theoretical and empirical studies (Mincer‟s Model) in
analyzing the likely effects of certain variables on wages.
In this study regarding the impacts of education and occupation mismatch
on wages, the researchers will use Mincer‟s human capital earnings function as
the basis. The researchers will use this function:
𝐿𝑛 𝑊𝑖 = 𝛼 + 𝛽𝑀𝐴𝑇𝐶𝐻 + 𝑢
Where:
𝐿𝑛 𝑊𝑖 = log 𝑜𝑓 𝑕𝑜𝑢𝑟𝑙𝑦 𝑤𝑎𝑔𝑒𝑠 /𝑒𝑎𝑟𝑛𝑖𝑛𝑔𝑠
MATCH= field of study and occupation category 1=matched 0=not matched
Education
Occupation
Matched
or
Unmatche
d
25
Effect
Wage
UNIVERSITY OF SANTO TOMAS
B. Beveridge Curve
To analyze the mismatch between vacant jobs and unemployed workers in
the Philippines, the researchers will use Shimer’s measurement in obtaining the
Beveridge Curve.
The vacancy rate is measured as the ratio of vacancies to vacancies plus
employment.
The unemployment rate is measured as the ratio of unemployment to the sum
of unemployment and employment.
Job- vacancy
rate
Unemployment
rate
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CHAPTER 3: DATA AND METHODOLOGY
A. The Model used by the Researchers in the Analysis of EducationOccupation Mismatch effect on wage
1. Research Design
In the study, the researchers used a quantitative explanatory research
methodology which attempts to explain the functional relationship between the
variable to be estimated (dependent variable) and the variable that accounts for
the changes (independent variable). The researchers worked on the
educational and work-related variables across the country. The dependent
variable is the log of hourly wage. The independent variable that is used is a
dummy or binary variable: MATCH variables (being educationally and
occupationally matched or unmatched).
2. Sources of Data
The cross-sectional data the researchers used was sourced from the
National Statistics Office‟s (NSO) regularly conducted Labor Force Survey‟s
(LFS) October 2011 (4th Quarter of 2011) public user file with wage data. The
researchers used the CSPro or Census and Survey Processing System which
is a public domain statistical package
provided by NSO to obtain the
necessary data to be used in the study.
3. Tools for Data Analysis
In doing the regression analysis, the researchers utilized the Statistical
Package for the Social Sciences (SPSS) 17.0.
The researchers also did
hypothesis testing using the t test. According to Gujarati (2004), in the
language of significance tests, a statistic is said to be statistically significant if
the value of the test statistic lies in the critical region. In this case the null
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UNIVERSITY OF SANTO TOMAS
hypothesis is rejected. By the same token, a test is said to be statistically
insignificant if the value of the test statistic lies in the acceptance region. In this
situation, the null hypothesis is not rejected.
4. Specific Methodology
With the objective to analyze the impact of education-occupation mismatch
on wage, the researchers used the LFS to obtain the total number of college
graduates. This was then classified according to the First Stage of Tertiary/
Baccalaurate Education provided also by the NSO thus arriving with the 8
Major Classifications of the Field of Study taken by the college graduates:
61-Education
62- Humanities and Arts
63-Social Sciences, Business and Law
64- Science
65- Engineering, Manufacturing and Construction
66-Agriculture
67- Health and Welfare
68-Services
For detailed sub-classifications of the 8 Major Classifications of the Field of
Study taken by the college graduates, see Appendix 1.
The researchers also obtained the Primary Occupations of the college
graduates for each of the 8 Major Classifications of the Field of Study.
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Table 1: Primary Occupations
PRIMARY OCCUPATIONS
Officials of Government and Special-Interest Organizations
Corporate Executives and Specialized Managers
General Managers or Managing-Proprietors
Supervisors
Physicists Mathematical and Engineering Science Professionals
Life Science and Health Professionals
Teaching Professionals
Other Professionals
Physical Science and Engineering Associate Professionals
Life Science and Health Associate Professionals
Teaching Associate Professionals
Related Associate Professionals
Office Clerks
Customer Services Clerks
Personal and Protective Service Workers
Models Salespersons and Demonstrators
Farmers and Other Plant Growers
Animal Producers
Forestry and Related Workers
Fishermen
Hunters and Trappers
Mining Construction and Related Trade Workers
Metal Machinery and Related Trades Workers
Precision Handicraft Printing and Related Trades Workers
Other Craft and Related Trades Workers
Stationary Plant and Related Operators
Machine Operators and Assemblers
Drivers and Mobile Plant Operators
Sales and Services Elementary Occupations
Agricultural Forestry Fishery and Related Laborers
Laborers in Mining Construction Manufacturing and Transport
Armed Forces
Non-Gainful Occupations
Other Occupations Not Classifiable
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UNIVERSITY OF SANTO TOMAS
The regression model to be estimated as presented here will be used for
each Major Classification of the Field of Study to determine the impact of
education-occupation mismatch on wage.
𝑳𝒏 𝑾𝒊 = 𝜶 + 𝜷𝑴𝑨𝑻𝑪𝑯 + 𝒖
Where:
𝐿𝑛 𝑊𝑖 = log 𝑜𝑓 𝑕𝑜𝑢𝑟𝑙𝑦 𝑤𝑎𝑔𝑒𝑠 /𝑒𝑎𝑟𝑛𝑖𝑛𝑔𝑠
MATCH= field of study and occupation category 1=matched 0=not matched
The dependent variable is the log of hourly wages and was obtained by
dividing the basic pay per day by normal working hours per day for the past
week. The reference period, being the „past week‟ or the past seven days
preceding the date of visit of the interviewer. The reason for taking the natural
logarithm of the wage variables is that they are highly skewed to the left. By
taking the log, the distribution becomes more symmetric (Neuman & Ziderman,
1991).
log 𝑜𝑓 𝑕𝑜𝑢𝑟𝑙𝑦 𝑤𝑎𝑔𝑒𝑠
= 𝑏𝑎𝑠𝑖𝑐 𝑝𝑎𝑦 𝑝𝑒𝑟 𝑑𝑎𝑦
/ 𝑛𝑜𝑟𝑚𝑎𝑙 𝑤𝑜𝑟𝑘𝑖𝑛𝑔 𝑕𝑜𝑢𝑟𝑠 𝑝𝑒𝑟 𝑑𝑎𝑦 𝑓𝑜𝑟 𝑡𝑕𝑒 𝑝𝑎𝑠𝑡 𝑤𝑒𝑒𝑘
The basic pay per day with the highest frequency was obtained from
each of the Primary Occupations under each Major Classification of the Field of
Study taken by the college graduates. This is to represent the basic pay per
day of the majority of the college graduates is receiving.
The normal working hours per day for the past week was obtained by
dividing the total number of hours worked during the past week by 8 hours
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UNIVERSITY OF SANTO TOMAS
which is the normal working hours per day for an employee as specified in the
Philippine Labor Law (Art. 84, Labor Code). The total number of hours worked
during the past week with the highest frequency was obtained from each of the
Primary Occupations under each Major Classification of the Field of Study
taken by the college graduates.
𝑛𝑜𝑟𝑚𝑎𝑙 𝑤𝑜𝑟𝑘𝑖𝑛𝑔 𝑕𝑜𝑢𝑟𝑠 𝑝𝑒𝑟 𝑑𝑎𝑦 𝑓𝑜𝑟 𝑡𝑕𝑒 𝑝𝑎𝑠𝑡 𝑤𝑒𝑒𝑘
= 𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑕𝑜𝑢𝑟𝑠 𝑤𝑜𝑟𝑘𝑒𝑑 𝑑𝑢𝑟𝑖𝑛𝑔 𝑡𝑕𝑒 𝑝𝑎𝑠𝑡
/ 𝑛𝑜𝑟𝑚𝑎𝑙 𝑤𝑜𝑟𝑘𝑖𝑛𝑔 𝑕𝑜𝑢𝑟𝑠 𝑝𝑒𝑟 𝑑𝑎𝑦
The MATCH variable was obtained by doing cross tabulation of the
Primary Occupations and the Field of Study. The Primary Occupations under
each Major Classification of the Field of Study were classified as Matched=1
and Unmatched=0 which is based and patterned on the Philippine Standard
Occupational Classification 1992 (updated for the year 2002), see Appendix 2
and also on the First Stage of Tertiary/ Baccalaurate Education provided by the
NSO. A matched individual is the one having the primary occupation in line with
his/her field of study. An unmatched individual is the one having the primary
occupation not in line with his/ her field of study.
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UNIVERSITY OF SANTO TOMAS
B. Beveridge Curve
1. Research Design
The researchers used correlational quantitative research design which
expresses the relationship between two variables numerically.
2. Sources of Data
In making an empirical analysis of the existence of the Beveridge Curve
in the Philippines, the researchers used the data of job vacancy rates obtained
from Bureau of Labor and Employment Statistics and the data of
unemployment rates was obtained from Philippine National Statistical
Coordination Board only for the years 1999 up to 2009.
3. Tools for Data Analysis
The researchers used the Statistical Package for the Social Sciences
(SPSS) 17.0 to analyze the vacancy-unemployment (v-u) ratio or the Beveridge
Curve.
4. Specific Methodology
The researchers obtained the correlation between the job vacancy rates
and unemployment rates to analyze the Beveridge Curve.
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UNIVERSITY OF SANTO TOMAS
CHAPTER 4: RESULTS AND DISCUSSION
A. Education-Occupation Mismatch Impact on Wage
The statistical table (See Appendix 3) shows the total number of college
graduates per Field of Study and their corresponding Primary Occupations.
The following shows the percentage of each field of study‟s graduates to the
total number of college graduates:
Education (61)- 23.14 %
Humanities and Arts (62)- 1.33 %
Social Sciences, Business and Law (63) -32.35%
Science (64) -7.13%
Engineering, Manufacturing and Construction (65) - 22.69%
Agriculture (66) - 3.55%
Health and Welfare (67) - 1.33%
Services (68)- 8.47%
The highest proportion (32.35%) of the total number of college graduates
who had already found jobs/ occupations are coming from the Social Sciences,
Business and Law. The least proportion (1.33%) of the total number of college
graduates who had already found jobs/ occupations are coming from the
Humanities and Arts and Health and Welfare.
Regression Analysis
The researchers consider the log of hourly earnings as the dependent
variable as run against the MATCH variable as the independent variable. The
reason for taking the natural logarithm of the wage variables is that they are
highly skewed to the left. By taking the log, the distribution becomes more
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UNIVERSITY OF SANTO TOMAS
symmetric (Neuman & Ziderman, 1991). By taking the natural logarithm of the
hourly wages shows that these become responsive to the changes in the
independent variable as shown in the two scatter diagram plots below. The
scatter plots presented here is only for Education field of study, since the
scatter plots for the other 7 fields of study also reflect the same pattern.
HOURLY WAGE/ EARNINGS
350
300
250
200
HOURLY
WAGE/
EARNINGS
150
100
50
0
0
10
20
30
LOG OF HOURLY EARNINGS
7
6
5
4
LOG OF HOURLY
EARNINGS
3
2
1
0
0
10
20
30
The results of the regression analysis (See Appendix 4) done for the 8 Major
Classifications of the Field of Study are explained on the next page.
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UNIVERSITY OF SANTO TOMAS
1. Education
Under the Field of Education (61), the regression model is:
log of hourly wages= 4.482 + 0.965MATCH
This presents that on the average, for every unit increase in the MATCH
variable, the log of hourly earnings would be increasing by 0.965 peso.
Hypothesis Testing
H0: There is no significant increase in wage when the education and
occupation of a college graduate is matched.
H1: There is a significant increase in wage when the education and occupation
of a college graduate is matched.
α=0.05
Degrees of freedom= n-k-1
Degrees of freedom= 26- 1-1 = 24
T critical= 1.711
T-stat= 2.54224502580694
Reject H0. Accept H1.This implies that if a college graduate under this Field of
Study pursues with the occupation related to his/her field of study can earn
higher wages rather than having occupation not related to his/ her field of
study.
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UNIVERSITY OF SANTO TOMAS
2. Social Sciences, Business and Law
Under the Field of Social Sciences, Business and Law (63), the regression
model is:
log of hourly wages= 4.270 + 0.662MATCH
This presents that on the average,
for every unit increase in the MATCH
variable, the log of hourly earnings would be increasing by 0.662 peso.
Hypothesis Testing
H0: There is no significant increase in wage when the education and
occupation of a college graduate is matched.
H1: There is a significant increase in wage when the education and occupation
of a college graduate is matched.
α=0.05
Degrees of freedom= n-k-1
Degrees of freedom= 27- 1-1 = 25
T critical= 1.708
T-stat= 3.5304477880851
Reject H0. Accept H1.This implies that if a college graduate under this Field of
Study pursues with the occupation related to his/her field of study can earn
higher wages rather than having occupation not related to his/ her field of
study.
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UNIVERSITY OF SANTO TOMAS
3. Services
Under the Field of Services (68), the regression model is:
log of hourly wages= 4.282 + 0.653MATCH
This presents that on the average, for every unit increase in the MATCH
variable, the log of hourly earnings would be increasing by 0.653 peso.
Hypothesis Testing
H0: There is no significant increase in wage when the education and
occupation of a college graduate is matched.
H1: There is a significant increase in wage when the education and occupation
of a college graduate is matched.
α=0.05
Degrees of freedom= n-k-1
Degrees of freedom= 25- 1-1 = 23
T critical= 1.714
T-stat= 2.25884756076435
Reject H0. Accept H1.This implies that if a college graduate under this Field of
Study pursues with the occupation related to his/her field of study can earn
higher wages rather than having occupation not related to his/ her field of
study.
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UNIVERSITY OF SANTO TOMAS
Based on the hypothesis testing conducted using t-test, the results show
that college graduates under these 3 fields of study (Education, Social
Sciences, Business and Law & Services) who pursue with the occupation
related to their field of study earn higher wages rather than having occupation
not related to their field of study. Thus, a college graduate earns higher wages
when his/her education and occupation is matched.
The result was supported by the study made by Patrick van Eijs and
Hans Heijke (1996) where it stated that efficiency and wages depend on the
matching of the demanded and attained abilities and therefore there will be a
higher wages if there will be a match on these two that causes a higher
productiveness. The study also concluded that through the matching of the
laborer‟s specific education skills with the occupational job characteristics
which yields efficiency, the human capital of the labor force is being utilized well
and this paves a way to achieve the right and deserved wage for the rendered
skill.
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UNIVERSITY OF SANTO TOMAS
4. Humanities and Arts
Under the Field of Humanities and Arts (62), the regression model is:
log of hourly wages= 4.437 - 0.010MATCH
This presents that on the average, for every unit increase in the MATCH
variable, the log of hourly earnings would be decreasing by 0.010 peso.
Hypothesis Testing
H0: There is no significant increase in wage when the education and
occupation of a college graduate is matched.
H1: There is a significant increase in wage when the education and occupation
of a college graduate is matched.
α=0.05
Degrees of freedom= n-k-1
Degrees of freedom= 19- 1-1 =17
T critical= 1.740
T-stat= -0.0486257070180501
Accept H0. Reject H1.This implies that if a college graduate under this Field of
Study pursues with the occupation related to his/her field of study can earn
lower wages rather than having occupation not related to his/ her field of study.
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UNIVERSITY OF SANTO TOMAS
5. Science
Under the Field of Science (64), the regression model is:
log of hourly wages= 4.479 + 0.051MATCH
This presents that on the average, for every unit increase in the MATCH
variable, the log of hourly earnings would be increasing by 0.051 peso.
Hypothesis Testing
H0: There is no significant increase in wage when the education and
occupation of a college graduate is matched.
H1: There is a significant increase in wage when the education and occupation
of a college graduate is matched.
α=0.05
Degrees of freedom= n-k-1
Degrees of freedom= 26- 1-1 =24
T critical= 1.711
T-stat= 0.197020752163872
Accept H0. Reject H1.This implies that if a college graduate under this Field of
Study pursues with the occupation related to his/her field of study can earn
lower wages rather than having occupation not related to his/ her field of study.
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UNIVERSITY OF SANTO TOMAS
6. Engineering, Manufacturing and Construction
Under the Field of Engineering, Manufacturing and Construction (65), the
regression model is:
log of hourly wages= 4.851- 0.193MATCH
This presents that on the average, for every unit increase in the MATCH
variable, the log of hourly earnings would be decreasing by 0.193 peso.
Hypothesis Testing
H0: There is no significant increase in wage when the education and
occupation of a college graduate is matched.
H1: There is a significant increase in wage when the education and occupation
of a college graduate is matched.
α=0.05
Degrees of freedom= n-k-1
Degrees of freedom= 27- 1-1 =25
T critical= 1.708
T-stat= -0.703149759265872
Accept H0. Reject H1.This implies that if a college graduate under this Field of
Study pursues with the occupation related to his/her field of study can earn
lower wages rather than having occupation not related to his/ her field of study.
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UNIVERSITY OF SANTO TOMAS
7. Agriculture
Under the Field of Agriculture (66), the regression model is:
log of hourly wages= 4.447- 0.243MATCH
This presents that on the average, for every unit increase in the MATCH
variable, the log of hourly earnings would be decreasing by 0.243 peso.
Hypothesis Testing
H0: There is no significant increase in wage when the education and
occupation of a college graduate is matched.
H1: There is a significant increase in wage when the education and occupation
of a college graduate is matched.
α=0.05
Degrees of freedom= n-k-1
Degrees of freedom= 29- 1-1 =27
T critical= 1.703
T-stat= -0.913513876447283
Accept H0. Reject H1.This implies that if a college graduate under this Field of
Study pursues with the occupation related to his/her field of study can earn
lower wages rather than having occupation not related to his/ her field of study.
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UNIVERSITY OF SANTO TOMAS
8. Health and Welfare
Under the Field of Health and Welfare (67), the regression model is:
log of hourly wages= 4.694- 0.468MATCH
This presents that on the average, for every unit increase in the MATCH
variable, the log of hourly earnings would be decreasing by 0.468 peso.
Hypothesis Testing
H0: There is no significant increase in wage when the education and
occupation of a college graduate is matched.
H1: There is a significant increase in wage when the education and occupation
of a college graduate is matched.
α=0.05
Degrees of freedom= n-k-1
Degrees of freedom= 18- 1-1 =16
T critical= 1.746
T-stat= -1.57964542504616
Accept H0. Reject H1.This implies that if a college graduate under this Field of
Study pursues with the occupation related to his/her field of study can earn
lower wages rather than having occupation not related to his/ her field of study.
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UNIVERSITY OF SANTO TOMAS
Based on the hypothesis testing conducted using t-test, the results show
that college graduates under these 5 Fields of Study (Humanities and Art,
Science, Engineering, Manufacturing and Construction, Agriculture, Health and
Welfare) who pursue with the occupation related to their field of study
(matched), earn lower wages; while if they pursue with the occupation not
related to his/her field of study (unmatched), they earn higher wages.
B. Beveridge Curve
The data presented on Table 2 are used in analyzing the existence of the
Beveridge curve in the Philippines.
Jobvacancy Unemployment
Year
Rate
Rate
2009
45.1
7.475
2008
52.5
7.4
2007
47.7
7.325
2006
47.9
8
2005
57.3
11.35
2004
62.3
11.825
2003
74.7
11.4
2002
70.9
11.4
2001
84.6
11.125
2000
68.5
11.175
1999
71.8
9.75
Table 2: Job-vacancy & unemployment rates
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UNIVERSITY OF SANTO TOMAS
Correlation of Unemployment rates and Job-vacancy rates
Unemployment
Job-vacancy rate
rate
Job-vacancy
rate
1
Unemployment
rate
0.768721874
1
Table 3. Correlation between job-vacancy and unemployment rates
The correlation coefficient between the job- vacancy rates and
unemployment rates resulted to a positive value of 0.768721874 which means
that there is a positive correlation. The two variables are associated; and they
move in the same direction in systematic way: as one gets larger, so does the
other; as one gets smaller, so does the other. The correlation coefficient of
0.768721874 is closer to 1 therefore implying strong positive correlation. This
positive correlation between the two variables greatly reflects that mismatch
occurs in the Philippine labor market as job vacancies increases, the
unemployment rate increases. This implies that as more jobs become available
for employment, more number of the labor force gets unemployed indicating
that there is no equilibrium in the labor market.
William Beveridge (1960) identified that the negative relationship
between unemployment and job vacancies depicts a balanced cycle in the
labor market implying that the job matching process in the labor market is
functioning well. Thus, the study‟s result showing a positive correlation implies
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UNIVERSITY OF SANTO TOMAS
that the job matching process in the labor market is not functioning well
mismatch, so the researchers could say that mismatch exists in the Philippines.
The result of the study showing that mismatch occurs in the Philippines
is also supported by Zimmer (2012) wherein in his study in Belgium, problems
of mismatch is also evident as it has both a significant pool of unfilled job
vacancies and persistent unemployment implying that both unemployment rate
and job vacancy rates move in the same direction.
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UNIVERSITY OF SANTO TOMAS
CHAPTER 5: CONCLUSION AND RECOMMENDATIONS
I. CONCLUSION
A. Education-Occupation Mismatch
The research was able to analyze the impact of education- occupation
mismatch on wage. Through estimating the coefficient of the Match variable
(education-occupation) along with the log of hourly earnings/wage, the
researchers conclude that that college graduates under these 3 fields of study Education, Social Sciences, Business and Law & Services who pursue with the
occupation related to their field of study earn higher wages rather than having
occupation not related to their field of study (lower wages).
The result was supported by Van Eijs and Heijke (1996) where it stated
that the matching of the laborer‟s specific education skills with the occupational
job characteristics yields efficiency, the human capital of the labor force is
being utilized well and this paves a way to achieve the right and deserved wage
for the rendered skill. Samson (2003) also concluded in their study that
matched workers earn more than their unmatched counterparts verified by the
regression analysis.
The researchers also conclude that college graduates under these 5
Fields of Study (Humanities and Art, Science, Engineering, Manufacturing and
Construction, Agriculture, Health and Welfare) who pursue with the occupation
related to their field of study (matched), earn lower wages; while if they pursue
with the occupation not related to his/her field of study (unmatched), they could
higher wages.
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UNIVERSITY OF SANTO TOMAS
These 5 out of 8 Major Classifications of the Fields of Study implied that
college graduates coming from these 5 Fields of study since they are earning
lower wages if they pursue with the occupation related to his/her field of study
(matched), tend to pursue occupations not related to his/her field of study
(unmatched) in order for them to earn higher wages. From, this point, the
researchers can conclude the existence of the education-occupation mismatch
in the Philippines.
Mismatch exists and causes underemployment, a situation in which a
worker is employed, but not in the desired capacity, whether in terms of
compensation, hours, or level of skills and experience (Lopez). Labor that falls
under the underemployment classification includes those workers that are
highly skilled but working in low paying jobs, workers that are highly skilled but
work in low skill jobs (Investopedia). Underemployment resulted from the
mismatch as can be seen in this research. In the 3 fields of study (Education,
Social Sciences, Business and Law & Services), underemployment can be
seen when unmatched individuals who are highly skilled work in a low paying
(lower wage) and low skill jobs. Moreover, in the 5 Fields of Study (Humanities
and Art, Science, Engineering, Manufacturing and Construction, Agriculture,
Health and Welfare), underemployment can be seen when unmatched
individuals who are highly skilled work but are working in low skill jobs.
B. Beveridge Curve
The researchers conclude that labor mismatch exists in the Philippine
labor market since the Beveridge Curve showed a positive correlation of
0.768721874 between the unemployment rate and job vacancy rate. This
presents the positive correlation between the two showing that mismatch exists
in the labor market (Beveridge. 1960). When the levels of unemployment and
48
UNIVERSITY OF SANTO TOMAS
job vacancies are both increasing, this illustrates that mismatch in the labor
market exists which is indicated by the qualification mismatch pertaining to the
mismatch between educational qualifications a worker possesses and the
prerequisites of the job (Shimer, 2005).
II.
RECOMMENDATIONS
For the Government
Since one of the main problems that education-occupation mismatch
brings is underemployment, which reflects the occurrence of low quality jobs
that hinders the existence of good job opportunities, workers are not able to
earn appropriate income that compensates their rendered skill. Therefore this
problem should be seriously resolved, and to address such, the researchers
recommend the following:

The researchers recommend the strengthening of the coordination of
the three training and education institutions – Department of Education
(DepEd), Commission on Higher Education (CHED) and Technical
Education and Skills Development Authority (TESDA) so as to have a
harmonized education and human resource development program.

The researchers recommend the government to provide enough job
opportunities (labor demand) that would cater the unemployed
individuals (labor supply) through the enhancement of the labor supply
side through development of human resources, labor productivity, and
technological advancement as well as the improvement of the labor
demand side.
49
UNIVERSITY OF SANTO TOMAS

The researchers would also like to recommend the government in order
to attain a balanced labor market (where Beveridge Curve presents as
inverse relationship between unemployment rate and job vacancy rate),
to find solutions on how to fill the job vacancies by focusing on
increasing the workers‟ competencies and work experiences.

The researchers recommend the government to properly utilize the
Official Job Portal of the Philippines, Phil-JobNet, which is an
automated job and applicant matching system which aims to fast-track
jobseekers search for jobs and employers search for manpower.

Based on the results of the study, the researchers would like to
recommend the government to help in the improvement of the
Education, Social Sciences, Business and Law & Services fields of
study as well as the occupations related to these fields of study so as to
attain matched individuals earning higher wages. The researchers
recommend that the Senior high school students to take these Fields of
study and pursue jobs related to these after college graduation for them
to attain higher wages at the same time applying the skills they have
learned in college.

Based from the results of the study that if the college graduates from
Humanities
and
Art,
Science,
Engineering,
Manufacturing
and
Construction, Agriculture, Health and Welfare Fields of study are
matched, they will earn lower wages and/ or if they are unmatched, they
will earn higher wages. Thus, the researchers recommend that the
college graduates from these fields of study should be flexible in
adapting various job opportunities. In line with this, the researchers
would like to recommend the government to boost the job opportunities
50
UNIVERSITY OF SANTO TOMAS
by providing sufficient incentives, proper working conditions, and
satisfying salaries for these fields of study so that the college graduates
from these fields would be able to achieve better earnings and at the
same applying their learned skills making them educationally and
occupationally matched.
For Future Researchers

The researchers recommend to the future researchers to deal with the
gathered primary individual data of the LFS so as to have a clearer and
precise study of the Mincer model (Human Capital Earning function). In
this regard, it is recommended to deal with the sex, age and work
experience of each individual so as to analyze the effect of these three
personal and work related variables on wage differentials.
51
UNIVERSITY OF SANTO TOMAS
BIBLIOGRAPHY
Books:

Gujarati. 2004. Basic Econometrics, Fourth Edition. The McGraw−Hill
Companies, 2004.

Mincer, J.
1993. Studies in Human Capital: Collected essays of
Jacob Mincer, Volume 1. Chapter 11. Edward Elgar Publishing
Company.

Philippine Standard Occupational Classification (PSOC) 1992.

Samson, J. et. Al. 2003. The Impact of Occupational Mismatch and
Education on Wages. University of the Philippines. School of
Economics.
Government Data:

Bureau of Labor and Employment Statistics

Bureau of Labor and Employment Statistics (BLES) Integrated
Survey
(BITS
2009-2010).
Retrieved
from
http://www.nscb.gov.ph/pressreleases/2012/PR201206_PP1_07_ows_bits.asp

National Statistics Office’s First Stage of Tertiary/ Baccalaurate
Education.

National Statistics Office’s Labor Force Survey’s October 2011 (4 th
Quarter of 2011) Public User File with Wage Data.
52
UNIVERSITY OF SANTO TOMAS

Philippine National Statistical Coordination Board
Journal Articles:

Ahola, S. 1999. The Matching Of Educational And Occupational
Structures In Sweden And Finland.

Ahola, S., Kivinen, O. & Rinne, R. 1992a. Transition from Secondary
to Higher
Education. In O. Kivinen & R. Rinne (eds.) Educational Strategies in
Finland in the 1990s Research Unit for the Sociology of Education.
University of Turku. Research Reports 8, pp 17-36. Turku.

Allen, J. & van der Velden, R. 2001. Educational Mismatches Versus
Skill Mismatches: Effects on Wages, Job Satisfaction and On-theJob Search. Oxford Economic Papers 3 (2001), 432-452. Oxford
University
Press.
Retrieved
from
http://arno.unimaas.nl/show.cgi?fid=10321.

Beveridge, L. 1960. Full Employment in a Free Society. Bradford and
Dickens, Drayton House. London.

Buchan J. & Calman L. 2004. “The global shortage of registered
nurses: An overview of issues and actions”. Geneva: International
Council
of
Nurses.
Retrieved
from
http://www.nurse.or.jp/nursing/international/icn/report/pdf/2012/02-042.pdf
53
UNIVERSITY OF SANTO TOMAS

Handel, M. 2003. Skills Mismatch in the Labor Market in Annual
Reviews.
University
Retrieved
of
Wisconsin,
from
Madison,
Wisconsin
53706.
http://www.northeastern.edu/socant/wp-
content/uploads/ARS_art.pdf

Knight, J.B. and R. H. Sabot. 1983. Educational Expansion and the
Kuznets Effect. American Economic Review.

Kuznets, S. 1955. Economic Growth and Income Inequality. The
American Economic Review. Vol. 45, No. 1 (Mar., 1955), pp. 1-28.
American
Economic
Association.
Retrieved
from
http://www.jstor.org/stable/1811581

Lopez, E. Over Employment, Underemployment, Unemployment
and Overtime. University of Santo Tomas.

Masuda, T. & Muta, H.1996. Vocational Education and Training in
Japan from Industry’s Perspective. Industry and Higher Education
11, 1, 43-52.

Muysken, J., Hoppe M. & Rieder, H. 2002. The impact of education
and mismatch on wages: Germany, 1984 – 1998.
University of
Maastricht. Netherlands

Neuman, S. & Ziderman, A.1991. Vocational Schooling, Occupational
Matching, and Labor Market Earnings in Israel.

RWANDA SKILLS SURVEY 2012 AGRICULTURE SECTOR REPORT
54
UNIVERSITY OF SANTO TOMAS

Shimer, R. 2005. Mismatch. National Bureau of Economic Research
Working
Paper
Series:
Massachusetts
Working
Paper
Avenue.
11888.
Retrieved
Cambridge,
from
http://www.nber.org/papers/w11888

Tilak, J. 1989. Education and Its Relation to Economic Growth,
Poverty and Income Distribution. World Bank Discussion Papers. The
World Bank. Washington, D.C.

Van Ejis, P. & Heijke, H. 1996. The Relation between the Wage, Jobrelated Training and the Quality of the Match between Occupations
and Types of Education.University of Limburg, Maastricht

Zimmer, H. 2012. Labour Market Mismatches. Economic Review
Journal. National Bank of Belgium.
Internet Articles:

http://filipinonurses.org/index.php/2011/11/being-a-volunteer-nurse-or-acall-center-agent-2/

http://www.edgewise.ph/2010/06/22/006-ask-edgewise/i-just-startedthis-job-but-i-received-a-better-offer-elsewhere/

N/A.
2010.
Job
Rate.Retrieved
Mismatch
from
Causes
High
Unemployment
http://affleap.com/job-mismatch-causes-high-
employment-rate/

N/A. 2012. Pinoy Nurses Told Not To Expect US Hiring Till 2020.
Retrieved from http://mb.com.ph/node/357253/pinoy-nur
55
UNIVERSITY OF SANTO TOMAS

http://www.investopedia.com/terms/u/underemployment.asp

http://www.ble.dole.gov.ph/philjobnet.asp

http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTEDUCATIO
N/0,,menuPK:28291~pagePK:149018~piPK:149093~theSitePK:282386,
00.html

http://www.merriam-webster.com/dictionary/occupation

http://www.thefreedictionary.com/

http://www.investopedia.com/terms/b/base-pay.asp#ixzz2LOnGtWQL
56
UNIVERSITY OF SANTO TOMAS
APPENDIX 1: 8 Major Classifications of the Field of Study
FIRST STAGE OF TERTIARY/BACCALAUREATE EDUCATION (NOT
LEADING DIRECTLY TO AN ADVANCED RESEARCH QUALIFICATION)
EDUCATION
614
61401
61401
61401
61401
61401
61404
61404
61404
61404
61404
61404
61404
61404
61404
61404
61404
61404
61404
61404
61408
61408
TEACHER TRAINING AND EDUCATION SCIENCES PROGRAMS
Programs in General Teacher Training
Bachelor of Elementary Education
Bachelor of Secondary Education
Bachelor of Science in Elementary and Secondary Education
Bachelor of Science in Pedagogy
Programs in Teacher Training with Specialization in Nonvocational Subjects
Bachelor of Home Economics and Livelihood Education for Teachers
Bachelor of Physical Education
Bachelor of Sports Science
Bachelor of Technology and Home Economics for Teachers
Bachelor of Arts in Language Education for Teachers
Bachelor of Science in Chemistry for Teachers
Bachelor of Science in English Education as Secondary Language
Bachelor of Science in Language Education for Teachers
Bachelor of Science in Mathematics for Teachers
Bachelor of Science in Music Education
Bachelor of Science in Physical Education
Bachelor of Science in Physics for Teachers
Bachelor of Science in Religious Education
Programs in Teacher Training for Teaching Practical or Vocational Subjects
Bachelor of Science in Industrial Education
61408
61412
61412
61412
61416
61422
61422
61472
61472
61499
61499
61499
61499
61499
61499
61499
61499
Bachelor of Science in Technician Teacher Education
Programs in Teacher Training for Teaching Preschool or Kindergarten
Bachelor of Science in Early Childhood Education
Bachelor of Science in Kindergarten/Preschool Education
Programs in Teacher Training for Teachers in Adult Education
Programs in Teacher Training for Teachers in Special Education
Bachelor of Special Education
Programs in Education Science in Support of Teaching
Bachelor of Science in Guidance and Counseling
Other Programs in Teacher Training and Education Sciences
Bachelor of Science in Agricultural Education
Bachelor of Science in Business/Commercial Education
Bachelor of Science in Extension Education
Bachelor of Science in Fishery Education
Bachelor of Science in Nursing Education
Bachelor of Science in Nutrition and Dietetics for Teachers
Bachelor of Science in Secretarial Education
HUMANITIES AND ARTS
621
62101
62101
62101
62104
62108
62122
62122
62122
62132
62132
62132
62132
62132
62132
62152
62152
62199
ARTS PROGRAMS
General Programs in Art Studies
Bachelor of Digital Arts
Bachelor of Science in Fine Arts/Bachelor of Fine Arts
Programs in Drawing and Painting
Programs in Sculpturing
Programs in Music
Bachelor of Music Liturgy
Bachelor of Science in Music/Bachelor of Music
Programs in Drama
Bachelor of Performing Arts
Bachelor of Arts in Speech and Drama
Bachelor of Arts in Speech and Theater Arts
Bachelor of Arts in Theater Arts
Bachelor of Science in Speech and Drama
Programs in Interior Design
Bachelor of Science in Interior Design
Other Programs in Arts
62199
Bachelor of Arts in Film and Audio-Visual Communication
622
62201
HUMANITIES PROGRAMS
General Programs in Humanities
57
UNIVERSITY OF SANTO TOMAS
62201
62211
62211
62211
62211
62211
62215
62215
62221
62231
62231
62241
62241
62241
62251
62251
62251
62261
62271
62271
62271
62281
62281
62281
62281
62281
62281
62299
Bachelor of Arts in Humanities
Programs in the Current or Vernacular Language and Its Literature
Bachelor of Arts in English
Bachelor of Arts in English Literature
Bachelor of Arts in Filipino
Bachelor of Arts in Philippine Literature
Programs in Other Living Languages and Their Literature
Bachelor of Arts in European Languages
Programs in "Dead" Languages and Their Literature
Programs in Linguistics
Bachelor of Arts in Linguistics
Programs in Comparative Literature
Bachelor of Arts in Comparative Literature
Bachelor of Arts in Literature
Programs in History
Bachelor of Arts in Development Studies
Bachelor of Arts in History
Programs in Archeology
Programs in Philosophy
Bachelor of Arts in Classical/Philosophy/Bachelor of Classical/Philosophy
Bachelor of Arts in Philosophy/Bachelor of Philosophy
Programs in Religion and Theology
Bachelor of Evangelical Ministry
Bachelor of Arts in Religion
Bachelor of Arts in Religious Studies
Bachelor of Arts in Divinity/Bachelor of Divinity
Bachelor of Arts in Theology/Bachelor of Theology
Other Programs in Humanities
SOCIAL SCIENCES, BUSINESS, AND LAW
631
63101
63101
SOCIAL AND BEHAVIORAL SCIENCE PROGRAMS
General Programs in Social and Behavioral Sciences
Bachelor of Arts in Behavioral Science
63101
63101
Bachelor of Arts in Social Science
Bachelor of Arts in Human Behavior Technology/Bachelor of Human Behavior
Technology
Bachelor of Science in Behavioral Science
Bachelor of Science in Human Behavior Technology
Programs in Economics
Bachelor of Arts in Economics
Bachelor of Arts in Applied Economics/Bachelor of Applied Economics
Bachelor of Science in Applied Economics
Bachelor of Science in Business Economics
Bachelor of Science in Economics
Programs in Political Science
Bachelor of Arts in Political Science/Bachelor in Political Science
Bachelor of Science in Foreign Service
Bachelor of Science in International Relations
Bachelor of Science in Political Economy
Programs in Sociology
Bachelor of Arts in Applied Sociology
Bachelor of Arts in Sociology
Bachelor of Science in Sociology
Programs in Demography
Bachelor of Science in Demography
Programs in Anthropology
Bachelor of Arts in Anthropology
Programs in Psychology
Bachelor of Arts in Applied Psychology
Bachelor of Science in Clinical Psychology
Bachelor of Science in Industrial and Organizational Psychology
Bachelor of Science in Psychology
Programs in Geography
Bachelor of Science in Geography
Programs in Studies of Regional Cultures
Bachelor of Arts in Arabic/Islamic Studies
Bachelor of Arts in International Studies
Bachelor of Arts in Philippine Arts
Bachelor of Arts in Philippine Studies
Other Programs in Social and Behavioral Sciences
63101
63101
63112
63112
63112
63112
63112
63112
63122
63122
63122
63122
63122
63132
63132
63132
63132
63133
63133
63142
63142
63152
63152
63152
63152
63152
63162
63162
63172
63172
63172
63172
63172
63199
58
UNIVERSITY OF SANTO TOMAS
632
63201
63201
63201
63202
63202
63202
63202
63202
63204
63204
63204
63207
63207
63222
63222
63222
63229
63229
63229
63229
63229
634
63401
63401
63401
63401
63401
63401
63401
63401
63401
63401
63404
63404
63404
63404
63404
63432
63432
63432
63432
63432
63434
63434
63434
63434
63436
63436
63436
63436
63439
63439
63439
63439
63439
63439
63439
63439
63439
63439
63439
63439
63439
63439
63439
JOURNALISM AND INFORMATION PROGRAMS
Programs in General Communication Arts
Bachelor of Arts in Communication
Bachelor of Arts in Media Studies
Programs in Journalism
Bachelor of Arts in Business Journalism/Bachelor in Business Journalism
Bachelor of Arts in Journalism/Bachelor in Journalism
Bachelor of Science in Business Journalism
Bachelor of Science in Journalism
Programs in Radio and Television Broadcasting
Bachelor of Arts in Broadcast Communication
Bachelor of Science in Broadcast Communication
Programs in Public Relations
Bachelor of Arts in Public Relations
Programs in Library Science
Bachelor of Arts in Library and Information Science/Bachelor of Library
and Information Science
Bachelor of Arts in Library Science/Bachelor of Library Science
Other Programs in Journalism and Information
Bachelor of Arts in Communication Arts/Mass Communication
Bachelor of Arts in Communication Research
Bachelor of Arts in Organizational Communication
Bachelor of Science in Mass Communication
BUSINESS AND ADMINISTRATION PROGRAMS
General Programs in Business Administration/Commerce
Bachelor of Arts in Business Administration/Bachelor in Business Administration
Bachelor of Arts in Entrepreneurial Management/Bachelor in Entrepreneurial
Management
Bachelor of Arts in Business Management/Bachelor of Business Management
Bachelor of Arts in Management and Social Work /Bachelor of Management
and Social Work
Bachelor of Science in Administration
Bachelor of Science in Business Administration
Bachelor of Science in Business Management
Bachelor of Science in Commerce
Bachelor of Science in Management
Programs in Secretarial
Bachelor of Science in Airline Secretarial/Administration
Bachelor of Science in Computer Secretarial
Bachelor of Science in Office Administration/Technology
Bachelor of Science in Secretarial Administration
Programs in Business Administration with Specialization in Accountancy
Bachelor of Science in Accountancy
Bachelor of Science in Business Administration and Accountancy
Bachelor of Science in Computer Accounting and Management
Bachelor of Science in Management and Accountancy
Programs in Business Administration with Specialization in Marketing
Bachelor of Arts in Advertising/Bachelor of Advertising
Bachelor of Arts in Advertising and Public Relations/Bachelor of Advertising
and Public Relations
Bachelor of Science in Marketing
Programs in Business Administration with Specialization in Finance and Investment
Bachelor of Science in Finance
Bachelor of Science in Real Estate
Bachelor of Science in Banking and Finance/Bachelor in Banking and Finance
Programs in Business Administration with Other Specialization
Bachelor of Arts in Legal Management
Bachelor of Arts in Business Engineering/Bachelor in Business Engineering
Bachelor of Arts in Agricultural Entrepreneurship/Bachelor of Agricultural
Enterpreneurship
Bachelor of Arts in Business Distributive Arts/Bachelor of Business Distributive Arts
Bachelor of Arts in Computer Management/Bachelor of Computer Management
Bachelor of Arts in Industrial Management/Bachelor of Industrial Management
Bachelor of Arts in Transportation Management/Bachelor of Transportation
Management
Bachelor of Science in Agri-Business Management
Bachelor of Science in Business Enterpreneurship
Bachelor of Science in Business Technology
Bachelor of Science in Economics and Cooperatives
Bachelor of Science in Fishery Business Management
Bachelor of Science in Home Arts and Enterpreneurship
Bachelor of Science in Industrial Management
59
UNIVERSITY OF SANTO TOMAS
63452
63452
63452
Programs in Public Administration
Bachelor of Arts in Public Administration/Bachelor of Public Administration
Bachelor of Science in Public Administration
63462
63462
63462
63462
63462
63462
63462
63462
63462
63462
63499
63499
63499
63499
63499
63499
63499
Programs in Institutional Administration/Management
Bachelor of Arts in Port Administration/Bachelor of Port Administration
Bachelor of Science in Airline Business Administration/Management
Bachelor of Science in Airline Management
Bachelor of Science in Airline Management and Accountancy
Bachelor of Science in Customs Administration
Bachelor of Science in Food Service Administration
Bachelor of Science in Hospital Administration
Bachelor of Science in Postal Management
Bachelor of Science in Shipping Management
Other Programs in Business Administration/Management
Bachelor of Arts in Legal and Indigenous Studies
Bachelor of Arts in Cooperatives/Bachelor of Cooperatives
Bachelor of Science in Cooperative Management
Bachelor of Science in Maritime Management
Bachelor of Science in Recreation Management
Bachelor of Science in Supply Management
638
63801
63801
63801
LAW PROGRAMS
General Programs in Law
Bachelor of Laws (LL.B.)/Juris Doctor (J.D.)
Bachelor of Science in Jurisprudence
SCIENCE
642
64202
64202
64202
64202
64202
64202
64202
64202
64202
64202
64202
64202
LIFE SCIENCES PROGRAMS
Programs in Biological Science
Bachelor of Science in Applied Biology
Bachelor of Science in Biochemistry
Bachelor of Science in Biological Science
Bachelor of Science in Biology
Bachelor of Science in Botany
Bachelor of Science in Entomology
Bachelor of Science in Human Biology
Bachelor of Science in Marine Biology
Bachelor of Science in Microbiology
Bachelor of Science in Molecular Biology and Biotechnology
Bachelor of Science in Pharmacology
64202
64202
64202
64209
64209
64209
64209
Bachelor of Science in Physiology
Bachelor of Science in Plant Science
Bachelor of Science in Zoology
Other Programs in Life Sciences
Bachelor of Arts in Applied Science/Bachelor of Applied Science
Bachelor of Science in General Science
Bachelor of Science in Natural Science
644
64412
64412
64412
64412
64412
64422
64422
64422
64432
64432
64432
64432
64432
64442
64442
64452
64452
64462
64462
64462
PHYSICAL SCIENCES PROGRAMS
Programs in Chemistry
Bachelor of Science in Chemical Research
Bachelor of Science in Chemical Technology
Bachelor of Science in Chemistry
Bachelor of Science in Industrial Chemistry
Programs in Geological Science
Bachelor of Science in Geology
Bachelor of Science in Volcanology
Programs in Physics
Bachelor of Science in Applied Physics
Bachelor of Science in Metallurgy
Bachelor of Science in Physics
Bachelor of Science in Physics-Mathematics
Programs in Astronomy
Bachelor of Science in Astronomy
Programs in Meteorology
Bachelor of Science in Meteorology
Programs in Oceanography
Bachelor of Science in Marine Science
Bachelor of Science in Oceanography
646
MATHEMATICS AND STATISTICS PROGRAMS
60
UNIVERSITY OF SANTO TOMAS
64601
64601
64611
64611
64611
64611
64621
General Programs in Mathematics
Bachelor of Science in Mathematics
Programs in Statistics
Bachelor of Science in Applied Statistics
Bachelor of Science in Experimental Statistics
Bachelor of Science in Statistics
Programs in Actuarial Science
64621
64699
64699
Bachelor of Science in Actuarial Science
Other Programs in Mathematics
Bachelor of Science in Applied Mathematics
648
64841
64841
64841
64841
64841
64841
64841
64841
64841
64841
64841
64841
64844
64844
64844
COMPUTING/INFORMATION TECHNOLOGY PROGRAMS
Programs in Computer Science and Information Technology
Bachelor of Arts in Information Technology/Bachelor in Information Technology
Bachelor of Science in Business Computer Applications
Bachelor of Science in Computer Applications
Bachelor of Science in Computer Data Processing Management
Bachelor of Science in Computer Science
Bachelor of Science in Computer Studies
Bachelor of Science in Information and Computer Science
Bachelor of Science in Information System/Management
Bachelor of Science in Information Technology
Bachelor of Science in Management Information System
Bachelor of Science in Software Technology
Programs in Electronic Data Processing
Bachelor of Science in Computer Data Processing and Information Management
Bachelor of Science in Data Processing
ENGINEERING, MANUFACTURING, AND CONSTRUCTION
652
65204
65204
65204
65204
65204
65204
65204
65204
65204
65204
65204
65204
65204
65212
65212
ENGINEERING AND ENGINEERING TRADES PROGRAMS
Programs in Aeronautical Engineering
Bachelor of Science in Aeronautical Engineering
Bachelor of Science in Aerospace Engineering
Bachelor of Science in Air Transportation
Bachelor of Science in Aircraft Maintenance Engineering
Bachelor of Science in Aircraft Maintenance Technology
Bachelor of Science in Aircraft Technology
Bachelor of Science in Aviation
Bachelor of Science in Aviation Electronics Engineering
Bachelor of Science in Avionics Engineering
Bachelor of Science in Avionics Technology
Bachelor of Science in Electrical Engineering Avionics
Bachelor of Science in Flying Technology
Programs in Chemical Engineering
Bachelor of Science in Ceramics Engineering
65212
65212
65212
65216
65216
65216
65218
65218
65222
65222
65222
65222
65222
65222
65222
65222
65222
65222
65222
65226
65226
65226
65226
65226
65226
65226
Bachelor of Science in Chemical Engineering
Bachelor of Science in Chemical Engineering Technology
Bachelor of Science in Textile Engineering
Programs in Civil Engineering
Bachelor of Science in Civil Engineering
Bachelor of Science in Construction Technology
Programs in Geodetic Engineering
Bachelor of Science in Geodetic Engineering
Programs in Electrical, Electronics and Computer Engineering
Bachelor of Science in Communications Engineering
Bachelor of Science in Computer Engineering
Bachelor of Science in Computer Technology
Bachelor of Science in Electrical Engineering
Bachelor of Science in Electrical Engineering Technology
Bachelor of Science in Electrical Technology
Bachelor of Science in Electronics and Communications Engineering
Bachelor of Science in Electronics Engineering
Bachelor of Science in Electronics Technology
Bachelor of Science in Instrumentation and Control Engineering
Programs in Industrial Engineering
Bachelor of Science in Industrial and Management Engineering
Bachelor of Science in Industrial Design
Bachelor of Science in Industrial Engineering
Bachelor of Science in Industrial Technology
Bachelor of Science in Management Engineering
Bachelor of Science in Manufacturing Engineering
61
UNIVERSITY OF SANTO TOMAS
65232
65232
65236
65236
65242
65242
65242
65242
65242
65250
65250
65250
Programs in Metallurgical Engineering
Bachelor of Science in Metallurgical Engineering
Programs in Mining Engineering
Bachelor of Science in Mining Engineering
Programs in Mechanical Engineering
Bachelor of Science in Automotive Technology
Bachelor of Science in Geothermal Engineering
Bachelor of Science in Mechanical Engineering
Bachelor of Science in Mechanical Technology
Programs in Sanitary Engineering
Bachelor of Science in Environmental and Sanitary Engineering
Bachelor of Science in Environmental Engineering
65250
65253
65253
65253
65263
65263
65281
65281
65281
65299
65299
65299
65299
65299
Bachelor of Science in Sanitary Engineering
Programs in Agricultural Engineering
Bachelor of Science in Agricultural Engineering
Bachelor of Science in Aquatic Resource Engineering
Programs in Forestry Engineering
Bachelor of Science in Forest Products Engineering
Programs in Marine Engineering
Bachelor of Science in Marine Engineering
Bachelor of Science in Naval Architecture and Marine Engineering
Other Programs in Engineering
Bachelor of Arts in Technology/Bachelor of Technology
Bachelor of Science in Electronics and Computer Technology
Bachelor of Science in Food Engineering
Bachelor of Science in Petroleum Engineering
654
65476
65476
65476
MANUFACTURING AND PROCESSING PROGRAMS
Programs in Clothing and Related Trades
Bachelor of Science in Clothing Technology
Bachelor of Science in Garment/Textile Technology
658
65801
65801
65812
65812
65822
65822
ARCHITECTURE AND BUILDING PROGRAMS
General Programs in Architecture and Town Planning
Bachelor of Science in Architecture
Programs in Landscape Architecture
Bachelor of Science in Landscape Architecture
Programs in Town Planning
Bachelor of Science in Town and Country Planning
AGRICULTURE
662
66201
66201
66203
66203
66203
66203
66206
66206
66206
66208
66208
66212
66212
66222
66222
66226
66249
66249
66249
66249
66249
66249
66249
66262
66262
66262
66262
AGRICULTURE, FORESTRY, AND FISHERY PROGRAMS
General Programs in Agriculture
Bachelor of Arts in Agricultural Technology/Bachelor of Agricultural Technology
Bachelor of Science in Agriculture
Programs in Animal Husbandry
Bachelor of Science in Animal Husbandry
Bachelor of Science in Animal Science
Bachelor of Science in Animal Technology
Programs in Horticulture
Bachelor of Arts in Horticulture Management/Bachelor of Technology in Horticulture
Management
Bachelor of Science in Horticulture
Programs in Agronomy
Bachelor of Science in Agronomy
Programs in Agricultural Economics
Bachelor of Science in Agricultural Economics
Programs in Food Science and Technology
Bachelor of Science in Food Technology
Programs in Soil and Water Sciences
Other Programs in Agriculture
Bachelor of Science in Agricultural Administration
Bachelor of Science in Agricultural Chemistry
Bachelor of Science in Agricultural Development
Bachelor of Science in Agricultural Management
Bachelor of Science in Rice Technology
Bachelor of Science in Sugar Technology
Programs in Forestry
Bachelor of Arts in Agro-Forestry Technology/Bachelor in Agro-Forestry Technology
Bachelor of Science in Agro-Forestry
Bachelor of Science in Forestry
62
UNIVERSITY OF SANTO TOMAS
66272
66272
66272
66272
66272
66272
Programs in Fishery Science and Technology
Bachelor of Science in Aquaculture
Bachelor of Science in Aquatic Resource Management and Technology
Bachelor of Science in Fisheries
Bachelor of Science in Fishing Technology
Bachelor of Science in Inland Fisheries
664
66432
66432
VETERINARY PROGRAMS
Programs in Veterinary Medicine
Bachelor of Science in Veterinary Technology
HEALTH AND WELFARE
672
67202
67202
67202
HEALTH PROGRAMS
Programs in Hygiene
Bachelor of Science in Community/Public Health
Bachelor of Science in Sanitary Science
67206
67206
67206
67208
67208
67208
67208
67208
67212
67212
67217
67217
67230
67230
67242
67242
67252
67252
67252
67252
67262
67262
67272
67272
67299
67299
67299
Programs in Medicine
Bachelor of Arts in Basic Medical Sciences
Doctor of Medicine
Programs in Rehabilitation Medicine
Bachelor of Science in Occupational Therapy
Bachelor of Science in Physical Therapy
Bachelor of Science in Respiratory Therapy
Bachelor of Science in Speech Pathology
Programs in Nursing
Bachelor of Science in Nursing
Programs in Medical X-Ray Techniques
Bachelor of Science in Radiologic Technology
Programs in Medical Technology
Bachelor of Science in Medical Technology
Programs in Dentistry
Doctor of Dental Medicine
Programs in Pharmacy
Bachelor of Science in Industrial Pharmacy
Bachelor of Science in Pharmaceutical Chemistry
Bachelor of Science in Pharmacy
Programs in Optometry
Doctor of Optometry
Programs in Nutrition and Dietetics
Bachelor of Science in Nutrition and Dietetics
Other Programs in Medical Diagnostic and Treatment
Bachelor of Science in Paramedics
Bachelor of Science in Rural Medicine
676
67632
67632
67632
67652
67652
67652
67652
SOCIAL SERVICES PROGRAMS
Programs in Social Welfare
Bachelor of Arts in Social Services/Social Work
Bachelor of Science in Social Services/Social Work
Programs in Community Development
Bachelor of Science in Community Development
Bachelor of Science in Development of Multi-Cultural Communities
Bachelor of Science in Rural Development Management
SERVICES
681
68101
68101
68101
68101
68132
68134
68134
68172
68172
68182
68182
68182
68182
68182
PERSONAL SERVICES PROGRAMS
General Programs in Home Economics (Domestic Science)
Bachelor of Science in Family and Child Development
Bachelor of Science in Home Economics
Bachelor of Science in Human Ecology
Programs in Home Economics with Emphasis on Household Arts
Other Programs in Home Economics
Bachelor of Science in Home Technology
Programs in Hotel and Restaurant Trades
Bachelor of Science in Hotel and Restaurant Management
Programs in Tourism
Bachelor of Arts in Tourism
Bachelor of Science in Tourism
Bachelor of Science in Tourism and Hotel and Restaurant Management
Bachelor of Science in Tourism and Travel Management
63
UNIVERSITY OF SANTO TOMAS
684
68404
68404
68404
TRANSPORT SERVICES PROGRAMS
Programs in Nautical Science
Bachelor of Science in Marine Transportation
Bachelor of Science in Nautical Science
685
68552
68552
68552
68552
ENVIRONMENTAL PROTECTION PROGRAMS
Programs in Environmental Studies
Bachelor of Arts in Technology in Environmental Management/Bachelor
of Technology in Environmental Management
Bachelor of Science in Coastal Resource Management
Bachelor of Science in Ecology
Bachelor of Science in Environmental Development/Environmental
Hygiene/Environmental Science
Bachelor of Science in Environmental Management
Bachelor of Science in Environmental Planning
686
68613
68613
68613
68613
SECURITY SERVICES PROGRAMS
Programs in Criminal Justice Education
Bachelor of Science in Criminal Justice/Criminology
Bachelor of Science in Forensic Science
Bachelor of Science in Industrial Security Management
68613
68617
68619
68619
68619
Bachelor of Science in Police/Law Enforcement Administration
Programs in Military
Other Programs in Civil Security
Bachelor of Arts in Peace and Security Studies
Bachelor of Science in Peace and Security Studies
68552
68552
68552
64
UNIVERSITY OF SANTO TOMAS
APPENDIX 2: Field of Study and Primary Occupations used for Matching
The following shows the Primary Occupations under each Major Classification
of the Field of Study which are considered to be matched which is based and
patterned on the Philippine Standard Occupational Classification 1992.
Education (61)
Supervisors, Teaching Professionals, and
Teaching Associate Professionals.
Humanities and Arts (62)
Teaching Professionals, Other Professionals,
Physical Science and Engineering Associate
Professionals,
Related
Associate
Professionals, Models, Salespersons and
Demonstrators, Precision, Handicraft, Printing
and Related Trades Workers.
Social Sciences, Business and Law
Officials of Government and Special-Interest
(63)
Organizations,
Corporate
Executives
and
Specialized Managers, General Managers or
Managing-Proprietors, Supervisors, Teaching
Professionals, Other Professionals, Related
Associate
Customer
Professionals,
Services
Office
Clerks,
Clerks,
Models,
Salespersons and Demonstrators, Other Craft
and Related Trades Workers, Sales and
Services.
Science (64)
Physicists,
Life
Professionals,
Science
Teaching
and
Health
Professionals,
Physical Science and Engineering Associate
Professionals,
Life
Science
and
Health
Associate Professionals.
Engineering,
Manufacturing
and Corporate
65
Executives
and
Specialized
UNIVERSITY OF SANTO TOMAS
Construction (65)
Managers, General Managers or ManagingProprietors, Physicists, Mathematical and
Engineering,
Physical
Science
and
Engineering Associate, Mining, Construction
and Related Trade Workers, Metal, Machinery
and Related Trades Workers, Stationary Plant
and Related Operators, Machine Operators
and Assemblers, Drivers and Mobile Plant
Operators, Laborers.
Agriculture (66)
General Managers or Managing-Proprietors,
Supervisors,
Farmers
and
Other
Plant
Growers, Animal Producers, Forestry and
Related Workers, Fishermen, Hunters and
Trappers, Metal, Machinery and Related
Trades
Workers,
Agricultural,
Forestry,
Fishery and Related Laborers.
Health and Welfare (67)
Life Science and Health Professionals, Life
Science and Health Associate Professionals,
Related Associate Professionals, Personal
and Protective Service Workers.
Services (68)
Officials of Government and Special-Interest
Organizations,
Supervisors,
Related
Associate Professionals, Customer Services
Clerks, Personal and Protective Service
Workers, Armed Forces.
66
UNIVERSITY OF SANTO TOMAS
APPENDIX 3: Total Number of College Graduates per Field of Study
and their corresponding Primary Occupations
67
UNIVERSITY OF SANTO TOMAS
APPENDIX 4: Regression Results for the 8 Fields of Study
1. Education
Coefficients
a
Standardized
Unstandardized Coefficients
Coefficients
B
Std. Error
Beta
(Constant)
4.482
.129
MATCH
.965
.380
Model
1
a.
.461
t
Sig.
34.765
.000
2.542
.018
t
Sig.
34.143
.000
3.530
.002
t
Sig.
30.246
.000
2.259
.034
Dependent Variable: LOG OF HOURLY EARNINGS
2. Social Sciences, Business and Law
Coefficients
a
Standardized
Unstandardized Coefficients
Coefficients
B
Std. Error
Beta
(Constant)
4.270
.125
MATCH
.662
.188
Model
1
a.
.577
Dependent Variable: LOG OF HOURLY EARNINGS
3. Services
Coefficients
a
Standardized
Unstandardized Coefficients
Coefficients
B
Std. Error
Beta
(Constant)
4.282
.142
MATCH
.653
.289
Model
1
.426
a. Dependent Variable: LOG OF HOURLY EARNINGS
68
UNIVERSITY OF SANTO TOMAS
4. Humanities and Arts
Coefficients
a
Standardized
Unstandardized Coefficients
Coefficients
B
Std. Error
Beta
(Constant)
4.437
.119
MATCH
-.010
.212
Model
1
-.012
t
Sig.
37.201
.000
-.049
.962
5. Science
Coefficients
a
Standardized
Unstandardized Coefficients
Coefficients
B
Std. Error
Beta
(Constant)
4.479
.114
MATCH
.051
.259
Model
1
.040
t
Sig.
39.402
.000
.197
.845
a. Dependent Variable: LOG OF HOURLY EARNINGS
6. Engineering, Manufacturing and Construction
Coefficients
a
Standardized
Unstandardized Coefficients
Coefficients
B
Std. Error
Beta
(Constant)
4.851
.167
MATCH
-.193
.275
Model
1
a.
-.139
Dependent Variable: LOG OF HOURLY EARNINGS
69
t
Sig.
28.988
.000
-.703
.488
UNIVERSITY OF SANTO TOMAS
7. Agriculture
Coefficients
a
Standardized
Unstandardized Coefficients
Coefficients
B
Std. Error
Beta
(Constant)
4.447
.148
MATCH
-.243
.266
Model
1
-.173
t
Sig.
29.997
.000
-.914
.369
a. Dependent Variable: LOG OF HOURLY EARNINGS
8. Health and Welfare
Coefficients
a
Standardized
Unstandardized Coefficients
Coefficients
B
Std. Error
Beta
(Constant)
4.694
.140
MATCH
-.468
.296
Model
1
-.367
a. Dependent Variable: LOG OF HOURLY EARNINGS
70
t
Sig.
33.625
.000
-1.580
.134
UNIVERSITY OF SANTO TOMAS
APPENDIX 5: DATA FOR EDUCATION (61)
TOTAL
NO. OF
HOURS
NORMAL
WORKED
WORKING
DURING
HOURS
WORKING
DURING
HOURLY
LOG OF
BASIC
PAST
HOURS
THE PAST
WAGE/
HOURLY
PAY
WEEK
PER DAY
WEEK
EARNINGS
EARNINGS
OCCUPATION
THE
Officials of Government and Special-Interest Organizations
1499
48
8
6
249.833333
5.520794
Corporate Executives and Specialized Managers
1499
48
8
6
249.833333
5.520794
Supervisors
1499
48
8
6
249.833333
5.520794
699
48
8
6
116.5
4.757891
1499
48
8
6
249.833333
5.520794
899
48
8
6
149.833333
5.009524
Physicists Mathematical and Engineering Science Professionals
Life Science and Health Professionals
Teaching Professionals
Other Professionals
1499
48
8
6
249.833333
5.520794
Physical Science and Engineering Associate Professionals
399
48
8
6
66.5
4.197202
Life Science and Health Associate Professionals
599
48
8
6
99.8333333
4.603502
1999
48
8
6
333.166667
5.808643
Related Associate Professionals
599
48
8
6
99.8333333
4.603502
Office Clerks
399
48
8
6
66.5
4.197202
Customer Services Clerks
299
48
8
6
49.8333333
3.908684
Personal and Protective Service Workers
1499
48
8
6
249.833333
5.520794
Models Salespersons and Demonstrators
299
48
8
6
49.8333333
3.908684
Mining Construction and Related Trade Workers
299
39
8
4.875
61.3333333
4.116323
Metal Machinery and Related Trades Workers
199
48
8
6
33.1666667
3.501545
Precision Handicraft Printing and Related Trades Workers
299
39
8
4.875
61.3333333
4.116323
Other Craft and Related Trades Workers
399
48
8
6
66.5
4.197202
Stationary Plant and Related Operators
399
48
8
6
66.5
4.197202
Machine Operators and Assemblers
399
48
8
6
66.5
4.197202
Drivers and Mobile Plant Operators
499
48
8
6
83.1666667
4.420847
Sales and Services Elementary Occupations
199
19
8
2.375
83.7894737
4.428307
Agricultural Forestry Fishery and Related Laborers
199
48
8
6
33.1666667
3.501545
Laborers in Mining Construction Manufacturing and Transport
399
48
8
6
66.5
4.197202
Armed Forces
499
48
8
6
83.1666667
4.420847
Teaching Associate Professionals
71
M
UNIVERSITY OF SANTO TOMAS
APPENDIX 6: DATA FOR HUMANITIES AND ARTS (62)
TOTAL
NO. OF
HOURS
NORMAL
WORKED
WORKING
DURING
OCCUPATION
HOURS
THE
WORKING
DURING
HOURLY
LOG OF
BASIC
PAST
HOURS
THE PAST
WAGE/
HOURLY
PAY
WEEK
PER DAY
WEEK
EARNINGS
EARNINGS
Officials of Government and Special-Interest Organizations
499
48
8
6
83.16667
4.420847
Corporate Executives and Specialized Managers
599
58
8
7.25
82.62069
4.41426
1499
48
8
6
249.8333
5.520794
Physicists Mathematical and Engineering Science Professionals
799
48
8
6
133.1667
4.891601
Life Science and Health Professionals
899
48
8
6
149.8333
5.009524
Teaching Professionals
499
48
8
6
83.16667
4.420847
Other Professionals
699
58
8
7.25
96.41379
4.568649
Physical Science and Engineering Associate Professionals
399
48
8
6
66.5
4.197202
Related Associate Professionals
299
19
8
2.375
125.8947
4.835446
Office Clerks
299
48
8
6
49.83333
3.908684
Customer Services Clerks
399
48
8
6
66.5
4.197202
Personal and Protective Service Workers
399
58
8
7.25
55.03448
4.00796
Models Salespersons and Demonstrators
499
48
8
6
83.16667
4.420847
Mining Construction and Related Trade Workers
499
39
8
4.875
102.359
4.628486
Precision Handicraft Printing and Related Trades Workers
299
39
8
4.875
61.33333
4.116323
Sales and Services Elementary Occupations
199
29
8
3.625
54.89655
4.005451
Agricultural Forestry Fishery and Related Laborers
399
48
8
6
66.5
4.197202
Laborers in Mining Construction Manufacturing and Transport
299
48
8
6
49.83333
3.908684
Armed Forces
699
58
8
7.25
96.41379
4.568649
Supervisors
72
M
UNIVERSITY OF SANTO TOMAS
APPENDIX 7: DATA FOR SOCIAL SCIENCES, BUSINESS & LAW (63)
TOTAL
NO. OF
HOURS
NORMAL
WORKED
WORKING
DURING
OCCUPATION
HOURS
THE
WORKING
DURING
HOURLY
LOG OF
BASIC
PAST
HOURS
THE PAST
WAGE/
HOURLY
PAY
WEEK
PER DAY
WEEK
EARNINGS
EARNINGS
Officials of Government and Special-Interest Organizations
1499
48
8
6
249.8333333
5.520794
Corporate Executives and Specialized Managers
1499
48
8
6
249.8333333
5.520794
General Managers or Managing-Proprietors
1499
58
8
7.25
206.7586207
5.331552
Supervisors
1499
48
8
6
249.8333333
5.520794
Physicists Mathematical and Engineering Science Professionals
499
48
8
6
83.16666667
4.4208466
Life Science and Health Professionals
299
48
8
6
49.83333333
3.9086841
Teaching Professionals
1499
48
8
6
249.8333333
5.520794
Other Professionals
1499
48
8
6
249.8333333
5.520794
Physical Science and Engineering Associate Professionals
399
48
8
6
66.5
4.1972019
Life Science and Health Associate Professionals
699
48
8
6
116.5
4.7578913
Teaching Associate Professionals
199
19
8
2.375
83.78947368
4.4283074
Related Associate Professionals
499
48
8
6
83.16666667
4.4208466
Office Clerks
399
48
8
6
66.5
4.1972019
Customer Services Clerks
599
48
8
6
99.83333333
4.6035021
Personal and Protective Service Workers
499
48
8
6
83.16666667
4.4208466
Models Salespersons and Demonstrators
499
48
8
6
83.16666667
4.4208466
Mining Construction and Related Trade Workers
499
48
8
6
83.16666667
4.4208466
Metal Machinery and Related Trades Workers
299
48
8
6
49.83333333
3.9086841
Precision Handicraft Printing and Related Trades Workers
599
48
8
6
99.83333333
4.6035021
Other Craft and Related Trades Workers
399
29
8
3.625
110.0689655
4.7011071
Stationary Plant and Related Operators
399
48
8
6
66.5
4.1972019
Machine Operators and Assemblers
399
48
8
6
66.5
4.1972019
Drivers and Mobile Plant Operators
499
48
8
6
83.16666667
4.4208466
Sales and Services Elementary Occupations
299
48
8
6
49.83333333
3.9086841
Agricultural Forestry Fishery and Related Laborers
199
48
8
6
33.16666667
3.5015454
Laborers in Mining Construction Manufacturing and Transport
299
48
8
6
49.83333333
3.9086841
Armed Forces
699
48
8
6
116.5
4.7578913
73
MA
UNIVERSITY OF SANTO TOMAS
APPENDIX 8: DATA FOR SCIENCE (64)
TOTAL
NO. OF
HOURS
NORMAL
WORKED
WORKING
DURING
OCCUPATION
HOURS
THE
WORKING
DURING
HOURLY
LOG OF
BASIC
PAST
HOURS
THE PAST
WAGE/
HOURLY
PAY
WEEK
PER DAY
WEEK
EARNINGS
EARNINGS
Officials of Government and Special-Interest Organizations
1499
48
8
6
249.833
5.520794
Corporate Executives and Specialized Managers
1499
48
8
6
249.833
5.520794
399
48
8
6
66.5
4.1972019
1499
48
8
6
249.833
5.520794
Life Science and Health Professionals
399
48
8
6
66.5
4.1972019
Teaching Professionals
299
48
8
6
49.8333
3.9086841
1499
48
8
6
249.833
5.520794
Physical Science and Engineering Associate Professionals
499
48
8
6
83.1667
4.4208466
Life Science and Health Associate Professionals
599
48
8
6
99.8333
4.6035021
Teaching Associate Professionals
599
48
8
6
99.8333
4.6035021
Related Associate Professionals
799
48
8
6
133.167
4.8916015
Office Clerks
499
48
8
6
83.1667
4.4208466
Customer Services Clerks
599
48
8
6
99.8333
4.6035021
Personal and Protective Service Workers
499
48
8
6
83.1667
4.4208466
Models Salespersons and Demonstrators
299
48
8
6
49.8333
3.9086841
Mining Construction and Related Trade Workers
399
48
8
6
66.5
4.1972019
Metal Machinery and Related Trades Workers
399
48
8
6
66.5
4.1972019
Precision Handicraft Printing and Related Trades Workers
299
48
8
6
49.8333
3.9086841
Other Craft and Related Trades Workers
399
48
8
6
66.5
4.1972019
Machine Operators and Assemblers
399
48
8
6
66.5
4.1972019
Drivers and Mobile Plant Operators
399
48
8
6
66.5
4.1972019
Sales and Services Elementary Occupations
499
48
8
6
83.1667
4.4208466
Agricultural Forestry Fishery and Related Laborers
199
19
8
2.375
83.7895
4.4283074
Laborers in Mining Construction Manufacturing and Transport
299
48
8
6
49.8333
3.9086841
Armed Forces
399
48
8
6
66.5
4.1972019
Other Occupations Not Classifiable
599
48
8
6
99.8333
4.6035021
Supervisors
Physicists Mathematical and Engineering Science Professionals
Other Professionals
74
MA
UNIVERSITY OF SANTO TOMAS
APPENDIX 9: DATA FOR Engineering, Manufacturing and
Construction (65)
TOTAL
NO. OF
HOURS
NORMAL
WORKED
WORKING
DURING
OCCUPATION
HOURS
THE
WORKING
DURING
HOURLY
LOG OF
BASIC
PAST
HOURS
THE PAST
WAGE/
HOURLY
PAY
WEEK
PER DAY
WEEK
EARNINGS
EARNINGS
Officials of Government and Special-Interest Organizations
1499
19
8
2.375
631.1579
6.4475561
Corporate Executives and Specialized Managers
1499
48
8
6
249.8333
5.520794
General Managers or Managing-Proprietors
1499
48
8
6
249.8333
5.520794
Supervisors
1499
48
8
6
249.8333
5.520794
Physicists Mathematical and Engineering Science Professionals
1499
48
8
6
249.8333
5.520794
Life Science and Health Professionals
1499
48
8
6
249.8333
5.520794
Teaching Professionals
1499
48
8
6
249.8333
5.520794
Other Professionals
1499
48
8
6
249.8333
5.520794
Physical Science and Engineering Associate Professionals
799
48
8
6
133.1667
4.8916015
Life Science and Health Associate Professionals
399
48
8
6
66.5
4.1972019
Teaching Associate Professionals
399
29
8
3.625
110.069
4.7011071
Related Associate Professionals
599
48
8
6
99.83333
4.6035021
Office Clerks
399
48
8
6
66.5
4.1972019
Customer Services Clerks
699
48
8
6
116.5
4.7578913
Personal and Protective Service Workers
699
48
8
6
116.5
4.7578913
Models Salespersons and Demonstrators
399
48
8
6
66.5
4.1972019
Mining Construction and Related Trade Workers
399
48
8
6
66.5
4.1972019
Metal Machinery and Related Trades Workers
399
48
8
6
66.5
4.1972019
Precision Handicraft Printing and Related Trades Workers
499
48
8
6
83.16667
4.4208466
Other Craft and Related Trades Workers
699
48
8
6
116.5
4.7578913
Stationary Plant and Related Operators
499
48
8
6
83.16667
4.4208466
Machine Operators and Assemblers
399
48
8
6
66.5
4.1972019
Drivers and Mobile Plant Operators
399
48
8
6
66.5
4.1972019
Sales and Services Elementary Occupations
299
48
8
6
49.83333
3.9086841
Agricultural Forestry Fishery and Related Laborers
299
48
8
6
49.83333
3.9086841
Laborers in Mining Construction Manufacturing and Transport
299
48
8
6
49.83333
3.9086841
1499
48
8
6
249.8333
5.520794
Armed Forces
75
M
UNIVERSITY OF SANTO TOMAS
APPENDIX 10: DATA FOR AGRICULTURE (66)
TOTAL
NO. OF
HOURS
NORMAL
WORKED
WORKING
DURING
OCCUPATION
Officials of Government and Special-Interest Organizations
Corporate Executives and Specialized Managers
HOURS
THE
WORKING
DURING
HOURLY
LOG OF
BASIC
PAST
HOURS
THE PAST
WAGE/
HOURLY
PAY
WEEK
PER DAY
WEEK
EARNINGS
EARNINGS
6
249.833333
5.520794
1499
48
8
599
48
8
6
99.8333333
4.603502
General Managers or Managing-Proprietors
1499
58
8
7.25
206.758621
5.331552
Supervisors
1499
48
8
6
249.833333
5.520794
899
48
8
6
149.833333
5.009524
1499
48
8
6
249.833333
5.520794
Teaching Professionals
799
48
8
6
133.166667
4.891601
Other Professionals
799
48
8
6
133.166667
4.891601
Physical Science and Engineering Associate Professionals
599
48
8
6
99.8333333
4.603502
Life Science and Health Associate Professionals
299
48
8
6
49.8333333
3.908684
Related Associate Professionals
499
48
8
6
83.1666667
4.420847
Office Clerks
299
48
8
6
49.8333333
3.908684
Customer Services Clerks
399
48
8
6
66.5
4.197202
Personal and Protective Service Workers
299
48
8
6
49.8333333
3.908684
Models Salespersons and Demonstrators
299
48
8
6
49.8333333
3.908684
Farmers and Other Plant Growers
199
19
8
2.375
83.7894737
4.428307
Animal Producers
199
48
8
6
33.1666667
3.501545
Forestry and Related Workers
199
39
8
4.875
40.8205128
3.709185
Fishermen
199
39
8
4.875
40.8205128
3.709185
Hunters and Trappers
199
39
8
4.875
40.8205128
3.709185
Metal Machinery and Related Trades Workers
499
48
8
6
83.1666667
4.420847
Other Craft and Related Trades Workers
299
48
8
6
49.8333333
3.908684
Machine Operators and Assemblers
399
48
8
6
66.5
4.197202
Drivers and Mobile Plant Operators
399
39
8
4.875
81.8461538
4.404841
Sales and Services Elementary Occupations
399
48
8
6
66.5
4.197202
Agricultural Forestry Fishery and Related Laborers
199
48
8
6
33.1666667
3.501545
Laborers in Mining Construction Manufacturing and Transport
299
48
8
6
49.8333333
3.908684
1499
48
8
6
249.833333
5.520794
199
48
8
6
33.1666667
3.501545
Physicists Mathematical and Engineering Science Professionals
Life Science and Health Professionals
Armed Forces
Other Occupations Not Classifiable
76
MA
UNIVERSITY OF SANTO TOMAS
APPENDIX 11: DATA FOR HEALTH & WELFARE (67)
TOTAL
NO. OF
HOURS
NORMAL
WORKED
WORKING
DURING
OCCUPATION
HOURS
THE
WORKING
DURING
HOURLY
LOG OF
BASIC
PAST
HOURS
THE PAST
WAGE/
HOURLY
PAY
WEEK
PER DAY
WEEK
EARNINGS
EARNINGS
Officials of Government and Special-Interest Organizations
1499
48
8
6
249.83333
5.52079403
Corporate Executives and Specialized Managers
1499
48
8
6
249.83333
5.52079403
Supervisors
1499
48
8
6
249.83333
5.52079403
Physicists Mathematical and Engineering Science Professionals
599
48
8
6
99.833333
4.60350213
Life Science and Health Professionals
399
48
8
6
66.5
4.19720195
Teaching Professionals
699
48
8
6
116.5
4.75789127
Other Professionals
899
48
8
6
149.83333
5.00952357
Life Science and Health Associate Professionals
399
48
8
6
66.5
4.19720195
Related Associate Professionals
599
48
8
6
99.833333
4.60350213
Office Clerks
499
48
8
6
83.166667
4.42084663
Customer Services Clerks
499
48
8
6
83.166667
4.42084663
Personal and Protective Service Workers
299
48
8
6
49.833333
3.9086841
Models Salespersons and Demonstrators
299
48
8
6
49.833333
3.9086841
Metal Machinery and Related Trades Workers
599
29
8
3.625
165.24138
5.10740731
Drivers and Mobile Plant Operators
399
39
8
4.875
81.846154
4.40484131
Sales and Services Elementary Occupations
499
48
8
6
83.166667
4.42084663
Agricultural Forestry Fishery and Related Laborers
299
48
8
6
49.833333
3.9086841
Laborers in Mining Construction Manufacturing and Transport
399
48
8
6
66.5
4.19720195
77
MAT
UNIVERSITY OF SANTO TOMAS
APPENDIX 12: DATA FOR SERVICES (68)
TOTAL
NO. OF
HOURS
NORMAL
WORKED
WORKING
DURING
OCCUPATION
Officials of Government and Special-Interest Organizations
HOURS
THE
WORKING
DURING
HOURLY
LOG OF
BASIC
PAST
HOURS
THE PAST
WAGE/
HOURLY
PAY
WEEK
PER DAY
WEEK
EARNINGS
EARNINGS
6.44756
1499
19
8
2.375
631.16
Corporate Executives and Specialized Managers
799
48
8
6
133.17
4.8916
Supervisors
499
48
8
6
83.167
4.42085
Physicists Mathematical and Engineering Science Professionals
699
48
8
6
116.5
4.75789
Life Science and Health Professionals
499
48
8
6
83.167
4.42085
1499
48
8
6
249.83
5.52079
Other Professionals
599
48
8
6
99.833
4.6035
Physical Science and Engineering Associate Professionals
399
48
8
6
66.5
4.1972
Life Science and Health Associate Professionals
399
48
8
6
66.5
4.1972
Teaching Associate Professionals
799
48
8
6
133.17
4.8916
Related Associate Professionals
499
48
8
6
83.167
4.42085
Office Clerks
499
48
8
6
83.167
4.42085
Customer Services Clerks
299
48
8
6
49.833
3.90868
Personal and Protective Service Workers
1499
48
8
6
249.83
5.52079
Models Salespersons and Demonstrators
299
48
8
6
49.833
3.90868
Mining Construction and Related Trade Workers
399
48
8
6
66.5
4.1972
Metal Machinery and Related Trades Workers
499
48
8
6
83.167
4.42085
Precision Handicraft Printing and Related Trades Workers
199
48
8
6
33.167
3.50155
Other Craft and Related Trades Workers
199
48
8
6
33.167
3.50155
Machine Operators and Assemblers
399
58
8
7.25
55.034
4.00796
Drivers and Mobile Plant Operators
399
48
8
6
66.5
4.1972
Sales and Services Elementary Occupations
299
48
8
6
49.833
3.90868
Agricultural Forestry Fishery and Related Laborers
299
48
8
6
49.833
3.90868
Laborers in Mining Construction Manufacturing and Transport
299
48
8
6
49.833
3.90868
Armed Forces
799
48
8
6
133.17
4.8916
Teaching Professionals
78
M