SOCIAL EDUCATION FOR economics-wp199.pdf

ADB Economics
Working Paper Series
Education Outcomes in the Philippines
Dalisay S. Maligalig, Rhona B. Caoli-Rodriguez,
Arturo Martinez, Jr., and Sining Cuevas
No. 199 | May 2010

ADB Economics Working Paper Series No. 199
Education Outcomes in the Philippines
Dalisay S. Maligalig, Rhona B. Caoli-Rodriguez,
Arturo Martinez, Jr., and Sining Cuevas
May 2010
(Revised: 17 January 2011)
Dalisay Maligalig is Principal Statistician; and Rhona Caoli-Rodriguez, Arturo Martinez, and Sining Cuevas are
Consultants at the Development Indicators and Policy Research Division, Economics and Research Department,
Asian Development Bank. This study was carried out under Regional Technical Assistance (RETA) 6364:
Measurement and Policy Analysis for Poverty Reduction. The authors benefited greatly from the insightful
comments of Anil Deolalikar, Socorro Abejo, Jesus Lorenzo Mateo, and Joel Mangahas. They also thank the
Philippine National Statistics Office and the Department of Education’s Research and Statistics Division for
providing the datasets used in this study. Any remaining errors are the authors’.

Asian Development Bank
6 ADB Avenue, Mandaluyong City
1550 Metro Manila, Philippines
www.adb.org/economics
©2010 by Asian Development Bank
May 2010
ISSN 1655-5252
Publication Stock No. WPS102229
The views expressed in this paper
are those of the author(s) and do not
necessarily reflect the views or policies
of the Asian Development Bank.
The ADB Economics Working Paper Series is a forum for stimulating discussion and
eliciting feedback on ongoing and recently completed research and policy studies
undertaken by the Asian Development Bank (ADB) staff, consultants, or resource
persons. The series deals with key economic and development problems, particularly
those facing the Asia and Pacific region; as well as conceptual, analytical, or
methodological issues relating to project/program economic analysis, and statistical data
and measurement. The series aims to enhance the knowledge on Asia’s development
and policy challenges; strengthen analytical rigor and quality of ADB’s country partnership
strategies, and its subregional and country operations; and improve the quality and
availability of statistical data and development indicators for monitoring development
effectiveness.
The ADB Economics Working Paper Series is a quick-disseminating, informal publication
whose titles could subsequently be revised for publication as articles in professional
journals or chapters in books. The series is maintained by the Economics and Research
Department.

Contents
Abstract v
I. Introduction 1
II. Conceptual Framework 3
A. Data Sources 5
B. Statistical Models 13
III. Results 18
A. Individual Education Outcomes 18
B. School Outcomes 21
C. Quality of Education Outcomes 23
IV. Policy Implications 26
A. Deployment of Teachers and Effective Class Size 26
B. Decentralization 30
C. On Making Access to Primary Education Equitable 32
D. On Working Children 36
E. Other DepEd Programs to Keep Children in School 38
F. On Gender Disparity 39
G. Age of Official Entry to Primary School 40
V. Conclusions and Recommendations 41
Appendix 1: Education for All Targets and Accomplishments, Primary Education 47
Appendix 2: Indicators from Basic Education Information System 48
Appendix 3: Preliminary Analysis—APIS 50
Appendix 4: Reasons for Not Attending School 52
References 58

Abstract
This paper identifies key determinants of individual, school, and quality of
education outcomes and examines related policies, strategies, and project
interventions to recommend reforms or possible reorientation. Two sets of data
were used: (i) data on school resources and outputs from the administrative
reporting systems of the Department of Education; and (ii) the 2002, 2004,
and 2007 Annual Poverty Indicator Surveys. Analysis of individual, school,
and quality of education outcomes showed that although school resources
such as pupil–teacher ratio is a key determinant for both individual and school
outcomes, and that per capita miscellaneous operating and other expenses are
significant factors in determining quality of education outcome, socioeconomic
characteristics are stronger determinants. Children of families in the lower-income
deciles and with less educated household heads are vulnerable and less likely
to attend school. Girls have better odds of attending school than boys. Working
children, especially males, are less likely to attend secondary school. On the
basis of these results, recommendations in the areas of policy and programs
are discussed to help address further deterioration, reverse the declining trend,
and/or sustain gains so far in improving basic education system performance
outcomes.

I. Introduction
Filipino parents value education as one of the most important legacies they can impart
to their children. They believe that having a better education opens opportunities that
would ensure a good future and eventually lift them out of poverty. Thus, they are willing
to make enormous sacrifices to send their children to school (Dolan 1991, De Dios 1995,
LaRocque 2004). However, with a poor family’s severely limited resources, education
tends to be less prioritized over more basic needs such as food and shelter. Hence, the
chances of the family to move out of poverty are unlikely. It is therefore, important that
the poor be given equitable access to education.
The 1987 Philippine Constitution declares that education, particularly basic education, is
a right of every Filipino. On this basis, government education policies and programs have
been primarily geared toward providing access to education for all. The Philippines is
committed to the World Declaration on Education for All (EFA) and the second goal of the
Millennium Development Goals (MDG)— to achieve universal primary education by 2015.
EFA’s framework of action has six specific goals in the areas of: (i) early childhood care
and education (ECCE); (ii) universal primary/basic education; (iii) life skills and lifelong
learning; (iv) adult literacy; (v) gender equality; and (vi) quality. In line with this framework
of action, the Philippine EFA 2015 National Action Plan (UNESCO 2010) adopted in 2006
was formulated as the country’s master plan for basic education.
In 2000, the Philippines reported that it has achieved substantial improvement in terms
of access to basic education, but still faces challenges in the areas of early childhood
care and development, internal efficiency, and learning outcomes (NCEFA 1999).
Through the government’s efforts to achieve the 2015 MDG targets, recent studies such
as the Philippines Midterm Progress Report on the MDGs (NEDA and United Nations
Country Team 2007, Table 1) assess that the probability of achieving universal primary
education (MDG 2) in the country is low (based on net enrollment rate, cohort survival
rate, and completion rate). Similarly, the 2009 EFA Global Monitoring Report (UNESCO
2008) identified the Philippines to be among the countries with decreased net enrollment
rate from 1999 to 2006, and with the greatest number of out-of-school children (more
than 500,000). The Philippines’s current performance in education based on the trends
identified by the EFA and MDG indicators as shown in Appendix Table 1 is not also
promising. It is quite likely that the EFA and MDG targets will not be met by 2015.

Overall, the Philippines has suffered a setback in most education outcome indicators.
Although signs of recovery have been registered by some indicators, national targets for
key EFA indicators such as intake and enrollment rates will still likely be missed in 2015.
How can the decline in the performance of EFA indicators of education outcomes be
averted and improvements in those that registered recovery be sustained? This paper
aims to address this question by identifying key determinants of selected major education
outcomes, and on this basis, examine concomitant or related policies, strategies, and
project interventions for purposes of recommending reforms or possible reorientation.
Previous studies have suggested that poverty incidence (socioeconomic status),
government expenditure on education (as a percentage of gross domestic product
[GDP]) and pupil–teacher ratio (PTR) are key determinants of school attendance or net
enrollment rate. Except for a few studies covering a specific area in the country, most
related studies in the Philippines examine the relationships of education outcomes and
inputs using exploratory correlations and regressions of inputs and factors that may
affect education outcomes. These studies do not have an explicit theoretical model to
guide the analysis, and hence could be considered to have been done on a piecemeal
basis, without being able to explore the relationships of all the major factors in one
comprehensive analysis. For example, Maligalig and Albert (2008) concluded that there
is evidence that government expenditure on education and poverty incidence are directly
related to net enrollment ratio, but failed to ascertain the degree of the relationships as
well as the efficacy of other factors that may affect school enrollment.
There are many other methods that could be employed in identifying key determinants
of education outcomes, such as the education production function, which has been
used by many studies cited throughout this paper. Another method is the randomized
evaluations that have already been done in other countries like Kenya, Nicaragua,
and United States; or the natural experiments study conducted in Indonesia by Duflo
(2001); or the qualitative methods that are being conducted as part of the Trends
in International Mathematics and Science Study. The education production function
approach usually refers to a mathematical equation between outcomes and inputs and
a statistical method for estimating those relationships. The success of this approach
is contingent upon available data and the application of suitable statistical methods in
estimating the production function. Both randomized evaluation and natural experiments
render controlled comparisons. However, both require extensive planning prior to the
implementation of the study.
For the purposes of this study, as randomized evaluations and natural experiment were
not possible, key determinants of education outcomes were identified by estimating an
education production function based on the combination of data from the Department of
Education (DepEd) administrative reporting systems, and the Annual Poverty Indicator
Survey (APIS) conducted by the National Statistics Office (NSO) in between the Family
2 | ADB Economics Working Paper Series No. 199

Income and Expenditure Survey (FIES). Section II of this paper identifies the conceptual
framework that was used; Section III presents the results; while Section IV discusses the
policy implications. The last section presents the conclusions and recommendations of
the study.
II. Conceptual Framework
Many studies on the determinants of education outcomes are based on an education
production function that defines a mathematical relationship between inputs and education
outcome1 Y such as
Y Y I F R e
= ( ) +
, , (1)
where Y is a function of I and F, which are individual characteristics and family
socioeconomic factors, respectively, R is school resources, and e represents unmeasured
factors influencing schooling quality. Depending on the availability of data, this
mathematical relationship is estimated using suitable statistical models, of which the
best is identified through evaluation of the model’s goodness of fit and adherence to
assumptions.
The output of an education production function is usually some achievement that can
be measured through indicators. Among these are intake and enrollment rates, cohort
survival rate, dropout rate, and repetition rate, which are all EFA indicators. Another
key education outcome indicator is the learning achievement rate or learning outcomes
usually measured through national standardized tests.
The education production function described in equation (1) requires both measures of
individual and family socioeconomic characteristics as well as school resources. Previous
studies in the Philippines as well as in other countries indicate that there are individual
and household characteristics that influence children’s participation and performance in
basic education (Bacolod and Tobias 2005, DeGraff and Bilsborrow 2003, UIS 2005).
These studies suggest that family background and socioeconomic factors are as
important as school resources in determining whether a child will attend school, survive,
and complete an education level, and achieve an acceptable level of learning outcome.
In fact, Hanushek (1986) concluded that socioeconomic factors are stronger determinants
compared to school resources.
Individual characteristics such as age, sex, and parents’ educational attainment are
important factors in achieving better education outcomes. For example, based on the
1 In economic theory, this should be output, which is the result of the production function, while outcome would be the utility of
the output. However, in this study, output and outcome are used interchangeably.
Education Outcomes in the Philippines | 3

2004 APIS, Maligalig and Albert (2008) concluded that, assuming all other factors stay
the same (ceteris paribus), boys are 1.39 times more likely not to attend school than
girls. Similarly, in examining Indonesia’s 1987 National Socioeconomic Survey, Deolalikar
(1993) found that males have significantly lower returns to schooling than females at
the secondary and tertiary levels. The returns to university education are 25% higher for
females than males. Deolalikar also cited some evidence that older household heads and
better-schooled female household heads provide relatively more schooling opportunities
for their female relatives. Furthermore, community characteristics such as proportion of
villages in the district of residence having access to all-weather roads, access by water,
lower secondary school, etc. have relatively few significant effects on school enrollment.
School resources, on the other hand, are typically the basic inputs in education, the
most fundamental being the classrooms and teachers. Other important inputs are the
curriculum, textbooks and other instructional materials, water and sanitation facilities such
as toilets, libraries, and science laboratories. Bacolod and Tobias (2005) find that the
presence of electricity is an important school input positively affecting learning outcome in
Cebu. As measure of school quality, school resources are expressed as PTR and pupil–
classroom ratio, among others.
Previous studies have mixed observations on the effects of school resources on
education outcomes. Case and Deaton (1999) found that prior to the democratic elections
in South Africa in 1999 and conditional on age, lower test scores, and lower probabilities
of being enrolled in education, schools with high PTRs discourage educational attainment.
In their study of time series data from 58 countries, Lee and Barro (2001) found strong
relationships between measures of school resources and measures of outcomes such
as subject test scores, dropout rate, and repetition rate. On the other hand, Hanushek
and Kimko (2000) concluded, based on data from 39 countries, that traditional measures
of school resources such as PTR and per capita education expenditures do not have
strong effects on test performance. Also, Hoxby (2000) on her study of 649 elementary
schools in the United States concluded that reduction in class size has no effect on
students’ achievement. Hanushek (2003) compiled 376 production functions from 89
individual publications on education outcomes across the United States and concluded
that the evidence on the PTR as an important determinant of education outcomes is
not conclusive. These studies, however, differ on the statistical methods and data used.
The suitability of the econometric methods was not considered nor was data quality
examined. As Case and Deaton (1999) have pointed out, many of these studies were
concerned with the estimation of detailed educational production functions that try to sort
out effects of different resources on education such as PTR, textbook-to-student ratio,
pupil–classroom ratio, school buildings, presence of library, per capita expenditure on
education, among others.

A. Data Sources
Education production functions will be modeled using two major sources: (i) the 2002, 2004,
and 2007 APIS conducted by the NSO; and (ii) administrative data obtained from the Basic
Education Information System (BEIS) and the National Educational Testing and Research
Center (NETRC) of DepEd as well as from its budget appropriations.
The first source of data consists of three rounds of APIS that used almost the same
questionnaire. These surveys are of national coverage with regions as domains,
barangays or enumeration areas as primary sampling units, and housing dwellings as the
ultimate sampling units. Households in the selected housing dwellings are enumerated on
the household’s income and expenditures and the socioeconomic characteristics of each
member of the household. A responsible adult in the household was asked about each
member’s age, sex, educational attainment, school attendance, reason for not attending
school, as well as household income and expenditures, among others. More than 50,000
households were surveyed covering the 85 provinces in the Philippines.
The APIS is undertaken during the intervening years of the FIES. Beginning 2004, the
2003 master sample design was used for all household surveys of national coverage
including APIS. The basis of the sampling frame for the 2003 master sample is the 2000
Census of Population and Housing as well as results of past national surveys, such as
the 2000 FIES, the 2001 Labor Force Survey, and the 1997 Family Planning Survey.
Administrative data from DepEd’s reporting systems stored at the division level could
either be from a province or an independent city. For purposes of consistency with APIS,
the province was set as the unit of analysis. Data were on the most recent five years
(2002–2007).
The APIS gathers information on the demographic, economic, and social characteristics
of households, which include health and education data on each family member. Data on
education include school attendance, highest educational attainment, and reasons for not
attending school. Among the cited reasons for absence from school are cost of education,
distance between home and school, availability of transportation, existence of illness or
disability, and whether the member is working or looking for work (Appendix 4).
BEIS was established in 2002 to improve the monitoring and evaluation of basic
education performance. Prior to BEIS, the basic education data system was laden with
an almost 3-year backlog. The BEIS significantly reduced data backlog with its quicker
consolidation and validation process. It includes data on school inputs (number of
teachers, classrooms, other school facilities) and outcome indicators crucial in assessing
basic education performance in terms of access, internal efficiency, and quality. For
school resources, the BEIS uses a color coding system that indicates the status of
divisions and even schools with respect to these resources.

The BEIS uses three modules. Module I is the Quick Count Module, which gets total
data from the schools (e.g., total enrollment, total number of teachers etc.) by the end of
December every year. The information is used for planning and budgeting for the next
school year. Module II is the School Statistics Module, which collects school data in detail
(e.g., enrollment by grade/year, age profiles of enrollees, etc.). This module is designed
to collect information from both public and private schools. Module III is the Performance
Indicators Module, which processes the data and presents the outcome indicators.
Figure 1 describes the BEIS data collection process. Annual data collection starts upon
the issuance of a DepEd order to collect public school profiles. The order is disseminated
down to the schools where base data on enrollment, dropouts, repeaters, number of
classrooms, teachers, etc. are manually recorded using annual data gathering forms
(government school profile forms for elementary and secondary levels) under Module
II. These forms are submitted to the division offices where they are encoded and
consolidated in MS Excel files. The division offices are also responsible for validating
the accuracy of information with the schools before they are submitted to the regional
offices for further consolidation. The regional offices then submit the data to the central
office’s Research and Statistics Division, which maintains and updates the BEIS annually,
processes the data, and presents the outcome indicators under Module III. The data
remains in MS Excel files that because of their bulk cannot be uploaded on the DepEd’s
website. Researchers and other users can only access from the internet a one-page fact
sheet on basic education statistics showing the national aggregates of major indicators
for the last 5 years. The researchers may obtain more information from the BEIS through
a written request addressed to the Research and Statistics Division, which provides the
information in soft copy. The BEIS is also internally accessible among DepEd’s various
offices and units through its local area network.
Figure 1: DepEd-BEIS Data Source and Collection
National Level: consolidation in BEIS; interpretation, evaluation, and reporting
Regional Level: consolidation of divisional data into regional data
Division Level: consolidation of school data; validation of data with the schools;
computation of gross and net intake rate; computation of gross and net
enrollment rates
School Level: collection of data on enrollment, existing resources, resource gaps,
drop-outs, repeaters; computation of pupil-teacher ratio, pupil−classroom ratio,
drop out rate, repetition rate, cohort survival rate

The DepEd intends to continuously improve BEIS. Under the BESRA, a proposal for
Enhanced BEIS is being explored. This involves developing an automated database
system where even data down the schools (School Information System) can be accessed
from the web. Moreover, DepEd is currently in the process of adopting an ICT-based data
collection scheme that will put in place effective quantitative and qualitative data collection
as well as student tracking systems.
Gross and net intake rates, gross and net enrollment rates, dropout rate, repetition rate,
and cohort survival rate are the key outcome indicators estimated and compiled by BEIS.
These indicators gauge the level of the children’s access to formal basic education and
the school effectiveness in keeping the children.
Indicators such as repetition rate, dropout rate, cohort survival rate, PTR, etc. are
computed based on actual intake and year-to-year enrollment. As such they can be
estimated at the school level and aggregated upward to district, division, regional,
and national levels. Intake and enrollment rates, however, can only be computed
at the division level based on the consolidated actual enrollment data, because the
disaggregation of population estimate from the NSO are available down to the division
level only.
The gross intake rate is the total number of enrollees in Grade 1, regardless of age,
expressed as a percentage of the population in the official primary education entry age,
which is currently 6 years old. On the other hand, net intake rate accounts for Grade 1
enrollees expressed as a percentage of the 6-year-old population. The gross enrollment
rate is defined as the total number of children, regardless of age, enrolled in a particular
education level, measured as a proportion of the age group corresponding to that
level. Meanwhile net enrollment rate (NER) accounts for the participation of children
who fall within a defined official school-age group.2 While the gross enrollment rate
reflects total participation and, to some extent, the capacity of the education system, the
net enrollment rate is indicative of both the quantity and quality of education system
performance and effectiveness with respect to the target age group.
2 Gross enrollment rate can be more than 100% as they include underaged and overaged children but unlike net enrollment
rate it does not reflect the quality of participation of the official school-age group. In a desirable situation, NER should be or
approaching 100%. It should be noted that values exceeding 100% are recorded in areas/divisions such as Pasig City and Cebu
City and other highly urbanized areas. One possible reason for such condition is that children from neighboring divisions
(usually from the province where the city is or from the peripheral provinces) also attend schools in these cities/divisions,
thus, the enrollment exceeds the school-age population in the host division. But it does not mean that the division has 100%
participation. For additional discussion on NER, refer to Box 1.

Box 1: Investigating the Accuracy of the Philippines’s Net Enrollment Rate
One of the key education indicators is the net enrollment rate (NER), which is chiefly used to
measure developments in primary education. In fact, both the EFA and MDG programs utilize
this to evaluate the progress in their respective Goal 2 objectives. On the basis of the NER
current trends (Box Figure 1), it is projected that the Philippines will not likely attain universal
primary education by 2015.
The NER is the ratio of the enrollment for the age group corresponding to the official school
age in the elementary/secondary level to the population of the same age group in a given year.
The official school-age population for the primary level in the Philippines is 6–11 years; thus, in
order to estimate for the NER, the total enrolled students aged 6-11 must be divided by the total
population of the same age group. In theory, NER should range from 0 to 100%. However, in
practice, as shown in Box Figure 2 where the box plots of NERs of provinces and independent
cities are shown, there are many data points with more than 100% NERs.
This situation merits a closer look at how the data are compiled. There are three possible
sources of errors: (i) the population projections in the 6–11 age group in provinces and cities
are not accurate; (ii) the total enrollment of ages 6–11 is not properly captured; or (iii) there are
many cross-provincial enrollees for some provinces and these are not captured at all in the
DepEd administrative reporting system (BEIS).a
Box Table 1 shows the comparison between APIS and DepEd data. The figures for total
population in the 6–11 age group that DepEd used to compute NER grew at a steady 2.34%
annually from 2002 to 2006 and dropped by 0.14% in 2007. The constant growth rate for 2002
to 2006 is equal to the national annual average population growth rate that the NSO computed
on the basis of the 1995 and 2000 Census of Population and Housing. To derive the 6–11
population in 2007, DepEd then adjusted the growth rate used and applied the average annual
growth rate from 2000 to 2007b on the 2000 Census 6–11 population. With a lower growth
a This can only be validated by a special survey that captures the school location and residence of the children of respondent
households. There is no strong evidence, however, to suggest that there is a significant number of cross-provincial enrollees.
b 2000 and 2007 are census years.
continued.
92
90
88
86
84
82
80
78
90.3
88.7
87.1
84.4
83.2
84.8
2002 2004 2005 2007
2003 2006
Box Figure 1: Net Enrollment Rate
Trend, 2002−2007 (percent)
250
200
150
100
50
2002 2004 2005 2007
2003 2006
Box Figure 2: Net Enrollment Rate
Distribution, 2002–2007 (percent)

Box 1. continued.
rate basis of 2.04%, the 2007 population consequently exhibited a declining trend since the
adjustment was not back-tracked. Usually, when new census figures become available, the
population projections are also updated. This is not yet the case in the current NER.
Therefore, the use of 2007 Census of Population and Housing estimates without back tracking
the series may have caused an artificial increase in the 2007 NER.
Box Table 1: Total Population and Enrollment of Children Aged 6−11, 2002−2007
Year Population, Aged 6–11
(millions)
Total Enrollment,
Aged 6–11 (millions)
NER
(%)
Growth (DepEd)
(%)
APIS DepEd APIS DepEd APIS DepEd Popu-
lation
Enrollment
2002 11.76 12.00 10.37 10.83 88.2 90.3 … …
2003 … 12.28 … 10.90 … 88.7 2.34 0.59
2004 12.59 12.57 11.11 10.95 88.2 87.1 2.34 0.45
2005 … 12.86 … 10.86 … 84.4 2.34 -0.80
2006 … 13.16 … 10.95 … 83.2 2.34 0.86
2007 13.04 13.14 11.59 11.15 88.9 84.8 -0.14 1.81
... means not available or not applicable.
Note: Annual population growth is 2.34% for 1995–2000 based on the 2000 census; and 2.04% for 2000–2007 based
on the 2007 census.
Another point investigated is the use of national population growth estimates instead of age-
specific population growth rates. The 2.34% growth rate applied by DepEd to the 2002–2006
population is the 1990–2000 average annual growth rate of the Philippines. Similarly, the
2.04% growth used for the 2007 estimate is the also the rate at the national level for the years
2000–2007. However, if the national average annual population growth rate projections for
2001–2005 is to be computed, it is only about 2.1%. And if the estimation is to be age–specific,
the average annual population growth rate for the 6–11 age group is only about 1.04%.c These
two figures are lower than the 2.34% that DepEd employed to project total population of ages
6–11. Box Figure 3 shows the various NER trends based on (i) the 2.34% population growth
rate used by DepEd for 2002–2006; (ii) the 2.04% rate if the population adjustment will be back
tracked; and (iii) the 1.04% rate, if the age-specific 6–11 growth rate is to be applied. Thus, the
type of population estimator used by DepEd has contributed to the rate of decline in NER from
2002 to 2006.
c Estimated based on the 2000 Census of Population and Housing population projections by age group that NSO publishes in
its website, and by assuming that the population counts are evenly distributed across ages in an age group.
continued.

To validate the total enrollment as compiled by BEIS, similar estimates from the Annual
Poverty Indicator Survey were derived. The APIS is a survey of national coverage that the
NSO conducts in the intervening years of the Family Income and Expenditure Survey. All
family members are asked about his/her age, whether he/she is attending school and if not,
the reason for not doing so, among others. Hence, APIS could also provide estimates of the
population in the primary age group as well as the population in the same age group who
are in school. The total enrollment estimates from APIS are within acceptable error margin
(one standard error) compared to the DepEd’s total enrollment and hence, there is no strong
evidence that DepEd’s total enrollment data is not accurate.
It should be noted, however, that based on APIS data, a substantial number of 6-year-olds are
not yet in primary school even though by DepEd’s guidelines, the official age of entry to primary
school is at 6 years old. About 830,900 6-year-old children were not in primary school in 2007;
37.5% have not started school yet; while 62.5% were still in preschool. This is equivalent to
about 6.4% of the total population in the 6–11 age group. On the other hand, examination of the
composition of enrolled 7-year old students showed that, although by DepEd guidelines, they
should be in the Grade 2 level, most of them are still in Grade 1. In 2002, half of the 7-year olds
who are enrolled are in Grade 1. And although this proportion steeply declined in 2004, it rose
again in 2007 resulting to a nearly equal number of 7-year-old students in Grade 1 and Grade 2.
This is an unexpected occurrence since it is anticipated that because DepEd has implemented
its guidelines on the official age of entry to primary school in 1995, the number of enrolled 7
year-olds in Grade 1 should have been declining since then. These findings suggest that though
the official school age starts at 6 years, there is still a significant percentage of families sending
their children to primary school at a later year, thus contributing to the “artificial” decline of the
NER.
Box Figure 4 shows the APIS and DepEd estimates of NER, which is another form of validation
that was used. While DepEd’s NER is steadily declining, the equivalent APIS indicator remained
steady between 2002 and 2004, and showed a slight increase by 2007.
Box 1: continued.
92
90
88
86
84
82
80
78
2002 2004
NER at 2.34% population growth NER at 2.04% population growth
NER at 1.04% population growth
2005 2007
2003 2006
Percent
Box Figure 3: Comparative NERs Based on Alternative Population Growths
continued.

Box Figure 4: NER Trends, 2002–2007 (percent)
92
90
88
86
84
82
80
78
DepEd
APIS 6-11
2002 2004 2005 2007
2003 2006
90.3
88.2
88.7 88.2
87.1
84.4
83.2
84.8
88.9
The four indicators discussed above—NER, gross enrollment rate, net intake rate,
gross intake rate—are compiled in BEIS at the division level using data from schools
as numerator and as denominator, the population projections for the corresponding age
groups from the NSO. A closer examination (see Box 1) of the net enrollment rate, which
is the main indicator for universal primary or universal basic education goals of both EFA
and MDG, reveals that there are flaws in the estimation process. For example, the fast
decline of NER as reflected in the BEIS data series seems to be caused by the higher
population projections from NSO.
Once the children are in school, the next order of business is how to keep them engaged
so that they are able to acquire the identified skills and levels of competencies defined
in the curriculum. How well the schools can keep the children from leaving before
completing a particular education level gauges the school’s internal efficiency. Indicators
of internal efficiency include cohort survival rate, dropout rate, and repetition rate. The
cohort survival rate in a certain education level is the percentage of a cohort of pupils/
students enrolled in the first year of that level who reach the last grade/year of that
particular education level. It indicates the holding power of the school. A desirable pattern
is that it should approach 100% and that its movement should have a negative relation
with the dropout rate.
Distortions in cohort survival rate are mainly the result of high dropout and repetition
rates. Dropout rate accounts for those pupils/students who leave school during the year
and those who complete the previous grade level but do not enroll in the next grade/
year level the following school year. It is expressed as a percentage of the total number
of pupils/students enrolled during the previous school year. Repetition rate serves to
measure the occurrence of pupils/students repeating a grade. It is technically defined as
Box 1: continued.

the percentage of a cohort of pupils enrolled in a grade at a given schoolyear who study
in the same grade the following schoolyear.
The National Achievement Test (NAT) is the primary indicator of school effectiveness
based on pupil/student scores in subjects like language, science, and math. The NAT is
administered by DepEd through its National Educational Testing and Research Center,
whose functions include analysis and interpretation of data for policy formulation and
recommendation. Making a time-series comparison of NAT results from 2002 to 2007 is
problematic since the tests are administered at different grade or year levels annually.
The NAT was first administered in 2002 to Grade 4 and 1st year high school students. It
included a diagnostic component conducted at the start of schoolyear to determine the
academic weaknesses or learning gaps of the pupil/students based on the curriculum-
prescribed learning competencies at a particular level. The results of this diagnostic test
are compared with the achievement tests administered to the same group of pupils at
the end of the schoolyear to determine learning progress. In the following schoolyears,
however, the NAT was administered in different grades and years.
Two indicators of school resources that will be used in the models are the miscellaneous
operating and other expenses budget (MOOE) and the personnel salary (PS) budget.
The budgeting division, working closely with Office of Planning Services, computes for
the MOOE based on a formula (per capita student cost and school-based). They use
the quick count data from BEIS to estimate the next schoolyear’s enrollment and the
MOOE. However, they also request the regional offices to submit MOOE proposals that
they only use for validation purposes. The budget for PS is computed based on current
staff complement and increases only for new hires and promotions. Data on PS and
MOOE used in this study were taken from various Congress-approved Government
Appropriations Acts based on the National Expenditure Program proposed by the
government. Using the DepEd budget, however, does not present the complete basic
education financing because it does not account for the contributions of private schools,
which comprise 8% of total elementary school enrollment and 21% of secondary school
enrollment.
These data also do not include the contributions of the private sector and local
government units. DepEd has forged partnerships with private and business sectors
in projects such as Adopt-a-School and is implementing other private sector initiatives
that have resulted in valuable contributions that are also quantifiable but are not being
captured in the BEIS or by any DepEd unit. Local government units also contribute
significantly to basic resources needed by the schools. Among these local sources is the
Special Education Funds (SEF) coming from the 1% real property tax earned by local
governments and earmarked for basic education as provided for in the Local Government
Code. The SEF is used for construction and rehabilitation of classrooms as well as for
funding salaries of locally hired teachers.

The available administrative data do not include individual and household characteristics
of the pupils/students (e.g., socioeconomic status and ethnic or linguistic variation).
Moreover, accuracy is often an issue with administrative data, especially since the
collector and processor of information are also its main users. As a result, over-reporting
or under-reporting to influence decisions on funding and other incentives can happen
(UIS 2008).
A more rigorous study that is also the approach taken by this research is to combine
education administrative data with census or household surveys. Although often
conducted less regularly, household surveys provide more information on the
characteristics of individuals and households that often influence decisions related to
education services made available by the government. Corresponding to the two major
data sources described above, two datasets were constructed: (i) the household/individual
data that combines APIS and the provincial-level PTR; and (ii) provincial-level data that
consists of data from BEIS, NETRC, and the Financial Management System but which
also includes provincial-level indicators from APIS such as the proportion of females,
median educational attainment of the household head, and median household per capita
income.
B. Statistical Models
On the basis of the available data described above, a modeling framework was
developed (see Figure 2). In this framework, the decision to attend school is considered
as an investment that promises future returns. First, it is hypothesized that the decision
whether to attend school or not is mainly influenced by personal circumstances. The
process of deciding whether to attend school or not usually starts at the household
level and is depicted by the dotted arrows pointing directly from household, personal
resources, to the decision of attending school. Once the household decides to send
the child to school, there are different possible education outcomes that are measured,
such as dropout rate, survival rate, repetition rate, and NAT score, among others. These
education outcomes are directly influenced by education inputs, but household and
personal resources are also contributing factors.

Figure 2: Model Framework
Household, Personal
Resources
Education Inputs
(School Resources)
(Individual
Outcome)
Decision to
attend school
School Outcomes
Repetition
Rate
Dropout
Rate
Survival
Rate
NAT
Score
Individual outcome (decision to attend school) is modeled using a combination of the
household/individual data from APIS and the provincial PTR from BEIS. All school
outcomes including the quality of education outcome are modeled using the combined
administrative data and provincial estimates of key individual and household variables
from APIS.
In the case of the APIS dataset, for each year (2002, 2004, and 2007), a probability
sample is drawn and hence, the set of households and individuals in the data set were
selected randomly. Because of this, a random effects model is explored, such that
subject specific parameters αi
{ } are treated as draws from an unknown population
(and thus may be considered random). Moreover, the outcome that will be modeled for
this data set is school attendance, a binary variable that can be modeled suitably by a
logistic regression using random effects likelihood estimation. Unlike the administrative
dataset, individuals, which are the unit of analysis, are only measured once; therefore,
if individuals are considered the subject in the model, a longitudinal analysis approach
is not possible. However, since the regions are the domains of the APIS and housing
dwellings are drawn from clusters or primary sampling units from strata defined within
regions (but are not similar across regions), the random effects that can be accounted for
clustering of responses are within the domains (region) and across years, such that
ln
P y
P y
tdi td
tdi td
td
=
( )
=
( )








= + ′
1
0
α
α
α β
xtdi . (1)
where ytdi is the education outcome of the ith individual in region d and year t, ′
xtdi is the
corresponding vector of explanatory variables, and αtd
is the domain-specific nested in
time parameter representing heterogeneity across time and regions. The results of the
random effects model are also compared with that of the more commonly used ordinary
logistic model.

Three types of explanatory variables are considered in the models: (i) individual
characteristics such as sex and age; (ii) household characteristics such as household
per capita expenditure, and age and educational attainment of the household head;
and (iii) PTR at the provincial level representing school resources. The factor other than
household characteristics that could affect the parents’ decision to send their children
to school is their perception on the capacity of the school. A measure of this perception
that is available is PTR because in general, parents believe that their children would get
better education if the classrooms are not crowded. Other indicators of school resources
were considered but dropped from the model because they were not used by parents or
individuals in their decision to attend school or not. These are the proxy for the average
teacher’s salary and the per capita MOOE. Moreover, these two indicators cover only the
public school system and there are no corresponding data from the private schools.
For school education outcomes such as the NAT overall rating, NAT average test scores
in Science, Math, English, and Filipino; dropout rate; cohort survival rate; and repetition
rates were considered. Since the BEIS dataset is the major data source for modeling
these education outcomes, the unit of analysis was the province, since this is the lowest
disaggregation level at which the full set of data across the most recent 5 years is
available. Also, for most of the provinces, data have been recorded for the most recent
5 years. Thus, longitudinal analysis3 was conducted instead of cross sectional analysis.
Longitudinal analysis is more complex than regression or time series analysis but it has
the ability to study dynamic relationships and to model differences among subjects. It
can be shown that the educational outcomes significantly vary across provinces. Hence,
provincial-specific parameters will be included in the model such that
E yit i it
( ) = + ′
α β
x (2)
where αi is the ith province-specific parameters, yit is the educational outcome at year
t and province I, while xit is the vector of explanatory variables. These variables are
further described herein. There are two distinct approaches for modeling the quantities
that represent heterogeneity among the subjects (in this case, provinces) αi
{ }: (i) fixed-
effects model in which αi
{ } are treated as fixed yet unknown parameters that need to
be estimated and (ii) random effects model in which αi
{ } are treated as draws from an
unknown population and thus are random variables such that
E yit i i it
α α β
( ) = + ′
x (3)
Considering that measures from all provinces that are the subjects or units of analysis
are included in the datasets, and that provincial-level measures were derived from data
3 Longitudinal analysis is a combination of various features of regression (cross-section and time series analysis). It is
very much like regression analysis because it examines a cross-section of subjects (unit of analysis). On the other
hand, it is similar to time series because subjects are observed over time. In this paper, instead of using the 5-year
BEIS data, modeling is restricted for the years when APIS were conducted since some APIS variables were merged
in the BEIS data.

of all schools in the province, the possibility of a provincial measure to vary because of
a random draw (sample) can be eliminated and hence, fixed effects model is deemed
appropriate.
Since the education production function is not complete without socioeconomic
characteristics that are not found in BEIS or any other government administrative
reporting system, some provincial-level indicators from the APIS such as the proportion
of females, median education attainment of the household head, and median household
income were combined with the dataset. As a consequence, only 2002, 2004, and 2007
data were included in the final data set.
There are many situations in educational and behavioral research in which multiple
dependent variables are of interest. Usually, separate analyses are conducted for each
of these variables even though they are likely to be correlated and have similar although
not identical set of predictor variables. In this research, a good example would be the
average NAT scores for English, Science, and Math that are also available for most of
the provinces. These subject NAT scores are highly correlated and hence, to accurately
capture this situation, an alternative modeling approach, the seemingly unrelated
regression (SUR) was used. SUR is a technique for analyzing a system of multiple
equations with cross-equation parameter restrictions and correlated error terms.
The SUR technique estimates separate error variances for each equation; hence separate
R2’s can be computed. Numerous parameter restrictions employed in SUR, however,
may lead to negative R2. A potential advantage of its application in panel data analysis
is to allow for same parameter estimates of the fixed effects using different correlated
dependent variables. Further, it moves away from the potential problem that unbalanced
data may cause under fixed or random effects framework.
Since separate data series for primary and secondary schools are provided in the
administrative dataset, separate models for primary and secondary age groups were
derived and examined. To apply these models in the APIS dataset, the primary and
secondary age groups have to be designated. The issue of the official age of entry to
primary education arose in the process. Per DepEd’s policy, the official entry age to
formal primary education is 6 years old. However, preliminary analysis of APIS revealed
that a substantial numbers of 6-year-olds were not yet in school (21.5% for 2002, 17.5%
for 2004, and 15.2% in 2007) and a significant proportion is still in preschool (27.2% for
2002, 26% for 2004, and 25.3% for 2007) (Table 1).

Table 1: Age-Specific Enrollment Rates, APIS 2002, 2004, 2007 (percent)
Age 2002 2004 2007
Enrolled Pre-
school
Primary Secondary Enrolled Pre-
school
Primary Secondary Enrolled Pre-
school
Primary Secondary
6 78.55 27.18 51.37 – 82.5 25.96 56.54 – 84.8 25.33 59.48 –
7 93.91 2.97 90.94 – 94.02 3.46 90.56 – 94.19 3.07 91.12 –
8 96.78 0.89 95.89 – 96.87 0.69 96.18 – 96.2 0.5 95.7 –
9 97.86 0.33 97.53 – 97.37 0.18 97.19 – 97.32 0.26 97.06 –
10 97.79 0.15 97.53 0.11 96.79 0.18 96.61 – 96.83 0.04 96.79 –
11 97.84 0.01* 93.6 4.23 96.76 – 91.92 4.73 96.26 0.06* 91.3 4.9
12 94.87 0.01* 56.65 38.21 94.16 – 56.23 37.88 94.44 0.1* 52.76 41.58
13 92.41 – 22.37 70.04 90.62 – 23.32 67.21 90.36 0.05* 21.74 68.57
14 88.66 – 10.46 78.1 86.56 – 11.09 75.33 86.76 – 10.29 76.47
15 84.62 – 4.39 79.33 82.85 – 4.76 76.67 82.2 0.04* 4.91 74.09
16 74.32 – 2.3 57.87 70.72 – 2.28 53.45 66.97 – 2.06 43.47
17 60.12 0.03* 0.76 23.73 56.6 – 1.01 23.07 54.38 – 1.16 20.86
– Zero values.
* Nonzero values; suspected to be encoding errors.
Source: Authors’ computations using APIS 2002, 2004, and 2007.
In fact, both the DepEd administrative and APIS data across years (2002 to 2007)
showed that less than half of 6-year-old children are not yet in primary school. BEIS
reported that 63.36% of Grade 1 enrollees are older than 6 years. Of these overaged
Grade 1 pupils, 63.44% are 7 years old. Parents appear to postpone enrollment at 6
years old and tend to send their children to school when they get older. And since this
study does not aim to determine when the child is sent to school but the decision whether
the child is sent to school or not, the age groups that will be used for primary and
secondary school were 7–12 and 13–16 years old, respectively.
In addition to data availability and results of previous studies, endogeneity issues are
also considered in determining the explanatory variables that will be included in the
models. Explanatory variables—such as total enrollment, number of teachers, budget
for personnel salary and wages, and budget for miscellaneous operating and other
expenses—which also vary according to the school size and consequently, the size of
the province are taken out of the list and instead, corresponding variables that are not
robust to school size are considered, such as PTR, average teacher salary, and per pupil
MOOE. The median per capita household income, median educational attainment of the
household head, and proportion of females for the appropriate school age group that
were estimated from APIS at the provincial level represent the household and individual
characteristics.

Preliminary analysis of APIS data for 13–16-year-olds as presented in Table 2 shows
that a sizeable number of 13–16-year-olds are already working and may not be able to
attend school. Hence, a binary variable corresponding to working or not could be a good
explanatory variable for the secondary school age group individual outcome model. But
having work can be viewed as an outcome of a child’s time allocation process (Khanam
and Ross 2005), and in this case, a possible endogeneity problem may exist. Moreover,
it is difficult to identify the true effect of work on school attendance since the factors
that encourage children to work tend to be the same conditions that discourage school
attendance. These issues, however, do not apply in the case of the APIS dataset in which
each family member was asked for his/her reason for not attending school. One of the
major reasons cited is “already working”.
Table 2: Working 13–16-Year-Olds by Age and Sex
Year Age Total Population (thousands) Already Working (percent)
Male Female Total Male Female Total
2002
13 910.52 893.16 1,803.69 11.51 6.07 8.81
14 864.14 814.48 1,678.62 17.05 7.96 12.64
15 948.41 848.66 1,797.07 21.57 8.62 15.45
16 821.95 758.80 1,580.75 27.28 12.57 20.22
All 3,545.01 3,315.10 6,860.12 19.21 8.67 14.12
2004
13 1,011.76 980.78 1,992.54 11.09 6.10 8.64
14 974.99 903.81 1,878.80 17.43 7.02 12.42
15 960.09 1,006.47 1,966.56 22.68 7.98 15.16
16 957.82 944.84 1,902.66 29.68 10.85 20.33
All 3,904.66 3,835.89 7,740.55 20.09 7.98 14.09
2007
13 1,142.57 1,082.80 2,225.37 9.68 5.11 7.45
14 1,078.04 1,062.66 2,140.70 13.91 7.52 10.74
15 1,082.29 1,182.89 2,265.18 20.55 9.84 14.96
16 1,055.42 1,119.36 2,174.78 27.63 14.85 21.05
All 4,358.32 4,447.71 8,806.03 17.77 9.39 13.54
Note: Values may not add up to totals due to rounding off.
Source: Authors’ computations using APIS data.
III. Results
A. Individual Education Outcomes
Table 3 presents the best models for log odds of attending school for the 7–12 and 13–16
age group. For the primary age group, age, sex, per capita expenditure of the household,
highest educational attainment of the household head, and PTR are the significant
explanatory variables.

Table 3: Random Effects Models for Log Odds of Attending School
Explanatory Variablesa Random Effects Logistic
Age: 7–12 Age: 13–16 Age: 7–12 Age: 13–16
Age = 8 0.69** 0.69**
Age = 9 1. 00 ** 1.00**
Age = 10 0.93** 0.93**
Age = 11 0.79** 0.79**
Age = 12 0.21** 0.21**
Age = 14 (0.36)** (0.36)**
Age = 15 (0.68)** (0.68)**
Age = 16 (1.48)** (1.48)**
Sex (1 = male) (0.43)** (0.30)** (0.43)** (0.3)**
log(per capita household expenditure) 1.03** 0.86** 1.04** 0.86**
(1 = if household head is male) 0.02 0.07** 0.02 0.08*
Age of household head 0.00 0.01** 0.00 0.01**
(1 = if household head is working) (0.05) 0.23** (0.05) 0.24**
Highest educational attainment of household head 0.13** 0.11** 0.13** 0.11**
Pupil–teacher ratio (0.02)** (0.01)** (0.01)** (0.01)**
(1 = if child is working) (2.29)** (2.28)**
Variance (random intercept due to year differences) 0.05 0.05
Variance (random intercept due to regional
differences)
0.13 0.17
Log likelihood of model (13376.87) (18530.94) (13333.15) (18469.04)
Pseudo R2 based from simple logistic model 0.14 0.28
Rescaled R2 0.02 0.11
Number of observations 91243 57011 91243 57011
AIC 26783.75 37089.87 26726.29 36996.08
BIC 26925.07 37215.18 27008.93 37255.66
** means statistically significant at 5% (p-value is at most at 0.05); * means significant at 10% (p-value is at most 0.10).
0.0 means magnitude is less than half of a unit.
a Similar models were estimated incorporating sex-slope interaction with pupil–teacher ratio. The results are presented in
Appendix 3. The variable is significant for the primary school model but not for the secondary school model.
Note: P-value is the probability of observing an extreme or more extreme value for the test statistic under the null hypothesis
that the parameter coefficient for the variable under consideration is zero. Smaller p-values suggest statistical significance.
The models use random intercepts to incorporate random variations due to differences in years and regions where the
observations come from. Random effects are characterized by their variance components.
Statistical significance of random effects is not directly estimated. Note that some multilevel-structural estimation
methods such as this do not allow the use of weights. But a preliminary analysis on the ordinary logistic regression results
reveals that there is no substantive difference between weighted and unweighted models. Results provided above are all
unweighted.
The Rescaled R2 provides a measure of the improvement on the amount of variation captured by including fixed effects in
the model (i.e., the null log likelihood is estimated from a pure random intercept-model).
Source: Authors’ computations using BEIS and APIS data.
Assuming all other variables stay in the same level (ceteris paribus), the following
conclusions can be derived from the model:
(i) As the child gets older up to 9 years old, the more she/he would be likely in
school. However, the odds taper off after 9 years old. In fact, when the child
reaches 12 years old, for the elementary age group model, the odds of attending
school decreased dramatically. In particular, the odds of attending school at
age 12 is approximately half than that of age 9. Figure 3 provides a graphical
representation of age-specific enrollment rates.

(ii) Girls are
1
0 4342021
exp .
−
( )
or 1.54 times more likely to attend school than boys.
(iii) A 1% increase in per capita household expenditure will translate to about 1.03%
increase in the odds for attending school.
(iv) The more educated the household head, the better the odds of the child to be in
school. In fact, the odds of attending school increase by 13% for every year of
increase in the educational attainment of the household head.
(v) A unit increase in PTR will reduce the odds of attending school by 2%.
In the case of the model for secondary school age children, all the explanatory variables
were significant. However, in terms of magnitude of the coefficients, the explanatory
variable with the strongest influence is if the child is working or not. If the child is working,
the odds of him/her not attending school is 9.87 times greater than when he/she is not
working, all other variables being equal. Other results on ceteris paribus assumption are
as follows:
(i) Older children are less likely to be attending school. From age 13 to 16, the odds
of attending school uniformly decrease. The steep decline is noticeable especially
between age 15 and 16.
(ii) Girls are 1.35 times more likely to attend school than boys.
(iii) A 1% increase in per capita household expenditure translates to about 0.86%
increase in the odds for attending school.
(iv) The more educated the household head, the better the odds of the child to be
in school—around an 11% increase for every year of increase in the educational
attainment of the household head.
(v) The child in a household with a head who is working is 1.26 times likely to be
attending school than a child whose household head is not working.
(vi) A unit increase in PTR will reduce the odds of attending school by 0.8%.
To probe further the odds of attending school at a different age, we can examine Figure 3
in which the proportion of school attendance by age group for the 2002, 2004, and 2007
APIS is presented. This figure illustrates the shift in signs for age when modeling odds
of attending school. Until the age of 9 or 10, there seems to be an upward trend of age-
specific enrollment rates, thereafter, age-specific enrollment rate declines.

Figure 3: Age-Specific Enrollment Rates (percent)
100
90
80
70
60
7 8
2002 2004 2007
9 10
Age
11 12 13 14 15 16
B. School Outcomes
On the basis of variability of education outcomes across observations from the panel data
considered, dummy variables for time period (year) and provinces were introduced to
explain heterogeneity across years and the variation across provinces, respectively.
Tables 4 and 6 present the estimates of the coefficients of the models, the p-values of the
corresponding tests of significance, and other model diagnostics for school efficiency and
quality of education outcomes, respectively.
Except for survival rate in secondary schools, the models above have good R2 values,4
which for this type of statistical model is a good measure of fit. Note, however, that there
are two models—primary dropout rate and survival rate—that do not have significant
explanatory variables but have significant provincial effects, though not reflected in
the table. This implies that the variations of primary dropout rate and survival rate
are largely determined by the variations of the dependent variables across provinces.
These variations represent those explanatory variables that were omitted in the models.
For example, the quality of school management varies across provinces, as well as
the financial support of local government units. These explanatory variables were not
represented in the models because there were no readily available and comprehensive
measures to represent them.
4 R2 measures the proportion of variation of the dependent variable (in this case, education outcome) that is explained by the
model. R2 ranges from 0 to 1. If it nears 1 it implies that the model has adequately explained the variations in the dependent
variable.

Table 4: Fixed Effects Models for Dropout Rate and Survival Rate
Explanatory Variables Education Outcomes
log(dropout rate) log(survival rate)
Primary Secondary Primary Secondary
log(per pupil MOOE) (0.07) (0.10) 0.04* (0.11)
Pupil–teacher ratio 0.03 (0.01) (0.02)** (0.00)
log(teacher’s salary)a 0.03 (0.12) (0.01) 0.33**
Median household head
educational attainment
(0.00) (0.06)** (0.01) 0.01
Median provincial household per
capita income
(0.00) 0.00 0.00 0.00**
Proportion of females (0.62) (0.42) 0.00 (0.27)
2004 (0.02) 0.00 (0.00) 0.02
2007 (0.01) (0.00) 0.01 (0.00)
Test for heteroskedasticity 0.11 0.00 0.00 0.01
Adjusted R2 0.82 0.58 0.70 0.18
** means statistically significant at 5% (p-value is at most 0.05); * means significant at 10% (p-value is at most 0.10).
0.0 means magnitude is less than half a unit.
a Similar statistical models where the proxy variable for teacher’s salary was normalized as a proportion of provincial per capita
income were also estimated. Still at the 0.05 level, the variable is not statistically significant.
Note: Unit of analysis is province for the years 2002, 2004, and 2007.
P-value is the probability of observing an extreme or more extreme value for the test statistic under the null hypothesis
For models that do not satisfy constant variance assumption, robust standard errors are used and the corresponding
p-values are reported.
The results above are based on the traditional view of fixed effects models where the panel effects (in this case, provincial
effects) are treated as parameters to be estimated. Estimation of fixed effects model using dummy variable regression
usually leads to high R2.
On the basis of the estimated fixed effects computed from the models presented in
Table 4, the top and bottom provinces were identified and listed in Table 5. The fixed
effects represent the characteristics that are unique to the provinces and hence, it may
be beneficial to have a closer look at the best performers to identify why they were above
the rest; and also, to examine those that need improvement the most to identify the
characteristics that could be enhanced.
Table 5: Key Performers in Selected Primary School Efficiency Indicators
Best Performers Needs Improvement
Dropout Rate Cohort Survival Rate Dropout Rate Cohort Survival Rate
Bataan 2nd District Bohol Basilan
Batangas 3rd District Iloilo Lanao del Sur
Davao del Sur 4th District Northern Samar Negros Occidental
Misamis Oriental Bulacan Quirino Sarangani
Mt. Province Rizal Sultan Kudarat Sulu
Note: In coming up with the list, provinces are ranked according to the computed fixed effects.
Source: Authors’ computations using BEIS data.

As indicated above, the secondary cohort survival rate R2
0 1797
=
( )
. has the lowest
model fit. This implies that even with the provincial effects that were used to represent
omitted variables that vary by province, there are still explanatory variables (not varying
by province) that are lacking in the secondary cohort survival rate model. A strong
possibility is that secondary-age children chose not to stay in school and work instead as
shown in the model for individual outcomes (decision to attend school).
For secondary schools dropout rate, the significant explanatory variable is median
household head educational attainment. An increase of 1 year in the median educational
attainment of the household head would result into a 5.9 percentage point reduction of
the dropout rate. Similarly, an increase of Pesos (P) 1,000 in the median provincial per
capita household income will increase the cohort survival rate by 2.3%. School resources,
represented by per pupil MOOE and PTR in the model, did not render significant
coefficients. There are two possible explanations for this. One, the school resources vary
widely across school districts within a province, but these variations cannot be reflected
in the provincial average that is used in the model, hence the relationship between
outcomes and school resources are not well estimated. Two, it is simply socioeconomic
characteristics that are more important in influencing school education outcomes.
C. Quality of Education Outcomes
Contrary to their minimal influence on school outcomes, per pupil MOOE and PTR have
a significant impact on the quality of education outcomes based on the result of modeling
NAT scores.
For the secondary repetition rate, the per pupil MOOE is significant but its sign is
counterintuitive. This is perhaps because per pupil MOOE only covers the public schools
that comprise only 79% of all secondary schools’ enrollment, and hence can only reflect
the public schools situation.
Per pupil MOOE and PTR are both significant determinants of primary NAT score. Ceteris
paribus, a 1% increase in per pupil MOOE translates to a 4.7% increase in the NAT
score, while a unit increase in the PTR results to a decrease of the NAT score by 1.18.
Note that the only budget school heads have a certain level of control over is MOOE. The
school MOOE is released to division offices that can disburse it directly to the schools in
the form of cash advance. The schools can exercise flexibility by realigning across the
MOOE items (e.g., participation in seminars/meetings and supplies) according to their
actual needs. Hence, in the model, per pupil MOOE can be viewed as the proxy indicator
for decentralization. On the other hand, the PS budget represented in the model by the
average teacher’s salary (the ratio of the budget for PS and the number of teachers) can
be taken as the proxy indicator for the status quo (no decentralization). That per pupil
MOOE is a significant determinant for the primary NAT score while the average teachers’
salary is not provides support to the potential of the continuing decentralization process. If

school heads are given the authority to determine and manage funds such as the MOOE
in accordance with their school development targets, then it can significantly affect quality
of education outcome such as the NAT score.
In addition to the MOOE and PTR, the median provincial per capita income is also a
significant determinant of primary NAT score outcome. Assuming all variables stay at the
same level, an increase of P1,000 in the median income translates to an 18.3% increase
in the NAT score. On the other hand, the median household head educational attainment
is the significant determinant of secondary school enrollment. A year increase in the
educational attainment results to an additional 1.14 to the NAT score.
Table 6: Quality of Education Production Functions
Education Inputs Education Outcomes
log(repetition rate) NAT Score
Primary Secondary Primary Secondary
log(per pupil MOOE) 0.06 0.40** 4.70** 2.73*
Pupil–teacher ratio 0.01 0.01 (1.18)** (0.19)
log(teacher’s salary)a 0.02 (0.35) (1.43) (0.06)
Median household head educational
attainment
0.00 (0.04) 0.74 1.15**
Median provincial per capita income 0.00* 0.00 0.00** 0.00
Proportion of females (0.71) (0.89) 1.47 0.42
2004 0.02 (0.13)** 2.30** 0.58
2007 0.02 0.00 1.01** 0.34
Test for heteroskedasticity 0.00 0.00 0.10 0.45
Adjusted R2 0.84 0.55 0.56 0.72
0.0 means magnitude is less than half a unit.
a Similar statistical models where the proxy variable for teacher’s salary was normalized as a proportion of provincial per capita
income were also estimated. Still at the 0.05 level, the variable is not statistically significant.
Note: P-value is the probability of observing as extreme or more extreme value for the test statistic under the null hypothesis that
the parameter coefficient for the variable under consideration is zero. Smaller p-values suggest statistical significance.
The results above are based on the traditional view of fixed effects models where the panel effects (in this case, provincial
effects) are treated as parameters to be estimated. Estimation of fixed effects model using dummy variable regression
usually leads to high R2.
A large part of the variations of the quality of education outcomes is explained by the
provincial effects and therefore, could be useful to identify which of the provinces are
the best-performing and least performing. On the basis of consistency of belonging to
the top 10 (or bottom 10) highest provincial average NAT scores between 2003 to 2007,
the best performing provinces for primary schools are Bataan, Biliran, Cavite, Eastern
Samar, Ilocos Norte, Leyte, Romblon, Surigao del Norte, and Surigao del Sur. The least
performers are Basilan, Lanao del Sur, Maguindanao, Sulu, and Tawi-tawi. For secondary
schools, the best performing provinces are Agusan del Sur, Biliran, Eastern and Western
Samar, Northern Samar, Southern Leyte, and Surigao del Norte; the least performing

are Basilan, Cotabato City, Maguindanao, Sarangani, Sulu, Tawi-tawi, and Zamboanga
Sibugay. Notably, all are in Mindanao and most of them in the Autonomous Region of
Muslim Mindanao, the region with the largest number of out of school children in the
primary school age group (83,520 or 14.1% of children in that age group) and secondary
age group (78,888 or 21.5%).
Since the NAT scores for English, Science, and Math are highly correlated, SUR modeling
was applied,5 where almost similar observations as discussed above can be observed
(Table 7). Note that a unit increase in PTR tends to have a negative impact on primary
NAT scores on key subjects (English, Science, Math) while educational attainment of
household head seems to yield a positive impact on average secondary NAT scores.
Table 7: Seemingly Unrelated Regression (SUR) Models for NAT Scores on English,
Science, and Math
Education Inputs NAT Score
Primary Secondary
log(per pupil MOOE) 0.31 1.09
Pupil–teacher ratio (0.30)** (0.04)
log(teacher’s salary) (3.87) 0.58
Median household head
educational attainment
0.25 0.63**
Median provincial per capita
income
0.00 (0.00)
Proportion of females (11.38)** (2.77)
R2 (%) 0.87, 3.26, 2.49,
6.75,2.93, 4.42, 2.88,
0.88, 4.98, 1.80, 5.80,
3.40, 4.49, 1.12, 2.10
(2.72), (2.37), (0.12), (3.71),
(3.34), (0.60), (2.75), (2.55),
(4.26), (2.74), (4.33), (2.10),
2.18, (00.32), 1.52
0.0 means magnitude is less than half of a unit.
Note: The system has 15 equations where the dependent variables are the scores on national achievement tests in language,
science, and mathematics from 2003 to 2007. Each equation has a different intercept to allow for varying degrees of
difficulty in each test.
P-value is the probability of observing an extreme or more extreme value for the test statistic under the null hypothesis
Sources: Authors’ computations using BEIS and APIS data.
5 Additional discussion is provided in the Statistical Models section.

IV. Policy Implications
Modeling the individual, school, and quality of education outcomes provided concrete
evidence on their key determinants. The PTR affects the individual outcomes for both age
groups and also has a direct effect on the NAT score at the primary level. Meanwhile,
the per pupil MOOE is significant in determining the NAT score at the primary level.
Socioeconomic characteristics (whether children were working, household income,
educational attainment of household head) proved to be the stronger determinants for all
types of education outcomes. Provincial effects are significant for both school and quality
of education outcomes. This section discusses how these results affect policy.
A. Deployment of Teachers and Effective Class Size
The result of this study on the effect of PTR on the odds of attending school and pupil/
student learning outcome reinforces the theory that quality schools attract families and
encourage them to access available education services (Bray 2002, UNICEF-UNESCO
2006). On the other hand, parents commonly equate overcrowding with low-quality
education and are thus discouraged to send their children to overcrowded schools. Bray
(2002) also noted that teachers’ morale tends to erode as the class size grows. It is
therefore vital for the education system to recognize this relation and examine current the
teacher hiring and deployment system.
The average PTR at the national level is 33.64 for primary schools and 39.36 for
secondary schools, both of which are considerably lower than 50, which is the target
of the Philippine EFA plan. However, provincial-level PTR varies widely from a very low
11.58–53.05 with a standard error of 6.88 for primary schools, and 10.66–84.54 with a
standard error of 7.98 for secondary schools (see Appendix Tables 5.1 and 5.2). These
ranges could be much wider if statistics are summarized at the district school level. These
summary statistics suggest that there is overcrowding in some areas like Maguindanao,
Rizal, and Lanao del Sur that may adversely affect individuals’ decisions to attend
school and their learning outcome (Figures 4 and 5). Overcrowding in schools tends to
put off families as it is recognized that for big classes, the teaching-learning quality is
compromised.

Figure 4: Distribution of Pupil–Teacher Ratios, Primary Education (pupils per teacher)
70
60
50
40
30
20
10
0
Maguindanao
Rizal
Maguindanao
Rizal
Maguindanao
Rizal Rizal
Maguindanao
Rizal
Maguindano
Rizal
2002 2004 2005 2007
2003 2006
Note: The rectangular box in the graph represents the 25th (lower hinge) and the 75th
(upper hinge) percentile of the data for each year. The line that cuts through the
rectangle shows the median point. The dots show the outliers in the set, as well
as the minimum and maximum values.
Figure 5: Distribution of Pupil–Teacher Ratios, Secondary Education (pupils per teacher)
80
70
60
50
40
30
20
10
0
2002 2004 2005 2007
2003 2006
Lanao del Sur
Rizal
Bohol
Tawi−tawi
Sultan Kudarat
Lanao del Sur
Rizal
Bohol Rizal
Lanao del Sur
Maguindanao
Rizal
Bulacan
Lanao del Sur
Rizal
Laguna
Lanao del Sur
Rizal
Note: The rectangular box in the graph represents the 25th (lower hinge) and the 75th
(upper hinge) percentile of the data for each year. The line that cuts through the
rectangle shows the median point. The dots show the outliers in the set, as well
as the minimum and maximum values.
The wide variation of PTRs across provinces suggests that the deployment of teachers
may not be equitable. One of the major impediments to rational distribution of teaching
assignments is Republic Act (RA) No. 4670 or the Magna Carta for Teachers of 1966,
which provides that teachers cannot be reassigned without their consent. The teachers

are thus protected from being transferred from one post to another based on whimsical
decisions from or abuse of power by school principals/heads and other higher officials.
However, when there is a real and urgent need for transfer arising from a shortage of
teachers in schools in other areas, RA 4670 can also be invoked. As early as 1999,
studies like the Philippine Education Sector Study (ADB and World Bank 1999, 60)
concluded that the Magna Carta constrains “the ability of local education authorities to
deploy teaching staff to meet local requirements” and “to redeploy teachers in response
to demographic shifts and to address teacher performance issues or for exposure and
training purposes.”
Recognizing this limitation, the Medium-Term Philippine Development Plan 2004–2010
included, among its priority legislative agenda, the amendment of this law with the vision
to balance teachers’ rights and privileges with responsibility and accountability. This
includes the promotion of the general welfare of teachers such as provision of additional
compensation, sufficient hardship allowance, and salary increment as warranted by
special assignments.
At present, the Magna Carta provides for special hardship allowance for teachers in
areas where they are “exposed to hardship such as difficulty in commuting to the place
of work or other hazards peculiar to the place of employment” (Section 19). It is also
provided that determining the areas considered to be difficult shall be the responsibility of
the DepEd Secretary. The hardship allowance shall be no less than 25% of the teacher’s
monthly salary. The allocation of the hardship allowance is determined and proposed by
division offices and are provided in the Government Appropriations Acts under the lump
sum allowances of regional offices. In cases where the allocation is insufficient, savings
from the DepEd field offices are tapped. The Department of Budget and Management
provided the updated Guidelines on the Grant of Special Hardship Allowance (National
Budget Circular Number No. 514, 5 December 2007).6 However, these additional
allowances and any incentive such as additional hazard pay (from budget savings) do not
seem attractive enough for effective deployment of teachers.
On the other hand, most pending initiatives in the legislature, such as the Senate7, to
amend the Magna Carta are focused on strengthening the rights and benefits of teachers,
and do not sufficiently address the issue on demand-based equitable deployment.
Technical deliberations on these bills are progressing slowly while the government,
despite the provision in the Medium-Term Philippine Development Plan, does not seem
to be taking a stronger stand on the amendment owing to its potentially political nature.
6 The guidelines cover classroom teachers and heads/administrators assigned to hardship posts, multigrade teachers, mobile
teachers, and nonformal education or alternative learning system (ALS) coordinators. Hardship posts are public schools or
community learning centers (in the case of ALS) located in areas characterized by transport inaccessibility and difficulty of
situation (e.g., places declared calamitous, hazardous due to armed conflict and extremely dangerous locations).
7 For example, Senate Bill Nos. 72, 156, 166. In 2008, a technical working group in the Senate was convened to review the Magna
Carta, study the different bills seeking to amend it, and consider the other proposed legislations related to the welfare and
benefits of teachers. The technical working group, which invites representatives from relevant government agencies, aims to
produce a consolidated bill that would address all the issues.

Any amendment to the Magna Carta should equally and sufficiently address both the
deployment and incentive issues. Provision of nonmonetary incentives should also be
considered (e.g., special certificate/recognitions, among others) in addition to additional
compensations. Otherwise, effective distribution of teachers to achieve the EFA goals will
remain remote.
Another issue related to teacher deployment is the standard on the most cost-effective
class size within the Philippine context. Although PTR is highly correlated with class size,
they are not the same. The PTR refers to the number of teachers and pupils/students,
while class size refers to the number of pupils/student regularly in a single teacher’s
classroom for whom the teacher is responsible. Small classes do not necessarily translate
to improvement in quality as there are other factors that influence the teaching-learning
process (e.g., teacher quality itself). Considering the instructional and cost requirement,
the DepEd needs to target the optimum class size and implement it continuously. At
present, target class size varies from year to year and from one planning exercise to
another. In examining information related to these indicators, it must also be considered
that some personnel occupying teaching items/positions are not really teaching but are
instead assigned to administrative and other responsibilities. As such the reported number
of teachers employed may not reflect the actual teaching complement of the schools or
division.
Another evidence of the shortage or faulty distribution of teachers and classrooms but
which was not adequately reflected in the datasets that were constructed for this study
is the implementation of multi-shift classes among some schools. The multi-shift class
system was implemented in 2004 for elementary and secondary levels. By 2007, around
13,800 and 1,250 classes were conducted as second and third shifts, respectively, at
the elementary level at 1:50 ratio. At the secondary level, around 7,990 were conducted
as second shift classes and 636 as third shift. Some classes were even held as fourth
shift (12 for elementary and 127 for secondary). Although this study failed to note
which provinces use the multi-shift approach, since PTR is a key determinant for both
school attendance and quality of education outcomes, it could be inferred that single-
shift classes result to better student learning outcome. Note that multi-shift classes are
indicative of high PTRs and therefore, it is expected that potential students in schools
with multi-shift classes are less likely to attend school and those that are already in multi-
shift classes are expected to obtain lower NAT scores.
As for the geographical allocation of classrooms, targeting is constrained by application
of RA 7880 (Fair and Equitable Allocation of the DECS8 Budget for Capital Outlay) which,
contrary to its title, hampers equitable distribution of classroom construction across the
country. RA 7880 provides for the pupil/student population as the basis of distributing 50%
of the budget for capital outlay, which includes school buildings, to legislative districts.
For those legislative districts with actual classroom shortage as reported through BEIS,
8 Department of Education, Culture and Sports (the name of DepEd prior to RA 9155 of 2001).

40% will be allocated and the remaining 10% can be determined by DepEd. However,
as indicated in previous discussions, PTR may not be reflected correctly because some
teaching positions have been designated to administrative work and there could be multi-
shift classes that could result to a lower PTR. To truly help address the classroom gap, a
large chunk of the capital outlay allocation of the DepEd budget should go to those areas
with actual classroom shortage and not to those with the highest student population, as
it does not follow that they have shortage in classrooms. Increase in allocation for other
areas should be based on actual increase in enrollment, which can be estimated through
enrollment trends and increase in school-age population. Other factors should also be
taken into account in distributing capital outlay—the current contribution and capability
of local government units to share in the provision of capital outlay items, and the
percentage of enrollment served by private schools.
B. Decentralization
The results of modeling the quality of education outcomes at the primary level showed
that per pupil MOOE is a significant determinant while the average personnel (teachers)
salary (PS) is not. Since MOOE is the only budget component that has been somewhat
decentralized, this result supports the continuing decentralization process.9
Note that this result came about despite problems in the disbursement of MOOE. As
a whole, MOOE constitutes the least of the entire DepEd budget, only around 13.6%
(2007). DepEd currently uses a cost per student estimation method in computing for
school MOOE. However, it is still considered to be inadequate to answer for the actual
operation needs of the schools. Moreover, prior to 2008, components of the MOOE for
schools were disbursed through the division offices in kind (e.g., supplies and materials).
Sometimes, they do not reach the schools and oftentimes they do not match the actual
needs of the schools.
In 2008, DepEd required division offices to distribute MOOE to schools in the form of
cash advance (drill-down policy). Such distribution of MOOE in cash directly to the
schools allows a certain level of control over responding to the actual needs of the
schools. However, some divisions and even schools may be reluctant about this scheme
at present because of the accompanying responsibility concerning accountability and
liquidation processes. This direct disbursement of MOOE to schools will be enhanced by
an equitable formula currently being developed in a study under the BESRA.10 The study
9 This result also supplements the results from a study conducted by Behrman et al. (2002) to find out the impact of local
government financial contributions to school performance. He found out that LGU share in education finance, the measurement
of which this study has failed to obtain, has a positive effect on the cohort survival rates and learning outcomes in public
primary schools in the Philippines, other things being equal.
10 DepEd formulated the Basic Education Sector Reform Agenda in 2005. The BESRA is a comprehensive sectorwide policy reform
that aims to facilitate the attainment of the Philippine National Action Plan for EFA 2015 targets by putting in place basic
education policies to support and sustain better performance of schools. Among the major targeted key result areas of BESRA
is establishing the specific policy reforms and mechanisms necessary for the success of school-based management (SBM). To
date, the following have been accomplished: (a) distribution of important resource materials such as primers on SBM School
Leadership, School-Community Partnership, and School Performance Accountability among 50 priority divisions; and (b)

is expected to develop a system of equitable allocation of MOOE down to the schools
and to estimate the MOOE necessary for the schools to operate within given standards
for the next 6 years, factoring in other sources of funds in addition to those coming from
DepEd. Such system is envisioned to significantly contribute to school empowerment
and is line with school-based management (SBM)11 that DepEd has introduced as early
as during the implementation of the Third Elementary Education Project (1998–2006).
This, however, is expected to create new challenges in the areas of school development
planning and financial management capacity for the school, in general, and for the school
heads, in particular. In anticipation that a system of transparency and accountability might
also bear down on the school heads, a sound support and capacity-building mechanism
should be put in place.
Another important consideration under the context of decentralization and the SBM
approach is empowering schools in the hiring of teaching staff. Currently, hiring of
teaching personnel (and school heads) is done at the division level. The school only
recommends its staffing complement based on actual needs. In a decentralized setup,
schools can be granted more influence in hiring teaching personnel in addition to
merely recommending the number of teachers needed. For example, school heads
can be involved in the actual screening and hiring decisions as they can see additional
qualifications best fit to the students’ learning needs.
Decentralization is considered to be the ultimate reform by which the delivery of basic
education services, both in terms of access and quality, can be improved. It is a shift
in governance framework arising from findings that the strategic planning for and
management of education service delivery in the Philippines were highly centralized and
hierarchical that field offices and schools have little power to introduce timely, relevant,
and tailor-fit innovations according to specific local contexts (EDCOM 1991, ADB
1999). However, the process of decentralization has been slow and replete with varying
approaches that have not rendered the ultimate goals.
Decentralizing the management, delivery, and even financing of basic education services
started with the Local Government Code of 1991. The Local Government Code provides
for the Special Education Fund collected from 1% of the real property tax in the municipal
government units. The Local Government Code also created local school boards whose
functions include decision making on how the SEF will be spent. The SEF is mandated to
be used for school building and rehabilitation. In actuality, however, SEF is also used to
development of Manuals on School Improvement Plan Preparation, School Governing Council and Assessment of School-based
SBM Practices, and continuing work toward the finalization of SBM Operations Manual. This Manual includes the guidelines on
the preparation of School Report Card (SRC). As a tool to assess school performance based on a set of standards and indicators,
the SRC is designed to supplement the School Improvement Plan preparation with important and objective data. The SRC is
also envisioned as the platform in developing a school-based information system for monitoring and evaluation. It has a lot of
potential in helping schools strategize to improve their performance and engage the community and other local stakeholders.
11 The SBM approach aims to lessen bureaucratic restrictions over the schools so that they are able to focus on actual delivery
of services and produce results. The higher-level offices within DepEd could then concentrate on supportive, facilitative, and
technical assistance functions.

fund the salaries of locally hired teachers employed to fill in shortages in teaching staff. In
addition, local government units also spend for education using funds outside of the SEF.
In 2001, the Governance of Basic Education Act (RA 9155) was enacted to redefine the
structure of DepEd to adjust for the trifocalization12 of the Philippine education system
management that occurred in the mid-1990s, and also to speed up the decentralization
process.13 RA 9155 thus sought to facilitate organizational changes in DepEd through the
empowerment of its field offices and the schools based on the argument that efficiency,
accountability, and manageability are better achieved when decision making is done
closer to the ground (Manasan and Gaffud 1999). But the existence of legal bases and
institutional reforms do not guarantee empowerment at the field offices. Recognizing
this and its commitment to EFA 2015 goals, the DepEd decided that a focused and
systematic approach is necessary in order to really implement decentralization. The
Department shifted its focus to the schools by attempting to directly bring reforms through
the SBM approach. In 2005, the DepEd launched the School First Initiative Program,
which underpinned the SBM approach. The SBM approach aims to lessen bureaucratic
restrictions over the schools so that they are able to focus on actual delivery of services
and produce results. The higher-level offices within DepEd could then concentrate
on supportive, facilitative, and technical assistance functions. This is the state of the
decentralization as of this writing. Note however, that indicators to evaluate the processes
described above are lacking and hence, there is only subjective monitoring of the
decentralization plan.
C. On Making Access to Primary Education Equitable
As the results of modeling the education production functions have indicated, merely
focusing on improving school resources such as building more classrooms, hiring more
teachers, and providing more textbooks may not be sufficient to improve individual,
school, and quality of education outcomes. Socioeconomic characteristics are stronger
determinants of these outcomes and vulnerable socioeconomic groups (those who are
poor and with less educated household heads) may not complete the basic education
as provided for by the Constitution. As Table 8 shows, educational attainment is directly
related to per capita household income. As one moves up the ladder of educational
attainment, it is expected that this will also translate to an increase in income. Notice the
differences in incomes of college-degree holders from the other kinds of workers. College
graduates tend to earn twice as much as the undergraduates, and more than three
times compared to high school graduates. Bearing in mind that the school-age children
12 Operationally, trifocalization means that the management and delivery of education services in the Philippines are done
through three agencies corresponding to each education level: (a) basic education; (b) middle-levels skills development that
includes technical-vocational education and training; and (c) higher education including postgraduate education. Prior to the
trifocalization, DepEd was the sole agency responsible for the governance of all education levels. The Technical Education
and Skills Development Authority was the first agency to be created through RA 7796 in 1994, with responsibility for the
middle-levels skills development that includes technical-vocational education and training. In 1995, the Commission on Higher
Education was created through RA 7722 as the agency to be concerned with the governance of higher education. These
legislations relieved DepEd of the functions of its Bureau of Technical-Vocational Education and Training and Bureau of Higher
Education.
13 The crafting and formulation of RA 9155 took off from the findings and recommendations of various studies and projects such as
the ADB-assisted Technical Assistance Decentralization of Basic Education Management and Third Elementary Education Project.

being studied shall assume the role as parents and/or heads of households in the future,
improving their educational outcomes can help break the cycle of poor education system
performance in the country.
Table 8: Nominal per Capita Household Income by Educational Attainment (thousands)
Education Attainment
of Household Head
2002 2004 2007
No grade completed 7.22 8.11 9.90
Elementary undergraduate 8.71 9.62 10.83
Elementary graduate 10.87 11.43 13.45
High school undergraduate 11.97 13.32 14.85
High school graduate 16.59 17.64 20.11
Vocational / postsecondary 22.70 24.19 25.26
College undergraduate 24.37 26.33 28.08
College graduate 52.91 49.82 53.83
Source: Authors’ computations using APIS and based on the educational attainment of household head.
Targeted interventions that could even out these disparities among socioeconomic groups
should therefore be implemented. For example, the government can affect the decisions
to attend school and sustain participation by influencing the beliefs and circumstances
of the households through advocacy, providing mechanisms of strengthening school
interaction with the community, and offering financial assistance.
Free access to basic education is provided by the government through public schools
but the indirect or personal costs of attending schools (e.g., transportation, school
supplies, clothing, etc.) bear heavy on the family resources, especially those from very
poor households. Figure 6 shows that the poorer the household, the less it spends
for education. The fact that the proportion of household expenditure for education has
decreased from 2002 to 2007 across the income deciles is consistent with the declining
trend in net enrollment rate and the increasing number of children not attending school.
Figure 6: Share of Expenditure on Education to Total Household Expenditure,
by Income Decile (percent)
80
60
40
20
0
1 2
2002 2004 2007
3 4
Decile
5 6 7 8 9 10

SOCIAL EDUCATION FOR economics-wp199.pdf

SOCIAL EDUCATION FOR economics-wp199.pdf

Recommended

Recommended

More Related Content

Similar to SOCIAL EDUCATION FOR economics-wp199.pdf

Similar to SOCIAL EDUCATION FOR economics-wp199.pdf (20)

More from AnnaLizaTadeo1

More from AnnaLizaTadeo1 (20)

Recently uploaded

Recently uploaded (20)

SOCIAL EDUCATION FOR economics-wp199.pdf