Chapter 6. Migration and education in the Philippines

Education plays a crucial role in development and growth. Migration and remittances have the potential to help improve educational outcomes and build future human capital stocks, but they also raise concerns about “brain drain”, as well as the impact on children left behind. This chapter investigates the interlinkages between education and migration in the Philippines, focusing on the impact of migration on educational expenditures and school attendance rates, the role of educational attainment in emigration decisions, and whether emigration and return migration are likely to affect human capital. It also explores whether and how education programmes such as school meals, conditional cash transfers and scholarships affect migration decisions. The findings have policy relevance in terms of matching education to the demands of the labour market, and meeting the increased demand for educational services in both the public and private sectors.

  

Emigration has become an important engine for development in the Philippines. Migration and remittances have the potential to play an important role in improving educational outcomes and future human capital stocks, but also raise concerns about “brain drain” and “brain waste” as many of the Philippines’ emigrants are highly educated but to take up unskilled jobs abroad.

Education and human capital generally play a critical role in driving economic growth in both advanced and emerging economies. The Philippines has a young population, which is expected to rise in number in the coming decades. However, youth unemployment is high. The youth bulge and large emigrant population have implications for the educational system and raise questions about how to best adapt education policy to meet future needs.

This chapter investigates the relationship between migration and education in the Philippines. Migration and education are closely linked through several channels. Emigration and return migration can change the skills composition in both countries of origin and destination. Migration and remittances can also influence school enrolment rates and educational investments. At the same time, educational policies and programmes may influence migration decisions and remittance patterns.

The chapter begins with an overview of the education sector in the Philippines, before investigating the role of education in migration decisions and migrants’ education acquisition abroad. It then presents the analysis of the impact of migration on educational expenditures and school attendance. The chapter also assesses the role of existing education policies on migration patterns. It concludes by drawing some conclusions for policy.

A brief overview of education in the Philippines

Until 2013, the educational system in the Philippines was organised into six years of elementary education, four years of secondary education, and higher education. Basic education – which consists of elementary (primary) and secondary education (high school) – is compulsory. Moreover, public basic education is free. The Enhanced Basic Education Act of 2013 is the most recent reform to the country’s educational system. It instituted the K to 12 programme, which makes kindergarten compulsory at five years old and adds two years of senior high school. These additional two years of high school bring the Philippines in line with the international standards of 12 years of basic education, i.e. six years of elementary and six years of high school. The first cohort of Filipino students began senior high school in June 2016 and will complete senior high school in 2018.

Despite being a developing country, the Philippines performs well in terms of educational indicators. Its mean years of schooling increased from 5.4 years in 1980 to close to 9 years in 2014, which is relatively high for the region (Figure 6.1). The Philippines also scores well for elementary school enrolment rates, at 96% in 2013, while secondary school enrolment rates were 67% (UNESCO, 2016).

Figure 6.1. Mean years of schooling is relatively high in the Philippines
Average number of years of education received by people aged 25 and older
picture

Source: Human Development Data, UNDP, http://hdr.undp.org/en/data.

 http://dx.doi.org/10.1787/888933458379

How does migration affect education in the Philippines?

Migration can affect the education and skills sector of a country of origin such as the Philippines through several pathways. A long-standing concern is the emigration of educated and highly skilled migrants, which is feared to result in brain drain. Another concern is the departure of migrant parents, which raises questions about the well-being of the children left behind. The schooling and academic performance of the children of emigrants may be adversely affected because of the absence of parental guidance and support. It is also possible that when both parents migrate, older children may have to assume the caregiving and other domestic responsibilities of adult members, which may force them to drop out of school (McKenzie and Rapoport, 2011; Hanson and Woodruff, 2003).

On the positive side, migration can increase household income through remittances, which can lead to greater investment in education (Adams, 2005; Cox-Edwards and Ureta, 2003; Hanson and Woodruff, 2003). If remittances received by the household are sufficient to cover basic needs, children should not need to work within or outside the household to support the family. Emigrants who obtain training or education abroad may bring back knowledge and skills that can be used in the country of origin. This “brain circulation” can therefore contribute to human capital accumulation. However, the various channels and complex interactions among all these phenomena make the relationship between migration and education complicated. The analysis below attempts to separate out the impact of individual channels – emigration, remittance, and return migration – on child and youth education and skills in the Philippines.

Highly educated individuals are more likely to plan to emigrate

Depending on the education profile of those who leave, emigration can either positively or negatively affect a country’s human capital stock. Decisions about educational attainment and emigration are often taken sequentially, but can also be made simultaneously. The first part of this chapter will examine the relationship between migration and education by analysing the role of education in emigration decisions.

Emigration is a selective process which is likely to involve younger, more educated and healthier individuals. The high level of education in the Philippines enhances their employment chances in the global labour market, contributing to high out-migration. Furthermore, more highly educated individuals are better able to access information, which is an important resource in making migration possible.

One way to evaluate how emigration affects human capital in the country of origin is to analyse the educational profile of those who plan to emigrate in the future.1 The IPPMD Philippine questionnaire asked adult household members whether they planned to live or work abroad in the future. Figure 6.2 shows that intentions to emigrate increase with education level. On average, 19% of all individuals in the sample are planning to emigrate, compared to 29% of individuals with post-secondary education.

Figure 6.2. Highly educated individuals are more likely to plan to emigrate
Share of individuals planning to emigrate (%), by education level
picture

Note: To better capture those individuals who have completed post-secondary education, the cut-off age for adults in these estimations is 20 years and above (compared to 15 years in other parts of the report).

Source: Authors’ own work based on IPPMD data.

 http://dx.doi.org/10.1787/888933458384

Other important determinants of intentions to emigrate include unemployment, living in a household that already has at least one emigrant, and high household wealth (results not shown in the table). It is interesting to note that those without employment are more likely to have intentions to emigrate. This is in line with the findings in Chapter 4, which also shows that 11% of current emigrants were unemployed before leaving the Philippines, while the overall unemployment rate in the IPPMD sample is 5%. Nevertheless, the great majority of those who emigrate are employed or in paid work before leaving the country, which confirms previous findings in the Philippines.

Return migration does little to build human capital since few emigrants acquire education abroad

One of the potential benefits of international migration is the acquisition of new knowledge and skills by migrants in destination countries. Return migrants who bring these skills back home can contribute to human capital accumulation in the origin country.

Box 6.1. The links between education and plans to emigrate

To further estimate the impact of education on the decision to emigrate, a probit regression with the following form was used:

picture

where picture is the intention of adult picture to emigrate, taking on a value of “1” if an individual plans to emigrate and “0” if not. picture represents a set of binary education level variables (no formal education being the reference category) of interest, while picture and picture are a set of observed individual and household characteristics believed to influence the outcome.a picture represents regional (municipality level) fixed effects and picture is the randomly distributed error term.

Table 6.1. Well-educated individuals are more likely to plan to emigrate

Dependent variable: Intentions to emigrate

Main variables of interest: Education level

Type of model: Probit

Sample: Individuals 20 years and above

Variables of interest

Sample

(1)

All

(2)

Urban

(3)

Rural

Elementary education

0.035

(0.033)

0.071

(0.044)

0.009

(0.048)

Lower secondary education

0.054*

(0.030)

0.070*

(0.042)

0.055

(0.041)

Upper secondary education

0.118***

(0.027)

0.132***

(0.038)

0.109***

(0.038)

Post-secondary education

0.158***

(0.027)

0.193***

(0.038)

0.146***

(0.038)

Number of observations

5 516

2 702

2 814

Note: Results that are statistically significant are indicated as follows: ***: 99%, **: 95%, *: 90%, Standard errors are in parentheses and robust to heteroskedasticity. To test robustness, the analysis was also carried out using a sample of individuals 25 years and above; this did not change the results.

Source: Authors’ own work based on IPPMD data.

a. The individual and household level control variables included in the regression were: age, sex, whether the individual lives in an urban area, household size, number of members in the household with tertiary education, whether the individual is unemployed and whether the household already has a migrant, and wealth status of the household (measured through an asset index using principal component analysis).

Table 6.2 displays the pre-migration education profile of surveyed current emigrants and return migrants. For both groups, those with post-secondary education comprise the largest share. In general, current emigrants had a higher level of educational attainment before leaving than return migrants. Overall, 9% of current emigrants and 6% of return migrants acquired education while they were abroad.2 It seems that as Filipino migrants are relatively well educated at their departure, few accumulate more education abroad – and this is especially the case for those who return. This suggests that the scope is limited for return migration to compensate for the loss of highly educated emigrants.

Table 6.2. Less than one in ten current emigrants and return migrants have received education abroad

Return migrants

Current emigrants

Educational level before departure (%)

No formal education

2.5

0.8

Elementary education completed

6.2

2.8

Lower secondary education completed

7.1

7.8

Upper secondary education

25.7

22.0

Post-secondary education

58.5

70.2

Share of migrants receiving education in country of destination

6.2

9.0

Note: The table displays education levels of current emigrants and return migrants (25 years and above) before leaving the Philippines, and the share of emigrants and return migrants that obtained education while being abroad.

Source: Authors’ own work based on IPPMD data.

Taken together, these results raise concerns over the attrition in human capital in the Philippines. However, as shown in the labour market chapter (Chapter 4), emigrants are mainly concentrated in a few sectors (primarily health and education). If there is an excess supply of skilled individuals, emigration can also help release pressure on the labour market and does not necessarily lead to skills shortages since the Philippines has a large pool of skilled professionals. This was pointed out in the stakeholder interviews, although several stakeholders mentioned that certain professions, such as scientists and engineers, are in short supply in the country. Another issue identified in the stakeholder interviews is the distribution of skilled professionals across the country. For example, the emigration of health professionals could lead to a deterioration of the health sector in rural areas, as it is much harder to recruit doctors and nurses to work in rural areas than urban areas. Skills shortages may also arise if colleges and universities design their curricula to meet global labour market demands rather than those of the local economy. This is a particular concern in private tertiary education institutions (Asis, 2006; Tan, 2009).

Emigration is positively linked to youth school attendance

As discussed above, the emigration of household members may negatively affect child and youth education enrolment rates and increase school drop-outs. In the Philippines, this is a recurrent concern in the court of public opinion – a concern magnified when women started participating in international labour migration (e.g. ECMI/AOS-Manila, SMC and OWWA, 2004; Asis and Ruiz-Marave, 2013). This is a view that is shared by many Filipino policy makers and stakeholders (Asis and Roma, 2010), as well as by stakeholders interviewed during the IPMMD study in the Philippines. On the other hand, remittances sent by emigrants can loosen financial constraints and allow households to pay for children’s schooling (Yang, 2008).

Given the high elementary school enrolment rates in the Philippines, the association between migration and educational attainment is more relevant for youth in secondary and tertiary education. School attendance for children aged between 6 and 14 years old in the IPMMD sample is 99% overall, and equally high for boys and girls, for children in the urban and rural areas, and for children coming from households with and without migration experience (data not displayed). However, school attendance declines for the age group 15-17, which corresponds to the last years of high school and first two years of tertiary education in the Philippines (Table 6.3). It also declines for young adults aged 18-22, which covers the latter years of tertiary education. The decline is particularly steep for the latter group, which reflects the national pattern (Asis and Battistella, 2013; Tan, 2009). There are also differences in school attendance for both groups by gender (higher school attendance among girls than boys), residence (higher among youth in urban areas than rural areas, but higher among young adults in rural areas than urban areas), and household migration characteristics (higher among youth and young adults belonging to households with at least one emigrant and households that receive remittances than those from households with no emigrant and not receiving remittances).

Table 6.3. School attendance rates are higher among children from households with migration experience

Household has at least one emigrant

Household has no emigrant

Household receives remittances

Household does not receive remittances

School attendance of youth (aged 15-17) %

Both sexes

89.6

74.4

89.3

73.8

Girls

92.0

77.4

92.6

76.2

Boys

87.5

72.3

86.7

71.7

Urban

92.5

70.4

91.2

68.9

Rural

86.8

79.6

87.3

79.1

School attendance of young adults (aged 18-22) %

Both sexes

38.9

29.28

36.6

30.3

Girls

40.5

26.1

36.0

28.2

Boys

37.6

32.7

37.2

32.6

Urban

36.7

29.7

34.6

30.7

Rural

41.4

28.9

39.1

30.0

Source: Authors’ own work based on IPPMD data.

The link between migration and education was further investigated using a regression framework for these two age groups (Box 6.2). The results show that for both age groups there is a positive association between migration and education attendance – for the 18-22 age group, the link is statistically significant. The link also seems to be stronger for migration than remittances. Although there is a strong positive link between receiving remittances and school attendance among the age group 15-17, when controlling for whether the household has an emigrant, the effect is no longer significant. For young people aged 18-22, the link between school attendance and remittances is negative, while the link with having an emigrant in the household is positive. This may be a result of the close association between migration and remittances: only 26 of the emigrant households do not receive remittances, and hence the effect of migration and remittances may be captured through emigration.

Box 6.2. The links between migration, remittances and youth school attendance

A regression framework was employed to estimate the effect of migration and remittances on school attendance using the following equation:

picture

where the unit of observation is youth picture, and the outcome variable picture is school attendance by youth in one of the two age groups (15-17 and 18-22) respectively. picture represents a migration variable including a binary variable for emigration, where “1” denotes if the youth lives in a household with at least one emigrant and “0” otherwise, while picture represent a binary variable for remittances, taking on value “1” if the household in which the youth lives is receiving remittances and “0” otherwise, picture and picture are a set of observed individual and household characteristics influencing the outcome. picture represents regional (municipality level) fixed effects and picture is the randomly distributed error term.a In an additional specification (presented in column 4 in Table 6.4), remittances are replaced by a binary variable indicating the presence of a return migrant in the household.

Four different specifications were carried out. Specification (1) investigates the link between receiving remittances and youth school attendance, controlling for all above-mentioned household characteristics, while column (2) simultaneously investigates the association between migration, remittances and youth school attendance. Columns (3) and (4) respectively investigate the association between school attendance of youth aged 18-22 and migration and remittances [column (3)] and return migration [column (4)].

Table 6.4. Migration is linked to higher school attendance

Dependent variable: Youth education attendance

Main variables of interest: Household has emigrant/receive remittance/has return migrant

Type of model: Probit

Sample: Youth aged 15-17 and 18-22

Variables of interest

Sample

(1)

Youth aged 15-17

(2)

Youth aged 15-17

(3)

Youth aged 18-22

(4)

Youth aged 18-22

Household has at least one emigrant

n/a

0.083

(0.070)

0.101**

(0.050)

n/a

Household receives remittances

0.100***

(0.035)

0.0336

(0.068)

-0.103*

(0.050)

n/a

Household has a return migrant

n/a

n/a

n/a

-0.006

(0.040)

Number of observations

575

575

908

908

Note: Results that are statistically significant are indicated as follows: ***: 99%, **: 95%, *: 90%. Standard errors are in parentheses and robust to heteroskedasticity. Return migration is not linked to school attendance by youth aged 15-17 (not displayed in table).

Source: Authors’ own work based on IPPMD data.

a. The set of independent variables includes age and sex of the youth, a binary variable indicating if the household in which the youth lives is located in an urban area, household size, the household’s dependency ratio (i.e. the share of youth children and elderly in the household in relation to adult members), the total number of children (aged 6-14) in the household, the number of children aged 0-14, the male-to-female ratio and finally a household asset wealth index.

The positive links between migration and youth school attendance found here imply that the presumed negative impact of migration on school attendance – i.e. that parental absence may lead to lesser parental guidance – is not borne out in this study.

Migration allows households to spend more on education

Remittances offer a financial resource to allow households to invest in educating their children. Several studies have found that remittances are invested in children’s education in the Philippines, thereby not only keeping children in school for longer but also increasing their human capital (e.g. Yang, 2008; Murata, 2011; Asis and Ruiz-Marave, 2013; Ducanes, 2015). Paying for a member’s schooling is in fact the most common activity undertaken by remittance-receiving households after a member left the household (Chapter 3). Even if basic education (elementary and secondary education) is free in the public school system, parents aspire to send their children to private schools, which they associate with better education. Family resources are especially needed for funding tertiary-level education, which is mostly provided by private institutions in the Philippines (ADB, 2012; Tan, 2009). There are also additional costs – such as transport, meals, and school projects – which can constitute a considerable expense for many households. The costs increase with the level of education, particularly at the tertiary level.

Figure 6.3 shows that migrant households – those with an emigrant, receiving remittances or with a return migrant – spend a higher share of their budget on average on education-related expenditures than those without migration experience. For example, households that receive remittances spend 7.7% of their budget on education on average, while the corresponding share for households without remittances is 5.5%. The same pattern holds true when looking at absolute yearly education expenditures: households with remittances spend more on average than households without remittances.

Figure 6.3. Households with migration experience spend on average a larger share of their budget on education
Share of annual budget spent on education (%)
picture

Note: The sample includes households with children and youth (aged 6-22 years old).

Source: Authors’ own work based on IPPMD data.

 http://dx.doi.org/10.1787/888933458391

More in-depth analysis, controlling for other individual and household factors in a regression framework, confirms that migration and remittances are positively associated with educational expenditures (Box 6.3). The results shown in Table 6.5 indicate that in the case of household expenditures on schooling, the size of both absolute and relative values is significantly positively associated with migration, remittances and return migration. The association between the amount of remittances a household receives and absolute educational expenditures is, however, only statistically significant when not controlling for migration. Having a return migrant is positively and statistically significantly associated with absolute educational expenditures, but not as a share of the total household budget.

Box 6.3. The links between migration and education expenditures

A regression framework similar to the one defined in Box 6.2 was employed to estimate the effect of migration and remittances on education expenditures using the following equation:

picture (1)

picture (2)

where the dependent variables picture in equation (1) and picture in equation (2) represent households’ educational expenditures measured in absolute (logged) values or as a share of total household annual budget respectively. picture represents a remittance variable for the amount of remittances received, while picture takes on value “1” if the household has at least one emigrant and “0” otherwise. picture are a set of observed household characteristics influencing the outcome.a picture represents regional (municipality level) fixed effects and picture is the randomly distributed error term. In an additional specification (presented in column 4 in Table 6.5), remittances are replaced by a binary variable indicating the presence of a return migrant in the household.

Four different specifications were carried out. Specification (1) investigates the link between receiving remittances and the (logged) amount of household budget spent on education while column (2) simultaneously investigates association between migration, remittances and the amount spent on education. Column (3) investigates the association between migration, remittances and the share of the total household budget that is spent on education. Finally, in column (4) the association between return migration and household expenditures on education is investigated (also controlling for households having an emigrant).

Table 6.5. Households receiving remittances spend more on education

Dependent variable: Logged amount of educational expenditures (column 1,2,4), Educational expenditures as share of total household expenditures (column 3)

Main variables of interest: Amount of remittances, having an emigrant/return migrant

Type of model: OLS

Sample: All households

Variables of interest

Dependent variable

(1)

Educational expenditure (amount)

(2)

Educational expenditure (amount)

(3)

Educational expenditure (share)

(4)

Educational expenditure (amount)

Amount of remittances household receives

0.036***

(0.007)

0.004

(0.014)

0.007***

(0.003)

n/a

Household has at least one emigrant

n/a

0.425***

(0.164)

0.006

(0.009)

0.481***

(0.085)

Household has a return migrant

n/a

n/a

n/a

0.219**

(0.110)

Number of observations

1 186

1 186

817

1 198

Notes: Results that are statistically significant are indicated as follows: ***: 99%, **: 95%, *: 90%. Standard errors are in parentheses and robust to heteroskedasticity. Educational expenditures as share of household yearly budget are not linked to return migration (not shown in table due to limited space).

Source: Authors’ own work based on IPPMD data.

a. The set of household and individual explanatory variables included in all specifications are the following: household size, household dependency ratio (defined as the number of children and elderly in the household as a share of the working population), mean education level of the adult members in the household, the number of young children (6-14 years old), the number of youth (15-17 years old) and the number of members of tertiary age in the household, a dummy for urban location and an asset index (based on principal component analysis) that aims to capture the wealth of the household.

One potential use of remittances is on private school education. Previous research has, for example, shown that remittances increase the probability of children attending private educational institutions in Latin America (Medina and Cardona, 2010). Descriptive statistics on the share of children and youth in private education indicate that income obtained from migration and remittances may partly be directed towards private schooling (Figure 6.4). Children and youth living in households that receive remittances are more likely to attend private schools than those in households without remittances. This holds for all age groups, from elementary school to tertiary education. However, the stakeholder interviews highlighted concerns over the accreditation process and verification of education quality of the many private higher education institutions in the country, including schools and programmes which were established partly to meet increased demand by emigrant and remittance-receiving households. Previous research has also shown that higher education institutions in the Philippines, which are often privately owned, are sensitive to overseas employment trends, and enrolment revenues are given priority over quality (Asis, 2006; Ortiga, 2015; Tan, 2009). Hence, the increased demand for educational services needs to be matched with investments in educational infrastructure as well as tools to monitor and assure education quality.

Figure 6.4. Remittance-receiving households are more likely to send their children to private schools
Share of students attending private education (%)
picture

Note: Statistical significance calculated using a chi-square test is indicated as follows: ***.99%, **.95%, *.90%. Remittances include all remittances, from former household members as well as from individuals (family and friends) that have never been part of the household.

Source: Authors’ own work based on IPPMD data.

 http://dx.doi.org/10.1787/888933458406

How do education policies in the Philippines affect migration?

The relationship between education policies and migration is multidimensional, and can affect migration patterns in several ways. Financial support for children’s education and the provision of training and programmes to match education supply and labour market demand may reduce the incentives to emigrate. On the other hand, welfare policies linked to education – such as cash transfers – can be used to finance emigration.

The IPPMD study identified a number of key policies in the education sector in the Philippines (Box 6.4) in order to analyse the link between education policies and migration outcomes, such as decisions to emigrate, to remit money and to return and stay in the home country.

Box 6.4. Education programmes in the household survey

After an assessment of the most relevant education programmes in the country implemented in recent years, a list of both in-kind and cash based programmes was identified and introduced into the IPPMD household questionnaire (listed in Figure 6.5). Households were asked if anyone had benefited from any of these educational programmes in the five years prior to the survey. Most of the programmes included in the survey target elementary and secondary schooling. All of the programmes are nationwide in scope, and many of them are needs-based. Questions on vocational training programmes were also included in the survey, these are analysed in the labour market chapter (Chapter 4).

Figure 6.5. Education policy programmes in the IPPMD survey
picture

Overall, 38% of the households in the sample with children of school age (6-20 years old) benefited from at least one of the education programmes included in the survey. In-kind distribution programmes, particularly the distribution of school textbooks and school meal programmes, were the most common (Figure 6.6). Of these, the school meal programme benefited the largest share of households – around 15%. Feeding programmes have been in place in public schools since 1997, implemented by the Department of Education (DepEd) together with local governments, businesses, NGOs, and community organisations. They began with the Supplemental Feeding Program (SFP), which served breakfast to elementary students identified as being malnourished. Its aim was to restore at least 70% of beneficiaries to normal nutritional status and to improve class attendance to 85-100%. In 2010, the programme was expanded and renamed the School-Based Feeding Program (SBFP). This provides breakfasts and lunches to elementary students (from 2012, kindergarten students were included). Although the scope is nationwide, budget constraints mean it can only reach a small percentage of malnourished school children. Other in-kind distribution programmes, such as textbooks, school supplies and uniforms, may be provided by the government or by private organisations.

Among the cash-based programmes, scholarships for tertiary education are the most prominent among the IPPMD households: 7.5% of households with children of school age have benefited from tertiary education scholarships in the past five years. Scholarships can be based on merit, or targeted at low-income groups, or a combination of both. A smaller proportion of households benefited from scholarships at the elementary and high school levels, which is explained by the fact that basic education (elementary and high school) is free. Scholarships at these levels offer assistance with school-related expenses. Scholarships can be provided by government bodies, officials (e.g. elected officials provide scholarship programmes), or by private organisations and individuals.

The conditional cash transfer programme (Pantawid ng Pamilyang Pilipino Program or 4Ps) is the largest social protection programme implemented by the government targeting extremely poor families. It began in 2007, when it covered families with children aged 14 and below. Since 2013 it has been extended to families with children under 18 years old. These families are given monthly cash assistance of PHP 500 (Philippine Pesos) to help them with health and nutrition expenses, and PHP 300 per child (for up to three children) to help with educational expenses. The cash assistance is conditional upon mothers seeking pre-natal and/or post-natal care and children attending school. A family with three children can receive up to PHP 1 400 per month.

The Educational Service Contracting (ESC) is a government scheme to provide grants for deserving elementary graduates to enrol in private high schools. One of its aims is to take the pressure off overcrowded public high schools.

Figure 6.6. In-kind distribution programmes are the most common education programmes
Share of households with school-age children benefiting in the five years prior to the survey
picture

Note: The sample includes households with children aged from 6 to 20 years old. Primary education corresponds to elementary education and secondary education to high school education.

Source: Authors’ own work based on IPPMD data.

 http://dx.doi.org/10.1787/888933458418

Cash-based education programmes appear to reduce emigration

Descriptive statistics in Figure 6.7 suggest that households with at least one emigrant are less likely to have benefited from all cash-based education policies except education service contracting. This could suggest that households that receive monetary support to send and keep their children and youth in school are less in need of considering emigration. However, it is also likely that the pattern is driven, for example, by household wealth, as the CCT programme is targeting poor households that may not have the sufficient funds to emigrate abroad. It is thus necessary to also control for other factors that might influence the decision to emigrate. The IPPMD survey collected data on beneficiaries of education programmes in the past five years prior to the survey, but not the exact year the household benefited from the programme. It is therefore not possible to identify emigrants who emigrated (or migrants who returned) after the household benefited from a policy. However, by restricting the sample to only include emigration and return migration in the past five years prior to the survey (and excluding households with emigrants that left more than five years ago and households with return migrants that came back more than five years ago), the analysis links policies to emigration that happened around the same time. The results of the regression analysis are presented in Box 6.5.

Figure 6.7. Households benefitting from cash-based education programmes are less likely to have emigrants
Share of households benefiting from education policies in the past five years, by migration experience
picture

Note: The sample includes households with children aged 6-20 years old. Households with emigrants include all households which had a member emigrating abroad in the five years prior to the study.

Source: Authors’ own work based on IPPMD data.

 http://dx.doi.org/10.1787/888933458422

Box 6.5. The links between education policy and emigration

To estimate the impact on emigration of benefitting from any education support programme, the following probit equation was applied:

picture

where picture represents household migration status, being a binary variable for the household either having at least one member planning to emigrate in the future (specification 1) having at least one emigrant who left in the five years prior to the survey (specification 2), receiving remittances (specification 3), or having a return migrant (specification 4). picture is the variable of interest and represents a binary variable indicating if the household has benefited from an education policy in the five years prior to the study (results presented in the upper part of the table). It takes on value “1” if the household has benefited from an education policy programme and “0” otherwise. Cash-based programmes (CCT and scholarships) are also analysed separately (results presented in the lower part of the table). picture are a set of observed individual and household characteristics influencing the outcome.a picture represents regional (municipality level) fixed effects and picture is the randomly distributed error term

Table 6.6. Cash-based education policies are negatively linked with emigration

Dependent variable: Plans to emigrate, having and emigrant/receiving remittances, have a return migrant

Main variables of interest: Household benefited from education programme/cash-based education programmes

Type of model: Probit

Sample: All households

Variables of interest

Dependent variable

(1)

Plan to emigrate

(2)

Household has an emigrant

(3)

Household receives remittances

(4)

Household has a return migrant

Household benefited from any education policy in the past 5 years

0.072***

(0.025)

-0.038

(0.028)

0.012

(0.016)

0.001

(0.013)

Number of observations

1 938

1 177

1 382

1 727

Cash-based programmes

Household benefited from conditional cash transfer

0.061

(0.054)

-0.130**

(0.057)

-0.160***

(0.055)

-0.028

(0.034)

Number of observations

1 938

1 177

1 382

1 727

Household benefited from scholarship programme

0.080**

(0.039)

-0.132***

(0.044)

-0.036

(0.036)

0.007

(0.018)

Number of observations

1 938

1 177

1 382

1 727

Notes: Results that are statistically significant are indicated as follows: ***: 99%, **: 95%, *: 90%. Standard errors are in parentheses and robust to heteroskedasticity.

a. The control variables include household size, household dependency ratio (defined as the number of children and elderly in the household as a share of the working population), mean education level of the adult members in the household, the number of young children (6-14 years old), the number of youth (15-17 years old), a dummy for urban location and an asset index (based on principal component analysis) that aims to capture the wealth of the household.

The results of the regression analysis (Table 6.6) show that households that have benefited from any type of education policy are more likely to have a member planning to emigrate in the future. However, overall there is no statistically significant link between benefitting from an education policy and having a member who emigrated in the past five years, receiving remittances or having a member who returned from overseas.

As discussed above, the main way that education policies potentially influence migration outcomes is by relieving households’ financial constraints. Cash-based education programmes may hence be particularly important in influencing migration decisions. The results of the analysis of the two main cash-based programmes in the Philippines, conditional cash transfer (CCT) programmes and scholarships, are shown separately in the second part of Table 6.6. These show that households benefitting from cash-based programmes (both CCTs and scholarships) are less likely to have had an emigrant leave the household in the past five years. They are also less likely to have received remittances. The fact that CCT programmes in the Philippines are directed towards poor households suggests that the results need to be interpreted with some caution as it is hard to establish causality. While the analysis did control for household wealth, more work is needed in order to fully understand the mechanisms linking CCT programmes and migration.

Households receiving scholarships are also less likely to have an emigrant, although they are more likely to have a member who is planning to emigrate. A potential explanation is that scholarships deter migration in the short term because individuals are still in education, but that they could be planning to emigrate once they have finished. This explanation also reflects the findings in the first part of the chapter that intentions to emigrate increase with education levels.

Furthermore, the analysis showed no statistically significant link between households with return migrants (who returned in the past five years) and benefiting from CCTs or scholarship programmes. This indicates that although education policies potentially deter emigration, benefiting from such policies is not sufficient to encourage emigrants to return.

Conclusions and policy recommendations

The analysis presented in this chapter shows that education is an important factor in the decision to emigrate. Adults educated to secondary level and above are more likely to plan to migrate than those with lower levels of education. As few return migrants obtain education while abroad, the loss of human capital from emigration is likely not compensated for by return migrants bringing back new skills.

On the other hand, the research finds that emigration and remittances have positive impacts on school attendance by young people and on household educational expenditures. Children in households that receive remittances are more likely to send their children to private schools – while this is a positive phenomenon, the demand created does put pressure on the education sector.

How are educational policies affecting migration? Conditional cash transfer programmes and scholarships seem to discourage beneficiary households from emigrating, perhaps by relieving financial constraints in a key sector such as education. Nevertheless, there appears to be a link between receiving scholarships and plans to emigrate, which could undermine the effect in the medium to longer run. Introducing conditionality into the design of cash-based education programmes could help deter emigration.

Policy recommendations are as follows:

  • The increased demand for educational services from remittance inflows should be met with investments in educational infrastructure, especially in teachers and building classrooms, to ensure universal access to education.

  • The use of remittances to finance private education calls for measures to monitor and verify the quality of private education institutions, including strengthening the accreditation process.

  • Collecting migration and remittance information in the design and evaluation of cash-based education programmes would allow policy makers to better understand the effects of such programmes on emigration patterns.

References

Adams, R.H. (2005), “Remittances, household expenditure and investment in Guatemala”, World Bank Policy Research Working Paper No. 3532, World Bank, Washington DC.

ADB (2012), Access Without Equity: Finding a Better Balance in Higher Education in Asia, Asian Development Bank, Mandaluyong City, www.adb.org/sites/default/files/publication/29769/access-without-equity.pdf.

Alburo, F.A. and D.I. Abella (2002), “Skilled labour migration from developing countries: Study on the Philippines”, International Migration Papers, 51, International Labour Office, International Migration Programme, Geneva, www.ilo.org/wcmsp5/groups/public/---ed_protect/---protrav/---migrant/documents/publication/wcms_201780.pdf.

Asis, M.M.B. (2006), “The Philippines: a culture of migration”, online article, Migration Information Source, 1 January, www.migrationpolicy.org/article/philippines-culture-migration.

Asis, M.M.B. and G. Battistella (2013), The Filipino Youth and the Employment-Migration Nexus, UNICEF Philippines and Scalabrini Migration Center, Makati City and Quezon City, www.unicef.org/philippines/media_21659.html.

Asis, M.M.B. and C. Ruiz-Marave (2013), “Leaving a legacy: parental migration and school outcomes in the Philippines”, Asian and Pacific Migration Journal, 22(13): pp. 349-376.

Asis, M.M.B. and G.M. Roma (2010), “Eyes on the prize: towards a migration and development agenda in the Philippines”, in Baggio, F. (ed.), Brick by Brick: Building Cooperation between the Philippines and Migrants’ Associations in Italy and Spain, Scalabrini Migration Center, Quezon City.

Cox-Edwards, A. and M. Ureta (2003), “International migration, remittances and schooling: evidence from El Salvador”, Journal of Development Economics, 72: 3, pp. 429-461.

DOST-SEI (2011), International Migration of Science and Technology Manpower-OFWs, Department of Science and Technology-Science and Education Institute, Manila, www.sei.dost.gov.ph/images/downloads/publ/sei_imig.pdf.

Ducanes, G. (2015), “The welfare impact of overseas migration on Philippine Households: analysis using panel data,” Asian and Pacific Migration Journal, 24(1): pp. 79-106.

ECMI/AOS-Manila, SMC and OWWA (2004), Hearts Apart: Migration in the Eyes of Filipino Children, Episcopal Commission on Migrants and Itinerant People-CBCP/Apostleship of the Sea-Manila, Scalabrini Migration Center and Overseas Workers Welfare Administration, Quezon City.

Hanson, G.H. and C. Woodruff (2003), “Emigration and educational attainment in Mexico”, mimeograph, University of California, San Diego.

McKenzie, D. and H. Rapoport (2011), “Can migration reduce educational attainment? Evidence from Mexico”, Journal of Population Economics 24(4): pp. 1331-1358. doi:10.1007/s00148-010-0316-x.

Medina, C. and L. Cardona (2010), “The effects of remittances on household consumption, education attendance and living standards: The case of Colombia”, Lecturas de Economía, 72: pp. 11–43.

Murata, A. (2011), “Effects of remittances on household expenditure, inequality and education expenditures: evidence from the Philippines”, doctoral thesis (DPhil), University of Sussex, Brighton.

Ortiga, Y.Y. (2015), “The flexible university: higher education and the global production of migrant labor”, British Journal of Sociology of Education, http://dx.doi.org/10.1080/01425692.2015.1113857.

Tan, E. (2009), Supply Response of Filipino Workers to World Demand, International Organization for Migration, Makati City.

UNESCO (2016), UIS.Stat (database), http://data.uis.unesco.org, accessed 20 October 2016.

Yang, D. (2008), “International migration, remittances and household investment: evidence from Philippine migrants’ exchange rate shocks”, Economic Journal, Royal Economic Society 118(528): pp. 591–630.

Notes

← 1. It is however important to keep in mind that intentions to emigrate are not always realised, and they do not perfectly predict future emigration.

← 2. The questionnaire included a set of questions related to the education of current emigrants and return migrants: current, including current education level, education level before the emigrant/return migrant left the Philippines and any education obtained while abroad.