The Feminization of International Migration and its e¤ects on the

Children Left behind: Evidence from the Philippines�

Patricia CortesBoston University - School of Management

October, 2011

Abstract

The last two decades have witnessed an increased feminization of international migration

and the Philippines is no exception. Whereas in the 1970s women formed about 15 percent

of the migrant labor force, in 2010, 55% of new hires of Filipino migrant workers were female.

Most of these women are married and many have children.

This paper explores the e¤ects of a mother migrating on her children�s wellbeing. We use

as control group children with migrant fathers to identify e¤ects coming from remittances from

those resulting from parental absence. Exploiting demand shocks as an exogenous source of

variation in the probability that the mother migrates, we �nd that children of migrant mothers

are approximately 5 percentage points more likely to be lagging behind in school compared to

children with migrant fathers. The negative e¤ect of the mother migrating is not explained

by gender di¤erences in income abroad or remittance behavior, supporting the hypothesis that

mother�s absence has a stronger detrimental e¤ect than father�s absence.

�Email: pcortes@bu.edu. I wish to thank the Philippine Overseas Employment Administration, in particular Helen

Barayuga, Nimfa de Guzman and Nerissa Jimena for assistance with the data. I acknowledge seminar participants

at IFAU, U of Chicago Booth, UCL and University of Milan for helpful comments. All errors remain my own.

1

1 Introduction

Transnational households, where a family member works in a foreign country while some or all of her

dependents reside in the sending country, are becoming increasingly common in many developing

countries. Millions of children from all parts of the world are growing up with at least one

parent living abroad. For example, Bryant (2005) estimates that 2-3 percent of Indonesian and

Thai children have been left behind by a parent. A UNDP study in Ecuador found in 2005 that 36

percent of migrant women and 40 percent of migrant men left their children back home. Save the

Children (2006) reports that around 1 million Sri Lankan children are left behind by their mothers,

who migrate in search of work. An estimated 170 000 in Romania have one or both parents working

abroad (New York Times, 2009) and a similar number is also observed in Moldova (Unicef, 2008).

The numbers in the Philippines are even more striking: close to 3,800,000 Filipinos1 �10 percent

of the country�s labor force� are working abroad as temporary migrants, not being allowed to

move overseas with their families. Most of these temporary migrants are married and many have

children, resulting in an estimated 1-3 million Filipino children with a parent living abroad2.

Most of the parents who leave do so to provide for their children economically. In the Philippines,

children in transnational households are not the poorest of the poor prior to migration of their

parents; most are fed on a daily basis and attend public schools. It is quality health care, good

schooling, a sturdy roof over their heads what parents seek for their children when they migrate.

Yang (2008) �nds that remittances increase educational expenditures and the likelihood of starting

relatively capital-intensive household enterprises.

However, children�s wellbeing depends not only on economic resources, but also on parental care.

Anecdotal evidence suggests that many children left behind grow up under serious emotional strain.

A survey by the Manila�s Scalabrini Migration Center (2000) to seven hundred school age children

shows that compared to their classmates, the children of migrant workers performed particularly

poorly in school, and were more likely to express confusion, anger and apathy. Studies based on

thorough interviews to children of migrant parents support these �ndings (Parrenas, 2005).

Concerns about the magnitude and nature of the negative e¤ects of migration on the children

left behind have grown as the gender composition of temporary migrants in the Philippines and

1POEA (2009)

http://www.poea.gov.ph/stats/Stock%20Estmate%202009.pdf2The 2007 Census counts 1.3 million Filipinos working abroad with at the very least 55 percent of them having

children. Assuming 2-3 children per migrant; Census data will suggest 1.5 to 2 million Filipino children with a parent

working abroad. Note that the Census number of immigrants is lower than the stock reported by the POEA because

it excludes households whose members migrated all together.

2

other countries has shifted from majority males to majority females. Whereas in the 1970s women

formed about 15 percent of the Filipino migrant labor force, in 2010 the share was 55 percent,

having reached a maximum of 75% in 2004 (see Figure 1). A similar situation is observed in Sri

Lanka, where more than 65 percent of emigrants are women. In 2006, out of 680,000 registered

overseas Indonesian migrants, more than 79% were women (World Bank)3. In countries where

gender roles are still very rigid and mother�s main role is to nurture and the father�s to be the

breadwinner, migration of mothers is perceived as a much larger disruption in a child�s life than

the absence of the father.4 Children with migrant fathers are cared by their mothers, who can

a¤ord to stay-at-home; fathers of children with migrant mothers, on the other hand, rarely become

the primary care givers. Instead, children are mostly under the care of extended kin, usually aunts

and grandmothers.

This paper investigates if and how mother migration has a di¤erential e¤ect on the well being of

the Filipino children left behind. We focus on the comparison with children with migrant fathers to

identify e¤ects coming from remittances from those resulting from lower parental time investments.

Constrained by the existing data, we focus on school performance as our main outcome, as measured

by the probability of lagging behind.

We work under the assumption that mothers want the best for their children and have rational

expectations about the magnitude of the direct negative e¤ects of their absence, and thus migrate

only if they expect their children to enjoy a net bene�t from their decision. This does not mean,

however, that identifying the di¤erential e¤ects of parental absence when the mother migrates is

not important; it provides policy makers in sending countries with valuable information about the

consequences of their migration policies and might also help provide better support services for

the left behind.5 It also expands our understanding of the role of parental time investments in the

human capital accumulation of children.

A major challenge in trying to evaluate the causal impact of migration is that female migrants are

not randomly allocated across households or regions. Families with migrant mothers are likely to

have particular (unobserved) characteristics that distinguish them from migrant father households.

3http://web.worldbank.org/4Sri Lanka�s Women�s A¤airs Minister Heman Ratnayake reportedly said that women "should think not twice but

thrice before leaving their young ones". She also urged women "not to desert their children for foreign jobs as this

can lead to a breakup in the family and other problems". (Oishi, 2005).5 It is common for governments to impose restrictions on who can migrate. In 1994, the Filipino government

imposed an age requirement of 25 for domestic workers. In Sri Lanka, the government banned the migration of

women with children younger than 5. The restriction was later repealed. In Bangladesh, professional and skilled

women have to be over twenty-one to work overseas and unskilled women have to be over thirty �ve. The Manpower

Department of Indonesia imposed age and marital status requirements for overseas migrants. Men who applied to

work in Saudi Arabia were required to be 18 years old, while women had to be either married or 25 years of age at a

minimum. Women with children under one year of age were not elegible to apply (Silvey, 2004).

3

Hence, by simply comparing household outcomes of families with migrant mothers to households

with migrant fathers, one cannot conclude that the observed di¤erences between them are solely a

consequence of the mother having migrated.

To solve this issue, we exploit demand shocks as a random source of variation that a¤ects the

probability that the mother instead of the father decides to work abroad. Philippines�migration

�ows are gender speci�c and highly channelized between local areas and foreign destinations; a

phenomenon mostly explained by the importance of social networks (SMC, 2006). Therefore,

economic shocks, changes in immigration laws, and even epidemics such as SARS in Hong Kong

should a¤ect the propensity to female migration vs. male migration di¤erently by local area. We

use con�dential administrative data from the Philippines Overseas Employment Administration

(POEA) to determine the top countries of destination for female and male migrants by province.

Using the destination distributions, we construct several instruments; some try to capture all

potential shocks, such as the set of top country of destination dummies interacted with year e¤ects.

Others, focus on more speci�c shocks, for example, on the 1994 policy that implemented a minimum

age requirement for domestic workers migrating to the Middle East. To proxy for economic shocks

we use the expected salary for a female migrant based her province�s distribution of destination

countries.

We use two main datasets for our analysis: the Census and the Survey of Overseas Filipinos

(SOF). The former has many more observations, but the later includes detailed information on the

migration experience of parents. We use the Census to establish robust and precisely estimated

e¤ects of having the mother migrate instead of the father on children�s outcomes. Both OLS and

IV estimations suggest that children�s educational outcomes are negatively a¤ected by the mother

migrating. We then use the SOF to explore the role of economics resources vs. parental time inputs

in explaining our �nding. Although we do �nd that remittances and income reduce the probability

that a child is lagging behind in school, controlling for them in the regressions does not reduce

the gap between children with migrant mothers vs. migrant fathers, suggesting that parental care

plays an important role in determining children�s educational outcomes.

The rest of the paper is organized as follows. The next section presents the literature review.

Section 3 develops a very simple model of education production. Section 4 discusses the data and

descriptive statistics. The empirical approach and the main results of the paper are presented in

Section 5, and Section 6 concludes.

2 Literature Review

4

Most of the literature on migration and the education of the left behind family has concentrated

on the role of remittances on households� schooling decisions. Evidence from several countries

suggests that families with migrants are more likely to send their children to school, using cash

from remittances to pay fees and other costs (UNDP, 2009). In Guatemala international migration

is associated with 48 percent higher expenditures in education (Adams Jr, 2005). Cox-Edwards

and Ureta (2003) �nd that in El Salvador remittances have a very large impact on the hazard of

leaving school.

Using exchange rate shocks as exogenous source variation in the value of remittances in the Philip-

pines, Yang (2008) estimates an elasticity of educational expenditures with respect to remittances

of 0.55. He also �nds weak evidence that the value of remittances slightly increases the likelihood

that a child aged 10-17 reports attending school.

The positive e¤ect of remittances on educational investments is very much in line with the emphasis

that development economists have placed on the role of resource constraints in explaining di¤erences

in school attainment. The e¤ects of migration on households go beyond increasing income. The

school performance of children is likely to depend too on time investments by adults, especially

parents. None of the papers mentioned above directly addresses this channel. There is, however,

a larger literature on the potential negative e¤ects of parental absence due to divorce or death on

a child�s wellbeing. In a survey paper, Haveman and Wolfe (1995) �nd that in the U.S. household

characteristics that are correlated with the amount of parental care are important determinants of

children�s educational attainment. They report that growing up in a single-parent or stepparent

family - controlling for income - has a large and negative e¤ect on educational attainment, as does

going through stressful events during childhood. Later work by Lang and Zagorsky (2001) and by

Corak (2001) using better data and more rigorous empirical strategies found much smaller negative

e¤ects of parental absence (by divorce or death) on children�s welfare and economic performance as

adults. A study by Getler, Levine and Ames (2004) in Indonesia, arguably a country with family

dynamics closer to those of the Philippines, found negative large e¤ects of parental death on a

child�s enrollment with no di¤erential e¤ect by gender of the child or of the deceased parent.

Though informative, these estimates are not directly comparable to ours since parental absence

because of migration is likely to di¤er from parental absence due to death or divorce. A recent

study by Lyle (2006) is closer in spirit to the present paper. The author uses military deployments

to estimate the e¤ect of parental absences on children�s academic achievement, and �nds that

parental absences adversely a¤ect test scores, especially of children with mothers in the army.

5

3 Theoretical Framework

In this section we develop a very simple model of education production that will help illustrate

the di¤erent channels through which parental migration a¤ect children outcomes, and how their

relative importance might vary with the gender of the parent who migrates. It will also prove useful

for the discussion of the types of selection we might observe in the data.

Assume that education (S) is produced using two inputs: economic resources (R) and parental

time (T ):

S(R; T ) (1)

where economic resources refer to household income, which is needed to pay for a private education

(which is assumed of better quality), for school supplies, or to prevent the need for the child to work.

We think of T as the amount (and quality) of time parents devote to their children, helping them

with homework, providing emotional support or giving guidance. This representation is clearly a

simpli�cation; we abstract from inputs that cannot be changed (innate ability) and note that levels

of others -such as quality of teachers and peers, and own e¤ort- are partially determined by R and

T .

Given the production function (1), parental migration (M) a¤ects the level of education as follows:

dS

dM=@S

@R� dRdM

+@S

@T� dTdM

(2)

Hence, the change in a child�s education due to parental migration depends on how it a¤ects the

two inputs, weighted by the marginal productivity of each. Given that we can safely assume

that dRdM > 0 and dT

dM < 0 and that marginal productivities are strictly positive, the e¤ect is not

unambiguously positive or negative.

3.1 Migrant Mother vs. Migrant Father

Assume for now, that the gender of the migrant parent is allocated randomly, or is independent of

each of the terms in (2). What di¤erential e¤ects on the education of the child should we expect

to see? As we will show in the data section of the paper, migrant mothers send signi�cantly fewer

remittances and earn less when working abroad than migrant fathers. This is due to most female

6

migrants working in relatively low remunerated occupations such as domestic workers.6 Therefore,@R@M (migrant mother) <

@R@M (migrant father):

What about @T@M (migrant mother) vs.

@T@M (migrant father)? In principle, who migrates -the mother

or the father- shouldn�t matter if the parent left behind carries and ful�lls all caring responsibili-

ties. However, gender roles are still very rigid in the Philippines; even in transnational households

concepts of fathering are limited to breadwinning while mothering centers on domestic-sphere ac-

tivities and emotional comfort and support. So, whereas in families of migrant fathers, the children

are cared by their mothers, who can a¤ord to stay home, fathers of children with migrant mothers

rarely become the primary care givers. Instead, children are mostly under the care of extended kin,

usually aunts that have families of their own or grandmothers who frequently feel they are too old

to raise children (Parrenas, 2005). Therefore, it is expected that most children will experience a

larger drop in parental time investments if the mother is the one who migrates.

Summarizing, ex-ante we expect children of migrant mothers to be unambiguously worse than

children of migrant fathers.

3.2 Selection

We can use our very simple model to predict the type of selection we should observe in the data.

Assume that (one of) the main objective of parents is to maximize their children�s education level,

therefore we will observe that the mother migrates if dSdM jmother> dS

dM jfather : For simplicity, alsoassume that all children�s production functions are identical.7Given that in average, male migrants

earn signi�cantly more than female migrants, when we observe the mother migrating we suspect that

the father had particularly low earnings potential. On the other hand, keeping earnings potential at

home and abroad constant, children of migrant mothers are likely to have a better support system.

Therefore, a priori, the selection can go in either direction. We will use the comparison between

the OLS and IV models to empirically establish the likely direction of the selection.

3.3 Other Channels

By shifting the migration prospects of the children, parental migration might also change the

perceived and real returns to education, and thus the incentives to study. A 2003 nationwide

survey of Filipino children in the ages 10-12 years found that 47 percent wanted to work abroad

someday; the percentage was higher - 60 percent - among the children of overseas Filipino workers.

6See next section for a more detailed description of the occupational distribution of temporary migrants.7We are abstracting, for example, from the complication of some children needing more parental attention to

succeed (larger @S@T), and from the selection this might cause.

7

The same study found that the courses children planned to take were those that would lead to jobs

that would be marketable abroad �nursing, mostly among girls; maritime courses, mostly among

boys.

The direction of migration prospects on educational incentives depends on the context; for example,

in Mexico, where low-skilled migration predominates, boys are shown to be more likely to drop out

of school to take up this option (McKenzie and Rapoport, 2006). On the other hand, emigration

of Fijians to high skilled jobs in Australia has encouraged the pursuit of higher education in Fiji

(Chand and Clemens, 2008). In the Philippines, given that migrants are selected from the right

tail of the education distribution, the prospect of migrating in the future is likely to increase the

returns to human capital, and thus the incentives for the children to attend and do well in school.

4 Data and Descriptive Statistics

This paper uses data from the Census, the Survey of Overseas Filipinos, the Labor Force Survey,

and a con�dential dataset containing information about all legal land based migrants collected by

the POEA. The Census, the SOF, and the LFS provide data on the basic economic and demographic

characteristics of Filipino households, including the migration status of their members. The main

advantage of the Census is its size; we have access to 100 percent of the 1990, 1995, 2000 and 2007

Censuses. On the other hand, the Surveys of Overseas Filipino (SOF) matched with the Labor

Force Survey has a much richer set of questions regarding the migration experience of household

members. The SOF is a supplement of the October Labor Force Survey, which includes information

on overseas workers who left abroad during the last �ve years. We use the 1993-2000 samples.

In this section, we present descriptive statistics of the three datasets to give the reader an overview of

the migration phenomenon in the Philippines. We start with Census data to explore the observable

di¤erences between female migrants, male migrants, and non-migrants, and how selection into

migration has changed over the years (see Table 1). We restrict the sample to women and men

aged 18-54. A few observations are worth mentioning. First, migrants - males and females- are

positively selected in terms of education. They are close to twice as likely as non-migrants to have

a college degree. Migrant women are also less likely to be married and have children than non-

migrants. However, there is a clear strong trend of more and more married women with children

migrating. Whereas in 1990 just a third of female migrants had children, by 2007 close to half of

them did. Trends for male migrants have gone in the opposite direction. Finally, the table shows

that migrants come disproportionally from certain regions of the country, and that the regional

distributions of female and male migrants are di¤erent. Note, however, that distribution of region

of origin of migrants has become less concentrated.

8

Table 2 also uses the Census to characterize the children of migrant mothers, migrant fathers, and

non-migrant parents. As expected by the statistics in Table 1, children of migrant parents belong

to better o¤ families, as shown by the education level of their parents and the number of siblings.

Note that the parent that migrates is usually the one with the highest education level. As suggested

by the age of the youngest sibling and the average age of the mother, migrant mothers tend to wait

longer to migrate. The average age of the youngest sibling in a family with a migrant mother is

one year older than the average when the father is a migrant and two years older than when both

parents are present. Perhaps not surprisingly, given the literature on gender roles and migration in

the Philippines (Parrenas, 2003), children of migrant mothers are more likely to live in extended

households. Mirrowing the distribution presented in Table 1, the regional distribution of migrating

mothers is also quite di¤erent from families with migrant fathers and non-migrant parents.

Census data provides a general picture of the characteristics of migrants in the Philippines and

their families, but has no information on the migration experience. For this, we have to turn to the

SOF, a rider of the October LFS dedicated to gather information about migrant households. In

Table 3 we present summary statistics of key migration variables, by time period, and by gender of

the migrating parent. We also include variables available in the Census to show that both samples

present a very similar picture. The average migrant has been away for close to 30 months and

have sent 50 000 pesos in remittances within a six month period, approximately the per capita

annual income of the Philippines. As observed in the table, migration experiences of mothers and

fathers di¤er in several respects: fathers have left more times, they have stayed abroad longer, they

are more likely to send remittances and, conditional on doing so, send close to 70 percent more

money than mothers. Note that fathers sending more remittances cannot be explained by mothers

preferring to bring money home when they come back.

Table 3 also shows that a very large share of migrants do not end up working on skilled occupations

at their destination country, despite having high education levels. Close to three out of four mothers

work as domestic workers, suggesting that women�s lower remittances can be explained, at least

partially, by receiving lower wages than men, who generally work on more pro�table occupations.

Another important migration dataset comes from the Philippine Overseas Employment Adminis-

tration (POEA), who provided us with information on all land based new hires from 1992 and

2009. Although the data is very incomplete for the earlier years, it gives a very clear picture of the

patterns of migration of Filipino women. Given that the data doesn�t include information on sea

based hiring, it is less useful for the study of male migration. Table 4 summarizes the data. A few

patterns emerge from the data. First, Filipinas specialize in a handful of occupations: household

workers, building caretakers, artists, nurses and machine operators. Second, occupations are tied to

speci�c countries; for example, Japan only admits Filipinas migrating as artists and Hong Kong as

domestic workers. As will be studied in a later section, occupation and country of destination de-

9

termine wages to a large degree. Migration opportunities of Filipinos depend mostly on government

actions (bilateral agreements with other countries), in contrast, for example, with the migration

phenomena of countries like Mexico. This characteristic of migration in the Philippines will be key

for the validity of our empirical strategy.

5 Empirical Analysis

We are limited by the Census and the LFS questions for the choice of our dependent variable. Our

main outcome is a dummy variable that takes a value of one if the child has dropped out of school

or is enrolled in a level below the expected for his/her age.

Our main empirical speci�cation is the following:

Laggingijt = �+ �MigrantMotherijt + Xijt + �Wjt + �t + �p + @lt + �Pt + "ijt (3)

where i is for individual, j for household, t for year, and p for province. Xijt are child speci�c

characteristics, including cohort �xed e¤ects, dummy for gender, and whether the child is the

oldest or youngest of the siblings; Wjt are household variables: age and education of the parents,

number of siblings, age of youngest sibling, age of oldest sibling and whether either parent is

head of household. �t and �p represent year and province �xed e¤ects respectively, @lt island

times year �xed e¤ects and Pt time-varying province characteristics, speci�cally, average household

expenditures, share of married woman that participate in the labor market, and share of women

with some college or more. We restrict the sample to children aged 8 to 17, who are either the

o¤spring of the head, or her grandchildren.8

OLS estimation of (3) is expected to be biased; the decision of whether the mother or the father

should migrate is likely correlated with unobserved determinants of children�s school performance,

"ijt: To address the omitted variable bias problem we propose using instrumental variables estima-

tion, which we discussed next.

5.1 Demand Shocks as Instruments

Our empirical strategy is to exploit exogenous variation in the relative returns of mother migrating

to father migrating ( dRdM (mother migrates) vs.

dRdM (father migrates) in our model) generated by

shocks to the demand for Filipino migrants by destination countries. Philippines�migration �ows8The categories of the variable relationship to head only allow us to match children to their parents if a parent is

the head or son or daughter of the head.

10

are highly channelized between local areas and foreign destinations, a phenomenon mostly explained

by the importance of social networks (SMC, 2006). In a survey conducted in 2004, 67 percent of

people that were preparing to migrate for the �rst time reported knowing a friend or relative at

their country of destination (SMG, 2005).

Using administrative data on the province of origin and country of destination of all legal female

migrants who left the country between 2004 and 2007, we construct country of destination dis-

tribution for male and female migrants for each of the 81 provinces in the Philippines. Although

the instruments are de�ned at the province level, for illustrative purposes we show in Table 5 the

distributions constructed at the regional level. It is clear from the table, that di¤erent regions of

the Philippines send female migrants to di¤erent parts of the world. Out of the 16 regions, United

Arab Emirates is the top destination for 4, Hong Kong for 3, Saudi Arabia for 5, Taiwan and

Kuwait for 2 each. And even within regions for which the top country of destination is the same,

there is signi�cant variation in the shares. A very di¤erent pattern is observed for men, where the

primary destination of land based migrants for all migrants is Saudi Arabia.

The variation in the composition of the top countries of destination across provinces suggests that

shocks to foreign countries, such as economic crisis, changes in immigration laws, and even epidemics

such as SARS in Hong Kong should a¤ect the propensity to migrate di¤erently by local area. We

use several instruments to implement this idea. First, we use top destination country dummies

interacted with 5-year period �xed e¤ects to model the net e¤ect of all types of shocks that a¤ect

the demand for foreign workers. Although using dummies has the advantage of capturing all types

of shocks on migration demand, they are also likely to proxy for unobserved shocks to provinces

(as we cannot include province X year FE) that might a¤ect educational outcomes of children. To

partially address this concern, we control for shocks that vary by year and by island, a geographic

aggregation larger than the province. Additionally, we include time-varying variables de�ned at

the level of the province. In particular, we control for economic growth (measured by the log of the

average household monthly expenditures), for changes in the share of married women participating

in the labor force and in the share of women with at least some college education.9 The two last

variables are chosen to proxy for women liberalization in the area, which might a¤ect both the

propensity of women to migrate and the educational outcomes of children.

Given that there is signi�cant variation in the share of all female migrants of a province that

go to the top destination, we also use the share of female migrants that go to each of the top �ve

destinations (Saudia Arabia, Hong Kong, Taiwan, UAE and Kuwait) interacted with year dummies

as instruments for the mother migrating. Doing so takes into account that a demand shock from

Saudi Arabia, for example, will a¤ect much more the migration opportunities of women in the

9We use the FIES survey for the years 1991, 1994 and 2000, and Census data for 1990, 1995, 2000 and 2007 to

construct these series.

11

province of Lanao del Sur, where close to 78% of migrant women migrate to that country, than the

opportunities of women from Sorsogon, where only 18% migrate to Saudi Arabia, even though it

is the top destination. We again include all the controls described in the paragraph above.

Finally, we try to identify more speci�c shocks to destination countries. To capture economic

shocks, we use the average salary of migrants working in the top destination and the expected

salary constructed using the distribution of destination countries in the province of origin.10 For

wages to be valid instruments they should be determined mostly by demand, and therefore not

likely to be correlated with unobserved shocks to the outcome. In the present context, this is

not an unreasonable assumption. In a later section we show that wages are mostly determined

by occupation abroad and country of destination and are unlikely to be in�uenced by changes in

supply by individual provinces in the Philippines. See Cortes and Pan (2011) for a discussion of

migration of domestic workers and wage setting in Hong Kong.

We also exploit a change in migration laws that negatively a¤ected the probability that women

migrated as domestic workers, especially to Saudi Arabia. In 1994, the Filipino Government im-

plemented a minimum age requirement of 25 years of age to women migrating as domestic workers.

The policy was a reaction to several cases of abuse reported by women working in Saudi Arabia.

Household workers migrating to some countries, in particular Hong Kong, were exempted because

employment terms provided adequate protective mechanisms against abuse. A sign that the policy

was established primarily for women migrating to Saudi Arabia was an increase in 1997 in the

minimum age requirement to 30 exclusively for women migrating there. A new policy was insti-

tuted at the end of 1998, which lowered the minimum age requirements for all countries to 21. To

capture the e¤ect of this shock, we use two instruments : (1) the interaction of a 1995 dummy with

a dummy equal to one if the province�s top country of destination is Saudi Arabia, (2) the share of

female migrants going to Saudi Arabia times a 1995 dummy.

Table 6 presents the �rst stages. For the sets of dummies used as instruments, we present the

F-test of the joint signi�cance of the instruments (p-value in parenthesis, standard errors clustered

at the province level). As observed, both sets of dummy instruments are good predictors of the

likelihood that the mother instead of the father migrates. Average salaries in top destinations are

also very statistically signi�cant and suggest that a one hundred dollar increase in the weighted

average monthly salary of a female migrant increases by about 10 percent the probability that the

mother migrates. The age requirement policy lowered signi�cantly the probability that the mother

migrated in provinces with a large share of migrants going to Saudi Arabia.

Given that we have more than one instrument we can perform overidenti�cation tests. Table A1 of

the Appendix shows the Hansen J statistic and corresponding p-value for a few combinations of the

10Because our POEA data expands from 1992-2007, we have to drop the census year 1990 from our analysis when

we use the wage instrument.

12

instruments. Results suggest that instruments that capture speci�c shocks - expected wages and

the imposition of the age requirement- are more likely to be uncorrelated with the error term; for

example, when we use as instruments the expected wage and the interaction of the 1995 dummy

with the share of female migrants going to Saudi Arabia, the p-value is larger than 0.5. We do

reject the null when we use the large set of dummies as instruments. However, as we will discuss

in the next section, the estimates of the e¤ects are very similar across all IV speci�cations.

5.2 Results using Census Data

Table 7 presents the OLS and IV estimation of (3). Standard errors are clustered at the province

level to address concerns of within province correlation of outcomes and serial correlation. The

�rst columns introduce step by step the variables included in the model. As observed, regardless of

the controls included in the regression, the OLS results are very stable and statistically signi�cant.

The magnitudes of the coe¢ cients suggest that children of migrant mothers are between 3 and 5

percentage points more likely to be lagging behind in school than children of migrant fathers. IV

e¤ects are also all positive and signi�cantly signi�cant. Implied IV e¤ects are larger than those

predicted by OLS suggesting positive selection: families that decided to send the mother abroad are

those characterized by closer family ties, a more involved father, or a better support system. The

magnitude of the IV estimates is large, having your mother migrate increases by about a standard

deviation the probability that you are lagging behind in school.

5.3 Results using SOF

The negative e¤ects of the mother migrating on her children�s educational outcomes might come

from two very di¤erent channels. One, suggested by the descriptive statistics of Table 3, is that

when the father is the one who migrates he is both more likely to send remittances, and conditional

on sending, remittances are larger. The second is that, even if mothers and fathers send the same

remittances, the mother�s absence results in much lower parental time inputs than when the father

is the one who leaves. To discern between these two very di¤erent channels, in this section we use

data from the Survey of Overseas Filipinos, which allows us to control for the remittance behavior

and income of parents abroad. The econometric speci�cation is very similar to (3); however, data

restrictions force us to change a few things; in particular, given the coding of the school attainment

variable we are only able to construct the variable lagging behind in school for children aged 11,

12, 13, 14 and 17.

Before introducing the migration variables, we present the estimation of the same speci�cation

used in the previous section to check that our conclusions from Census data are robust to using a

di¤erent sample. As observed in Table 8, the OLS coe¢ cient of having a migrant mother is 0.10,

13

larger than when the Census is used, but similarly positive and statistically signi�cant. The IV

results, although very imprecisely estimated (the �rst stage is not as strong, see Table A2), are

also positive, and, as in Table 7, larger than the OLS. We �nd reassuring that our results are very

similar when two completely separate samples are used.

Now we turn to adding migration controls (Table 9). The variables we use are: a dummy for

having sent remittances in the last six months, value of remittances, log (remittances) conditional

on having sent remittances, number of months abroad and a dummy for having brought cash when

traveling home. We focus mostly on the OLS results, but present the IV results in the Appendix

(Table A3). Two main observations emerge from the exercise. First, the magnitude and signi�cance

of dummy for mother migrating changes very little when remittances controls are added, suggesting

that the gap in the school performance between children of migrant mothers and migrant fathers

cannot be explained away by di¤erences in remittance behavior. However, this doesn�t mean that

remittances do not a¤ect school performance of children. As observed, the dummy for having sent

remittances is negative and statistically signi�cant: receiving remittances from your migrant parent

reduces the probability of lagging behind in school by about 3.5 percentage points. Amounts also

matter; increasing the value of the remittances by 10 percent reduces the probability of lagging

behind by close to 0.2 percent. Number of months abroad and a dummy for bringing cash home

are far from statistically signi�cant.

5.3.1 Salary Imputation

In this subsection, we explore further if the di¤erence in the probability of lagging behind between

children with migrant mothers and children with migrant fathers is due, at least partially, to

di¤erences in economic resources. We make use of the POEA data set, which as described in a

previous section, includes wage data, as well as occupation and country of destination. We �rst

use the POEA data to check if female migrants do earn less than their male counterparts. We run

wage regressions that include a gender dummy and other controls. Column 1 of Table 10 shows

the coe¢ cient of the female dummy of a simple wage regression which only includes year �xed

e¤ects. The female dummy coe¢ cient suggests that women earn close to 43% more than men! The

di¤erence disappears, however, when we include a dummy for female working in Japan. Recall

from Table 4 that female migrants to Japan work exclusively as dancers and most are single, and

thus will not be included in our educational outcomes regressions. The female coe¢ cient, once

we control for migrants to Japan, suggests that female migrants earn in average a statistically

signi�cant 0.4 percent less than their male counterparts. This number, though negative, is very

small compared to the di¤erences observed in the SOF data on remittances. A likely explanation is

that the POEA data only includes land based migrants. Sea based migrants represent about a third

of male OFWs and earn signi�cantly more than land based male migrants. In summary, wage data

14

supports the evidence that migrant mothers do earn less than migrant fathers, and consequently,

send signi�cantly less remittances than migrant fathers.

The second exercise we do with the POEA data is to construct a measure of the migrant�s income

and include it in the regressions as an alternative to remittances. We estimate the wage of the

migrant based on variables available in both datasets (namely gender, occupation, year and country

of destination) and give migrants in the SOF dataset an estimated wage. For this exercise to work,

wages have to be determined mostly by the characteristics described above. Columns (3) to (5) of

Table 10 present wage regressions including these controls and the corresponding R-squared. As

observed, more than 93 percent of the variation in wages of Filipino migrants can be explained by

variation in country of destination, occupation, and gender.

We use the estimates in Column (5) to impute wages to migrants in the SOF data. Table 11

presents the estimation of (3) including as a regressor the (log of ) imputed wage. Once again, the

migrant mother coe¢ cient remains basically unchanged when the imputed wage is added to the

regression suggesting that even if economic resources do matter for educational outcomes of children

(the wage coe¢ cient is negative and statistically signi�cant) they cannot explain why children with

migrant mothers are doing worse than children with migrant fathers.

6 Conclusion

In many developing countries, an increasing number of children are growing up with their mother

working abroad. This paper explores the potential consequences of the mother migrating on her

children�s wellbeing, in particular, on their school achievement.

Using children with migrant fathers as a control group, and demand shocks as an exogenous source

of variation in the probability that the mother migrates, we �nd that mother�s absence has a larger

negative e¤ect on parental time investments than father�s absence. This result is likely to generalize

to countries where gender roles are very persistent, like in the Philippines.

It is important to stress that the results of this paper do not necessarily imply that children of

migrant mothers would be better o¤ have their mother not migrated. Assuming mothers want the

best for their children and that they have accurate expectations of the magnitude of the potential

direct negative e¤ects of their absence, mothers migrate because they expect their children to enjoy

a net bene�t from their decision. By identifying that negative e¤ects of parental absence are larger

when the mother migrates than when the father migrates, this paper provides policy makers in

15

sending countries with better information about the consequences of their migration policies and

might also help provide better support services for the left behind.

References

[1] Adams Jr., R. H. (2005) �Remittances, Household Expenditure and Investment in Guatemala�.

Policy Research Working Paper No. 3532. Washington DC: World Bank.

[2] Bryant, J. (2005). "Children of International Migrants in Indonesia, Thailand and the Philip-

pines: A Review of Evidence and Policies". Innocenti Working Paper No. 2005-05

[3] Chand, Satish and Michael Clemens (2008) "Skilled Emigration and Skill Creation: A quasi-

experiment". Center for Global Development. Working Paper Number 152

[4] Corak, Miles (2001) "Death and Divorce: The Long-Term Consequences of Parental Loss on

Adolescents". Journal of Labor Economics Vol 19, No. 3: pp. 682-715.

[5] Cox Edwards, A. and M. Ureta. (2003). �International Migration Remittances, and Schooling:

Evidence from El Salvador.�Journal of Development Economics 72 (2): 429-461.

[6] Cortes P. and Jessica Pan (2011) "Outsourcing Household Production: Foreign Domestic

Helpers and Native Labor Supply in Hong Kong".

[7] Getler, Paul, David I. Levine, and Minnie Ames (2004). "Schooling and Parental Death". The

Review of Economics and Statistics, Vol. 86 No. 1: pp 211-225.

[8] Haveman, Robert and Barbara Wolfe (1995). "The Determinants of Children�s Attainments:

A Review of Methods and Findings," Journal of Economic Literature, vol. 33 no. 4.

[9] Lang, Kevin and Jay L. Zagorsky (2001) "Does Growing up with a Parent Absent Really

Hurt?" The Journal of Human Resources Vol. 36, No. 2: pp. 253-273.

[10] Lyle, David (2006) "Using Military Deployments and Job Assignments to Estimate the E¤ect of

Parental Absences and Household Relocations on Children�s Academic Achievement", Journal

of Labor Economics, vol. 24, no. 2.

[11] McKenzie, David and Hillel Rapoport (2006) "Can migration reduce educational attainment?

Evidence from Mexico" BREAD Working Paper No. 124.

[12] Parrenas, Rhacel (2005) Children of Global Migration: Transnational Families and Gendered

Woes, Stanford University Press.

[13] Scalabrini Migration Center (2003) Hearts Apart: Migration in the Eyes of Filipino Children.

16

[14] Silvey, Rachel (2004) "Transnational Domestication: State power and Indonesian Migrant

Women in Saudi Arabia". CLARA working paper, No. 17.

[15] Scalabrini Migration Center (2005) Preparing to Work Abroad: Filipino Migrants�Experiences

prior to Deployment.

[16] Yang, Dean (2008) �International Migration, Remittances, and Household Investment: Evi-

dence from Philippine Migrants�Exchange Rate Shocks.�The Economic Journal 118 (528):

591-630

[17] UNDP (2009) Overcoming barriers: Human mobility and development, Human Development

Report.

[18] Unicef (2007) Ecuador. Memorias, Seminario Familia, ninez y Migracion.

[19] Unicef (2008) The impact of parental deprivation on the development of children left behind

by moldovan migrants.

17

250,000

Figure 1. New Hires of Overseas Filipino Workers (OFWs) by Gender

200,000

150,000

100,000

50,000

0

,

0

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Females MalesSource: POEA 18

Table 1. Characteristics of Migrants vs. Non-migrants: Census Data

OFW Not OFW OFW Not OFW OFW Not OFW OFW Not OFW OFW Not OFW OFW Not OFW OFW Not OFW OFW Not OFW

Share of Sample 0.011 0.021 0.021 0.027 0.017 0.025 0.023 0.034

Age 30.90 31.80 31.55 32.08 31.79 32.53 33.14 33.13 34.99 31.81 34.75 32.15 35.20 32.59 36.33 33.01

Single 49.93 28.66 46.45 28.26 40.38 27.69 40.96 27.21 21.91 35.66 25.83 35.45 24.26 34.86 21.37 36.15Married 44.31 66.71 46.49 64.84 48.46 61.04 49.36 61.13 76.90 62.54 71.68 60.50 70.22 56.51 73.85 55.07Widowed 2.83 3.15 3.03 3.13 2.86 2.93 2.50 2.90 0.44 0.96 0.58 1.01 0.73 1.02 0.55 1.05Divorced/Separated 2.65 1.10 2.68 1.12 3.58 1.61 4.22 1.86 0.54 0.53 0.63 0.57 0.94 0.87 0.99 1.09Other (Live-in partner) 0.28 0.38 1.35 2.65 4.72 6.73 2.96 6.90 0.21 0.31 1.28 2.47 3.85 6.74 3.24 6.64

Head or Spouse 0.35 0.64 0.38 0.65 0.39 0.63 0.41 0.64 0.64 0.59 0.61 0.60 0.60 0.58 0.63 0.57

Dummy for Child 0-18 0.30 0.56 0.32 0.56 0.45 0.61 0.48 0.59 0.71 0.60 0.66 0.59 0.52 0.49 0.64 0.54Dummy for Child 0-2 0.04 0.25 0.05 0.23 0.12 0.24 0.10 0.22 0.24 0.29 0.22 0.27 0.15 0.20 0.19 0.21Dummy for Child 3-5 0.09 0.25 0.09 0.25 0.13 0.25 0.15 0.22 0.27 0.27 0.25 0.27 0.18 0.21 0.22 0.21

Primary school 0.13 0.44 0.13 0.37 0.17 0.33 0.08 0.26 0.11 0.44 0.09 0.39 0.13 0.37 0.06 0.31Some high school 0.08 0.12 0.08 0.13 0.11 0.15 0.05 0.12 0.06 0.13 0.05 0.13 0.08 0.16 0.03 0.13High school grad 0.25 0.17 0.29 0.21 0.23 0.20 0.28 0.29 0.23 0.19 0.22 0.22 0.17 0.20 0.19 0.27Some college 0.18 0.13 0.23 0.15 0.34 0.23 0.26 0.18 0.21 0.13 0.30 0.15 0.45 0.22 0.28 0.17College degree + 0.37 0.14 0.28 0.14 0.14 0.09 0.33 0.15 0.39 0.10 0.34 0.10 0.17 0.06 0.44 0.11

Ilocos 22.28 5.55 16.93 5.24 13.42 5.27 10.52 4.91 9.08 5.69 8.04 5.45 7.44 5.43 6.22 5.13Cayagan 7.32 3.67 7.22 3.54 7.19 3.46 8.09 3.33 1.91 3.91 1.82 3.8 2.56 3.71 2.52 3.64Central Luzon 13.07 10.55 12.16 10.63 12.72 10.94 13.51 11.29 18.1 10.55 16.34 10.65 15.45 10.92 17.98 11.16Southern Tagalog 16.12 13.64 16.28 15 15.71 15.86 16.16 16.76 20.25 13.81 22.04 14.9 21.23 15.66 24.16 16.15Bicol 2.38 5.76 2.62 5.74 3.55 5.42 2.64 5 1.97 5.91 1.97 5.86 3.29 5.65 2.55 5.23Western Visayas 6.77 8.72 7.55 8.13 8.18 7.73 8.09 7.17 6.73 8.87 7.57 8.28 9.06 7.87 7.86 7.56Central Visayas 2.4 7.39 2.62 7.36 4.58 7.36 3.47 7.18 3.85 7.29 4.43 7.26 6.56 7.26 5.89 7.15Eastern Visayas 1.14 3.34 1.17 2.67 1.8 4.14 1.49 3.85 1.22 3.51 1.04 2.79 2.31 4.32 1.82 4.11Western Mindanao 2.01 3.88 2.79 3.96 2.55 3.85 2.56 3.39 0.99 4.03 1.47 4.08 1.71 3.93 1.49 3.54Northern Mindanao 0.64 3.49 1.01 3.55 1.26 3.49 2.3 4.34 0.67 3.63 0.99 3.66 1.6 3.59 2.38 4.48Southern Mindanao 1.63 6.83 3.22 6.72 4.04 6.71 3.47 4.76 1.02 7.19 1.78 7.03 2.83 7 1.99 4.99Central Mindanao 0.81 2.85 1.52 2.99 2.11 2.89 4.89 3.89 0.52 2.97 0.87 3.13 1.37 2.97 1.53 4.21National Capital R. 18.58 16.2 17.88 17.12 14.34 15.58 14.18 15.66 31.33 14.51 28.05 15.41 19.09 14.35 19.57 14.12Cordillera A. R. 2.33 1.45 4.41 1.76 3.33 1.69 3.34 1.68 0.73 1.52 1.36 1.87 1.28 1.79 1.27 1.8AR. in Muslim Mindanao 2.17 3.95 1.87 2.91 4.34 3.13 4.34 4.52 1.25 3.73 1.5 3.05 3.16 2.95 1.83 4.36Caraga 0.35 2.72 0.76 2.66 0.9 2.48 0.96 2.26 0.37 2.89 0.75 2.78 1.05 2.58 0.95 2.39

No. Observations 154,747 13,713,890 325,140 15,336,382 374,043 17,785,167 577,539 20,589,745 13,615,973 232,210 15,365,655 390,121 419,675 18,003,447 738,952 20,885,455

Women and men ages 18-54Children's variables only available for women that can be potentially matched with children based on the relationship to head classification.

Men2000 2007199519951990 19902000 2007

Women

19

Table 2. Descriptive Statistics Children by Migration Status of Parents - Census Data 1990-2007

None Mother Father None Mother Father None Mother Father None Mother Father

Child: Relationship to Head (Child) 0.94 0.85 0.95 0.97 0.86 0.95 0.94 0.86 0.94 0.97 0.86 0.96

Lagging in School 0.35 0.14 0.10 0.34 0.21 0.14 0.29 0.17 0.12 0.31 0.15 0.10Age 12.18 12.14 12.06 12.25 12.40 12.20 12.19 12.44 12.22 12.30 12.29 12.16

Number of Siblings 4.26 3.48 3.69 4.15 3.37 3.49 4.05 3.29 3.37 4.02 3.14 3.27Age of oldest sibling 14.97 14.56 14.51 14.90 14.69 14.45 14.83 14.69 14.50 14.81 14.25 14.22Age of youngest siblings 6.38 8.27 7.19 6.49 8.55 7.34 6.73 8.79 7.67 7.04 8.82 7.89

Mother's Age 39.84 37.39 38.57 39.85 38.00 38.84 40.04 38.48 39.66 40.49 37.99 39.99Father's Age 42.98 40.55 40.88 42.78 40.99 41.13 42.78 40.95 41.82 43.28 40.93 42.22

Father's educationElementary 0.61 0.32 0.16 0.54 0.29 0.12 0.50 0.27 0.12 0.45 0.18 0.05Some HS 0.11 0.12 0.09 0.12 0.11 0.07 0.14 0.14 0.08 0.12 0.10 0.04HS Grad 0.13 0.25 0.27 0.17 0.29 0.25 0.16 0.27 0.19 0.23 0.34 0.22Some College 0.08 0.15 0.20 0.09 0.16 0.27 0.14 0.24 0.43 0.11 0.21 0.29College Plus 0.07 0.16 0.27 0.08 0.14 0.28 0.05 0.07 0.16 0.08 0.15 0.38

Mother's educationElementary 0.63 0.24 0.26 0.54 0.21 0.19 0.48 0.18 0.16 0.40 0.08 0.08Some HS 0.12 0.12 0.11 0.13 0.11 0.09 0.15 0.14 0.11 0.14 0.08 0.06HS Grad 0.11 0.26 0.23 0.16 0.31 0.26 0.16 0.28 0.21 0.25 0.36 0.29Some College 0.06 0.14 0.15 0.08 0.18 0.21 0.13 0.28 0.33 0.10 0.25 0.25College Plus 0.08 0.23 0.25 0.09 0.18 0.26 0.06 0.10 0.17 0.10 0.21 0.31

Ilocos 0.06 0.23 0.09 0.06 0.17 0.07 0.05 0.16 0.07 0.05 0.12 0.05Cayagan 0.04 0.09 0.02 0.04 0.08 0.01 0.04 0.10 0.02 0.03 0.09 0.02Central Luzon 0.10 0.15 0.22 0.10 0.13 0.18 0.10 0.13 0.18 0.10 0.14 0.20Southern Tagalog 0.14 0.18 0.21 0.15 0.18 0.24 0.15 0.16 0.23 0.16 0.17 0.27Bicol 0.07 0.03 0.02 0.07 0.03 0.02 0.07 0.04 0.03 0.07 0.03 0.03Western Visayas 0.10 0.05 0.04 0.09 0.06 0.06 0.09 0.07 0.08 0.08 0.07 0.07Central Visayas 0.08 0.02 0.03 0.08 0.02 0.05 0.08 0.03 0.06 0.07 0.03 0.06Eastern Visayas 0.04 0.01 0.01 0.03 0.01 0.01 0.05 0.02 0.02 0.05 0.01 0.02Western Mindanao 0.04 0.02 0.01 0.05 0.02 0.01 0.04 0.02 0.01 0.04 0.02 0.01Northern Mindanao 0.04 0.01 0.01 0.04 0.01 0.01 0.04 0.01 0.01 0.05 0.02 0.02Southern Mindanao 0.08 0.01 0.01 0.07 0.03 0.01 0.07 0.04 0.02 0.05 0.03 0.02Central Mindanao 0.03 0.01 0.00 0.03 0.01 0.01 0.03 0.02 0.01 0.04 0.05 0.01National Capital R. 0.10 0.17 0.31 0.11 0.17 0.29 0.10 0.11 0.19 0.11 0.14 0.20Cordillera A. R. 0.01 0.02 0.01 0.02 0.05 0.01 0.02 0.05 0.01 0.02 0.04 0.01AR. in Muslim Mindanao 0.04 0.01 0.01 0.03 0.01 0.01 0.04 0.02 0.02 0.06 0.04 0.02Caraga 0.03 0.00 0.00 0.03 0.01 0.01 0.03 0.01 0.01 0.03 0.01 0.01

N. Obsv. 13,158,717 81,452 209,674 13,150,918 177,022 291,516 15,844,798 157,150 278,721 16,300,100 266,307 497,314

1990 1995 2000 2007

20

Table 3. SOF Descriptive Statistics 1993-2002

93-96 97-99 00-02 93-96 97-99 00-02

Share of sample 0.349 0.382 0.340

Family - children caractertistics

Male 0.53 0.55 0.50 0.52 0.52 0.53Lags in school 0.26 0.27 0.24 0.13 0.17 0.13Number Siblings 3.60 3.67 3.09 3.68 3.70 3.20Age youngest sibling 8.74 8.58 8.89 6.92 7.09 8.01Age oldest sibling 17.95 20.31 14.76 15.94 17.53 14.83

Father - Some College 0.16 0.19 0.18 0.26 0.33 0.30Father - College Plus 0.11 0.12 0.15 0.22 0.21 0.33

Mother - Some College 0.23 0.23 0.21 0.21 0.23 0.25Mother - College Plus 0.16 0.18 0.20 0.23 0.25 0.29

Migration Characteristics

Number of Months Aways 22.23 29.88 29.13 21.29 33.97 35.81Intended Length of stay 28.78 28.60 28.91 23.04 23.12 21.62Has returned? 0.22 0.20 0.20 0.31 0.21 0.25Has send remittances? 0.72 0.73 0.73 0.74 0.85 0.80Value of Remittance | >0 21221 32528 42020 37574 57104 78327Bring cash home? 0.09 0.18 0.13 0.16 0.23 0.21Log of Imputed Salary 5.92 6.02 6.05 6.26 6.37 6.36Imputed Salary POEA Data ($ month) 458 510 510 650 751 733

Hhld Helpers 78 Hhld Helpers 79 Hhld Helpers 80.02Prof. Nurses 3.02 Prof. Nurses 4 Prof. Nurses 4.42

Performing Arts 1.68 Production workers 1.09 Sewers, etc 2.36

Ship related occup. 18 Ship related occup. 19 Ship rel occup. 20.5Carpenters 9.44 Vehicle Drivers 6.01 Construction 7.85

Motor Vehicle Drivers 7.43 Eletricians 5.38 Eletricians 6.55

HK 28.55 HK 27.57 HK 33.57Saudi Arabia 23.12 Taiwan 14.98 Saudi Arabia 15.16

Singapore 11.72 Saudi Arabia 14.77 Taiwan 9.33

Saudi Arabia 51.49 Saudi Arabia 43.52 Saudi Arabia 42.9Japan 7.2 Japan 7.73 USA 7.95USA 6.58 USA 7.33 Japan 6.87

Number of Obs: 4849

00-0297-99

97-99 00-02

Top 3 Occupation for Migrant Mothers

Top 3 Occupation for Migrant Fathers

Top 3 Destination for Migrant Mothers

Top 3 Destination for Migrant Fathers

Migrant Mother Migrant Father

93-96

93-96

93-96

93-96

97-99 00-02

97-99 00-02

21

Table 4. Main Destinations of Female OFW by Occupation 1992-2009

Occupation: Artist

Share of Female OFWs 0.39 0.23 0.10 0.04 0.04

Destination Share Destination Share Destination Share Destination Share Destination ShareHong Kong 33.3 Japan 98.7 Taiwan 62.5 Saudi Arabia 63.1 Taiwan 99.33Saudi Arabia 16.4 South Korea 0.9 Saudi Arabia 13.1 UK 10.5 UAE 0.21Kuwait 12.5 Hong Kong 0.2 Canada 10.0 USA 8.7 Malaysia 0.18UAE 10.2 Israel 9.8 UAE 3.4 Jordan 0.11Qatar 4.9 Total 723,707 UAE 3.1 Singapore 2.9 Saudi Arabia 0.04Malaysia 3.7 Qatar 0.5 Kuwait 2.3 Bahrain 0.03Lebanon 3.6 Barhain 0.3 Irealnd 2.3Taiwan 3.3 Lybia 2.1 Total 120,416Singapore 3.0 Total 303,015 Qatar 1.2Bahrain 1.4 Canada 0.6Oman 1.3Italy 1.3 Total 139,326

Total 1,212,601

Mean age 31.69 Mean age 26.08 Mean age 33.29 Mean age 31.47 Mean age 27.06Share single 0.58 Share single 0.98 Share single 0.58 Share single 0.59 Share single 0.90

Source: POEA

Domestic Worker Professional Nurse Machine OperatorBuilding Caretakers

22

Table 5. Migrants' Top Destinations, by Region and Gender- 1992-2009

RegionTop 1 Share Top 2 Share Top 3 Share Top 1 Share Top 2 Share Top 3 Share

1 HK 0.32 TAIWAN 0.16 UAE 0.13 SA 0.43 TAIWAN 0.16 QATAR 0.142 HK 0.27 UAE 0.22 TAIWAN 0.11 SA 0.47 TAIWAN 0.18 QATAR 0.123 TAIWAN 0.20 SA 0.19 UAE 0.16 SA 0.46 QATAR 0.15 UAE 0.114 SA 0.21 TAIWAN 0.18 UAE 0.17 SA 0.41 QATAR 0.19 UAE 0.125 UAE 0.22 SA 0.20 HK 0.14 SA 0.48 QATAR 0.15 UAE 0.126 UAE 0.21 HK 0.19 SA 0.15 SA 0.52 QATAR 0.12 UAE 0.117 TAIWAN 0.21 UAE 0.16 KUWAIT 0.15 SA 0.40 QATAR 0.27 UAE 0.108 UAE 0.23 SA 0.21 HK 0.14 SA 0.50 QATAR 0.17 UAE 0.119 SA 0.32 UAE 0.24 KUWAIT 0.21 SA 0.62 QATAR 0.11 UAE 0.1010 UAE 0.22 KUWAIT 0.20 SA 0.15 SA 0.54 QATAR 0.22 UAE 0.0811 KUWAIT 0.23 SA 0.23 UAE 0.21 SA 0.56 UAE 0.10 QATAR 0.1012 SA 0.29 UAE 0.22 KUWAIT 0.19 SA 0.60 QATAR 0.15 UAE 0.0713 SA 0.29 UAE 0.19 TAIWAN 0.12 SA 0.45 QATAR 0.14 UAE 0.1414 HK 0.36 TAIWAN 0.13 UAE 0.12 SA 0.39 TAIWAN 0.13 QATAR 0.1015 SA 0.54 UAE 0.18 KUWAIT 0.14 SA 0.85 QATAR 0.06 UAE 0.0516 KUWAIT 0.21 SA 0.21 UAE 0.19 SA 0.51 QATAR 0.17 UAE 0.12

Source: POEA data, based on landbased new hires.

Female Migrants Male Migrants

23

Table 6. First Stage - Sample: Children 8-17 with one migrant parent, Census Data

(1) (2) (3) (4) (5) (6)

Top country of destination FE * Year FE 3.280(F-statistic) (0.000)

Share to each top 5 destination* Year FE 5.010(F-statistic) (0.000)

Average salary to main country of destination 0.020(in hundreds) (0.010)

Weighted Average Salary, top 5 destinations 0.113(in hundreds) (0.044)

1995 dummy * top destination = SA dummy -0.03(0.010)

1995 dummy * share of female migrants to SA -0.20(0.057)

Province FE X X X X X X

Year FE X X X X X X

Island*year FE X X X X X X

Province time-varying controls X X X X X X

Child and HHld Controls X X X X X X(including Cohort dummies)

Number of observations 1,824,553 1,824,553 1,552,367 1,552,367 1,824,553 1,824,553

Controls:- Child's: gender dummy, cohort dummies, dummy for being oldest child, dummy for being youngest child- Household's: Mother's and father's education dummies, mother's age, father's age, dummy for head of hhld being one of the parents, number of siblings, age of youngest sibling, age of oldest sibling'- Province: log(avg hhld expenditures), share of married women working, share of women with college or more educationStd. errors clustered at the province level

Dep. Variable : Dummy for Mother OFW

24

Table 7. Census Results - OLS and IV

(1) (2) (3) (4) (5) (6) (7) (8) (9)OLS OLS OLS IV IV IV IV IV IV

Mother OFW 0.052 0.045 0.036 0.467 0.235 0.332 0.533 0.590 0.688(0.005) (0.002) (0.002) (0.107) (0.111) (0.173) (0.189) (0.150) (0.203)

Year FE X X X X X X X X X

Province FE X X X X X X X X

Island*year FE X X X X X X X X

Province time-varying controls X X X X X X X X

Child and HHld Controls X X X X X X X(including Cohort dummies)

Instrument Top country Share of migrants Avg. salary Weighted Avg Salary 1995 dummy X dummy 1995 dummy Xof dest. Dummy to each top 5 dest. top destination Top 5 destinations top country=SA share to SA

X year FE X year FE

No. of Observations 1,824,553 1,824,553 1,824,553 1,824,553 1,824,553 1552367 * 1552367* 1,824,553 1,824,553

Sample: Children ages 8-17 with one migrant parent.Controls:- Child's: gender dummy, cohort dummies, dummy for being oldest child, dummy for being youngest child- Household's: Mother's and father's education dummies, mother's age, father's age, dummy for head of hhld being one of the parents, number of siblings, age of youngest sibling, age of oldest sibling'- Province: log(avg hhld expenditures), share of married women working, share of women with college or more educationStd. errors clustered at the province level* Number of observations is reduced because there is no data to construct the instrument for 1990

Dependent Variable : Dummy for Lagging in School

25

Table 8. SOF (1993-2000) - OLS and IV

Census sample(1) (2) (3) (4) (5) (6) (7) (8)

OLS OLS OLS OLS IV IV IV IV

Mother OFW 0.039 0.099 0.106 0.094 0.204 0.142 0.648 0.336(0.002) (0.017) (0.014) (0.016) (0.175) (0.134) (0.664) (0.708)

Year FE X X X X X X X X

Province FE X X X X X X X

Island*year FE X X X X X X X

Province time-varying controls X X X X X X X

Child and HHld Controls X X X X X X X(including Cohort dummies)

Instrument Top country Share of migrants Avg. salary Weighted Avg Salaryof dest. Dummy to each top 5 dest. top destination Top 5 destinations

X year FE X year FE

Number of observations 884,731 4,849 4,849 4,849 4,036 4,036 4,036 4,036

Controls:- Child's: gender dummy, cohort dummies, dummy for being oldest child, dummy for being youngest child- Household's: Mother's and father's education dummies, mother's age, father's age, dummy for head of hhld being one of the parents, number of siblings, age of youngest sibling, age of oldest sibling'- Province: unemployment, labor force participation of married women, unemployment of married womenStd. errors clustered at the province level

Dependent Variable : Dummy for Lagging in School (children ages 11, 12, 13, 14 and 17)SOF Sample

26

Table 9. SOF (1993-2000) - OLS with migration controls

(1) (2) (3) (4) (5) (6)OLS OLS OLS OLS OLS OLS

Mother OFW 0.094 0.092 0.090 0.084 0.097 0.094(0.016) (0.016) (0.016) (0.016) (0.016) (0.016)

Dummy for remittances > 0 -0.033(0.014)

Value of Remittances -1.580E-07(2.23E-07)

Log(remittances) -0.016(0.010)

Number of Months abroad 0.0002(0.016)

Bring Cash home -0.006(0.017)

Year FE X X X X X X

Province FE X X X X X X

Island*year FE X X X X X X

Province time-varying controls X X X X X X

Child and HHld Controls X X X X X X(including Cohort dummies)

Number of observations 4,849 4,849 3,754 3,754 4,716 4,849

Controls:- Child's: gender dummy, cohort dummies, dummy for being oldest child, dummy for being youngest child- Household's: Mother's and father's education dummies, mother's age, father's age, dummy for head of hhld being one of the parents, number of siblings, age of youngest sibling, age of oldest sibling'- Province: unemployment, labor force participation of married women, unemployment of married womenStd. errors clustered at the province level

(children ages 11, 12, 13, 14 and 17)Dependent Variable : Dummy for Lagging in School

27

Table 10. Explaining OFWs salary variation using POEA data (landbased, new hires)

(1) (2) (3) (4) (5)Female Dummy 0.431 -0.005 -0.030 0.016 -0.035

(0.001) (0.001) (0.001) (0.001) (0.001)

Year dummies X X X X X

FemaleXJapan dummy X X X X

Country of destination dummies X X

Occupation Dummies X X

R-Squared 0.16 0.79 0.86 0.88 0.93

Number of Observations 2,388,492 2,388,492 2,387,207 2,379,082 2,377,797

Robust standard errors in parenthesis

Dep. Variable: Log(monthly salary in US$)

28

Table 11. SOF (1993-2000) - OLS with imputed migrant wage

(1) (2) (3)OLS OLS IV*

Mother OFW 0.089 0.091 0.151(0.019) (0.019) (0.141)

Log(monthly wage) - Imputed -0.014 -0.014(0.005) (0.006)

Year FE X X X

Province FE X X X

Island*year FE X X X

Province time-varying controls X X X

Child and HHld Controls X X X(including Cohort dummies)

Number of observations 3,485 3,485 3,485

Controls:- Child's: gender dummy, cohort dummies, dummy for being oldest child, dummy for being youngest child- Household's: Mother's and father's education dummies, mother's age, father's age, dummy for head of hhld being one of the parents, number of siblings, age of youngest sibling, age of oldest sibling'- Province: unemployment, labor force participation of married women, unemployment of married women*Instrument: Share of migrants to each top 5 dest. X year FEStd. errors clustered at the province level

Dependent Variable : Dummy for Lagging in School (children ages 11, 12, 13, 14 and 17)

29

Table A1. Overidentification tests for instruments used in the estimation of Eq (3)

Instruments Hansen J statistic p-value

Set of top country of destinatio dummies X year FE 25 0.009

Share going to top destinations X year FE 36 0.001

Average salary top country of destination 3.250 0.0711995 dummy X top destination SA

Weighted Avg Salary Top 5 destinations 0.420 0.5171995 dummy X share to SA

Average salary top country of destination 3.20 0.07Weighted Avg Salary Top 5 destinations

1995 dummy X top destination SA 0.37 0.551995 dummy X share to SA

30

Table A2. First Stage - Sample: Children 8-17 with one migrant parent, SOF data

(1) (2) (3) (4)

Share of migrants to each top 5 destination* Year FE 3.290(F-statistic, p-value in parenthesis) (0.000)

Main country of destination FE * Year FE 2.890(F-statistic, p-value in parenthesis) (0.000)

Weighted Average Salary, top 5 destinations 0.134(in hundreds) (0.143)

Average salary to main country of destination 0.045(in hundreds) (0.032)

Province FE X X X X

Year FE X X X X

Island*year FE X X X X

Province time-varying controls X X X X

Child and HHld Controls X X X X(including Cohort dummies)

Number of observations 4,070 4,070 4,070 4,070

Controls:- Child's: gender dummy, cohort dummies, dummy for being oldest child, dummy for being youngest child- Household's: Mother's and father's education dummies, mother's age, father's age, dummy for head of hhld being one of the parents, number of siblings, age of youngest sibling, age of oldest sibling'- Province: unemployment, labor force participation of married women, unemployment of married womenStd. errors clustered at the province level.

Dep. Variable : Dummy for Mother OFW

31

Table A3. SOF (1993-2000) - IV with migration controlsInstrument: Share of migrants to each top 5 dest. X year FE

(1) (2) (3) (4) (5)

Mother OFW 0.140 0.083 0.012 0.111 0.126(0.135) (0.153) (0.162) (0.135) (0.133)

Dummy for remittances > 0 -0.033(0.018)

Value of Remittances -1.320E-07(4.32E-07)

Log(remittances) -0.029(0.029)

Number of Months abroad 2.758E-04(4.54E-04)

Bring Cash home 0.003(0.023)

Year FE X X X X X

Province FE X X X X X

Island*year FE X X X X X

Province time-varying controls X X X X X

Child and HHld Controls X X X X X(including Cohort dummies)

Number of observations 4,036 3,129 3,129 3,938 4,036

Controls:- Child's: gender dummy, cohort dummies, dummy for being oldest child, dummy for being youngest child- Household's: Mother's and father's education dummies, mother's age, father's age, dummy for head of hhld being one of the parents, number of siblings, age of youngest sibling, age of oldest sibling.- Province: unemployment, labor force participation of married women, unemployment of married womenStd. errors clustered at the province level.

Dependent Variable : Dummy for Lagging in School (children ages 11, 12, 13, 14 and 17)

32