Determinants of secondary school dropouts in vietnam panel data evidence

UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

INSTITUTE OF SOCIAL STUDIES
THE HAGUE
THE NETHERLANDS

VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

DETERMINANTS OF SECONDARY SCHOOL
DROPOUT IN VIETNAM:
A PANEL DATA EVIDENCE

BY

LE ANH KHANG

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

HO CHI MINH CITY, JULY 2012


UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

INSTITUTE OF SOCIAL STUDIES
THE HAGUE
THE NETHERLANDS

VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

DETERMINANTS OF SECONDARY SCHOOL
DROPOUT IN VIETNAM:
A PANEL DATA EVIDENCE
A thesis submitted in partial fulfilment of the requirements for the degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS

By

LE ANH KHANG

Academic Supervisor:
Dr. LE VAN CHON

HO CHI MINH CITY, JULY 2012


ACKNOWLEDGEMENT
Joining classes of quantitative research project with STATA & VHLSS2008,
hold by the Faculty of Development Economics of HCMC University of Economics
and the applied econometrics seminar by Prof. Dr. Ardeshir Sepehri from University
of Manitoba, Canada, have encouraged and yielded me confident to move this paper
ahead.
I would like to express my thanks to Mr. Phung Thanh Binh, Mr. Truong Thanh
Vu, Mr. Nguyen Khanh Duy, Ms. Ngo Hoang Thao Trang, and Mr. Dang Dinh Thang
and all other people participated for arranging and conducting the quantitative
research project with STATA & VHLSS2008.

I would like to express my appreciation to Dr. Nguyen Hoang Bao who
introduced the logistic regression model in explaining school dropout behavior at a
“sharing experience in doing research” seminar on August, 2010, hold by the Faculty
of Development Economics of HCMC University of Economics.
I would like to express my gratitude to Prof. Dr. Ardeshir Sepehri who has
sparked the idea of analyzing VHLSS dataset by panel data methods to capture the
unobserved heterogeneity.
I would like to express my sincere thanks to Dr. Le Van Chon, my supervisor,
who provides me directive suggestions during the thesis performing.
I would like to thank all professors in the teaching board of MDE program, who
have helped me accumulate valuable knowledge to acquire this study.
To all my friends in MDE class 16, who give me emotional encouragements, I
would like to express my thanks.
Finally, I would like to express my deeply appreciation to my parents, to my
wife and my son, to my family for spiritual supports. In particular, I dedicate this
thesis to my father.


ABSTRACT
This paper investigates the socioeconomic determinants of dropout behavior of
Vietnamese children in secondary schools using the Vietnam Household Living
Standard Survey for 2006 and 2008 and logistic regression model for Panel data.
Determinants are considered at individual, household, schooling, and regional levels.
My findings reveal that the unobserved individual characteristics account for 17% in
propensity of dropping out of secondary schools in different years 2006 and 2008.
Furthermore, the results disclose that child gender, child age, child ethnic, child
inactive days, household expenditure, household head gender, household head
education, the number of children between 1 and 17 years old, cost of school, urbanrural, and regions have statistically significant relationship with secondary school
dropout.
Key Words: secondary school dropout; panel; logistic model; random effects;

Vietnam

1


TABLE OF CONTENTS
CHAPTER 1:INTRODUCTION ............................................................................. 6
CHAPTER 2:LITERATURE REVIEW .................................................................. 8
2.1

A standard model of household schooling investment decision ............... 8

2.2

Empirical studies of school dropout in the world ................................... 10

2.3

Empirical Studies of school dropout in Vietnam .................................... 12

CHAPTER 3:VIETNAMESE SECONDARY EDUCATION – AN OVERVIEW15
CHAPTER 4:RESEARCH METHODOLOGY .................................................... 20
4.1

Data ......................................................................................................... 20

4.2

Methodology ........................................................................................... 22

CHAPTER 5:THE RESULTS ............................................................................... 29
5.1

Descriptive Statistics ............................................................................... 29

5.2

Dropout rates and Children characteristics ............................................. 31

5.2.1 Dropout rates and Household characteristics ...................................... 32
5.2.2 Dropout rate and School characteristics .............................................. 35
5.2.3 Dropout rates and Regional characteristics ......................................... 36
5.3

Regression Results .................................................................................. 37

CHAPTER 6:CONCLUSION ................................................................................ 45
REFERENCES ....................................................................................................... 47
APPENDIX .......................................................................................................... 52

2


LIST OF TABLES
Table 4.1: Description of the variables .................................................................. 26
Table 5.1: Descriptive statistics ............................................................................. 30
Table 5.2: Regression results of the Random-effects models ................................ 37
Table 5.3: The estimation of dropout probability, given initial probalibity P0 ...... 38

3

LIST OF FIGURES
Figure 3.1: Secondary school dropout rates ................................................................... 15
Figure 3.2: Gross enrollment rate by urban-rural .......................................................... 16
Figure 3.3: Gross enrollment rate by gender .................................................................. 17
Figure 3.4: Gross enrollment rate by region .................................................................. 17
Figure 3.5: Average expense on secondary education per schooling person in the past
12 months by urban-rural............................................................................ 18
Figure 3.6: Average expense on secondary education per schooling person in the past
12 months by gender ................................................................................... 18
Figure 3.7: Average expense on secondary education per schooling person in the past
12 months by gender ................................................................................... 19
Figure 3.8: Average expense on secondary education per schooling person in the past
12 months by income quintile..................................................................... 19
Figure 5.1: Dropout rate and child age .......................................................................... 32
Figure 5.2: Dropout rate and household expenditure quintile ....................................... 32
Figure 5.3: Dropout rate and years of schooling of household head ............................. 33
Figure 5.4: Dropout rate and number of children between 1 and 17 years old.............. 34
Figure 5.5: Dropout rate and cost of school ................................................................... 35
Figure 5.6: Dropout rate and region ............................................................................... 36

4


LIST OF ABBREVIATIONS
GSO: General Statistics Office
HH: Household
LMP: Linear Probability Model
MOET: Ministry of Education and Training

MP: Maximum Likelihood
NA: Not Applicable
OR: Odds Ratio
RE: Random-effects
VHLSS: Vietnam Household Living Standard Survey

5


CHAPTER 1:

INTRODUCTION

During the past two decades, Vietnam has achieved important results in education
in terms of increased enrollment, improved school infrastructure and diversified
schooling forms (MOET, 2006). However, Vietnam is still facing critics on the quality
of education and struggling with the phenomenon of dropping out of school. The net
enrollment rates1 were 95.5% at primary level, 82.6% at lower secondary, and 56.7% at
upper secondary level (GSO, 2011). The effects of school dropout are expounded in the
costs of individual, community, and society. Specifically, individual faces risk in
finding jobs; country struggles low-skilled labor force; and society expands rich and
poor gap. These effects have raised concerns to many researchers around the world in
examining factors affect school dropout, and from that appropriate policies are
proposed to policy makers to find ways to mitigate the phenomenon.
There are many factors which could influence early dropping out of school.
Empirical studies point out four groups of influential factors: individual characteristics,
household characteristics, school characteristics, and regional characteristics. However,
most of the empirical studies in Vietnam utilized the cross-sectional data to examine
the effects of these factors on the dropout behavior (Behrman & Knowles, 1999; Vo
Thanh Son et al., 2001; Vo Tri Thanh & Trinh Quang Long, 2005; Nguyen Linh

Phuong, 2006). Cross-sectional data face the possible problem of heteroskedasticity,
specifically the unobserved individual effects. Panel data are advocated to control for
this.
By addressing above issues, in this research, I am aiming at using panel data,
rather than cross-sectional data, to examine the influences of the socioeconomic

1

Net enrollment rate at z level is the number of pupils who in the ages of z level (according to the education law
in 2005) and currently keep schooling at z level as a percentage of z level aged population. Where, z is primary or
lower secondary or upper secondary. For example, if z is primary level then the net enrolment rate at primary
level is a percentage of the number of pupils who aged from 6 to 11 years old and currently keep schooling at
primary level over the number of primary level aged population.

6


determinants on the dropout behavior of pupils in secondary schools in Vietnam with
the aid of logistic regression model.
My study is endeavored to achieve three main objectives: (1) To determine factors
theoretically affecting the decision of dropping out of school; (2) To examine factors
statistically explaining the dropout behavior in secondary schools in Vietnam; and (3)
To implicate ways to reduce the secondary school dropout rates in Vietnam. The main
question of the research is: “What are the determinants of secondary school dropout in
Vietnam?” To answer this question, I divide it into two subquestions: (1) What are the
determinants theoretically influencing the decisions of dropping out of school? (2) Do
these determinants statistically explain the dropout behavior in secondary schools in
Vietnam? The first subquestion will be answered by recalling literature review and
empirical studies in the world and in Vietnam. Determinants obtained by the first
subquestion will be used for the second one by applying econometric method to

analyze the secondary data VHLSS.
The paper is continued with a set of sections. Section II recalls the literature
review and empirical studies in the world and in Vietnam. Section III provides an
overview of education system in Vietnam with a brief picture of dropout situation
during 2000-2006. Section IV describes the dataset used and research methodology.
Section V presents the results based on descriptive statistics and econometric models.
Section VI comes up with main conclusions and policy implications.

7


CHAPTER 2:

LITERATURE REVIEW

The issue of school dropout has attracted numerous researchers around the world.
The starting point to understand the decision of dropping out of school is the standard
theory on human capital investment, originally developed by Ben-Porath (1967) and
Becker (1964). The theory states that benefits and costs generated by additional
schooling, e.g. future income improving; expenditure on schooling tuition; opportunity
costs of entering the labor market late, etc., will be compared by individuals. If the
marginal rate of return to additional schooling exceeds the marginal cost of education,
individuals will keep schooling. The limitation of this theory is the assumption that
individuals face no resource constraints. This assumption does not seem to hold in
reality. Moreover, dropping out of school is not individual decision. Children don’t
decide by themselves but mostly by their parents. The household schooling decision
theory releases the assumption of no resource constraints and considers an existing
relationship between parents and children, in which parents play a principal role and
children as an agent. In parents’ view, children’s education is considered as both
consumption goods and investment goods. Parents spend resource on children’s

education because well-educated children bring satisfaction to them. Parents invest in
children’s education with the hope that they will receive support from children later in
life. A standard model of household decision-making in terms of children’s education
is discussed in detail in a paper by Vo Tri Thanh and Trinh Quang Long (2005). In this
section, I would like to briefly recall this model and also underline empirical papers in
the world and in Vietnam related to the main implications of the model.
2.1

A standard model of household schooling investment decision
The household schooling investment decision model begins with an assumption

that households are considered as unitary households. It means there is no difference in
preferences of parents. If parents’ preferences are not the same, they are then supposed
to behave as if they are maximizing a single utility function.

8


Suppose a household includes a father, a mother, and N children, in which N
children are divided into m daughters and n sons. The parents’ life is divided into two
periods. They work and raise children in the first period. They retire in the second
period. In the first period, income from working after subtracting a proportion of
investment in their children’s education is considered as household consumption. In the
second period, their consumption depends on the remittances that their children return
to them. The amounts of remittances in turn depend on the level of children’s education
acquired in the first period. Hence, there is a trade-off in parents’ schooling decision
between consumption in the first period and consumption together with children’s
wealth in the second period. A utility function which represents the identical
preferences of parents is expressed as follows:
U  U (C1 , C 2 , I d 1 ,..., I dm , I s1 ,..., I sn )

(2.1)

Where, C1 and C 2 are household consumption in the first and second periods
respectively. I di (i = 1 … m) and I sj (j = 1 … n) are incomes earned by the ith
daughter and jth son in second period respectively.
The equation 2.1 can be expressed in another form as follows:
U  F (C1 )  G (C 2 , I d 1 ,..., I dm , I s1 ,..., I sn )

(2.2)

By a series of arguments which we can see in Vo Tri Thanh and Trinh Quang
Long (2005), the demand for quantity of daughters’ and sons’ schooling are pointed
out as a function of the cost of education, parents’ wage rates on the labor market,
children characteristics, unearned income, parents’ education, household and
community factors as follows:
S di  S di (wm , w f ,V , P, S m , S f , Z di , Z sj , H ) and

S sj  S sj (wm , w f ,V , P, S m , S f , Z di , Z sj , H )

Where,

9

(2.3)


S di (i = 1… m) and S sj (j = 1… n) are the education of the ith daughter and jth son.
wm and w f are mother’s and father’s wage rates respectively.

V is unearned income such as parent’s satisfaction from well-educated children.
P is the direct cost of education, including tuition fees, books, uniforms, etc.
S m and S f are mother’s and father’s education respectively.
Z di and Z sj are daughter and boy characteristics respectively.

H is other household and community factors.
The implications of the model are the wage rates of parents on labor market, the
unearned income, the cost of schooling, the parents’ education, the children individual
characteristics, and other household and community factors do have effects on the
parental decision of investing in their children’s education (for more details, see Vo Tri
Thanh & Trinh Quang Long, 2005).
2.2

Empirical studies of school dropout in the world
The determinants of dropping out of school are empirically divided into four

groups of factors: (1) individual characteristics; (2) household characteristics; (3)
school characteristics; and (4) regional characteristics.
Numerous papers examine the influence of individual characteristics on the
dropout decision. The returns to education of boys, predicted by parents, are higher
than those of girls (Schultz, 1993). Therefore, benefits of investing in education for
girls may be lower than boys. Moreover, in developing countries and in rural areas, the
opportunity cost of educating girls is higher than that of boys as girls are supposed to
perform more household works than boys. As consequence, the demand for girls’
schooling will be lower (Glick & Sahn, 1998). The older the age is the greater the
tendency of school dropout. Children in working age tend to engage in the labor market
to assist their parents. Older children often go along with higher opportunity costs and
lower marginal benefits could discourage parents from investing in education for them

10

(Ben-Porath, 1967). Child labor has a positive relationship with school dropout
(Admassie, 2002). Working absorbs much of children’s time instead of using it for
schooling. Energy exhausted from labor affects children’s performance at school.
Children with poor mental health have a positive relationship with dropout status.
There are a few researches on how health issues directly affect school dropout
(Pridmore, 2007). But in general, researches indicate that poverty often results in poor
health and under-nutrition. Through there, children’s educational access and attainment
are severely jeopardized (Glewwe & Jacoby, 1995; Alderman et al., 2001; Grira, 2001;
Ghuman et al., 2006).
Household characteristics are empirically considered as important determinants of
dropping out of school. Children, whose parents have difficulties in finance, are more
likely to drop out of school. Children in low income families are heavier affected in
terms of school completion than children in high income families (Duncan et al., 1998;
Glick & Sahn, 1998). Children with more educated parents are less likely to drop out of
school than ones with less educated parents (Glick & Sahn, 1998). Children receive
more supports in learning from their educated parents would help them stay in school
longer (Sabates et al., 2010). Specifically, mother’s education has a stronger effect on
children in school than father’s education. Moreover, the effects of parents’ education
on school dropout are different between boys and girls. The dropout probability on
girls is larger than boys (Tansel, 1997). The higher the number of children is, the larger
the probability of school dropout, because of the budget constraints on families
(Psacharopoulos & Arriagada, 1989). In households which have higher number of
children, the dropout probability on girls is higher than boys (Parish & Willis, 1993)
due to raising demand on girls’ childcare.
School characteristics are factors that may influence school dropout. Many studies
show that high schooling fees increase the probability of school dropout (Wolfe &
Behrman, 1984; Chernichovsky, 1985; Al-Samarrai & Peasgood, 1998; Zimmerman,

11


2001; Dostie & Jayaraman, 2006). Distance to school plays an important role in
developing countries as it may force children to discontinue education, especially in
rural areas where most of schools are located far from children’s houses, and the means
of transportation are not well developed (Bilquees & Saqib, 2004). Poor education
quality discourages children to remain in school and parents’ motivation to keep their
children schooling (Coleman, 1966; Oakland, 1986a & 1986b; Brown & Park, 2002;
Hanum, 2003; Hanushek et al., 2006).
Regional characteristics are another factors affect school dropout. The dropout
probability is higher for children living in rural areas than in urban areas (McCaul,
1989; Ono, 2000). School enrollment is significantly affected by geographic
disparities. Poor regions tend to have higher school dropout rates (Vo Tri Thanh &
Trinh Quang Long, 2005).
2.3

Empirical Studies of school dropout in Vietnam
Empirical studies in Vietnam find that all above four groups of factors have strong

influences on the school dropout.
Behrman and Knowles (1999) estimate the relationships between household
income and the school success of children in Vietnam by utilizing data from the 1996
Vietnam Social Sector Financing Survey. They find that there is five times higher in
the income elasticity of completed grades compare to the median estimate of earlier
studies. For grades completed per year of school, this relationship is even strongest.
Moreover, between boys and girls, this association is quite difference. This difference
suggests that girls’ schooling is considered to be more luxury than boys’ schooling.
Furthermore, the paper indicates that school fees are only one-third of what households
directly consume on education. Thus, school fees exemptions are necessary but the

influence in school enrollment by this exemption is not widely.
Vo Thanh Son et al. (2001) utilize the data from VHLSS 1998 to explore
variables associated with dropping out of school. Some evidences are provided from

12


their study: (1) children from households in the poorest quintile have higher dropout
rates compared with ones from households in the top quintile; (2) an increase in school
fees leads to increase in dropout rates; (3) gender matters are not evidentially at
primary level, but it does appear in secondary level. Specifically, it becomes wider at
upper secondary level, compared to the lower one. This is considered as evidence that
girls tend to have higher dropout rate than boys.
Vo Tri Thanh and Trinh Quang Long (2005) identify the underlying determinants
of the schooling dropout in Vietnam by separately using data from three Vietnam’s
Living Standard Surveys conducted in 1992/93, 1997/98 and 2001/02. They explore
that: (1) the household’s per capita expenditure and the direct costs of school have
strong effects on the dropout probability; (2) when household's per capita expenditure
on girls increases, girls would benefit more than boys. But they would suffer more than
boys from an increase in the direct costs of school. However, these differences have
been gradually narrowed substantially; (3) dropout phenomenon is a regional
specification. Different regions have different effects on dropout rates; (4) dropout
situation is very much dependent on the public funding for education.
Nguyen Linh Phuong (2006) investigates the effects of parental socioeconomic
status, school quality, and community factors on the enrollment and achievement of
children in rural areas in Vietnam by using VHLSS 1998 data. The paper reveals that:
(1) the levels of household expenditures and parental education have significant
impacts on educational enrollment and outcomes. Especially, mother's education is
more important in determining school enrollment than educational outcome, while
father’s education expands the probability of learning; (2) the dropout probability of

girls is higher than boys; (3) school fees do not determine school enrollment as the
exemption or reduction in these fees already applied to many of children in poor
families.

13


Le Thi Nhat Phuong (2008) utilizes VHLSS 2004 and 2006 separately to examine
the socioeconomic determinants of school dropout for Vietnamese children aged 11-18.
She finds that: (1) age and household size have significantly positive effects on the
dropout probability; (2) the dropout rates are also shown to vary between girls and
boys, but this gender gap has narrowed substantially. Moreover, minority girls confront
more obstacles in remaining in school than minority boys; (3) the school dropout rate is
also very sensitive to the changes in household’s income and costs of school. However,
the costs of school have different impacts on families in different quintiles; (4) region
is another determinant affecting children’s decision to drop out of school; (5) the
parents’ perception of the value of education may increase the child’s probability of
school retention.
Ngo Hoang Thao Trang (2010) employs VHLSS 2006 to examine the effects of
individual, household, community, and regional levels on the dropout behavior of
children in secondary schools in Vietnam by using the logistic regression model. She
finds that: (1) age, working hours per year, parents’ education, regions have large
effects on the probability of leaving school; (2) household expenditure, the number of
siblings, the proportion of pupils with reduced contributions, the pupil to teacher ratio,
the pupil to classrooms ratio and the proportion of classrooms with good blackboards
have small effects on the probability of leaving school. However, the existence of
children’s working hours per year in her model might leads to incorrect standard errors
and inefficient estimation because of causal relationship between child work and
school dropout.

14


CHAPTER 3:

VIETNAMESE SECONDARY EDUCATION – AN OVERVIEW

The Vietnamese national educational system is regulated by the Education Law (2005).
According to this law, educational levels include general education with primary education,
lower secondary education, and upper secondary education. Primary education is conducted in
05 years of schooling, from the 1st to the 5th grade, where the age of commencement to the
1st class is six. Lower secondary education requires 04 years of schooling, from the 6th to the
9th grade. Pupils entering the 6th grade must complete the primary education programme, at
the age of 11. Upper secondary education is conducted in 03 years of schooling, from the 10th
to the 12th grade. Pupils entering the 10th grade must have a Lower Secondary Education
Diploma, at the age of 15. In this section, I focus on lower secondary and upper secondary
educations to graphically provide a brief picture about dropout trends during the past period
1999-2011.

The MOET (2011) reported statistical data on education from 1999 to 2011, in
which the dropout rates were calculated but only available from 1999-2000 to 20042005 (Appendix 1). Figure 3.1 provides dropout rates in secondary schools. The
dropout rate decreased from over 8% in 1999-2000 to around 5% in 2004-2005 at
lower secondary education, while it increased at upper secondary education from over
7% in 1999-2000 to above 8% in 2004-2005.
Figure 3.1: Secondary school dropout rates

Dropout rate (%)

10
8

6
4
2
0
1999-2000

2000-2001

2001-2002

2002-2003

Lower secondary

2003-2004

Upper secondary

Source: MOET (2011)

15

2004-2005


However, according to Vo Tri Thanh and Trinh Quang Long (2005, p.25), this
data contains the issue of underestimating of school dropout in Vietnam because it does
not count pupils who stop schooling after completed a given grade. Given that issue, a
more precise definition are introduced by considering a child to be dropped out if
he/she did not enroll in school in the 12 months prior to the survey, although this

definition still faces the issue of ignoring a small number of children who postpone
their education in the 12 months prior to the survey but intended to return to school in
the coming years.
Figure 3.2: Gross enrollment rate by urban-rural

Enrollment rate (%)

120
100
80
60
40
20
0

Urban
Rural

Lower
secondary

Upper
secondary

2006

Lower
secondary

Upper

secondary

2008

Lower
secondary

Upper
secondary

2010

Source: GSO (2006, 2008, 2010)

GSO of Vietnam issued the reports on education based on VHLSS2006,
VHLSS2008 and VHLSS2010 (Appendix 2). Some figures from these reports are
quoted here to briefly provide a picture of secondary education in Vietnam in recent
years. Figures 3.2 provides gross enrollment rate vary across urban-rural. The gross
enrollment rates were not varying much in lower secondary level compared to upper
secondary level.

16


The gross enrollment rates, distinguished by gender are revealed in figures 3.3.
There was not much disparity in lower level, while female had higher enrollment rates
than male in upper secondary level.

Enrollment rate (%)

Figure 3.3: Gross enrollment rate by gender
120
100
80
60
40
20
0

Male
Female

Lower
Upper
Lower
Upper
Lower
Upper
secondary secondary secondary secondary secondary secondary
2006

2008

2010

Source: GSO (2006, 2008, 2010)

The gross enrollment rates, separated by region, are displayed in figure 3.4. At
secondary education, the North West and Mekong River Delta suffered the lowest
enrollment rates. The rates were more serious in upper secondary level.

Figure 3.4: Gross enrollment rate by region

Enrollment rate (%)

120
100
80
60
40
20
0
Lower secondary

Upper secondary

Lower secondary

2006

Upper secondary
2008

Red River Delta

North East

North West

North Central Coast

South Central Coast

Central Highlands

South East

Mekong River Delta

Source: GSO (2006, 2008)

17


Average expenses on secondary education per schooling person in the past 12
months, divided by urban-rural are provided in figure 3.5. Expenses on secondary
education were double in urban area compared to rural area.
Figure 3.5: Average expense on secondary education per schooling person in the
past 12 months by urban-rural
3,500

VND 1,000

3,000
2,500
2,000

Urban

1,500
Rural

1,000
500
0
Lower
secondary

Upper
secondary

2004

Lower
secondary

Upper
secondary

2006

Lower
secondary

Upper
secondary

2008

Source: GSO (2004, 2006, 2008)

Average expenses on secondary education per schooling person in the past 12
months, separated by gender are revealed in figure 3.6. Expenses on secondary
education for female were slightly higher than male.
Figure 3.6: Average expense on secondary education per schooling person in the
past 12 months by gender
2,500

VND 1,000

2,000
1,500

Male

1,000

Female

500
0
Lower
secondary

Upper
secondary

2004

Lower
secondary

Upper
secondary

2006

Source: GSO (2004, 2006, 2008)

18

Lower
secondary

Upper
secondary

2008


Average expenses, divided by region are disclosed in figure 3.7. Expenses were
highest in South East region while North West region got the lowest.
Figure 3.7: Average expense on secondary education per schooling person in the
past 12 months by gender
4,000
3,500

VND 1,000

3,000
2,500

2,000
1,500
1,000
500
0
Lower s econdary

Upper s econdary

Lower s econdary

2006

Upper s econdary
2008

Red River Delta

North Eas t

North Wes t

North Central Coas t

South Central Coas t

Central Highlands

South Eas t

Mekong River Delta

Source: GSO (2006, 2008)

Average expenses, separated by income quintiles are revealed in figure 3.8. The
richest spend four times higher then the poorest.
Figure 3.8: Average expense on secondary education per schooling person in the
past 12 months by income quintile
4,000
3,500

VND 1,000

3,000
2,500
2,000
1,500
1,000
500
0
Lower secondary

Upper secondary

Lower secondary

2006
Quintile 1

Quintile 2

Upper secondary
2008

Quintile 3

Source: GSO (2006, 2008)

19

Quintile 4

Quintile 5


CHAPTER 4:
4.1

RESEARCH METHODOLOGY

Data
The data in this research is mainly based on the Vietnam Household Living

Standard Survey (VHLSS), conducted by General Statistic Office (GSO), in 2006 and
2008. GSO regularly conducts VHLSS every two years in order to evaluate living
standards for policy-making and socio-economic development planning.
VHLSS2006 and VHLSS2008 are implemented nationwide, consisting of a
sample of 45,945 households, in which 36,756 households for income survey, and
9,189 households for both income and expenditure. The sample includes 3,063
communes which were represented at national, regional, rural/ urban and provincial

levels. The information collected in the survey was organizationally implemented in 2
rounds by directly face-to-face interviewing with household heads and key community
officials.
Data which are repeated measurements on the same individual at different points
in time are called panel data or longitudinal data. In this research, I am using panel data
to examine the school dropout phenomenon at secondary level from grade 6 to 12.
Only children age from 11 to 18 years old who have accomplished primary level and
keep schooling in 2006 are considered. Additionally, children at 18 years of age who
have accomplished upper secondary level are eliminated out of the sample. For
example, in 2006, a child at 11 years old who finished primary level will be considered
in 2006 sample. In 2008, he/she is at 13 years old will continuously be included in
2008 sample. There are some issues in the sample that I need to point out. Some
children who 11 years old in 2006 but 14 years old in 2008, or who 17 years old in
2008 but accomplished upper secondary level, or who 17 years old in 2006 but 18
years old in 2008. Why is that? The answer is because age is calculated to age rounded
at the time of the survey. For example, a child who born in 1994 but in the month after
the surveyed month in 2006, so he/she is not enough months to be recorded as 12 years

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old, but 11 years old instead. At the survey time in 2008, the surveyed months are after
the born month. Hence he/she is enough months to be recorded as 14 years old. Similar
to children who are 17 years old in 2008 but already finished upper secondary level.
These children are also eliminated out. Only children satisfy above conditions in 2006
are maintained to see whether they drop out of school in 2008, when they are
reinterviewed.
The panel data help us capture factors effect children’s dropout behavior during
period 2006-2008 better than cross-sectional data. In detail, suppose that we consider a
cross-sectional dataset in 2008 only. Children who are considered as dropout are

evaluated through the question asking whether he/she is schooling in previous 12
months. Suppose there is a child not schooling in previous 12 months then he is
considered as dropped out in the sample. The problem is that we are not sure whether
this child just left school in previous 12 months or long time ago, say 24 months or 36
months ago. Then factors that we base on such as his household expenditure, cost of
schooling, … which also be captured in the period of 12 months prior to the survey, are
wrong in analyzing the effect. By using panel data, we assure that all children are
schooling in 2006 and dropout decisions only happen in period 2006-2008. Hence,
factors which effect dropout decisions are better evaluated.
After consolidated to remove errors and inconsistencies, the sample data remains
1,869 children from ages 11 to 17 in 2006 and 12 to 18 in 2008, who are both
interviewed in 2006 and 2008, in which 1,610 children keep schooling, 259 children
being dropped out. The dropout rate was generally around 13.86%.

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4.2

Methodology
I am concerning on the probability of secondary school dropout over two-year

periods, specifically, the determinants of the probability p of the occurrence of the
school dropout rather than non-dropout that occurs with probability of 1-p. In
regression analysis, I want to measure how the probability p varies across children as a
function of regressors. Hence, dependent variable is a binary outcome. There are two
standard models for binary outcome: the probit model and the logit model. These
models are fitted by maximum likelihood (MP). A linear probability model (LPM)
which is fitted by ordinary least squares can also be used. However, LPM faces
problems, e.g., heteroscedasticity, the difficulty of interpreting probabilities which

greater than 1 and less than 0, constant marginal effects (Gujarati, 1995). Then, Logit
and Probit models are alternative choices. Many papers choose logit model because of
its mathematical simplicity (Gujarati, 2003). In this study, Logit model is employed.
After decided logit model is analysis model, the question is which estimator is
suitable to treat this nonlinear panel model? One way is to explore the variations of
time-varying regressors. If between variation is the most variation rather than within
variation, then FE estimator is not expected to be very efficient as it builds on within
variation only. Other way is using appropriate statistic software to fit the model by FE
estimator. If the outcome is “convergence not achieved” or “substantially larger
standard errors” because of the loss of time-invariant observations and only within
variation of the regressors is used, then FE estimator is not appropriated and RE
estimator is alternative selection. The point is that I want to eliminate the endogeneity
of dropout decision by randomly assigning this to children. Then each child will have
an autonomous dropout decision which is not correlated with dropout determinants and
this autonomous dropout decision is random and follows a normal distribution with

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