Does internal parental migration affect child school participation and work in Indonesia?

Abstract

This study aims to analyse how internal parental migration influences school participation and work for children aged 10–15 years in Indonesia as measured by four categories, namely, (a) children attending school, (b) working children, (c) children attending school and working and (d) children who do not attend school and do not work (idle). The method used in this study is a multinomial logit model, while the data used is from the 2015 Inter-Census Population Survey (SUPAS) provided by Indonesian Central Board of Statistics (BPS). The results show that parental internal migration generally leads to a reduction in school participation rates and an increase in work participation rates among the children of migrant parents. The impact is greater when the mother migrates. However, the negative impact of parental internal migration on children’s participation in education and child labour decreases as the duration of migration increases. In addition, this study also provides some important findings regarding the important aspects that can affect child school participation and work, including the quality of parents’ work after migration, parents’ education, child’s age, family size and family welfare, including the quality of the environment where the children live.

Keywords:

• Internal migration
• child education
• child labour
• SUPAS

Reviewing Editor:

• Grace Spencer, Anglia Ruskin University, Cambridge, UK

Subjects:

• Labour Economics
• Childhood and Adolescence
• Parents

1. Introduction

Indonesia is a developing country with a population of more than 270 million people. The large population offers potential use of human resources to support development. According to the Central Board of Statistics (BPS), however, approximately 9% of Indonesian people lived under poverty in 2019. Poverty in Indonesia has a multidimensional nature since the phenomenon is intricately related to people’s inability to access social, economic, cultural and political resources and positively participate in the community (Firdausy & Budisetyowati, 2022; Nurwati, 2008).

Migration, both internal and international, is an important strategy often devised by economically challenged people to improve their livelihoods and to raise their socioeconomic statuses. Because of the limited employment opportunities in their places of origin, more poor families are migrating to obtain better opportunities and income (Rizky et al., 2017). In the case of Indonesia, there are at least two factors that encourage people to migrate, namely, an excess of labour force in their places of origin and income inequality across regions (World Bank, 2016).

Internal migration is predominant in Indonesia, accounting for as much as 9% of the total population, while international migration accounts for approximately 3% of the population (Sugiyarto et al., 2019). Internal migration, in which people move across provinces or districts, is estimated to increase annually (see, for example, Figure 1). In 2021, internal migration in Indonesia increased to 13.2% or as many as 35.5 million people. According to BPS (2022), internal migration rates have remained around 13% from 2015 to 2021 and even over the past decade. The distribution patterns have also remained consistent, with the majority of internal migration occurring in the islands of Java, Sumatra and Kalimantan, predominantly involving male migrants (BPS, 2022). Previous research also explains that migration trends in Indonesia have remained unchanged, where the island of Java is the main migration destination because it acts as a core economic region in Indonesia (Pardede et al., 2020; Wajdi et al., 2015).

Figure 1. Population by migration status and gender, 2015–2021 (in thousands). Source: Badan Pusat Statistik (BPS, 2016, 2020b, 2022).

The distribution of internal migrants in Indonesia is dominated by people aged 20–39 year, which account for 57.9% or more than half of the total number of internal migrants in Indonesia. Based on this fact, most migrants in Indonesia are people from young and productive age groups. On the other hand, internal migration is also dominated by married migrants, who account for 56.1% (BPS, 2020b). Assuming there are children in the households, such a migration pattern gives rise to the phenomenon of parental migration.

This phenomenon can be viewed from two perspectives: migration as a collective decision (such as family or parental migration) or as an individual decision (individual migration). Meanwhile, parental migration tends to carry exorbitant cost because it can potentially damage families and children. As a collective decision, family migration can be more detrimental to children’s development (Kusadokoro & Hasegawa, 2017; Lu, 2014; Lu et al., 2020; Wen & Lin, 2012; Xi & Wang, 2024). For example, parental migration can impact the growth and development of children, especially their health and academic performance (Wen & Lin, 2012). Furthermore, a study by Kusadokoro and Hasegawa (2017) shows that most children in Turkey suffer the negative impacts resulting from internal migration, where they have to leave school and engage in economic activities to help increase their household income. A recent study conducted by Xi and Wang (2024) found that parental migration, especially from families with poor economic backgrounds, had a very negative impact on children’s growth and development and suggested the need to revitalise the economy and life in rural areas to reduce parental migration.

In Indonesia, problems in children’s lives, both in terms of education and work as a result of parental migration, are also apparent. First, from educational viewpoint, the BPS data show that school enrolment of children (aged 7–18) tends to decrease as they age (Figure 2). School participation rates of children aged 7–12 years (ages for elementary school) reached more than 95%, while those of children aged 13–15 and 16–18 years (ages for middle and high school) were lower, at approximately 94% and 70%, respectively.

Figure 2. School participation by children of different age groups (in years), 2016–2019. Source: BPS (2024).

Secondly, in terms of employment, it is estimated that in 2018, there were nearly 1 million child labourers aged 5–17 years (BPS, 2019). Parental migration that is not accompanied by post-migration economic stability can lead to significant financial stress for the family. This financial pressure may compel children to contribute to the household income, potentially resulting in them dropping out of school and entering the workforce (Basu, 2003; Shimada, 2015). Figure 3 shows the three most common types of work done by child labour in Indonesia. During 2019–2021, child labour was most commonly employed in the services sector and the percentage tends to increase. Many children help their parents in agricultural sector. As long as the work does not pose hazards and takes less time, children under 15 years can work for short periods of time so that they can still attend school. Nevertheless, many of them are regarded as economic assets by their parents in pursuit of harvest targets, so they have to work long hours, lifting heavy loads, spraying pesticides and many other duties, which potentially disrupts their education and often makes them drop out from school.

Figure 3. Distribution of child labour by work field (%) 2019–2021. Source: BPS (2019, 2020a, 2021).

The emergence of child labour will have a dire impact: children lose the opportunities to amass human capital through education (Shimada, 2015). Education is a learning process by which students can actively improve their potentials, and all children must be given the equal right to access it. Indonesia as a nation is still struggling to meet the objective of universalisation of nine-year mandatory basic education. Statistical data for 2015–2020 show that on the average, Indonesian students only enjoy seven to eight years of education. There exists a huge educational discrepancy between Indonesian rural and urban areas. For example access to school is more difficult in rural areas, while access to schools in urban areas is easier thanks to better public transportation and equitable availability of schools (Muhaimin et al., 2020). In practice, rural areas had the largest number of child workers, which totalled 550,078 in 2015, while in urban areas there were 431,823 child workers in the same period. In addition, there is arguably a stark contrast in the availability and quality of infrastructure between rural and urban areas. To make things worse, there is an enormous gap between urban and rural areas: the limited number of teachers in rural areas compared to that in urban areas.

Compared to the other developing countries, Indonesia and Vietnam share the same rank (83rd) in terms of the vulnerability of child labour. Vietnam’s child labour force is dominated by children from rural areas where as much as 85%, 67% of which work in agriculture (Giang et al., 2021). Low household incomes keep Vietnamese children engaged in work. In Indonesia, rising household income reduces boys’ working hours, but the impact on girls is unclear and it was found that increased level of welfare and education of parents, especially mothers, reduces child labour in Indonesia (Chang, 2006), that the higher the mother’s final education will reduce children’s work involvement.

This study examines the effect of parental (internal) migration on school participation and work among children aged 10–15 years in Indonesia. Despite the growing literature, there is no consensus on the circumstances under which children benefit or suffer from parental migration. In addition, the problems regarding education participation and the high number of working children are yet to be solved.

Children aged 10–15 years in this study are categorised into four types of activities: (a) attending school, (b) working, (c) attending school and working and (d) not school and not working (idle) (see Figure 4). The study uses data from the 2015 Inter-Census Population Survey (SUPAS), which furnish information on migratory status as well as the activities performed by children aged 10 years and over.

Figure 4. Activities of children aged 10–15 years in Indonesia based on the migratory status of the parents. Source: BPS (2016).

The following section will discuss the main literature used in this study, followed by a discussion of the research methods and the empirical results of the findings. This paper concludes with conclusion and some political implications.

2. Literature review

Internal migration is an important strategy that people or families develop and work out use to improve their standards of living. Although internal migration can provide economic benefits, it can also have social costs, that affect children’s growth. Several previous studies have confirmed that internal migration may lead to negative child educational outcomes and push children to enter labour market (Antman, 2011; Kusadokoro & Hasegawa, 2017; Lu, 2014; Lu et al., 2020; Xi & Wang, 2024).

Lu (2014) analysed the relationship between parental migration and children’s education in Mexico and Indonesia. Using data from Mexican Family Life Survey and Indonesian Family Live Survey, the results show that children who are left behind by parents who migrate internationally have a worse level of education than those who live with both parents. Internal migration also has a negative effect on children’s education, although the level is lower than that of the international migration.

Furthermore, Kusadokoro and Hasegawa (2017) analysed how internal family migration affects the school lives and work situations of children in Turkey. The results show that children living in migrant families tend to leave school and become more involved in economic activities. Furthermore, unsuccessful parental migration will further negatively impact their children’s education and lead to child labour.

However, it is generally understood that migration can have a positive effect on children’s lives due to positive returns in the form of remittances and higher incomes (Acharya & Gonzalez, 2014; Yabiku & Agadjanian, 2017). Kusumawardhani and Warda (2011) further explain that parental migration can affect the relationship with children in the family in three ways. First, remittances sent by parents can serve as a financial addition to help them stay in school, which causes a reduction in child labour. Secondly, the departure of parents to migrate causes a lack of adult attention and supervision of their children’s growth. Hence, parental migration increases the likelihood of older children joining the labour market in search of additional income. Thirdly, parental migration may encourage future migration by household members, including children. This is related to the effect of information and networks owned by migrant parents in increasing the chances of their children becoming migrants and reducing the latter’s desire to go to school in their places of origin.

Furthermore, Yabiku and Agadjanian (2017) explain that the impact of migration by parents can have positive effects on children’s education if the migrant parents become economically successful. Different impacts my result, depending on the children’s social demographic characteristics. Findings from their research show that boys from migrant families tend to have lower dropout rates than boys from non-migrant families. In line with that, Acharya and Gonzalez (2014) also found that migration from poor households in Nepal resulted in increased investment in children’s education and higher participation in children’s education.

Genicot et al. (2017) explain the positive relationship between child labour and parental migration. The results of their research in Brazil show a significant relationship between internal migration and child labour. Genicot et al. (2017) estimate that a 10% increase in migrants in Brazil results in a reduction in child labour by 2.8%. This finding is in line with the classic theoretical framework of labour market equilibrium in which internal labour migration will decrease labour wages at the destination. This will result in a decrease in the supply of child labour because there would be no incentive for them to enter the labour market. Furthermore, Genicot et al. (2017) also pinpoint a significant increase in migrant children’s school participation. In line with that, De Paoli and Mendola (2014) found that international labour mobility played a role in reducing child labour in disadvantaged households through changes in the local labour market.

Several previous studies have also explored the relationship between migration and working children and school participation, especially in Indonesia (Arlini et al., 2019; Listiani, 2018; Nguyen & Purnamasari, 2011; Parinduri & Thangavelu, 2011). Listiani (2018) analysed the impact of parental internal migration on children’s growth in Indonesia. The results show that the impact of migration on children’s growth can differ, depending on the gender of the migrating parent. Paternal migration tends to have a positive impact on child growth. Conversely, maternal migration tends to have a negative impact on child growth. Arlini et al. (2019) find that parental migration have a positive impact on children’s school participation. Even so, the impact can vary depending on the children’s aged. The negative impact of parental migration is felt more by younger children. On the other hand, research conducted by Nguyen and Purnamasari (2011) and Parinduri and Thangavelu (2011) found no significant relationship between parental migration and children’s educational outcomes.

3. Research method

The study uses a multinomial logit model to determine how parental internal migration affects children’s participation in work and schooling. Using this method, we can observe how the distribution of activities by children changes due to internal migration by parents. This method will also reduce the possibility of error distribution problems and heteroscedasticity violations due to nonmetric dependent variables (Gujarati, 2011). Mathematically, the multinomial logit model used in this study is as follows: (1) $$Y_i={\alpha }_0+{\alpha }_1{\mathit{\text{MigrationStatus}}}_i+\psi X_i+{\epsilon }_i$$(1) where Y is the activity status of the children, that are categorised into the following 4 groups:

1. Children attending school, namely, children aged 10–15 years who are attending school and whose main activity is only going to school.

2. Working children, namely, children aged 10–15 years whose main activity is working 28 h or more per week.

3. Children attending school and working, namely, children aged 10–15 years whose main activity is school but who also work less than 28 h per week.

4. Children not attending school and not working (idle), namely, children aged 10–15 years who do not go to school and do not work.

The focus on children aged 10–15 years is consistent with the objective of Indonesian government-sponsored universalisation of 9-year mandatory basic education that encourage children to attend school until – at least – they turn 15 (see Jones & Pratomo, 2016). The 2015 Inter-Census Population Survey (SUPAS) data covers individuals starting from aged 10 years old, therefore it is not possible to cover children below 10-year-old. However, the category is also in line with BPS’s definition of working age population: individuals between 15 and 64 years old. In other words, children below 15 assumed to be at school. Related with the data, it seems possible to have multiple children from the same household, but there is no information about the household level in the survey as the survey provides only individual characteristics.

The main independent variable in this study is the migration status of parents, which consists of two variables (father and mother). The first is migratory status, which is a dummy variable consisting of two categories, namely, parents’ decision to migrate. The second is the duration of the internal migration. The coefficient vector (ψ) on the variable X acts as a control variable that includes three main groups, namely, (a) individual characteristics of children related to individual characteristics of children (age and sex); (b) characteristics of the parents (age, education, employment) and (c) socioeconomic status of the household (place of residence (village/city), number of children, household welfare (living conditions) and gross regional domestic product). Table 1 presents the details of variables and their measurements.

Table 1. Definition of variables.

No	Variable	Definition	Type	Measurement
Dependent variable
1	Participation of children in school and work	Activities carried out during the past week by children aged 10–15 years	Discrete	Nominal, where: 1 = attend school, 2 = working, 3 = school and work, 4 = not in school and work (idle)
Independent variable: individual characteristics of the child
2	Age of child	Age on last birthday	Continuous	Years
3	Child sex	Sex of children aged 10–15 years which consists of male and female	Discrete	0 = female, 1 = male
Independent variable: parent’s characteristics
4	Father’s migration status	People who move provinces and then settle somewhere	Discrete	0 = non-internal migrants 1 = internal migrant parents (province)
5	Father migration duration	Length of stay at migration destination	Continuous	Nominal, where: 0 = no migration 1 = < 5 years 2 = 5–9 years 3 = > 9 years
6	Father’s age	Age	Continuous	Years
7	Father’s education	Diploma/Certificate of Highest Completion of Study (STTB)	Continuous	Nominal, where: 1 = does not have an elementary school diploma; 6 = elementary/MI/equivalent; 9 = junior school/equivalent; 12 = senior school/equivalent; 13 = higher education (diploma and bachelor)
8	Father has a steady job	The type of a person’s position in work and the field of activity of the job/business/company/office where the person works, or produced by the company/office where the respondent works.	Discrete	0 = other 1 = if father has a permanent job
9	Father works in the agricultural sector		Discrete	0 = other 1 = if father works in the non-agricultural sector
10	Father works in the trade sector		Discrete	0 = other 1 = if father works in the trade sector
11	Father works in the service sector		Discrete	0 = other 1 = if father works in the service sector
12	Mother’s migration status	People who move provinces and then settle in one place	Discrete	0 = internal non-migrants 1 = internal migrant parents (province)
13	Mother migration duration	Length of stay at the migration destination	Continuous	Nominal, where: 0 = no migration 1 = < 5 years 2 = 5–9 years 3 = > 9 years
14	Mother’s age	Age	Continuous	Years
15	Mother’s education	Diploma/Certificate of Highest Educational Level	Continuous	Nominal, where: 1 = does not have an elementary school diploma; 6 = Elementary/MI/equivalent; 9 = SMP/MTs/equivalent; 12 = SMA/MA/equivalent; 13 = higher education (diploma and bachelor)
16	Mother Job: Housewife	The type of a person’s position in work and the field of activity of the job/business/company/office where the person works, or produced by the company/office where the respondent works.	Discrete	0 = other 1 = if the mother takes care of the household
17	Mother has a steady job		Discrete	0 = other 1 = if the mother’s employment status is permanent job
Independent variable: socio-economic status of households
18	Living in the Village	Introduction of village or sub-district places to indicate rural or urban areas	Discrete	0 = urban 1 = rural
19	Number of children	Number of children in the household	Continuous
20	Living Condition: Dirt floor	The predominant type of floor in the house	Discrete	0 = non-timber/land 1 = wood/papa/land
21	Living Condition: There is electricity	Energy sources used by households	Discrete	0 = non-electric 1 = electric
22	Regional GDP	GRDP log of province of residence	Continuous	Rupiah

The marginal effect of the variables is also estimated in this study. Greene (2003) explains that marginal effects are different from basic coefficients in the logit model, where the influence value of the estimated coefficient is relatively small (minor) compared to the impact of the estimated marginal effect which tends to be wider (larger). The marginal effect for a dummy independent variable (d) is expressed as follows (Greene, 2003): (2) $$\text{Marginal\hspace{0.17em}\hspace{0.17em}effect}=\text{Prob}\left[Y=1\left|{\overline{x}}_{\left(d\right)},\hspace{0.17em}d=1\left]-\mathit{\text{Prob}}\right[Y=1\right|{\overline{x}}_{\left(d\right)},d=0\right]$$(2) where Prob [Y = 1|x̅_(d), d = 1] is the probability of the outcome variable (Y) being equal to 1 when the variable d is 1, while Prob [Y = 1|x̅_(d), d = 0] is the probability of the Y being equal to 1 when the variable d is 0. x̅_(d) represents the average value of all other variables in the model when the variable d is at a certain value.

The data used in this study are secondary data in the form of microdata from the results of the 2015 Inter-Census Population Survey (SUPAS) collected by the Indonesian Central Bureau of Statistics. SUPAS 2015 collected data from 40,750 census blocks with a total of 652,000 households. The data collected by SUPAS 2015 perfectly suit the needs of this study, especially because they contain data on population movements (migration) at the household level. Compared to similar data sources such as The National Socioeconomic Survey (SUSENAS), SUPAS is considered superior due to a greater number of observations as it is based on the census, whereas SUSENAS is based on a survey. Table 2 presents the summary statistics of the variables in this research.

Table 2. Summary statistics.

Variables	Mean	Std. dev	Min	Max
Migration status and parental characteristics
Father’s migratory status	0.180	0.384	0	1
Father migration duration	0.494	1.082	0	3
Father’s age	44.574	7.469	16	95
Father’s education	7.990	4.427	1	19
Father has a steady job	0.306	0.461	0	1
Father works in the agricultural sector	0.467	0.499	0	1
Father works in the trade sector	0.154	0.361	0	1
Father works in the service sector	0.152	0.359	0	1
Mother’s migration status	0.157	0.364	0	1
Mother migration duration	0.430	1.021	0	3
Mother’s age	40.130	6.658	16	79
Mother’s education	7.581	4.235	1	19
Mother job: housewife	0.638	0.481	0	1
Mother has a steady job	0.115	0.319	0	1
Individual characteristics of children
Child age	12.485	1.713	10	15
Boy	0.515	0.500	0	1
Household characteristics
Live in the village	0.817	0.387	0	1
Number of children	2.917	1.389	1	15
Living condition: dirt floor	0.249	0.433	0	1
Living condition: availability of electricity	0.932	0.252	0	1
Regional GDP	12.410	1.204	9.922	14.190

4. Results and discussion

Table 3 presents the multinomial logit regression results regarding the impact of parental internal migration on children activity status. The results show that there is a positive and significant relationship between parental migration status and children’s work participation. On the other hand, parents’ migratory status is negatively and significantly related to children’s school participation. For children whose fathers migrate, the tendency for children to go to school and work increases by 1.3%. Meanwhile, children with migrant mothers show 3.6% decreased tendency to attend school, 0.3% increased tendency to work and 2.6% increased tendency to be idle. However, this relationship weakens with the duration of parental migration. This means that the negative impact of parental migration decisions on children activity status only occurs in the short term.

Table 3. Marginal effect of multinomial logit regression results.

	School		Work		School and Work		Idle
	dy/dx	P>|z|	dy/dx	P>|z|	dy/dx	P>|z|	dy/dx	P>|z|
Migration status and parental characteristics
Father’s migratory status	−0.011	0.198	0.000	0.818	0.013	0.014**	−0.002	0.720
Father’s migration duration	0.004	0.138	0.000	0.820	−0.005	0.001***	0.001	0.824
Father’s age	0.000	0.000***	0.000	0.787	0.000	0.000***	0.000	0.531
Father’s education	0.003	0.000***	0.000	0.000***	0.000	0.013**	−0.002	0.000***
Father has a steady job	0.003	0.038**	0.000	0.076*	−0.006	0.000***	0.002	0.042**
Father works in the agricultural sector	−0.010	0.000***	0.000	0.015**	0.011	0.000***	−0.002	0.190
Father works in the trade sector	−0.002	0.249	−0.001	0.005***	0.005	0.000***	−0.002	0.215
Father works in the service sector	0.000	0.875	−0.001	0.000***	0.003	0.018**	−0.002	0.265
Mother’s migratory status	−0.036	0.001***	0.003	0.071*	0.006	0.248	0.026	0.007***
Mother’s migration duration	0.010	0.001***	−0.001	0.038**	−0.001	0.660	−0.008	0.001***
Mother’s age	0.000	0.716	0.000	0.002***	0.000	0.002***	0.000	0.397
Mother’s education	0.004	0.000***	0.000	0.000***	−0.001	0.000***	−0.003	0.000***
Mother’s job: housewife	0.023	0.000***	−0.001	0.000***	−0.027	0.000***	0.005	0.000***
Mother has a steady job	0.007	0.001***	0.000	0.364	−0.014	0.000***	0.006	0.001***
Individual characteristics of children
Child age	−0.017	0.000***	0.002	0.000***	0.005	0.000***	0.009	0.000***
Boy	−0.015	0.000***	0.003	0.000***	0.007	0.000***	0.005	0.000***
Household characteristics
Live in the village	−0.007	0.000***	0.000	0.968	0.011	0.000***	−0.005	0.001***
Number of children	−0.004	0.000***	0.000	0.000***	0.003	0.000***	0.001	0.001***
Living condition: dirt floor	−0.019	0.000***	0.001	0.000***	0.006	0.000***	0.012	0.000***
Living condition: availability of electricity	0.054	0.000***	−0.002	0.000***	−0.025	0.000***	−0.026	0.000***
Regional GDP	−0.003	0.000***	0.000	0.380	−0.001	0.013**	0.003	0.000***

*** p < .01, **p < .05, *p < .1.

The results of this study are supported by previous research which states that children whose parents migrate will generally help or even take over household work and reduce their effective time for pursuing their education (Kandel, 2003; Van de Glind, 2010). The same thing was also stated by Shimada (2015) where the post-migration economic uncertainty experienced by families will also encourage children to help the family economy. Even if migrating children are involved in education, their school performance tends to be worse than non-migrating children (Giannelli & Mangiavacchi, 2010; Salah, 2008). But there are unique results when parental migration is examined more deeply by gender, children with migrating mothers tend to experience decreased engagement in school due to environmental changes and lack of consistent attention. Factors such as changes in school due to migration, lack of supervision and lack of parental involvement might affect children’s academic performance in the context of maternal labour migration. Another interesting finding of this study is the dominant gender roles played by mothers: they are highly influential within the households, in which working women have a dual role within the domestic and public spheres (Berniell et al., 2021). Therefore, when mothers migrate, some aspects of their domestic roles (their children’s education, for example) are neglected. Meanwhile, when fathers migrate, the effect on children is not as great as when mothers migrate. When making decisions to migrate, parents expect to get better jobs and earn higher wages, but in reality they confront ruthless competition (Hakim, 2011), which often adds to the economic burden on the family, so children are forced to work for a living.

This finding is in line with research by Kusumawardhani and Warda (2011) and Ferrone and Giannelli (2015), where migrant families who initially migrated faced the income uncertainty (remittances); thus, the children were forced to become involved in economic activities. The low participation in children’s education is also caused by the lack of presence of parents in children’s social lives. Lack of parental supervision and attention will create a situation where children work to replace the duties of adults, thereby reducing school attendance. In addition, Kusadokoro and Hasegawa (2017) found that a decrease in children’s school participation is related to the unstable work of migrant parents, so the wages received are erratic, which causes children to enter the workforce.

However, the negative impact of internal migration by parents decreases with the duration of the migration. This means that the level of child participation in education will be higher and the likelihood of working will be lower for long-term migrant families. This is basically due to better migration returns and more stable incomes for migrant families. A similar discussion is found in the study by Manning and Pratomo (2013) and Srivastava (2020), who explain that long-term migrant workers tend to focus on better employment sectors with higher incomes because they have adapted to the environment and have social capital. Successful migration has the potential to increase children’s educational outcomes and tends to reduce the proportion of children who work to help the family.

Furthermore, maternal migration has a greater short-term impact than paternal migration. Children with migrant mothers tend to work or even leave school while remaining unemployed (idle). This finding resonates the previous research that also found a greater negative impact from maternal migration (Chen et al., 2019; Cortes, 2015). Chen et al. (2019) explain that maternal migration has a significant negative effect on children’s social competence and school participation. Fathers who stay at home tend to spend less time with their children, and they are more likely to let their children’s education lag (Cortes, 2015; Lam & Yeoh, 2018). This shows that the low interaction between mother and child proves to negatively affect children’s motivation to participate in school activities.

The estimation results in Table 3 also show the influence of parental characteristics on children’s educational activities and work. First, the level of the father’s education has a significant positive effect on the children’s likelihood to engage in school activities. This finding echoes the finding of previous research by Mansuri (2006), which indicate a positive correlation between parents’ education and children’s school participation. Similar results were also revealed by De Paoli and Mendola (2014), where fathers (as the heads of households) with low education tend to encourage their children to work. These two arguments reveal that the level of education completed by parents, especially fathers, influences children’s participation in school or work.

Secondly, working fathers who have regular jobs and work in the formal sector tend to have children who participate in school. Yabiku and Agadjanian (2017) explain that fathers who are economically successful prioritise their children’s education over the other activities. In line with the father’s employment status, father’s occupation also affects children’s participation in school and work. Children with fathers who migrate and work in agriculture, trade and services sectors will tend to attend school and work. In Indonesia, most of these occupations are informal in nature. Informal work tends to be flexible and can be performed by anyone from any age group or range. This triggers children from migrant families to participate in economic activities performed by their parents in that field, besides their school activities. This is in line with the findings of Majumder (2011), who revealed that most children (more than 80%) work with their parents, especially in informal sector work, which can also be performed by children. Furthermore, Majumder (2011) explains that children’s participation in work can generate nearly 20% of the family income, which causes a decrease in children’s interest in attending school by 85%.

Thirdly, the level of the mother’s education also has a positive influence on children’s participation in school. Kusadokoro and Hasegawa (2017) explained that parental education positively influences children’s choices to go to school and negatively influences children’s choices to work. Highly educated mothers will encourage children to prioritise school activities over other activities. In addition to education, the mother’s employment status also influences children’s choice of activities. Children of mothers who work in the formal sector are more likely to participate in school and are less likely to participate in work. This condition is of course triggered by the mother’s economic stability which enables her to send her child to school and stress the importance of education.

Furthermore, children’s participation in school and work is also affected by the mother’s status as a housewife. Children of mothers who work as housewives tend to be more likely to participate in school activities. If the mother stays at home, the likelihood of children doing domestic work significantly decreases while school participation increases (De Paoli & Mendola, 2014).

In addition to the characteristics of parents, the characteristics of children are also a dictate children’s decision to participate in school and/or to work. In this study, the factors related to the characteristics of children were age and sex. First, the estimation results in Table 3 show that the younger a child is, the higher the school enrolment rate tends to be. Conversely, the older the child is, the more opportunities there are for them to choose to work, go to school and work, or not go to school and not work.

This finding is in line with previous research conducted by De Paoli and Mendola (2014) and Majumder (2011), where older children tend to choose to work. One major consideration for children who opt to join the workforce is their physical strength: their ability to physically perform the required work activities. Children’s psychological conditions also change and vary as they age, when they acquire the ability to perform simple arithmetics and perform simple work activities, allowing them to earn a fairly good income.

Secondly, children’s activity statuses also differ depending on the their gender. Girls from migrant families tend to have higher enrolment in school than boys. De Paoli and Mendola (2014) stated in their research results that boys are more frequently involved in the labour market, while girls are significantly more involved in domestic household work. This fact is explained by Genicot et al. (2017), who find that older male children choose to work rather than go to school. This is also related to the characteristics of households in Indonesia, which is dominated by patriarchal culture, where boys are culturally burdened with the responsibility of taking care of the household.

Household socioeconomic characteristics also contribute to determining children’s school and work participation. First, children from migrant families who live in villages tend to have lower school participation compared to other activities. This is in line with the research of De Paoli and Mendola (2014), which revealed that children in rural areas generally contribute to the labour market. Secondly, children’s school participation tends to be lower when they live with extended migrant families. Due to the enormous family economic burden, children tend to choose to engage in both school and work. This finding is in line with Mansuri (2006), who explain that the extended family has a negative effect on school participation.

Thirdly, the facilities and infrastructure factors available at home, such as the availability of electricity and the types of floors in the house, have a significant effect on the status of children’s activities. The estimation results children living in houses with dirt floor show decreased tendency to attend school and increased tendency. On the other hand, the availability of electricity in the household will increase children’s tendency to go to school and vice versa. These results are in line with research by Bacolod and Ranjan (2008) and Kusadokoro and Hasegawa (2017), who explain that household welfare determines children’s decisions to work and to attend.

Fourthly, the regional gross domestic product (GRDP) also affects children’s the choices to attend school and to work. The results of this study suggest that an increase in the GRDP of a region is associated with a decreased tendency of children to attend school. This finding is different from previous research conducted by Khairunnisa et al. (2015) and Susanto and Udjianto (2019), where GRDP has a positive relationship children’s school participation. In Indonesia, the local government has a scholarship program for elementary and high school levels so that even though these regions have low GRDP levels, the school enrolment rates are still high.

5. Conclusion

The main objective of this study was to determine the influence of parental internal migration on the school and work participation of children aged 10–15 years. Using SUPAS data for 2015, this study shows that internal migration by parents generally tends to reduce the level of participation of children in school and encourages them to participate in work. The impact is greater when the mother migrates. However, the negative impact of internal migration by parents decreases with the duration of the migration. This means that the level of child participation in education will be higher and the likelihood of working will be lower in long-term migrant families.

This study strengthens the argument that parental migration has a significant influence on children’s lives. In line with several other empirical findings, there is a negative relationship between parental migration and children’s lives. However, this negative relationship will decrease over time. This means that children who have lived in migrant families for a long time tend to feel less negative impacts compared to children from new migrant families. In addition, this study also offers several important findings related to factors that affect children’s school enrolment and work. The success rate of parental migration is a major factor that encourages children’s school participation and reduces the possibility of children joining the workforce. Migrant parents who have good jobs will tend to have economic security, which incidentally reduces the need for children to work to help the family financially. Furthermore, depending on the children’s individual characteristics, work participation tends to increase among older and male children. Meanwhile, children’s school participation tends to increase among younger and female children. In addition, improving school infrastructure to create a comfortable learning environment for children has proved to increase the level of children’s school participation. Statistical data show that the majority of working children come from families with relatively poor housing conditions.

Hopefully, findings from his study can make useful contribution to the government, enabling them to create policies that support equal job opportunities which will minimise labour migration. To avoid migration failure resulting from low competitiveness, the government can support the existing labour force by improving skills in the form of training and non-formal education. This effort is expected to reduce the involvement of children under the working age in employment and increase their involvement in education. In addition, the provision of regulations that favour workforce protection is also necessary so that workers are guaranteed their basic rights and protected from any forms of discrimination, which in the long run will bring welfare for workers and their families.

In the mean time, this study puts the emphasis on the impact of parental migration on children’s participation in education and work, regardless of whether their children migrate or are left in their places of origin. The impact of parental migration can be different for children who are left behind in their places of origin and for children who join their migrant parents – so further research is expected to take these into account. Future research can also analyse in more depth the impact of migration not only on children’s educational participation but also on the outcomes or quality of educational participation.

Ethics approval

Ethical approval was waived because this analysis used secondary data that does not contain personally identifiable information.

Consent form

Consent to participate was not an issue for the same reasons.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research received no external funding.

Notes on contributors

Susilo

Susilo is a lecturer in labour economics at Brawijaya University Indonesia. His research focuses on labour economics, public finance and migration.

Muhammad Afif Khoiruddin

Muhammad Afif Khoiruddin is a lecture assistant and researcher in the Department of Economics, Brawijaya University- Indonesia. His research interest in labour economics and economic development.

Devanto Shasta Pratomo

Devanto Shasta Pratomo is a professor in labour economics at Brawijaya University-Indonesia. His research focuses on labour economics, education and migration.

Muhammad Salahudin Al Ayyubi

Muhammad Salahudin Al Ayyubi is doctoral candidate and researcher in the Department of Economics, Brawijaya University- Indonesia. His research interest in labour economics and economic development.