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UNIVERSITY OF SANTO TOMAS
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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
UNIVERSITY OF SANTO TOMAS
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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 Committee on Oral Examination with the grade of____________.
_________________________ Emmanuel Lopez, Ph.D. Chairman
__________________ _______________________ Alvin Ang, Ph.D. Carlos Manapat, Ph.D. Panel Member 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 Education-Occupation 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 cost-
effective 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 + 𝑑𝑋2 + 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 the vacancy rate and the
unemployment rate (Shimer, 2005).
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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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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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IV. CONCEPTUAL FRAMEWORK
A. The Model used by the Researchers in the Analysis of Education-
Occupation 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
Matched
or
Unmatche
d
Effect Wage Education
Occupation
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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.
Unemployment
rate
Job- vacancy
rate
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CHAPTER 3: DATA AND METHODOLOGY
A. The Model used by the Researchers in the Analysis of Education-
Occupation 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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28
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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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 𝑡𝑒 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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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 𝑡𝑒 𝑝𝑎𝑠𝑡 𝑤𝑒𝑒𝑘
= 𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑜𝑢𝑟𝑠 𝑤𝑜𝑟𝑘𝑒𝑑 𝑑𝑢𝑟𝑖𝑛𝑔 𝑡𝑒 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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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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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
UNIVERSITY OF SANTO TOMAS
34
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.
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.
0
50
100
150
200
250
300
350
0 10 20 30
HOURLY WAGE/ EARNINGS
HOURLY WAGE/ EARNINGS
0
1
2
3
4
5
6
7
0 10 20 30
LOG OF HOURLY EARNINGS
LOG OF HOURLY EARNINGS
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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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36
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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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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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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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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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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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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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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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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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.
Year
Job-
vacancy
Rate
Unemployment
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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Correlation of Unemployment rates and Job-vacancy rates
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
Job-vacancy rate
Unemployment
rate
Job-vacancy
rate 1
Unemployment
rate 0.768721874 1
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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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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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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
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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.
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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
UNIVERSITY OF SANTO TOMAS
51
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.
UNIVERSITY OF SANTO TOMAS
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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/PR-
201206_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 (4th
Quarter of 2011) Public User File with Wage Data.
UNIVERSITY OF SANTO TOMAS
53
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-the-
Job 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/2012/02-04-
2.pdf
UNIVERSITY OF SANTO TOMAS
54
Handel, M. 2003. Skills Mismatch in the Labor Market in Annual
Reviews. University of Wisconsin, Madison, Wisconsin 53706.
Retrieved from 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
UNIVERSITY OF SANTO TOMAS
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Shimer, R. 2005. Mismatch. National Bureau of Economic Research
Working Paper Series: Working Paper 11888. Cambridge,
Massachusetts Avenue. Retrieved 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, Job-
related 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-a-
call-center-agent-2/
http://www.edgewise.ph/2010/06/22/006-ask-edgewise/i-just-started-
this-job-but-i-received-a-better-offer-elsewhere/
N/A. 2010. Job Mismatch Causes High Unemployment
Rate.Retrieved from 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
UNIVERSITY OF SANTO TOMAS
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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
UNIVERSITY OF SANTO TOMAS
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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 TEACHER TRAINING AND EDUCATION SCIENCES PROGRAMS 61401 Programs in General Teacher Training 61401 Bachelor of Elementary Education 61401 Bachelor of Secondary Education 61401 Bachelor of Science in Elementary and Secondary Education 61401 Bachelor of Science in Pedagogy 61404 Programs in Teacher Training with Specialization in Nonvocational Subjects 61404 Bachelor of Home Economics and Livelihood Education for Teachers 61404 Bachelor of Physical Education 61404 Bachelor of Sports Science 61404 Bachelor of Technology and Home Economics for Teachers 61404 Bachelor of Arts in Language Education for Teachers 61404 Bachelor of Science in Chemistry for Teachers 61404 Bachelor of Science in English Education as Secondary Language 61404 Bachelor of Science in Language Education for Teachers 61404 Bachelor of Science in Mathematics for Teachers 61404 Bachelor of Science in Music Education 61404 Bachelor of Science in Physical Education 61404 Bachelor of Science in Physics for Teachers 61404 Bachelor of Science in Religious Education 61408 Programs in Teacher Training for Teaching Practical or Vocational Subjects 61408 Bachelor of Science in Industrial Education 61408 Bachelor of Science in Technician Teacher Education 61412 Programs in Teacher Training for Teaching Preschool or Kindergarten 61412 Bachelor of Science in Early Childhood Education 61412 Bachelor of Science in Kindergarten/Preschool Education 61416 Programs in Teacher Training for Teachers in Adult Education 61422 Programs in Teacher Training for Teachers in Special Education 61422 Bachelor of Special Education 61472 Programs in Education Science in Support of Teaching 61472 Bachelor of Science in Guidance and Counseling 61499 Other Programs in Teacher Training and Education Sciences 61499 Bachelor of Science in Agricultural Education 61499 Bachelor of Science in Business/Commercial Education 61499 Bachelor of Science in Extension Education 61499 Bachelor of Science in Fishery Education 61499 Bachelor of Science in Nursing Education 61499 Bachelor of Science in Nutrition and Dietetics for Teachers 61499 Bachelor of Science in Secretarial Education HUMANITIES AND ARTS 621 ARTS PROGRAMS 62101 General Programs in Art Studies 62101 Bachelor of Digital Arts 62101 Bachelor of Science in Fine Arts/Bachelor of Fine Arts 62104 Programs in Drawing and Painting 62108 Programs in Sculpturing 62122 Programs in Music 62122 Bachelor of Music Liturgy 62122 Bachelor of Science in Music/Bachelor of Music 62132 Programs in Drama
62132 Bachelor of Performing Arts 62132 Bachelor of Arts in Speech and Drama 62132 Bachelor of Arts in Speech and Theater Arts 62132 Bachelor of Arts in Theater Arts 62132 Bachelor of Science in Speech and Drama 62152 Programs in Interior Design 62152 Bachelor of Science in Interior Design 62199 Other Programs in Arts 62199 Bachelor of Arts in Film and Audio-Visual Communication 622 HUMANITIES PROGRAMS 62201 General Programs in Humanities
UNIVERSITY OF SANTO TOMAS
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62201 Bachelor of Arts in Humanities 62211 Programs in the Current or Vernacular Language and Its Literature 62211 Bachelor of Arts in English 62211 Bachelor of Arts in English Literature 62211 Bachelor of Arts in Filipino 62211 Bachelor of Arts in Philippine Literature 62215 Programs in Other Living Languages and Their Literature 62215 Bachelor of Arts in European Languages 62221 Programs in "Dead" Languages and Their Literature 62231 Programs in Linguistics 62231 Bachelor of Arts in Linguistics 62241 Programs in Comparative Literature 62241 Bachelor of Arts in Comparative Literature 62241 Bachelor of Arts in Literature 62251 Programs in History 62251 Bachelor of Arts in Development Studies 62251 Bachelor of Arts in History 62261 Programs in Archeology 62271 Programs in Philosophy 62271 Bachelor of Arts in Classical/Philosophy/Bachelor of Classical/Philosophy 62271 Bachelor of Arts in Philosophy/Bachelor of Philosophy 62281 Programs in Religion and Theology 62281 Bachelor of Evangelical Ministry 62281 Bachelor of Arts in Religion 62281 Bachelor of Arts in Religious Studies 62281 Bachelor of Arts in Divinity/Bachelor of Divinity 62281 Bachelor of Arts in Theology/Bachelor of Theology 62299 Other Programs in Humanities SOCIAL SCIENCES, BUSINESS, AND LAW 631 SOCIAL AND BEHAVIORAL SCIENCE PROGRAMS 63101 General Programs in Social and Behavioral Sciences 63101 Bachelor of Arts in Behavioral Science 63101 Bachelor of Arts in Social Science 63101 Bachelor of Arts in Human Behavior Technology/Bachelor of Human Behavior Technology 63101 Bachelor of Science in Behavioral Science 63101 Bachelor of Science in Human Behavior Technology 63112 Programs in Economics 63112 Bachelor of Arts in Economics 63112 Bachelor of Arts in Applied Economics/Bachelor of Applied Economics 63112 Bachelor of Science in Applied Economics 63112 Bachelor of Science in Business Economics 63112 Bachelor of Science in Economics 63122 Programs in Political Science 63122 Bachelor of Arts in Political Science/Bachelor in Political Science 63122 Bachelor of Science in Foreign Service 63122 Bachelor of Science in International Relations 63122 Bachelor of Science in Political Economy 63132 Programs in Sociology 63132 Bachelor of Arts in Applied Sociology 63132 Bachelor of Arts in Sociology 63132 Bachelor of Science in Sociology 63133 Programs in Demography 63133 Bachelor of Science in Demography 63142 Programs in Anthropology 63142 Bachelor of Arts in Anthropology 63152 Programs in Psychology 63152 Bachelor of Arts in Applied Psychology
63152 Bachelor of Science in Clinical Psychology 63152 Bachelor of Science in Industrial and Organizational Psychology 63152 Bachelor of Science in Psychology 63162 Programs in Geography 63162 Bachelor of Science in Geography 63172 Programs in Studies of Regional Cultures 63172 Bachelor of Arts in Arabic/Islamic Studies 63172 Bachelor of Arts in International Studies 63172 Bachelor of Arts in Philippine Arts 63172 Bachelor of Arts in Philippine Studies 63199 Other Programs in Social and Behavioral Sciences
UNIVERSITY OF SANTO TOMAS
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632 JOURNALISM AND INFORMATION PROGRAMS 63201 Programs in General Communication Arts 63201 Bachelor of Arts in Communication 63201 Bachelor of Arts in Media Studies 63202 Programs in Journalism 63202 Bachelor of Arts in Business Journalism/Bachelor in Business Journalism 63202 Bachelor of Arts in Journalism/Bachelor in Journalism 63202 Bachelor of Science in Business Journalism 63202 Bachelor of Science in Journalism 63204 Programs in Radio and Television Broadcasting 63204 Bachelor of Arts in Broadcast Communication 63204 Bachelor of Science in Broadcast Communication 63207 Programs in Public Relations 63207 Bachelor of Arts in Public Relations 63222 Programs in Library Science 63222 Bachelor of Arts in Library and Information Science/Bachelor of Library and Information Science 63222 Bachelor of Arts in Library Science/Bachelor of Library Science 63229 Other Programs in Journalism and Information 63229 Bachelor of Arts in Communication Arts/Mass Communication 63229 Bachelor of Arts in Communication Research 63229 Bachelor of Arts in Organizational Communication 63229 Bachelor of Science in Mass Communication 634 BUSINESS AND ADMINISTRATION PROGRAMS 63401 General Programs in Business Administration/Commerce 63401 Bachelor of Arts in Business Administration/Bachelor in Business Administration 63401 Bachelor of Arts in Entrepreneurial Management/Bachelor in Entrepreneurial Management 63401 Bachelor of Arts in Business Management/Bachelor of Business Management 63401 Bachelor of Arts in Management and Social Work /Bachelor of Management and Social Work 63401 Bachelor of Science in Administration 63401 Bachelor of Science in Business Administration 63401 Bachelor of Science in Business Management 63401 Bachelor of Science in Commerce 63401 Bachelor of Science in Management 63404 Programs in Secretarial 63404 Bachelor of Science in Airline Secretarial/Administration 63404 Bachelor of Science in Computer Secretarial 63404 Bachelor of Science in Office Administration/Technology 63404 Bachelor of Science in Secretarial Administration 63432 Programs in Business Administration with Specialization in Accountancy 63432 Bachelor of Science in Accountancy 63432 Bachelor of Science in Business Administration and Accountancy 63432 Bachelor of Science in Computer Accounting and Management 63432 Bachelor of Science in Management and Accountancy 63434 Programs in Business Administration with Specialization in Marketing 63434 Bachelor of Arts in Advertising/Bachelor of Advertising 63434 Bachelor of Arts in Advertising and Public Relations/Bachelor of Advertising and Public Relations 63434 Bachelor of Science in Marketing 63436 Programs in Business Administration with Specialization in Finance and Investment 63436 Bachelor of Science in Finance 63436 Bachelor of Science in Real Estate 63436 Bachelor of Science in Banking and Finance/Bachelor in Banking and Finance 63439 Programs in Business Administration with Other Specialization 63439 Bachelor of Arts in Legal Management 63439 Bachelor of Arts in Business Engineering/Bachelor in Business Engineering 63439 Bachelor of Arts in Agricultural Entrepreneurship/Bachelor of Agricultural
Enterpreneurship 63439 Bachelor of Arts in Business Distributive Arts/Bachelor of Business Distributive Arts 63439 Bachelor of Arts in Computer Management/Bachelor of Computer Management 63439 Bachelor of Arts in Industrial Management/Bachelor of Industrial Management 63439 Bachelor of Arts in Transportation Management/Bachelor of Transportation Management 63439 Bachelor of Science in Agri-Business Management 63439 Bachelor of Science in Business Enterpreneurship 63439 Bachelor of Science in Business Technology 63439 Bachelor of Science in Economics and Cooperatives 63439 Bachelor of Science in Fishery Business Management 63439 Bachelor of Science in Home Arts and Enterpreneurship 63439 Bachelor of Science in Industrial Management
UNIVERSITY OF SANTO TOMAS
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63452 Programs in Public Administration 63452 Bachelor of Arts in Public Administration/Bachelor of Public Administration 63452 Bachelor of Science in Public Administration 63462 Programs in Institutional Administration/Management 63462 Bachelor of Arts in Port Administration/Bachelor of Port Administration 63462 Bachelor of Science in Airline Business Administration/Management 63462 Bachelor of Science in Airline Management 63462 Bachelor of Science in Airline Management and Accountancy 63462 Bachelor of Science in Customs Administration 63462 Bachelor of Science in Food Service Administration 63462 Bachelor of Science in Hospital Administration 63462 Bachelor of Science in Postal Management 63462 Bachelor of Science in Shipping Management 63499 Other Programs in Business Administration/Management 63499 Bachelor of Arts in Legal and Indigenous Studies 63499 Bachelor of Arts in Cooperatives/Bachelor of Cooperatives 63499 Bachelor of Science in Cooperative Management 63499 Bachelor of Science in Maritime Management 63499 Bachelor of Science in Recreation Management 63499 Bachelor of Science in Supply Management 638 LAW PROGRAMS 63801 General Programs in Law 63801 Bachelor of Laws (LL.B.)/Juris Doctor (J.D.) 63801 Bachelor of Science in Jurisprudence SCIENCE 642 LIFE SCIENCES PROGRAMS 64202 Programs in Biological Science 64202 Bachelor of Science in Applied Biology 64202 Bachelor of Science in Biochemistry 64202 Bachelor of Science in Biological Science 64202 Bachelor of Science in Biology 64202 Bachelor of Science in Botany 64202 Bachelor of Science in Entomology 64202 Bachelor of Science in Human Biology 64202 Bachelor of Science in Marine Biology 64202 Bachelor of Science in Microbiology 64202 Bachelor of Science in Molecular Biology and Biotechnology 64202 Bachelor of Science in Pharmacology 64202 Bachelor of Science in Physiology 64202 Bachelor of Science in Plant Science 64202 Bachelor of Science in Zoology 64209 Other Programs in Life Sciences 64209 Bachelor of Arts in Applied Science/Bachelor of Applied Science 64209 Bachelor of Science in General Science 64209 Bachelor of Science in Natural Science 644 PHYSICAL SCIENCES PROGRAMS 64412 Programs in Chemistry 64412 Bachelor of Science in Chemical Research 64412 Bachelor of Science in Chemical Technology 64412 Bachelor of Science in Chemistry 64412 Bachelor of Science in Industrial Chemistry 64422 Programs in Geological Science 64422 Bachelor of Science in Geology 64422 Bachelor of Science in Volcanology 64432 Programs in Physics
64432 Bachelor of Science in Applied Physics 64432 Bachelor of Science in Metallurgy 64432 Bachelor of Science in Physics 64432 Bachelor of Science in Physics-Mathematics 64442 Programs in Astronomy 64442 Bachelor of Science in Astronomy 64452 Programs in Meteorology 64452 Bachelor of Science in Meteorology 64462 Programs in Oceanography 64462 Bachelor of Science in Marine Science 64462 Bachelor of Science in Oceanography 646 MATHEMATICS AND STATISTICS PROGRAMS
UNIVERSITY OF SANTO TOMAS
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64601 General Programs in Mathematics 64601 Bachelor of Science in Mathematics 64611 Programs in Statistics 64611 Bachelor of Science in Applied Statistics 64611 Bachelor of Science in Experimental Statistics 64611 Bachelor of Science in Statistics 64621 Programs in Actuarial Science 64621 Bachelor of Science in Actuarial Science 64699 Other Programs in Mathematics 64699 Bachelor of Science in Applied Mathematics 648 COMPUTING/INFORMATION TECHNOLOGY PROGRAMS 64841 Programs in Computer Science and Information Technology 64841 Bachelor of Arts in Information Technology/Bachelor in Information Technology 64841 Bachelor of Science in Business Computer Applications 64841 Bachelor of Science in Computer Applications 64841 Bachelor of Science in Computer Data Processing Management 64841 Bachelor of Science in Computer Science 64841 Bachelor of Science in Computer Studies 64841 Bachelor of Science in Information and Computer Science 64841 Bachelor of Science in Information System/Management 64841 Bachelor of Science in Information Technology 64841 Bachelor of Science in Management Information System 64841 Bachelor of Science in Software Technology 64844 Programs in Electronic Data Processing 64844 Bachelor of Science in Computer Data Processing and Information Management 64844 Bachelor of Science in Data Processing ENGINEERING, MANUFACTURING, AND CONSTRUCTION 652 ENGINEERING AND ENGINEERING TRADES PROGRAMS 65204 Programs in Aeronautical Engineering 65204 Bachelor of Science in Aeronautical Engineering 65204 Bachelor of Science in Aerospace Engineering 65204 Bachelor of Science in Air Transportation 65204 Bachelor of Science in Aircraft Maintenance Engineering 65204 Bachelor of Science in Aircraft Maintenance Technology 65204 Bachelor of Science in Aircraft Technology 65204 Bachelor of Science in Aviation 65204 Bachelor of Science in Aviation Electronics Engineering 65204 Bachelor of Science in Avionics Engineering 65204 Bachelor of Science in Avionics Technology 65204 Bachelor of Science in Electrical Engineering Avionics 65204 Bachelor of Science in Flying Technology 65212 Programs in Chemical Engineering 65212 Bachelor of Science in Ceramics Engineering 65212 Bachelor of Science in Chemical Engineering 65212 Bachelor of Science in Chemical Engineering Technology 65212 Bachelor of Science in Textile Engineering 65216 Programs in Civil Engineering 65216 Bachelor of Science in Civil Engineering 65216 Bachelor of Science in Construction Technology 65218 Programs in Geodetic Engineering 65218 Bachelor of Science in Geodetic Engineering 65222 Programs in Electrical, Electronics and Computer Engineering 65222 Bachelor of Science in Communications Engineering 65222 Bachelor of Science in Computer Engineering 65222 Bachelor of Science in Computer Technology 65222 Bachelor of Science in Electrical Engineering
65222 Bachelor of Science in Electrical Engineering Technology 65222 Bachelor of Science in Electrical Technology 65222 Bachelor of Science in Electronics and Communications Engineering 65222 Bachelor of Science in Electronics Engineering 65222 Bachelor of Science in Electronics Technology 65222 Bachelor of Science in Instrumentation and Control Engineering 65226 Programs in Industrial Engineering 65226 Bachelor of Science in Industrial and Management Engineering 65226 Bachelor of Science in Industrial Design 65226 Bachelor of Science in Industrial Engineering 65226 Bachelor of Science in Industrial Technology 65226 Bachelor of Science in Management Engineering 65226 Bachelor of Science in Manufacturing Engineering
UNIVERSITY OF SANTO TOMAS
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65232 Programs in Metallurgical Engineering 65232 Bachelor of Science in Metallurgical Engineering 65236 Programs in Mining Engineering 65236 Bachelor of Science in Mining Engineering 65242 Programs in Mechanical Engineering 65242 Bachelor of Science in Automotive Technology 65242 Bachelor of Science in Geothermal Engineering 65242 Bachelor of Science in Mechanical Engineering 65242 Bachelor of Science in Mechanical Technology 65250 Programs in Sanitary Engineering 65250 Bachelor of Science in Environmental and Sanitary Engineering 65250 Bachelor of Science in Environmental Engineering 65250 Bachelor of Science in Sanitary Engineering 65253 Programs in Agricultural Engineering 65253 Bachelor of Science in Agricultural Engineering 65253 Bachelor of Science in Aquatic Resource Engineering 65263 Programs in Forestry Engineering 65263 Bachelor of Science in Forest Products Engineering 65281 Programs in Marine Engineering 65281 Bachelor of Science in Marine Engineering 65281 Bachelor of Science in Naval Architecture and Marine Engineering 65299 Other Programs in Engineering 65299 Bachelor of Arts in Technology/Bachelor of Technology 65299 Bachelor of Science in Electronics and Computer Technology 65299 Bachelor of Science in Food Engineering 65299 Bachelor of Science in Petroleum Engineering 654 MANUFACTURING AND PROCESSING PROGRAMS 65476 Programs in Clothing and Related Trades 65476 Bachelor of Science in Clothing Technology 65476 Bachelor of Science in Garment/Textile Technology 658 ARCHITECTURE AND BUILDING PROGRAMS 65801 General Programs in Architecture and Town Planning 65801 Bachelor of Science in Architecture 65812 Programs in Landscape Architecture 65812 Bachelor of Science in Landscape Architecture 65822 Programs in Town Planning 65822 Bachelor of Science in Town and Country Planning AGRICULTURE 662 AGRICULTURE, FORESTRY, AND FISHERY PROGRAMS 66201 General Programs in Agriculture 66201 Bachelor of Arts in Agricultural Technology/Bachelor of Agricultural Technology Bachelor of Science in Agriculture 66203 Programs in Animal Husbandry 66203 Bachelor of Science in Animal Husbandry 66203 Bachelor of Science in Animal Science 66203 Bachelor of Science in Animal Technology 66206 Programs in Horticulture 66206 Bachelor of Arts in Horticulture Management/Bachelor of Technology in Horticulture Management 66206 Bachelor of Science in Horticulture 66208 Programs in Agronomy 66208 Bachelor of Science in Agronomy 66212 Programs in Agricultural Economics 66212 Bachelor of Science in Agricultural Economics 66222 Programs in Food Science and Technology
66222 Bachelor of Science in Food Technology 66226 Programs in Soil and Water Sciences 66249 Other Programs in Agriculture 66249 Bachelor of Science in Agricultural Administration 66249 Bachelor of Science in Agricultural Chemistry 66249 Bachelor of Science in Agricultural Development 66249 Bachelor of Science in Agricultural Management 66249 Bachelor of Science in Rice Technology 66249 Bachelor of Science in Sugar Technology 66262 Programs in Forestry 66262 Bachelor of Arts in Agro-Forestry Technology/Bachelor in Agro-Forestry Technology 66262 Bachelor of Science in Agro-Forestry 66262 Bachelor of Science in Forestry
UNIVERSITY OF SANTO TOMAS
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66272 Programs in Fishery Science and Technology 66272 Bachelor of Science in Aquaculture 66272 Bachelor of Science in Aquatic Resource Management and Technology 66272 Bachelor of Science in Fisheries 66272 Bachelor of Science in Fishing Technology 66272 Bachelor of Science in Inland Fisheries 664 VETERINARY PROGRAMS 66432 Programs in Veterinary Medicine 66432 Bachelor of Science in Veterinary Technology HEALTH AND WELFARE 672 HEALTH PROGRAMS 67202 Programs in Hygiene 67202 Bachelor of Science in Community/Public Health 67202 Bachelor of Science in Sanitary Science 67206 Programs in Medicine 67206 Bachelor of Arts in Basic Medical Sciences 67206 Doctor of Medicine 67208 Programs in Rehabilitation Medicine 67208 Bachelor of Science in Occupational Therapy 67208 Bachelor of Science in Physical Therapy 67208 Bachelor of Science in Respiratory Therapy 67208 Bachelor of Science in Speech Pathology 67212 Programs in Nursing 67212 Bachelor of Science in Nursing 67217 Programs in Medical X-Ray Techniques 67217 Bachelor of Science in Radiologic Technology 67230 Programs in Medical Technology 67230 Bachelor of Science in Medical Technology 67242 Programs in Dentistry 67242 Doctor of Dental Medicine 67252 Programs in Pharmacy 67252 Bachelor of Science in Industrial Pharmacy 67252 Bachelor of Science in Pharmaceutical Chemistry 67252 Bachelor of Science in Pharmacy 67262 Programs in Optometry 67262 Doctor of Optometry 67272 Programs in Nutrition and Dietetics 67272 Bachelor of Science in Nutrition and Dietetics 67299 Other Programs in Medical Diagnostic and Treatment 67299 Bachelor of Science in Paramedics 67299 Bachelor of Science in Rural Medicine 676 SOCIAL SERVICES PROGRAMS 67632 Programs in Social Welfare 67632 Bachelor of Arts in Social Services/Social Work 67632 Bachelor of Science in Social Services/Social Work 67652 Programs in Community Development 67652 Bachelor of Science in Community Development 67652 Bachelor of Science in Development of Multi-Cultural Communities 67652 Bachelor of Science in Rural Development Management SERVICES 681 PERSONAL SERVICES PROGRAMS 68101 General Programs in Home Economics (Domestic Science)
68101 Bachelor of Science in Family and Child Development 68101 Bachelor of Science in Home Economics 68101 Bachelor of Science in Human Ecology 68132 Programs in Home Economics with Emphasis on Household Arts 68134 Other Programs in Home Economics 68134 Bachelor of Science in Home Technology 68172 Programs in Hotel and Restaurant Trades 68172 Bachelor of Science in Hotel and Restaurant Management 68182 Programs in Tourism 68182 Bachelor of Arts in Tourism 68182 Bachelor of Science in Tourism 68182 Bachelor of Science in Tourism and Hotel and Restaurant Management 68182 Bachelor of Science in Tourism and Travel Management
UNIVERSITY OF SANTO TOMAS
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684 TRANSPORT SERVICES PROGRAMS 68404 Programs in Nautical Science 68404 Bachelor of Science in Marine Transportation 68404 Bachelor of Science in Nautical Science 685 ENVIRONMENTAL PROTECTION PROGRAMS 68552 Programs in Environmental Studies 68552 Bachelor of Arts in Technology in Environmental Management/Bachelor of Technology in Environmental Management 68552 Bachelor of Science in Coastal Resource Management 68552 Bachelor of Science in Ecology 68552 Bachelor of Science in Environmental Development/Environmental Hygiene/Environmental Science 68552 Bachelor of Science in Environmental Management 68552 Bachelor of Science in Environmental Planning 686 SECURITY SERVICES PROGRAMS 68613 Programs in Criminal Justice Education 68613 Bachelor of Science in Criminal Justice/Criminology 68613 Bachelor of Science in Forensic Science 68613 Bachelor of Science in Industrial Security Management 68613 Bachelor of Science in Police/Law Enforcement Administration 68617 Programs in Military 68619 Other Programs in Civil Security 68619 Bachelor of Arts in Peace and Security Studies 68619 Bachelor of Science in Peace and Security Studies
UNIVERSITY OF SANTO TOMAS
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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
(63)
Officials of Government and Special-Interest
Organizations, Corporate Executives and
Specialized Managers, General Managers or
Managing-Proprietors, Supervisors, Teaching
Professionals, Other Professionals, Related
Associate Professionals, Office Clerks,
Customer Services Clerks, Models,
Salespersons and Demonstrators, Other Craft
and Related Trades Workers, Sales and
Services.
Science (64) Physicists, Life Science and Health
Professionals, Teaching Professionals,
Physical Science and Engineering Associate
Professionals, Life Science and Health
Associate Professionals.
Engineering, Manufacturing and Corporate Executives and Specialized
UNIVERSITY OF SANTO TOMAS
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Construction (65) Managers, General Managers or Managing-
Proprietors, 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.
UNIVERSITY OF SANTO TOMAS
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APPENDIX 3: Total Number of College Graduates per Field of Study and their corresponding Primary Occupations
UNIVERSITY OF SANTO TOMAS
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APPENDIX 4: Regression Results for the 8 Fields of Study
1. Education
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 4.482 .129 34.765 .000
MATCH .965 .380 .461 2.542 .018
a. Dependent Variable: LOG OF HOURLY EARNINGS
2. Social Sciences, Business and Law
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 4.270 .125 34.143 .000
MATCH .662 .188 .577 3.530 .002
a. Dependent Variable: LOG OF HOURLY EARNINGS
3. Services
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 4.282 .142 30.246 .000
MATCH .653 .289 .426 2.259 .034
a. Dependent Variable: LOG OF HOURLY EARNINGS
UNIVERSITY OF SANTO TOMAS
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4. Humanities and Arts
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 4.437 .119 37.201 .000
MATCH -.010 .212 -.012 -.049 .962
5. Science
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 4.479 .114 39.402 .000
MATCH .051 .259 .040 .197 .845
a. Dependent Variable: LOG OF HOURLY EARNINGS
6. Engineering, Manufacturing and Construction
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 4.851 .167 28.988 .000
MATCH -.193 .275 -.139 -.703 .488
a. Dependent Variable: LOG OF HOURLY EARNINGS
UNIVERSITY OF SANTO TOMAS
70
7. Agriculture
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 4.447 .148 29.997 .000
MATCH -.243 .266 -.173 -.914 .369
a. Dependent Variable: LOG OF HOURLY EARNINGS
8. Health and Welfare
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 4.694 .140 33.625 .000
MATCH -.468 .296 -.367 -1.580 .134
a. Dependent Variable: LOG OF HOURLY EARNINGS
UNIVERSITY OF SANTO TOMAS
71
APPENDIX 5: DATA FOR EDUCATION (61)
OCCUPATION
BASIC
PAY
TOTAL
NO. OF
HOURS
WORKED
DURING
THE
PAST
WEEK
WORKING
HOURS
PER DAY
NORMAL
WORKING
HOURS
DURING
THE PAST
WEEK
HOURLY
WAGE/
EARNINGS
LOG OF
HOURLY
EARNINGS MATCH
Officials of Government and Special-Interest Organizations 1499 48 8 6 249.833333 5.520794 0
Corporate Executives and Specialized Managers 1499 48 8 6 249.833333 5.520794 0
Supervisors 1499 48 8 6 249.833333 5.520794 1
Physicists Mathematical and Engineering Science Professionals 699 48 8 6 116.5 4.757891 0
Life Science and Health Professionals 1499 48 8 6 249.833333 5.520794 0
Teaching Professionals 899 48 8 6 149.833333 5.009524 1
Other Professionals 1499 48 8 6 249.833333 5.520794 0
Physical Science and Engineering Associate Professionals 399 48 8 6 66.5 4.197202 0
Life Science and Health Associate Professionals 599 48 8 6 99.8333333 4.603502 0
Teaching Associate Professionals 1999 48 8 6 333.166667 5.808643 1
Related Associate Professionals 599 48 8 6 99.8333333 4.603502 0
Office Clerks 399 48 8 6 66.5 4.197202 0
Customer Services Clerks 299 48 8 6 49.8333333 3.908684 0
Personal and Protective Service Workers 1499 48 8 6 249.833333 5.520794 0
Models Salespersons and Demonstrators 299 48 8 6 49.8333333 3.908684 0
Mining Construction and Related Trade Workers 299 39 8 4.875 61.3333333 4.116323 0
Metal Machinery and Related Trades Workers 199 48 8 6 33.1666667 3.501545 0
Precision Handicraft Printing and Related Trades Workers 299 39 8 4.875 61.3333333 4.116323 0
Other Craft and Related Trades Workers 399 48 8 6 66.5 4.197202 0
Stationary Plant and Related Operators 399 48 8 6 66.5 4.197202 0
Machine Operators and Assemblers 399 48 8 6 66.5 4.197202 0
Drivers and Mobile Plant Operators 499 48 8 6 83.1666667 4.420847 0
Sales and Services Elementary Occupations 199 19 8 2.375 83.7894737 4.428307 0
Agricultural Forestry Fishery and Related Laborers 199 48 8 6 33.1666667 3.501545 0
Laborers in Mining Construction Manufacturing and Transport 399 48 8 6 66.5 4.197202 0
Armed Forces 499 48 8 6 83.1666667 4.420847 0
UNIVERSITY OF SANTO TOMAS
72
APPENDIX 6: DATA FOR HUMANITIES AND ARTS (62)
OCCUPATION
BASIC
PAY
TOTAL
NO. OF
HOURS
WORKED
DURING
THE
PAST
WEEK
WORKING
HOURS
PER DAY
NORMAL
WORKING
HOURS
DURING
THE PAST
WEEK
HOURLY
WAGE/
EARNINGS
LOG OF
HOURLY
EARNINGS MATCH
Officials of Government and Special-Interest Organizations 499 48 8 6 83.16667 4.420847 0
Corporate Executives and Specialized Managers 599 58 8 7.25 82.62069 4.41426 0
Supervisors 1499 48 8 6 249.8333 5.520794 0
Physicists Mathematical and Engineering Science Professionals 799 48 8 6 133.1667 4.891601 0
Life Science and Health Professionals 899 48 8 6 149.8333 5.009524 0
Teaching Professionals 499 48 8 6 83.16667 4.420847 1
Other Professionals 699 58 8 7.25 96.41379 4.568649 1
Physical Science and Engineering Associate Professionals 399 48 8 6 66.5 4.197202 1
Related Associate Professionals 299 19 8 2.375 125.8947 4.835446 1
Office Clerks 299 48 8 6 49.83333 3.908684 0
Customer Services Clerks 399 48 8 6 66.5 4.197202 0
Personal and Protective Service Workers 399 58 8 7.25 55.03448 4.00796 0
Models Salespersons and Demonstrators 499 48 8 6 83.16667 4.420847 1
Mining Construction and Related Trade Workers 499 39 8 4.875 102.359 4.628486 0
Precision Handicraft Printing and Related Trades Workers 299 39 8 4.875 61.33333 4.116323 1
Sales and Services Elementary Occupations 199 29 8 3.625 54.89655 4.005451 0
Agricultural Forestry Fishery and Related Laborers 399 48 8 6 66.5 4.197202 0
Laborers in Mining Construction Manufacturing and Transport 299 48 8 6 49.83333 3.908684 0
Armed Forces 699 58 8 7.25 96.41379 4.568649 0
UNIVERSITY OF SANTO TOMAS
73
APPENDIX 7: DATA FOR SOCIAL SCIENCES, BUSINESS & LAW (63)
OCCUPATION
BASIC
PAY
TOTAL
NO. OF
HOURS
WORKED
DURING
THE
PAST
WEEK
WORKING
HOURS
PER DAY
NORMAL
WORKING
HOURS
DURING
THE PAST
WEEK
HOURLY
WAGE/
EARNINGS
LOG OF
HOURLY
EARNINGS MATCH
Officials of Government and Special-Interest Organizations 1499 48 8 6 249.8333333 5.520794 1
Corporate Executives and Specialized Managers 1499 48 8 6 249.8333333 5.520794 1
General Managers or Managing-Proprietors 1499 58 8 7.25 206.7586207 5.331552 1
Supervisors 1499 48 8 6 249.8333333 5.520794 1
Physicists Mathematical and Engineering Science Professionals 499 48 8 6 83.16666667 4.4208466 0
Life Science and Health Professionals 299 48 8 6 49.83333333 3.9086841 0
Teaching Professionals 1499 48 8 6 249.8333333 5.520794 1
Other Professionals 1499 48 8 6 249.8333333 5.520794 1
Physical Science and Engineering Associate Professionals 399 48 8 6 66.5 4.1972019 0
Life Science and Health Associate Professionals 699 48 8 6 116.5 4.7578913 0
Teaching Associate Professionals 199 19 8 2.375 83.78947368 4.4283074 0
Related Associate Professionals 499 48 8 6 83.16666667 4.4208466 1
Office Clerks 399 48 8 6 66.5 4.1972019 1
Customer Services Clerks 599 48 8 6 99.83333333 4.6035021 1
Personal and Protective Service Workers 499 48 8 6 83.16666667 4.4208466 0
Models Salespersons and Demonstrators 499 48 8 6 83.16666667 4.4208466 1
Mining Construction and Related Trade Workers 499 48 8 6 83.16666667 4.4208466 0
Metal Machinery and Related Trades Workers 299 48 8 6 49.83333333 3.9086841 0
Precision Handicraft Printing and Related Trades Workers 599 48 8 6 99.83333333 4.6035021 0
Other Craft and Related Trades Workers 399 29 8 3.625 110.0689655 4.7011071 1
Stationary Plant and Related Operators 399 48 8 6 66.5 4.1972019 0
Machine Operators and Assemblers 399 48 8 6 66.5 4.1972019 0
Drivers and Mobile Plant Operators 499 48 8 6 83.16666667 4.4208466 0
Sales and Services Elementary Occupations 299 48 8 6 49.83333333 3.9086841 1
Agricultural Forestry Fishery and Related Laborers 199 48 8 6 33.16666667 3.5015454 0
Laborers in Mining Construction Manufacturing and Transport 299 48 8 6 49.83333333 3.9086841 0
Armed Forces 699 48 8 6 116.5 4.7578913 0
UNIVERSITY OF SANTO TOMAS
74
APPENDIX 8: DATA FOR SCIENCE (64)
OCCUPATION
BASIC
PAY
TOTAL
NO. OF
HOURS
WORKED
DURING
THE
PAST
WEEK
WORKING
HOURS
PER DAY
NORMAL
WORKING
HOURS
DURING
THE PAST
WEEK
HOURLY
WAGE/
EARNINGS
LOG OF
HOURLY
EARNINGS MATCH
Officials of Government and Special-Interest Organizations 1499 48 8 6 249.833 5.520794 0
Corporate Executives and Specialized Managers 1499 48 8 6 249.833 5.520794 0
Supervisors 399 48 8 6 66.5 4.1972019 0
Physicists Mathematical and Engineering Science Professionals 1499 48 8 6 249.833 5.520794 1
Life Science and Health Professionals 399 48 8 6 66.5 4.1972019 1
Teaching Professionals 299 48 8 6 49.8333 3.9086841 1
Other Professionals 1499 48 8 6 249.833 5.520794 0
Physical Science and Engineering Associate Professionals 499 48 8 6 83.1667 4.4208466 1
Life Science and Health Associate Professionals 599 48 8 6 99.8333 4.6035021 1
Teaching Associate Professionals 599 48 8 6 99.8333 4.6035021 0
Related Associate Professionals 799 48 8 6 133.167 4.8916015 0
Office Clerks 499 48 8 6 83.1667 4.4208466 0
Customer Services Clerks 599 48 8 6 99.8333 4.6035021 0
Personal and Protective Service Workers 499 48 8 6 83.1667 4.4208466 0
Models Salespersons and Demonstrators 299 48 8 6 49.8333 3.9086841 0
Mining Construction and Related Trade Workers 399 48 8 6 66.5 4.1972019 0
Metal Machinery and Related Trades Workers 399 48 8 6 66.5 4.1972019 0
Precision Handicraft Printing and Related Trades Workers 299 48 8 6 49.8333 3.9086841 0
Other Craft and Related Trades Workers 399 48 8 6 66.5 4.1972019 0
Machine Operators and Assemblers 399 48 8 6 66.5 4.1972019 0
Drivers and Mobile Plant Operators 399 48 8 6 66.5 4.1972019 0
Sales and Services Elementary Occupations 499 48 8 6 83.1667 4.4208466 0
Agricultural Forestry Fishery and Related Laborers 199 19 8 2.375 83.7895 4.4283074 0
Laborers in Mining Construction Manufacturing and Transport 299 48 8 6 49.8333 3.9086841 0
Armed Forces 399 48 8 6 66.5 4.1972019 0
Other Occupations Not Classifiable 599 48 8 6 99.8333 4.6035021 0
UNIVERSITY OF SANTO TOMAS
75
APPENDIX 9: DATA FOR Engineering, Manufacturing and Construction (65)
OCCUPATION
BASIC
PAY
TOTAL
NO. OF
HOURS
WORKED
DURING
THE
PAST
WEEK
WORKING
HOURS
PER DAY
NORMAL
WORKING
HOURS
DURING
THE PAST
WEEK
HOURLY
WAGE/
EARNINGS
LOG OF
HOURLY
EARNINGS MATCH
Officials of Government and Special-Interest Organizations 1499 19 8 2.375 631.1579 6.4475561 0
Corporate Executives and Specialized Managers 1499 48 8 6 249.8333 5.520794 1
General Managers or Managing-Proprietors 1499 48 8 6 249.8333 5.520794 1
Supervisors 1499 48 8 6 249.8333 5.520794 0
Physicists Mathematical and Engineering Science Professionals 1499 48 8 6 249.8333 5.520794 1
Life Science and Health Professionals 1499 48 8 6 249.8333 5.520794 0
Teaching Professionals 1499 48 8 6 249.8333 5.520794 0
Other Professionals 1499 48 8 6 249.8333 5.520794 0
Physical Science and Engineering Associate Professionals 799 48 8 6 133.1667 4.8916015 1
Life Science and Health Associate Professionals 399 48 8 6 66.5 4.1972019 0
Teaching Associate Professionals 399 29 8 3.625 110.069 4.7011071 0
Related Associate Professionals 599 48 8 6 99.83333 4.6035021 0
Office Clerks 399 48 8 6 66.5 4.1972019 0
Customer Services Clerks 699 48 8 6 116.5 4.7578913 0
Personal and Protective Service Workers 699 48 8 6 116.5 4.7578913 0
Models Salespersons and Demonstrators 399 48 8 6 66.5 4.1972019 0
Mining Construction and Related Trade Workers 399 48 8 6 66.5 4.1972019 1
Metal Machinery and Related Trades Workers 399 48 8 6 66.5 4.1972019 1
Precision Handicraft Printing and Related Trades Workers 499 48 8 6 83.16667 4.4208466 0
Other Craft and Related Trades Workers 699 48 8 6 116.5 4.7578913 0
Stationary Plant and Related Operators 499 48 8 6 83.16667 4.4208466 1
Machine Operators and Assemblers 399 48 8 6 66.5 4.1972019 1
Drivers and Mobile Plant Operators 399 48 8 6 66.5 4.1972019 1
Sales and Services Elementary Occupations 299 48 8 6 49.83333 3.9086841 0
Agricultural Forestry Fishery and Related Laborers 299 48 8 6 49.83333 3.9086841 0
Laborers in Mining Construction Manufacturing and Transport 299 48 8 6 49.83333 3.9086841 1
Armed Forces 1499 48 8 6 249.8333 5.520794 0
UNIVERSITY OF SANTO TOMAS
76
APPENDIX 10: DATA FOR AGRICULTURE (66)
OCCUPATION
BASIC
PAY
TOTAL
NO. OF
HOURS
WORKED
DURING
THE
PAST
WEEK
WORKING
HOURS
PER DAY
NORMAL
WORKING
HOURS
DURING
THE PAST
WEEK
HOURLY
WAGE/
EARNINGS
LOG OF
HOURLY
EARNINGS MATCH
Officials of Government and Special-Interest Organizations 1499 48 8 6 249.833333 5.520794 0
Corporate Executives and Specialized Managers 599 48 8 6 99.8333333 4.603502 0
General Managers or Managing-Proprietors 1499 58 8 7.25 206.758621 5.331552 1
Supervisors 1499 48 8 6 249.833333 5.520794 1
Physicists Mathematical and Engineering Science Professionals 899 48 8 6 149.833333 5.009524 0
Life Science and Health Professionals 1499 48 8 6 249.833333 5.520794 0
Teaching Professionals 799 48 8 6 133.166667 4.891601 0
Other Professionals 799 48 8 6 133.166667 4.891601 0
Physical Science and Engineering Associate Professionals 599 48 8 6 99.8333333 4.603502 0
Life Science and Health Associate Professionals 299 48 8 6 49.8333333 3.908684 0
Related Associate Professionals 499 48 8 6 83.1666667 4.420847 0
Office Clerks 299 48 8 6 49.8333333 3.908684 0
Customer Services Clerks 399 48 8 6 66.5 4.197202 0
Personal and Protective Service Workers 299 48 8 6 49.8333333 3.908684 0
Models Salespersons and Demonstrators 299 48 8 6 49.8333333 3.908684 0
Farmers and Other Plant Growers 199 19 8 2.375 83.7894737 4.428307 1
Animal Producers 199 48 8 6 33.1666667 3.501545 1
Forestry and Related Workers 199 39 8 4.875 40.8205128 3.709185 1
Fishermen 199 39 8 4.875 40.8205128 3.709185 1
Hunters and Trappers 199 39 8 4.875 40.8205128 3.709185 1
Metal Machinery and Related Trades Workers 499 48 8 6 83.1666667 4.420847 1
Other Craft and Related Trades Workers 299 48 8 6 49.8333333 3.908684 0
Machine Operators and Assemblers 399 48 8 6 66.5 4.197202 0
Drivers and Mobile Plant Operators 399 39 8 4.875 81.8461538 4.404841 0
Sales and Services Elementary Occupations 399 48 8 6 66.5 4.197202 0
Agricultural Forestry Fishery and Related Laborers 199 48 8 6 33.1666667 3.501545 1
Laborers in Mining Construction Manufacturing and Transport 299 48 8 6 49.8333333 3.908684 0
Armed Forces 1499 48 8 6 249.833333 5.520794 0
Other Occupations Not Classifiable 199 48 8 6 33.1666667 3.501545 0
UNIVERSITY OF SANTO TOMAS
77
APPENDIX 11: DATA FOR HEALTH & WELFARE (67)
OCCUPATION
BASIC
PAY
TOTAL
NO. OF
HOURS
WORKED
DURING
THE
PAST
WEEK
WORKING
HOURS
PER DAY
NORMAL
WORKING
HOURS
DURING
THE PAST
WEEK
HOURLY
WAGE/
EARNINGS
LOG OF
HOURLY
EARNINGS MATCH
Officials of Government and Special-Interest Organizations 1499 48 8 6 249.83333 5.52079403 0
Corporate Executives and Specialized Managers 1499 48 8 6 249.83333 5.52079403 0
Supervisors 1499 48 8 6 249.83333 5.52079403 0
Physicists Mathematical and Engineering Science Professionals 599 48 8 6 99.833333 4.60350213 0
Life Science and Health Professionals 399 48 8 6 66.5 4.19720195 1
Teaching Professionals 699 48 8 6 116.5 4.75789127 0
Other Professionals 899 48 8 6 149.83333 5.00952357 0
Life Science and Health Associate Professionals 399 48 8 6 66.5 4.19720195 1
Related Associate Professionals 599 48 8 6 99.833333 4.60350213 1
Office Clerks 499 48 8 6 83.166667 4.42084663 0
Customer Services Clerks 499 48 8 6 83.166667 4.42084663 0
Personal and Protective Service Workers 299 48 8 6 49.833333 3.9086841 1
Models Salespersons and Demonstrators 299 48 8 6 49.833333 3.9086841 0
Metal Machinery and Related Trades Workers 599 29 8 3.625 165.24138 5.10740731 0
Drivers and Mobile Plant Operators 399 39 8 4.875 81.846154 4.40484131 0
Sales and Services Elementary Occupations 499 48 8 6 83.166667 4.42084663 0
Agricultural Forestry Fishery and Related Laborers 299 48 8 6 49.833333 3.9086841 0
Laborers in Mining Construction Manufacturing and Transport 399 48 8 6 66.5 4.19720195 0
UNIVERSITY OF SANTO TOMAS
78
APPENDIX 12: DATA FOR SERVICES (68)
OCCUPATION
BASIC
PAY
TOTAL
NO. OF
HOURS
WORKED
DURING
THE
PAST
WEEK
WORKING
HOURS
PER DAY
NORMAL
WORKING
HOURS
DURING
THE PAST
WEEK
HOURLY
WAGE/
EARNINGS
LOG OF
HOURLY
EARNINGS MATCH
Officials of Government and Special-Interest Organizations 1499 19 8 2.375 631.16 6.44756 1
Corporate Executives and Specialized Managers 799 48 8 6 133.17 4.8916 0
Supervisors 499 48 8 6 83.167 4.42085 1
Physicists Mathematical and Engineering Science Professionals 699 48 8 6 116.5 4.75789 0
Life Science and Health Professionals 499 48 8 6 83.167 4.42085 0
Teaching Professionals 1499 48 8 6 249.83 5.52079 0
Other Professionals 599 48 8 6 99.833 4.6035 0
Physical Science and Engineering Associate Professionals 399 48 8 6 66.5 4.1972 0
Life Science and Health Associate Professionals 399 48 8 6 66.5 4.1972 0
Teaching Associate Professionals 799 48 8 6 133.17 4.8916 0
Related Associate Professionals 499 48 8 6 83.167 4.42085 1
Office Clerks 499 48 8 6 83.167 4.42085 0
Customer Services Clerks 299 48 8 6 49.833 3.90868 1
Personal and Protective Service Workers 1499 48 8 6 249.83 5.52079 1
Models Salespersons and Demonstrators 299 48 8 6 49.833 3.90868 0
Mining Construction and Related Trade Workers 399 48 8 6 66.5 4.1972 0
Metal Machinery and Related Trades Workers 499 48 8 6 83.167 4.42085 0
Precision Handicraft Printing and Related Trades Workers 199 48 8 6 33.167 3.50155 0
Other Craft and Related Trades Workers 199 48 8 6 33.167 3.50155 0
Machine Operators and Assemblers 399 58 8 7.25 55.034 4.00796 0
Drivers and Mobile Plant Operators 399 48 8 6 66.5 4.1972 0
Sales and Services Elementary Occupations 299 48 8 6 49.833 3.90868 0
Agricultural Forestry Fishery and Related Laborers 299 48 8 6 49.833 3.90868 0
Laborers in Mining Construction Manufacturing and Transport 299 48 8 6 49.833 3.90868 0
Armed Forces 799 48 8 6 133.17 4.8916 1