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ADB EconomicsWorking Paper Series
Education Outcomes in the Philippines
Dalisay S. Maligalig, Rhona B. Caoli-Rodriguez,
Arturo Martinez, Jr., and Sining CuevasNo. 199 | May 2010
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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 or Poverty Reduction. The authors beneted greatly rom the insightul
comments o Anil Deolalikar, Socorro Abejo, Jesus Lorenzo Mateo, and Joel Mangahas. They also thank thePhilippine National Statistics Ofce and the Department o Educations Research and Statistics Division or
providing the datasets used in this study. Any remaining errors are the authors.
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Asian Development Bank
6 ADB Avenue, Mandaluyong City
1550 Metro Manila, Philippines
www.adb.org/economics
2010 by Asian Development BankMay 2010
ISSN 1655-5252
Publication Stock No. WPS102229
The views expressed in this paper
are those of the author(s) and do not
necessarily reect 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 Pacic 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 Asias development
and policy challenges; strengthen analytical rigor and quality of ADBs 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.
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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 Ofcial 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 AnalysisAPIS 50Appendix 4: Reasons for Not Attending School 52
References 58
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Abstract
This paper identies 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 pupilteacher ratio is a key determinant for both individual and school
outcomes, and that per capita miscellaneous operating and other expenses aresignicant 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.
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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 sacrices to send their children to school (Dolan 1991, De Dios 1995,
LaRocque 2004).However, with a poor familys 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 providingaccess 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.
EFAs framework of action has six specic 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 countrys 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 efciency, and learning outcomes (NCEFA 1999).
Through the governments 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) identied the Philippines to be among the countries with decreased net enrollmentrate from 1999 to 2006, and with the greatest number of out-of-school children (more
than 500,000). The Philippiness current performance in education based on the trends
identied 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.
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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 beaverted 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 pupilteacher ratio (PTR) are key determinants of school attendance or net
enrollment rate. Except for a few studies covering a specic 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 mayaffect 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 efcacy 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 randomizedevaluations that have already been done in other countries like Kenya, Nicaragua,
and United States; or the natural experiments study conducted in Indonesia by Duo
(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 identied 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 Ofce (NSO) in between the Family
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Income and Expenditure Survey (FIES). Section II of this paper identies 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 denes a mathematical relationship between inputs and education
outcome1Ysuch as
Y Y I F R e= ( ) +, , (1)
where Yis a function of Iand F,which are individual characteristics and family
socioeconomic factors, respectively, Ris school resources, and e represents unmeasured
factors inuencing schooling quality. Depending on the availability of data, this
mathematical relationship is estimated using suitable statistical models, of which the
best is identied through evaluation of the models goodness of t 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 inuence childrens 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 determinantscompared 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 o the production unction, while outcome would be the utility othe output. However, in this study, output and outcome are used interchangeably.
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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 Indonesias 1987 National Socioeconomic Survey, Deolalikar
(1993) found that males have signicantly lower returns to schooling than females at
the secondary and tertiary levels. The returns to university education are 25% higher forfemales 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 signicant 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) nd that the
presence of electricity is an important school input positively affecting learning outcome inCebu. 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 measuresof 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,pupilclassroom ratio, school buildings, presence of library, per capita expenditure on
education, among others.
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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 ResearchCenter (NETRC) of DepEd as well as from its budget appropriations.
The rst 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 households income and expenditures and the socioeconomic characteristics of each
member of the household. A responsible adult in the household was asked about each
members 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 DepEds 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 ve years
(20022007).
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 signicantly 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 efciency, 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.
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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 proles of enrollees, etc.). This module is designedto 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 proles. 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 prole forms for elementary and secondary levels) under Module
II. These forms are submitted to the division ofces where they are encoded and
consolidated in MS Excel les. The division ofces are also responsible for validating
the accuracy of information with the schools before they are submitted to the regionalofces for further consolidation. The regional ofces then submit the data to the central
ofces 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 les that because of their bulk cannot be uploaded on the DepEds
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 DepEds various
ofces 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, pupilclassroom ratio,drop out rate, repetition rate, cohort survival rate
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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 collectionas 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 childrens 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 computedat 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 ofcial 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 dened 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 childrenwho fall within a dened ofcial school-age group.2 While the gross enrollment rate
reects 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 reect the quality o participation o the ocial 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 or such condition is that children rom neighboring divisions
(usually rom the province where the city is or rom 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, reer to Box 1.
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Box 1: Investigating the Accuracy o the Philippiness Net Enrollment Rate
One of the key education indicators is the net enrollment rate (NER), which is chiey used to
measure developments in primary education. In fact, both the EFA and MDG programs utilizethis 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 ofcial school
age in the elementary/secondary level to the population of the same age group in a given year.
The ofcial school-age population for the primary level in the Philippines is 611 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 611 age group in provinces and cities
are not accurate; (ii) the total enrollment of ages 611 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 gures for total
population in the 611 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 611
population in 2007, DepEd then adjusted the growth rate used and applied the average annualgrowth rate from 2000 to 2007b on the 2000 Census 611 population. With a lower growth
a This can only be validated by a special survey that captures the school location and residence o the children o respondent
households. There is no strong evidence, however, to suggest that there is a signicant number o 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 20072003 2006
Box Figure 1: Net Enrollment Rate
Trend, 20022007 (percent)
250
200
150
100
50
2002 2004 2005 20072003 2006
Box Figure 2: Net Enrollment Rate
Distribution, 20022007 (percent)
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Box 1. continued.
rate basis of 2.04%, the 2007 population consequently exhibited a declining trend since theadjustment was not back-tracked. Usually, when new census gures 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 articial increase in the 2007 NER.
Box Table 1: Total Population and Enrollment o Children Aged 611, 20022007
Year Population, Aged 611
(millions)
Total Enrollment,
Aged 611 (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% or 19952000 based on the 2000 census; and 2.04% or 20002007 based
on the 2007 census.
Another point investigated is the use of national population growth estimates instead of age-
specic population growth rates. The 2.34% growth rate applied by DepEd to the 20022006
population is the 19902000 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
20002007. However, if the national average annual population growth rate projections for
20012005 is to be computed, it is only about 2.1%. And if the estimation is to be agespecic,
the average annual population growth rate for the 611 age group is only about 1.04%. c These
two gures are lower than the 2.34% that DepEd employed to project total population of ages
611. Box Figure 3 shows the various NER trends based on (i) the 2.34% population growth
rate used by DepEd for 20022006; (ii) the 2.04% rate if the population adjustment will be back
tracked; and (iii) the 1.04% rate, if the age-specic 611 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 o 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.
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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 DepEds total enrollment and hence, there is no strong
evidence that DepEds 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 DepEds guidelines, the ofcial 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 611 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 ofcial 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 ndings suggest that though
the ofcial school age starts at 6 years, there is still a signicant percentage of families sending
their children to primary school at a later year, thus contributing to the articial 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 DepEds 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
782002 2004
NER at 2.34% population growth NER at 2.04% population growth
NER at 1.04% population growth
2005 20072003 2006
Percent
Box Figure 3: Comparative NERs Based on Alternative Population Growths
continued.
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Box Figure 4: NER Trends, 20022007 (percent)92
90
88
86
84
82
80
78
DepEd
APIS 6-11
2002 2004 2005 20072003 2006
90.3
88.288.7 88.2
87.1
84.4
83.2
84.8
88.9
The four indicators discussed aboveNER, gross enrollment rate, net intake rate,
gross intake rateare 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 aws in the estimation process. For example, the fast
decline of NER as reected 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 engagedso that they are able to acquire the identied skills and levels of competencies dened
in the curriculum. How well the schools can keep the children from leaving before
completing a particular education level gauges the schools internal efciency. Indicators
of internal efciency 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 rst 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 dened as
Box 1: continued.
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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 isadministered 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 rst 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 Ofce 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 schoolyears enrollment and the
MOOE. However, they also request the regional ofces 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 thegovernment. Using the DepEd budget, however, does not present the complete basic
education nancing 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 quantiable but are not being
captured in the BEIS or by any DepEd unit. Local government units also contribute
signicantly to basic resources needed by the schools. Among these local sources is theSpecial Education Funds (SEF) coming from the 1% real property tax earned by local
governments and earmarked forbasic 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.
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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 inuence 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 inuence 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 inuenced 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, personalresources, 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 inuenced by education inputs, but household and
personal resources are also contributing factors.
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Figure 2: Model Framework
Household, Personal
ResourcesEducation 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 schooloutcomes 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 specic parameters i{ } 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 administrativedataset, 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 dened 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
lnP y
P y
tdi td
tdi td
td
=( )=( )
= +
1
0
xtdi . (1)
where ytdi is the education outcome of the ith individual in region dand yeart, x
tdiis the
corresponding vector of explanatory variables, and td is the domain-specic nested intime 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.
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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 childrento 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
teachers 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 modelingthese 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 signicantly vary across provinces. Hence,
provincial-specic parameters will be included in the model such that
E yit i it ( ) = + x (2)
where i
is the ith province-specic 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) i{ } : (i) xed-
effects model in which i{ } are treated as xed yet unknown parameters that need to
be estimated and (ii) random effects model in which i{ } are treated as draws from anunknown population and thus are random variables such that
E yit i i it ( ) = + x (3)
Considering that measures from all provinces that are the subjects or units of analysisare 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.
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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, xed effects model is deemed
appropriate.
Since the education production function is not complete without socioeconomiccharacteristics 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 nal 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 ofthe 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
R2s can be computed. Numerous parameter restrictions employed in SUR, however,
may lead to negative R2.A potential advantage of its application in panel data analysisis to allow for same parameter estimates of the xed effects using different correlated
dependent variables. Further, it moves away from the potential problem that unbalanced
data may cause under xed 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 ofcial age of entry to
primary education arose in the process. Per DepEds policy, the ofcial entry age to
formal primary education is 6 years old. However, preliminary analysis of APIS revealed
that a substantial numbers of6-year-olds were not yet in school (21.5% for 2002, 17.5%
for 2004, and 15.2% in 2007) and a signicant proportion is still in preschool (27.2% for
2002, 26% for 2004, and 25.3% for 2007) (Table 1).
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Table 1: Age-Specic 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 Secondar
6 78.55 27.18 51.37 82.5 25.96 56.54 84.8 25.33 59.487 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.
12 94.87 0.01* 56.65 38.21 94.16 56.23 37.88 94.44 0.1* 52.76 41.5
13 92.41 22.37 70.04 90.62 23.32 67.21 90.36 0.05* 21.74 68.5
14 88.66 10.46 78.1 86.56 11.09 75.33 86.76 10.29 76.4
15 84.62 4.39 79.33 82.85 4.76 76.67 82.2 0.04* 4.91 74.0
16 74.32 2.3 57.87 70.72 2.28 53.45 66.97 2.06 43.4
17 60.12 0.03* 0.76 23.73 56.6 1.01 23.07 54.38 1.16 20.8
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 712 and 1316 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 variablessuch as total enrollment, number of teachers, budget
for personnel salary and wages, and budget for miscellaneous operating and other
expenseswhich 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.
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Preliminary analysis of APIS data for 1316-year-olds as presented in Table 2 shows
that a sizeable number of 1316-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 childs time allocation process (Khanamand Ross 2005), and in this case, a possible endogeneity problem may exist. Moreover,
it is difcult 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 1316-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 8.8114 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 of.
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 712 and 1316
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 signicant
explanatory variables.
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Table 3: Random Eects Models or Log Odds o Attending School
Explanatory Variablesa Random Eects Logistic
Age: 712 Age: 1316 Age: 712 Age: 1316
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 = i household head is male) 0.02 0.07** 0.02 0.08*
Age o household head 0.00 0.01** 0.00 0.01**
(1 = i household head is working) (0.05) 0.23** (0.05) 0.24**
Highest educational attainment o household head 0.13** 0.11** 0.13** 0.11**
Pupilteacher ratio (0.02)** (0.01)** (0.01)** (0.01)**
(1 = i child is working) (2.29)** (2.28)**
Variance (random intercept due to year diferences) 0.05 0.05Variance (random intercept due to regional
diferences)
0.13 0.17
Log likelihood o model (13376.87) (18530.94) (13333.15) (18469.04)
Pseudo R2 based rom simple logistic model 0.14 0.28
Rescaled R2 0.02 0.11
Number o 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 signicant at 5% (p-value is at most at 0.05); * means signicant at 10% (p-value is at most 0.10).
0.0 means magnitude is less than hal o a unit.a Similar models were estimated incorporating sex-slope interaction with pupilteacher ratio. The results are presented in
Appendix 3. The variable is signicant or the primary school model but not or the secondary school model.
Note: P-value is the probability o observing an extreme or more extreme value or the test statistic under the null hypothesis
that the parameter coecient or the variable under consideration is zero. Smaller p-values suggest statistical signicance.
The models use random intercepts to incorporate random variations due to diferences in years and regions where theobservations come rom. Random efects are characterized by their variance components.
Statistical signicance o random efects is not directly estimated. Note that some multilevel-structural estimation
methods such as this do not allow the use o weights. But a preliminary analysis on the ordinary logistic regression results
reveals that there is no substantive diference between weighted and unweighted models. Results provided above are all
unweighted.
The Rescaled R2 provides a measure o the improvement on the amount o variation captured by including xed efects in
the model (i.e., the null log likelihood is estimated rom 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 inschool. 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-specic enrollment rates.
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(ii) Girls are1
0 4342021exp .( )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 signicant. However, in terms of magnitude of the coefcients, the explanatory
variable with the strongest inuence 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 schoolaround 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 gure 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-
specic enrollment rates, thereafter, age-specic enrollment rate declines.
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Figure 3: Age-Specic Enrollment Rates (percent)
100
90
80
70
607 8
2002 2004 2007
9 10
Age
11 12 13 14 15 16
Source: Authors computations using APIS data.
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 coefcients of the models, the p-values of the
corresponding tests of signicance, and other model diagnostics for school efciency andquality 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 t. Note, however, that there
are two modelsprimary dropout rate and survival ratethat do not have signicant
explanatory variables but have signicant provincial effects, though not reected 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 nancial support of local government units. These explanatory variables were notrepresented in the models because there were no readily available and comprehensive
measures to represent them.
4 R2 measures the proportion o variation o the dependent variable (in this case, education outcome) that is explained by themodel. R2 ranges rom 0 to 1. I it nears 1 it implies that the model has adequately explained the variations in the dependent
variable.
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Table 4: Fixed Eects Models or 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)
Pupilteacher ratio 0.03 (0.01) (0.02)** (0.00)
log(teachers 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 o emales (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)
Number o observations 251 247 251 247
Test or heteroskedasticity 0.11 0.00 0.00 0.01
Adjusted R2 0.82 0.58 0.70 0.18
** means statistically signicant at 5% (p-value is at most 0.05); * means signicant at 10% (p-value is at most 0.10).
0.0 means magnitude is less than hal a unit.a Similar statistical models where the proxy variable or teachers salary was normalized as a proportion o provincial per capita
income were also estimated. Still at the 0.05 level, the variable is not statistically signicant.
Note: Unit o analysis is province or the years 2002, 2004, and 2007.
P-value is the probability o observing an extreme or more extreme value or the test statistic under the null hypothesis
that the parameter coecient or the variable under consideration is zero. Smaller p-values suggest statistical signicance.
For models that do not satisy constant variance assumption, robust standard errors are used and the corresponding
p-values are reported.
The results above are based on the traditional view o xed efects models where the panel efects (in this case, provincial
efects) are treated as parameters to be estimated. Estimation o xed efects model using dummy variable regression
usually leads to high R2.
Source: Authors computations using BEIS and APIS data.
On the basis of the estimated xed effects computed from the models presented in
Table 4, the top and bottom provinces were identied and listed in Table 5. The xed
effects represent the characteristics that are unique to the provinces and hence, it may
be benecial 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 Perormers in Selected Primary School Efciency Indicators
Best Perormers 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 xed efects.
Source: Authors computations using BEIS data.
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As indicated above, the secondary cohort survival rate R2
0 1797=( ). has the lowestmodel t. 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 asshown in the model for individual outcomes (decision to attend school).
For secondary schools dropout rate, the signicant 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 signicant
coefcients. There are two possible explanations for this. One, the school resources vary
widely across school districts within a province, but these variations cannot be reected
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 inuencing school education outcomes.
C. Quality o Education Outcomes
Contrary to their minimal inuence on school outcomes, per pupil MOOE and PTR have
a signicant 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 signicant 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 reect
the public schools situation.
Per pupil MOOE and PTR are both signicant 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 ofces that can disburse it directly to the schools in
the form of cash advance. The schools can exercise exibility by realigning across the
MOOE items (e.g., participation in seminars/meetings and supplies) according to theiractual 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 teachers 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 signicant determinant for the primary NAT score while the average teachers
salary is not provides support to the potential of the continuing decentralization process. If
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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 signicantly 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 asignicant 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 signicant 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*
Pupilteacher ratio 0.01 0.01 (1.18)** (0.19)
log(teachers 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 o emales (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
Number o observations 251 247 252 246
Test or heteroskedasticity 0.00 0.00 0.10 0.45
Adjusted R2 0.84 0.55 0.56 0.72
** means statistically signicant at 5% (p-value is at most 0.05); * means signicant at 10% (p-value is at most 0.10).
0.0 means magnitude is less than hal a unit.a Similar statistical models where the proxy variable or teachers salary was normalized as a proportion o provincial per capita
income were also estimated. Still at the 0.05 level, the variable is not statistically signicant.
Note: P-value is the probability o observing as extreme or more extreme value or the test statistic under the null hypothesis that
the parameter coecient or the variable under consideration is zero. Smaller p-values suggest statistical signicance.
The results above are based on the traditional view o xed efects models where the panel efects (in this case, provincial
efects) are treated as parameters to be estimated. Estimation o xed efects model using dummy variable regression
usually leads to high R2.
Source: Authors computations using BEIS and APIS data.
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
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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 or NAT Scores on English,
Science, and Math
Education Inputs NAT Score
Primary Secondary
log(per pupil MOOE) 0.31 1.09
Pupilteacher ratio (0.30)** (0.04)
log(teachers salary) (3.87) 0.58
Median household head
educational attainment
0.25 0.63**
Median provincial per capita
income
0.00 (0.00)
Proportion o emales (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
** means statistically signicant at 5% (p-value is at most 0.05); * means signicant at 10% (p-value is at most 0.10).
0.0 means magnitude is less than hal o a unit.
Note: The system has 15 equations where the dependent variables are the scores on national achievement tests in language,
science, and mathematics rom 2003 to 2007. Each equation has a diferent intercept to allow or varying degrees o
diculty in each test.
P-value is the probability o observing an extreme or more extreme value or the test statistic under the null hypothesis
that the parameter coecient or the variable under consideration is zero. Smaller p-values suggest statistical signicance.
Sources: Authors computations using BEIS and APIS data.
5 Additional discussion is provided in the Statistical Models section.
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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 signicant 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 signicant for both school and quality
of education outcomes. This section discusses how these results affect policy.
A. Deployment o Teachers and Eective 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
11.5853.05 with a standard error of 6.88 for primary schools, and 10.6684.54 with astandard 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.
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Figure 4: Distribution o PupilTeacher Ratios, Primary Education (pupils per teacher)
70
60
50
40
30
20
10
0
Maguindanao
Rizal
MaguindanaoRizal
Maguindanao
RizalRizal
Maguindanao
Rizal
Maguindano
Rizal
2002 2004 2005 20072003 2006
Note: The rectangular box in the graph represents the 25th (lower hinge) and the 75th
(upperhinge) percentile o the data or each year. The line that cuts through the
rectangle shows the median point. The dots show the outliers in the set, as wellas the minimum and maximum values.
Source: Authors computations using BEIS data.
Figure 5: Distribution o PupilTeacher Ratios, Secondary Education (pupils per teacher)
80
70
60
50
40
30
20
10
0
2002 2004 2005 20072003 2006
Lanao del SurRizalBoholTawitawiSultan Kudarat
Lanao del Sur
RizalBohol Rizal
Lanao del SurMaguindanaoRizal
Bulacan
Lanao del Sur
RizalLaguna
Lanao del Sur
Rizal
Note: The rectangular box in the graph represents the 25th (lower hinge) and the 75th
(upperhinge) percentile o the data or 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.
Source: Authors computations using BEIS data.
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
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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 ofcials.
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 20042010
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, sufcient 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 difculty 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 difcult shall be the responsibility of
the DepEd Secretary. The hardship allowance shall be no less than 25% of the teachers
monthly salary. The allocation of the hardship allowance is determined and proposed by
division ofces and are provided in the Government Appropriations Acts under the lump
sum allowances of regional ofces. In cases where the allocation is insufcient, savings
from the DepEd eld ofces are tapped. The Department of Budget and Management
provided the updated Guidelines on the Grant of Special Hardship Allowance (NationalBudget 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 benets of teachers,
and do not sufciently 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, mobileteachers, and nonormal education or alternative learning system (ALS) coordinators. Hardship posts are public schools or
community learning centers (in the case o ALS) located in areas characterized by transport inaccessibility and diculty o
situation (e.g., places declared calamitous, hazardous due to armed conict 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 diferent bills seeking to amend it, and consider the other proposed legislations related to the welare and
benets o teachers. The technical working group, which invites representatives rom relevant government agencies, aims to
produce a consolidated bill that would address all the issues.
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Any amendment to the Magna Carta should equally and sufciently address both the
deploym