Education Outcomes in the Philippines (No. 199)

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 Bank6 ADB Avenue, Mandaluyong City1550 Metro Manila, Philippineswww.adb.org/economics

©2010 by Asian Development BankMay 2010ISSN 1655-5252Publication Stock No. WPS102229

The views expressed in this paperare those of the author(s) and do notnecessarily reflect the views or policiesof 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 47Appendix 2: Indicators from Basic Education Information System 48Appendix 3: Preliminary Analysis—APIS 50Appendix 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

90.388.7

87.184.4

83.2

84.8

2002 2004 2005 20072003 2006

Box Figure 1: Net Enrollment Rate Trend, 2002−2007 (percent)

250

200

150

100

502002 2004 2005 20072003 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

782002 2004

NER at 2.34% population growth NER at 2.04% population growth NER at 1.04% population growth

2005 20072003 2006

Perc

ent

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

DepEdAPIS 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 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

ResourcesEducation Inputs

(School Resources)

(IndividualOutcome)

Decision toattend school

School Outcomes

Repetition Rate

DropoutRate

Survival Rate

NATScore

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 α 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 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

lnP y

P ytdi td

tdi tdtd

=( )=( )

= + ′

1

0

α

αα βx tdi . (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 βx (2)

where α i is the ith province-specific parameters, yit is the educational outcome at year t and province I, while x it 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) α i{ } : (i) fixed-effects model in which α i{ } are treated as fixed yet unknown parameters that need to be estimated and (ii) random effects model in which α i{ } are treated as draws from an unknown population and thus are random variables such that

E yit i i itα 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 4.912 94.87 0.01* 56.65 38.21 94.16 – 56.23 37.88 94.44 0.1* 52.76 41.5813 92.41 – 22.37 70.04 90.62 – 23.32 67.21 90.36 0.05* 21.74 68.5714 88.66 – 10.46 78.1 86.56 – 11.09 75.33 86.76 – 10.29 76.4715 84.62 – 4.39 79.33 82.85 – 4.76 76.67 82.2 0.04* 4.91 74.0916 74.32 – 2.3 57.87 70.72 – 2.28 53.45 66.97 – 2.06 43.4717 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 8.8114 864.14 814.48 1,678.62 17.05 7.96 12.6415 948.41 848.66 1,797.07 21.57 8.62 15.4516 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 8.6414 974.99 903.81 1,878.80 17.43 7.02 12.4215 960.09 1,006.47 1,966.56 22.68 7.98 15.1616 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 7.4514 1,078.04 1,062.66 2,140.70 13.91 7.52 10.7415 1,082.29 1,182.89 2,265.18 20.55 9.84 14.9616 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 0.05Variance (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 0.28Rescaled R2 0.02 0.11Number of observations 91243 57011 91243 57011AIC 26783.75 37089.87 26726.29 36996.08BIC 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 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 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

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 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)

Number of observations 251 247 251 247

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 that the parameter coefficient for the variable under consideration is zero. Smaller p-values suggest statistical significance. 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 BasilanBatangas 3rd District Iloilo Lanao del SurDavao del Sur 4th District Northern Samar Negros OccidentalMisamis Oriental Bulacan Quirino SaranganiMt. 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** 0.00Proportion of females (0.71) (0.89) 1.47 0.422004 0.02 (0.13)** 2.30** 0.582007 0.02 0.00 1.01** 0.34Number of observations 251 247 252 246Test for heteroskedasticity 0.00 0.00 0.10 0.45Adjusted R2 0.84 0.55 0.56 0.72

** 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: 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

** 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 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 that the parameter coefficient for the variable under consideration is zero. Smaller p-values suggest statistical significance.

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

MaguindanaoRizal

MaguindanaoRizal

MaguindanaoRizal Rizal

MaguindanaoRizal

Maguindano

Rizal

2002 2004 2005 20072003 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.

Source: Authors’ computations using BEIS data.

Figure 5: Distribution of Pupil–Teacher Ratios, Secondary Education (pupils per teacher)

80

70

60

50

40

30

20

10

02002 2004 2005 20072003 2006

Lanao del SurRizalBoholTawi−tawiSultan Kudarat

Lanao del Sur

RizalBohol RizalLanao del SurMaguindanaoRizal

Bulacan

Lanao del SurRizalLaguna

Lanao del SurRizal

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.

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


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 9.90Elementary undergraduate 8.71 9.62 10.83Elementary graduate 10.87 11.43 13.45High school undergraduate 11.97 13.32 14.85High school graduate 16.59 17.64 20.11Vocational / postsecondary 22.70 24.19 25.26College undergraduate 24.37 26.33 28.08College 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)

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60

40

20

01 2

2002 2004 2007

3 4

Decile

5 6 7 8 9 10



What are the government initiatives for these vulnerable groups? DepEd issued the policy of “no school contributions collection” policy upon enrollment starting 2001. This prohibits any kind of contribution from parents for their children to enroll and attend school. Another direct assistance program is the Food for School Program that started in 2005 and which involves distribution of rice to families, through school children, in the amount of one kilo a day based on the students’ school attendance. It has benefited around 4.5 million families as of 2008. Selection of target areas is also based on FIES results in identifying the provinces and on small area estimates in identifying the municipalities. Other related initiatives include the School Milk Project, which aims to provide supplementary nourishment to undernourished children by serving chocolate flavored milk for 120 feeding days. Another intervention is the Breakfast Feeding Program, which aims to improve learning capacities of students by serving the specially formulated fortified noodles for breakfast. Both programs are aimed at Grade 1 pupils in selected schools. School-based feeding programs and take home rations proved to have a positive affect on school enrollment, attendance, and dropout rates, especially with a sound targeting mechanism (Janke 2004, Manasan and Cuenca 2007). However, an issue on the timing and duration should be considered. When school feeding is provided as a stand-alone incentive, class attendance may cease when the feeding ends. As such, it is important to implement school feeding as an integrated and complementary program together with other interventions with the objectives of sustaining school attendance and reducing the risk of dropout among children. Moreover, the implementation of these school feeding programs has been marred by controversies on procurement and delays in arrival and distribution, undermining their potential success and possible expansion. For instance, the procurement of noodles used for the Breakfast Feeding Program was the subject of controversy related to overpricing (more than double the price of regular commercial noodles). The DepEd explained that the higher price is due to the higher production cost of the specially-fortified noodles designed to address malnutrition. Thus, the DepEd noodles and the commercial noodles are not comparable at all. This controversy could have been anticipated and avoided by pro-actively informing the public on cost and how it is offset by the nutritional (and educational such as better absorption) benefits. This way, transparency is ensured to protect the integrity and sustainability of the program.

Overall, it seems that these programs are not as effective as they were envisioned to be, as evidenced by the results in the models. In general, children in the vulnerable groups still have the least chance of attending school, implying that these interventions did not influence the target children. The analysis of the relationships between the education outcomes and their determinants also showed that the rise in per capita household expenditure will likely increase the odds of attending school in both the primary and secondary levels. Therefore, the obvious course of action to encourage school attendance is to subsidize a portion of the household expenditure, i.e., food, consequently increasing the household budget for other nonfood expenditures. Based on the design of the abovementioned programs, they are expected to affect the expenditures on food.


However, in these kinds of endeavors, there should also be a strong link between the program design and its execution to the objective and desired outcome. For example, working on the premise of the linkage between the decision to go to school and household expenditures, programs should be designed and implemented in the manner that would affect the household where the students belong to and not just the students themselves. The decision to attend school is a function of the household characteristics and not just that of the individual’s.

Such is the program design of the new government initiative, the Pantawid Pamilyang Pilipino Program (4Ps) started in January 2008 in 6,000 households, growing to 377,143 households by the end of 2008. Essentially, the 4Ps aims to improve the socioeconomic situation and to break the intergenerational poverty cycle through provision of cash assistance to poor households selected using an objective poverty targeting mechanism based on the results of FIES, small area estimates, and saturation surveys of households, among others. The 4Ps is based on a conditional cash transfer (CCT) scheme that become a popular intervention because of its notable success and the positive effect observed in basic education participation in countries in Latin America (e.g., Mexico and Brazil) and Africa, among others (Son 2008). In the Philippines, the 4Ps grants a family with a maximum of three children in school a P1,400 monthly subsidy or P15,000 annually if they comply with certain conditions/requirements, which include regular school attendance and provision of health care (e.g., vaccines, medical check-ups, participation in health, nutrition, and population seminars). Specifically, for education, the financial assistance can only be given upon compliance with certain conditions including attendance (85% of the time) in daycare or preschool classes if the households have 3–5-year-old children, and enrollment and sustained attendance for households with children aged 6–14 years old. As of June 2009, the number of 4Ps beneficiary families has reached 695,746 poor households nationwide. Meanwhile, in September 2009, President Gloria Macapagal-Arroyo announced her plans to expand the coverage of the program to one million poor families in response to the effects of the global financial crisis.

The implementation of the 4Ps is led by the DSWD in close coordination and cooperation with DepEd, Department of Public Works and Highways, Department of Interior and Local Government, and local government units. The DepEd is responsible for providing the necessary resources (e.g., school and teachers) to accommodate beneficiary children and for monitoring. The schools are also used as venues for responsible parenthood seminars and some health-related component (e.g., deworming of children) of the program. After a year of pilot implementation, an evaluation is now being conducted as to its overall cost efficiency and effectiveness with respect to targeting and implementation.


D. On Working Children

A striking result of the analysis in the preceding section is that in 2007, 13.54% of children belonging to the secondary education-age group were already working (14.09% in 2004 and 14.12% in 2002), and boys are almost twice as likely to be working as girls. The results of modeling school attendance also indicated that assuming all factors equal, working children are 9.78 times more likely not to attend school. Therefore, the substantial number of working children, especially boys, is probably the major factor of the low NER in secondary school (61.2% in 2007).

The analysis shows that for the students in the secondary level, there is a trade-off between earning money (by working) and attending school: either one foregoes the current payment received in favor of future returns, or vise-versa. The results further suggest that the present income received holds more weight than future income. Therefore, in order for a person to place more importance on future earnings provided by better education and to attend school at present, he/she must be compensated for the opportunity costs he/she will incur. That is, be compensated with the acceptable level of returns now for the earnings to be foregone by attending school. If the opportunity cost will not be offset, any program to encourage working children to come back to school will likely not succeed.

These assumptions are further strengthened by the analyses below.

As shown in Figure 7, more than half of the working children are in the bottom 30% of the income groups and hence, poverty is probably the main reason why children start working early and decide not to continue school anymore. Children are compelled to start working for a living to supplement their family’s meager income. Hence, even if they have a good understanding of better pay when they finish college (or even secondary school only), they do not have any other option but to work to help their family survive. In addition, when children have started earning income and have tasted financial independence, they may see less value in going back to school. Also, because children belonging to this age group are more aware of the stigma of being overaged in school (ASPBAE 2007), they no longer make any effort of attending school.

Although there is a slight decline in the percentage of working children from 2002 to 2007, current DepEd programs to counter the trend seem inadequate. One of these programs is the Alternative Learning System (ALS). Under ALS, out-of-school youth and adults who have not completed basic education can avail of nonformal education and literacy classes. The ALS has an Assessment and Equivalency Program where elementary and secondary education equivalency certificates can be obtained upon passing respective examinations. The Assessment and Equivalency Program allows individuals to reenter the formal education system.


Figure 7: Proportion of Working 13–16-Year-Olds, by Income Decile (percent)

25

20

15

10

5

0

1 2

2002 2004 2007

3 4

Decile

5 6 7 8 9 10


Another program that DepEd is implementing related to the alternative delivery of formal secondary education especially for working students is the Drop-out Reduction Program, the counterpart of Drop-Out Intervention Program in elementary schools. The Drop-out Reduction Program is comprised of interventions that include the Open High School Program, which was started in 2005 in 28 secondary schools nationwide with an enrollment of 5,000 students. Open High School students are still enrolled in formal secondary schools but lessons are delivered in a flexible manner through modules and with minimal teacher–student interaction during the school year. Students are selected from those to be at risk of dropping (i.e., poor attendance and performance). The program is accompanied by trainings for school principals, Open High School coordinators, and guidance counselors for effective management and continuous improvement of modules. Interventions under the Drop-Out Intervention Program and Drop-out Reduction Program are also called an Alternative Delivery Mode for basic education.

DepEd also attempted to integrate technical vocational education with the high school curriculum so that graduates are to some extent ready with practical skills when they complete basic education. This concept has the potential to make secondary education more attractive, especially to boys. A related issue to be considered is the short duration of basic education in the Philippines. Additional components such as technical-vocational skills development can further cram the curriculum and thus compromise the quality of schooling.

Systematic evaluation of the efficiency and feasibility of the programs described above, if any, are not available to the public and hence, their effectiveness will remain in question. Also, these programs are only implemented in a limited scale and do not appear to be


available across all public schools in the country. Moreover, public awareness of these programs is marginal and hence, they may not be familiar to the target group of working children. Most important, however, is that while the programs offer alternatives to the formal secondary education approach, they do not address the major cause why children opt to work—poverty and/or hunger.

E. Other DepEd Programs to Keep Children in School

In addition to those discussed under working children, DepEd is also implementing other programs to keep children in school. The Drop-out Intervention Program for elementary schools includes specific interventions like the Instructional Management by Parents, Community and Teachers, and the Modified In-School, Out-of-School Approach. The former is a combination of self-learning materials and an in-school off-school mode of engaging hard-to-reach pupils; while the latter is being implemented in schools with very high enrollments targeting school children who are in difficult and different circumstances that limit their school attendance and hamper their performance, thus increasing their risk of dropping out. These approaches allow children flexibility in terms of physical school attendance while being able to keep up with the lessons.

Another program is the Student Tracking System, which is an integral component of the Child Friendly School System implemented through the Country Program for Children sponsored by the United Nations Children’s Fund. The Student Tracking System is a tool to regularly monitor the condition the pupils and his/her family (e.g., punctuality and attendance, health, socioeconomic status). Based on the information compiled, the teacher is able to identify children who are at risk of dropping out and can thus immediately address the needs of these children. The attempt to expand and the implementation of the Child Friendly School System and the use of the Student Tracking System encountered challenges such as maintaining the quality of the program. Therefore, DepEd’s plan to institutionalize the Child Friendly School System was put on hold and the implementation is still limited to project areas of UNICEF’s Country Program for Children, which cover 19 provinces and five cities.

It appears that DepEd is implementing many programs to boost the enrollment rates for both primary and secondary schools. However, these programs do not entirely address the major reasons of children for not attending school indicated by respondents in the 2002, 2004, and 2007 APIS (see Appendix 4). These programs were not also able to identify the regions and perhaps, provinces that are more vulnerable than others. For example, 14.1% of children aged 7–12 years or 83,520 were not in school in the Autonomous Region of Muslim Mindanao in 2007. Based on the APIS results, their major reason for not attending school is lack of interest. Lack of interest seems to be the primary reason of boys not to attend school in many regions, but for females in Central Luzon, Bicol, and Southern Mindanao, for example, the primary reason is the high cost


of education. In fact, high cost of education ranks second among the reasons for not attending school.

For secondary schools, the National Capital Region and Cordillera Autonomous Region are the best performers in terms of the percentage of children not in school (see Appendix Tables 4d to 4f). The biggest numbers of children not in school were in Western Visayas, Central Luzon, and the Cavite-Laguna-Batangas-Rizal zone. Unlike the primary school-age children, the number of children not in school for secondary school greatly increased from 2002 to 2007. The three major reasons for nonattendance are lack of personal interest, high cost of education, and employment or looking for work. Males in general are not interested in going to school but females indicate high cost of education as a reason.

Given that basic education is provided free, other costs of schooling could be bearing down on the households such as transportation, food, and school supplies. The government, therefore, needs to address this problem by helping those families that lack necessary funds to send their children to school. The CCT program discussed in the previous section has probably the most potential for mitigating this problem. Since a major conditionality for the continued transfer of cash to beneficiary households is class attendance of school children, families are likely to sustain their attendance in school most probably using a portion of the cash itself.

F. On Gender Disparity

Results of modeling individual education outcomes show that boys have lesser odds of attending school for both primary and secondary level age groups. Over the years, girls have been outperforming boys in the most education outcome indicators. The finding of this study, that girls have better chances of being sent to schools than boys, is consistent with DepEd administrative data that girls have been doing better than boys in terms of access indicators (e.g., net enrollment and net intake rates). Gender disparity worsens at the secondary level where the performance gap is five times wider than in the elementary level with respect to participation rate. And based on their lower gross enrollment rate, overaged boys are also likely to stay away from school than girls.

DepEd should determine empirical reasons behind this trend in order to implement the appropriate interventions. UNESCO (2010, 35) cites a study on causative factors of dropping out in selected schools across the country, where a negative attitude toward or low motivation is a reason specific for boys for not attending classes. Similarly, the various APIS results on the reasons for not attending school in both age groups consistently revealed that lack of personal interest is the number one reason among boys, while it is the high cost of education for girls. The findings of such survey should serve as wake-up call for the government.


However, the better performance of girls in education outcomes based on national aggregates should not cause complacency among policy makers and education leaders. Throughout the country, pockets of discrimination and gender stereotyping still exists. The same way that difficult circumstances force boys to work in farms and elsewhere to augment family income, girls are also tapped for household work and taking care of younger siblings (ASPBAE 2007).

G. Age of Official Entry to Primary School

A critical issue related to the improvement of access to primary education is the policy on timely entrance of children to formal primary education, which is currently 6 years. The policy was implemented beginning 1995 but after more than 10 years, while the proportion of 6-year-old children who are not yet in school has been declining based on APIS results, there is still a substantial number of 6-year-olds who are not in school. Also, in the 2007 APIS, a third of those who are already in school are not yet in Grade 1 but are still in preschool (Table 2). Parents postpone sending their 6-year-old children to school for a variety of reasons as shown in Table 9.

Table 9: Reasons of 6-Year-Olds for not Attending School (percent)

Reasons 2002 2004 2007

Cannot cope with school work 13.35 12.98 2.79High cost of education 11.20 10.95 7.10Illness / disability 2.43 3.03 2.02Lack of personal interest 24.89 22.92 11.76Schools are far / no school within barangay 8.89 6.83 5.31Other reasons (combined) 39.24 43.29 71.01Memo items: Total number of 6-year-olds not currently in school (thousands)

403.57 365.37 311.95

Percentage not in school 21.4 17.5 15.2

Source: Author’s computations using APIS data.

DepEd also needs to reexamine the rationale behind its policy on primary school entry age and how it is being implemented. If the rationale is still valid and would work best to achieve the basic education goals, then restrategizing to improve its implementation is crucial and urgent. A fundamental approach is information dissemination and advocacy among parents, especially those with very young children who will soon reach the official entry age for primary school. The importance of starting school at the right age and progressing accordingly should be emphasized. In analyzing patterns of school attendance or nonattendance in selected countries, UIS (2005) concluded that age is among the most important determinants. Starting school late increases the likelihood of dropping out before completion. Moreover, late entrants are at risk, especially when the age gap is too obvious, because they are ridiculed in school (ASPBAE 2007).


V. Conclusions and Recommendations

Basic education in the Philippines has suffered a major performance setback in recent years. The country’s chances of fulfilling its commitment to EFA and relevant MDG goals are unlikely, unless the key determinants of these goals (education outcomes) are identified, and policies and programs reoriented toward improving the performance of these key determinants to arrest the declining trends in education outcomes.

The results of modeling the 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 per pupil MOOE is a significant factor in determining quality of education outcome, socioeconomic characteristics are stronger determinants of education outcomes. Children of families in the lower-income deciles and with less educated household heads are vulnerable and less likely to attend school. Females have better odds of attending school than males. Working children, especially males, are less likely to attend secondary school.

DepEd has been implementing many programs to counter the declining trends of key indicators of education outcomes. As discussed in the preceding section, these programs vary in complexity, from alternative approaches to formal education, to augmentation of basic needs of vulnerable families. DepEd has also introduced interventions that are potentially effective in influencing demand for basic education among households, particularly poor households. Among these are the conditional school feeding and the take home food ration (e.g., rice) programs, which studies worldwide have demonstrated to be effective in both giving assistance to the poor, at the same time ensuring school attendance among poor children.

The analysis of related major policies reveals, however, that while these policies aim to provide equitable access to education for all Filipinos, a few are countereffective. Among these are the provisions on the deployment/reassignment of teachers under the Magna Carta for Teachers, and the pupil/student population-bias allocation of classroom construction under the Fair and Equitable Allocation of the DECS Budget for Capital Outlay Act. The government has long recognized these impediments but has been unable to take a stronger stand and appropriate representation toward amending these laws.

It also appears that DepEd has many programs and policies whose effectiveness has not been adequately evaluated before and after their implementation. It is vital for DepEd to keep track of the actual “value-added” of these interventions to be able to assess and determine the most cost-efficient ones for strengthening and expansion, if necessary. Implementing numerous and perhaps duplicative programs are costly, wasteful, and ineffective.


As mentioned at the beginning of the paper, randomized evaluations and natural experiments are gaining ground in the context of policy assessment. This approach can be applied to assess programs and to determine the most effective and cost-efficient education policies. But while the randomized control trial approach is attractive in the sense that the impact of policies can be measured quantitatively, the key challenge lies on designing the appropriate “experiment” that will answer the research questions of interest. Moreover, this method requires extensive planning and is expensive to implement.

Due to the approach’s operational difficulty, randomized evaluations are still not the primary method utilized in program assessments. A possible way to address this problem is to design surveys—one of the most common data sources—which incorporate the concept of natural randomized experiments. Incorporating randomized experiments in the framework is expected to facilitate more in-depth analysis of causality between outcomes and type of interventions, consequently decreasing the costs in generating information.

Other concerns that should be considered are the effects of project implementation, positive or otherwise, on the teachers who are the ultimate implementers and field workers. Administration of these programs is an additional responsibility for the teachers; extra workloads they are usually not appropriately compensated for. This concern must be taken into consideration in the design and implementation plans.

The key determinants of education outcomes that have been identified by this research can be used in designing more effective programs to increase school attendance, and keep children in school to receive better quality education. For example, one key determinant of school attendance is per capita household expenditure. Had this been known, more support for programs such as conditional cash transfer to provide assistance to targeted vulnerable groups (per capita household expenditure may be an indicator for determining such groups) may have been provided, instead of allocating more resources for school feeding programs that benefit Grade 1 pupils only.

Furthermore, depending on the type of education outcomes, projects may also be designed to take more household characteristics into consideration, rather than just the individual profiles of students. This approach may be effective for outcomes geared toward the primary level of education. To illustrate, results of the analyses showed that the more educated the household head, the better the odds of the child being in school. In fact, for students at the primary level, the odds of attending school increase by 13% for every year of increase in the educational attainment of the household head. Given this information, programs similar to conditional cash transfer, which target families that have household heads with lower educational attainment, can be designed.

On the other hand, for programs addressing the secondary level, the reverse approach may be more successful, i.e., giving better emphasis on the individual characteristics.


Results showed that older children at the secondary level are less likely to attend school. The probable reason for this may be the greater capability of older children to earn money. Therefore, in this case, the decision to attend school or not lies more on the individual. This study, therefore, presents alternative approaches in designing or implementing education programs and provides empirical evidence on why these options should be given consideration.

Specifically, to help address further deterioration, reverse the declining trend, and/or sustain gains in improving the basic education system performance outcomes, this study offers the following recommendations in the areas of policy and programs based on the results of modeling education outcomes and the examination of related polices and programs:

(i) Fasttrack the amendment to the Magna Carta for Teachers, giving equal attention to the privileges, benefits, responsibility, and accountability involved in the mission of basic education in the country. Such amendment is expected to help improve equitable deployment of teachers according to where they and their competencies are needed. The amendment should also be dovetailed to new policy developments arising from reform initiatives on teacher education and training, career-path and professional development, and teaching-learning quality assurance, among others, like those formulated in BESRA (e.g., Teacher Education and Development Program and the National Competency-Based Teacher Standards). A concomitant and equally important issue is ensuring that teachers are able to concentrate on their teaching responsibilities rather than being saddled with additional tasks arising from the implementation of various programs that can adversely affect the teaching-learning progress in the class.

(ii) Amend/improve other pertinent laws proven to be countereffective in achieving the country’s basic education goals such as the Fair and Equitable Allocation of the DECS Budget for Capital Outlay Act to help improve equitable distribution of school resources, focusing on schools where there are severe shortages in classrooms and other facilities.

(iii) This study has shown that net enrollment rate has not improved because almost half of 6-year-old children (311,946 in 2007) are not yet in school. Of those 6-year olds who are in school, about half are still in preschool. DepEd declared in 1995 that the official age of entry to primary school is 6 years old. But 14 years from this declaration, compliance is still low. It is time that DepEd revisit this policy and make necessary changes, if necessary.

(iv) One of the results of this study showed that MOOE, which is the only budget component managed by school heads, is a key determinant of quality of education. MOOE is smaller compared to the personnel budget (which is not


yet devolved), and is one of only two school resources that are crucial to improving the quality of education outcomes. It is therefore important that the decentralization process that was started in 1991 and which has metamorphosed several times under various leaderships in DepEd be fully implemented. The guidelines and procedures for the current SBM, including the full implementation of school-based MOOE, must be finalized. The government should also provide necessary mechanisms to ensure that the SBM initiative is sustained despite the frequent changes in DepEd leadership. Implementation of SBM should be accompanied by appropriate support mechanisms for the school heads such as capability building. The extent by which schools heads can be involved in selection and hiring of their teachers should also be explored, taking into consideration matching of particular learning needs in the schools and the teacher-applicant’s competencies in addition to the required core qualifications.

(v) One key factor that is lacking in the implementation of decentralization is the system of indicators by which performance of school districts can be evaluated. While there are a variety of indicators that BEIS compiles, and there are also indicators for meeting EFA goals and the MDG 2, some of these indicators cannot be compiled at the school district level. For example, net enrollment rate, which is both an EFA and MDG 2 indicator cannot be compiled at the school district level because the projection of the total 6–11-year-old or 12–15-year-old population in the area covered by the school district is not available, and neither would it be an effective indicator even when data is available because students cross-enroll between school districts. A set of indicators that is workable and meaningful should be developed and regularly maintained and disseminated. DepEd can spearhead the conduct of methodological research on the available data as well as consultations with major stakeholders in identifying this set of indicators. Transparency with respect to the evidenced-based performance of the school can generate active participation of the various stakeholders in the community of the school district, and innovative approaches to providing equitable access to education may develop.

(vi) BEIS must also record and store the set of indicators at the school level discussed above and also studies such as this research to facilitate monitoring by DepEd. While BEIS has moved toward a more systematic data compilation method, it needs to acquire and implement a more modern technology for organizing and disseminating the data that it compiles. It took considerable time to prepare the data used for this research in analysis-ready format because BEIS data are maintained in individual spreadsheet formats with no metadata codes that could link indicators and provinces. An automated database system presents a good solution because it allows users easy, versatile, and secured data access.


(vii) Results of household surveys such as the APIS and Functional Literacy, Education and Mass Media Survey should supplement BEIS indicators in providing evidenced-based performance evaluation of education outcomes. These surveys can also validate trends of BEIS indicators as well as serve as basis for in-depth research.

(viii) DepEd should undertake an in-depth methodological study on how the indicators that use population projections of certain age groups, such as net enrollment rate, gross enrollment rate, gross intake rate, and net intake rate, are measured. The population projections that are used should be consistent with published projections of the NSO and should be updated and back-tracked whenever new data from the Census of Population and Housing becomes available. Policies and programs of DepEd are monitored on the basis of these indicators that are not accurate, especially at the subnational level.

(ix) A comprehensive and impartial evaluation of all government programs designed to stimulate demand for schooling should be conducted. One important point to consider, for instance, is whether the separate and seemingly fragmented implementation of various interventions such as school feeding and food distribution among pupils can be integrated into a single, more systematic, and cost-effective program in which improved targeting as well as regular evaluation mechanisms should be incorporated. Since the government is also currently experimenting with CCT, it should also be explored whether combining the implementation of the school feeding program to CCT will be more cost-effective and workable. The 4Ps CCT program currently being piloted in the country is promising and, like in other countries, is designed to be implemented through concerted efforts among key government social service agencies.

(x) Other programs that the government must assess objectively in terms of effectiveness and coverage are its various dropout prevention programs that involve different intervention activities in both elementary and secondary education levels. These specific interventions need to be assessed in terms of actual contribution, as well as possible duplications, in arresting dropout cases. Children who are already in school should be, at all costs, encouraged to stay in school. A particular attention should be focused on children who are working, which are prevalent among secondary education-aged children. A two-track intervention approach may be explored. First, flexibility in school attendance and design and delivery of lessons must be adopted. Second, the children must be constantly engaged in counseling for consistent drilling down of the importance of further education in both individual and societal terms.

(xi) The finding that, in general, a greater proportion of secondary education-aged children tend to stop or forego schooling for work warrants special attention from


education leaders and decision makers. It is also pragmatic to assume that for most children, secondary education will be the highest educational attainment and hence, a curriculum that includes practical life-skills components that will already prepare children to earn their living after graduation must be developed and implemented.

(xii) The fact that secondary education-aged boys are particularly susceptible to staying out of school, engaging in labor, and developing a despondent attitude toward education deserves an even deeper examination and attention among education leaders. Determining what causes them to lose interest in schooling and what can be done to reverse such attitude is imperative. DepEd’s current initiative to integrate a technical-vocational component may be a welcome development for boys, but the Department should be able to weigh additional costs against the real short-term (e.g., attracting boys back to high schools) and long-term (e.g., acquisition of practical skills among secondary students) benefits of this move.

(xiii) One approach would be to develop technical-vocational curriculum for high school students that would give them better chances of being hired by enterprises in their community. Schools can develop partnerships or solicit sponsorships from successful businesses that would welcome technical/vocational student trainees.

(xiv) Moreover, in line with the country’s commitment to EFA, there should be more efforts in getting back pupils/students who have dropped out as well as bringing in those school-age children who have not been in school. A cautionary analysis, however, was given by Bray (2002) that bringing in the last percentage of children not in school will be more costly. As such, it is crucial that the government decide on the most cost-effective interventions, while taking into account the wide in-country geographic and cultural differences that characterize the Philippines. For those who are already past school-age and have not completed basic education, the role of Alternative Learning Systems should be strengthened and promoted.

This study could have provided more insights to the key determinants of education outcomes if a complete set of data was available at the school level, including indicators of school management performance; contribution of the local government and the private sector to the school; including additional teachers, books, and other in-kind resources. The analysis at the provincial level that was employed by this study carries an implicit assumption that schools in a province are homogeneous, which is not often the case. The variations across schools would have further enriched this research. Moreover, measures of school resources that were lacking and which were enumerated above would have improved the education production function models.


Appendix 1: Education for All Targets and Accomplishments, Primary Education

Goals and Indicators Targets* Observed Data2005 2010 2015 2002 2003 2004 2005 2006 2007

Goal 1 - Early Childhood Care and EducationGross enrollment ratio in pre-primary … … … 18.44 18.36 19.23 20.53 21.15 21.54 3–4 years old (daycare centers) 30 60 100 … … … … … … 5 years old (pre-school education) 70 80 100 … … … … … …Percentage of Grade 1 entrants with Early Childhood Education experience

67 100 … 54.24 55.97 60.46 60.72 62.78 64.86

Goal 2 – Universal Primary Education/Universal Basic Education

PRIMARY Gross enrollment rate 110.6 115.3 120 108.3 106.1 104.2 101.1 99.9 102Net enrollment rate 92.19 95.1 98.1 90.3 88.7 87.1 84.4 83.2 84.8Gross intake rate 121.55 120.55 120.18 128.1 124.0 122.7 115.2 117.5 119.2Net intake rate 77.5 86.25 95 41.3 41.6 41.3 36.6 39.7 45.6Dropout rate … … … 7.45 9.08 8.96 9.91 8.79 8.47Repetition rate … … … 2.25 2.35 2.43 2.93 2.53 2.47

SECONDARY Gross enrollment rate 88.32 99.14 110 83.55 84.82 83.94 80.53 79.5 81.4Net enrollment rate 67.72 77.73 87.73 59.00 60.15 59.97 58.54 58.59 61.91Dropout rate … … … 13.91 15.5 15.24 18.07 15.81 15.06Repetition rate … … … 2.81 2.4 1.93 3.74 3.33 3.23

Goal 3 - Life Skill and Lifelong LearningYouth literacy (15–24)1 and Life Skills … … … … 95.1 … 94.5 94.5 94.4Transition Rate (primary to secondary) … … … 97.62 95.85 93.05 102.55 97.53 99.32

Goal 4 - Adult LiteracyAdult literacy rate* … … … … 93.4 … … … …

Goal 5 - Gender Equality in Basic Education

PRIMARY GPI for gross enrollment rate … … … 0.99 0.98 0.99 0.98 0.98 0.98GPI for net enrollment rate … … … 1.02 1.02 1.02 1.02 1.02 1.02GPI for cohort survival rate … … … 1.13 1.14 1.17 1.15 1.14 1.13GPI for completion rate … … … 1.14 1.16 1.18 1.16 1.14 1.15SECONDARY GPI for gross enrollment rate … … … 1.09 1.09 1.09 1.09 1.08 1.07GPI for net enrollment rate … … … 1.17 1.18 1.18 1.18 1.18 1.16GPI for cohort survival rate … … … 1.14 1.13 1.13 1.19 1.12 1.17GPI for completion rate … … … 1.15 1.17 1.16 1.24 1.15 1.20

Goal 6 - Quality of Basic EducationPRIMARY Cohort survival rate 73.26 78.96 84.67 72.4 71.8 71.3 70 73.4 75.3Completion rate 70.12 75.58 81.04 71.6 70.2 69.1 68.1 71.7 73.1Pupil–classroom ratio … … … 40.1 39.6 39.3 38.8 38.7 37.6Pupil–teacher ratio … … … 35.8 35.8 35.5 35.1 35.0 35.0SECONDARY Cohort survival rate 70.26 77.26 83.00 76.99 77.71 78.09 67.32 77.33 79.91Completion rate 63.36 69.32 75.27 74.8 71.7 72.4 61.0 72.1 75.4Student–classroom ratio … … … 60.28 60.96 58.77 56.59 55.68 54.41Student–teacher ratio … … … 40.20 41.65 40.98 39.47 39.17 38.75

… means not available or not applicable.GPI = gender parity index.* Simple literacy computed from the 2003 Functional Literacy, Education and Mass Media Survey.Sources: Department of Education-Basic Education Information System, National Statistical Coordination Board.


Appendix 2: Indicators from Basic Education Information System

Average Repetition Rate is the average percentage of pupils/students enrolled in a given grade/year in a given school year, who study in the same grade/year the following school year.

Cohort Survival Rate is the proportion of enrollees at the beginning grade or year who reach the final grade or year at the end of the required number of years of study.

Completion Rate is the percentage of first year entrants in a level of education who complete/finish the level in accordance with the required number of years of study.

Department of Education Budget, MOOE, refers to the annual budget for maintenance and other operating expenses.

Department of Education Budget, PS, refers to the annual budget for personnel services expenses.

Dropout rate is the percentage of pupils/students who leave school during the year for any reason as well as those who complete the previous grade/year level but fail to enroll in the next grade/year level the following school year to the total number of pupils/students enrolled during the previous school year.

Gross Enrollment Rate refers to the total enrollment in a given level of education as a percentage of the population, which, according to national regulations, should be enrolled at this level, i.e., age 6–11 for elementary and 12–15 for secondary. It is a measure of the “capacity” of a region’s elementary and secondary schools.

Gross Intake Rate is the percentage of the population at the official elementary-entrance age who are new entrants in the first grade of elementary education, regardless of age.

Mean Percentage Score (MPS) indicates the ratio between the number of correctly answered items and the total number of test questions, or the percentage of correctly answered items in a test.

National Achievement Rate refers to the degree of performance in different subject areas in various levels of education.

Net Enrollment Rate pertains to the ratio of 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.

Net Intake Rate is the percentage of the population at the official elementary school-entrance age who are new entrants in the first grade of elementary education, and who are of the same age.

Pupil–Class Ratio is an Education for All Goal 6 indicator. It refers to the average number of pupils per class, which gives a rough indication of class size. It is used to assess the efficiency of resource utilization, and indirectly, to assess the teaching/learning process.


Pupil/Student–Classroom Ratio pertains to the average number of pupils/students per classroom in elementary/secondary education in a given school year.

Pupil/Student–Teacher Ratio is the average number of pupils/students per teacher in a grade/year in a given school year.

Transition Rate (from primary level to intermediate level, Grade IV to Grade V; elementary to secondary), is the percentage of pupils who graduate from one level of education and move on to the next higher level.


Appendix 3: Preliminary Analysis—APISAppendix Table 3.1: 7–12 Years Old—Summary Statistics of Variables Used in the Education Outcome Production Function, APIS 2002, 2004, 2007

Indicator Year Mean Median Standard Deviation

Minimum Maximum

Age 2002 9.47 9 1.70 7 122004 9.42 9 1.72 7 122007 9.51 10 1.71 7 12

All years 9.47 9 1.71 7 12

Per capita expenditure(thousand pesos)

2002 9.55 6.14 13.65 0.69 587.632004 10.92 7.13 12.94 0.55 367.672007 11.77 7.98 13.68 0.78 433.04

All years 10.79 7.10 13.45 0.55 587.63

Age of household head (years)

2002 44.50 42 11.22 12 962004 43.32 41 10.64 15 982007 44.44 42 11.21 7 99

All years 44.08 42 11.04 7 99

Educational attainment of household head (years)

2002 7.84 7 3.70 0 162004 8.08 8 3.94 0 162007 8.19 8 3.98 0 16

All years 8.04 8 3.89 0 16

Note: All postgraduate degree holders are encoded as 16 for the value on educational attainment.Source: Authors’ computations using 2002, 2004, and 2007 APIS data on 7–12-year-old population group.

Appendix Table 3.2: 13–16 Years Old—Summary Statistics of Variables Used in the Education Outcome Production Function, APIS 2002, 2004, 2007


Minimum Maximum

Age 2002 14.46 14 1.11 13 162004 14.49 14 1.12 13 162007 14.50 15 1.12 13 16

All years 14.48 14 1.12 13 16

Per capita expenditure(thousand pesos)

2002 10.91 7.00 15.31 0.79 451.802004 11.90 7.91 13.11 0.95 307.052007 12.93 8.61 16.39 0.94 788.49

All years 12.00 7.89 15.07 0.79 788.49

Age of household head(years)

2002 47.44 45 10.16 13 992004 46.68 45 9.96 13 982007 47.53 46 10.30 13 99

All years 47.22 45 10.15 13 99

Educational attainment of household head (years)

2002 7.91 7 3.75 0 162004 8.01 7 3.95 0 162007 8.12 8 4.02 0 16

All years 8.02 7 3.92 0 16

Note: All postgraduate degree holders are encoded as 16 for the value on educational attainment.Source: Authors’ computations using 2002, 2004, and 2007 APIS data on 13–16-year-old population group.


Appendix Table 3.3: Summary Statistics of Provincial Level Indicators, APIS 2002, 2004, 2007


Minimum Maximum

Proportion of females(7 to 12)

2002 0.49 0.49 0.04 0.38 0.642004 0.50 0.50 0.04 0.40 0.712007 0.49 0.49 0.04 0.33 0.61

All years 0.49 0.49 0.04 0.33 0.71Median household per capita income (thousand pesos)

2002 8.27 6.96 4.23 3.54 24.02004 9.33 7.81 4.48 3.98 28.32007 10.77 8.86 5.08 5.10 27.48

All years 9.47 8.03 4.71 3.54 28.3Median household head educational attainment(years)

2002 7.14 6.50 1.54 3.00 10.502004 7.16 6.50 1.59 3.00 10.502007 7.71 6.50 1.71 3.00 10.50

All years 7.34 6.50 1.63 3.00 10.50

Source: Authors’ computations using 2002, 2004, and 2007 APIS data.

Appendix Table 3.4: Exploratory Model for Log Odds of Attending School (with sex-slope dummy for pupil–teacher ratio)

Explanatory Variables Age Groups

Age: 7–12 Age: 13–16Age = 8 0.69**Age = 9 1.01**Age = 10 0.93**Age = 11 0.80**Age = 12 0.21**Age = 14 (0.36)**Age = 15 (0.68)**Age = 16 (1.48)**Sex (1 = male) (1.07)** (0.20)log(per capita household expenditure) 1.03** 0.86**(1 = if household head is male) 0.02 0.06Age of household head 0.00 0.01**(1 = if household head is working) (0.05) 0.23**Highest educational attainment of household head 0.13** 0.11**Pupil–teacher ratio (0.03)** (0.01)**(1 = if child is working) (2.29)**Sex(1=Male)*Pupil–teacher ratio 0.02** (0.00)Sex(1=Male)*(1=if household head is male) (0.00) 0.02Variance(random intercept due to year differences) 0.04 0.05Variance(random intercept due to regional differences) 0.13 0.17Log likelihood of model (13372.06) (18530.68)Number of observations 91243 57011AIC 26778.12 37093.37BIC 26938.28 37236.58

** 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 of a unit.


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6.5

28.4

…11

.9…

2.9

4.7

…2.

80.

6M

IMA

ROPA

13.0

0 3

.3…

…14

.60.

57.

32.

08.

627

.03.

225

.71.

2…

…3.

92.

73.

2Bi

col R

egio

n19

.30

2.4

12.2

8.0

8.2

5.0

1.9

8.6

20.9

25.9

…1.

5…

……

…2.

85.

1W

este

rn V

isay

as29

.70

3.0

…5.

715

.97.

03.

913

.210

.020

.6…

1.9

…2.

82.

1…

4.6

12.2

Cent

ral V

isay

as50

.27

5.8

6.6

11.0

6.1

3.2

3.2

3.4

10.8

19.1

3.8

5.8

3.4

5.1

5.0

0.4

8.3

4.8

East

ern

Visa

yas

31.1

5 4

.81.

03.

56.

46.

60.

34.

820

.535

.22.

93.

7…

0.8

…1.

17.

46.

0W

este

rn

Min

dana

o18

.40

3.8

10.5

4.0

11.7

5.0

1.7

1.3

7.9

16.9

7.8

11.8

…1.

86.

5…

7.2

6.0

Nor

ther

n M

inda

nao

19.5

5 3

.32.

38.

39.

817

.87.

26.

52.

730

.1…

……

1.0

0.5

1.1

3.9

8.8

Sout

hern

M

inda

nao

20.1

4 3

.56.

3…

12.6

30.1

1.9

7.1

3.9

18.2

2.3

3.6

……

3.0

2.9

…8.

0

Cent

ral

Min

dana

o25

.75

4.5

3.7

2.8

28.9

25.5

5.7

4.1

9.3

8.8

…3.

2…

5.7

……

…2.

2

NCR

18.6

9 1

.53.

13.

630

.317

.86.

06.

45.

28.

2…

…2.

1…

……

6.2

11.1

CAR

1.8

2 0

.8…

17.2

……

9.7

10.3

21.8

15.7

…6.

4…

9.4

……

…9.

4A

RMM

75.8

816

.28.

29.

25.

44.

00.

61.

815

.122

.58.

89.

8…

1.7

3.3

4.5

2.4

2.7

Cara

ga11

.41

2.9

11.4

21.8

30.1

16.0

18.2

9.8

11.7

25.5

……

……

4.1

……

3.3

... m

eans

not

ava

ilabl

e or

not

app

licab

le.

Not

e:

No

regu

lar t

rans

port

atio

n, h

ouse

keep

ing,

and

fini

shed

sch

oolin

g ar

e lu

mpe

d un

der O

ther

Rea

sons

. So

urce

: A

utho

rs’ c

ompu

tatio

ns u

sing

200

2 A

PIS

data

on

7–12

age

pop

ulat

ion

grou

p.

Appendix 4: Reasons for Not Attending School


App

endi

x Ta

ble

4.2:

Rep

orte

d Re

ason

s am

ong

7–12

Age

Gro

up fo

r N

ot A

tten

ding

Sch

ool,

2004

Regi

onN

ot

curr

entl

y in

sch

ool

(per

sons

, th

ousa

nds)

Not

cu

rren

tly

in

scho

ol (%

)

Reas

ons

for

not a

tten

ding

sch

ool (

%)

Cann

ot c

ope

wit

h sc

hool

w

ork

Hig

h co

st o

f ed

ucat

ion

Illne

ss/

Dis

abili

tyLa

ck o

f per

sona

l in

tere

stSc

hool

s ar

e fa

r/N

o sc

hool

in

barang

ay

Empl

oym

ent/

Lo

okin

g fo

r w

ork

Oth

erre

ason

sO

ther

s

Fem

ale

Mal

eFe

mal

eM

ale

Fem

ale

Mal

eFe

mal

eM

ale

Fem

ale

Mal

eFe

mal

eM

ale

Fem

ale

Mal

eFe

mal

eM

ale

Iloco

s Re

gion

11.5

11.

8…

6.3

9.6

15.9

…19

.913

.524

.6…

2.9

……

……

3.6

3.7

Caga

yan

Valle

y14

.88

3.1

…2.

115

.422

.14.

410

.58.

530

.7…

……

……

…4.

22.

2Ce

ntra

l Luz

on25

.11

2.0

…4.

18.

516

.912

.72.

310

.428

.2…

……

1.9

…1.

96.

26.

8CA

LABA

RZO

N41

.23

2.6

1.2

2.5

10.0

10.2

10.4

12.9

11.5

22.7

1.2

1.3

…1.

31.

41.

57.

74.

1M

IMA

ROPA

19.5

94.

64.

1…

4.4

11.2

5.7

3.9

17.3

25.9

10.2

2.8

…1.

21.

3…

5.5

6.6

Bico

l Reg

ion

35.1

54.

15.

99.

01.

118

.92.

29.

09.

221

.7…

…1.

1…

1.1

…2.

218

.7W

este

rn V

isay

as41

.35

4.2

3.4

4.3

4.4

7.3

8.7

8.5

9.1

21.4

9.5

7.4

1.1

1.0

…1.

05.

27.

6Ce

ntra

l Vis

ayas

41.9

44.

53.

16.

011

.212

.64.

61.

114

.427

.61.

12.

0…

4.9

……

4.0

7.4

East

ern

Visa

yas

24.4

83.

84.

41.

37.

212

.23.

04.

212

.840

.80.

02.

81.

42.

7…

…2.

94.

3W

este

rn

Min

dana

o34

.62

6.5

3.0

10.1

7.7

5.0

…5.

924

.330

.52.

11.

01.

1…

1.2

2.9

2.1

3.0

Nor

ther

n M

inda

nao

20.6

53.

62.

13.

57.

94.

22.

03.

715

.237

.73.

91.

6…

3.7

2.1

…4.

08.

4

Sout

hern

M

inda

nao

30.7

25.

21.

26.

98.

214

.22.

49.

87.

517

.19.

94.

7…

2.3

1.2

1.2

5.2

8.3

Cent

ral

Min

dana

o48

.31

8.6

1.4

2.9

15.9

18.5

4.5

5.4

9.5

17.7

6.8

10.7

…1.

81.

3…

2.2

1.3

NCR

32.0

92.

32.

16.

913

.123

.46.

115

.93.

05.

2…

……

…1.

5…

5.0

17.7

CAR

5.06

2.3

3.4

3.8

6.6

7.2

6.8

3.1

24.7

30.8

3.5

6.6

……

……

3.5

…A

RMM

67.5

212

.95.

55.

53.

54.

80.

5…

16.0

21.2

9.7

12.4

…0.

51.

51.

09.

38.

5Ca

raga

11.0

32.

9…

…11

.610

.06.

39.

98.

040

.51.

92.

4…

1.7

…1.

74.

21.

8

... m

eans

not

ava

ilabl

e or

not

app

licab

le.

Not

e:

No

regu

lar t

rans

port

atio

n, h

ouse

keep

ing,

and

fini

shed

sch

oolin

g ar

e lu

mpe

d un

der O

ther

Rea

sons

. So

urce

: A

utho

rs’ c

ompu

tatio

ns u

sing

200

4 A

PIS

data

on

7–12

age

pop

ulat

ion

grou

p.


App

endi

x Ta

ble

4.3:

Rep

orte

d Re

ason

s am

ong

7–12

Age

Gro

up fo

r N

ot A

tten

ding

Sch

ool,

2007

Regi

onN

ot

curr

entl

y in

sch

ool

(per

sons

, th

ousa

nds)

Not

cu

rren

tly

in

scho

ol (%

)

Reas

ons

for

not a

tten

ding

sch

ool (

%)

Cann

ot c

ope

wit

h sc

hool

w

ork

Hig

h co

st o

f ed

ucat

ion

Illne

ss/

Dis

abili

tyLa

ck o

f per

sona

l in

tere

stSc

hool

s ar

e fa

r/N

o sc

hool

in

barang

ay

Empl

oym

ent/

Look

ing

for

wor

k

Oth

erre

ason

sO

ther

s

Fem

ale

Mal

eFe

mal

eM

ale

Fem

ale

Mal

eFe

mal

eM

ale

Fem

ale

Mal

eFe

mal

eM

ale

Fem

ale

Mal

eFe

mal

eM

ale

Iloco

s Re

gion

15.7

12.

2…

……

11.1

17.3

2.9

3.0

31.9

……

……

11.4

2.9

11.3

8.2

Caga

yan

Valle

y18

.20

3.9

…1.

84.

111

.36.

09.

920

.938

.11.

9…

……

…3.

8…

2.2

Cent

ral L

uzon

39.2

32.

93.

2…

17.1

20.3

5.9

12.3

6.7

25.2

……

……

……

4.5

4.8

CALA

BARZ

ON

49.3

43.

12.

4…

13.6

11.0

13.7

3.7

7.2

22.5

……

…2.

31.

1…

13.9

8.5

MIM

ARO

PA27

.31

5.5

2.3

3.9

7.1

11.8

9.8

2.4

16.0

19.0

6.7

8.8

……

……

3.8

8.4

Bico

l Reg

ion

32.4

83.

54.

32.

910

.514

.28.

68.

33.

225

.91.

4…

……

……

13.6

7.1

Wes

tern

Vis

ayas

39.5

83.

7…

…5.

75.

64.

013

.48.

618

.74.

24.

0…

3.1

1.4

5.5

10.7

15.1

Cent

ral V

isay

as39

.72

4.1

1.3

…4.

96.

75.

14.

314

.135

.03.

72.

51.

2…

1.3

1.2

7.5

11.3

East

ern

Visa

yas

31.2

64.

4…

4.3

5.4

11.7

1.4

9.0

12.3

33.7

……

……

1.3

2.5

6.7

11.7

Wes

tern

M

inda

nao

35.3

46.

92.

24.

86.

26.

01.

22.

317

.631

.64.

66.

9…

…1.

13.

36.

65.

6

Nor

ther

n M

inda

nao

24.9

94.

1…

…3.

313

.58.

83.

215

.934

.5…

……

1.6

1.7

…7.

010

.5

Sout

hern

M

inda

nao

31.9

95.

41.

22.

613

.215

.25.

91.

39.

017

.19.

95.

01.

2…

…1.

15.

511

.8

Cent

ral

Min

dana

o28

.20

4.7

…2.

79.

415

.57.

48.

24.

216

.95.

67.

3…

1.3

1.3

1.5

5.2

13.5

NCR

28.2

52.

0…

…5.

211

.49.

014

.114

.311

.2…

2.1

…1.

9…

3.5

13.9

13.5

CAR

6.23

2.7

…5.

8…

5.8

3.2

9.9

7.4

52.8

…3.

2…

……

2.9

6.0

2.9

ARM

M83

.52

14.1

1.8

0.8

7.3

9.0

0.5

…17

.213

.67.

79.

3…

…2.

91.

315

.213

.4Ca

raga

16.1

84.

1…

1.7

1.6

5.5

8.6

6.9

10.4

38.3

3.7

5.4

……

1.6

…3.

412

.9

... m

eans

not

ava

ilabl

e or

not

app

licab

le.

Not

e:

No

regu

lar t

rans

port

atio

n, h

ouse

keep

ing,

and

fini

shed

sch

oolin

g ar

e lu

mpe

d un

der O

ther

Rea

sons

. So

urce

: A

utho

rs’ c

ompu

tatio

ns u

sing

200

7 A

PIS

data

on

7–12

age

pop

ulat

ion

grou

p.


App

endi

x Ta

ble

4.4:

Rep

orte

d Re

ason

s am

ong

13–1

6 A

ge G

roup

for

Not

Att

endi

ng S

choo

l, 20

02

Regi

on

Not

cu

rren

tly

in s

choo

l (p

erso

ns,

thou

sand

s)

Not

cu

rren

tly

in

scho

ol (%

)

Reas

ons

for

not a

tten

ding

sch

ool (

%)

Cann

ot c

ope

wit

h sc

hool

w

ork

Hig

h co

st o

f ed

ucat

ion

Illne

ss/

Dis

abili

tyLa

ck o

f per

sona

l in

tere

stSc

hool

s ar

e fa

r/N

o sc

hool

in

barang

ay

Empl

oym

ent/

Look

ing

for

wor

k

Oth

erre

ason

sO

ther

s

Fem

ale

Mal

eFe

mal

eM

ale

Fem

ale

Mal

eFe

mal

eM

ale

Fem

ale

Mal

eFe

mal

eM

ale

Fem

ale

Mal

eFe

mal

eM

ale

Iloco

s Re

gion

44.8

711

.91.

05.

019

.619

.94.

82.

43.

724

.8…

…6.

64.

06.

9…

1.0

0.4

Caga

yan

Valle

y38

.08

14.4

…2.

716

.220

.00.

80.

36.

630

.20.

71.

42.

710

.34.

1…

1.4

2.7

Cent

ral L

uzon

97.3

014

.4…

4.2

17.2

15.9

2.6

3.6

7.2

19.8

……

7.9

13.0

4.4

1.1

1.5

1.5

CALA

BARZ

ON

99.2

813

.20.

32.

711

.917

.12.

93.

04.

523

.33.

62.

77.

311

.83.

70.

41.

53.

4M

IMA

ROPA

34.6

115

.83.

61.

611

.019

.21.

84.

010

.624

.8…

1.0

5.2

10.8

2.6

0.5

0.7

2.6

Bico

l Reg

ion

85.2

118

.42.

23.

516

.112

.73.

53.

28.

323

.51.

00.

94.

612

.01.

50.

52.

93.

6W

este

rn V

isay

as79

.82

13.4

2.4

4.3

10.6

13.7

4.1

4.4

9.6

28.3

…0.

37.

64.

83.

60.

52.

83.

0Ce

ntra

l Vis

ayas

92.8

118

.90.

50.

94.

815

.10.

91.

84.

438

.9…

…10

.016

.13.

10.

80.

22.

6Ea

ster

n Vi

saya

s74

.45

20.8

1.4

0.8

6.5

15.9

2.7

3.3

11.3

45.2

1.1

2.2

1.9

3.3

1.0

0.5

2.4

0.6

Wes

tern

M

inda

nao

44.7

416

.71.

21.

410

.612

.20.

82.

78.

938

.72.

71.

73.

98.

84.

60.

7…

1.1

Nor

ther

n M

inda

nao

47.4

714

.01.

61.

616

.618

.32.

21.

310

.829

.6…

1.3

4.0

7.8

0.5

1.3

1.0

2.3

Sout

hern

M

inda

nao

58.2

616

.61.

1…

12.4

21.5

3.7

5.9

7.8

22.0

1.0

…4.

811

.51.

11.

31.

74.

3

Cent

ral M

inda

nao

59.4

917

.60.

4…

20.2

24.5

…1.

13.

522

.33.

00.

93.

915

.13.

7…

1.6

…N

CR62

.72

8.3

3.0

…19

.723

.43.

30.

86.

020

.8…

…3.

38.

60.

40.

85.

24.

7CA

R12

.98

9.9

0.8

5.5

10.7

14.4

2.2

3.0

6.9

33.7

…2.

43.

47.

44.

31.

61.

12.

4A

RMM

46.9

217

.9…

1.8

8.9

9.4

0.7

0.7

14.9

30.9

2.5

7.3

2.0

8.2

9.5

1.9

…1.

1Ca

raga

30.6

813

.9…

…15

.315

.22.

56.

75.

541

.00.

70.

9…

6.9

3.1

…0.

91.

3

... m

eans

not

ava

ilabl

e or

not

app

licab

le.

Not

e:

No

regu

lar t

rans

port

atio

n, h

ouse

keep

ing,

and

fini

shed

sch

oolin

g ar

e lu

mpe

d un

der O

ther

Rea

sons

.So

urce

: A

utho

rs’ c

ompu

tatio

ns u

sing

200

2 A

PIS

data

on

13–1

6 ag

e po

pula

tion

grou

p.


App

endi

x Ta

ble

4.5:

Rep

orte

d Re

ason

s am

ong

13–1

6 A

ge G

roup

for

Not

Att

endi

ng S

choo

l in

2004

Regi

onN

ot

curr

entl

y in

sch

ool

(per

sons

, th

ousa

nds)

Not

cu

rren

tly

in

scho

ol (%

)

Reas

ons

for

not a

tten

ding

sch

ool (

%)

Cann

ot c

ope

wit

h sc

hool

w

ork

Hig

h co

st o

f ed

ucat

ion

Illne

ss/

Dis

abili

tyLa

ck o

f per

sona

l in

tere

stSc

hool

s ar

e fa

r/N

o sc

hool

in

barang

ay

Empl

oym

ent/

Look

ing

for

wor

k

Oth

er

reas

ons

Oth

ers

Fem

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About the PaperDalisay S. Maligalig, Rhona B. Caoli-Rodriguez, Arturo Martinez, Jr., and Sining Cuevas examine the key determinants of individual, school, and quality of education outcomes using extensive data from both the administrative reporting systems of the Department of Education and the Annual Poverty Indicators Survey conducted by the Philippine National Statistics Office. They conclude that although school resources such as pupil–teacher ratio are 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 of education outcomes.

About the Asian Development BankADB’s vision is an Asia and Pacific region free of poverty. Its mission is to help its developing member countries substantially reduce poverty and improve the quality of life of their people. Despite the region’s many successes, it remains home to two-thirds of the world’s poor: 1.8 billion people who live on less than $2 a day, with 903 million struggling on less than $1.25 a day. ADB is committed to reducing poverty through inclusive economic growth, environmentally sustainable growth, and regional integration. Based in Manila, ADB is owned by 67 members, including 48 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance.

Asian Development Bank6 ADB Avenue, Mandaluyong City1550 Metro Manila, Philippineswww.adb.org/economicsISSN: 1655-5252Publication Stock No. WPS102229 Printed in the Philippines

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Education Outcomes in the Philippines (No. 199)