Report number 194677 Educational Access and Disparities in Myanmar November 2024 2 + 1 = ....... 2 + 4 = ....... 4 - 2 = ....... 7 - 3 = ....... kKgGc sSzZv © 2024 The World Bank 1818 H Street NW, Washington, DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org Some rights reserved This work is a product of the staff of The World Bank. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Rights and Permissions The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given. Attribution—Please cite the work as follows: Fukao, T., S. Katwal, M. M. Thwin, and A. P. Kyaw. 2024. Educational Access and Disparities in Myanmar. Washington, DC: World Bank. All queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@worldbank.org. Table of Contents Executive Summary ........................................................................................................................ 1 1. Introduction ............................................................................................................................. 2 2. Data and methodology ............................................................................................................. 4 3. State of education access: Levels, disparities, and changes .................................................... 6 3.1 Changes in access at the aggregate national level .......................................................... 6 3.2 Disparities in access to school education ........................................................................ 8 3.3 OOSC: Who are they and why are they not in school? ................................................ 15 4. Coping with disruptions in schooling .................................................................................... 22 4.1 Patterns of student enrollments across school types ..................................................... 22 4.2 Access to online education and parental support .......................................................... 26 5. Conclusions ........................................................................................................................... 30 References ..................................................................................................................................... 33 Annex ............................................................................................................................................ 35 List of Tables Table 3.1: Profiles of children (6–17 year-olds) who are out of school and who are in school, 2024 (%)........................................................................................................................................ 16 Table 3.2: Descriptive statistics (sample mean) for primary and secondary school-age children, 2024............................................................................................................................................... 17 Table 3.3: Determinants of schooling status (being out of school) among 6–17-year-old children ....................................................................................................................................................... 18 Table 4.1: Distribution of enrolled children (6–17 years) across state and non-state schools, 2017 and 2023 (%) ................................................................................................................................. 24 Table 4.2: Profile of students enrolled in state versus non-state schools, 2023 and 2024 (%) ..... 25 Table 4.3: Distribution of children (ages 6–17) who used online education in the past 12 months, 2023 and 2024 (%) ........................................................................................................................ 27 Table 4.4: Determinants of enrollment in state versus non-state schools, access to online education, and family support (probit models) ............................................................................. 29 Table A1:Profiles of children (6–17-year-olds) who are out of school and who are in school, 2024 (%)…………………………………………………………………………………….……35 List of Figures Figure 3.1: Enrollment rate by level of education, 2017–2024 ...................................................... 8 Figure 3.2: Primary, middle, and high school NER by gender, 2017–2024 ................................... 9 Figure 3.3: Primary, middle, and high school NER by language spoken at home, 2017–2024 ... 10 Figure 3.4: Primary, middle, and high school NER by ethnicity, 2024 ........................................ 10 Figure 3.5: Primary, middle, and high school NER by disability status, 2024 ............................. 11 Figure 3.6: Primary, middle, and high school NER by wealth group, 2017–2024....................... 12 Figure 3.7: Primary, middle, and high school NER by location (urban and rural areas), 2017– 2024............................................................................................................................................... 13 Figure 3.8: Primary, middle, and high school NER by best/worst-performing states, 2017–2024 ....................................................................................................................................................... 14 Figure 3.9: Primary, middle, and high school NER by conflict intensity (across townships), 2024 ....................................................................................................................................................... 15 Figure 3.10: Reasons for dropping out of school (6–17-year-old children) ................................. 20 Figure 3.11: Reasons for dropping out of school across the years (6–17-year-old children) by the year of dropout .............................................................................................................................. 21 Figure 3.12: Reasons for dropping out by gender and age group ................................................. 22 Figure 4.1: Distribution of enrolled students (ages 6–17) by school type, 2023 and 2024 .......... 23 Figure A1: Number of people displaced since February 2021 and remain displaced (as of June 2024) ............................................................................................................................................. 36 Figure A2: Number of people internally displaced within Myanmar (as of June 2024) .............. 37 List of Boxes Box 2.1: Regression models for the determinants of out of school status ...................................... 6 Box A1: RAPID framework ......................................................................................................... 38 Acknowledgment This report was authored by a team comprising Tsuyoshi Fukao (Senior Education Specialist and Task Team Leader, HEAED), Saurav Katwal (Consultant Economist, HEAED), Mar Mar Thwin (Education Specialist, HEAED), and Aung Phyo Kyaw (Education Consultant, HEAED). It was prepared under the guidance of Mariam Sherman (Country Director for Myanmar, Cambodia, and Lao PDR), Alberto Rodriguez (Regional Director, HEADR), and Cristian Aedo (Practice Manager, HEAED) The team thanks Tara Beteille (Program Leader, HEADR) who was the peer reviewer for this report. Abbreviations ACLED Armed Conflict Location and Event Data CDM Civil Disobedience Movement CSO Central Statistical Organization FCDO Foreign Commonwealth and Development Office FCV Fragility, Conflict, and Violence GER Gross Enrollment Rate IDP Internally Displaced Person JRF Joint Response Framework MLCS Myanmar Living Conditions Survey MoE Ministry of Education MSPS Myanmar Subnational Phone Survey NER Net Enrollment Rate NFE Non-Formal Education NGO Nongovernmental Organization OOSC Out-of-School Children RAPID Reach Access Prioritize Increase Develop SEA-PLM Southeast Asia Primary Learning Metrics SEAMEO Southeast Asian Ministers of Education Organization UNDP United Nations Development Programme UNESCO United Nations Educational, Scientific, and Cultural Organization UNHCR United Nations High Commissioner for Refugees UNICEF United Nations Children’s Fund USAID United States Agency for International Development WASH Water, Sanitation, and Hygiene Executive Summary Myanmar's education sector made significant progress in expanding access and achieving gender parity in enrollment rates before the COVID-19 pandemic. However, the sector faced severe disruptions due to the pandemic, military coup, and deteriorating economic situation, leading to prolonged school closures, weakened public education, and mounting learning losses. A World Bank report (Bhatta et al. 2023) highlighted a significant decline in enrollment rates, with 28 percent of children ages 6–17 out of school in 2022–23 and widening disparities across income groups and gender at the high school level. This report provides a comprehensive analysis of the current state, trends, and disparities in education access in Myanmar, using data from Myanmar Living Conditions Survey 2017 (MLCS 2017), Myanmar Subnational Phone Survey (MSPS) 2023, and MSPS 2024. Despite the recent disruptions to the education sector, the findings reveal remarkable improvements in access to education across various levels, except for lower secondary education. Primary education net enrollment rate (NER) reached 92 percent in 2024, surpassing pre-pandemic levels, and upper secondary NER increased by 28 percent between 2023 and 2024. Despite these improvements, disparities in educational access persist across demographic and socioeconomic characteristics. Females exhibit significantly higher enrollment rates than males at middle and high school levels, with the female NER exceeding males by 28 percent and 43 percent, respectively. The school enrollment disparity between the wealthiest and poorest remains pronounced, particularly at middle and high school levels, with the NER for the poorest being 25 percent and 59 percent lower, respectively. Moreover, rural NER consistently lags urban NER at the middle school level by 11–14 percent, and areas with higher conflict levels exhibit lower enrollment rates across all age groups. The report also sheds light on the primary drivers of school dropout, with economic hardship emerging as the main reason, cited by 41 percent of respondents. Other significant factors include school closures (14 percent), safety concerns (11 percent), and disengagement from learning (6 percent). In terms of coping strategies, while most students are enrolled in state schools, there has been a shift toward non-state schools among certain groups, particularly those from wealthier households, urban areas, and high-conflict zones. For instance, non-state school enrollment remains relatively high among students from the wealthiest households. Although there was a slight drop in share between 2023 and 2024, it remains 40 percent higher than in 2017. Similarly, urban children comprise 52 percent of all non-state school enrolled children while only accounting for 27 percent of all children enrolled in state schools. However, non-state schools face resource constraints, presenting an opportunity for targeted support to improve access to quality education. The adoption of online education remains limited, with only 4 percent of children ages 6 –17 using online learning platforms. Notably, out-of-school children (OOSC) and those from low- income and rural households have the least access to these resources, highlighting the need for targeted interventions to bridge the digital divide. 1 Addressing inequalities in educational access is critical for Myanmar's future stability and prosperity. By prioritizing targeted interventions, increasing instructional efficiency, supporting psychosocial well-being, and leveraging evidence-based strategies, Myanmar can work toward ensuring equitable access to quality education for all children, regardless of their background or circumstances. 1. Introduction Myanmar’s education sector experienced significant progress in expanding access across all levels in the years leading up to the COVID-19 pandemic. The country achieved notable improvements in enrollment rates, particularly at the secondary level, and attained gender parity in net enrollment rates (NERs) for both primary and secondary education (Bhatta and Katwal 2022b; CSO, UNDP, and World Bank 2018). In 2018, Myanmar fared relatively well in both access to education and gender parity in enrollment, particularly when compared to countries with similar income levels and its regional peers. In terms of access (enrollment), Myanmar consistently outperformed the global average at all levels of education. Moreover, among its regional peers, Myanmar ranked above Cambodia and Lao PDR in terms of enrollment rates. Notably, Myanmar's gender parity in education access at the secondary and tertiary levels also exceeded expectations based on its income level and surpassed that of Lao PDR and Cambodia (Bhatta and Katwal 2022b). Despite these achievements, Myanmar faces persistent challenges in delivering quality education, with student performance lagging regional standards. The 2019 Southeast Asia Primary Learning Metrics (SEA-PLM) assessments revealed that a staggering 89 percent of grade 5 students in Myanmar performed below the minimum proficiency levels in reading, with even lower percentages among poor students and linguistic minorities, aligning with the World Bank's estimated learning poverty rate of 89.5 percent for 2019 (Azevedo 2020; Bhatta and Katwal 2022a, b; UNICEF and SEAMEO 2020). Myanmar's performance in both math and reading falls below the average of other participating Southeast Asian countries (Bhatta and Katwal 2022a). The education sector in Myanmar experienced severe disruptions due to the pandemic and the subsequent military coup, resulting in prolonged school closures and a weakened public education system (Bhatta and Katwal 2022b; Bhatta et al. 2023; Frontier 2022; Insecurity Insight 2021). The extended school closures have obstructed access to education and exacerbated the learning crisis, potentially leading to severe long-term socioeconomic consequences, such as reduced human capital accumulation, lower future earnings, increased poverty and inequality, and hampered economic growth (Azevedo 2020; Bhatta and Katwal 2022a; Schady et al. 2023). These challenges have been further compounded by Myanmar’s deteriorating economic situation since the military takeover in 2021. According to the World Bank’s Myanmar Economic Monitor report, the economy was projected to grow by only 1 percent by March 2024 (World Bank 2024). The spread of conflict across Myanmar since October 2023 has led to the displacement of approximately 3,177,100 people, disrupting trade routes and increasing transportation costs. As a result, Myanmar's economy remains about 10 percent smaller than it was 2 in 2019, unlike other East Asian nations that have rebounded from the pandemic. Households continue to struggle with the lasting impacts of recent economic shocks, prompting increased migration, both within Myanmar and abroad, as a means of coping. The United Nations High Commissioner for Refugees (UNHCR) reports a total of 3,454,600 internally displaced persons (IDPs) in Myanmar as of November 4, 2024. Among these, 277,500 were displaced before February 1, 2021, and 3,177,100 after that date (UNHCR 2024). The largest concentration of IDPs is in North-West Myanmar, with 1,671,300, primarily in Sagaing Region (1,249,700). South-East Myanmar hosts 966,100 IDPs, including significant numbers in Kayin State (223,200) and Shan State (South) (141,100). In North-East Myanmar, there are 189,800 IDPs, with 137,800 in Kachin State. Rakhine State reports 346,400 IDPs, with 234,200 in the Central region and 112,200 in the North. Additionally, Yangon Region has 3,500 IDPs. It is crucial to note that the figures provided are subject to rounding adjustments and dynamic changes due to fluid cross-border movements. The escalation of armed conflict post-February 2021 suggests a heightened risk of protracted displacement for affected populations. Myanmar Emergency Overview map released by UNHCR is provided in the annex (Figure A1 Figure A2). The mounting learning losses are expected to have severe consequences. As a result of the compounded disruptions, it is estimated that the average learning-adjusted years of schooling for the current cohort of school-age children will decrease by 1.9 to 2.2 years, implying that on average, children in Myanmar have learned nothing during the period of school closures. The learning poverty rate is expected to increase to 100 percent, and average annual earnings per student are projected to decline by 11–13 percent (Azevedo et al. 2022; Bhatta and Katwal 2022a). Further, education spending in Myanmar has declined, with the first year-on-year declines in nominal spending for education since FY2011/12. Expenditure within the education sector has experienced its most significant decline in almost a decade compared to the overall budget. In FY2021/22, education received just 5.6 percent of the total government expenditure, hitting its lowest point since FY2011/12 (World Bank 2022). Despite a slight recovery in its portion of total spending in the following FY2022/23, the impact of rising inflation means that real (constant kyat) expenditure on education has remained stagnant and unchanged since FY2017/18 (World Bank 2023). External contributions to the Ministry of Education (MoE) budget have nearly disappeared (World Bank 2022), further exacerbating the financial challenges faced by the sector. This reduction in financial support along with decrease in the number of teachers due to the dismissal of those participating in the civil disobedience movement (CDM) suggests a likely decline in the quality of teaching and learning for students who remain in school. To address these urgent challenges, development partners put forward the Myanmar Joint Response Framework (JRF) for the Education Sector 2022–2025 in March 2022. The JRF focuses on supporting community-based and open learning modalities through civil society organizations and nongovernmental organizations (NGOs) in the near term, with plans to progressively increase engagement as the political and operating environment improves. However, significant financial, operational, and coordination challenges remain in providing the much-needed educational support to Myanmar's children during this crisis (Aedo et al. 2023). 3 A recent World Bank report (Bhatta et al. 2023), using Myanmar Subnational Phone Survey 2023 (MSPS 2023) and Myanmar Living Conditions Survey 2017 (MLCS 2017) data, has highlighted the significant decline in school enrollment rates since the onset of COVID-19). The report noted that approximately 28 percent of children ages 6–17 were out of school in the 2022–23 academic year, with reductions in enrollment occurring across all ages and education levels. This trend contrasts with the experience of many other Southeast and South Asian countries, where enrollments largely recovered after the pandemic subsided and schools reopened. The report also emphasized the widening disparities in access across income groups and the increasing gender gap in favor of females at the high school level (Bhatta et al. 2023). The report builds on this earlier work and sheds light on more recent trends in education access and equity in Myanmar. Building upon this context, the present report aims to provide a comprehensive analysis of the current state, trends, and disparities in education access in Myanmar, using data from MLCS 2017, MSPS 2023, and the new round of MSPS, that is, MSPS 2024. The report focuses on the following key questions: (1) What is the current state of enrollment rates across different education levels? (2) How do enrollment rates vary across different demographic and socioeconomic groups? (3) What are the main factors contributing to children being out of school? (4) How are households coping with disruptions in schooling, including the use of online education and non-state schools? The report is structured as follows: Section 2 outlines the data and methodology, Section 3 examines the state of education access and disparities, Section 4 explores household coping strategies, and Section 5 summarizes the findings and provides concluding remarks. 2. Data and methodology Sampling MSPS 2024 covered 303 out of 330 townships in Myanmar, representing about 95 percent of the country's population. It employed a stratified random sampling approach, using a frame of approximately 150,000 households from 321 townships. Within each township, households were divided into two groups based on the household head's education level, with a maximum of 32 households sampled per township. Given ongoing conflicts, MSPS continues to be conducted via phone, implementing measures to mitigate potential biases associated with this methodology. The survey design ensures representation of often overlooked groups and maintains comparability with other subnationally representative surveys. Some states such as Shan and Rakhine were oversampled due to higher poverty rates, remoteness, and conflict intensity. This approach allows MSPS to track subnational well-being changes over time while ensuring compatibility with other high-quality surveys in Myanmar. Detailed sampling methodology is available in the technical documentation (Sinha Roy 2023, 2024). Data sources The analyses of access and disparities in this study are primarily based on household survey data from three sources: the two rounds of Myanmar Subnational Phone Survey (MSPS 4 2023 and MSPS 2024) and MLCS 2017. MSPSs are nationally representative household surveys that allow relevant indicators to be estimated at the subnational (state/regional) level and include a relatively comprehensive set of questions on education access and other relevant demographic and household characteristics. MSPS 2023 was conducted between November 2022 and March 2023 and MSPS 2024 was conducted between November 2023 and March 2024. The nationally representative MLCS 2017, which was conducted face to face, also includes all the relevant questions on education access and information on demographic and household characteristics. These datasets allow us to analyze the trends in education access, compare the current state of education access and disparities with the situation a year back, and examine conditions before the pandemic and the military transition. Other data used in the analyses of access include conflict data from the Armed Conflict Location and Event Data (ACLED) project. Analytical approach The analysis employs simple descriptive statistics and regression methods to examine the current status, trend, and disparity in education access, focusing on primary and secondary school-age children between the age of 6 and 17. The assessment encompasses an overview of the education access, changes in status of out-of-school children (OOSC) from 2017 to 2023, and disparities in access across various individual and household dimensions, including gender, disability status, socioeconomic status, language, and ethnicity. The main indicator used to measure education access is the NER and the trends and disparities highlight the differences across age groups by presenting the analysis that is disaggregated by age group—primary school age (6– 10 years), middle school age (11–14 years), and high school age (15–17 years). To augment the descriptive analysis, regression models provide robust insights into the determinants of out-of- school status. In addition, regressions are also employed to analyze the correlates of schooling choice, online education uptake, and parental engagement in their children’s education. Detailed information on the regression models is presented in Box 2.1. 5 Box 2.1: Regression models for the determinants of out-of-school status The key outcomes at the child level are out-of-school status (enrolled versus not enrolled), type of school attended (state versus non-state), use of online education (using online education versus not using), and parental engagement in their studies (time adults in household invest in education of child). A simple probit regression model is used to analyze the determinants or correlates of these four outcomes. The outcome ,ℎ,a, for child i from household h residing in township a is modeled as follows: ,ℎ,a = 0 + ,ℎ, + ,ℎ,a, where ,ℎ,a is a matrix of explanatory variables representing different student and household characteristics, and ,h,a is a random error term. The coefficient vector represents the average effects of the various explanatory variables on the outcome, while controlling for the effects of the other variables included in the model. The key explanatory variables of interest in all the regressions include child’s gender (male versus female), child’s home language (Myanmar versus other), household location (urban versus rural), household socioeconomic status (wealth quintile), and household’s exposure to conflict (township-level conflict intensity as measured by the log of number of conflict incidents per capita). The regressions specific to access to online education and parental engagement also include enrollment status and school type as explanatory variables. Household wealth quintiles were generated from the wealth index. The wealth index utilized in this study is derived from the first principal components of a set of observable dwelling characteristics and ownership of consumer durables. These characteristics encompass housing quality, ownership of a motorized vehicle, refrigerator, television, wardrobe, rice cooker, digital device, and whether the dwelling is connected to the electrical grid. 3. State of education access: Levels, disparities, and changes 3.1 Changes in access at the aggregate national level Access to education in Myanmar has witnessed remarkable advancements across various levels, showing significant improvements, except at the lower secondary level which has witnessed further decline. Despite the ongoing political turmoil, access to primary education in Myanmar has witnessed a remarkable surge within just one year. The NER at the primary level showcased a remarkable 27 percent increase between 2023 and 2024, reaching an impressive 92 percent in 2024 (Figure 3.1, Panel A). Primary enrollment has surpassed the pre-pandemic NER of 90 percent from 2017, indicating a promising recovery in education access at the primary level. While this increase could be partly attributed to a sense of urgency among parents concerned about their children's education after 2–3 years of discontinued schooling, the magnitude of the increase raises questions about the underlying factors contributing to this trend. Such a rapid rise in NER within a year, amidst the prevailing challenges, is quite surprising and warrants further investigation to understand the ground reality. While the NER provides insights into the participation of the official age group, the gross enrollment rate (GER) offers a more comprehensive picture of overall enrollment, including students who may be overage or underage for their grade level. The primary GER has risen significantly from 104 percent in 2023 to 135 percent in 2024, indicating that a significant proportion of students enrolled in primary education are overage for their grade level. In fact, 6 nearly half (45 percent) of middle school-age children (11–14 years) are enrolled in primary schools (Katwal et al. 2024). This remarkable increase in primary school enrollment has not been due to a significant rise in informal education. According to the MSPS 2023–24 data, an overwhelming majority (approximately 97 percent) of enrolled primary school-age children are attending formal education institutions. This high enrollment rate in formal schooling suggests a strong desire among parents and communities to ensure their children receive a structured education, even amidst the prevailing circumstances. However, it is crucial to delve deeper into the quality of education being provided and the sustainability of these enrollment rates. While enrollment figures are encouraging, there is a possibility that children, although enrolled, might not necessarily be attending school regularly due to various factors, including disruptions caused by the political situation. Ensuring consistent attendance and quality learning outcomes should be a priority alongside efforts to maintain and further improve enrollment rates. Furthermore, at the upper secondary level, there has been a 28 percent upswing in the NER between 2023 and 2024. Despite this improvement, it is important to note that the upper secondary NER still stands at a modest 29 percent, indicating considerable room for growth in expanding access and ensuring a larger proportion of the age-appropriate population is enrolled in upper secondary education. The upper secondary GER in Myanmar is notably higher than the NER, standing at 39 percent in 2024. Interestingly, the GER has remained relatively stable between 2023 and 2024, with only a marginal increase of 1 percentage point from 38 percent in 2023. While upper secondary enrollment rates have shown some improvement, it is worth noting that attendance at matriculation exams has been significantly declining since 2019. This trend suggests that despite some recovery in enrollment, fewer students are completing their upper secondary education and qualifying for tertiary studies. Encouragingly, tertiary enrollment has also shown improvement, registering a 25 percent increase from the previous year. Anecdotal evidence suggests that this might be due to students being informed by their universities that failure to enroll this year would result in their dismissal from the higher education system, making it their last opportunity to reenroll. This sense of urgency among students could have contributed to the observed increase in tertiary enrollment rates. In stark contrast, the NER at the lower secondary level has experienced a concerning decline of 14 percent between 2023 and 2024. As a result, the current lower secondary NER stands at a significantly lower rate of 37 percent, representing a staggering 45 percent decrease compared to the NER recorded in 2017. Similarly, the lower secondary GER has been on a consistent downward trend, falling from 82 percent in 2017 to 63 percent in 2023, and further declining to 56 percent in 2024. This alarming trend highlights the need for targeted interventions and policies aimed at improving access to and retention in lower secondary education. 7 Figure 3.1: Enrollment rate by level of education, 2017–2024 Panel A. Net Enrollment rate (NER) Panel B. Gross Enrollment Rate (GER) Source: Original figure based on MLCS 2017, MSPS 2023, and MSPS 2024. 3.2 Disparities in access to school education The gender gap in NER exhibits significant variation across different education levels and has undergone notable changes in recent years. In 2024, at the primary level, the gender difference is relatively minimal, with the NER for females slightly surpassing that for males by approximately 2 percent (Figure 3.2). This marks a reversal from 2023, where female primary NER lagged the rate for males by around 7 percent. However, at the middle school level, the gender gap has widened considerably. In 2017, female NER exceeded male NER by only 2 percent, but by 2024, this gap had expanded to a significant 28 percent mainly due to a relatively large drop in male enrollment. Similarly, at the high school level, females exhibit significantly higher enrollment rates compared to males, with the female NER exceeding that of males by approximately 42 percent in 2024. Although this gap has narrowed compared to the 2023 gender gap of 61 percent, it remains notably higher than the 24 percent gap observed in 2017. It is important to note that the recent conscription law may have contributed to lower enrollment rates for male students in upper secondary education in 2024. This could be causing some families to keep their older male children out of school to avoid potential military service. 8 Figure 3.2: Primary, middle, and high school NER by gender, 2017–2024 Source: Original figure based on MLCS 2017, MSPS 2023, and MSPS 2024. The enrollment gap between Myanmar-speaking and non-Myanmar-speaking children is relatively small across all levels of education. In 2024, at the primary level, the gap in NER between the two groups is merely 3 percentage points. Interestingly, both demographics have shown significant improvement in primary school enrollment rates over the past year, with Myanmar speakers increasing from 73 percent to 92 percent and non-Myanmar speakers1 from 72 percent to 89 percent (Figure 3.3). Notably, at the middle school level, the NER gap between Myanmar and non-Myanmar speakers has significantly narrowed since 2017. While both groups saw a decline in enrollment, the convergence was primarily due to a sharp drop in enrollment among Myanmar speakers. This gap, which was 22 percentage points in 2017, reduced to 4 percentage points in 2023, and further decreased to 1 percentage point in 2024. Interestingly, both groups experienced a decline in NER in the past year, underscoring the shared challenges faced by Myanmar-speaking and non-Myanmar-speaking children in accessing education. At the high school level, the dynamics of the NER gap between the two groups have exhibited a unique evolution since 2017, diverging from the trends observed at the primary and middle school levels. In 2017, Myanmar speakers had 24 percentage points higher NER than non-Myanmar speakers. By 2023, this gap had decreased to 8 percentage points in favor of non-Myanmar speakers, and as of now, it stands at just 2 percentage points due to relatively better progress among Myanmar speakers. 1 The term ‘non-Myanmar speakers’ refers to individuals who primarily speak a language other than Myanmar at home. This includes those whose most commonly spoken household language is one of the following ethnic languages: Shan, Karen, Kachin (Jamephaw, Lisu, Rawang, Khamti Shan), Chin, Mon, Kayah (also known as Kayah, Ka-yun, Yin Talai, or Yin Baw), Rakhine, Chinese, Hindi/Gorkha, Arabic, Bengali, Punjabi, or any other language not mentioned. 9 Figure 3.3: Primary, middle, and high school NER by language spoken at home, 2017 –2024 Source: Original figure based on MLCS 2017, MSPS 2023, and MSPS 2024. The data reveal a relatively small difference in NERs across the majority and minority ethnic groups in Myanmar. The Bamar majority exhibits slightly higher enrollment rates compared to other ethnic groups at all levels of schooling. At the primary level, 93 percent of Bamar children are enrolled, while the enrollment rate for other ethnic groups stands at 90 percent (Figure 3.4). This gap persists at the middle and high school levels, with 37 percent of Bamar children attending middle school compared to 36 percent of other ethnic groups, and 30 percent of Bamar youth enrolled in high school versus 26 percent of their non-Bamar counterparts. While these differences in enrollment rates may appear modest, they highlight the need for continued efforts to ensure equitable access to education across all ethnic groups in Myanmar. Figure 3.4: Primary, middle, and high school NER by ethnicity, 2024 100% 93 90 80% 60% 37 36 40% 30 26 20% 0% Primary Middle High Bamar Other Source: Original figure based on MSPS 2024. 10 The disparities in educational access between children with and without disabilities become strikingly evident at the secondary school level, revealing a significant gap in opportunities for students with special needs to progress through the education system. While 92 percent of children without disabilities are enrolled in primary education, the enrollment rate for children with disabilities is even higher, at 95 percent, suggesting that efforts to promote inclusive primary education have been relatively successful at the primary school level (Figure 3.5). Nonetheless, this trend reverses dramatically at higher levels of schooling. Only 24 percent of children with disabilities attend middle school, compared to 37 percent of their non-disabled peers. The gap widens further at the high school level, where a mere 5 percent of youth with disabilities are enrolled, in stark contrast to the 30 percent enrollment rate among those without disabilities. These findings underscore the urgent need for targeted interventions and support mechanisms to ensure that children with disabilities have equal opportunities to progress through the education system and access higher levels of learning. Figure 3.5: Primary, middle, and high school NER by disability status, 2024 100% 95 92 80% 60% 37 40% 30 24 20% 5 0% Primary Middle High Disabled Not disabled Source: Original figure based on MSPS 2024. The disparity in school enrollment between the wealthiest and poorest segments of the population remains pronounced, particularly at the middle and high school levels. Following the pandemic, the gap widened considerably at the primary level as well, but over the past year, significant improvements, particularly among the poorest demographic, have substantially reduced this gap. As of 2024, the NERs among the wealthiest and poorest primary-age children stand at 92 percent and 87 percent,2 respectively, representing a relatively small 5 percentage point gap (Figure 3.6). Although the gap in middle school enrollment has narrowed somewhat, it remains considerable, with the NER for the poorest group approximately 25 percent (or 12 percentage points) lower than that of the wealthiest group. In comparison, the corresponding gaps in 2017 and 2023 were 35 percent and 38 percent, respectively. Similarly, the enrollment disparity between the wealthiest and poorest children at the high school level remains substantial. In 2024, the high 2The primary NER for children from households in the second, third, and fourth wealth quintiles stands at 94 percent, 94 percent, and 93 percent, respectively. 11 school NER was 46 percent among the wealthiest and 19 percent among the poorest, indicating that the NER for the poorest group is approximately 59 percent lower than that among the wealthiest group. Figure 3.6: Primary, middle, and high school NER by wealth group, 2017 –2024 Source: Original figure based on MLCS 2017, MSPS 2023, and MSPS 2024. Disparities in NERs between urban and rural areas persist, particularly at the middle and high school levels, with discernible trends emerging over the years. The primary school NER remains relatively high for both urban and rural children, with a minimal gap of only 2 percentage points between the two groups (Figure 3.7). Both urban and rural areas experienced significant increases in enrollment between 2023 and 2024, with urban enrollment rising by 22 percentage points from 71 percent to 93 percent, and rural enrollment increasing by 18 percentage points from 73 percent to 91 percent. However, at the middle school level, the urban-rural gap has remained relatively stable over the years, with rural NER consistently lagging urban NER by 14 percent in 2017, 13 percent in 2023, and 11 percent in 2024. Conversely, there has been a notable reduction in the urban-rural gap in high school NER, which decreased from 20 percentage points in 2017 to 6 percentage points in 2023, and further declined to 4 percentage points in 2024. It is noteworthy that between 2023 and 2024, NER increased at both primary and high school levels for both urban and rural children, while middle school enrollment declined among both urban and rural children. 12 Figure 3.7: Primary, middle, and high school NER by location (urban and rural areas), 2017 –2024 Source: Original figure based on MLCS 2017, MSPS 2023, and MSPS 2024. A significant gap persists in enrollment across different states and regions, and this disparity has widened at all education levels between 2017 and 2024. Figure 3.8 illustrates the NERs for the best-performing (highest NER) and worst-performing (lowest NER) states and regions. It is evident that there has been a notable divergence in access to education at all three levels during this period, with the gap particularly pronounced at the high school level. In 2024, the NER for children from the worst-performing states and regions at the high school level is 87 percent lower than that for children from the best-performing states. This gap was 53 percent in 2017. Similarly, at the primary school level, the gap increased from 11 percent to 23 percent, and at the middle school level, it widened from 38 percent to 65 percent during the same period. When comparing the NERs between 2023 and 2024, it appears that although the gap across states has narrowed at the primary level, it has widened at the middle and high school levels. This increase can be attributed to the ongoing political crisis, which has affected different states and regions to varying degrees. 13 Figure 3.8: Primary, middle, and high school NER by best/worst-performing states, 2017–2024 Source: Original figure based on MLCS 2017, MSPS 2023, and MSPS 2024. In Myanmar, the impact of conflict intensity on education access is starkly evident when examining enrollment rates across different age groups and townships. Data reveal that in townships with lower conflict intensity, enrollment rates for primary school-age children are significantly higher, with 97 percent enrolled compared to 86 percent in townships experiencing higher conflict levels (Figure 3.9). Similarly, for middle school-age children, enrollment rates drop from 44 percent in low-conflict areas to 29 percent in high-conflict areas, highlighting the disruptive effect of conflict on educational continuity. The impact is equally evident among high school-age children, with enrollment rates declining sharply from 34 percent in low-conflict areas to 23 percent in high-conflict areas. These disparities underscore the immense challenges faced by children living in conflict- affected regions, where ongoing violence and insecurity disrupt access to education, particularly at the middle and high school levels. Addressing these disparities requires targeted interventions to mitigate the impact of conflict on education access, including efforts to ensure the safety of schools and students, provide psychosocial support, and strengthen educational infrastructure in conflict-affected areas. By prioritizing education in these regions and implementing targeted strategies, Myanmar can work toward ensuring that all children have equal opportunities to access quality education, regardless of their geographical location or exposure to conflict. 14 Figure 3.9: Primary, middle, and high school NER by conflict intensity (across townships), 2024 100% 97 86 80% 60% 44 40% 34 29 23 20% 0% Primary Middle High Conflict intensity (1st quartile) Conflict intensity (4th quartile) Source: Original figure based on MSPS 2024. 3.3 OOSC: Who are they and why are they not in school? The disparities between OOSC and those enrolled in school are stark and multifaceted, reflecting patterns across various demographic indicators. Table 3.1 highlights that 59 percent of OOSC are of high school age, a stark contrast to the 18 percent of in-school children in the same age group. Additionally, 47 percent of OOSC come from the poorest two quintiles, compared to 42 percent of enrolled children. OOSC are also predominantly rural, with 77 percent living in rural areas, compared to 71 percent of their in-school counterparts. Children from high-conflict townships are disproportionately out of school, with 30 percent of OOSC coming from the highest conflict areas in contrast to 20 percent from the least affected regions. Moreover, there is a noticeable gender imbalance among the OOSC, with males accounting for 58 percent and females constituting 42 percent of OOSC. This disparity stands in stark contrast to the nearly equal distribution of males and females among in-school children and the overall child population. The gender gap is most pronounced at the high school-age group, where males comprise an alarming 63 percent of OOSC, while females make up only 37 percent while the overall distribution of high school-age children is 54 percent males and 46 percent females (Table 3.1 and Error! Reference source not found.). Although less severe, the disparity persists a t the primary school age, with males accounting for 55 percent of OOSC and females representing 45 percent. Across all three age groups, a higher percentage of OOSC come from the poorest households (bottom wealth quintile). Among primary, middle, and high school-age children, 37 percent, 30 percent, and 26 percent, respectively, belong to the poorest families. Interestingly, a significantly larger proportion of primary school-age OOSC come from relatively wealthy households—33 percent from the top two wealth quintiles—compared to their older counterparts, where the figures range from 28 to 29 percent. 15 Exposure to conflict is also strongly associated with school attendance, particularly among primary school-age children and, to a lesser extent, among middle school-age children. A staggering 38 percent of primary school-age OOSC come from townships with the highest levels of conflict (top 25 percent in terms of per capita conflict incidents), while the corresponding figure for middle school-age children is 33 percent. In contrast, only 8 percent of primary school-age OOSC come from townships in the bottom quartile of conflict incidents, and the figure rises to 20 percent for middle school-age children. Notably, the distribution of high school-age OOSC across the conflict intensity quartiles is relatively balanced. Table 3.1: Profiles of children (6–17 year-olds) who are out of school and who are in school, 2024 (%) OOSC Children Primary Middle High All who are school school school All children in school age age age Gender Male 55 49 63 58 48 50 Female 45 51 37 42 52 50 Total 100 100 100 100 100 100 Age group Primary school age (6–10) 100 13 46 40 Lower secondary school age (11–14) 100 28 36 34 Upper secondary school age (15–17) 100 59 18 26 Total 100 100 100 100 100 100 Consumption quintile Poorest quintile 37 30 26 28 20 22 2nd quintile 13 21 20 19 22 22 3rd quintile 18 22 25 23 21 22 4th quintile 15 20 19 19 18 18 Richest quintile 18 8 10 10 18 16 Total 100 100 100 100 100 100 Location Rural 81 70 79 77 71 72 Urban 19 30 21 23 29 28 Total 100 100 100 100 100 100 Conflict/non-conflict Bottom quartile 8 20 22 20 29 27 2nd quartile 13 19 27 23 28 27 3rd quartile 40 29 24 27 23 23 Top quartile 38 33 27 30 21 22 Total 100 100 100 100 100 100 Source: Original calculations based on MSPS 2024. 16 The average age is 11.65 years for the overall sample of children ages 6–17, with the average ages for the primary, middle, and high school-age subsamples being 8.01, 12.59, and 16.02 years, respectively. The gender distribution is relatively balanced, with 50 percent of the overall sample being female, and similar proportions observed across the subsamples, ranging from 46 to 52 percent (Table 3.2). The majority of children (83 percent) speak the Myanmar language, with consistent percentages across all subsamples. The distribution of children across wealth quintiles is relatively even, with some minor variations. For instance, a slightly higher proportion of middle school-age children (27 percent) belong to the bottom quintile compared to the overall sample (23 percent) and the other subsamples (21 percent). In the overall sample, 13 percent of children reside in households headed by females. The average years of education for household heads is 5.85 years, with minimal differences across the subsamples. The average number of children per household is 2.44; this is consistent across all subsamples. Urban residents comprise 28 percent of the overall sample, with the middle school- age subsample having a slightly higher proportion (31 percent) compared to the primary and high school-age subsamples (26 percent each). The conflict intensity, measured as the log of violent incidents per capita, is similar across all subsamples and the overall sample, ranging from −8.82 to −8.75. Table 3.2: Descriptive statistics (sample mean) for primary and secondary school-age children, 2024 All Primary Middle High children school-age school-age school-age (6–17) children children children (6–10) (11–14) (15–17) Female 0.50 0.52 0.51 0.46 Age 11.65 8.01 12.59 16.02 Myanmar language speakers 0.83 0.81 0.84 0.84 Wealth index (Bottom quintile) 0.23 0.21 0.27 0.21 Wealth index (2nd quintile) 0.20 0.19 0.21 0.21 Wealth index (3rd quintile) 0.22 0.26 0.17 0.22 Wealth index (4th quintile) 0.18 0.17 0.18 0.20 Wealth index (Top quintile) 0.17 0.17 0.17 0.16 Female-headed household 0.13 0.12 0.13 0.15 Household head's years of education 5.85 6.00 5.87 5.59 Number of children in household 2.44 2.44 2.51 2.35 Urban 0.28 0.26 0.31 0.26 Conflict intensity (log of violent incidents per capita) −8.78 −8.82 −8.75 −8.76 Source: Original calculations based on MSPS 2024. The probit regression analysis presented in Table 3.2 sheds light on the factors influencing the likelihood of children being out of school in Myanmar. The results reveal several statistically significant associations between various child, household, and location characteristics and the probability of a child being out of school. These findings provide valuable insights into the disparities in education access and the challenges faced by specific groups of children in the country. 17 One of the most striking results is the gender disparity in school attendance, particularly among high school-age children. The analysis shows that being female is associated with a 15 percent lower likelihood of being out of school for this age group, a statistically significant finding. This highlights the urgent need to address the barriers that disproportionately affect male high school-age children and implement targeted interventions to promote their school attendance and completion. The child's age also emerges as a significant predictor of school attendance across all age groups. A one-year increase in age is associated with a 4 percent higher likelihood of being out of school overall, with the effect being most pronounced among high school-age children (8 percent) and middle school-age children (5 percent), both statistically significant. This underscores the importance of focusing on retention and completion efforts, particularly for older children who may face increased pressure to drop out of school. Household socioeconomic status, as measured by wealth quintiles, plays a crucial role in determining children's school attendance. Compared to children from the bottom wealth quintile, those from higher wealth quintiles have a significantly lower likelihood of being out of school. The effect is most substantial for high school-age children from the top wealth quintile, who are 35 percent less likely to be out of school than their counterparts from the bottom quintile. This finding emphasizes the need to address the economic barriers to education and to provide targeted support to children from disadvantaged households. The educational attainment of the household head is also significantly associated with children's school attendance. A one-year increase in the household head's years of education is associated with a 1 percent lower likelihood of a child being out of school overall, with the effect being most pronounced for high school-age children (3 percent), both statistically significant. This suggests that parental education and awareness play a vital role in promoting children's education and that efforts to improve adult literacy and education levels can have positive intergenerational effects. Lastly, the regression analysis reveals that children living in high-conflict areas are more likely to be out of school. Residing in a high-conflict township is associated with a 3 percent higher likelihood of being out of school overall. This effect is particularly notable for high school-age children, who are 4 percent more likely to be out of school if they live in a high-conflict area. This finding underscores the detrimental impact of conflict on children's education and the need for targeted interventions and support for children in conflict-affected areas. Table 3.3: Determinants of schooling status (being out of school) among 6 –17-year-old children Model 1 Model 2 Model 3 Model 4 All Primary Middle High school school age school age age Child characteristics Child is female −0.0554*** −0.0141 −0.0119 −0.1471*** (0.0186) (0.0147) (0.0266) (0.0453) Age of child 0.0393*** −0.0120** 0.0511*** 0.0797*** (0.0029) (0.0059) (0.0126) (0.0258) 18 Model 1 Model 2 Model 3 Model 4 All Primary Middle High school school age school age age Primary language at home is Myanmar −0.0082 −0.0534** 0.0037 0.1639* (0.0321) (0.0236) (0.0440) (0.0846) Household socioeconomic and demographic characteristics Wealth quintile (reference: Bottom quintile) 2nd wealth quintile −0.1367*** −0.1125*** −0.1010** −0.2420*** (0.0289) (0.0370) (0.0414) (0.0724) 3rd wealth quintile −0.0793** −0.1009** −0.0256 −0.1400* (0.0331) (0.0393) (0.0551) (0.0715) 4th wealth quintile −0.0913*** −0.1057*** −0.0600 −0.1678** (0.0311) (0.0388) (0.0481) (0.0687) Top wealth quintile −0.1636*** −0.1048*** −0.1527*** −0.3513*** (0.0323) (0.0399) (0.0425) (0.0676) Is a female-headed household −0.0216 −0.0144 −0.0816** 0.0435 (0.0236) (0.0207) (0.0377) (0.0576) Household head's years of education −0.0086*** 0.0017 −0.0089** −0.0277*** (0.0023) (0.0017) (0.0037) (0.0059) Number of children in household 0.0084 −0.0034 0.0140 0.0360** (0.0073) (0.0079) (0.0110) (0.0174) Location Urban −0.0134 −0.0093 0.0081 −0.0240 (0.0246) (0.0161) (0.0380) (0.0548) High-conflict area 0.0274*** 0.0231*** 0.0175 0.0430* (0.0095) (0.0067) (0.0139) (0.0234) Number of obs. 5,632 2,327 1,971 1,334 Pseudo R-squared 0.22 0.21 0.15 0.18 Source: Original calculations based on MSPS 2024. Note: Robust standard errors presented in parentheses. Regression model used is probit (reported results are marginal effects); sample restricted to 6 –17-year-old children; dependent variable is schooling status (1 if out-of-school and 0 otherwise); coefficients represent marginal effects; standard errors in parentheses. a. Conflict intensity is measured as the log of per capita conflict incidents at the township level. ***p < 0.01, **p < 0.05, *p < 0.1. Economic hardship overwhelmingly emerges as the primary driver compelling children to leave schools. Figure 3.10 illustrates that a striking 41 percent of respondents cited insufficient household income as the main reason for school dropout among children ages 6–17. This stark finding underscores the profound impact of poverty on educational opportunities, with families often grappling to fulfill basic needs and compelled to prioritize income-generating activities over schooling. Moreover, an additional 14 percent of respondents attribute dropout to school closures, reflecting the disruptive effect of external factors on educational continuity. Safety concerns also loom large, with 11 percent of respondents citing it as a reason for dropout, highlighting the complex interplay between security challenges and access to education. Furthermore, 6 percent of respondents indicated disengagement from the learning process as a reason for dropping out, emphasizing the importance of fostering an engaging educational environment to retain students in schools. 19 Figure 3.10: Reasons for dropping out of school (6–17-year-old children) Source: Original figure based on MSPS 2024. The data reveal an intriguing shift in the reasons behind children ages 6–17 dropping out of school in recent years. While economic constraints were more prevalent as a contributing factor in earlier years—among children who last attended school in 2019 or earlier—the more recent dropouts have been significantly influenced by school closures, safety concerns, and disengagement from the learning process. This trend reflects the profound disruptions to children's education caused by the COVID-19 pandemic and the aftermath of the military coup, underscoring the multifaceted challenges that have emerged in ensuring continuous and secure access to quality education. Notably, among children who last attended school in 2022 or 2023, about 13 percent cited safety concerns as the reason for dropping out, while 8 percent attributed their departure to disengagement from the learning process (Figure 3.11). These figures highlight the urgent need to address the climate of insecurity and the potential erosion of educational engagement, which may have been exacerbated by the unprecedented upheavals experienced during this period. This shift in the dynamics of educational exclusion demands a multifaceted response, one that not only tackles economic barriers but also prioritizes the restoration of safe and conducive learning environments as well as the implementation of strategies to reengage and retain students within the educational system. 20 Figure 3.11: Reasons for dropping out of school across the years (6 –17-year-old children) by the year of dropout 100% 20 23 24 80% 4 8 12 13 2 3 60% 4 8 2 40% 36 60 38 20% 4 1 18 14 15 0% 2019 or prior 2020 or 2021 2022 or 2023 School closed School too far Insufficient household income Grade low compared to age Disengaged from learning process Safety concerns Other reasons Source: Original figure based on MSPS 2024. The reasons for dropping out of school vary significantly across age groups and gender. Notably, insufficient household income emerges as the overarching reason for educational exclusion, further reinforcing the pervasive impact of poverty on access to education. However, the data highlight that dropouts due to economic hardships are particularly pronounced among the middle and high school-age groups. Among males, a staggering 52 percent of those in the middle school-age group and 43 percent in the high school-age group cited financial constraints as the reason for dropping out, in stark contrast to only 3 percent of primary school-age males (Figure 3.12). A similar pattern is observed among females, with 40 percent of middle school and 45 percent of high school females citing economic reasons, compared to 17 percent of primary school- age girls. Interestingly, safety concerns emerge as a significant factor contributing to dropouts among primary school-age children, particularly females. A concerning 26 percent of primary school- age males and 34 percent of their female counterparts are out of school due to safety issues. However, the prevalence of dropouts attributed to safety concerns decreases substantially among older age groups. Only 9 percent of middle school-age children of both genders, 13 percent of high school-age males, and 8 percent of high school-age females have dropped out due to safety concerns. These nuanced insights underscore the need for targeted interventions tailored to the specific challenges faced by different age groups and genders. While addressing economic barriers remains a priority across all segments, particular attention must be given to alleviating financial constraints for adolescents to prevent dropouts during crucial educational stages. Furthermore, 21 ensuring a safe and secure learning environment is paramount, especially for younger children who are more vulnerable to safety risks, hindering their access to education. Figure 3.12: Reasons for dropping out by gender and age group 100% 6 14 20 16 25 25 28 22 23 24 23 32 9 80% 41 5 9 23 13 13 9 8 11 8 34 60% 8 5 7 5 24 14 7 3 6 4 26 3 4 1 16 1 40% 3 2 52 17 40 29 27 11 43 41 46 40 43 45 6 20% 2 5 4 2 31 4 4 3 21 21 3 26 1 14 16 11 14 18 14 11 11 0% Primary Middle High Total Primary Middle High Total Primary Middle High Total All Male Female School closed School too far Insufficient household income Grade low compared to age Disengaged from learning process Safety concerns Other reasons Source: Original figure based on MSPS 2024. 4. Coping with disruptions in schooling 4.1 Patterns of student enrollments across school types Myanmar has over 16 types of schools, but the overwhelming majority of students are enrolled in public or state basic education institutions. As depicted in Figure 4.1, state basic education schools—comprising basic education primary schools, basic education middle schools, and basic education high schools—are the educational institutions where the majority of school students, approximately 76 percent, are enrolled in the academic year 2023–2024. Additionally, 18 percent of enrolled students attend other types of state schools, including basic education branch schools and basic education post-primary schools. Consequently, out of the enrolled children, a staggering 94 percent of students are enrolled in state schools, while only 6 percent are enrolled in non-state institutions with 3.5 percent of children attending private schools. Remarkably, despite the changed political context following the military coup, state schools continue to cater to the educational needs of the majority of students who have remained within the school system. This underscores the pivotal role played by state-run educational institutions in ensuring access to education for the nation's youth, regardless of the prevailing circumstances. On the other hand, the role of non-formal schooling remains limited with less than 3 percent of enrolled children enrolled in non-formal education (NFE) institutions. There is no significant difference in community 22 school3 enrollment between 2023 and 2024, 0.16 percent in 2023 and 0.22 percent in 2024. Enrollment in NFE4 class is also limited with just 0.03 percent of children enrolled in them.5 Figure 4.1: Distribution of enrolled students (ages 6–17) by school type, 2023 and 2024 Basic education high school 34.87 33.14 Basic education Primary school 23.64 26.90 Basic education middle school 21.62 15.87 State Branch high school 2.63 6.58 Basic education post-primary school 5.94 6.43 Branch middle school 1.72 3.31 Branch primary school 1.32 1.64 Private school 4.37 3.50 Monastic school 1.272.00 Other school 0.501.11 Individual learning home with a teacher guide 0.28 0.41 Non-state Community school 0.16 0.22 Other religious school 0.13 0.17 NFE Class . 0.03 Ethnic school . 0.03 NGO-run school 0.19 0.01 0% 5% 10% 15% 20% 25% 30% 35% 40% 2023 2024 Source: Original figure based on MSPS 2024. Note: In Myanmar, basic and branch upper secondary education (high school) serves students across all three levels (primary, middle, and high). The proportion of children enrolled in NFE classes and ethnic schools is not available for 2023. The data underscore the enduring significance of state schools, particularly basic education primary, middle, and high schools, in catering to the educational needs of the majority of Myanmar’s children. While the proportion of students attending non-state schools saw a slight increase from 5 percent to 8 percent between 2017 and 2023, recent trends indicate a reversal, with the non-state share dropping back to 6 percent in 2024 (Table 4.1). 3 Community schools refer to educational institutions or classes established through local community initiatives in informal settings, particularly in conflict-affected areas within non-ethnic regions of Myanmar. 4 NFE class refers to a non-formal education class designed specifically for OOSC. These classes are operated jointly by the MoE and the United Nations Children’s Fund (UNICEF). It is important to note that these are not NFE schools per se, but rather a targeted program aimed at providing educational opportunities to children who have dropped out of the formal school system. 5 The relatively low reported share of non-state school enrollment may underestimate actual figures due to the sensitive nature of questions regarding school type in the current context. Survey respondents may have been hesitant to disclose attendance at non-state educational institutions, given the complex political situation in Myanmar. Therefore, these statistics should be interpreted with appropriate caution, considering potential response bias. 23 There are significant differences in state and non-state school enrollment distribution across different demographic and socioeconomic groups. In the 2023–24 academic year, a slightly higher proportion of males (7 percent) were enrolled in non-state schools compared to females (5 percent), suggesting a potential gender gap in access to non-state educational opportunities for girls. Moreover, enrollment in non-state schools has declined across age groups in the 2023–24 academic year compared to the previous year, marking a reversal of the increasing trend observed between 2017 and 2023. Despite this decline, non-state enrollments in primary and middle school- age groups remain higher than observed in 2017, indicating sustained interest in non-state educational options at these levels. Examining enrollment dynamics through the lens of socioeconomic status reveals noteworthy trends. The share of students from the poorest households (bottom wealth quintile) enrolled in non-state schools has decreased, dropping to 2 percent in 2024 compared to 4 percent in 2017 and 2023, a 50 percent reduction, suggesting that economic challenges resulting from the pandemic and political crisis may disproportionately affect the poor. Conversely, non-state school enrollment remains relatively high among students from the wealthiest households (top wealth quintile), with a slight drop in share between 2023 and 2024, yet remaining 40 percent higher than in 2017 for this group. The trend in non-state school enrollment differs significantly across urban and rural areas. Rural non-state school enrollment remains steady at 4 percent, while among urban children, although lower than in 2023, it remains about one-third higher than in 2017. Notably, children living in townships experiencing the highest level of conflict have seen an increase in non-state enrollment since the previous year, jumping from 6 percent in 2023 to 8 percent in 2024, representing a 33 percent increase. Table 4.1: Distribution of enrolled children (6–17 years) across state and non-state schools, 2017 and 2023 (%) 2017 2023 2024 State Non-state Total State Non-state Total State Non-state Total All 95 5 100 92 8 100 94 6 100 Gender Male 95 5 100 91 9 100 93 7 100 Female 95 5 100 93 7 100 95 5 100 Age group Primary school age 96 4 100 92 8 100 94 6 100 Middle school age 96 4 100 94 6 100 95 5 100 High school age 92 8 100 87 13 100 92 8 100 Wealth quintile Poorest quintile 96 4 100 96 4 100 98 2 100 2nd quintile 96 4 100 96 4 100 96 4 100 3rd quintile 97 3 100 94 6 100 96 4 100 4th quintile 92 8 100 87 13 100 92 8 100 Richest quintile 90 10 100 84 16 100 86 14 100 Location Rural 96 4 100 94 6 100 96 4 100 24 2017 2023 2024 State Non-state Total State Non-state Total State Non-state Total Urban 91 9 100 85 15 100 88 12 100 a Conflict intensity Bottom quartile — — — 94 6 100 98 2 100 2nd quartile — — — 87 13 100 95 5 100 3rd quartile — — — 89 11 100 90 10 100 Top quartile — — — 94 6 100 92 8 100 Source: Original calculations based on MLCS (2017), MSPS (2023), and MSPS 2024. Note: a. Conflict intensity is measured as the log of per capita conflict incidents at the township level. A comparative analysis of the profiles of children enrolled in non-state schools versus state schools in 2024 further confirms that children in non-state schools are disproportionately from the higher wealth quintiles and are more concentrated in urban and high-conflict areas. As shown in Table 4.2, compared to children enrolled in state schools, non-state school children are significantly more likely to be males than females. While the state school children are relatively evenly distributed across the wealth groups, a much larger share of non-state school students come from households belonging to the highest wealth quintile (42 percent) and a disproportionately smaller share of non-state schools are from the poorest households (8 percent), a finding consistent with the discussion in the preceding paragraph (Table 4.2). Finally, with regard to the location of students, a disproportionately larger share of non-state school students tend to come from urban areas compared to children from state schools—urban children comprise 54 percent of all non- state school enrolled children while only accounting for 27 percent of all children enrolled in state schools. Similarly, a larger share of non-state school students is from higher-conflict areas compared to students enrolled in state schools. Table 4.2: Profile of students enrolled in state versus non-state schools, 2023 and 2024 (%) 2023 2024 All All State Non-state children State Non-state children Gender Male 48 54 49 47 56 48 Female 52 46 51 53 44 52 Total 100 100 100 100 100 100 Age group Primary school age (6–10) 44 44 45 46 47 46 Middle school age (11–14) 39 26 37 36 29 36 High school age (15–17) 17 29 18 17 24 18 Total 100 100 100 100 100 100 Wealth quintile Poorest quintile 21 10 20 23 8 22 2nd quintile 22 9 21 21 13 21 3rd quintile 21 14 21 22 14 21 4th quintile 23 39 24 18 24 19 Richest quintile 13 27 14 17 42 18 25 2023 2024 All All State Non-state children State Non-state children Total 100 100 100 100 100 100 Location Rural 74 48 72 73 46 71 Urban 26 52 28 27 54 29 Total 100 100 100 100 100 100 Conflict intensitya Bottom quartile 26 17 25 30 12 29 2nd quartile 33 22 32 29 22 28 3rd quartile 24 40 26 21 38 23 Top quartile 16 21 16 20 29 21 Total 100 100 100 100 100 100 Source: Original calculations based on MSPS (2023) and MSPS 2024. Note: a. Conflict intensity is measured as the log of per capita conflict incidents at the township level. 4.2 Access to online education and parental support The adoption of online education in Myanmar remains limited, with only a small fraction of children ages 6–17 using online learning platforms in recent years. The data reveal that the overall usage of online education remains relatively low, with only 4.1 percent of children in this age group accessing online learning in 2023 and a slight decrease to 4.0 percent in 2024 (Table 4.3). However, when examining the data through the lens of various subgroups, significant disparities emerge, highlighting the uneven landscape of digital education access in the country. Interestingly, the share of online education users is two times greater among enrolled students than among children who are not enrolled in school, which suggests that online education is used more as a supplementary tool rather than as a substitute for traditional schooling. One of the most striking disparities is observed between children enrolled in non-state and state schools. In 2023, 13.7 percent of children in non-state schools used online education, while only 3.3 percent of those in state schools did so. Although there is a decline in usage among non- state school students to 10.7 percent in 2024, the gap between the two groups remains significant, with state school students’ usage increasing only marginally to 3.5 percent. This stark difference underscores the need to bridge the digital divide between state and non-state educational institutions to ensure equitable access to online learning opportunities. Socioeconomic status emerges as another critical determinant of access to online education. Children from the richest quintile demonstrate the highest adoption rates, with 10.4 percent using online education in 2023, compared to a mere 1.5 percent among those from the poorest quintile. While there is a decline in usage among the richest quintile to 7.8 percent in 2024, the disparity persists, with the poorest quintile’s usage increasing only slightly to 2.5 percent. This highlights the urgent need for targeted interventions to support disadvantaged communities in accessing digital learning resources. 26 Geographical location also plays a significant role in shaping access to online education. Urban children exhibit a higher usage rate of 8 percent in 2023, compared to 2.7 percent among their rural counterparts. Despite a slight decline in urban usage to 7.0 percent in 2024 and a marginal increase in rural usage to 2.8 percent, the urban-rural divide remains significant. Bridging this gap requires investments in digital infrastructure and connectivity in rural areas to ensure that children, regardless of their location, can benefit from online learning opportunities. Interestingly, the data reveal variations in online education adoption across different levels of conflict intensity. Areas with higher conflict intensity, represented by the third quartile, show the highest usage rate of 5.5 percent in 2023, increasing to 6.6 percent in 2024. In contrast, areas with the lowest conflict intensity, such as the bottom quartile, exhibit lower adoption rates of 2.8 percent in 2023 and 3.6 percent in 2024. This suggests that conflict-affected regions may have unique challenges and opportunities in terms of online education access, warranting further investigation and tailored interventions. Gender disparities in online education usage appear less pronounced, with males showing a slightly higher adoption rate of 4.7 percent in 2023 compared to 3.5 percent among females. However, this trend has reversed in 2024, with female usage increasing to 4.2 percent and male usage declining to 3.7 percent. Moreover, the use of online education among high school-age children has shown a significant decline between 2023 and 2024—5.8 percent of children in this age group used online education in 2023 compared to 3.6 percent in 2024. This decline warrants attention to ensure that older students continue to have access to digital learning resources as they progress through their educational journey. Table 4.3: Distribution of children (ages 6–17) who used online education in the past 12 months, 2023 and 2024 (%) Used online education (past 12 months) 2023 2024 All 4.1 4.0 Enrollment status Not enrolled 2.3 1.5 Enrolled 4.7 4.5 School type State 3.3 3.5 Non-state 13.7 10.7 Gender Male 4.7 3.7 Female 3.5 4.2 Age group Primary school-age group (6–10) 3.9 3.8 Middle school-age group (11–14) 2.9 4.4 High school-age group (15–17) 5.8 3.6 Wealth quintile 27 Used online education (past 12 months) 2023 2024 Poorest quintile 1.5 2.5 2nd quintile 1.6 3.3 3rd quintile 2.9 2.9 4th quintile 6.3 4.4 Richest quintile 10.4 7.8 Location Rural 2.7 2.8 Urban 8.0 7.0 Conflict intensitya Bottom quartile 2.8 3.6 2nd quartile 3.7 2.8 3rd quartile 5.5 6.6 Top quartile 4.3 3.5 Source: Original calculations based on MSPS 2023 and MSPS 2024. Note: a. Conflict intensity is measured as the log of per capita conflict incidents at the township level. The regression analyses provide valuable insights into the factors influencing children ’s enrollment in state versus non-state schools, their use of online education, and parental engagement in their learning. These findings not only support the descriptive analysis but also shed light on the complex interplay of various demographic, socioeconomic, and contextual factors. Consistent with the descriptive findings, the regression results in Table 4.4 (model 1) demonstrate that children from wealthier households are more likely to enroll in non-state schools compared to their counterparts from poorer households, even when controlling for other factors such as student demographics, household characteristics, and location. Notably, the probability of children being enrolled in non-state schools is over 7 percent higher among those from the wealthiest households compared to those from the poorest households. Similarly, the household head’s education level emerges as a significant positive determinant of enrollment in non-state schools. However, the analysis also reveals gender disparities, with female children being about 1.7 percent less likely to be enrolled in non-state schools. The number of children in the household is also negatively associated with non-state school enrollment. On the other hand, urban children and those residing in high-conflict townships are more likely to be enrolled in non-state schools compared to their rural and low-conflict counterparts, respectively. The regression results also confirm that enrolled children and those in non-state schools are significantly more likely to use online education compared to OOSC and those enrolled in state schools (Table 4.4, model 2). Furthermore, urban children and those from more educated 28 families exhibit a higher likelihood of using online education. Surprisingly, household wealth does not show a statistically significant association with online education usage. Parental engagement in children’s learning is another crucial aspect explored in the regression analysis (Table 4.4, model 3). In the current context of high out-of-school rates and many enrolled children falling behind their expected grade levels (Katwal et al. 2024), parental involvement is particularly important. The findings reveal that the most significant determinants of parental engagement are children’s enrollment status and household wealth. Enrolled children and those from wealthier households are more likely to receive family support and guidance in their education compared to OOSC and those from less wealthy households. Interestingly, children in non-state schools are not more likely to receive family support than those in state schools. The analysis also highlights other factors influencing parental engagement. Older children are less likely to receive parental guidance, possibly due to their greater independence and parents ’ knowledge limitations in supporting higher-grade studies. The household head’s education level is positively associated with parental engagement, while children from female-headed households are more likely to receive educational support. Urban children, however, are less likely to receive guidance compared to their rural counterparts. These findings underscore the need for targeted interventions to address disparities in educational access, online learning, and parental engagement. The interventions should focus on supporting disadvantaged households, promoting gender equality, and enhancing parental involvement across all socioeconomic groups and geographic locations. Table 4.4: Determinants of enrollment in state versus non-state schools, access to online education, and family support (probit models) Model 1 Model 2 Model 3 Non- Online Guardian state/State Education help Child characteristics Child is female −0.0169** 0.0029 0.0351 (0.0082) (0.0076) (0.0228) Age of child −0.0006 0.0010 −0.0392*** (0.0013) (0.0012) (0.0030) Primary language at home is not Myanmar 0.0369 0.0118 −0.0066 (0.0228) (0.0153) (0.0397) Child is enrolled in school 0.0378** 0.1886*** (0.0157) (0.0369) Child is enrolled in non-state school 0.0286*** −0.0261 (0.0086) (0.0372) Household socioeconomic and demographic characteristics Wealth quintiles (reference: Bottom quintile) 2nd wealth quintile 0.0118 −0.0049 0.1249*** (0.0112) (0.0185) (0.0336) 29 Model 1 Model 2 Model 3 Non- Online Guardian state/State Education help 3rd wealth quintile 0.0164 −0.0144 0.1307*** (0.0124) (0.0168) (0.0383) 4th wealth quintile 0.0351*** −0.0158 0.1195*** (0.0132) (0.0169) (0.0378) Top wealth quintile 0.0742*** −0.0050 0.1162*** (0.0171) (0.0169) (0.0351) Is a female-headed household −0.0031 0.0158 0.0769** (0.0123) (0.0111) (0.0325) Household head's years of education 0.0024** 0.0046*** 0.0109*** (0.0010) (0.0008) (0.0030) Number of children in household −0.0078** −0.0040 0.0005 (0.0038) (0.0033) (0.0097) Household location Urban 0.0356*** 0.0259*** −0.0617** (0.0108) (0.0086) (0.0256) Conflict intensitya 0.0100** 0.0047 0.0309*** (0.0043) (0.0045) (0.0111) Number of observations 5,469 5,413 5,469 Pseudo R-squared 0.15 0.11 0.15 Source: Original calculations based on MSPS 2024. Note: Dependent variables in the models are as follows. Model 1: school type = 1 if non-state school and 0 otherwise; Model 2: online education = 1 if child used online education in the past 12 months and 0 otherwise; Model 3: family support = 1 if average daily minutes of parental guidance is greater than the median and 0 otherwise. a. Conflict intensity is the log of per capita conflict incidents at the township level. Robust standard errors in parentheses. All three models account for state/region fixed effects. ***p < 0.01, **p < 0.05, *p < 0.1. 5. Conclusions Myanmar's education system has seen some improvement since the last academic year, with overall enrollment rates increasing. However, a closer examination of the data reveals persistent and alarming disparities in educational access across various demographic and socioeconomic groups. The MSPS survey data highlight the strong association between conflict and education access with areas experiencing higher levels of conflict consistently exhibiting lower enrollment rates across all age groups. The survey findings underscore the urgent need for targeted interventions to address the widening gaps in educational access. Male children, those from low-income households, and middle and high school-age students are particularly vulnerable, with significantly lower enrollment rates compared to their counterparts. Moreover, the stark disparities across states at all 30 age groups further emphasize the need for a comprehensive and context-specific approach to tackling educational inequalities. The COVID-19 pandemic and ongoing political conflict have exacerbated the challenges faced by Myanmar’s education system, leading to prolonged school closures and significant learning losses. Consequently, a growing number of children, particularly those in middle and high school, are falling behind their expected grade levels and failing to attend age-appropriate classes (Katwal et al. 2024). This disengagement from the learning process is further compounded by rising school dropouts due to safety concerns. While online education has the potential to bridge some of these gaps, its current uptake remains low, at only 4 percent, overall. Notably, the children who stand to benefit the most from online learning, such as OOSC and those from low-income and rural households, have the least access to these resources. Increasing the accessibility of online education could serve not just as a valuable supplementary learning tool among enrolled students but also among the OOSC. To effectively address the multifaceted challenges facing Myanmar's education system, it is crucial to prioritize the fourth and fifth aspects of the RAPID 6 framework.7 This includes increasing the efficiency of instruction through catch-up learning approaches, such as targeted instruction, structured pedagogy, self-guided learning, and increased learning time. Additionally, developing psychosocial health and well-being by building teachers’ capacity to support students, investing in teacher well-being and resilience, and prioritizing students’ safety and nutrition is paramount. Given the limited resources available, it is essential to focus on evidence-based, cost-effective interventions that have proven to yield significant improvements in student learning outcomes. The World Bank’s ‘Best Buys’ report (Banerjee et al. 2023) offers valuable insights into high-impact, low-cost interventions such as structured pedagogy programs and teaching at the right level. By leveraging these evidence-based strategies and rigorously evaluating the impact of interventions, Myanmar can optimize its resources and maximize returns on investment in education. The observed shift in schooling choices toward non-state schools, among certain groups of the society, also presents an opportunity to improve access by providing targeted support to these institutions and their students. Non-state schools in Myanmar often face resource constraints that hinder their ability to provide quality education and maintain financial viability. Offering grants for infrastructure expansion, teaching materials, and staff salaries, as well as providing direct funding for teacher salaries or teaching materials, as seen in other fragility, conflict, and violence (FCV) countries such as Afghanistan and Yemen (Aedo et al. 2023), could significantly bolster these schools. Additionally, providing cash transfers to students attending non-state schools could help cover tuition, fees, and other educational expenses, further improving access to education. 6 Reach Access Prioritize Increase Develop. 7 See Box A1. 31 In conclusion, while Myanmar has made progress in overall enrollment rates, the disparities in educational access across various groups and the negative relationship between exposure to conflict and access to education remain deeply concerning. Addressing these inequalities is critical for Myanmar’s future stability and prosperity. By prioritizing targeted interventions, increasing the efficiency of instruction, supporting psychosocial well-being, and leveraging evidence-based, cost-effective strategies, Myanmar can work toward ensuring equitable access to quality education for all its children, regardless of their background or circumstances. Investing in education is not only a path to individual empowerment but also a critical step toward building a more resilient, inclusive, and prosperous society. Recommendations Addressing disparities in access • Targeted support for vulnerable groups o Implement conditional cash transfer programs for low-income families, particularly in rural and conflict-affected areas. o Develop mobile schools and flexible learning programs for children in remote or conflict- affected regions. o Create inclusive education programs for children with disabilities, including teacher training and infrastructure adaptations. • Gender-specific interventions o Launch awareness campaigns to promote male enrollment in upper secondary education. Enhancing parental and community engagement • Community learning centers o Establish community learning centers that can provide supplementary education and serve as hubs for educational resources. Addressing conflict-related challenges • Safe schools initiative o Implement a ‘Safe Schools’ program to protect educational institutions from conflict- related violence. o Provide psychosocial support for students and teachers affected by conflict. To ensure these good practices benefit students in both state and non-state schools, a collaborative approach involving all education stakeholders is essential. This includes fostering partnerships between non-state providers, NGOs, and international organizations. Sharing resources across different school types and ensuring inclusive policy-making processes will maximize impact. A comprehensive monitoring and evaluation system will be crucial to track progress and adjust strategies as needed. This approach will promote a unified effort to improve education access and quality for all students in Myanmar, regardless of the type of school they attend. 32 References Aedo, C., T. Beteille, S. D. Bhatta, J. Stevens, M. M. Thwin. 2023. Breakdown and Revitalization: Preventing a Lost Generation of Educational Attainment in Myanmar. Washington, DC: World Bank Group. Azevedo, J. P. 2020. “Learning Poverty: Measure and Simulations.” Policy Research Working Paper 9446, World Bank, Washington, DC. Azevedo, J. P., M. Akmal, M. H. Cloutier, H. Rogers, and Y. N. Wong. 2022. “Learning Losses during COVID-19.” Banerjee, A., T. Andrab, R. Banerji, S. Dynarski, R. Glennerster, S. Grantham-Mcgregor, K. Muralidharan, B. Piper, J. S. Chanduvi, H. Yoshikawa, S. Ruto, and S. Schmelkes. 2023. 2023 Cost-Effective Approaches to Improve Global Learning - What Does Recent Evidence Tell Us Are “Smart Buys” for Improving Learning in Low- and Middle-Income Countries? Washington, DC: World Bank Group. Bhatta, S. D., and S. Katwal. 2022a. Learning in Myanmar, Pre- and Post-Covid-19. Washington, DC: World Bank. Bhatta, S. D., and S. Katwal. 2022b. Pre-Covid-19 Trends and Disparities in Education Outcomes in Myanmar. Washington, DC: World Bank. Bhatta, S. D., S. Katwal, S. Sinha Roy, R. Van der Weide, U. Sharma, A. P. Kyaw, and M. M. Thwin. 2023. Education in Myanmar: Where Are We Now? Washington, DC: World Bank. CSO (Central Statistical Organization), UNDP (United Nations Development Programme), and World Bank. 2018. Myanmar Living Conditions Survey 2017: Key Indicators Report. Nay Pyi Taw and Yangon, Myanmar: Ministry of Planning and Finance, UNDP, and World Bank. Frontier. 2022. “Education vs. Revolution: School Reopenings Bring Hard Choices.” Frontier Myanmar, January 17, 2022. https://www.frontiermyanmar.net/en/education-vs- revolution-school-reopenings-bring-hard-choices/. Insecurity Insight. 2021. Violence Against or Obstruction of Education in Myanmar v. August 2021. Geneva, Switzerland: Insecurity Insight. https://bit.ly/MyanmarEducationJuly2021. Katwal, S., T. Fukao, M. M. Thwin, and A. P. Kyaw. 2024. Preventing a Lost Generation: Understanding the State of Out of School Children in Myanmar. Washington, DC: World Bank. 33 Schady, N., A. Holla, S. Sabarwal, J. Silva, and A. Y. Chang. 2023. Collapse and Recovery: How the COVID-19 Pandemic Eroded Human Capital and What to Do about It. Washington, DC: World Bank. Sinha Roy, S. 2023. “Myanmar Subnational Phone Surveys (MSPS) of the World Bank: Coverage, Reliability, Representativeness.” Washington, DC: World Bank. Sinha Roy, S. 2024. “Myanmar Subnational Phone Surveys (MSPS): Round 2023-24: Survey Design Technical Document.” Washington, DC: World Bank. UNHCR (United Nations High Commissioner for Refugees). 2024. “Weekly Myanmar Displacement Maps/Figures, November 2024.” UNHCR Representation Office, Yangon, Myanmar. UNICEF (United Nations Children’s Fund), and SEAMEO (Southeast Asian Ministers of Education Organization). 2020. SEA-PLM 2019 Main Regional Report, Children’s Learning in 6 Southeast Asian Countries. Bangkok, Thailand: UNICEF and SEAMEO - SEA-PLM Secretariat. World Bank. 2022. Myanmar Fiscal Monitoring: Myanmar Budget Brief. November 2022. Washington, DC: World Bank. World Bank. 2023. Myanmar Fiscal Monitoring: Myanmar Budget Brief. November 2023. Washington, DC: World Bank. World Bank. 2024. Myanmar Economic Monitor, June 2024. Washington, DC: World Bank. World Bank, Bill and Melinda Gates Foundation, FCDO (Foreign, Commonwealth and Development Office), UNESCO (United Nations Educational, Scientific, and Cultural Organization), UNICEF (United Nations Children’s Fund), and USAID (United States Agency for International Development). 2022. Guide for Learning Recovery and Acceleration: Using the RAPID Framework to Address COVID-19 Learning Losses and Build Forward Better. Washington, DC: World Bank. 34 Annex Table A1:Profiles of children (6–17-year-olds) who are out of school and who are in school, 2024 (%) Primary school age Middle school age High school age OOSC Enrolled All OOSC Enrolled All OOSC Enrolled All children children children children children children Gender Male 55 47 48 49 49 49 63 47 54 Female 45 53 52 51 51 51 37 53 46 Total 100 100 100 100 100 100 100 100 100 Consumption Quintile Poorest quintile 37 18 19 30 25 26 26 16 21 2nd quintile 13 21 21 21 22 22 20 25 22 3rd quintile 18 26 26 22 15 17 25 20 22 4th quintile 15 18 17 20 19 18 19 19 19 Richest quintile 18 17 17 8 19 17 10 20 16 Total 100 100 100 100 100 100 100 100 100 Location Rural 81 73 74 70 69 69 79 71 74 Urban 19 27 26 30 31 31 21 29 26 Total 100 100 100 100 100 100 100 100 100 Conflict/non-conflict Bottom quartile 8 28 27 20 27 27 22 32 28 2nd quartile 13 30 29 19 27 26 27 26 26 3rd quartile 40 22 23 29 23 24 24 23 23 Top quartile 38 20 21 33 22 24 27 20 23 Total 100 100 100 100 100 100 100 100 100 Source: Original calculations based on MSPS 2024. 35 Figure A1: Number of people displaced since February 2021 and remain displaced (as of November 2024) Source: UN in Myanmar (Myanmar Emergency Overview Map). 36 Figure A2: Number of people internally displaced within Myanmar (as of November 2024) Source: UN in Myanmar (Myanmar Emergency Overview Map and Statistics). 37 Box A1: RAPID framework Formulated by UNICEF, UNESCO, and the World Bank, the RAPID framework for establishing programs for learning recovery and accelerated learning includes the following short-term interventions: 1. Reaching every child and retaining them in school: (a) reopening schools safely and keeping them open; (b) conducting reenrollment campaigns, strengthening early warning systems to identify potential dropouts, and involving families; and (c) providing free schooling, meals, and cash transfers. 2. Assessing learning levels regularly: (a) providing classroom-level assessment tools and conducting diagnostic/formative assessments to inform instruction and (b) conducting system-level (national, subnational) assessments of learning levels and losses to inform policy. 3. Prioritizing teaching the fundamentals: (a) foundational learning (mainly numeracy and literacy but also socio- emotional skills and digital skills) and (b) prerequisites for future learning by adjusting the curricula and time allocations and training teachers accordingly. 4. Increasing the efficiency of instruction, including through catch-up learning: (a) using approaches such as targeted instruction, structured pedagogy, self-guided learning, tutoring, and increase in learning time and (b) supporting teachers by providing continuous training, coaching, access to technology, and counseling/peer support. 5. Developing psychosocial health and well-being: (a) building teachers’ capacity to support students’ well -being and identify students in need of specialized services; (b) supporting teacher well-being and resilience; and (c) investing in students’ safety, nutrition, and water, sanitation, and hygiene (WASH). Source: World Bank et al. 2022. 38