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Systematic Review

Determinants of Child Poverty in Low Middle-Income Countries - Southeast Asia: A Systematic Literature Review

[version 1; peer review: awaiting peer review]
Bazlin Fadilah
https://orcid.org/0009-0003-1000-2982
1, Siti Munawaroh
https://orcid.org/0009-0002-7597-4179
1, Arif Hermawan
https://orcid.org/0009-0003-1673-0147
1
Bazlin Fadilah
https://orcid.org/0009-0003-1000-2982
1, Siti Munawaroh
https://orcid.org/0009-0002-7597-4179
1, Arif Hermawan
https://orcid.org/0009-0003-1673-0147
1
PUBLISHED 17 Jul 2026
Author details Author details
OPEN PEER REVIEW
REVIEWER STATUS AWAITING PEER REVIEW

Abstract

The multidimensional roots of child poverty in Southeast Asia’s middle-income economies remain largely under-explored, even as the problem itself continues to pose a significant regional challenge. This study aims to identify and synthesize the determinants that drive the spread of child poverty in the Southeast Asian region. This systematic literature review (SLR) was carried out following the PRISMA 2020 protocol. Literature searches were conducted on Scopus, PubMed, and Google Scholar databases for articles published over the last ten year (2015-2025). The screening process for the initial 1,751 articles was facilitated by an AI-based application, Rayyan, to ensure the objectivity and efficiency of the selection based on the PEO framework. A synthesis of qualified articles shows that child poverty in Southeast Asia is triggered by complex interactions between socio-economic, health, geographical, disaster and cultural factors.

Keywords

Child Poverty, Child Deprivation, Multidimensional Poverty, Southeast Asia, Low Middle-Income Countries

Corresponding author: Bazlin Fadilah
Competing interests: No competing interests were disclosed.
Grant information: This work was supported by The Indonesia Endowment Fund for Education [Lembaga Pengelola Dana Pendidikan] (LPDP), a scholarship program under the auspices of the Ministry of Finance of The Republic of Indonesia.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Copyright:  © 2026 Fadilah B et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite: Fadilah B, Munawaroh S and Hermawan A. Determinants of Child Poverty in Low Middle-Income Countries - Southeast Asia: A Systematic Literature Review [version 1; peer review: awaiting peer review]. F1000Research 2026, 15:1183 (https://doi.org/10.12688/f1000research.185580.1) First published: 17 Jul 2026, 15:1183 (https://doi.org/10.12688/f1000research.185580.1) Latest published: 17 Jul 2026, 15:1183 (https://doi.org/10.12688/f1000research.185580.1)

Introduction

Child poverty is recognized as a multifaceted issue, reflecting a situation in which essential rights are not fulfilled. (UNICEF 2023). Currently, one in five children worldwide lives in extreme poverty. A total of 417 million children in low- and middle-income countries experience deprivation in more than two essential living needs, including education, health, shelter, nutrition, sanitation, and clean water (UNICEF 2025). Consequently, children are the most at risk of falling into a cycle of intergenerational poverty.

Due to its complexity, child poverty spans material, psychosocial, and multidimensional deprivations. Material lack involves unmet physical needs such as inadequate education, sanitation, clean water, healthcare, and housing (Dat et al. 2015; Isaura et al. 2022; Cortes-Cely et al. 2026; Sánchez Vargas & Dip 2026) which directly dictates a child’s social life (Shaoqi et al. 2018). Consequently, impoverished children face diminished physical and mental health, poor social development, and increased risks of violence or neglect, severely limiting their future prospects (Crawford 2017; Ozoemenam et al. 2022).

Southeast Asia, where over half the population is young (ASEAN Secretariat 2023), faces a critical gap between monetary poverty reduction and child well-being (World Bank 2024). Poverty affects 23.4% of children in the Philippines (UNICEF 2025), 11.44% in Indonesia (BPS 2024), and up to 20% in Cambodia and Laos via multidimensional deprivations (Alkire et al. 2025). These figures confirm that children in this region are two to three times more likely to be impoverished than adults (OPHI 2025).

Child poverty arises from an intricate interplay of determinants. While household characteristics influence the micro level, geographical and infrastructural factors such as inadequate health facilities, sanitation, and clean water perpetuate poverty at a higher level. In Southeast Asia, these deficiencies are prevalent in rural areas, where poor sanitation directly drives stunting (Isaura et al. 2022; Blankenship et al. 2020; Cetthakrikul et al. 2018; Yunitasari et al. 2022). Furthermore, existing social protection systems often remain fragmented and child-insensitive, focusing on general households rather than age-specific needs. Analysing these complexities is essential for mapping contextual policies and understanding the unique barriers to children’s rights within the region’s heterogeneous dynamics.

Despite extensive literature, child poverty determinants in lower-middle-income Southeast Asian countries remain understudied at a regional level. This study employs a Systematic Literature Review (SLR) to synthesize fragmented, single-country evidence from the last decade into a holistic framework. By identifying dominant determinants, it aims to bridge theoretical gaps and provide practical strategies for breaking the intergenerational poverty cycle.

Methods

This study utilizes a Systematic Literature Review (SLR) design, adhering to the PRISMA 2020 protocol to ensure transparency, accuracy, and replicability (Page et al. 2021; Sarkis-Onofre et al. 2021). The review aims to comprehensively synthesize child poverty determinants within Southeast Asia. Research focus was structured using the PEO (Population, Exposure, Outcome) framework, a standard for analyzing qualitative cause-and-effect relationships in health (Capili 2020; Aboagye et al. 2021; Hosseini et al. 2024). Specifically, the study examines the child population (0–18 years) subjected to socio-economic, environmental, and geographical poverty drivers in Southeast Asia’s lower-middle-income nations.

Information sources and search strategy

The literature search strategy was carried out on three reputable databases in February 2026, namely Scopus, PubMed, and Google Scholar, which resulted in a total of 1,751 articles. The three databases were searched for literature using boolean operators; (TITLE-ABS-KEY (“Child” OR “Childhood”) AND (“Poverty” OR “Deprivation”) AND (“Indonesia” OR “Vietnam” OR “Philippines” OR “Lao PDR” OR “Myanmar” OR “Cambodia” OR “Timor Leste” OR “Southeast Asia”)) AND PUBYEAR >2014 AND PUBYEAR <2026 AND (LIMIT-TO (OA, “all”)) AND (LIMIT-TO (AFFILCOUNTRY, “Indonesia”) OR LIMIT-TO (AFFILCOUNTRY, “Philippines”) OR LIMIT-TO (AFFILCOUNTRY, “Cambodia”) OR LIMIT-TO (AFFILCOUNTRY, “Myanmar”) OR LIMIT-TO (AFFILCOUNTRY, “LAOS”)) AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (LANGUAGE, “ENGLISH”)). After removing 143 duplicates using automatic reference management (Herwansyah et al. 2022), 1,608 articles were selected based on specific inclusion and exclusion criteria.

Eligibility criteria, selection process, and data collection process

Study feasibility was determined by rigorous inclusion and exclusion criteria. Inclusion was limited to empirical, full-text English articles (quantitative, qualitative, or mixed methods) published between 2015 and 2025, focusing on multidimensional child poverty in Indonesia, Vietnam, the Philippines, Cambodia, Lao PDR, Myanmar, and Timor-Leste. Conversely, non-empirical works like narrative reviews and protocols without primary data were excluded. To enhance collaborative screening and efficiency, the AI-driven Rayyan platform was utilized (Ouzzani et al. 2016; Johnson & Phillips 2018). Screening was conducted independently by three reviewers against the PEO-based criteria, and any disagreements were resolved through discussion until consensus was reached. Finally, data from eligible studies were qualitatively synthesized to explore the socio-economic, environmental, and cultural determinants of child poverty in Southeast Asia.

Result

Study selection

Following PRISMA 2020 guidelines (Page et al., 2021), the study selection was conducted in distinct stages on Figure 1. An initial search on February 6, 2026, across Scopus (n = 1,154), PubMed (n = 397), and Google Scholar (n = 200) identified 1,751 records. After removing 143 duplicates, 1,608 articles were screened by title and abstract, leading to the exclusion of 1,511 irrelevant works. The remaining 97 articles underwent full-text eligibility assessment, where 51 were excluded for reasons such as incorrect outcomes, populations, or study designs. A further 10 articles were removed following in-depth screening due to non-conforming data. Ultimately, 36 articles met all criteria and were included in the qualitative synthesis.

a6e16dfb-c449-4910-9958-c899af569f8f_figure1.gif

Figure 1. PRISMA 2020 flow diagram.

Geographical distribution of the studies

Among the selected final review studies, twenty-three were conducted in Indonesia, three in the Philippines, three in Cambodia, three in Myanmar, three in Vietnam, and one in Laos. A detailed distribution of the study is presented in Figure 2.

a6e16dfb-c449-4910-9958-c899af569f8f_figure2.gif

Figure 2. Geographical distribution of the studies (n = 36).

Data extraction

Based on Table 1, the data extracted from the 36 included studies are presented according to author, study design, country of study, population and sample, outcome, and key results. The extraction reveals a predominance of cross-sectional and quantitative designs, with Indonesia accounting for the majority of the studies and the remaining evidence distributed across the Philippines, Cambodia, Myanmar, Vietnam, and Laos. The outcomes examined span a range of child poverty indicators such as stunting, acute respiratory infection, child labour, and early marriage, reflecting the multidimensional nature of deprivation in the region. These extracted characteristics provide the basis for the thematic synthesis of determinants discussed in the following section.

Table 1. Overview of the record (study design, country of study, population & sample, outcome, and results).

No.AuthorsStudy designCountry of studyPopulation & sampleOutcomeResults
1Mauludyani et al. 2025Cross-sectional study using multilevel linear regression analysisIndonesiaPopulation: 514 districts/cities in Indonesia. Population: children under five years of age (toddlers)Sanitation and drinking water, food security1. At the district level, poverty, food expenditure >65%, lack of access to clean water, and length of schooling for women are significantly positively associated with stunting.
2Fatimah et al. 2025Comparative study with cross-sectional designIndonesiaPopulation: Poor households in the urban area of Cianjur Regency. Sample: Total 126 respondents (mothers with children under the age of five (toddlers)Food Security1. Food Security: The majority of households experience food insecurity (92.1% in urban areas and 96.7% in rural areas)
3Alristina et al. 2025Cross-sectional study using multilevel linear regression analysisIndonesiaPopulation: All mothers or primary caregivers of children attending Posyandu in Surabaya. Sample:
657 mothers who had at least one child between the ages of 36 and 59 months		Household income, parental education, employment status, food security1. Prematurity: Low maternal education was associated with a 3-fold higher risk of preterm birth (AOR = 3.23; p < 0.001). In addition, prematurity correlates with home ownership.
4Manrique-de Hitta et al. 2025Cross-sectional analyticsPhilippinesPopulation: Primary caregivers with infants and young children aged 0–23 months living in the Fourth District of Camarines Sur. Sample: 628 peoplehousehold income, parental education, Sanitation and drinking water, parental employment status, food security1. Stunting factors for infants aged 0–6 months include age (risk decreases), economic status, and non-college maternal education (lower risk), while for children aged 6–24 months, risk increases with age and is higher in males.
5Mitra et al. 2025Case-control studyIndonesiaPopulation: All children aged 12–59 months in the Fifty Health Center Working Area, Pekanbaru, Indonesia. Sample: 108 childrenHousehold income, parental education, parenting, parental employment status1. Sociodemographic factors (knowledge, attitudes, and income) are indirectly related to stunting through its influence on parenting (β = 1.33; p < .001) 2. Parenting (practices of feeding, hygiene, sanitation, and health services) has a direct impact on stunting (β = 0.09; p = .049)
6Agushybana et al. 2025Cross-sectional study designIndonesiaPopulation: Households in Indonesia that represent 83% of the population in 13 provinces. Sample: 3397 childrenparental education, Sanitation and drinking water, spatial gaps, parents’ employment status, maternal health, spatial gaps1. Key stunting drivers include older child age, low birth weight (LBW), incomplete prenatal care, poor sanitation, and lack of maternal health (KIA) books. Interestingly, in 1997, paternal elementary education was linked to higher stunting rates compared to fathers with no formal schooling.
7Khoiruddin et al. 2025Quantitative using binary and multinomial logit regressionIndonesiaPopulation: Children in Indonesia. Sample: 261,673 children aged 5–15 yearsparental education, parental employment status, asset ownership, spatial gap, Smoking culture at home1. Informal work is consistently associated with a higher risk of multidimensional child poverty, especially in rural households 2. Single parent status, large number of children, and smoking increase children’s vulnerability to deprivation 3. There is a real territorial gap, where children in rural areas face a much higher risk of deprivation than in urban areas
8Idawati et al. 2024Cross-sectional surveyIndonesiaPopulation: All married women under the age of 19 in rural areas of Aceh Province. Sample: 507 femalesEarly Marriage1. Parental influence is the primary driver of early marriage in Aceh, with a 10.34 times higher risk for women under heavy parental pressure. Economics also play a key role; women from the poorest families are 2.23 times more vulnerable than those from the wealthiest.
9Ayu et al. 2024Observational quantitative analytics with case-control designIndonesiaPopulation: All short and very short toddlers living in rural areas in Batu Bara Regency. Sample: 100 households with short and very short toddlers with an age range of 12–59 monthsParental education, Sanitation and drinking water, maternal health, smoking culture at homeResearch identified several major risk factors for stunting: 1. Low maternal education, Birth weight, Formula feeding for less than 6 months, Lack of access to proper drinking water, Poor maternal nutritional status during pregnancy, Low family wealth index.
10Seran & Sengkoen 2024Quantitative with descriptive and inferential analysis methods using multiple linear regressionIndonesiaPopulation: Households with children under five in the Belu Regency area, especially in Atambua City and Halilulik Village. Sample: 100 households with children under the age of five (toddlers)Parental Education, Household Income, Parent’s Employment Status1. Nutritional factors contribute the largest to the incidence of stunting (13.41%) 2. Children with low nutritional intake have a 4.913 times greater risk of stunting 3. In addition, low maternal education levels (≤ junior high school) increased the risk of stunting by 8,081 times, and low-income families.
11Laksono et al. 2024Secondary data analysis with a cross-sectional study design using the two-stage stratified sampling methodIndonesiaPopulation: All Indonesian children under the age of five (toddlers) living in urban poor communities. Sample: 43,284 childrenParental Education, Parental Employment Status1. Factors that significantly increase the risk of stunting include: low maternal education, unemployed mothers, the poorest poverty rate, no ANC during pregnancy, older children (compared to the 0–11 month group), and male children
12Iwo et al. 2024Longitudinal research using data from the Study of the Tsunami Aftermath and Recovery (STAR)IndonesiaPopulation: Individuals and households in 13 districts in Aceh and North Sumatra. Sample: 5,429 childrenParental Education, Post-Disaster 1. Disaster is one of the causes of children’s deprivation
13Simaremare et al. 2024Observational studies with a cross-sectional approachIndonesiaPopulation: All households in Indonesia. Sample: 9,243 children aged 0–59 months from 60 disadvantaged areas in IndonesiaSanitation and drinking water1. Multivariate analysis showed that factors significantly related to increased risk of diarrhea were the age of children 12–23 months and 24–35 months, as well as a history of ISPA in the past month.
14Fu. et al. 2023Qualitative research using curriculum vitae analysis and thematic analysisCambodiaPopulation: Children living in residential care institutions (RCIs) in Cambodia. Sample: 25 children aged 12 to 17 yearsIncomplete family structure1. Children feel material benefits and educational opportunities at RCI compared to previous difficult conditions 2. Peer support who have “similar sad stories” is an important factor for children’s adaptation
15Andrestian et al. 2023Exploratory qualitative investigation with phenomenological methodsIndonesiaPopulation and sample: Youth marriage perpetrators, families, health workers, and related government agencies in South Kalimantan.Parental Education, Parenting, Early Marriage1. Teenage marriage in South Kalimantan is triggered by economic motives, adolescent desires, social pressure, and lack of motivation to complete formal education.
16Bustos et al. 2023Cross-sectional study using multilevel linear regression analysisPhilippinesPopulation: Children living in extreme poverty in 10 regional regions of the Philippines. Sample: 2,945 children aged between 6 months and 12 yearsSpatial Gaps1. Enrollment in 4Ps was associated with a lower chance of stunting (adjusted OR = 0.55), although this relationship was influenced by geographic factors of the area of residence
17Htet et al. 2023Repeated cross-sectional surveys or panel studiesMyanmarPopulation: Households in rural Chin State (mountainous), Magway Region (plain/dry zone), and Ayeyarwady Region (river delta). Sample: 3,745 childrenParent Education, Sanitation and Drinking Water, Food Security, Maternal Health1. Child age and short maternal height are consistently determinants of stunting in all three regions
18Utami et al. 2023Qualitative: empirical legal researchIndonesiaPopulation and sample: children who enter into early marriage.Early Marriage1. Early marriage causes vulnerability to children’s rights, including vulnerability to education, the right to sustainable livelihood, the right to growth and development, and the right to be free from violence.
19Um et al. 2023Analysis of secondary data from the cross-sectional population survey (Cambodia Demographic and Health Survey/CDHS)CambodiaPopulation: Children under the age of five in Cambodia. Sample: 29,171 children aged 0–59 monthsParental education, Sanitation and drinking water, parental employment status, smoking culture at home1. ISPA risk increases with child age (6–35 months), maternal smoking, and poor toilet hygiene. Conversely, high maternal education, breastfeeding, top-tier wealth, and more recent survey data correlate with lower risk.
20Santri et al. 2023Cross-sectional studyIndonesiaPopulation of families with children under the age of 5 years in Indonesia whose data is recorded in SDKI 2017. Sample: 4,953 households with children under 5 years of ageparental education, Sanitation and drinking water, spatial gaps, smoking culture at home1. Symptoms of ISPA are significantly related to rural residence, high wealth index, frequency of father’s daily smoking, and low level of father’s education.
21Tran et al. 2023Longitudinal studies that are a follow-up to a double-blind randomized controlled trialVietnamPopulation: Descendants of women participating in preconception micronutrient supplementation (PRECONCEPT) studies in 20 communes. Sample: 4,186 childrenParent education1. Maternal factors, home environment, and child nutrition mitigate cognitive gaps (7–42%) in early age 2. A high-quality home environment reduces the socio-emotional gap by 43% at the age of 6–7
22Herliana & Douiri 2017Cross-sectional studyIndonesiaPopulation: Children aged 12–59 months in Indonesia. Sample: 14,401 childrenparental education, Access to health facilities, parental employment status1. There are 13 factors that are significantly related to low immunization coverage, including: living in the Maluku and Papua regions, older children (36–47 months), high birth households, large family sizes, uneducated mothers, and the poorest households.
23Suyanto et al. 2023Mixed method approachIndonesiaPopulation: Girls who perform early marriage in the Horseshoe area, East Java. Sample: 300 respondents for quantitative surveys and 30 informants for in-depth interviews.Parental Education, Early Marriage, Patriarchy1. Early marriage is driven by economic and sociocultural factors, including the old virginity stigma for those over 20. Consequently, 57.7% of respondents sacrificed their education. Strong patriarchy persists, burdening wives with domestic labor even during pregnancy and exposing them to domestic violence.
24Kharisma et al. 2022This study uses secondary cross-sectional data with the Instrumental Variable (IV) analysis methodIndonesiaPopulation: All children in Indonesia covered by the IFLS-5 survey. Sample: 9,950 observations of children aged 5 to 14 yearsparental education, household income, parental employment status, Incomplete family structure, spatial gap1. Core parental presence (both parents) reduces child labor probability by 0.440 points versus single-mother households. Additional risks include increasing child age, the household head’s employment status, and large family size.
25Kang & Kim 2019QuantitativeMyanmarPopulation: 76990 children. Samples: 4217 ages 0–59 monthsParents’ employment status, maternal health1. Stunting is related to: age and gender in children, nutritional status in mothers, access to care facilities, mother’s work, mother’s health status,
26Ulep & Casas 2022Cross-sectional studies using linear probability model (LPM) and Oaxaca-Blinder decomposition methodPhilippinesPopulation: All children aged 0–60 months in the Philippines covered in the national survey. Sample: 1,881 childrenParent education, Sanitation and drinking water, food security, maternal health1. Maternal factors (height, education, and BMI) account for more than 50% of the total gap. 2. Specifically, maternal height contributes 26%, maternal education 18%, maternal BMI 17%, quality of prenatal care 12%, dietary diversity 12%, and child iron supplementation 5% to stunting inequality.
27Yunitasari et al. 2022Mixed methodsIndonesiaPopulation: Mothers involved in Maternal and Child Nutrition Security projects in rural areas. Sample: 152 mothers with children aged 0–23 monthsParental education, Sanitation and drinking water, access to health facilities, smoking culture at home1. Stunting drivers include male gender, older age, poverty, and poor ANC access. Critical contributors involve sanitation–water synergy, genetic misconceptions, condensed milk misuse, and cigarette spending prioritized over nutrition.
28Blankenship et al. 2020Secondary data analysis using nationally representative data from the Demographic and Health Survey (DHS)MyanmarPopulation: Children under the age of 5 in Myanmar. Sample: 3,981 childrenSanitation and drinking water, parental employment status, maternal health, spatial disparities1. Stunting is driven by short maternal stature (<145 cm), perceived small birth size, non-clinical delivery, maternal employment, unsafe water, specific topography (delta/coastal/highlands), and poverty. Wasting stems from maternal underweight, open defecation, maternal work, and coastal residency.
29Karpati et al. 2020Secondary analysis of national survey data using the Multiple Overlapping Deprivation Analysis (MODA) methodologyCambodiaPopulation: Children in Cambodia. Sample: 7,906 children under five years old (0–59 months)Parental education, Sanitation and drinking water, maternal health, spatial gaps1. Children who do not experience deprivation in three or more dimensions have a significant reduction in the probability of stunting. 2. Other significant risk factors include the mother’s height < 145 cm, being in the two lowest quintiles of wealth, and the child’s increasing age
30Boulom et al. 2020Cross-sectional surveyLaosPopulation: All residents in 23 villages in Nong District are isolated and difficult to access. Sample: 173 households with a father, mother, and at least one child aged 12–47 monthsHousehold income, asset ownership, food security1. Factors that are significantly positively related to nutritional status include asset ownership (mobile phones or electric rice mills), collection of non-timber forest products (insects/ant eggs), and diversity of household diets. 2. As many as 90% of households experience food insecurity.
31Sandra et al. 2020Quantitative research using the Ordinary Least Square (OLS) multiple regression analysis methodIndonesiaPopulation: All districts and cities in 34 provinces in Indonesia. Sample: 301 districts/cities in Indonesia, with a focus on children aged 11–17 yearsParent Education1. Educational participation has a negative and significant effect on child labor; The higher the child’s attendance at school, the lower their time to work
32Adam & Salim 2018This study uses a survey design with a cross-sectional study approachIndonesiaPopulation: All students at SDN Salulayang, Mamuju District. Sample: 90 childrenHousehold income, parental education1. There was a significant relationship between maternal education level (p = 0.005) and parental income (p = 0.006) and stunting incidence. 2. Higher levels of education facilitate access to nutritional information, while adequate income allows families to obtain optimal nutritional intake
33Minh et al. 2016Cross-sectional designVietnamPopulation: 11,663 Women in Vietnam. Sample: 1,383 women who had live births in the last two yearsParental education, Sanitation and drinking water, spatial gaps1. Stunting barriers are disproportionately high in poor rural ethnic minorities (38% vs. <1% wealthy urban). Low education and inadequate facilities significantly restrict access to professional birth attendants and ANC, directly increasing child mortality risks.
34Bima et al. 2022Qualitative approach with cross-sectional study designIndonesiaPopulation: Children living in poor households in urban areas in Indonesia. Sample: Children aged 6–17 years old as main participants in six urban villages in three cities (North Jakarta, Makassar, and Surakarta)Parental Education, Identity1. Poor children in the city experience the impact of their parents’ economic difficulties. 2. In addition, it was found that 37% of poor children in urban areas do not have birth certificates, which hinders their access to various government assistance programs.
35Tantan & Astuti 2021Quantitative research using the Multiple Overlapping Deprivation Analysis (MODA) frameworkIndonesiaPopulation: Children living in the Jakarta Metropolitan Area (JMA) which includes the DKI Jakarta area, part of West Java, and part of Banten. Sample: Children aged 0–17 years oldFood Security1. The Health and Food and Nutrition dimensions have the highest level of deprivation, where about 1 in 2 children experience it
36Dat et al. 2015Longitudinal analysis using Young Lives survey data with an empirical approach of logit ordered regression model (cross-sectional) and fixed-effects model (panel)VietnamPopulation: Children in Vietnam. Sample: includes children aged 11–12 years in Round 2 and 14–15 years old.Sanitation and drinking water, parents’ employment status1. Children tend to prioritize deprivation that has a direct impact on their well-being, such as shelter and water and sanitation, over long-term deprivation such as children’s health and employment.

Discussion

This review of 36 studies investigates child poverty determinants in six Southeast Asian nations: Indonesia, Cambodia, Myanmar, the Philippines, Laos, and Vietnam. By analysing cases of stunting, ARI, child mortality, and labour, the research demonstrates that child poverty is an accumulation of human rights failures rather than mere income deficiency. Based on Table 2, the study classifies these drivers into seven 6 dimensions: household, infrastructure, spatial, cultural, disaster-related, and environmental.

Table 2. Summary of study findings on determinants of child poverty in Southeast Asia.

Dimensions/themesCountryIndicatorStudiesKey finding
Household Characteristics Indonesia, Cambodia, Myanmar, Philippines, Laos, Vietnam

  • • Household income

  • • Parent education

  • • Mother’s parenting style

  • • Employment status

  • • Asset ownership

  • • Food insecurity

  • • Incomplete family structure

  • • Maternal health (low weight and height)

7
24
2
13
2
8
2
7

Infrastructure Gap Indonesia, Myanmar, Vietnam

  • • Sanitation and drinking water

  • • Access to healthcare facilities

142

Spatial Gaps Indonesia, Myanmar, Vietnam

  • • Rural and urban areas

9

Culture Indonesia

  • • Patriarchy

  • • Early marriage

1
4

Disaster Indonesia

  • • Post-disaster impact

1

  • • 1 reported that disasters are one of the causes of children’s deprivation (Iwo et al., 2024)

Household characteristics

Household characteristics are primary determinants of child poverty across Indonesia, Cambodia, Myanmar, Laos, the Philippines, and Vietnam. Evidence from Indonesia and Myanmar shows that low income and lack of assets correlate strongly with multidimensional deprivation and stunting (Mauludyani et al. 2025; Seran & Sengkoen 2024; Mitra et al. 2025; Htet et al. 2023). Conversely, productive assets protect children from food insecurity (Khoiruddin et al. 2025; Htet et al. 2023; Boulom et al. 2020). Maternal factors specifically low education, unemployment, and poor physical health are critical triggers for stunting and low health literacy (Laksono et al. 2024; Mauludyani et al. 2025; Ayu et al. 2024; Agushybana et al. 2025; Herliana & Douiri 2017). Furthermore, incomplete family structures exacerbate vulnerability to poverty and child labor (Khoiruddin et al. 2025; Kharisma et al. 2022; Fu et al. 2023).

Infrastructure gap

Infrastructure gaps specifically limited access to sanitation, clean water, and healthcare are primary determinants of child health in Indonesia, Myanmar, Cambodia, and Vietnam. In Indonesia, untreated water and poor sanitation triple the risk of stunting by compromising infant metabolism and immunity (Mauludyani et al. 2025; Ayu et al. 2024; Yunitasari et al. 2022). Similar environmental correlations exist in Myanmar, where maternal decision-making is pivotal (Htet et al. 2023), and in Cambodia, where poor water quality triggers acute respiratory infections (Um et al. 2023). Beyond physical health, these environments hinder cognitive development and school attendance. This is further exacerbated by spatial and socio-economic stratification; underprivileged families often rely on lower-tier health centers, while urban, affluent groups access superior hospital services, highlighting deep systemic development inequalities (Bima et al. 2022).

Spatial gaps

Spatial disparities function as structural determinants of child deprivation in Southeast Asia, with rural areas facing the highest severity. In Indonesia, urban superiority is evident as urban children face a 50% lower stunting risk than rural peers due to stable infrastructure and healthcare access (Agushybana et al. 2025). Conversely, rural Indonesian children suffer from higher respiratory disorders and diarrhea, driven by environmental dust and physical barriers to clinics (Santri et al. 2023; Simaremare et al. 2024). Similar patterns emerge in the Philippines, where mountainous urban and lowland rural areas face stunting due to food distribution barriers (Bustos et al. 2023). In Myanmar, a nutritional dichotomy exists coastal areas are wasting epicenters due to poor sanitation, while mountainous regions suffer from chronic stunting caused by isolation (Blankenship et al. 2020). These spatial injustices often intersecting with ethnic minority status in Vietnam lock rural families into low-wage informal labour, perpetuating intergenerational poverty (Minh et al. 2016; Khoiruddin et al. 2025). Meanwhile, many children in urban areas lack birth certificates, which results in the loss of access to education, social assistance, and healthcare (Bima et al. 2022; Li et al. 2023).

Culture

Indonesian sociocultural norms often prove more influential than law in driving early marriage, typically as a reaction to school dropout or social shaming (Suyanto et al. 2023; Utami et al. 2023). Reinforced by patriarchal systems, girls are frequently treated as economic assets and subjected to domestic burdens rather than receiving an education (Idawati et al. 2024; Fitriahadi et al. 2025). This environment prioritizes communal identity over individual educational rights, locking children into traditional cycles of marriage (Suyanto et al. 2023; Andrestian et al. 2023).

Disaster impact

Natural disasters significantly drive child poverty in Indonesia by eroding family assets and disrupting educational investment (Iwo et al. 2024; Khoiruddin et al. 2025). Infrastructure destruction and limited financial safety nets often trap children in long-term poverty cycles following these shocks (Schmidt et al. 2021; Bastos et al. 2001). Notably, the statistical impact is profound: a rise in disaster victims can increase the likelihood of child poverty by up to 5.7 times (Daoud et al. 2016), underscoring the role of environmental shocks in regional deprivation.

Unsafe environment

In Indonesia and Cambodia, unsafe household environments, especially smoking cultures, are the main behavioral and structural factors that exacerbate multidimensional child poverty and threaten their survival (Khoiruddin et al. 2025; Behbod et al. 2018; Anniina et al. 2026). In Indonesia, parental smoking habits trigger the risk of ISPA and high stunting rates (29.5%) due to the diversion of nutrition funds for cigarette shopping and health misinformation (Santri et al. 2023; Yunitasari et al. 2022; Belvin et al. 2015). Meanwhile, in Cambodia, smoking (especially by mothers) was found to increase the risk of ISPA and pneumonia symptoms in toddlers by up to 1.6 times compared to non-smoking households (Um et al. 2023; Hatt & Waters 2006; Gehring et al. 2006). Collectively, these findings underscore the urgency of creating a safe domestic environment to ensure the fulfilment of children’s nutrition and physical health.

Limitation

The results of data extraction on child poverty in Southeast Asia show limited scope due to the dominance of studies centered in Indonesia, so that findings in other countries have not been represented in a diverse manner. This inequality can be seen from the number of literatures dominated by Indonesia as many as 23 articles, while other countries have very minimal representation, namely the Philippines, Cambodia, Vietnam, and Myanmar with only 3 articles each, and Laos with 1 article. This condition causes the picture of child poverty in the Southeast Asian region as a whole to not be mapped comprehensively and balanced.

Conclusion

This review identifies child poverty in Southeast Asia as a multidimensional failure of rights across six dimensions: household, infrastructure, spatial, cultural, disaster, and environment. The findings reveal that financial aid alone cannot mitigate these complexities. Effective poverty alleviation requires a transition toward integrated, cross-sectoral strategies that pair equitable infrastructure development with inclusive, context-sensitive policies. Shifting the focus to the collective fulfilment of basic rights is essential for breaking the intergenerational poverty cycle.

Notes on contributors

Bazlin Fadilah is a Master’s candidate in Social Development and Welfare at the Gadjah Mada University, Yogyakarta, Indonesia. Her research interests include poverty, childhood, and social policy.

Siti Munawaroh is a Master’s Candidate in Social Development and Welfare at the Gadjah Mada University, Yogyakarta, Indonesia. Her research interest include poverty and empowerment.

Arif Hermawan is a Master’s Candidate in Social Development and Welfare at the Gadjah Mada University, Yogyakarta, Indonesia. His research interest include poverty, gender, and empowerment.

AI statement

Rayyan AI: Data management efficiency regarding abstract and full text-screening.

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Data availability

Underlying data

No underlying data are associated with this article, as all analysed data were extracted from previously published studies listed in the references.

Extended data

Zenodo: Determinants of Child Poverty in Low Middle-Income Countries - Southeast Asia: A Systematic Literature Review. https://doi.org/10.5281/zenodo.21029098 (Fadilah et al. 2026).

This project contains the following extended data:

  • • [PRISMA 2020 Flowchart]

  • • [Geographical Distribution of The Studies]

  • • [PRISMA 2020 Checklist]

  • • [Search Strategy]

  • • [Data Extraction]

  • • [Summary of The Findings]

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

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Fadilah B, Munawaroh S and Hermawan A. Determinants of Child Poverty in Low Middle-Income Countries - Southeast Asia: A Systematic Literature Review [version 1; peer review: awaiting peer review]. F1000Research 2026, 15:1183 (https://doi.org/10.12688/f1000research.185580.1)
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