Major Influencing Factors of Out-of-School Children in West Java Province, Indonesia (ANGGRAYNI)

Major Influencing Factors of Out-of-School Children in WestJava Province, Indonesia
Yenni ANGGRAYNI*
Abstract: In 2022, a high percentage of out-of-school children (OOSC) within the age groupof 13–18 years indicated a significant challenge to achieving universal access to high-quality
education. Therefore, this research aims to determine the major factors underlying this issue by
applying logistic regression analysis and testing the relationship among independent variables using
generalized structural equation modeling (GSEM). This case study, located in West Java Province,
Indonesia, involved a sample of 1,148 children selected through a cross-section purposive sampling
design. The results of the logistic regression analysis indicated that the main factors of OOSC withthe highest odds ratio are excessive absence for more than one month, students working while
attending school, and poor academic performance. Furthermore, the results of GSEM demonstrate
the significant contribution of teacher factors to student absenteeism, children working while
attending school, and low academic achievement. Therefore, it is necessary to improve teacher
quality, particularly teachers’ social competence, increase student-teacher relationships, change
students’ perceptions of education from bad to good, and increase parent/guardian involvement ineducating their children to address the issue of OOSC in Indonesia.
Keywords: absenteeism, working status, poor academic performance, teacher quality.

1. Introduction
The fourth Sustainable Development Goal, “Quality Education for All,” guarantees lifelong
learning and universal access to high-quality education. According to the United Nations Children’s
Fund (UNICEF), globally, 222 million children and adolescents are facing crises, with 78.2 million
not attending school in 2022. The school enrollment rate, particularly in developing countries, isconcerning. In 2022, in Indonesia, a developing Southeast Asian country, 55.152 million are schoolage children (7–18 years old), accounting for 19.99% of the overall population.1 Consequently, thequality of children’s education will significantly affect Indonesian economic growth and impact thecountry’s economy.
In the academic year 2022–2023, Indonesia’s net enrollment rate (NER) was 91.81% forprimary school (7–12 years), 79.35% for lower secondary education (13–15 years), and 68.87%* Doctoral Student, Graduate School of Policy Science, Ritsumeikan University
Email: ps0558fe@ed.ritsumei.ac.jp, yenni.anggrayni@bappenas.go.id
Received on November 21, 2023, accepted after peer reviews on April 4, 2024; published online: July 9, 2024
©Asia-Japan Research Institute of Ritsumeikan University:
Asia-Japan Research Academic Bulletin, 2024
ONLINE ISSN 2435-306X, Vol.5, 12
1 According to the Statistics of Indonesia report (2023), the population of Indonesia is projected to be 275.77 million
people in 2022. According to the Ministry of Education, Culture, Research and Technology (2023), the number ofschool-age children in Indonesia (7–18 years old) in 2022 was 55.152 million people.
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for upper secondary education (16–18 years) (Ministry of Education, Culture, Research, andTechnology 2023). This indicated that many school-age children (13–18 years) lack access to
education. Hence, the global issue of out-of-school children (OOSC) highlights a serious deficiency
in Indonesia’s education system. The OOSC in Indonesia, as defined, are children between theages of 7 and 18 years with limited access to formal and informal education. The OOSC category
includes those who have never formally enrolled in a school, those who dropped out after being
registered, and those who graduated but did not continue to a higher level.2
The UNESCO Institute for Statistics (UIS) has been assessing school dropout rates in 192
countries since 2000. According to Statistics Indonesia, OOSC in Indonesia reached approximately
4.17 million in 2018, which is less than the estimated 6.9 million by the UIS in 2015 (UIS 2019).3
The number of OOSC increased to 4.29 million in 2019. To mitigate this issue, the Indonesian
government introduced the Education Operational Assistance program known as “Biaya
Operasional Pendidikan” (BOP)4 in 2019 to promote the reentry of OOSC into the education systemthrough non-formal education.5 Nevertheless, this program could only reduce the number of OOSC
by around 210,000, leaving 4.08 million OOSC in 2020.
In 2020, the Indonesian government accelerated the implementation of compulsory education,
extending the education period from 9 to 12 years,6 to increase the number of students whocompleted upper secondary school (Kusumah 2021). In the same year, the Indonesian government
implemented a comprehensive nationwide strategy to address the problem of OOSC to decrease
the number of OOSC. However, this approach was only able to reduce the number of children
not attending school by approximately 150,000, with a total of 3.93 million OOSC in 2021. In
2022, the number of OOSC increased by 150,000, reaching 4.08 million, indicating that thepolicies implemented by the Indonesian government have failed to decrease the number of OOSC
consistently and significantly.
Several factors contribute to the decisions of students to drop out of school, such as limited
ability or motivation, reduced expected benefits from school, and limited financial resources (Tsaneva2017). Based on the 2018 Program for International Student Assessment (PISA) results, one ofIndonesia’s main issues that leads to dropping out of school is excessive absenteeism (OECD 2019).
Moreover, the findings of the 2021 national socioeconomic survey of sociocultural and educational
2 According to the National Strategy for handling out-of-school children (OOSC) year 2020, the definition of out-ofschool children in Indonesia are those aged 7–18 years without access to formal or non-formal education.
3 The UNESCO Institute of Statistics (UIS) conducted estimations for several countries with the largest out-of-school
populations in 2015 and used a new model approach in 2020. The estimation results conducted by the UIS in 2015
demonstrate that the population of OOSC in Indonesia in 2018 will reach 6.9 million children. However, Statistics
Indonesia data in 2019 reveal that the number of children outside of school in 2018 was 4.17 million.
4 Based on the Minister of Education and Culture Regulation No. 7 of 2019, beginning in 2019, the central
government allocated non-physical DAK for Operational Assistance for the Delivery of Equal Education known as“Biaya Operasional Pendidikan” (BOP). BOP funds are allocated to regions based on the number of students in nonformal education institutions/units organized according to basic (packages A and B) and secondary (package C)
education levels.
5 The Strategic Plan of the Ministry of Education and Culture of the Republic of Indonesia (2020–2024): it is a
strategy for decreasing the number of OOSC by returning them to the education system through non-formal education,
which pertains to equivalency education and consists of packages A (equivalent to primary school), B (equivalent tolower secondary school), and C (equivalent to upper secondary school).
6 Strategic Plan of the Ministry of Education and Culture of the Republic of Indonesia (2020–2024): one of thestrategies is to increase equal access to educational services at all levels and accelerate the implementation of a 12year compulsory education period.
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Major Influencing Factors of Out-of-School Children in West Java Province, Indonesia (ANGGRAYNI)

modules conducted by the Central Statistics Agency in Indonesia support the continued relevanceof this rationale. The survey results indicate that economic issues, such as the inability to pay
school fees, or to get a job, and feelings of shame are due to economic conditions for a significant
proportion of OOSC (47.19%). In addition, a proportion of 30.39% is due to marriage, domestic
responsibilities, dissatisfaction with education, peer harassment, disability, and the cumulative
effects of the COVID-19 pandemic. Meanwhile, 20.5% of the respondents did not specify why they
did not attend school. Furthermore, the results point out that economic issues composed the highest
percentage of OOSC problems in 2021. This implies that cash-assisted transfer programs, such as
School Operational Assistance, known as “Bantuan Operasional Sekolah” (BOS) and the Smart
Indonesia Program, known as “Program Indonesia Pintar” (PIP), which have been in place since
20087 and 2015,8 respectively, have yet to solve economic issues that lead to school-age children
dropping out of school.
A strategy of educational decentralization has been implemented since 2000.9 Despite
this national strategy being established to address OOSC problems throughout the country, theimplementation of the plan in each region depends on the financial capacity of each district or city.
Decentralization can improve educational outcomes, provided the local governments, communities,
and schools have the resources and political will (Leer 2016).
The West Java Province provides the necessary resources and political commitment to handle
the OOSC issue. The implementation of the national strategy for addressing OOSC in West Java
Province is conducted in the following manner. Since 2020, the local government of West Java
Province has been introducing cash transfer assistance programs (BOS and PIP) to complement the
12-year compulsory education policy by providing free education up to upper secondary school.
Furthermore, in the same year, efforts have been made to encourage OOSC between the ages of
7 and 21 years into the education system through formal (open schools)10 or non-formal education(equivalency programs).11 Nevertheless, these strategies have yet to be effective in West Java
Province. The cumulative number of OOSC recorded in 2022 in West Java Province was 912,084,
which was the most among the 34 provinces in Indonesia.12
Therefore, it is essential to examine the main factors of OOSC in West Java Province, which
has the largest population in Indonesia and is located close to the capital city. This will enable theformulation of more appropriate policy proposals to address the problem of OOSC in West Java
Province. The majority of the ethnic groups in the country are in this province, showing the same
7 Minister of Education, Culture, Research and Technology Regulation Number 63 of 2022: educational unit
operational assistance funds known as “Bantuan Operasional Sekolah” (BOS) refer to a specific type of non-physicalallocation funds designated to provide financial support for non-personnel operational expenses of educational units.
8 Minister of Education and Culture Regulation Number 10 of 2020: The Smart Indonesia Program known as“Program Indonesia Pintar” (PIP) denotes assistance in the form of cash transfers, which expands access and learning
opportunities from the government given to students and students from poor or vulnerable families since 2014.
9 Educational decentralization was when the central government transferred administrative and fiscal responsibility
for health and secular education to district governments.
10 According to the Regulation of the Minister of Education and Culture of the Republic of Indonesia No. 72 of 2013,
an open school is a form of formal education that stands alone but is part of a formal regular school that provides
education using independent learning methods.
11 According to Statistics Indonesia in 2022, the number of OOSC in 34 provinces in Indonesia is 4,082,000. The
highest OOSC is in West Java Province with 912,084 children, and the lowest is in North Kalimantan Province with
9,451 children.
12 Based on Statistics Indonesia, West Java ranks first as the province with the largest population in Indonesia, withthe population of 49,405,800 in 2022.
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characteristics as tribes in other regions. Thus, several suggestions for helping the Indonesian
government to overcome the OOSC problem in Indonesia can be obtained from the analyses of
OOSC in West Java Province.

2. Previous Studies
International research conducted by the UIS (2015) highlights the presence of obstacles, suchas financial, access, and capacity, which hinder the achievement of basic education in Bangladesh,
Kyrgyzstan, and Vietnam. Financial limitations indicate that poor households struggle to coverdirect and indirect educational expenses in Pakistan, Sri Lanka, and Tajikistan, which is similar
to the case of several African countries, such as Uganda and Nigeria, such that children must beallowed to work while attending school (Moyi 2013; Okoh et al. 2020). Kamran and Deen (2017)
reveal that a strong correlation exists between the limited educational attainment of the household
head in impoverished families and insufficient parental involvement in the educational pursuitsof children in Pakistan. Moreover, child labor and inadequate parental support are found to
significantly influence decreased academic performance among students in Thailand (Vayachuta
et al. 2016) and increase the likelihood of grade repetition of OOSC in Guinea-Bissau (Marshall et
al. 2020). In addition, Okoh et al. (2020) observe that the decision of Nigerian students to refrainfrom attending school is influenced by constraints in access such as distance to the educational
institution.
The results of previous research conducted in Indonesia demonstrated similar results in which
the employment status of the head of the household, family welfare, being part of a large family,
having siblings who dropped out of school, working while schooling, and the high average cost of
education are the leading causes of OOSC in a group of school children aged 7–18 years (Gunturand Lobo 2015; Muttaqin et al. 2017; Setyadharma 2017; and Zuilkowski et al. 2019). Apart fromindividual and family factors, Setyadharma (2017) analyzed school factors and government policies
as causes of children dropping out of school. The results demonstrated that other factors, such asliving in rural areas, attending schools in urban areas, poor national exam scores of lower secondary
schools, and frequent transfers of students are the causes of children dropping out of school at theupper secondary school level in Central Java Province, Indonesia.
Previous research findings provide insight into developing variables that capture elementsthat influence children dropping out of school. The results indicate several variables that fall under
the individual (e.g., gender and home location [urban or rural areas]) and family (education of headof household and number of family members) categories as the most frequently mentioned ones inprevious research. Moreover, quantitative analysis suggests that they are critical variables. Basedon previous studies conducted in national and specific regions in Indonesia, such as Nusa Tenggara
and Central Java Province, research that examines the influence of school and teacher factors on thedecision of children to become OOSC is lacking.
Therefore, this study fills the research gap by adding school and teacher factors to complement
individual and family variables. School factors in this study include distance from home to school,
type of school (public or private), receipt of government cash assistance, and being bullied by peers
at school. Further, teacher factors include students’ perceptions of teacher quality at school andstudent-teacher relationships. Another factor related to teachers is the quality of discussions between
parents and teachers. Student-teacher relationships and discussions between parents and teachers
indicate teachers’ quality regarding their social competence. The social competence of teachers isdemonstrated through the following: (1) They are inclusive, act objectively, and do not discriminate
against students; (2) They communicate effectively, empathize, and are polite toward fellow educators,
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Major Influencing Factors of Out-of-School Children in West Java Province, Indonesia (ANGGRAYNI)

and (3) They communicate effectively verbally and in writing with students and their parents.
Although previous studies conducted by other researchers and surveys led by the Central
Statistics Agency have shown the causes of dropping out of school, the roots of the problem stillneed to be clarified. Research regarding children’s decisions to drop out of school as well as
the reasons and processes behind them still need to be researched. Thus, this research aims todetermine the main factors of OOSC by adding school and teacher factors as independent variablescausing OOSC. Furthermore, the relationship between these factors will be analyzed to determine
the direct and indirect effects of OOSC.

3. Methodology(1) Study Area
This study used a purposive sampling method based on general and specific criteria. The
general criteria are based on the completion of 9 years and 12 years of compulsory education (grossenrollment rate (GER) > 95%).13 The specific criteria for regency areas are the classification of
the number of children not in school based on high, medium, and low categories. Meanwhile, theparticular criteria for city areas are based on city characteristics and spatial location. The scopeof this research is three regencies (i.e., Ciamis, Garut, and Subang) and three cities (i.e., Bogor,
Bandung, and Cirebon). The determination of the six regencies/cities is based on data on GER
achievements and number of OOSC in West Java Province published by Statistics Indonesia in 2022with the following details:
1) The GER achievements of Ciamis Regency demonstrate the completion of 9 years and 12
years of compulsory education with the number of children not attending school under the
low category (16,536).
2) The GER achievements of Garut Regency present the completion of 9 years of compulsory
education (99.60%) with the need to complete 12 years of compulsory education andnumber of children not attending school under the high category (57,487).
3) The GER achievements of Subang Regency include the completion of 9 years ofcompulsory education with GER achievements of more than 100% and the number ofchildren not attending school under the medium category (28,648).
4) The GER achievements of Bogor City indicate that 9- and 12-year compulsory education
still needs to be completed with the number of children not attending school under the low
category (17,612). Based on characteristics, Bogor is one of the buffer or satellite cities of
the national capital, Jakarta. Meanwhile, based on spatial location, the city of Bogor is in
the west of West Java Province.
5) The GER achievements of Bandung City suggest that 9- and 12-year compulsory
education still needs to be completed with the number of children not attending school
under the high category (30,652). Based on characteristics, Bandung City is the capital ofWest Java Province. Meanwhile, it is right in the center of West Java Province based on
spatial location.
13 According to the Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia, the grossenrollment rate (GER) is defined as the percentage of students registered at a certain level of education compared withthe population of a certain school-age group. The 9-year compulsory education is considered completed if the GER
achievement is more than 95% at the lower secondary school level. The 12-year compulsory education is considered
completed if the GER achievement is more than 95% at the upper secondary school level.
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6) The GER achievements of Cirebon City indicate that 9-year and 12-year compulsory
education still needs to be completed with the number of children not attending school
under the lowest category (5,402). Based on characteristics, Cirebon City is the smallest
one in West Java Province with a spatial location in the east and a border area with Central
Java Province.

(2) Data and Variables
The present study utilizes primary methods of data collection, specifically a cross-sectional
design with a quantitative research approach using a questionnaire, to investigate the factors that
contribute to OOSC. The questionnaire assesses several aspects, including individual, family,
school, and teacher characteristics. The modified version of the questionnaire was previously
developed by Setyadharma (2017), from which the determinants of student dropout are also derived.
This study introduced variables that were excluded from the initial iteration of the Setyadharma(2017) models. In addition, the assessment consists of an open-ended response section in which
students are provided with a box for articulating their own reasoning or rationale for the causes of
OOSC.
The study recruited 1,148 participants who are students of formal and non-formal education
with 574 for each category. This study defined students of non-formal education as individuals withexperience of dropping out of formal education. Meanwhile, students of formal education are thosecurrently enrolled in formal schools.
The variables are OOSC (dependent) and gender, home location, working while at school,
perception of education, academic performance, suspension from school, absenteeism for morethan one month (excessive absenteeism), household head education of at least a university degree,parental academic support, family size, inability to pay school fees, school type, participation in acash transfer program, distance from school exceeding 10 km, bullying by peers or teachers, teacher
quality, and quality student-teacher relationships (independent).

(3) Data Analysis
Analysis was conducted using Stata 17. Descriptive statistics were used to observe thecharacteristics of OOSC. Moreover, a chi-square test was performed to test the significance of theassociation between the dependent and independent variables.
The equation for logistic regression analysis is derived as follows:
𝜋𝜋𝜋𝜋𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 �1−𝜋𝜋𝜋𝜋� = 𝛼𝛼𝛼𝛼 + 𝛽𝛽𝛽𝛽1 𝑋𝑋𝑋𝑋1+𝛽𝛽𝛽𝛽2 𝑋𝑋𝑋𝑋2+𝛽𝛽𝛽𝛽3 𝑋𝑋𝑋𝑋3 +…. 𝛽𝛽𝛽𝛽𝑛𝑛𝑛𝑛 𝑋𝑋𝑋𝑋𝑛𝑛𝑛𝑛 ,(1)

Where 𝜋𝜋𝜋𝜋 …is probability of OOSC, 𝛽𝛽𝛽𝛽1…. 𝛽𝛽𝛽𝛽𝑛𝑛𝑛𝑛 are regression coefficients indicating the relative
effect of particularly dependent variables on the dependent variable, and 𝜒𝜒𝜒𝜒1… 𝜒𝜒𝜒𝜒𝑛𝑛𝑛𝑛 are independent
variables.
The study employed GSEM for analysis to assess relationships between factors and the main
outcome variable (OOSC) as direct and indirect effects. Each domain of mediation was a binary
variable that the Bernoulli family fitted and a logit link function. The Bernoulli family refers to abinary response variable (i.e., working while attending school, academic performance, absenteeism,and OOSC). Collinearity was diagnosed using the Variance Inflation Factors (VIF) test. VIF values
detected no multicollinearity among dependent variables. Analysis began with a hypothesizedmodel (Figure 1). Modifications were taken interactively by adding a path link. Ultimately, the6

Major Influencing Factors of Out-of-School Children in West Java Province, Indonesia (ANGGRAYNI)

study retained an identified model with minimum information criteria. The final model was selectedbased on the path coefficient’s statistical significance, the relationship’s theoretical meaningfulness,and the minimum information criteria. The determinant factors of OOSC were identified according
to adjusted odds ratios with corresponding 95% confidence intervals (CI) and p-values <0.05.

4. Results and Discussion
The sample consisted of 1,148 respondents, with descriptive statistics of the characteristics
sample of OOSC (Appendix 1). According to Statistics of Indonesian data (2022) there were 912,084
OOSC in West Java Province in 2022. The OOSC category size was 30,098 (0.6%) for primaryschool-aged children (7–12 years), 203,755 (8.53%) for lower secondary school-age children (13–15
years), and 678,231 (27.16%) for upper secondary school (16–18 years). Table 1 reveals that 1,148
children (in school and out-of-school care) participated in this study, including 358 lower secondary
school-age children (13–15 years), 597 upper secondary school-age children (16–18 years), and
193 overage children (19–21 years). According to the research, there were 574 OOSC, including
93 lower secondary school-age children (13–15 years), 300 upper secondary school-age children(16–18 years), and 181 overage adolescents (19–21 years). This study focuses on the gender-based
characteristics of children; thus, the proportion of males and females was comparable with 677(58.97%) females and 471 (41.02%) males. The cases of children working while at school are 125(21.77%) in the in-school group of children and 346 (60.27%) in the group of OOSC. Based on
the characteristics of children with excessive absenteeism, the study observed striking differences
between in-school and OOSC, namely, 15 (2.61%) and 86 (14.98%), respectively. Surprisingly, thenumber of children with low academic performance does not significantly differ between children
in and out of school, namely 360 (62.71%) and 487 (84.84%), respectively.
Table 1. Weighted percentage distribution of out-of-school children (OOSC) by characteristics
No. Demographic Variables
1. Age
13–15
16–18
19–21
2. Gender
FemaleMale
3. Home location
Rural
Urban
4. Working while at school
Yes
No
5. Absenteeism more than
one month
Yes
No
6. Academic performance
Low
High

In-school
Number
Proportion

Out-of-School
Number
Proportion

Total
Number
Proportion

265
297
12

46.16
51.74
2.09

93
300
181

16.20
52.26
31.53

358
597
193

31.18
52.00
16.81

361
213

62.89
37.10

316
258

55.05
44.94

677
471

58.97
41.02

284
290

49.47
50.52

321
253

55.92
44.07

605
543

52.70
47.29

125
449

21.77
78.22

346
228

60.27
39.72

471
677

41.02
58.97

15
559

2.61
97.38

86
488

14.98
85.01

101
1,047

8.79
91.20

360
214

62.71
37.28

487
87

84.84
15.15

847
301

73.78
26.21

Source: Author 2023
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Table 2 shows that the major factors of OOSC in West Java Province are excessive
absenteeism, lack of parental encouragement for children to study well, children working whileat school, low academic achievement, the lack of discussion of academic achievement by parents
with teachers, unreceived government cash assistance program, inability to pay school fees, homedistance from school of >10 km, home location (urban or rural areas; with an alpha of 1%), andbullying by peers or teachers (with an alpha of 5%).
Table 2. Logistic regression models for examining the main factors of OOSC
Variables

OOSC
Odds ratio

Individual factors
Gender (male = 1, female = 0)
Home location (urban = 1, rural = 0)
Working while at school (yes = 1, no = 0)
Academic performance (low: 5.00–8.50 = 1, high > 8.50 = 0)
Perception of education (bad = 1, good = 0)
Absenteeism more than one month (yes = 1, no = 0)
Family factors
Education of head of household at least a university degree
Academic support from parents:a. Your parents ever encouraged you to study well (no support = 1, support = 0)
b. Your parents ever talked/discussed with teacher about your development/activities in school(no support = 1, support = 0)
Family size
Inability to pay school fees (yes = 1, no = 0)
School factors
School distance more than 10 km (yes = 1, no = 0)
Government’s cash transfer program (unreceived = 1, received = 0)
Bullying by peers or teachers (yes = 1, no = 0)
School type (private = 1, public = 0)
Teacher factors
Student–teacher relationships (bad = 1, good = 0)0.969
(0.153)
1.661***(0.254)4.212***(0.677)2.803***(0.495)
1.198(0.535)10.51***(4.296)0.141***(0.0538)4.444***(2.154)2.718***(0.449)1.203***(0.0538)
1.898***(0.289)1.802***(0.339)1.942***(0.299)1.613**(0.346)
0.769*(0.117)1.731(0.777)1.995(1.304)Teacher quality (bad = 1, good = 0)

Note. Dependent variable: out-of-school children (OOSC = 1, in school = 0).
A constant is also included in each model, but its coefficient is not reported.
***p ≤ 0.01, **p ≤ 0.05, and *p ≤ 0.1.
8Major Influencing Factors of Out-of-School Children in West Java Province, Indonesia (ANGGRAYNI)Table 3 indicates that home location, working while at school, academic performance,
absenteeism, household head education of at least a university degree, academic support fromparents, family size, inability to pay school fees, home distance from school more than 10 km,
unreceived government cash transfer program, and bullying by peers and/or teachers exerted direct
effects on OOSC. Meanwhile, other variables exert direct and indirect effects on OOSC such as
working while at school, absenteeism, household head education of at least a university degree,
inability to pay school fees, and bullying by peers. Meanwhile, variables that indirectly influence
OOSC are gender, perception of education, school type, relationships between teachers and students,
and teacher quality.
Table 3. Generalized structural equation modeling coefficient estimates of the direct, indirect,
and total effects factors on out-of-school children.
Variables
Individual factors
Gender (male = 1, female = 0)
Home location (urban = 1, rural = 0)
Working while at school (yes = 1, no = 0)
Academic performance (low: 5.00–8.50 = 1, high > 8.50 = 0)
Perception of education (bad = 1, good = 0)
Absenteeism more than one month (yes = 1, no = 0)
Family factors
Education of head of household at least a university degree
Academic support from parents:a. Your parents ever encouraged you to study well
(no support = 1, support = 0)
b. Your parents ever talked/discussed with teachers about yourdevelopment/activities in school(no support = 1, support = 0)
Family size
Inability to pay school fees (yes = 1, no = 0)
School factors
Home distance more than 10 km from school (yes = 1, no = 0)
Unreceived cash transfer from the government to poor students(yes = 1, no = 0)
Bullying by peers or teachers (yes = 1, no = 0)
School type (private = 1, public = 0)
Teacher factors
Student-teacher relationships (bad = 1, good = 0)
Teacher quality (bad = 1, good = 0)

Direct Effects
B (95% CI)

Indirect
Effects
B (95% CI)

Total Effects
B (95% CI)–
0.53***
1.40***
1.10***–
2.40***

1.00***–
0.67***
0.82**
0.70***

1.00***
0.53***
2.07***
1.10***
0.82**
3.10***−1.90***−1.20***−3.10***

1.60***–

1.60***

0.99***–

0.99***

0.18***
0.59***–
0.79***

0.18***
1.38***

0.57***
0.64***––

0.57***
0.64***

0.46**

0.95***
0.55**

1.41***
0.55**

1.11***
0.92**

1.11***
0.92*****p ≤ 0.01, **p ≤ 0.05, and *p ≤ 0.1.
According to the logistic regression (Table 2) and GSEM (Table 3) analyses, the major factorsof OOSC with the highest odds ratio and total effects are absenteeism (OR = 10.51, B = 3.10),
working while at school (OR = 4.21, B = 2.07), lack of support from parents (OR = 4.44, B = 1.60),
low academic performance (OR = 2.80, B = 1.1), and the lack of discussion by parents with teachers
about the academic development their children (OR = 2.71, B = 0.9).
Furthermore, the results of logistic regression analysis of home location indicate that the odds
ratio of children living in urban areas becoming OOSC is higher than that of school-aged children
living in rural areas. This result differs from that of Setyadharma (2017) in Central Java Province,
Indonesia. The reason is that opportunities for informal sector employment in urban West Java
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Province are more significant than those in rural areas, which enables children to attend school
while working. The results demonstrated that the odds ratio of children who work while at school is
4.21 higher than children who do not work while at school. The results of GSEM analyses (Figure 1)
provide a more in-depth explanation of the findings reported by Lobo (2015), which identified thatchildren do not go to school in East Nusa Tenggara Province because they are busy working while
attending school.
Figure 1 shows that teacher quality, inability to pay school fees, and household head educationof at least a university degree indirectly influence OOSC but directly influence children who work
while attending school. An education of at least a university degree for the head of the household
exhibited a negative coefficient, which indicates that children whose heads of household with abachelor’s degree can reduce the possibility of children working while attending school. Meanwhile,
teacher quality exerted a higher direct effect on children working than the inability to pay school
fees. This finding implies that children work while attending school not only because they cannot
pay school fees but also because of their perceptions of the low quality of teachers.

Figure 1. Path analysis using coefficient estimates generalized structural equation modeling ofout-of-school children in West Java Province
Source: Author
In Indonesia, teacher quality can be reflected through the competence of teachers (Panggabeanand Himawan 2016). The Indonesian government assessed the teacher quality of 2.9 million teachersin Indonesia through the Teacher Competency Test in 2015.14 Data from the Ministry of Education,
Culture, Research, and Technology (MoECRT) demonstrate that the average Teacher Competency
14 According to the Education Balance Data released by the Ministry of Education and Culture of the Republicof Indonesia in 2016, the Indonesian government-conducted Teacher Competency Test has set a national minimum
competency standard of a score of 55 for two fields, namely, pedagogical and professional competence.
10

Major Influencing Factors of Out-of-School Children in West Java Province, Indonesia (ANGGRAYNI)Test score in West Java Province was 58.97, higher than the national minimum competency standard
for teachers of the Republic of Indonesia, namely a score of 55.00. The Indonesian government
has implemented a professional teacher education program from 2017 to the present to improveteacher competency.15 Teachers who graduate from the teacher education program in Indonesia
will receive a professional certificate. Alternatively, data from the MoECRT in 2022 suggested thatthe percentages of teachers in West Java Province with a professional license were only 36.5% and56.9% at the lower and upper secondary school levels, respectively.16 This statistic implies that theperceptions of students of the low quality of teachers are relevant to the low average test scores forteacher competency and the low percentage of teachers with professional certificates. Therefore, theefforts of the regional government of West Java Province to guarantee free education up to the upper
secondary school level since 2020 are insufficient for overcoming the problem of children working
while attending school, because solving the issue of low-quality teachers in West Java Provinceremains essential.
The results of GSEM also depict that another factor with the highest direct effect on OOSC isexcessive absence (more than one month). The path analysis in Figure 1 implies that the factors that
directly influence excessive absenteeism are gender (male), school type (private), poor relationships
between teachers and students, and experiencing bullying by peers. Moreover, the results suggestthat poor relationships between teachers and students exert the highest direct effect on excessive
absenteeism compared with other factors. These findings, in line with the 2018 PISA results forIndonesia at the national level, display three characteristics of students with excessive absenteeism,
namely, being male, attending private schools, and experiencing bullying from peers (OECD 2019).
The originality of this research is in finding new factors that cause excessive absenteeism, namely
the student-teacher relationships. Therefore, enhancing the quality of student-teacher relationships isessential to decrease the prevalence of students with excessive school absences.
Figure 1 depicts that children working while attending school, excessive absenteeism, andperceptions of education directly influence the low academic achievement of students. However, theperception of education exerted a more significant direct influence on low academic achievement
than working while attending school and excessive absenteeism. A method for measuring students’
perceptions about education is determining whether or not students like school or consider it awaste of time. The results show that the perception of students who do not like school and believethat school is a waste of time directly influences their low academic achievement. Thus, changing
children’s perception of education from bad to good is essential to increase academic performance.
If children like attending school and believe that education is not a waste of time, it can decreaseabsenteeism and prioritize school over working.
Furthermore, the results of the GSEM analysis indicate important factors regarding parents’
academic support; namely, the variables “encouraged to study well” and “discussed with teacher”
are the most significant direct influences on OOSC. These findings align with the 2018 PISA resultsfor Indonesia; schools with students from the socioeconomic background coming from the lowest
25–50% category face limited involvement in learning from parents and a lack of parental initiativein knowing their children’s academic performance (OECD 2019).
Thus, the findings of this study revealed the issue of OOSC not only in West Java Province
15 Based on the 2005 Teacher and Lecturer Law, teachers in Indonesia must obtain four empathetic competencies,
namely, personality, pedagogy, professional, and social.
16 According to the Education Balance Data released by the Ministry of Education, Culture, Research, andTechnology of the Republic of Indonesia in West Java Province in 2022, teachers with a professional license are inservice teachers who participated in an experienced teacher education program and obtained an educator certificate.
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Volume 5, 12. July 2024

but also nationwide. This is due to the characteristics of West Java Province being similar toother regions in Indonesia, such as the problem and implementation of the strategy for handling
OOSC issues, which are national policies. The ineffectiveness of central government policies, as
executed through the national strategy for addressing OOSC in West Java Province, has resulted ina struggle to reduce the number of children not attending school effectively. The national and local
governments so far have focused mainly on addressing difficulties from the economic perspective ofstudents, such as direct financial assistance, which, according to the findings in this research, is not
the main reason for OOSC. This study indicates that the teacher and school environment elements
play a crucial role in the primary factors contributing to children’s absenteeism from school.
Ultimately, this research suggests the need for policies to improve teacher quality, increase thequality of student-teacher relationships, change student perceptions of education from bad to good,and increase parent/guardian involvement in learning to decrease the number of OOSC. These
factors are related to the teachers’ social competence. Therefore, increasing teacher competence,
particularly social competencies, through professional teacher education programs can be a strategic
policy to address the problem of OOSC not only in West Java Province but also nationwide inIndonesia.

Conclusion and Recommendation
This study identified the major factors of OOSC in West Java Province, Indonesia, using
a quantitative approach comprising logistic regression analysis and GSEM. The major factors of
OOSC, based on the result of logistic regression analysis, are student absenteeism, working while
attending school, low academic achievement, lack of encouragement from parents to children to
study well until completion of education, lack of discussion between parents and teachers about theacademic progress of children, and non-receipt of cash transfer from the government, inability to
pay school fees, home distance from school more than 10 km, home location (urban), and bullying
by peers and teachers.
Furthermore, an essential result of GSEM analysis refers to the relationship between the
independent variables that directly or indirectly influence decisions about OOSC. The variables
that directly affect OOSC with the highest coefficient estimates are absenteeism, working while atschool, low academic performance, lack of support from parents, and lack of discussion between
parents and teachers. The variables that indirectly influence OOSC are teacher factors, such as thelow quality of teachers, poor student-teacher relationships, and bad student perception of education.
Thus, the results of the analyses in this study suggest the need for policies to improve teacher
quality, particularly in increasing teachers’ social competencies in Indonesia.
The limitations of this study include absenteeism, teacher quality, and student-teacher
relationships of direct student voice in the questionnaire for both OOSC and school students. Future
research on this topic is necessary to examine the impact of professional teacher education programson teacher competencies and the teachers’ perspectives on the programs required to enhance theircompetencies, particularly social competencies.

Acknowledgments

This work was supported by the JICA SDGs Global Leader research grant.

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Major Influencing Factors of Out-of-School Children in West Java Province, Indonesia (ANGGRAYNI)

References
Books and Articles

Kamran, R., and S. ul Deen. 2017. Out of School Children: Causes behind Primary Dropout.
Journal of Inclusive Education, 1(1), 35–51. <https://ojs.aiou.edu.pk/index.php/jie/article/
view/238> (Accessed on September 16, 2022).
Kusumah, C. K. 2021. 12-Years Compulsory Education Policy and Education Participation
Completeness: Evidence from Indonesia. The Journal of Indonesia Sustainable Development
Planning, 2(2), 187–201. <https://doi.org/10.46456/jisdep.v2i2.138> (Accessed on November 21,
202ha2).
Leer, J. 2016. After the Big Bang: Estimating the Effects of Decentralization on Educational
Outcomes in Indonesia through a Difference-in-differences Analysis. International Journalof Educational Development, Elsevier, 49, 80–90. <http://doi: 10.1016/j.ijedudev.2016.02.005>(Accessed on September 18, 2022)
Guntur, R. D. and M. Lobo. 2015. An Analysis of Factors That Affect Out of School Junior High
Aged Children Using Logistic Regression Method. Asia-Pacific Collaborative Education
Journal, 11(2), 23–36. <https://db.koreascholar.com/Article/Detail/311254> (Accessed on
September 4, 2022).
Marshall, J. H., M. Nicolai, and R. da Silva. 2020. Out-of-School Children in Guinea-Bissau: A
Mixed-Methods Analysis. International Journal of Educational Development, 77, 102223.
<https://doi.org/10.1016/j.ijedudev.2020.102223> (Accessed on October 10, 2022).
Moyi, P. 2013. Primary School Attendance and Completion among Lower Secondary School AgeChildren in Uganda. Current Issues in Education, 16(2). <https://cie.asu.edu/ojs/index.php/
cieatasu/article/view/1111/491> (Accessed on April 18, 2023).
Muttaqin, T., R. Wittek, L. Heyse, and M. van Duijn. 2017. Why Do Children Stay out of School inIndonesia? Jurnal Perencanaan Pembangunan (The Indonesian Journal of Dev. Planning),
1(2), 93–108. <https://doi.org/10.36574/jpp.v1i2.12> (Accessed on June 17, 2022).
Okoh, C. N., Emenike J. A., A. Doma, and Mary O. A. 2020. Out of School Children: Enhancing
Factors and Consequences for Sustainable Development in North Central Geo-political Zone,
Nigeria. American Journal of Educational Research, 8(10), 804–811. <https://doi.org/10.12691/
education-8-10-10> (Accessed on September 16, 2022).
Panggabean, M., and K. K. Himawan. 2016. The Development of Indonesian Teacher Competence
Questionnaire. Journal of Educational, Health and Community Psychology, 5(2). <http://
journal.uad.ac.id/index.php/Psychology/article/view/5134> (Accessed on September 5, 2023)
Priyambada, A., A. Suryahadi, and S. Sumarto. 2005. Child Labor in Indonesia during the
Economic Crisis: The Trade-Off between School and Work. SMERU Working Paper. <http://smeru.or.id/sites/defa> (Accessed on August 8, 2022)
Soule, N. and H. Curtis. 2021. High School Home Visits: Parent-Teacher Relationships and Student
Success. The School Community Journal, 31(2), 131–153. < https://files.eric.ed.gov/fulltext/
EJ1323070.pdf> (Accessed on October 17, 2023).
Setyadharma, Andryan. 2017. Upper Secondary School Dropout: Lessons from Central Java
Province, Indonesia. Massey University New Zealand. <https://mro.massey.ac.nz/bitstream/
handle/10179/11854/02_whole.pdf> (Accessed on July 8, 2022).
Tsaneva, Magda. 2017. Does School Matter? Learning Outcomes of Indonesian Children after
Dropping out of School. World Development Perspectives, 6, 1–10. <https://www.researchgate.
net/publication/315331424> (Accessed on July 3, 2022).

13

Asia-Japan Research Academic Bulletin

Volume 5, 12. July 2024

Vayachuta, P., A. Ratana-Ubol, and W. Soopanyo. 2016. The Study of ‘Out-of-School’ Children andYouth Situations for Developing a Lifelong Education Model for ‘Out-of-School’ Children
and Youth. SHS Web of Conferences, 26, 01015. <https://doi.org/10.1051/shsconf/20162601015>(Accessed on September 9, 2023).
Zuilkowski, S. S., U. Samanhudi, and I. Indriana. 2019. “There Is No Free Education Nowadays”:
Youth Explanations for School Dropout in Indonesia. Compare: A Journal of Comparativeand International Education, 49(1), 16–29. <https://www.tandfonline.com/doi/full/10.1080/030
57925.2017.1369002> (Accessed on July 13, 2023).

Statistics, Reports and Websites

Ministry of Education, Culture, Research, and Technology (MoECRT). 2020. “Rencana Strategis
Kementerian Pendidikan Dan Kebudayaan 2020-2024”. (website) Available at <https://
kspstendik.kemdikbud.go.id/storage/uploads/file-renstra/1710990484.pdf> (Accessed on April
14, 2023).
———. 2022a. “Peraturan Pemerintah (PP) N0. 4/2022 Tentang Perubahan Atas Peraturan
Pemerintah No. 57/2021 Tentang Standar Nasional Pendidikan”. (website) Available at <https://peraturan.bpk.go.id/Home/Details/196151/pp-no-4-tahun-2022> (Accessed on April 14, 2023).
———. 2022b. “Jumlah Anak Tidak Sekolah di Indonesia.” Accessed Available at <https://pd.data.
kemdikbud.go.id/ATS/index.php/publik/rekap_data_ats> (Accessed on April 14, 2023).
———. 2022c. “Statistik Pendidikan Provinsi Jawa Barat Tahun 2022” (website) Availabe at<https://jabar.bps.go.id/publication/download.html?nrbvfeve=YWYyZmE4YjhmNWMyOGZj
YmZhNzM1ZTA4&xzmn=aHR0cHM6Ly9qYWJhci5icHMuZ28uaWQvcHVibGljYXRpb24v
MjAyMy8xMi8yOS9hZjJmYThiOGY1YzI4ZmNiZmE3MzVlMDgvc3RhdGlzdGlrLXBlbmRpZGlrYW4tcHJvdmluc2ktamF3YS1iYXJhdC0yMDIyLmh0bWw%3D&twoadfnoarfeauf=Mj
AyNC0wNi0yMCAxMDowNjowOA%3D%3D> (Accessed on September 7, 2023)———. 2023a. “Statistik Sekolah Menengah Pertama (SMP) Tahun 2022–2023”. (website)
Available at <https://data.kemdikbud.go.id/publikasi?sort_by=SORT_BY_CREATED_AT_
DESC&s=Statistik+Sekolah+Menengah+Pertama+%28SMP%29+Tahun+2022%2F2023>(Accessed on April 14, 2023).
———. 2023b. “Statistik Sekolah Menengah Atas (SMA) Tahun 2022/2023”. (website) Available
at <https://data.kemdikbud.go.id/publikasi?year=2023&sort_by=SORT_BY_CREATED_
AT_DESC&s=Statistik+Sekolah+Menengah+Atas+%28SMA%29+Tahun+2022%2F2023>(Accessed on April 14, 2023).
———. 2023c. “Statistik Sekolah Menengah Kejuruan (SMK) Tahun 2022/2023”. (website)
Available at <https://data.kemdikbud.go.id/publikasi?sort_by=SORT_BY_CREATED_AT_
DESC&s=Statistik+Sekolah+Menengah+Kejuruan+%28SMK%29+Tahun+2022%2F2023>(Accessed on April 14, 2023).
———. 2023d. “Statistik Pendidikan Masyarakat (Dikmas) Tahun 2022/2023”. (website)
Available at <https://data.kemdikbud.go.id/publikasi?sort_by=SORT_BY_CREATED_AT_
DESC&s=Statistik+Pendidikan+Masyarakat+%28Dikmas%29+Tahun+2022%2F2023>(Accessed on April 14, 2023).
Ministry of Education and Culture (MoEC). 2019a. “Pendidikan di Indonesia Belajar dari Hasil
PISA 2018: Programme for International Student Assessment” (website) Availabe at <https://
repositori.kemdikbud.go.id/16742/1/Laporan%20Nasional%20PISA%202018%20Indonesia.
pdf>(Accessed on September 20, 2023).

14

Major Influencing Factors of Out-of-School Children in West Java Province, Indonesia (ANGGRAYNI)

OECD. 2019. “PISA 2018 Results: Insight and Interpretations” (website) Available at <https://www.
oecd.org/pisa/PISA%202018%20Insights%20and%20Interpretations%20FINAL%20PDF.pdf>(Accessed on September 25, 2023).
UIS. 2015. Out-of-School Children and Adolescents in Asia and the Pacific: Left Behind on the
Road to Learning Opportunities for All. Available at <https://uis.unesco.org/sites/default/files/
documents/out-of-school-children-and-adolescents-in-asia-and-pacific-left-behind-on-theroad-to-learning-opportunities-for-all-2015-en.pdf> (Accessed on September 17, 2022).
———. 2019. New Methodology Shows that 258 Million Children, Adolescents and Youth Are
Out of School Out-of-School Children. Available at <https://uis.unesco.org/sites/default/files/
documents/new-methodology-shows-258-million-children-adolescents-and-youth-are-outschool.pdf> (Accessed on September 25, 2022).

15

Description

16

Dummy for children denoting whether they are ever inability to pay school fees while they wereattending in school (yes = 1, no = 0)

Dummy denoting that children last school was more than 10km from their home (yes = 1, no = 0)

Dummy for children denoting whether they received money from the government’s poor student’s
assistance program or not at in school (unreceived = 1, received = 0)

Dummy for children denoting whether they ever victim of bullying was attending in school (ever = 1,
never = 0)

School distance

Government’s cashtransfer program

Bullying by peers orteachers

Dummy for children denoting their teacher quality (bad = 1, good = 0)

Dummy for children denoting that their ever victim of bullying were attending in school (poor = 1,
good = 0)

Teacher quality

Student–teacher
relationships

Teacher

Dummy denoting children school type (private school = 1, public school = 0)

School type

SchoolIndicates number of household members (including household head)

1,148

1,148

1,148

1,148

1,148

1,148

1,148

1,148

1,148

b. Have your parents ever encouraged you to study well in school (never = 1, ever = 0)

Inability to pay school
fees

1,148

1,148

a. Have your parents ever talked/discussed with teacher about your development/activities in school(never = 1, ever = 0)

Indicates the household head a university degree (yes = 1, no = 0)

Family size

Parents academic
support

Education of head ofhousehold at least auniversity degree

Family1,148Absenteeism

Dummy for children denoting their whether ever absenteeism more than one month(ever absenteeism = 1, never = 0)

1,148

1,148

Perception of education A combination of dummy for children denoting whether they think school was a waste of time or not
and/or whether they like school or not (bad = 1, good = 0)

Dummy for children denoting whether or not they ever earned money when they were in school(yes = 1, no = 0)

Working while atschool

1,148

1,148

Dummy for children denoting home location (urban = 1, rural = 0)

Home location

1,148

1,148

Obs

Academic performance Dummy for children denoting their academic performance (low (5.01–8.50) = 1, high (>8.50) = 0)

Dummy for children denoting gender (male = 1, female = 0)

GenderIndividual

Out-of-school children Dummy for children denoting whether or not they are OOSC (out-of-school children = 1, in school = 0)

Dependent variable

Variable

Appendix Table 1. Descriptive statistics of characteristic sample

0.028

0.045

0.181

0.610

0.195

0.460

0.471

5.628

0.053

0.663

0.089

0.087

0.039

0.737

0.410

0.472

0.409

0.50

0.167

0.208

0.385

0.487

0.396

0.498

0.499

2.176

0.224

0.472

0.285

0.283

0.194

0.440

0.492

0.499

0.491

0.500

0

0

0

0

0

0

0

3

0

0

0

0

0

0

0

0

0

1

1

1

1

1

1

1

23

1

1

1

1

1

1

1

1

1

Mean Std Dev Min Max

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