Impacts of migration and migrant’s gender on children’s school enrollment and child work in Viet nam.pdf

1. i
UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES
HO CHIMINH CITY THE HAGUE
VIETNAM THE NETHERLANDS
VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
IMPACTS OF MIGRATION AND MIGRANT’S GENDER
ON CHILDREN’S SCHOOL ENROLLMENT AND CHILD
WORK IN VIET NAM
BY
VÕ THỊ THU HOÀI
MASTER OF ARTS IN DEVELOPMENT ECONOMICS
HO CHI MINH CITY, APRIL 2014

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UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES
HO CHIMINHCITY THE HAGUE
VIETNAM THE NETHERLANDS
VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
IMPACTS OF MIGRATION AND MIGRANT’S GENDER
ON CHILDREN’S SCHOOL ENROLLMENT AND CHILD
WORK IN VIET NAM
A thesis submitted in partial fulfilment of the requirements for the degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS
By
VÕ THỊ THU HOÀI
Academic Supervisor
Dr. TRAN TIEN KHAI

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CERTIFICATION
“I certify that the substance of this dissertation has not already been submitted for any
degree and is not currently submitted for any other degree
I certify that to the best of my knowledge and help received in preparing this dissertation
and all source used, have been acknowledged in this dissertation”
VO THI THU HOAI

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ACKNOWLEDGEMENT
This thesis would have not been fulfilled without special assistances from some
individuals, group, family who have contributed to my studying process.
Foremost, I would like to express my sincere gratitude to my supervisor Dr.
Tran Tien Khai who continuously support of my M.A thesis by his patience, enthusiasm
and immense knowledge. His guidance helped me all the time to research and writing the
thesis.
Secondly, I would like to thank Dr. Truong Dang Thuy because of his dedicated
support in research method during the time of this thesis. All of his help strongly to find
the solution and improves this paper.
Besides, my sincere thanks also go to Dr. Nguyen Trong Hoai, Dr. Pham Khanh Nam
who supervised and motivated the Class MDE 17 to finish the course on time.
It is grateful to thank my classmates of MDE17 MDE18, VNP staffs for
stimulating discussions, for the fun time we had together.
Last and not the least, I would like to express my grateful thank to my family
who are always beside me, give me the birth and support me throughout my life
April, 2014
VO THI THU HOAI
Email: hoai.vtt@vnp.edu.vn

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ABSTRACT
Accompany with development trend in over the world, migration flow plays important
role in Vietnamese economy. It contributes to the significant income and raises the living
standard for household in the country, specially, in rural areas. This paper tries to
measure the impact of migration on children’s schooling enrollment and child work,
addition to, determine how migrant’s gender matter in this impact. The context is applied
in rural areas in Vietnam with the dataset of VHLSS 2010 by Instrument variable method
to deal with the problem of endogeneity of migration. From the first stage of migrant
indicator, it indicates that instrument historical migration network and number of male
adults will impact on migrant indicator significantly and these instruments are the strong
instruments. The results show that the presence of migrant in the household will make the
children take part in school more, at the same time make children work less. Besides, not
like others researches, gender of migrant and time of migrant using in household don’t
have meaning with children’s welfare.
Keywords: migration, migrant’s gender, children’s school enrollment, child work

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TABLE OF CONTENT
ABSTRACT................................................................................................................................... v
CHAPTER 1: INTRODUCTION................................................................................................ 1
1.1. Problem statement.......................................................................................................... 1
1.2. Research Objectives ....................................................................................................... 3
1.3. Research question........................................................................................................... 3
1.4. Research scope................................................................................................................ 3
1.5. Structure of the research............................................................................................... 3
CHAPTER 2: LITERATURE REVIEW ................................................................................... 5
2.1. Definition of key concept ............................................................................................... 5
2.2. Theoretical literature ..................................................................................................... 5
2.3. Empirical literature...................................................................................................... 12
2.4. Conceptual framework ................................................................................................ 16
CHAPTER 3: METHODOLOGY ............................................................................................ 18
3.1. Endogeneity problem................................................................................................... 18
3.2. Endogeneity of migration ............................................................................................ 18
3.3. Estimated equation....................................................................................................... 20
3.3.1. Validity of Instrument variable:.......................................................................... 20
3.3.2. IVs methods........................................................................................................... 20
3.3.3. Estimated equation:.............................................................................................. 24
3.3.4. Method to run IVs regression .............................................................................. 26
3.4. Data................................................................................................................................ 28
3.4.1. Source of data........................................................................................................ 28
3.4.2. Variables description and measurement: ........................................................... 29
CHAPTER 4: OVERVIEW OF MIGRATION IN CASE OF VIETNAM........................... 36
4.1. Socio-economic setting and migration in Vietnam.................................................... 36
4.1.1. Migration aboard.................................................................................................. 36
4.1.2. Internal migration................................................................................................. 37
4.2. Characteristics of migrant........................................................................................... 39

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4.2.1. International migrant ........................................................................................... 39
4.2.2. Internal migrant.................................................................................................... 44
CHAPTER 5: EMPERICAL ANALYSIS................................................................................ 46
5.1. Descriptions of variables.............................................................................................. 46
5.2. Estimation results......................................................................................................... 51
5.3. Interpretation of results............................................................................................... 57
CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS........................................... 63
6.1. Conclusions ....................................................................................................................... 63
6.2. Recommendations:........................................................................................................... 64
6.3. Limitations........................................................................................................................ 65
REFERENCE.............................................................................................................................. 67
APPENDIX.................................................................................................................................. 70

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LIST OF FIGURES
Figure 2.1: Conceptual framework about impact of migration and migrant’s gender on
children’s welfare.................................................................................................................. 1717
Figure 3.1: Histogram of expenditure per capita a year .......................................................... 31
Figure 3.2: Histogram of natural logarithm of expenditure per capita a year ........................ 31
Figure 4.1: International migration trend from 2000 to 2010 (Department of oversea
database)................................................................................................................................... 40
Figure 4.2: Number of male and female international migrant from 2006-2010 (IOM).......... 41
Figure 4.3: Main destinations of international migrant from 2000-2010 (IOM)..................... 42
Figure 4.4: Structure of international migration labor of Viet Nam from 2006-2010 (IOM,
2011).......................................................................................................................................... 43

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LIST OF TABLES
Table 3.1: Description and measurement of variables ............................................................. 32
Table 5.1: T-test between household with non-migrant and household with migrant.............. 48
Table 5.2: T-test between household with male migrant and household with female migrant. 50
Table 5.3: Factors affecting migration indicator in the household (results of first stage
regression of Instrumental variables) ....................................................................................... 53
Table 5.4: Factors affecting children’s school enrollment and child work (results of second
stage regression of Instrumental variables).............................................................................. 56

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ABBREVIATIONS
BLUE: Best linear unbiased estimates
DID: Difference in difference
GSO: General statistic office
IOM: International Organization for Migration
IVs: Instrumental variables
OLS: Ordinary least square
PSM: Propensity score matching methods.
VHLSS: Vietnam Household Living Standard Survey
UN: United Nations
UNDP: United Nations Development Programme
US: United Stages

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CHAPTER 1: INTRODUCTION
1.1. Problem statement
According to analysis of International Organization for Migration (IOM), there were
more than 215 million external migrants (live outside of their countries because of many
reasons) and over 700 million internal migrants (within country) in 2009. The remittance
has increased gradually in recent years, besides that, remittance flow in developing
countries was estimated to have totaled $401 billion in 2012. Global remittance included
developed countries were estimated about $529 billion (World Bank, 2012). Remittance
plays an important in economic development by reducing poverty, leading to higher
living standard of human such as health, education, access technologies…improving
access to formal financial sector, better preparedness for disasters, …etc. Moreover, in
coming decades, under the pressure of demographic forces, globalization and climate
change, migration will increase sharply. It needs to have advanced data to research about
the impacts of migration and remittances on human life and economy. From that, the
policy maker can have the correct direction for migration sector in the future.
From economic renovation in 1986, Viet Nam had many remarks in development
process. Among them, shifting from centrally-planned economy to a socialist-oriented
market economy for industrialization, modernization was a long step in policies and
economic aspect. Beside the diversification and multilateral development of open-door
economic, more and more Vietnamese aboard to live and work. External migration
contributes to economy and raises the living standard of remaining people. In 2012, Viet
Nam was rated in seventh among the biggest receiving remittances countries ($ 10
billion) with about 4 million oversea Vietnamese. From 1991, it was accounted about 60-
70% remittances comparing to foreign investment. Remittance not only plays an
important role in Vietnam’s economy, but also affects to the demographic of Vietnamese
people. According to United Nations Development Programme (2009), estimated amount

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of money which migrant send back to their family reaches 5.5 billion dollars in 2007. In
parallel with that, internal migration from rural to urban has become a general upward
trend in Viet Nam a few last year. Not only male, the share of female migrants has
strongly dominated and played an important role in migrant worker (a half of internal
migration in 2009). Most migrants have close relationship with their origin families
through sending remittances and affecting demographic characteristics of members in the
household. It is so useful for examining the impacts of migration on children’s welfare. In
reality, education investment for children occupies a big part of expenditure amount in a
household. So, the matter is whether migration contributes to school enrollment of
children or not and how children in migrant household will differ with no migrant
household’s children. From that, need deeper research about whether gender of migrant
and time of migrant living in household can impact directly to school enrollment. Some
suggestions can be concluded in reality which need to be confirmed are: (1) Migrant help
children have more school enrollment; (2) if household has female migrant, the level of
school enrollment of children is better than household having male migrant. Two issues,
school enrollment and child work, of course, are interconnected closely. When migration
affects to children’s school enrollment, it will affect to child work also. Besides that, there
were many reports and papers such as Cuong (2008), Pfau and Long (2006) research
about migration and remittance before. Meanwhile impact of migration and migrant’s
gender on children’s school enrollment and child work in Vietnam has not been studied
and need more papers having the deep view about this aspect.
By applying data of Vietnam household living standard survey (VHLSS) 2010, this
paper is expected to use a suitable regression to investigate the impacts of migration and
migrant’s gender on children’s school enrollment and child work in Viet Nam for that
time clearly. After that, a main key factor of migrant which wasn’t recommended in the
similar previous research, that is time of migrant spending in household will be used to

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find out how migrant’s monitor effect children’s welfare. Finally, it hopes that the results
of research will be helpful for migration policy.
1.2. Research Objectives
The aiming objectives of this research are to meet the following objectives:
a) To examine the impacts of migration and migrant’s gender on children’s school
enrollment and child work
b) To make recommendations for government about the policies and programs
which tend to promote the effectiveness of migration in children development
as well as human capital investment.
1.3. Research question
The research will try to address some questions like that:
a) How do migration and migrant’s gender significantly affect children’s school
enrollment?
b) How do migration and migrant’s gender significantly affect child work?
1.4. Research scope
As we know, in rural areas most of the family face to the poor and not sure to
guarantee a complete life. Clearly, the impact level of migration mostly happens strongly
in rural areas where economic situation play a key important role in outcome of people.
Therefore, the research focuses on impact of migration and migrant’s gender on
children’s welfare in rural areas in 2010. It aims to rural-urban and rural-aboard migration
flow.
1.5. Structure of the research
This research will be classified into 6 main parts. The first part introduces overview
about the research, raising the problem and objectives as well as scope of the research.

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The second part is the literature is the solid basis theory and previous empirical
experience before relating to research. The third part shows methodology, estimated
equation in the research. It also talks about the data which used for methodology. The
next part is to know how social-economic characteristic of Vietnam impact on migration
and overview of migration case in Vietnam. The fifth part show the result of model after
running the regression, and explain the result appear in the model. The final part is
conclusion and recommendation, limitation need to take experience in the research.

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CHAPTER 2: LITERATURE REVIEW
2.1. Definition of key concept
Children: United Nations Convention on the Rights of the Child considers children as
a human being under 18 years old. Even in some definitions, child includes the fetus and
the unborn. It is important to note that in most paper, the author will define children by
their own purpose by disaggregating children group into specified group. For the purpose
of this research relating to school and labor supply of child so children which is
concerned in the thesis is limited schooling ages from 6 to 18 years old child. This
definition will be used throughout the paper.
Migration: General familiar definition of migration is the movement of human from
one place to another. According to International Organization for Migration, there is not
the universally definition exists. The term migrant can be applied for personal, family
moving to another place for a better life without intervention of external forces. Migration
can be permanent, if that person will never return to his/her origin places, or for a long
term he/she move to other places and live in that destination over at least 12 months. A
short term migrant is the person who moves to other place at least 3 months but less than
1 year. Under definitions, traveler in short term or business persons will not be considered
as migrant. However, migrant includes certain kinds of short-term migrant such as
seasonal worker, farm-worker. In the context of this paper, migrants are the people who
work in another place over 6 last months and it doesn’t count permanent migrants who
have permanent residence in other places different from origin.
2.2. Theoretical literature
2.2.1. Lewis model (1954)
In Lewis’s theory, traditional agriculture and industrial sector exists simultaneously.
Because of the disparity between two sectors, if evaluate the productivity and human

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resource of each sector, traditional agriculture which has surplus labor and low
productivity will move in to industrial sector which has high productivity and high
technology level. Lewis shows that 30% of income difference between two sectors will
make the labor move to the higher income. For a long time, the gap of income between
them is lower, the wage in agricultural sector will equal to the wage of industrial sector.
At that time the attractiveness to promote the movement will be disappear.
Although, theory of Lewis is considered as the basis model of development but it was
also criticized by other economists. Lewis thought that the relationship of reinvestment or
capital accumulation and number of jobs is positive. Clearly, when investing to the
technology, the labor force will be reduced. Moreover, Lewis shows the supply of labor in
agriculture is elastic and limitless. According to Todaro (2003), surplus of labor in rural is
limited and quite small. Thirdly, Lewis assumed that in the industrial sectors, wage will
be constant instead of increase over time. In addition, diminishing return of industrial
sector need to be argued, in while, in real the return of industrial sector also increase in
some specific fields.
2.2.2. The push pull theory of migration (1966)
Push pull theory (Lee, 1966) is neoclassical theory which explains why people move
for rural-rural, rural- urban, urban-urban and international migration. It emphasizes the
trends of people move from low to high income area or from densely to sparsely
population area or because of fluctuation of business cycle.
Decision to migrate and process of migration can be summarized by 4 main factors:
Characteristics of origin, characteristics of destination, intervening obstacles (cost, border,
etc…) and nature of people. Among them we separate it into two kind of factor, push
factors include demographic grow low living standards, people lack of conditions to have
a better life such as economic opportunities, political repression, infrastructure, access to

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clean water, sanitation, etc (in the origin area)...Pull factors include demand of labor, land,
economic opportunities, political freedom, good infrastructure in the destination and so on
(in the destination). A potential migrant will consider the balance of all the attractive and
interactive factors and attractive factors of destination along with difficulties of
intervening obstacle and nature of human to decide migration.
Some weak-points of theory could be given such as it couldn’t say why the reason to
move. It judged that economic growth is the main reason for people moving and unable to
predict the movement in the future. It also didn’t account the fact that some people have
less ability to have migrating decision, people also differ in their ability to act of
migrating decision although they want to migrate.
2.2.3. Todaro model (Todaro, 1969)
Corresponding to macroeconomic theory is microeconomic, Todaro model is
considered as a predecessor of theory for rural migration, Todaro model is the basic
theory not only for internal migration but also international migration because of valuable
cores. Todaro explained that the persistent of internal migration is to face the
unemployment in the destination places. The existence of underemployment and
unemployment of urban areas affects the probability of rural migration when they have
difficulties in finding a job over there. In contrast to other previous paper when
considering labor transfer process include one- stage, Todaro showed that this process
include two-stage. First stage is the movement of unskilled rural worker to urban areas
and spend time in one “urban traditional” sectors. The second-stage is the phenomenon
when there is the permanent movement in modern sector jobs.
Although Todaro only concerned about internal migration but income hypothesis is
also relevant with international migration. The interdependence of mixing effect from
industrial expansion, productivity growth, differential in earning capacity of urban versus

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rural activities leads to the migration and occupational distribution in urban places. When
evaluating the determinant of urban labor supply it needs to concern about the rural-
urban expected income. Differential income will be adjusted so unemployment rate will
be a problem with urban supply when rural worker migrate to find a job with expected
income. From that it said that raising income by creation employment in rural area
s will help reducing out-migration in rural areas. So the policy which government can
support is development programs make rural life more attractive. One more important
matter affect the decision making of migrant is the balance between the risks and the
probabilities of unemployed in one period with certain wage and the opportunities to have
a permanent job. It also depends on the behavioral and spirit of migrant who prefer which
fundamental role among them.
2.2.4. Chain migration theory
Chain migration is the relationship between the previous migrants and current
migrants through the movement of migrant from this place to other place follows previous
migrants. Previous migrants will establish the transportation, accommodation,
information…so make it convenient for migrant to migrate. Follows migrants path
therefore, knowledge of destination increase, obstacles decrease so attractiveness of
destination increase. Lee’s (1966) argued that migration facilitates information of
destination back to origin. Moreover, Böcker (1994) emphasized migration network
reduce the risks as well as material and psychological cost for next migrants.
Chain migration and the expectation of migration may increase illegal immigration or
migrant will find destination not as attractive as expected. On the other hand, this theory
doesn’t offer the insight into the mechanism which migration network can lead to weaken
migration system. It also didn’t indicate that counteract of external and internal processes
lead to increase migration.

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To evaluate the influence of any economic-social factors or program of the
government on standard living of individuals, household, communities, a familiar method
is impact evaluation. The core method of impact evaluation which Khandker et al (2010)
concerned in his research was to choose a good comparison group with treatment group.
Two groups have the similar characteristics but the difference is the intervention elements
of treatment group and comparison group. The below equation represents a direct effect
of T on the program Y in context subject i with or without intervention:
Y_i= * X_i+ * T_i +ε_i
Where T is the dummy variable with the value 1 if subject with intervention, 0 if
subject without intervention
X: the observed characteristics of subject and other characteristics affect subject i.
ε: Error term representing unobserved characteristics which affect Y.
The accuracy of impact evaluation depends on the reasonable assumptions as well as
exogenous factors. Khandker et al(2010) also represented some various economics
methods to address selection bias and calculating the effect of interventions.
2.2.4.1. Randomized evaluation method
Randomized evaluation identifies a group that has the same observed characteristics
and assigning randomly interference to evaluate the effectiveness of one project or
program. Randomized evaluation method is used to address an array of well-known bias.
It can resolve the selection problem that plagues treatment effect estimates. Researcher is
allowed to design behavioral parameters that are difficult to estimate by other methods.
The results of randomized evaluation are transparent and typically and highly credible to
policymaker. But randomized evaluation is quite rare because the way to collect data
costly including expensive and take much time. Moreover, it have little or no external

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validity, usually estimated impacts are local. Randomized evaluation also prone to biases
so misleading inference, and feasible in practice.
2.2.4.2. Propensity score matching methods (PSM)
Propensity score matching estimates an average treatment from observable data and
help to match treatment with similar control units observationally. So to compare the
intervention effects between treatment and control group which bases on the observed
characteristics. The key advantage of PSM is to use linear combination of covariates for a
single score, so it can balance treatment and control group on a large number of covariates
without losing a large number of observations. But PSM only counts observable
covariates only, all factors affect treatment but they can’t be observable, can’t be
mentioned in the method. Another issue is that PSM requires large sample so maybe
overlap between treatment and control group. From that, biases may be created after
matching because of missing unobservable variables and overlapping data. PSM has
another disadvantage when assuming no unobserved differences and this is often
implausible.
2.2.4.3. Difference- in-Differences or double difference methods (DID)
Difference-in-Difference examines the effect of intervention of program by taking out
the different between control and treatment group before and after intervening. DID is
easy to calculate standard errors by control variables which may reduce the residual
variance. DID eliminates fixed difference not related to treatment and can study treatment
with different treatment intensity. But DID method assume that the unobserved
heterogeneity does not change in time but clearly when the trend change, it will make
biased for the result. In case unknown characteristics make control and treatment group
react differently. DID will easily overestimate or underestimate the true effect.

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Furthermore, DID require data which have 2 pre-intervention periods of data. The lack of
data can be applied DID method.
2.2.4.4. Instrumental variables ( IVs) methods
Instrumental variables method proves its effectiveness and flexibility when allowing
the selection bias on unobserved characteristics vary over time. It can correct the bias
through found instruments which impact on participation status but no relationship with
unobserved characteristics. IVs can control endogenous in the model effectively, thus it is
usually used to address the problem of omitted variable bias, unobservable variable, errors
in variables or measurement errors of endogenous variable by permitting them remain in
the error term. But IVs can be deal with some problems when instrument correlate with
errors term, lead to inconsistence. More popularly, authors usually have to face with weak
instruments and many confounders which results in imprecise and biased result. Due to
the first-stage equation, that instrument shows the weak predicting ability. In the second-
state, predict the outcome exactly is limited. So the most important problem of IVs is how
to choose the good and suitable instrument variables
2.2.4.5. Regression discontinuity (RD) design and pipeline method
Regression discontinuity which is extent from IVs accounts for selection or
participation on observed and unobserved characteristics. Then, compare participant and
non-participant groups which base on the closed location around criteria. It can yield an
unbiased estimate of treatment at the discontinuity. RD also takes advantage when
assigning the benefit as criteria for design of social policy. But RD only looks at sub-
group of sample and assignment rule, in practice, often not implemented strictly. When
estimating at the discontinuity, fewer observations exist to ensure not bias in the results.
Pipeline comparison uses variation in the timing of program’s implementation to
establish eligible comparison groups who is suitable to join the program but not receiving

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the intervention. Pipeline comparison can combine RD if treatment is located on the basis
of exogenous characteristics.
In this research, I will use instrumental variable method because of endogeneity
appearing in model. IVs will solve the bias which is caused by endogenous variable by
finding the instrument variables which associate with participation but no relationship
with unobserved characteristics. IVs method allows us to count for observed and
unobserved heterogeneity as well as ensure the result which is not affected by time-
variant situation. The bias of result will be solved completely when comparing to other
methods.
2.3. Empirical literature
There has been not any quantitative research about the impact of migration and
gender matters on child’s welfare in Vietnam. But in the world, for years, many theories
and empirical studies which were relevant to this impact.
One paper published to quantify the impact of remittances upon school enrollment
was a study of Cox-Edwards and Ureta (2003) on the risk of school dropout. The authors
used cross-sectional data from Encuesta de Hogares de Propósitos Multiples 1997 and
estimated how remittances influence Salvadorian households’ educational. The authors
found out, via income effect, remittance relieve the budget constraints, it makes good
condition for the household afford and allows children spend more time in school. The
result is that remittances reduce significantly the dropout aged 6 to 24. To be consistent
with dropout decrease, authors expect the right thing is that remittances contribute
positively to school enrollment. However, one of concerns becoming the limitation of this
paper is that the authors didn’t address potential sample selectivity issues and endogeneity
of remittances.

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Indeed, with the same hypothesis, Hanson and Woodruff (2003) used Mexico’s 2000
Census data and recognized that there was the complex interaction between remittances
and migration. Remittances and school enrollment have the positive relationship. In
Mexico, in migrant family, children (from 10 to 15 year-olds) complete more grades in
school than others. But the authors also noted that negative labor shocks which affected to
parents would make the children have to spend more time to work instead of spending
their time in the school. It implied that there was the spurious negative relationship
between migration and children schooling enrollment. One concern is to overcome the
limitation some previous paper, the authors knew to control the endogeneity of
remittances by using IV with historical migration rates and household characteristics
instrument.
Acosta (2006) realized that remittances can affect child outcome and labor supply in
El Savador, the key outcomes for the growth of developing country. The paper proved
that the estimating of remittance was taken into account for selection and endogeneity
problem which different from estimates presented in previous studies. To solve relating
problem, Acosta (2006) used Propensity score matching and instrumental variable method
to run regression, instrument variable which was used in the paper is migration network
and household migration history (number of international migrants who returns two or
more one year ago) for remittance receipt. The result showed that remittance had positive
relationship with school enrollment but not for all range of age, only for age from 11-14
(boys) and 11-17 (girls) year old. Moreover, negative side of remittance is to increase of
child work for wage. However, concerning adult labor supply, the robust result showed
that female labor supply is lower in recipients of remittance but higher with male labor
supply.
Mansuri (2006) analyzed the relationship of economic migration undertaken by men
and it showed that children in migrant’s household attain more time in school and remain

24. 14
in school with more accumulating years comparing to non-migrant household. Mansuri
(2006) used two strategies to address the endogeneity, the first strategy is to use
instrument variables, the second strategy is to confine attention to migrant household and
to use information on the year of initial migration to exploit the fact which affect to
children school enrollment. With instrument variables, the paper found two instruments,
one is migration network with a level village and another is the number of male adult in
household with household level. Discrimination happened in Pakistan because girls in
migrant household headed by female were more likely to drop out of school than boys.
Moreover, both boys and girls in migrant household headed by female tend to work more
than migrant household headed by male. Education in Pakistan depended much on
income flows in the household and traditionally structure of household and social culture.
On differential impact of gender on wage income, expenditure and production of
migrant-sending household, Pfeiffer and Taylor (2008) used probit-method and control
potential endogeneity by using historical migration gender as instrumental variable. They
saw that impacts of migrant left behind for sending household according to the genders.
Male migration was more likely to engage production activities than female migration.
That was explained like the role of female migration in household production activities,
they only participate in a little of work in a subset of production activities. On the other
hand, female migration had the negative relationship with education, female migration
decrease the investment in education. However, male migration had negative impact. This
was interpreted that the lack of monitor of female migration on schooling investment or
the signal from international migrant about the low return of low skilled migrant.
By contrast, migration can affect school enrollment negatively, one typical example,
McKenzie and Rapoport (2007) recognized that migration and school enrollment of 16 to
18 year olds girls and 12 to 18 year olds boys in Mexico have significant negative
relationship. The argument of the authors is that migration affect school enrollment via

25. 15
three ways: the income effect brought by remittances, direct effect of adult migration that
requires more demand on child work, indirect effect of incentives to invest for education.
One specific thing of migration in Mexico is that children in migrant household likely to
self migrate to US via illegal ways. In this context, children in migrant household are
unlikely better than children in non migrant household. Once remittance became essential
for survival of population in Mexico, it generated the dependency of household to
remittances for household members who left behind. Change in consumption, reduce
labor supply, enjoy more time in relax and more need of remittance in the future. The
absent of adults in the household may increase the child’s responsibilities in the
household, greater demand for older children to run and support the family. It makes
difficult to attend school like other normal children.
To overcome some limitations in previous paper about El Savador, by using panel
data and controlling household fixed effect method, Acosta (2011) also did another
research and it found that female migration tend to reduce the child work in domestic and
non-domestic. But it was not true for male migrant whom seemed to stimulate the child
work in domestic labor. In addition, male migration seemed not to affect to school
enrollment of child while female migration had the inverse impact when reducing the
child school enrollment. The reason for the negative impact of female migration on school
enrollment maybe because of the absent of monitoring of female in managing funds or
difference in the use of remittances by gender of the recipient person or the child-adult
labor substitution.
According to Nguyen T, and Purnamasari R. (2011), the impact of international
migration on sending household was affected a lot by migrant’s gender in context of
Indonesia. They used Family Life survey from 1993 to 2007 and instrumental variable
method for regression the result. As the whole, household with only female migrant
reduced child work. Surprisingly, household with some male migrants household seemed

26. 16
to be insignificant with child work. Interestingly, either migrant’s gender seemed not to
be significant with school enrollment of child, the result can be explained that the absent
of parent impact negatively in return to education, specially, women absent involving
child care and monitoring children.
Emigration can decrease or increase the school enrollment of children, the final result
of impact will depend on the level to offset the effect of disruptions happened in migrant
household. The effect of migration on children school enrollment equals to total sum
effect of migration on children school enrollment via its impact on family income and
family structure. Example, migration can increase the school enrollment of children if
remittances can be used for investing in education and that household remain a female
household head to take care their children. But if migration increases the migration
decision of children in migrant household, it will lead to negative result with children
school enrollment.
This part of research builds up two important aspects relating to the migration and
migrant’s gender. Firstly, migration has the impacts on child work and children school
enrollment of sending household. To get the good results, it needs to be solved the
endogeneity problem by using instrument variable. Secondly, it determines that the
migrant’s gender combines with migration can create above significant impacts.
2.4. Conceptual framework
According to previous paper – Nguyen V.P (2011) which researched the way which
female and male uses the international remittance in Vietnam, Nguyen V.P realized that
shifting income from remittance affect intra-household decision making, gender of
remaining adults in the household are likely to spend in different way. Role of female
increases when migrating to earn money, they have the power and contribute their voice
in the household. But the absence of female adults create disconnects and loss of control

27. 17
with children’s monitor. So, impact of migrant’s gender on children’s school enrollment
and child work base much on total sum of all aspects. Through various results which are
concerned in the literature review, it is expected the impact of migration and gender of
migrant on children’s school enrollment and child work like below:
Figure 2.1: Conceptual framework about impact of migration and migrant’s gender on children’s welfare
The layout of conceptual framework advices that children’s school enrollment and
child work can be impacted by migration and interaction of migration and migrant’s
gender. Net impact of migrant’s gender becomes ambiguous matter, depend on above
concerning factors. With migration factor, migration doesn’t always show its
effectiveness when relaxing credit constraints, raising the living standard of all members
in the household. Nguyen T and Purnamasari (2011) didn’t see relationship between
migration and children’s welfare. The net impact of migration also bases on the level of
effect of each factor including remittance, the offset of presence of other family member
and the absence of migrant in the household.
Migration
School enrollment Child work
Migrant’s gender
School enrollment Child work

28. 18
CHAPTER 3: METHODOLOGY
3.1. Endogeneity problem
In statistical model, endogeneity appears if there is the correlation between
independent variable and error term. Some reasons can lead to endogeneity like
measurement error, omitted variable, auto-regression, auto-correlated or simultaneity in
simultaneous equations models. When model leaves out incorrectly one or some variables
which are significant in model, these omits correlate with dependent variables and
independent variables. Other factors exist in models can under-overestimating the models
instead of the correct variables. With measurement error, data can be reported error or
coded error. If measurement error is independent variable, assumption is not violated and
thus no endogeneity. In contrast, when measurement error is included in the independent
variable, the problem of endogeneity arises. In dynamic model, even simultaneity happens
if two variables are codetermined and affect to each other, endogeneity arises also.
Another reason like auto-regression which presents the output variable depends linearly
on its own previous value or auto-correlated which implies the cross-correlation of a
signal with itself. Consequence of this problem is to make regression coefficient of OLS
biased. Some popular methods are used to solve endogeneity problem like IVs and
Heckman selection correction.
3.2. Endogeneity of migration
After reviewing some mentioned literatures, it is realized that the problem we have to
face in estimating effect of migration on children school enrollment and child work is the
endogeneity. There are likely to have the correlation between explanatory variable and
error term by reasons of: reversed causality, omitted variables, self-selection leading to
bias. The relationship between children’s welfare and migration which may be happened
in two sides create reversed causality. Children’s welfare can influence migration and

29. 19
migration can influence children’s welfare reversely. In schooling outcome case, in the
household where it needs income to invest for education of children, so it demands family
members to make decision for migration with the purpose to fund children’s education. In
child work case, to avoid depending on income from child work, the household must to
migrate for living and get the better opportunity for financing their household’s economic.
Some variables which are so difficult to observe such as ability, connectedness or concern
of children’s education and child work result bias, they may lead to some overestimated
effects such as positive effect for schooling and negative effect for child work outcomes.
The last issue of endogeneity is self-selection. The migrant household can be selected
randomly and sorted into migration basing on unobservable characteristics.
The first rule when using instrumental variables method is to choose the best and
fitted instrument variable. Basing on the papers which concerned about endogeneity of
migration and remittance in literature review, most of authors were likely to choose
historical migration networks, household characteristics, distance… as instrument
variables. On the aspects of overview about the context of Vietnam, there is no data about
of distance from origin place to destination place of migrant. Historical migration network
in destination to support the remaining people have more information, relax the budget,
create more opportunities for migration easily. And one more factor can be considered
because it is so common with the reality condition: the number of male adult in the
household. Due to the immobilization and ability of female, they usually stay at home to
take care of children. If the household has more male adults, the higher of ability to
migrate will be. Clearly, two variables will affect directly to indicator of household
migrant but not correlate to children school enrollment or child work. So, in the first stage
of IVs method, household migrant indicator is considered as dependent variable. It
includes two instrument variables: historical migration network, number of male adults
and other independent variables in second stage.

30. 20
3.3. Estimated equation
3.3.1. Validity of Instrument variable:
In place of migrant household variable, Nguyen, T and Purnamasari, R (2011) as well
as many Acosta (2006), Pfeiffer and Taylor (2008) used migration network history as a
category of instrumental variable for endogeneity of migration. The assumption in this
instrumental method is that migration networks don’t affect children’s welfare but
through the ability to have migrants in household. Meanwhile, Mansuri (2006) used
number of male adults such as instrument of migration. The most important of impacts of
migration network history and number of male adults are effects of human and physical
capital intensity to induce more migration in the neighbor communities. Mostly,
migration network creates the social network in destination places and lower the cost of
migrating via the support and information. Number of male adults makes the household
have more convenient conditions to distribute the labor in the household, therefore
motivate migration. In order to check the validity of instrument variable, we test
endogeneity by Durbin Wu-Hausman test and then test the strength of instrument variable
by F-statistic.
3.3.2. IVs methods
The instrumental variables method is used widely in econometrics and is one among
the method to obtain the consistent parameter estimates.
a) Inconsistent in Ordinary Least Square (OLS)
Consider one scalar regression model with dependent variable y and independent
variable x. When obtaining a consistent in estimating of a will gives the endogenous
change in x. Dependent and regressor variables are measured via deviations from
regression model specifies:

31. 21
y=a*x+u (2)
where in u is error term. Regression of y on x yields OLS estimate a^ of a.
Standard regression results make the assumption that error term not correlated with
regressors. There is only effect of x on y and error term on y like the path below:
x y
u
no relationship between x and u.
But in some situations, there is the correlation between x and u, the standard to ensure
the consistent in OLS is violated. This phenomenon appears if there are some omitted
factors or some measurement errors, auto-regression… in the model. Error term affect x
and make the impact of x on y higher level. An appropriate diagram will be like this:
x y
u
x become have two effects on y. The goal to evaluate the effect of x on y via a, now
combine the effect of u also, giving a^ >a. Using calculus, we have y= a*x+ u(x) with
derivative:
dy/dx=a+ du/dx
From the giving information on dy/dx, the total effect which estimates on y is a+
du/dx rather than a alone. The OLS estimations become inconsistent and biased for a.
b) Instrumental variable

32. 22
One treatment method is via observational data to find instrument z which the
changes in property in z will not influence y but change the value of x. The diagram will
be like below:
z x y
u
There is the correlation between y and z but indirectly through changes of x, it means
such correlation is the indirect path of z which influence x at the same time in turn
determines of y. If the correlation of z and y is directly, it will be another case of
econometrics and z is not considered an instrumental variable. Summary, z is called as an
instrumental variable or instrument for the regressor x in model y= a*x+ u, if z correlated
with x but uncorrelated with error term u in the model. Two assumptions which have to
be ensured in the model to conclude that z is the instrumental variable. The first
assumption is z not to be a regressor of y, y is regressed by x. The errors from missing z
will be absorbed in the error term u. At that time, z correlated with the error term. The
second assumption is there is the association between instrument and variable which is
instrumented.
The causal estimated calculation is estimated the change of dy/dz and dx/dz as:
aIV=
𝑑𝑦/𝑑𝑧
𝑑𝑥/𝑑𝑧
The obvious way to estimate dy/dz is to use OLS of y on z with slope estimate (z’z)-
1
z’y. Similarly, to estimate dx/dz we use OLS of x on z with slope estimate (z’z)-1
z’x.
Detail like below:
. aIV=
(z’z)−1z’y
(z’z)−1z’x.
=(z’x)-1
z’y

33. 23
This is called 2SLS estimators. To find the 2SLS estimators, it is a good way to
implement two-step procedures. The idea of instrumental variable is to find a proxy for x
which not correlated with error term u. The first state will generate the proxy, the second
stage will substitute the value of proxy for x and the result will be regressed by OLS. The
proxy must meet the requirement that it belongs in the second stage and predict the value
of x but doesn’t belong in first stage. Other words, we have to find instrument z that
impact directly on x and not impact on y. Technical conditions on z as follows:
Corr(z,u)=0
Corr (z,x)#0
The first equation can be described:
x=a’z+ε (1)
then estimate a new set values of the variable x^,
x=x^+€
Now, x^ will replaced for x in the equation y=ax+u
Rewrite: y=ax^+a€+u
The new equation will produce the consistent estimates of all parameter including a.
After that, using simply technical correction to correct the standard errors, including
testing weak instrument, endogeneity testing.
Testing for endogeneity can obtain by get the residual ε^ from the first stage then put
ε^ in estimated equation (2), y=ax+δε^+u. If δ^ is significant, so x has endogeneity. To
test weak instrument, obtaining F-statistics on the estimators of the instrumental variables
we run the first stage, check the test: Ho: δ1=…=δm=0. If F-statistic is high, instruments

34. 24
are weak and 2SLS estimators will not be consistent or have large standard errors. Thus,
in the problem where OLS break down because of the correlation of right-hand-side
variables and disturbances, IV is the good choice to get the consistent estimates.
3.3.3. Estimated equation:
When estimating migration problem, it needs to be noted is endogeneity. To solve
endogeneity, we can use instrumental variable (is also called two-stage least squares
2SLS) approach. Historical migration network variable and number male adults in
household will be showed as instrumental variables to evaluate their impact on
migrationdirectly, and impact on the children’s welfare indirectly.
From the literature review, basing on the theories and the model which authors build
to evaluate the impact of migration on children’s welfare, model which appeared in the
paper of Nguyen T, and Purnamasari R. (2011) and Mansuri (2006), at the same time,
from the purpose of this paper to check about migration and characteristic of migrant can
affect to children’s welfare. The estimating equation is for each household i:
M_i=β1 + β2*X_i + β3*Migration_network + β4*No_male_adult + ε_i
(1- First stage)
Y_i=α1 + α2*X_i+ α3 *M^_i+ α4*female_i*M^_i + α5*t_migrant*M^_i + _i
(2- Second stage)
In the first stage:
M: indicator for having a migrant.
Migration network in 2008 will be applied as instrument variable for M in 2010 which
evaluated by the percentages of household in the village with migrants. Historical
migration network has the meaning to create a useful network. In destination, old

35. 25
migrants have the experience, information, opportunity to have new job. From their
relationship between them and the remaining people, they will transfer the tool,
knowledge and opportunity for migration in easier condition. The lager migration
network is, the more people migrate.
Number of male adult is another instrument variable for M in 2010 which is evaluated
by the total number of male adult who over 18 year olds. Male adults will affect
directly to the ability of migration in the household. The less male adult is, the less
opportunity to migrate. Meanwhile, male migrate to get more income for taking care
the household.
X_i: set of head household, household, village characteristics include log of per capita
expenditure and other variables mentioned in section IV.
ε_i : Error term
In the second stage:
Y_i: include children’s school enrollment and child work (children from 6 to 18 years
old who also go to school or work in the last 12 months according to the 2010
VHLSS.
X_i: set of household, head household, village characteristics mentioned in section
section IV.
t_migrant: Time of migrant living in the household in 1 year
Female_it: Dummy variable which show that households with only female migrant,
omitted category can be referred as households with at least some male migrants.
The paper put the interactive variables of gender of migrant and indicator of migrant,
interactive variables of time of migrant living in household and indicator of migrant in

36. 26
the model. The reason can be explained that gender of migrant and time of migrant
living in household will affect children school enrollment and child work via the
occurrence of migrant.
: un-observed components for household create the error
3.3.4. Method to run IVs regression
As the general estimated equation which concerned previous part, the detail
equation can be written like below:
First stage:
H_migrant=β1 + β2*HH_school + β3*HH_age + β4*HH_gender + β5*HH_work+
β6*ln_exp_pc + β7*Pro_females + β8*H_csize + β9*H_child + β10*Com_agri +
β11*Enterprise + β12*Big_road + β13*Electro + β14*Market + β15*Ele_school +
β16*Jun_school + β17*High_school +β18
*
Migra_net +β19*No_male_ad +ε_i
After running regression of first stage, H_migranthatregressor is predicted from
H_migrant. Then, we run second stage by putting H_migranthat and other relating
variables in second stage model.
Second stage:
Detail equation of children’s school enrollment can be written:
Enroll =β1 +β2*H_migranthat + β3*H_migranthat*female_it +
β4*H_migranthat*t_migrant+ β5*HH_school + β6*HH_age + β7*HH_gender +
β8*HH_work+ β9*ln_exp_pc + β10*Pro_females + β11*H_csize + β12*H_child +
β13*Com_agri + β14*Enterprise + β15*Big_road + β16*Electro + β17*Market +
β18*Ele_school + β19*Jun_school + β20*High_school + _i

37. 27
Detail equation of child work can be written:
C_labor =β1 + β2*H_migranthat + β3*H_migranthat*female_it +
β4*H_migranthat*t_migrant+ β5*HH_school + β6*HH_age + β7*HH_gender +
β8*HH_work+ β9*ln_exp_pc + β10*Pro_females + β11*H_csize + β12*H_child +
β13*Com_agri + β14*Enterprise + β15*Big_road + β16*Electro + β17*Market +
β18*Ele_school + β19*Jun_school + β20*High_school + _i
Estimation strategy includes there steps:
Step 1: Running the first stage model and then replacing the predicted value of
endogenous variable which calculated in first stage into second stage. In detail, run first
stage regression with dependent variable: indicator of migrant and independent variable:
household head attributes, household attributes, village attributes and two main key
instruments (historical migration network and number of male adults)
After checking the validity of endogeneity for migrant indicator and strength of key
instruments in first stage, using predicted migrant indicator to create two variables which
interacted between migrant indicator and migrant’s gender or time of migrant living in
home. After then, run second stage with dependent variables including children’s school
enrollment and child work.
Step 2: The paper will use diagnostic tests to check the appropriate model and avoid
the biased coefficient and standard error. Missing relevant variable or including irrelevant
variable in model can lead to errors in the model and wrong evaluation. Gujarati (2003)
concerned about the strategy to build model by two ways: General to simple can make the
coefficients less precise because of less variables. However, the model still gets unbiased
for all coefficients and hypothesis testing is still available. Against, simple to general
method can exclude the relevant variables out so it makes the biased, error in the model
and hypothesis testing become invalid. From that, most of the studies use general to

38. 28
simple method. In this paper, this method is superior to do diagnostic tests. Begin with the
least insignificant variables, we drop out one by one and re-estimate the model. To be
sure that the significance of remaining variables is improved, Wald test will be used to
check whether we should keep or drop out the insignificant variables in the model.
With cross-sectional data, one problem needs to be noticed is heteroskedasticity
when existing non-constant error variance. Heteroskedasticity violates OLS assumptions
because least square estimator becomes insignificant and the results can’t be reliable. We
use Breusch-Pagan to test heteroskedasticity and use heteroskedasticity-robust to adjust
the heteroskedasticity in the model.
Step 3: After running regression and use diagnostic tests, make the report of final
results and interpretation the results.
3.4. Data
3.4.1. Source of data
Mainly data used in this research is Vietnam household living standard surveys
(VHLSS) 2010 was conducted by General Statistics Office (GSO) with the technical
support from World Bank. The survey included the information relating to living standard
to make plan and policy for commune’s development in Viet Nam. The sample was
selected from 69.360 households in 3.133 communes/wards, including 22.365 households
for income, 37.596 households for income and other issues, 9.399 households for income,
expenditure and other issues, representative for rural and urban and regional levels.
Information was collected in 2 years: 2010 and 2011 through face to face interview. The
content of survey consisted of List of household member, Health, Income, Education,
Expenditure, Durable goods, Accommodation.
VHLSS 2010 is the last survey about wholly household living standard, contains
information of current migration as well as household characteristics. Information of

39. 29
income in VHLSS is quite weak because there is no information about the value of
housing and land ownership. The advantage of this survey is to have the clear information
for food and non-food items such as health, education, durables. So in this paper, to
replace the indicator for welfare we use per capital expenditure. In addition to, VHLSS
2010 is rich in education and labor variables to help us determine the impacts of
migration and migrant gender on children education indicators and child work indicators:
% of children 6-18 in the household who has gone to school, % of children 6-18 in the
household who has worked in the last 12 months respectively.
With detailed information of migrant gender in household, we can classify them into
2 groups: household with at least one male migrant, household with only female migrants.
3.4.2. Variables description and measurement:
Standard to choose right migrant bases on the last movement which migrant live in
rural areas and go far away from their households for working. Time to go far away their
households is over 6 last months. Non-migrants are defined as people lives in rural areas,
still live over there, and not move to other places for working in 6 last months.
The dependent variables are the percentage of children of one household who has
gone to school and has worked as labor force in last 12 months. The independent
variables include the social-economic factors which affect directly on the children’s
school attainment and child work. Among those variables, gender of migrant and
indicator for having migrant are the most important variables.
As above concerning about the non-economic factors which also affect to migration
beside economic factors, it is classified into 4 sets of variables depends on the purpose
using instrumental variable model. The first set is head household characteristics, the
second one is migrant’s characteristics alternatively, village characteristic are considered

40. 30
also. The last one includes the instrumental variables for indicator of migration historical
network migration and number of male adults in the household.
It is expected that in household characteristics will have positive impacts on children
if economy situation (Ln_pc_exp) can ensure the living standard, number of children
(h_child, h_csize) in that household is small. Perception of head household depends much
on age, gender, education (hh_age, hh_gender, hh_school, hh_work), such as young age,
female head, high education level, much time working of household will ensure the
children care better. Migrant’s characteristics are the key impacts which we want to
examine how the effect of these variables in this paper, including time of migrants living
in household (ti_migrant), gender of migrants (female_mi) and indicator for household
migrant (h_migrant). The thesis also considers more about village characteristic, it is
hoped that the presence of big road, electronic, market and health center ( Big_road,
Electro, Market, Health_center) as well as the education system elementary, junior or
high school (ele_school, jun_school, high_school) will make children’s welfare better.
The proportion people work in agriculture field (com_agri) brings the negative and the
number of enterprise (Enterprise) in the villages has the positive relationship with
children’s welfare hopefully. At the same time, the thesis checks whether migration
indicator is affected by the historical network migration (migra_net) and number of male
adults (no_male_ad). Historical network migration and number of male adults will have
positive relationship with migrant indicator as suggested.
One among the most important variables is per capita expenditure which measures the
welfare of the household for a long-term. The variable reflects the welfares is not income
because the shortage of income data in the household from the asset management system
as well as from the culture of Vietnamese, they don’t talk much with strange person about
the real income. However, as below histogram of distribution of per capita expenditure in
a year, it is not normal distribution and skews to the right.

41. 31
Figure 3.1: Histogram of expenditure per capita a year
Logarithm (natural) transformation of per capita expenditure helps to reduce the
skewness of the distribution of per capita expenditure significantly. Right now, the
logarithm (natural) per capita expenditure becomes normal distribution which is satisfied
with the OLS regression.
Figure 3.2: Histogram of natural logarithm of expenditure per capita a year
Relative to details of variables in the model, we have description, how to measure of each
variable and justification of their impact in the model as below table
0
.1
.2
.3
.4
Fraction
0 50000 100000 150000 200000 250000
exp_pc
0
.05
.1
.15
Fraction
7 8 9 10 11 12
ln_exp_pc

42. 32
Table 3.1: Description and measurement of variables
Variables Description Measurement Justification for their
inclusion in the model
Dependent variable (Y)
ENROLL Percentage of
children 6-18
who go to
school in the
household in last
12 months
Fraction of
children from 6-18
who go to school
among total
children from 6-18
in that household
- To determine the difference
of children school
enrollment. From that, find
out how to improve school
enrollment rate.
CLABOR Percentage of
children 6-18
years old in the
household that
work as labor
force in last 12
month
Fraction of
children from 6-18
years olds who
work as labor force
among total
children from 6-18
in that household
in the last 12
months
- To capture the variation of
child work under the effects
of migration.
- It is expected to be negative
relationship with migration.
Explanatory variables (X)
Household characteristic (X)
H_CSIZE Number of
children from 6-
18 years old in
the household
Number of people - In large families, it seems to
have the problem when
caring the children education
and labor outcome.
H_CHILD Number of
children under 6
years old in the
household
Number of people - It is to be expected that more
children under 5 years old in
household will decrease the
children school enrollment
and increase the child work
rate.
LN_ PC_EXP Logarithm
(natural) total
Natural logarithm
of expenditure for
- More total expenditure,
more school enrollment and

43. 33
Variables Description Measurement Justification for their
inclusion in the model
expenditure per
capita in year.
all non-food and
food items of one
member in the
household in 1 year
less child work.
PRO_FEMAL
ES
Proportion of
female adults in
the household
Number of female
adult over number
of adults in the
household
- To check the affects of role
of proportion of females to
children’s welfare. It is
expected that more female
adults will increase
children’s welfare.
Head household characteristics (X)
HH_SCHOOL Number of
school years
completed by
household head
Years - With well-educated
household head, it will bring
about more opportunities for
children ‘s welfare
HH_AGE Age of
household head
Calendar years - To understand the role of
household head age with the
children’s welfare
HH_GENDER Binary variable - Female household
head is coded 0
- Male household
head is coded 1
- Female head household
tends to encourage children
to have more school
enrollment and reduce child
work
HH_WORK Number of
average working
hours in a day of
head household
Number - Head household work hard
can ensure the good
economic situation and
children’s welfare
Migrant’s characteristics (X)
H_MIGRANT Binary variable - Migrant
household is
- Access to know the impact
of current migrant household

44. 34
Variables Description Measurement Justification for their
inclusion in the model
coded 1
- Non-migrant
household is
coded 0
on child outcomes.
TI_MIGRANT Number of
months which
migrants
spending in
household in last
1 year.
Number of months - Children, who live in
household with migrant who
spending more time to live
with household will have
more chances to be cared.
FEMALE_MI Binary variable Household with
only female
migrants is coded 1
Household with at
least some male
migrants is coded 0
- To know clearly difference
of how migrant’s gender
matter affect children’s
school enrollment and child
work
- It is hoped that male migrant
will affect positively
children’s school enrollment
and negatively child work.
Village attributes
COM_AGRI The proportion
of people work
in agriculture
field in that
commune
Number of people
work in agriculture
over number of
labor
- To know the role of
agriculture with children’s
welfare
ENTERPRISE The number of
enterprise in
the commune
Number - Check the role of other kinds
of economy beside
agriculture on children’s
welfare

45. 35
Variables Description Measurement Justification for their
inclusion in the model
BIG_ROAD
ELECTRO
MARKET
HEALTH_CEN
TER
Binary variable The presence of
each element in the
commune is coded
1. If against, no
element is coded 0
- Whether the better condition
to live in the commune will
make children’s welfare
better or not
ELE_SCHOOL
JUN_SCHOOL
HIGH_SCHOO
L
Binary variable The presence of
each element in the
commune is coded
1. If against, no
element is coded 0
- It is expected that the
presence of education
system will make children
go to school more and from
that reduce child work.
Indicator of Migration (M)- Instrumental variables
MIGRA_NET Percentage of
households in
the district with
migrants in 2008
Fraction of
households having
migrants among
households in that
district in 2008
- Evaluate the impact of
migration network on
migrant indicator
NO_MALE_A
D
Number of male
adults in the
household
Number of male
adults who are over
18 years old
- Evaluate the impact of
number of male adult in the
household on migrant
indicator.

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CHAPTER 4: OVERVIEW OF MIGRATION IN CASE OF VIETNAM
4.1. Socio-economic setting and migration in Vietnam
4.1.1. Migration aboard
In the context of globalization and integration over the world, the major trend is
international migration. According to research of IOM, there are about 215 million
international migrants which is equivalent to 3.3 percent of global population. Vietnam is
not the exceptional case, with development advances after Doi Moi (1986), Vietnam has
integrated and developed strongly in the global economy wave, migration has become the
major issue in Vietnam. Demand for labor, services, improving living standard, income
as well as the demand for social security make people migrate more and more. Migration
flow becomes diversified in scale and migration form. In order to support the migration
issue, Vietnam published more swimming policies, open-door policy and oriented to
industrialize and modernize the economy. Policy mechanism helps people have more
chances to migrate. Right now, migrants continue contributing the country economy and
human development of Viet Nam.
In developed countries or some countries where economy is better than Vietnam,
population is older, local people don’t want to do the “inferior” job, hard job, harmful
working, low income. So it needs big supply of services and labor from aboard. Disparity
between the poor and the rich among the countries and areas create the demand for
migration. The trend to work in fixed-term contracts becomes popular in Asia. Being a
developing country, Vietnam’s population is ranked in the top of the world with about
86,9 million people, 75% of population lives in rural, it needs more 1.71 million jobs a
year (MOLISA,2009). Situation of big amount of population and lack of finding jobs
make excessive in labor. In addition to, some reasons make labor unemployment such as
low specialization level, cheap salary…. Young population creates Vietnam’s advantage
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in labor supply but requirement of job supply and stable income are the big challenges for
Vietnam.
Over two decades, taking advantage and eliminating the difficulty, challenge,
specially, overcoming the negative impact of two economic global crisis, Vietnam got out
of under-developed country to become a developing country. Appearance of country
change, position and strength is confirmed solidly than in the past. Position of Vietnam is
higher in international market and makes the premises for pushing industrialization,
modernization to raise the living standard of Vietnamese. After 25 years old from Doi
Moi, with the process of open-door period and international integration, it has pushed the
demand and created the good environment for migration. After WTO participation,
demand for migration with many purposes has been diversified. Law of supply and
demand about labor, service, disparities of living standard and income between Vietnam
and other countries push the migration flow. The development of technology allow
migrant easy to contact to each other in order to make a network, at the same time the
development of international transportation make it easy for travelling with cheaper fee
and more convenient than before. The overview of migration aboard in 25 years shows
the diversified categories of migration aboard, increasing migration scale, reason to
migrate is more complicated, specially, with the participation of female and children.
4.1.2. Internal migration
Internal migration is not the new phenomenon in Vietnam society but essential part
of history. After unification, big movement from urban to rural area happened strongly.
Migration trend was due to the planning of government to bring the urban residents to
new areas for safe reason and they came back to their origin places after war also. From
1970, movement program which determined by government to take people to new
economic areas continued formulating the main migration flow in Vietnam. In 1990s,
some changes in organized migration policies of government didn’t focus on new
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economic areas migration but resettlement combines with environment improvement
programme.
Besides the government’s programme, from 1990, free migration began developing.
The movement was out controlled by the government and related directly to the policies
of “DoiMoi” in 1986. This was the starting point for the transition process from centrally
economy to market orientation economy. The reforms of “Doi Moi” mitigated the
conditions which constraint the citizens from movement. Detail, by canceling the system
of subsidiary which related to “ho khau” registering and began assigning land by the
piece. This policy didn’t put under the constraint with farm land, allowed farmer to be
more active after transferring or leasing land.
“Doi Moi” also pushed the economic development quickly and contributed to help
Vietnam hunger eradication and poverty reduction. Private enterprises developed strongly
with foreign investment, it attracted labor and encouraged people move to the areas which
supplies many jobs for labor. In parallel with this migration trend, less and less people
didn’t work in agriculture field and traditional production, so they chose to move out their
home places and found other jobs. Industrialization and economic development happened
in manufacturing sector and in industrial zone which were near or in the city, it made the
disparities between rural and urban bigger. The income for working in industrial sector
and manufacturing sector is six times comparing to income from agricultural sector (UN,
2003), so demand of people is to raise the income by working in different place.
Impact of market renovation and strong economic development constitute the
motivation for migration. Some sectors such as textile, servant sector attract mostly
female labor. Role’s change of female in agriculture relate to migration trend although
there are many discusses about that. Some arguments say that the attenuation of
agriculture sector, migration is one way for female to find a job because “farming job is a
few and only for male usually”. In contrast, others say that there is the feminization
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