What happens to the learning outcomes of left-behind children when parents are away? Evidence from four pacific island countries

Vu, Trang Thu; Suryadarma, Daniel Working Paper What happens to the learning outcomes of left-behind children when parents are away? Evidence from four pacific island countries ADBI Working Paper, No. 1449 Provided in Cooperation with: Asian Development Bank Institute (ADBI), Tokyo Suggested Citation: Vu, Trang Thu; Suryadarma, Daniel (2024) : What happens to the learning outcomes of left-behind children when parents are away? Evidence from four pacific island countries, ADBI Working Paper, No. 1449, Asian Development Bank Institute (ADBI), Tokyo, https://doi.org/10.56506/XZLJ5478 This Version is available at: https://hdl.handle.net/10419/301954 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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EVIDENCE FROM FOUR PACIFIC ISLAND COUNTRIES Trang Thu Vu and Daniel Suryadarma No. 1449 May 2024 Asian Development Bank Institute The Working Paper series is a continuation of the formerly named Discussion Paper series; the numbering of the papers continued without interruption or change. ADBI’s working papers reflect initial ideas on a topic and are posted online for discussion. Some working papers may develop into other forms of publication. The Asian Development Bank refers to “China” as the People’s Republic of China. Suggested citation: Vu, T. T. and D. Suryadarma. 2024. What Happens to the Learning Outcomes of Left- Behind Children When Parents Are Away? Evidence from Four Pacific Island Countries. ADBI Working Paper 1449. Tokyo: Asian Development Bank Institute. Available: https://doi.org/10.56506/XZLJ5478 Please contact the authors for information about this paper. Email: [email protected] Trang Thu Vu is a research associate and Daniel Suryadarma is a senior research fellow, both at the Asian Development Bank Institute, Tokyo, Japan. The views expressed in this paper are the views of the author and do not necessarily reflect the views or policies of ADBI, ADB, its Board of Directors, or the governments they represent. ADBI does not guarantee the accuracy of the data included in this paper and accepts no responsibility for any consequences of their use. Terminology used may not necessarily be consistent with ADB official terms. Discussion papers are subject to formal revision and correction before they are finalized and considered published. We thank seminar participants at the Australian National University and Asian Development Bank for their feedback. The views expressed are the views of the authors and do not necessarily reflect the views or policies of ADBI, ADB, its Board of Directors, or the governments they represent. Asian Development Bank Institute Kasumigaseki Building, 8th Floor 3-2-5 Kasumigaseki, Chiyoda-ku Tokyo 100-6008, Japan Tel: +81-3-3593-5500 Fax: +81-3-3593-5571 URL: www.adbi.org E-mail: [email protected] © 2024 Asian Development Bank Institute ADBI Working Paper 1449 Vu and Suryadarma Abstract When parents migrate from home to another place and leave their children behind, it can have a large influence on their children. This study explores the relationship between parental migration and the learning outcomes of left-behind children aged 7–14 in Pacific Island economies, particularly Fiji, Kiribati, Samoa, and Tonga, by using the Multiple Indicator Cluster Survey 6. Using propensity score matching, we observe no significant effects in Fiji, Kiribati, and Tonga. However, our findings indicate that in Samoa, children whose parents are absent from home make significant improvements in their reading skill, despite struggling in mathematics. In addition, living in an extended family could benefit Samoan children when a parent is away. We also find that the effects in Samoa are isolated among girls. Keywords: Pacific Island countries, left-behind children, foundational skills JEL Classification: I25, I30, F22 ADBI Working Paper 1449 Vu and Suryadarma Contents 1. INTRODUCTION .......................................................................................................... 1 2. FIJI, KIRIBATI, SAMOA, AND TONGA: ECONOMIC SITUATION AND LABOR MOBILITY ............................................................................................... 2 3. DATA, IDENTIFICATION STRATEGY AND DESCRIPTIVE STATISTICS ................ 4 3.1 Data and Main Variables .................................................................................. 4 3.2 Identification Strategy ....................................................................................... 4 3.3 Parental Migration ............................................................................................ 5 3.4 Learning Outcomes of Children in Fiji, Kiribati, Samoa, and Tonga ................. 6 4. ESTIMATION RESULTS ............................................................................................ 10 4.1 Correlates of Having Migrant Parents ............................................................ 10 4.2 The Impact of Parental Migration on Children’s Literacy and Numeracy ....... 11 4.3 Heterogeneity Analysis by Sex and Living with the Extended Family ............ 12 5. CONCLUSION ........................................................................................................... 13 REFERENCES ...................................................................................................................... 15 APPENDIX ............................................................................................................................. 20 ADBI Working Paper 1449 Vu and Suryadarma 1 1. INTRODUCTION Voluntary migration is mainly economically motivated. For the poor or those in rural areas, migration is considered a prominent livelihood strategy (Stark and Bloom 1985; Bryan, Chowdhury, and Mushfiq Mobarak 2014). Given the extent of economically motivated migration worldwide, hundreds of millions of children are left behind when their parents migrate. For instance, more than a third of all children residing in rural People’s Republic of China (PRC)—amounting to about 61 million—are left behind by one or both migrant parents. More than 40% in rural South Africa, about one-third in Ghana, Moldova, and Georgia, and around a quarter of all children in the Philippines are left behind (Fellmeth et al. 2018; Antia et al. 2020). We estimate the impact of the absence of parents on their children’s learning outcomes in Fiji, Kiribati, Samoa, and Tonga. We contribute to the small body of research focusing on the impact of parent migration on children’s literacy and numeracy. Ours is the first study in the region, in which nationally representative household surveys containing objective measures of children’s foundational skills only became publicly available recently. The absence of parents due to migration can significantly impact children’s outcomes. Remittances are the most visible and expected benefit of migration (Stark 1991; Stark and Taylor 1991). The transfer helps make schooling and healthcare more affordable and accessible, reduces child labor, increases children’s leisure time (Basu et al. 2010; Antman 2013; Alcaraz, Chiquiar, and Salcedo 2012), and improves children’s health outcomes (Frank and Hummer 2002; Antón 2010). Yang (2008) finds a positive effect of international remittances on child education and labor in the Philippines. Binci and Giannelli (2012) conclude that internal remittances increase school attendance and reduce child labor in Viet Nam. Nguyen and Nguyen (2015) find that receiving international remittances helps children increase completed grades by around 2%. Hildebrandt et al. (2005) find that children in international migrating households in Mexico have lower rates of infant mortality and higher birth weights. Carletto, Covarrubias and Maluccio (2011) document that the nutrition intake of children in migrant-sending households in Guatemala improves faster than that of children in households without migrants. The benefits of parental migration, however, do not always materialize on the leftbehind children. Migration does not necessarily lead to a higher income for migrantsending households. Migration can also prevent these households from engaging in high-return but labor-intensive activities (Taylor 2010). Regular absence of the most productive household members may also impose monetary or nonmonetary costs on the family and community members left behind (Gibson, McKenzie, and Stillman 2011a and 2011b). When parents migrate, children may receive less care and attention – for instance, less time for reading books, helping with homework, and attending school meetings to learn about child issues. Nguyen (2016) finds that although parental migration helps families increase per capita consumption, it does not improve the health (weight and height) or cognitive ability (math score, and Peabody Picture Vocabulary Test) of children aged 5–8 years in Ethiopia, India, Peru, and Viet Nam. Meng and Yamauchi (2017) show that when Chinese rural workers migrate to cities for work, the left-behind children, especially boys, experience sizeable health and education adverse effects. In addition, children may have to do more housework and/or take care of other family members. Many studies show the adverse effects of parental migration on children’s education (McKenzie and Rapoport 2011; Frisancho Robles and Oropesa 2011; Coffey 2013; Lu 2014; Robles-Llana 2021). ADBI Working Paper 1449 Vu and Suryadarma 2 Another disadvantage for children is the social impacts of extended family separations. Withers (2022) finds evidence in Pacific Island economies on the difficulties of maintaining relationships at a distance, including managing childcare. The absence of parents can also lead to a relationship where children receive only one-sided love. Children and caring in relation to finance are the second-ranked social issue, after extramarital affairs, frequently occurring among seasonal workers in the Pacific. Across many Pacific Islands countries (PICs), labor mobility plays a vital role in supporting living standards. Internal migration has increased significantly and intensively over the past five decades (Naidu and Vaike 2016). Also, a considerable number of people from these islands live abroad, forming diaspora communities (Doan, Dornan, and Edwards 2023). The countries have received substantial amounts of remittances, with some having one of the world’s highest ratios of remittances to gross domestic product. Notably, Tonga, Samoa, Fiji, and Kiribati are among the top 10 recipients of remittances as a share of the GDP in the Asia and the Pacific region. In Tonga, remittances account for over a fifth of national income, and in Samoa, they reach 35% (World Bank 2023). Despite the increasing trend of migration in PICs, the existing literature on migration and child development in the region remains limited (World Migration Report 2020). There is a lack of contextually relevant research that needs to be set at both national and regional levels for comparison. A major reason for the scarcity of research in PICs on this topic is the lack of data availability (UNICEF 2020). Most of the studies in the PICs focus on evaluating international seasonal mobility programs (Gibson, McKenzie, and Stillman 2011b; Gibson and McKenzie 2014; Gibson and Bailey 2021). Of these, only a few examine the effect on school attendance (Gibson and McKenzie 2014; Gibson et al. 2019). Moreover, studies that examine the impacts of migration on the math and reading proficiency of children, especially for children between the ages of 7 and 14, do not yet exist. We use the sixth round of the UNICEF Multiple Indicator Cluster Surveys (MICS6) in 2019 for the four countries. We implement a propensity score matching (PSM) approach to establish causality, particularly the Mahalanobis matching technique. We find no evidence in Fiji, Kiribati, and Tonga that children with at least one parent migrating have a different probability of acquiring foundational reading or mathematics skills from those living with both parents. In Samoa, the left-behind children have a higher chance of acquiring foundational reading skills by 11.7 percentage points, but a lower chance of acquiring foundational mathematics skills by 8.6 percentage points. These are large effects given the low levels of literacy and numeracy in the country. The rest of this paper is structured as follows: Section 2 provides a succinct background of the four countries; Section 3 discusses data and the estimation strategy; we present the results and a discussion in Section 4; Section 5 provides the conclusion. 2. FIJI, KIRIBATI, SAMOA, AND TONGA: ECONOMIC SITUATION AND LABOR MOBILITY The four Pacific Island countries of Fiji, Kiribati, Samoa, and Tonga vary significantly in size and economic development level. Fiji is the largest country in terms of both land area and population within the region. Table 1 shows that Fiji has the highest GDP per capita among the four, while Kiribati has the lowest. The most recent poverty headcount data from the World Development Indicators, using the $3.65 a day poverty ADBI Working Paper 1449 Vu and Suryadarma 3 line (2017 PPP), show that the poverty rate in Fiji was 12.4% in 2019, increased from 6% in 2013. In Samoa, the poverty rate was 10.5% in 2013; it was 19.5% in Kiribati in 2019, and 13.9% in 2015 for Tonga, which was also higher than its 9.6% in 2009. In general, the economies of the four countries rely heavily on a combination of industries, with agriculture, fishing, and tourism being the primary contributors to economic growth. Subsistence farming and handicrafts are also major sources of income. The majority of households, particularly on the outer islands, are self-sufficient (Georgeou et al. 2022). Table 1: Country Background, 2021 Country Characteristics Fiji Kiribati Samoa Tonga Land area (sq. km) 18,270 810 2,830 720 Population (million) 902,899 121,388 200,144 106,759 GDP per capita (constant 2015 US$) 4,708.1 1,473.9 3,972.2 4,629.9 Urban share (%) 58 56 18 23 Remittances (% of GDP) 7.8 8.2 25.3 39 Compulsory education, duration (years) NA 9 8 15 Source: The World Bank, data bank. Data on remittances are from 2020. These economies have been facing various and persistent challenges. Geographically isolated, there is a vast distance between them and larger markets and natural resources (Gibson 2007). They are also vulnerable to natural disasters like cyclones and tsunamis, and they have limited infrastructure and small domestic markets. Furthermore, climate change poses a substantial threat, as rising sea levels and extreme weather patterns directly impact these low-lying island nations (Asian Development Bank 2022). Across many (PICs), labor mobility plays an essential role in supporting living standards as well as being a way of coping with the effects of climate change and disaster risks. Internal migration has increased significantly and intensively over the past five decades (Naidu and Vaike 2016). Also, a considerable number of people from these islands live abroad, forming diaspora communities (Doan et al. 2023), taking advantage of labor mobility schemes from the Australian and New Zealand governments (Doan, Dornan, and Edwards 2023).1 Overseas migrants work primarily in New Zealand, Australia, and the United States. The countries receive substantial amounts of remittances, with some having one of the world’s highest ratios of remittances to GDP. As Table 1 shows, remittances as a share of GDP range from 7.8% in Fiji to 39% in Tonga. 1 Three of the main labor mobility schemes in the Pacific regions that migrants apply for are the Seasonal Worker Program (SWP) since 2012, the Pacific Labor Scheme (PLS) in Australia, and the Recognized Seasonal Employer (RSE) since 2007 in New Zealand. Migrants work mostly in labor-intensive sectors such as horticulture, aquaculture, cane and cotton agriculture, forestry, hospitality, tourism, fisheries, meat processing, and the elderly care sector (Australia) and viticulture industries. The stay is up to nine months within the SWP and up to three years within the PLS. ADBI Working Paper 1449 Vu and Suryadarma 4 3. DATA, IDENTIFICATION STRATEGY AND DESCRIPTIVE STATISTICS 3.1 Data and Main Variables We use the sixth round of the UNICEF Multiple Indicator Cluster Surveys (MICS6) from 2019. The MICS is a household survey that contains basic demographic information about all household members, women and men aged 15–49, and children under five and aged 5–17 years living in the household. Moreover, the MICS includes a reading and mathematics assessment administered to children aged 7–14 years. From each sampled household, one child was randomly selected. The survey is representative at the national level. It uses a multistage, stratified cluster sampling approach. The MICS6 is the first household-based learning assessment in the region that has been made publicly available.2 It is also the first survey in the region to measure the foundational literacy and numeracy skills of children across different ages, allowing direct observation of children’s foundational skills over the early years of primary through lower secondary school. We use the UNICEF definition of literacy and numeracy (UNICEF 2020): A child is literate if they can read 90% of the words in a simple 70- to 80-word story and answer five questions about the story. The five questions are categorized into two groups: literal questions (i.e., starting with “what”) and inferential questions (i.e., starting with “why”). A child is numerate if they can correctly answer 21 math questions on number identification, number comparison, basic one- and two-digit addition, and number patterns (i.e., 3, 5, 7, _). We consider this a strict measure, as a child has little room for error to be seen as acquiring foundational skills. One wrong answer would mean that the child is considered not to have literacy or numeracy skills. Further work is still needed to check the sensitivity of the threshold (Silberstein 2021). We define left-behind children as those who have at least one parent not living in the same household. The parent(s) can be on another island (internal economic migration) or in another country (international economic migration). We exclude children whose parents have passed away, live in another household on the same island, are divorced, or are in institutions. Therefore, we compare the outcomes of two groups of children. One group consists of those who have at least one parent living abroad or on another island, and the other group includes children living with both parents. 3.2 Identification Strategy The model that we use is expressed in Equation (1): 𝑌 !" = 𝛽#+ 𝛽$𝑃𝑎𝑟𝑒𝑛𝑡𝑠𝑀𝑖𝑔𝑟𝑎𝑡𝑒!"+/𝛽%𝑋!" + /𝜀!", (1) where 𝑌 !" are learning outcomes of child i in household h. We have two learning outcomes: (i) a binary variable equal to one if a child is proficient at reading as defined by the MICS, and zero otherwise; (ii) a binary variable equal to one if a child is proficient at mathematics as defined by the MICS, and zero otherwise. 𝑃𝑎𝑟𝑒𝑛𝑡𝑠𝑀𝑖𝑔𝑟𝑎𝑡𝑒!" is the binary variable with the value of one if at least one parent of child i has migrated (and thus no longer lives in household h), and zero if the child is 2 In addition, there is the Pacific Islands Literacy and Numeracy Assessment (PILNA). However, the assessment focuses on the numeracy and literacy proficiency skills of Year 4 and Year 6 students at school: https://eqap.spc.int/PILNA. ADBI Working Paper 1449 Vu and Suryadarma 11 Table 3: Correlates of Having at Least One Migrant Parent (1) (2) (3) (4) (5) At Least One Parent Migrates Fiji Kiribati Samoa Tonga Pooled Sample Age of child –0.00264 –0.00208 –0.000372 0.00278 –0.000445 (0.00391) (0.00484) (0.00376) (0.00535) (0.00236) Male 0.0264 0.0206 –0.00101 0.0237 0.0162* (0.0168) (0.0204) (0.0172) (0.0265) (0.00970) Child’s functional difficulties (aged 5–17years) –0.0119 –0.00114 0.0165 –0.0693 –0.00104 (0.0286) (0.0235) (0.0208) (0.0480) (0.0134) Household size –0.0173*** –0.00549 –0.00304 –0.0114* –0.00585*** (0.00556) (0.00393) (0.00226) (0.00630) (0.00189) Mother’s education, secondary school or higher 0.0162 0.0474** 0.0317 0.0116 0.0298** (0.0228) (0.0238) (0.0285) (0.0290) (0.0124) Father’s education, secondary school or higher –0.336*** –0.378*** –0.372*** –0.446*** –0.381*** (0.0147) (0.0244) (0.0109) (0.0266) (0.00843) Rural area –0.0755*** –0.00774 –0.0875*** –0.0420 –0.0413*** (0.0188) (0.0230) (0.0231) (0.0485) (0.0148) Observations 1,461 1,097 1,198 766 4,522 Note: Logistic regression, marginal effect is shown. The regression also includes fixed effects for regions in each country. *** p < 0.01, **p < 0.05, * p < 0.1. 4.2 The Impact of Parental Migration on Children’s Literacy and Numeracy We implement nearest-neighbor matching using Mahalanobis distance, in which each individual in the treatment group is matched with the nearest individual in the control group. As we show in the previous subsection, the matching is based on age, gender, parental education, household size, children’s functional disabilities, living in rural area, and region fixed effects.5 The predicted propensity scores for the treatment group, which includes children who have at least one migrant parent, and the control group are shown in Figures A1 and A2 in the Appendix. Common support is presented by the overlap in the distribution of the propensity scores of the two groups. Table 4 shows the impact of parental migration on children’s probability of having foundational literacy and numeracy. The estimates show that overall, there is no evidence that having at least one parent migrating significantly affects the probability of obtaining either skill (Column 5). Having a migrant parent only has a statistically significant effect in the case of Samoa (Column 3). In this country, having a migrant parent increases the chance of being literate by 11.7 percentage points, but reduces the likelihood of being numerate by 8.6 percentage points. These are large effects given the low share of Samoan children who have literacy or numeracy skills (Figure 4). 5 Because age and household size are continuous covariates, we include a bias-correction term based on those two covariates. ADBI Working Paper 1449 Vu and Suryadarma 12 Table 4: Propensity Score Matching Results: Mahalanobis Matching (1) (2) (3) (4) (5) Fiji Kiribati Samoa Tonga Pooled Sample Reading skill At least one parent migrates –0.0572 0.0220 0.117* 0.0516 0.0258 (0.0501) (0.0545) (0.0705) (0.0729) (0.0342) Math skill At least one parent migrates –0.0177 0.0905 –0.0861* –0.0472 –0.000736 (0.0578) (0.0560) (0.0484) (0.0782) (0.0345) Observations 1,461 1,097 1,198 766 4,522 Notes: This table shows estimated differences in reading and math skills between the treatment and the control group. The set of variables used in the matching consists of the age of children, sex, education of mother and father, household size, indicator for living in rural area, and region indicators. Standard errors in parentheses are clustered at cluster level; *** p < 0.01, **p < 0.05, * p < 0.1. 4.3 Heterogeneity Analysis by Sex and Living with the Extended Family Alternative living arrangements could play a crucial role in understanding the potential impact of the absence of parents on the well-being of left-behind children (Doss et al. 2022; Pritadrajati 2023). When one or both parents are absent, children are more likely to live in an extended family, and their welfare will depend on the family members. Extended family could be considered an important informal support for the children of migrants, particularly when both parents are away from home, or the remaining partner may struggle to adapt to take care of the child. On the other hand, living in an extended family, such as with grandparents or other relatives, could be a potential source of deficit in terms of values and parenting roles. In some cases, a child might be abused (Withers 2022). Reflecting our finding on the significant impacts of parents’ migration on children’s literacy and numeracy in Samoa but not in other countries (Table 4), in Table 5 we find a large negative and significant effect of parent migration on the numeracy of Samoan children not living with their extended family (19 percentage points), while there is no evidence that parent migration affects the numeracy of children living in extended families. We also find a positive and large effect of parent migration on the literacy of children living in an extended family in the country (16.2 percentage points). These findings imply that in Samoa, the extended family arrangement protects or even benefits children’s learning outcomes in cases where at least one parent migrates. Parent migration could also have different effects on boys and girls. Booth and Tamura (2022) and Wang (2019) find a more negative impact of parental migration on the education outcomes of boys than girls. Cortes (2015) and Apatinga, Kyeremeh, and Arku (2022) find that boys are significantly more negatively affected than girls, even though there is wide evidence that education expenditures on girls are more sensitive to income changes. Similarly, Acosta (2011) shows that remittances can improve education for girls but not for boys in El Salvador. However, Meyerhoefer and Chen (2011) find that parental labor migration in the PRC is associated with a decrease in the education of girls rather than boys, since girls have to spend more time on housework when parents are absent from home. Chang et al. (2019) found that left-behind girls were negatively affected by one parent migrating, especially if it was the father. These results could be different depending on the context and norms around gender roles. ADBI Working Paper 1449 Vu and Suryadarma 13 Table 5: Parental Migration and Children’s Learning Outcomes by Extended Family and Gender across Four Countries Fiji Kiribati Samoa Tonga Extended Family No Yes No Yes No Yes No Yes Reading skill At least one parent migrates –0.0673 0.0514 0.0578 –0.0862 0.0490 0.162* 0.0575 0.154 (0.0618) (0.104) (0.0713) (0.0714) (0.128) (0.0881) (0.0939) (0.109) Math skill At least one parent migrates –0.0214 0.110 0.0126 0.121 –0.19*** –0.0381 –0.0530 0.0527 (0.0711) (0.0993) (0.0544) (0.0883) (0.0711) (0.0489) (0.0926) (0.106) N 1,315 146 890 207 804 394 589 177 Gender Female Male Female Male Female Male Female Male Reading skill At least one parent migrates –0.0568 –0.0206 –0.0778 0.143* 0.166*** 0.0643 0.0357 0.0312 (0.0824) (0.0590) (0.0602) (0.0843) (0.0616) (0.117) (0.0859) (0.109) Math skill At least one parent migrates –0.0131 –0.00494 0.193** –0.0302 –0.102* –0.103 –0.0949 –0.0213 (0.0852) (0.0797) (0.0954) (0.0513) (0.0534) (0.0717) (0.0996) (0.102) N 694 767 543 554 551 647 362 404 Notes: Estimated using Mahalanobis matching; extended family encompasses individuals beyond the nuclear family (i.e., excluding the household head, spouse, and children); standard errors in parentheses; *** p < 0.01, **p < 0.05, * p < 0.1. In the context of the four countries that we examine, Table 5 shows mixed evidence on the impact of parental migration on the literacy and numeracy of boys and girls. In Samoa, parental migration only affects girls’ outcomes. It has a large and adverse effect on girls’ chances of acquiring numeracy skills by –10.2 percentage points, but a positive impact on the probability of having literacy skills by 16.7 percentage points. On the other hand, in Kiribati, parental migration has a positive and significant effect on boys’ literacy by 14.3 percentage points. It also improves girls’ chances of being numerate by 19.3 percentage points. These are all large effects. Unfortunately, the lack of data availability precludes us from delving deeper into the potential explanations of these findings.6 5. CONCLUSION Parents play a pivotal role in shaping their children’s educational journey and overall development. Their presence and involvement create a foundation that impacts a child’s learning outcomes. The absence of parents from home may pose challenges for the academic performance of a child. However, if the absence is economically motivated, the higher income could compensate for the loss of the parents’ presence. We examine the impact of parent migration on the foundational literacy and numeracy of children left behind. The context is four Pacific Island countries characterized by a high rate of migration and heavy reliance on remittances. Given data constraints, this is the first study to address the topic in the context of Pacific Island countries. Therefore, our findings should be seen as a first step, with future research hopefully delving deeper into the issue. 6 We also estimate the impact by whether parents migrate to another island in the country or overseas. We find no evidence of impact heterogeneity. ADBI Working Paper 1449 Vu and Suryadarma 14 Our matching estimates indicate that in Samoa, children for whom at least one parent migrates make significant improvements in their literacy but have lower numeracy. The heterogeneity analysis shows that living in an extended family could benefit Samoan children when a parent is away. We also find that the effects in Samoa are isolated among girls. We do not observe a significant impact in other countries. In the Pacific countries where data are available, therefore, migration has a relatively muted effect on the learning outcomes of children. This may indicate that the positive income effect and the negative absence effect may cancel each other out. But there may be other reasons, as we find some evidence of heterogeneity by gender and by living arrangements. Unfortunately, we do not have access to more detailed data, for example on household education spending, to delve deeper. 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ADBI Working Paper 1449 Vu and Suryadarma 20 APPENDIX Table A1: Basic Education System in Fiji, Kiribati, Samoa, and Tonga Primary School Junior Secondary Senior Secondary Fiji 6 4 2 No compulsory English instruction Free Kiribati 6 3 4 Compulsory Free Samoa 8 5 2 Free and mandatory Samoan and English (from third year) English Highly selective Tonga 6 3 3 Free for governmentsponsored schools Source: Fiji: https://www.studycountry.com/guide/FJ-education.htm. Kiribati: https://www.epdc.org/sites/default/files/documents/EPDC_NEP_2018_Kiribati.pdf. Samoa: https://www.studycountry.com/guide/WS-education.htm. Figure A1: Propensity Score