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Asia & the Pacific Policy Studies, vol. 2, no. 3, pp. 494–516
doi: 10.1002/app5.102
Original Article
What Determines Learning among Kinh and Ethnic Minority
Students in Vietnam? An Analysis of the Round 2 Young Lives
Data
Paul Glewwe, Qihui Chen and Bhagyashree Katare*
Abstract
An analysis of the Young Lives data collected in
2006, involving a younger cohort (aged 5) and
an older cohort (aged 12), yields three important findings regarding the Kinh–ethnic minority gaps in mathematics and reading skills in
Vietnam. First, large disparities exist even
before children start primary school. Second,
language may play an important role:
Vietnamese-speaking ethnic minority children
scored much higher than their non-Vietnamesespeaking counterparts, even though tests could
be taken in any language the child chooses.
Third, Blinder–Oaxaca decompositions indicate that higher parental education among
Kinh children explains about one third of the
gap for both cohorts. For the older cohort, Kinh
households’ higher income explains 0.2–0.3
standard deviations (SDs) of the gap (1.3–1.5
SDs). More time in school, less time spent
working, and better nutritional status each
explain about 0.1 SDs of the mathematics
* Glewwe: Department of Applied Economics,
University of Minnesota, St Paul, MN 55108, USA;
Chen: Department of Applied Economics, College
of Economics and Management, China Agricultural
University, Beijing 100 083, China; Katare: Department of Agricultural Economics, Purdue University,
West Lafayette, IN 47907, USA. Corresponding
author: Glewwe, email ⬍[email protected]⬎. We
thank Laura Camfield, Stefan Dercon, Helen
Pinnock, Caitlin Porter and Chi Truong for helpful
comments on earlier drafts of this article.
score gap; Kinh children’s more years of
schooling explains about 0.3 SDs of the
Peabody Picture Vocabulary Test score gap.
Key words: cognitive skills, ethnic minority,
Blinder-Oaxaca decomposition, Vietnam,
education
1. Introduction
Vietnam is one of the poorest countries in
south-east Asia, but since the late 1980s it has
enjoyed a high rate of economic growth, which
is almost certainly due to its Doi Moi policies,
which in effect replaced Vietnam’s planned
economy with a market economy. Like many
formerly socialist countries, Vietnam has long
had relatively high levels of education compared to other low income countries. Its
primary school gross enrolment rate has been
close to 100 per cent since the early 1990s, and
its secondary school gross enrolment rate
increased from about 32 per cent in the early
1990s to 57 per cent in the late 1990s and
about 85 per cent in 2006 (Glewwe 2004;
General Statistics Office 2007).
While education is often measured in terms
of years of schooling completed, the benefits
of education are ultimately determined by the
skills individuals acquire in school. Thus, it is
important to understand what individual,
family and school characteristics lead to the
acquisition of academic skills. Unfortunately,
thorough studies of the determinants of aca-
© 2015 The Authors. Asia and the Pacific Policy Studies
published by Crawford School of Public Policy at The Australian National University and Wiley Publishing Asia Pty Ltd.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License,
which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial
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Glewwe et al.: Young Lives Data Analysis
demic skills in developing countries are somewhat rare owing to lack of data on both
academic skills and their determinants.
This article examines the acquisition of
mathematics and reading skills in Vietnam,
using the Round 2 Young Lives household
survey data collected in 2006. While Vietnam
has long had unusually good performance in
education as measured by years of education
completed, its relatively short school day may
limit the skills students acquire in school.
Moreover, ethnic minority students have much
lower outcomes, in terms of both years of
schooling and test scores, than ethnic Vietnamese (Kinh). Thus, this article focuses on
explaining the Kinh–ethnic minority learning
gap in Vietnam.
The rest of this article proceeds as follows.
Section 2 briefly describes Vietnam’s education system, and reviews several recent studies
on the determinants of school enrolment and
student learning in Vietnam. Section 3
describes the data. Section 4 presents the
methodological framework underlying the
analysis. The results for the younger cohort
(aged 5 in 2006) and older cohort (aged 12 in
2006) are given in Sections 5 and 6, respectively. The final section draws conclusions and
presents some suggestions for future research.
2. Literature Review
Vietnam’s education system has three levels:
primary, secondary and tertiary. Primary education (Grades1–5) is for children aged 6–10.
Secondary education consists of lower secondary education (Grades 6–9) for children aged
11–14, and upper secondary education (Grades
10–12) for children aged 15–17. Various types
of tertiary education, ranging from university
degree programs to a wide variety of technical
training, are available for the population aged
18 years and older. The vast majority of Vietnam’s schools are public (governmentoperated) schools.
Relative to its low income level, Vietnam
has achieved remarkable success in terms of
basic education outcomes. While its gross
domestic product per capita in 2004 (US$502)
was less than one half the average of East
495
Asian and Pacific countries and a quarter of the
average of middle-income countries, it has
similar literacy rates to those two groups of
countries (Dang 2007). Its primary school
completion rate, 92 per cent, is even slightly
higher than the average for the abovementioned groups of countries; gross enrollment rates in Vietnam were 90 per cent, 76 per
cent and 16 per cent at the primary, secondary
and tertiary levels, respectively, in 2006
(World Bank 2008).
Several recent studies have examined the
educational performance of ethnic minority
children in Vietnam.1 Dang (2003), while not
focusing on test scores, estimates the determinants of years of schooling in rural Vietnam.
Using data from the 1997–1998 Vietnam
Living Standards Survey, he finds significant
Kinh–ethnic minority gaps in years of schooling. His regressions include separate ethnic
minority dummy variables for the Northern
Uplands, Central Highlands and Mekong
Delta regions (where most ethnic minorities in
Vietnam reside), as well as a general ethnic
minority dummy for ‘all other regions’ in
Vietnam. He finds that while ethnic minority
children in the Northern Uplands tend to have
more years of schooling than Kinh children
(significant at the 10 per cent level), ethnic
minority children in the Central Highlands and
the Mekong Delta, as well as in ‘all other
regions’ of Vietnam, had one to two fewer
years of schooling than Kinh children after
controlling for the aforementioned variables.
These negative impacts are especially strong in
the Central Highlands and regions other than
the three regions just mentioned. Note that
since many ethnic minority children live in
remote areas, the distance-to-the-nearest-town
effect will have an additional negative impact
on their years of schooling. Similarly, the
qualifications of teachers in their schools are
likely to be lower, further reducing their years
of schooling.
1. There are also several more recent papers on student
performance in Vietnam that do not emphasise Kinh–
ethnic minority differences. To avoid making this article
overly long, and to focus on issues pertaining to ethnic
minorities, we do not review those papers here.
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More recent papers have examined gaps in
test scores between Kinh and ethnic minority
children in Vietnam. Himaz (2009) examined
mathematics and reading (vocabulary) skills
using the Round 2 Young Lives data for the
older cohort children (aged 12 when tested in
2006). She regressed scores on the mathematics test and the Peabody Picture Vocabulary
Test (PPVT) on a set of child and household
characteristics and a dummy variable for the
mother belonging to an ethnic minority.2 She
found that mothers’ and fathers’ education
had strong positive effects for both tests, as
did child nutrition (measured by the heightfor-age z-score). Household size and having
an ethnic minority mother had strong negative effects on both tests. The negative effect
of household size probably reflects the fact
that the household wealth variable is not in
per capita terms; as discussed below, once
household consumption is expressed in per
capita terms there is no additional explanatory power of household size. In general, virtually all of Himaz’s results are consistent
with many other studies of the determinants
of test scores in developing countries (see
Glewwe & Kremer 2006 for a review). They
confirm the importance of parental education
and child nutrition, as well as negative outcomes for ethnic minority children even
after controlling for these two important
determinants.
A completely different data source on test
scores was used by the World Bank, which
conducted two reading and mathematics
studies with Vietnam’s Ministry of Education
and Training in 2001 (World Bank 2004) and
2007 (World Bank 2011). Both studies focus
on children in the last year of primary school
(Grade 5). The general findings are as one
would expect: children with better-educated
parents and from wealthier households have
higher scores on the two tests. Also, ethnic
2. The child and mother ethnic minority status variables
are extremely highly correlated; of the 989 observations
that have both variables, there are only 26 cases where the
child was from an ethnic minority while the mother was
not, or the mother was from an ethnic minority but the
child was not.
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minority children are found to have lower
scores, but neither study provides a detailed
analysis of the reasons for this.
Two very recent papers have examined the
gap in per capita household expenditures
between Kinh and ethnic minority households
in Vietnam, which are of interest since those
gaps can lead to gaps in student learning.
Baulch et al. (2010) examined ethnic inequality in household expenditure per capita, using
data from the Vietnam Living Standards
Surveys of 1993 and 1998 and the Vietnam
Household Living Standard Surveys (VHLSS)
of 2002, 2004 and 2006. The study adopts the
Blinder–Oaxaca decomposition method to
identify the sources of the ethnic gap in per
capita expenditure, examining both differences
in characteristics and differences in returns to
those characteristics. Their (mean and
quantile) decomposition results show that at
least a half of the ethnic gap in per capita
expenditure can be attributed to the lower
returns to characteristics received by ethnic
minority households.
Nguyen et al. (2014) also examine ethnic
inequality in household expenditures per
capita, using data from the 2006 VHLSS.
More specifically, they investigate: (i) how
language barriers (inability to speak Vietnamese) may hinder ethnic minority households
from taking advantage of their acquired skills
and attributes; (ii) whether commune infrastructure works for or against ethnic minorities; and (iii) the extent to which differences
in endowments, as opposed to returns to
endowments, explain the ethnic gap in per
capita household expenditures. Applying the
Blinder–Oaxaca decomposition technique
(using instrumental variables to consistently
estimate the impacts of household and
commune level variables), they find the following: first, removing language barriers significantly reduces ethnic inequality through
enhancing ethnic minorities’ gains from education; second, returns to education favour the
Kinh–Chinese majority in mixed communes,
which suggests that the special needs of
minority students are not adequately
addressed, or that there exists unequal treatment in the labour market. Third, except for
© 2015 The Authors. Asia and the Pacific Policy Studies
published by Crawford School of Public Policy at The Australian National University and Wiley Publishing Asia Pty Ltd
Glewwe et al.: Young Lives Data Analysis
hard-surfaced roads, there is little difference
in the benefits generated from enhanced infrastructure at the commune level across ethnic
groups. Finally, in contrast to other studies
(including Baulch et al. 2010), as much as
49–66 per cent of the ethnic gap in per capita
household expenditures is due to differences
in endowments rather than differences in the
returns to endowments.
Finally, three recent qualitative studies have
examined education and gaps between Kinh
and ethnic minority children in Vietnam.
Truong (2009) conducted a qualitative study
based partially on the Young Lives data.
Without attempting to estimate the determinants of test scores, Truong documents
large Kinh–ethnic minority gaps in school
enrolment and dropout rates and in test scores.
This article focuses on Bao Ly commune in
Lao Cai province, which has a high
H’mong population, and on Ea Mua commune
in Phu Yen province, which has a sizeable
Cham population. The H’mong children in
Bao Ly commune are much more likely to
drop out of school (by age 12), and score far
lower on reading, writing and mathematics
tests, compared to Kinh children in that
commune.
Second, a study by the World Bank (2009)
argues that lack of bilingual instruction and
lack of ethnic minority instructors in schools
with ethnic minorities are barriers to learning
among ethnic minority students. Third, a
report by the Vietnam Academy of Social
Sciences (2009) points out that ‘Ethnic minority people who are not fluent in the Vietnamese
language are 1.9 times more likely to be poor
than ethnic minority people who are fluent in
Vietnamese, and 7.8 times more likely to be
poor than the Kinh/Chinese people’.
This article is the first to examine the determinants of test scores and the Kinh–ethnic
minority gaps in test scores in detail using the
Young Lives data from Vietnam. More importantly, it is the first article to apply the Blinder–
Oaxaca decomposition to understand test score
gaps in Vietnam using any data source. These
gaps are arguably one of the most important
sources of socio-economic inequality in
Vietnam today.
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3. Data
This article analyses the Round 2 Young Lives
data from Vietnam, which were collected in
2006. The Round 1 survey in 2002 collected
data on 2,000 children aged 6–17 months
(younger cohort) and 1,000 children aged 7.5–
8.5 years (older cohort). Strictly speaking, this
sample is not representative of Vietnam as a
whole. Instead, five of Vietnam’s 63 provinces
were selected to be ‘representative’ of most of
Vietnam’s regions. However, comparisons
with the nationally representative Vietnam
Household Living Standards Survey suggest
that the Young Lives sample is broadly representative of Vietnam as a whole (see Table A1
in Appendix 1). Section 3.1 explains the sampling procedure, and following it, section
3.2 describes the data, focusing on the tests
administered in round 2. For more details on
the sampling procedure, see Tran et al. (2003).
3.1 Sample
Vietnam can be divided into eight socioeconomic regions: North-West, North-East,
Red River Delta, North Central Coast, South
Central Coast, South-East, Central Highlands,
and Mekong River Delta. To ensure that the
Young Lives sample included a major urban
centre, a new ‘region’ (‘Cities’) was created
consisting of all major urban provinces
(Hanoi, Ho Chi Minh City, Da Nang, Hai
Phong and Ba Ria-Vung Tau). Of these nine
‘regions’, five (North-East, Red River Delta,
Cities, South Central Coast, and Mekong River
Delta) were chosen as ‘representative’ of
Vietnam in the sense that they: (i) include
regions in the northern, central and southern
areas of Vietnam; (ii) include urban, rural and
mountainous areas; (iii) are relatively poor;
and (iv) reflect some unique factors of
Vietnam, such as areas prone to natural disasters and areas heavily affected by past wars.
From each of these five regions, a ‘typical’
province was chosen after consultation with
both government and international experts.
The five selected provinces (see Figure 1)
were: Lao Cai (North-East region), Hung Yen
(Red River Delta), Da Nang (Cities), Phu Yen
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Figure 1 The Five Provinces Featured in the Young
Lives Survey
(South Central Coast) and Ben Tre (Mekong
River Delta). Within each province, at least
four communes (‘sentinel sites’) were chosen,
giving greater weight to poor communes.3
More specifically, all communes in the province were ranked by poverty level: poor,
average, better off and rich. Two communes
were selected from the poor group, one from
the average group, and one from the above
average group (combining ‘better off’ and
‘rich’). The selection of communes from each
group was not random; the criteria considered
include: (i) whether the commune represents
common provincial/regional features; (ii)
3. Vietnam is divided into about 65 provinces. Each province is divided into districts, and each district is further
divided into communes. These are all political units.
Overall, Vietnam has over 10,000 communes.
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whether there was commitment from the local
government for the research; (iii) feasibility in
terms of research logistics; and (iv) population
size. If a selected commune had a population
of less than 6,000 persons, a ‘similar’
commune at the same poverty level was
selected to assure that 100 Younger Cohort
children could be found in that ‘sentinel site’.
Following this procedure, 31 communes were
finally selected: seven in Lao Cai, six in Hung
Yen, four in Da Nang, six in Phu Yen, and eight
in Ben Tre. In the second round of data collection in 2006, the number of communes
increased to 34 because one commune in Da
Nang province was split into two between
2002 and 2006, and another in Da Nang was
split into three.
In each selected commune, lists were compiled of all children born between 1 January
1993 and 31 December 1994 and between 1
January 2000 and 31 December 2001. These
2-year ranges were used due to uncertainty
about the exact starting date of data collection.
Random sampling was then applied in each
commune to select 100 children of 6.0–17.9
months and 50 children aged 7.5–8.5 years at
the time of the fieldwork (July–November
2002). The refusal rate was only 1.2 per cent
(=36/3000) and replacement sampling was
used in the case of refusals.
For reasons including child deaths, refusals
and households’ moving away from Vietnam,
30 of the 2,000 younger cohort children and 10
of the 1,000 older cohort children in round 1
did not participate in round 2, leaving 1,970
younger cohort children and 990 older cohort
children in the round 2 sample. The main
reasons for the sample attrition in the younger
cohort are that the child died (11 cases) and
that the household could not be found (13),
with the remaining cases being refusal or
moving away from Vietnam. At least 82 households, and perhaps as many as 95, of the 1,970
households that were re-interviewed moved
within Vietnam from 2002 to 2006, and all
were found and re-interviewed. (There are 82
households that clearly moved, and another 13
for which missing data make it unclear
whether they moved.) The main reasons for
sample attrition in the older cohort are that the
© 2015 The Authors. Asia and the Pacific Policy Studies
published by Crawford School of Public Policy at The Australian National University and Wiley Publishing Asia Pty Ltd
Glewwe et al.: Young Lives Data Analysis
child died (two cases), the household refused
to participate (two cases), or the household
could not be found (three cases). Finally, of the
990 older cohort households re-interviewed in
2006, 14 moved within Vietnam and were
found and re-interviewed, and another household was interviewed, but missing data make it
impossible to determine whether that household moved.
3.2 Tests
The cognitive skills tests administered to the
children varied by cohort and the round of the
survey. In round 1, the younger cohort was too
young to take any tests. In round 2, they took
two tests: the Cognitive Development Assessment test of basic quantitative skills (CDA-Q)
and the PPVT test. For more information on
these tests, see Cueto et al. (2009). The former
test is the quantitative sub-test of the CDA test
developed by the International Evaluation
Association (IEA). For each of its 15 items, a
child is shown a picture and asked a question
(e.g. ‘Look at the bowls of eggs. Point to the
bowl that has the most eggs.’) and then chose
the best answer from three or four choices.
Each correct answer scores 1 point; an incorrect or blank answer scores 0. The total
CDA-Q score is the number of correct
answers.4
The PPVT mainly tests vocabulary acquisition of children aged 2.5 years and older. It
consists of 17 sets ranked in order of difficulty.
Each set includes 12 items, each item containing a picture. The child is asked to say the
name of the object or activity in the picture. An
initial, ‘basal’ set is given based on a child’s
age and is selected to be very easy for the
child. If the child correctly answers 11–12
items in the basal set, the next more difficult
set is administered. The child is given increasingly difficult sets until reaching a set that is
too difficult (i.e. unable to correctly answer at
least five items in the set). The PPVT score is
the number of correctly answered items out of
all sets taken by the child, plus the number of
4. One of these 15 questions was found to have poor
statistical properties based on Item Response Theory, and
was dropped from the calculation of the total score.
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items in all sets that were easier than the basal
set, under the assumption that the child would
have correctly answered all items in these
easier sets.5
In round 2, the CDA-Q scores are missing
for 64 of the 1,970 younger cohort children for
unknown reasons, leaving 1,906 children with
CDA-Q scores. The PPVT scores are missing
for 223 children, mainly because the test was
not administered properly to most of these
children, leaving 1,747 children with PPVT
scores. The reasons for the missing test scores
are as follows. First, the basal set of questions
was too difficult for 97 of the younger cohort
children. Second, for 104 children, the test was
stopped ‘too early’, that is before the child had
reached the set that was too difficult for him or
her. If either of these two mistakes in test
administration occurs, the test is considered
invalid and no score is contained in the data.
This leaves 1,769 children (=1,970—201), but
another 22 also had missing PPVT scores,
leaving a sample of 1,747 with PPVT scores.
Of these 22, 18 suffered from physical or
mental disabilities that precluded them from
taking the test, two were prevented from taking
the test due to insufficient lighting, and one
could not take the test due to a vision or
hearing problem.
Next, consider the tests taken by the older
cohort, who were 8 years old in round 1 (2002)
and 12 years old in round 2 (2006). Very
simple reading and writing tests were administered to these children in both rounds, and a
very simple mathematics test was administered in round 1. Most of the children received
the highest scores on these tests, leaving little
variation to be explained, so these tests will not
be analysed in this article.
The older cohort also took a mathematics
test in round 2, which consists of 10 questions
(five multiple-choice and five open-ended
questions) chosen from the Trends in International Mathematics and Science Study developed by the IEA in 2003, focusing on number
5. As in the CDA-Q test, some items were found to have
poor statistical properties in the Vietnam context and thus
were excluded from the calculation of the total PPVT
scores.
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sense and basic mathematical skills. One question was excluded from the analysis owing to
potential gender bias, yielding a maximum
score of 9. The older cohort also took the same
PPVT test taken by the younger cohort in
round 2.
Of the 990 older cohort children tested in
round 2, mathematics scores are missing for
nine cases; for two children the test conditions
were not appropriate, but no reason is specified
for the other seven. PPVT scores are missing
for 45 children, for the following reasons.
First, one child did not take the test because of
a serious mental or physical disability. Second,
for 11 children the PPVT test was stopped too
early, and for 31 children the basal set was not
correctly set during test administration.
Finally, the test conditions were inadequate
(insufficient light) for two children. Note that
14 children did not have their tests stopped at
the right point, but such continuing the test
when one should stop still allows one to obtain
the correct score (the one that would have been
recorded if the test had been stopped at the
correct point), and so these observations still
have valid test scores.
4. Methodological Issues6
Education increases individuals’ well-being
primarily through their acquisition of cognitive (e.g. literacy and numeracy) and noncognitive (e.g. social and organisational)
skills. This skill acquisition process can be
studied using the education production function framework, which provides crucial guidance on how to use education data to estimate
the causal determinants of acquired skills.
How cognitive skills are ‘produced’ depends
on many factors (or ‘inputs’), including child,
household, and school variables. The relationship between skills learned (A) and these
factors can be described in a cognitive skills
production function:
A = a (Q, C, H, I )
(1)
6. For a more detailed discussion of the issues discussed
in this section, see Glewwe and Lambert (2010).
September 2015
where Q is the set of school and teacher characteristics, C is child characteristics and H is
household characteristics that affect learning; I
is educational ‘inputs’ that households contribute, such as children’s years of schooling and
daily attendance, and purchases of textbooks
and other educational materials.
Unfortunately, production functions are
often difficult to estimate. To see why, consider
a simple linear specification of equation (1):
A = β0 + βQ1Q1 + βQ 2 Q 2 + … + βC1C1
+ βC 2 C2 + … + βH1H1 + βH 2 H 2
(1′)
+ … + β I1 I1 + β I 2 I 2 + … + u A
where each variable in Q, C, H and I is shown
explicitly. Assuming linearity is not very
restrictive if one adds squared and interaction
terms to the variables in (1). The ‘error term’
(uA) accounts for all variables in (1) that are
not in the data, as well as for measurement
errors in A and in the right-hand-side variables
in (1′).
By standard econometric theory, the causal
impacts of the observed variables in (1′) on
learning (the βs) can be consistently estimated
by using ordinary least-squares (OLS) only if
uA is uncorrelated with ALL of the observed
‘explanatory’ variables. Unfortunately, uA is
likely to be correlated with those variables, and
so will cause biases in OLS estimates of the βs
in (1′) for three reasons.7
4.1 Omitted Variables
No dataset contains all the explanatory variables in equation (1). Difficult-to-observe variables include: teachers’ motivation and head
teachers’ management skills (Q), children’s
ability and motivation (C), parents’ willingness and capacity (H) to help, and their time
spent helping (I) with children’s schoolwork.
These variables, if unobserved, are probably
correlated with some observed variables in
(1′), causing omitted-variable biases. For
7. A fourth problem, selection and attrition bias, is not
discussed here as it seems unlikely to be a problem with
the Young Lives data, which tests all children whether or
not they are currently in school. See Glewwe and Lambert
(2010) for an explanation of this type of bias.
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Glewwe et al.: Young Lives Data Analysis
example, ‘high-quality’ schools are usually
better in many dimensions, both observed and
unobserved. This produces positive correlation
between uA and observed school quality variables, causing overestimation of the impacts of
those variables. Omitted-variable bias can also
lead to underestimation. For example, high
school quality may lead parents to reduce time
spent helping their children, generating negative correlation between school quality and uA
(e.g. working through some unobserved parental efforts). Omitted-variable bias affects estimates of the βs not only for observed variables
that are correlated with uA but also for
observed variables that are uncorrelated with
u A.
4.2 Endogenous Program Placement
School quality could also be correlated with uA
if governments improve schools with unobserved education problems (Pitt et al. 1993).
Governments may also raise school quality in
areas with good education outcomes, if those
areas have political influence (World Bank
2001). The former causes underestimation of
school quality impacts on learning, while the
latter causes overestimation.
4.3 Measurement Error
Even the best survey data collected in developing countries contain many errors. Data on
child, household, and school characteristics
may be inaccurate or out of date. Because measurement error is the difference between the
true and observed values of a variable, it
causes uA to be correlated with the observed
variable. Random measurement error typically
causes underestimation of true impacts, while
non-random errors could cause either underestimation or overestimation.
This article addresses each of these estimation problems. Regarding the first problem,
omitted-variable bias, the best approach is to
collect data on as many of the explanatory
variables in equation (1′) as possible. Given
the richness of the Young Lives data, many,
and perhaps even most, of the variables in (1′)
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are available. In addition, in regressions the
impacts of all school quality variables can be
controlled for by using commune fixed effects.
Strictly speaking, this works only if the school
quality variables do not interact with the other
variables that determine test scores, which is
unlikely to hold. Nevertheless, the ability of
these dummies to fully control for the impacts
that do not include interaction terms should
remove the most serious estimation problems
due to unobserved school quality variables.
A related point is that community fixed
effects work best if all children enroll in the
school located in their commune and there is
only one school per commune (or there are
multiple schools of similar quality). While
children generally enroll only in schools in
their commune, about half of the communes
have more than one school (see Appendix 1
Table A2). Yet in most of the communes with
multiple primary schools, those schools were
probably very similar, since they had names
such as ‘An Hoa Primary School No. 1’ and
‘An Hoa Primary School No. 2’, and only three
older cohort students were attending private
primary schools. We tried to obtain the names
of primary schools attended for older cohort
children (which would allow us to use school
fixed effects instead of commune fixed
effects), but we have only the current school
attended, which for most older cohort children
is the lower secondary school.
The second problem, endogenous program
placement, seems unlikely to be a problem in
the Vietnamese context. District governments
are responsible for financing primary and preschool education in Vietnam, and historically
there has been little attempt by the central
Government to provide additional resources to
poorer districts (World Bank 1997). The
amount of money transferred by provincial or
district governments to schools is a set amount
per pupil, or more recently a set amount per
school-aged child, without reference to academic performance (World Bank 2003; Nordic
Consulting Group 2008). Although in recent
years the Government has instituted some programs that are intended to provide more
resources to poorer districts and communes,
the amount of funds allocated by these initia-
© 2015 The Authors. Asia and the Pacific Policy Studies
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502
Asia & the Pacific Policy Studies
tives is very small (less than 1 percent of total
school expenditure) and the share of primary
school expenditure received from these
sources appears to be uncorrelated with
district-level poverty rates (Nordic Consulting
Group 2008), which suggests little reason to
worry about bias from endogenous program
placement. Finally, regressions using school/
commune fixed effects will avoid bias due to
any omitted school variables, as explained in
the previous paragraph.
The third estimation problem, measurement
error bias, can be addressed using instrumental
variables as long as the measurement errors in
the instruments are uncorrelated with those in
the explanatory variables. The variable most
likely to suffer from measurement error is
household expenditure per capita, which will
be instrumented using a household wealth
index. Another variable that could suffer from
measurement error is the height-for-age
z-score, an indicator of early childhood malnutrition. The measurement error arises from
the fact that there is variation in height, and
weight, even among healthy children, which
implies that this z-score is a noisy measure of
early childhood nutritional status. In estimates
not reported (but available upon request), we
used each child’s weight-for-age z-score as an
instrument for his/her height-for-age z-score.
This had little effect on the results, and in any
case it is unlikely to be a valid instrument
because the same ‘noise’ in height-for-age also
affects weight-for-age (among healthy children some are naturally taller, and they will
usually also be heavier).
Given that the main aim of this article is to
examine the underlying causes of the Kinh–
ethnic minority learning differences, the
decomposition proposed by Blinder (1973)
and Oaxaca (1973) is adopted to explore these
differences. More specifically, consider estimating equation (1′) separately for the Kinh
(subscripted k) and ethnic minority (subscripted m) populations:8
8. In Vietnam, the Chinese ethnic minority is not considered disadvantaged, and shares many cultural similarities
with the Kinh, so in virtually all studies of ethnic minorities in Vietnam, the Chinese are grouped together with the
Kinh and together they are considered the ‘ethnic major-
September 2015
A k = β0 k + β k ′ x k + u Ak
(2)
A m = β0 m + βm ′ x m + u Am
(Kinh)
(3)
(ethnic minority)
where all the coefficients in equation (1′)
except the constant term have been incorporated into the β vectors, and all of the explanatory variables have been incorporated into the
x vectors. Note that the impacts of the x variables on test scores (the βs) can differ across
the two ethnic groups.
Since the mean values of the error terms (uAk
and uAm) in equations (2) and (3) equal zero,
the following relationships hold, where ‘bars’
indicate mean values of variables for the
respective ethnic group:
A k = β0 k + β k ′ x k
(2′)
A m = β0 m + βm ′ xm
(3′)
Thus, the between-group difference in the
mean test scores can be expressed as:
A k – A m = (β0 k – β0 m ) + (β k ′ x k – βm ′ xm ) (4)
The term (β k ′ x k − βm ′ xm ) can be decomposed into two parts, one reflecting the
between-group difference in the means of the x
variables and the other reflecting the betweengroup difference in the coefficients:
A k – A m = (β0 k – β0 m ) + β k ′ ( x k – xm )
(5)
+ (β k – βm ) ′ xm
This decomposition can also be done in
another, analogous, way, which multiplies the
between-group differences in the means by βm
and multiplies the between-group differences
in the βs by x k :
ity’. This article follows this classification, although in
practice it makes no difference because only one of the
2,000 younger cohort children is Chinese, and none of the
1,000 older cohort children is Chinese.
© 2015 The Authors. Asia and the Pacific Policy Studies
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Glewwe et al.: Young Lives Data Analysis
A k – A m = (β0 k – β0 m ) + βm ′ ( x k – xm )
+ (β k – βm ) ′ x k
(6)
In most cases, these two decompositions
should give similar results. Both will be shown
below.
To see how to interpret these decompositions, consider equation (5). The first term,
β0k − β0m, shows the differences in test scores
unaccounted for by mean differences in the x
variables, nor by the differences in their
impacts (the βs), for some reference value for
x (averaged over all groups). Many studies
implicitly set the reference value of x to zero,
but this could be misleading if some x variables never attain a value of zero. For example,
consider the case of only one explanatory variable for a test score, namely years in school.
Suppose that years of school has a stronger
effect on Kinh children than on ethnic minority
children, perhaps because the former attend
higher quality schools. If all children have had
4 or more years of schooling, fitting separate
regression lines for both Kinh and ethnic
minority children will lead to a line with a
higher slope for Kinh children, which if
extended back to the point where years of
schooling equals 0 could lead to an intercept
for Kinh children that is lower than that for
ethnic minority children, that is, β0k ⬍ β0m,
which could be mistakenly interpreted as ‘discrimination’ against Kinh children even if the
regression line for the Kinh lies above that of
the ethnic minority children for all values of
years of schooling in the population. The point
here is that interpretation of β0k − β0m must be
done very cautiously. This article sets the reference value as the lowest value for each x
variable; thus β0k − β0m is the difference in the
test scores of a child for whom all the x variables take their lowest value. This should be
kept in mind when interpreting the contribution of β0k − β0m to A k − A m .
The second term in equation (5),
β k ′ ( x k − xm ) , is the sum, over all the x variables, of the contributions of the Kinh–ethnic
minority differences in the mean values of the
x variables to explaining the Kinh–ethnic
minority differences in the mean test scores.
For example, as will be seen below, on
503
average, Kinh children have better-educated
parents than do ethnic minority children, and
educated parents have a direct, positive impact
on test scores. In fact, the contributions of each
of the x variables can also be obtained separately from the regression estimates, and
indeed are shown below.
Finally, the last term in equation (5),
(β k − βm ) ′ xm , is the sum, over all the x variables, of the contributions to the Kinh–ethnic
minority gap in mean test scores caused by the
Kinh–ethnic minority differences in the
impacts of the x variables on test scores. For
example, it may be that parental education has
a higher impact on test scores for the Kinh than
for ethnic minorities, perhaps due to higher
school quality among the Kinh population.
Again, while this term sums up these effects
for all the x variables, the impact for each x
variable can be identified in the regression
(shown below).
Recall that this article controls for differences in school quality by controlling for
commune fixed effects. Note that, while in most
of the communes (22 or 23 out of 31, depending
on the cohort) are ‘segregated’ (either all Kinh
or all ethnic minority), the effects of the school
variables could vary by ethnic group in the (8 or
9 out of 31) ‘mixed’ communes. Thus, in those
communes alone the regressions include an
interaction term between the commune dummy
and the ethnic minority dummy to capture this
(potential) differential.
A final issue with the use of commune fixed
effects is that there is no longer a constant term
for either ethnic group. In effect, the constant
term for the Kinh is the (weighted) average of
the commune dummy variables for the communes in which the Kinh population resides,
and the same for the ethnic minority population (in which case the interaction term
between the ethnic minority dummy and the
commune dummy in the ‘mixed’ communes
must be added to the commune dummy). The
averages of these dummy variables are shown
in the results below. Note that they represent
differences in school quality as well as the
(β0k − β0m) term; these two differences cannot
be distinguished in estimation using commune
fixed effects.
© 2015 The Authors. Asia and the Pacific Policy Studies
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504
Asia & the Pacific Policy Studies
5. Analysis for the Younger Cohort
This section examines the test scores of the
younger cohort who were 5 years old when
tested in 2006.The analysis first compares the
test scores of Kinh and ethnic minority children, first for the entire sample and then for the
subsample of ‘mixed’ communes. It then presents regression estimates of the determinants
of test scores, and uses them to explain the
Kinh–ethnic minority gaps in test scores.
Table 1 presents means and standard deviations (SDs) of the CDA-Q and PPVT scores,
first for the full sample and then separately for
the two ethnic groups. Panel A shows statistics
for all students, while panel B is limited to the
nine ‘mixed’ communes.
Beginning with panel A, the Kinh–ethnic
minority gap in the CDA-Q (mathematics)
score is quite large: the mean score of the Kinh
is 10.2 (out of 14) while that of ethnic minorities is 7.4. The difference of 2.8 points is
equivalent to 1.1 SDs of the distribution of test
scores. The mean PPVT scores were 39.4 and
September 2015
23.5 for the Kinh and ethnic minorities, respectively. The gap, 15.9 points, is equivalent to 0.9
SDs of the distribution of test scores.
It is also informative to distinguish between
ethnic minority children whose ‘mother
tongue’ is Vietnamese and those whose mother
tongue is not. (The ethnic groups least likely to
speak Vietnamese as their mother tongue are
the H’mong (3 per cent) and the Dao (24 per
cent), while those most likely to speak Vietnamese are the Cham (97 per cent) and the Ba
Na (64 per cent).) The former group (about one
third of ethnic-minority children) scored much
higher than the latter group. More specifically,
the mean CDA-Q score for the former group
(8.3) is about 0.5 SDs larger than that for the
latter group (6.9). The difference on the PPVT
test is even higher: the former group scored
(30.1) about 0.6 SDs higher than the latter
group (20.1).
As explained above, part of the Kinh–ethnic
minority gap in the test scores may reflect the
fact that the two ethnic groups live in different
communities and attend different schools. To
Table 1 Mean Test Scores for Ethnic Majority and Ethnic Minority Children (Younger Cohort, 5 Years Old)
Student type
A: All communes
Full sample
Kinh
Ethnic minority
Ethnic minority (speaks Vietnamese)
Ethnic minority (speaks other language)
B: Mixed communes
Full sample
Kinh
Ethnic minority
Ethnic minority (speaks Vietnamese)
Ethnic minority (speaks other language)
Variable (raw score)
Mean
Standard deviation
Number of observations
CDA-Q
PPVT
CDA-Q
PPVT
CDA-Q
PPVT
CDA-Q
PPVT
CDA-Q
PPVT
9.79
37.0
10.20
39.4
7.36
23.5
8.26
30.1
6.88
20.1
2.51
18.2
2.29
18.0
2.34
12.2
2.09
12.1
2.32
10.7
1,906
1,747
1,631
1,480
275
267†
95
92
180
174
CDA-Q
PPVT
CDA-Q
PPVT
CDA-Q
PPVT
CDA-Q
PPVT
CDA-Q
PPVT
8.94
32.1
9.78
37.0
7.86
25.8
7.90
29.8
7.83
23.2
2.35
14.8
1.97
14.6
2.36
12.6
2.23
13.3
2.45
11.5
369
356
209
202
160
154
61
60
99
94
†Small discrepancies in some of these figures are due to missing data on whether one ethnic minority child speaks
Vietnamese or another language.
CDA-Q, Cognitive Development Assessment test-quantitative; PPVT, Peabody Picture Vocabulary Test.
© 2015 The Authors. Asia and the Pacific Policy Studies
published by Crawford School of Public Policy at The Australian National University and Wiley Publishing Asia Pty Ltd
505
Notes: Per capita expenditure is instrumented, using a household wealth index. Significance is based on robust standard errors, clustered at the commune level. Sample size is 1,815.
Significance at the 10%, 5% and 1% levels is denoted by *, ** and ***, respectively.
0.069
0.113
0.174
0.000
0.066
0.001
0.027
0.0
0.025
−1.184
0.0
0.0
0.0
0.480
0.0
0.0
0.0
0.0
0.911
0.113
0.174
0.000
0.009
0.001
0.027
0.000
0.025
−2.027
0.0
0.0
0.0
0.535
0.0
0.0
0.0
0.0
1.943
8.37
7.72
0.49
15.28
3.977
5.541
6.739
17.62
Log (per capita expenditure)
Father’s education
Mother’s education
Girl
Child’s age
Height-for-age z-score
Log (educ. expenditure)
Time spent in nursery
Time spent in pre-school
Average const. (segregated)
Average const. (mixed)
0.087
0.022**
0.031***
0.015
0.042***
0.001
0.009
0.000
0.004
−1.226
−0.868
1.128***
0.022**
0.031***
0.015
0.006
0.001
0.009
0.000
0.004
−2.545
−2.325
−1.043***
0.0
0.0
0.0
−0.035**
0.0
0.0
0.0
0.0
1.135
3.24
2.11
0.463
13.71
2.863
2.501
0.555
11.48
(β k − β m ) ′ xm
β m ′ ( xk − xm )
(β k − β m ) ′ xk
xm
xk
βk − βm
βm
βk
Variables
Table 2 Regression Estimates for CDA-Q Test, Younger Cohort
‘control for’ differences in communities and
schools, panel B of Table 1 limits the comparison across ethnic groups to the nine ‘mixed’
communes. This does reduce the gaps somewhat. The difference in the CDA-Q score (1.9)
is 32 per cent smaller than the gap when comparing all communes (2.8). Similarly, the difference in the PPVT score (11.2) is 30 per cent
smaller than the gap when comparing all communes (15.9). Nevertheless, there are still
large Kinh–ethnic minority gaps even in the
‘mixed’ communes. Note also that the gap
between Vietnamese-speaking and nonVietnamese-speaking ethnic minorities is much
smaller than for the full ethnic minority sample;
indeed for the CDA-Q test there is almost no
difference.
To understand better the nature of the gaps,
we next present regressions that attempt to
explain the Kinh–ethnic minority gaps in the
CDA-Q (Table 2) and PPVT scores (Table 3).
The test scores are standardised to have an
SD of 1, for ease of interpretations of the
coefficients.
Recall that the Blinder–Oaxaca decomposition divides the overall Kinh–ethnic minority
gaps in mean test scores into three parts: (i) the
differences attributable to the differences in
the means of the xs; (ii) the differences due to
different impacts of the xs (the βs) on test
scores; and (iii) differences in the constant
terms (the β0s). In fact, the younger cohort
includes only 283 ethnic minority children, so
the precision of the estimated βs for ethnic
minorities is often low, making these estimates
not significantly different from those for the
Kinh children. In this case, allowing separate
estimates for the two ethnic groups can
produce large apparent ‘explanations’ of the
test scores gaps that are, in fact, not statistically significant. Thus, whenever the difference βk − βm was not statistically significant
for a given variable, the two associated βs were
constrained to be equal. This should also
increase the precision of estimates of the
impact of differences in the means of the x
variables on the Kinh–ethnic minority test
scores gaps.
The first and second columns of Table 2
show the estimates of βk in equation (5) and
β k ′ ( xk − xm )
Glewwe et al.: Young Lives Data Analysis
© 2015 The Authors. Asia and the Pacific Policy Studies
published by Crawford School of Public Policy at The Australian National University and Wiley Publishing Asia Pty Ltd
Notes: Per capita expenditure is instrumented, using a household wealth index. Significance is based on robust standard errors, clustered at the commune level. Sample size is 1,668.
Significance at the 10%, 5% and 1% levels is denoted by *, ** and ***, respectively.
−0.621
0.0
0.0
0.0
0.493
0.0
0.0
0.0
0.0
0.718
0.103
0.185
−0.001
0.029
0.056
0.024
0.011
0.023
−1.063
0.0
0.0
0.0
0.550
0.0
0.0
0.0
0.0
1.943
8.37
7.72
0.49
15.28
3.977
5.541
6.739
17.62
Log (per capita expenditure)
Father’s education
Mother’s education
Girl
Child’s age
Height-for-age z-score
Log (educ. expenditure)
Time spent in nursery
Time spent in pre-school
Avg. const. (segregated)
Avg. const. (mixed)
0.341***
0.020**
0.033***
−0.041
0.054***
0.050
0.008
−0.002
0.004
−2.094
−1.913
0.888***
0.020**
0.033***
−0.041
0.018**
0.050
0.008
−0.002
0.004
−2.086
−2.408
−0.547**
0.0
0.0
0.0
0.036***
0.0
0.0
0.0
0.0
1.135
3.24
2.11
0.463
13.71
2.863
2.501
0.555
11.48
(β k − β m ) ′ xm
β m ′ ( xk − xm )
(β k − β m ) ′ xk
xm
xk
βk − βm
Variables
βk
βm
Table 3 Regression Estimates for PPVT Test, Younger Cohort
0.276
0.103
0.185
−0.001
0.085
0.056
0.024
−0.012
0.024
Asia & the Pacific Policy Studies
β k ′ ( xk − xm )
506
September 2015
those of βm in equation (6), respectively.9 The
third column shows the difference in the two
estimates. (The actual regression coefficients
are those in columns 1 and 3 of Table 2, and
statistical significance is shown for those two
columns; column 2 is calculated as the difference, and calculating its statistical significance
is straightforward.) Somewhat surprisingly,
household per capita expenditure has no significant effect on Kinh children’s CDA-Q
scores, but it has a large and statistically significant effect among ethnic minority children.
This difference reflects partly the fact that
ethnic minorities are poorer, and income
effects may be stronger for the poor.
Both father’s and mother’s education have
strong impacts on the test scores of both
groups of children, although the betweengroup differences in their impacts were not
statistically significant. Child age had a significant impact on the CDA-Q test for Kinh children but not for ethnic minority children.
Presumably, this reflects the fact that older
children are, ceteris paribus, more mature and
thus have acquired more skills; recall that
almost none of these children have started
primary school so there is no variation in the
sense that older children have been at school
longer. It is not clear why this ‘maturity’ effect
does not show up very strongly among ethnic
minority children.
For all of the remaining variables—child
sex, height-for-age z-scores, educational
expenditure on the child, months spent in a
community nursery (crèche) from birth to 36
months of age, and months spent in a preschool since 36 months of age—neither the
coefficient nor the Kinh–ethnic minority
difference in the coefficients is statistically
significant.
The last two lines in Table 2 show the
average constant term (average community
fixed effect) for the two ethnic groups, separately for segregated (all Kinh or all ethnic
minority) and mixed communes. Two main
lessons can be drawn from these figures. First,
9. These are instrumental variable estimates; to minimise
measurement error bias, the (log of) per capita expenditure
variable is instrumented by an index of household wealth.
© 2015 The Authors. Asia and the Pacific Policy Studies
published by Crawford School of Public Policy at The Australian National University and Wiley Publishing Asia Pty Ltd
Glewwe et al.: Young Lives Data Analysis
for both ethnic groups the constant terms are
fairly similar whether they live in segregated
or mixed communes; in particular, there is no
large advantage for ethnic minority children
and no disadvantage for Kinh children from
living in a mixed commune. Second, there is a
Kinh–ethnic minority gap of 1.3–1.5 SDs of a
test score, even after controlling for all other
variables. This is rather surprising given that
the unadjusted difference in Table 1 (about 1.1
SDs) was smaller. However, as discussed
above, these differences in the (average) β0
terms are difficult to interpret and may simply
reflect ‘adjustments’ in the constant term to
accommodate differences in the ‘slope’ parameters that are statistically significant across the
two groups.
The Blinder–Oaxaca decomposition can be
used to see how much of the observed Kinh–
ethnic minority gap in the mean CDA-Q scores
is explained by the differences in the means of
the x variables (shown in columns 4 and 5 of
Table 2) and how much is explained by differences in the impacts of those variables. Note
that any variable that has no significant
explanatory power for either group will have
little role to play.
The role played by per capita expenditure is
the strongest. Since Kinh children live in
wealthier households, they can gain (using the
ethnic minority coefficient) about 0.9 SDs of a
test score, although this effect almost disappears when using the small Kinh coefficient
(0.1 SDs). More consistent across the two
decompositions, the difference in those two
coefficients is highly favorable to ethnic
minority children; they benefit much more
than Kinh children from an increase in household income, adding 1.2–2.0 SDs to their test
scores. Overall (combining the two separate
parts of the decomposition), rather than
explaining why ethnic minorities have lower
CDA-Q scores than do Kinh children, household income in effect increases the gap by
about 1.1 SDs.
Higher fathers’ and mothers’ education
among Kinh children together explain about
0.3 SDs of the gap in the CDA-Q scores. Intuitively, parental education raises test scores,
and Kinh children’s parents are much more
507
educated than the parents of ethnic minority
children; their fathers have, on average, 5 more
years of education and their mothers have
almost 6 more years of education. Child age
also explains a sizeable part of the Kinh–ethnic
minority gap; Kinh children ‘mature’ in some
way that ethnic minority children do not,
which boosts their scores by about 0.5 SDs.
But exactly what is happening here is far from
clear; it is unlikely to be a ‘biological’ maturation so presumably it may reflect something
about Kinh culture. On the other hand one
cannot rule out that this difference could be
spurious; there are nine explanatory variables
in Table 2, and even if all the differences in the
parameters between Kinh and ethnic minority
children were zero, there is a 1 out of 20
chance that any given difference will be significant at the 5 per cent, and this may be such
a case.
Overall, while the regression coefficients
usually have the expected signs, it is not clear
that the Blinder–Oaxaca decomposition is providing clear insights into the causes of the differences in the CDA-Q scores among the
younger cohort. Perhaps the main lesson is that
it matters little whether Kinh or ethnic minority children live in segregated communities or
mixed communities.
Next, consider the Kinh–ethnic minority
gap in the PPVT scores. The estimates in
Table 3 are analogous to those for the CDA-Q
test (Table 2). The first notable result is that,
for Kinh children, household expenditure per
capita has a strong and statistically significant
impact, in contrast with the small and insignificant impact it has on the CDA-Q scores.
The impact is even stronger for ethnic minority
children, and the difference in these impacts is
quite large and statistically significant.
In addition, both mothers’ and fathers’ education have significantly positive impacts on
both ethnic groups’ PPVT scores, as they did
on the CDA-Q scores (although the betweengroup differences in the coefficients are again
statistically insignificant). As with the CDA-Q
test, child age has a strong positive impact on
PPVT scores for both groups, although the
impact on ethnic minority children is much
smaller than on Kinh children, the difference
© 2015 The Authors. Asia and the Pacific Policy Studies
published by Crawford School of Public Policy at The Australian National University and Wiley Publishing Asia Pty Ltd
508
Asia & the Pacific Policy Studies
being statistically significant. Intuitively, children of better-educated parents learn more
quickly, and as they get older (even before
entering primary school), they learn more. For
other variables, as seen in Table 2, none has a
statistically significant effect for either ethnic
group, and the differences were also statistically insignificant.
Finally, the last two lines of Table 3 show
average constant terms across communes for
both ethnic groups, again separately for segregated and mixed communes. As before, it
matters little whether either group of children
lives in a segregated or mixed commune. More
interestingly, the between-group differences
are much smaller than in Table 2: only about
0.5 SDs of a test score for mixed communes,
and no difference at all for segregated communes. This implies that most of the raw difference in PPVT scores (about 0.9 SDs) is
explained by the regression results.
Now turn to the Blinder–Oaxaca decompositions for the Kinh–ethnic minority gap in the
PPVT scores, which was about 0.9 SDs of the
distribution of the PPVT score. The two
decompositions indicate that the difference in
the mean of (log) per capita expenditures
accounts for 0.3–0.7 SDs of this gap. But the
coefficient is much higher for ethnic minorities, which implies that, for an average ethnic
minority child, there is a 0.6–1.1 SD benefit.
As before, rather than explaining the gap, the
combined effect of per capita expenditure
increases it by about 0.3 SDs.
Similar to the case with the CDA-Q scores,
differences in the means of parental education
explain about 0.3 SDs of the Kinh–ethnic
minority gap, after summing the effects of both
parents. Child age has a much larger impact on
Kinh children than on ethnic minority children, explaining a very large part, about 0.5
SDs, of that gap. As one would expect, none of
the other variables provides any sizeable
explanation of the gap, given that they are all
statistically insignificant.
6. Analysis for the Older Cohort
This section analyses the data for the older
cohort children, who were 12 years old in
September 2015
2006. As in Section 5, the analysis first documents the Kinh–ethnic minority gaps in the
test scores, first for the entire sample and then
for mixed communes. It then presents estimates of a cognitive skills production function
that attempt to explain the determinants of test
scores and the gaps in test scores.
Table 4 presents means and SDs of the
mathematics and PPVT scores for the full
sample and separately for the two ethnic
groups. Panel A shows statistics for all students, while panel B limits the sample to the
nine mixed communes.
As with the younger cohort, the Kinh–ethnic
minority gap is quite large: the difference in
the mean mathematics score between the Kinh
(7.8 out of 9) and ethnic minorities (5.3) is 2.5
points, equivalent to 1.3 SDs of the distribution
of test scores. Similarly, the mean PPVT score
for the Kinh (142.3) was much higher than that
for ethnic minorities (104.3). This 38-point
gap is equivalent to 1.5 SDs of the distribution
of test scores. Moreover, as seen with the
younger cohort, Vietnamese-speaking ethnic
minority children do much better than their
non-Vietnamese-speaking counterparts.
Limiting the above comparison to the nine
mixed communes reduces the Kinh–ethnic
minority gaps to some extent. The gap in the
mathematics test (1.8) is 28 per cent smaller
than the gap when comparing all communes
(2.5). Similarly, the gap in the PPVT score
(25.2) is 34 per cent smaller than the gap when
comparing all communes (38.0). Nevertheless,
there are still large Kinh–ethnic minority gaps
even among children in the same commune.
Note also that in the mixed communes the gaps
between the Vietnamese-speaking and nonVietnamese-speaking ethnic minority children
are much smaller than was the case for all
communes.
To understand better the nature of the gaps,
turn next to regression estimates and Blinder–
Oaxaca decompositions for the Kinh–ethnic
minority gaps in the mathematics (Table 5)
and PPVT (Table 6) scores. Again, the test
score has been standardised to have an SD of 1.
The first and second columns of Table 5
show the estimates of βk in equation (5) and
those of βm in equation (6), respectively, for the
© 2015 The Authors. Asia and the Pacific Policy Studies
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Glewwe et al.: Young Lives Data Analysis
509
Table 4 Mean Test Scores for Ethnic Majority and Ethnic Minority Children (Older Cohort, 12 Years Old)
Student type
Variable (raw score)
A: All communes
Full sample
Math (IEA)
PPVT
Kinh
Math (IEA)
PPVT
Ethnic minority
Math (IEA)
PPVT
Ethnic minority (speaks Vietnamese)
Math (IEA)
PPVT
Ethnic minority (speaks other language) Math (IEA)
PPVT
B: Mixed communes
Full sample
Math (IEA)
PPVT
Kinh
Math (IEA)
PPVT
Ethnic minority
Math (IEA)
PPVT
Ethnic minority (speaks Vietnamese)
Math (IEA)
PPVT
Ethnic minority (speaks other language) Math (IEA)
PPVT
Mean
Standard deviation
Number of observations
7.44
137.6
7.75
142.3
5.28
104.3
6.27
119.8
4.18
86.8
1.92
26.1
1.51
18.8
2.78
41.5
2.31
31.6
2.85
45.0
981
945
855
827
126
118†
66
63
60
54
6.62
130.4
7.44
141.8
5.64
116.6
5.86
117.9
4.32
117.9
2.32
29.1
1.58
18.6
2.66
33.3
2.47
30.7
3.40
33.3
217
206
118
113
71
68
49
48
22
19
†Small discrepancies in some of these figures are due to missing data on whether one ethnic minority child spoke
Vietnamese or another language.
IEA, International Evaluation Association; PPVT, Peabody Picture Vocabulary Test.
mathematics score of the older cohort. The
third column shows the few cases of statistically significant differences in the two
estimates.
Household per capita expenditure has a significantly positive impact on the mathematics
scores of both ethnic groups (the difference
between the two coefficients is statistically
insignificant). Both mothers’ and fathers’ education also have significantly positive impacts
on the mathematics scores of both groups.
Turning to child characteristics, although
there are no gender differences for Kinh children there is a strong gender difference for
ethnic minorities: girls scored about 0.3 SDs
higher than boys, ceteris paribus. Years of
schooling has a strong and statistically significant impact on both groups of children, with a
significantly larger impact on the ethnic
minorities. Also, the hours per day spent in
school has a strong and significantly positive
impact on both ethnic groups (although the
difference between these two impacts is statistically insignificant). Finally, hours spent
working has a negative impact, significant at
the 10 per cent level for both groups.
The last five variables in Table 5 measure
different aspects of health and disability, and
three are statistically significant for both ethnic
groups (with no statistically significant different impacts by ethnic group). First, the heightfor-age z-score has a significantly positive
impact. Second, children who have difficulty
understanding what their parents are saying
have much lower scores (0.7 SDs lower).
Third, children who had had an injury or
episode of illness in the last 4 years that was so
severe that the parents thought they might die
have significantly lower scores (0.2 SDs
lower).
Examination of the average constant terms
again shows that, it matters little whether Kinh
students are in a segregated or a mixed
commune. However, for ethnic minority students, living in a mixed commune implies a
drop of about 1.2 SDs in a test score relative to
living in a segregated commune. Moreover,
ethnic minority students seem to do worse in
© 2015 The Authors. Asia and the Pacific Policy Studies
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0.269**
0.024**
0.024***
0.014
−0.008
0.009
0.256***
0.141***
0.010
−0.048*
0.003
0.064**
−0.023
−0.668***
−0.043
−0.244**
−3.327
−3.474
βk
0.269**
0.024**
0.024***
0.014
0.271*
0.009
0.358**
0.141***
0.010
−0.048*
0.003
0.064**
−0.023
−0.668***
−0.043
−0.244**
−3.380
−4.582
βm
0.0
0.0
0.0
0.0
−0.279*
0.0
−0.102*
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
βk − βm
2.085
8.515
7.651
6.027
0.502
15.163
5.954
4.504
2.901
1.826
1.913
3.728
0.208
0.015
0.063
0.056
xk
1.384
2.902
1.619
2.905
0.503
13.669
5.133
4.000
1.579
3.495
0.291
2.721
0.007
0.031
0.086
0.055
xm
0.701
5.613
6.032
3.122
−0.001
1.494
0.821
0.504
1.322
−1.669
1.622
1.007
0.201
−0.016
−0.023
0.001
( xk − xm )
0.0
0.0
0.0
0.0
−0.140
0.0
−0.607
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
(β k − β m ) ′ xk
0.189
0.135
0.145
0.044
−0.000
0.013
0.294
0.071
0.013
0.080
0.005
0.064
−0.005
0.011
0.001
−0.000
β m ′ ( xk − xm )
0.0
0.0
0.0
0.0
−0.150
0.0
−0.688
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
(β k − β m ) ′ xm
0.189
0.135
0.145
0.044
0.000
0.013
0.210
0.071
0.013
0.080
0.005
0.064
−0.005
0.011
0.001
−0.000
β k ′ ( xk − xm )
Notes: Per capita expenditure is instrumented, using a household wealth index. Significance is based on robust standard errors, clustered at the commune level. Sample size is 893.
Significance at the 10%, 5% and 1% levels is denoted by *, ** and ***, respectively.
Log (per capita exp.)
Father’s education
Mother’s education
Log (educ. expend.)
Girl
Child’s age
Years of schooling
Hours in school/day
Hours spent studying/day
Hours spent working/day
Extra math class (last 6 months)
Height-for-age z-score
Hearing problem
Understands what parents say
Long-term health problem
Serious illness/injury (last 4 years)
Average const. (segregated)
Average const. (mixed)
Variables
Table 5 Regression Estimates for Mathematics (IEA) Test, Older Cohort
510
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0.396***
0.024***
0.007
−0.030
−0.080
0.025***
0.320***
0.036
0.001
−0.011
0.021*
0.037
−0.618***
−0.191*
−0.148*
−0.104
−3.267
−3.370
βk
0.396***
0.024***
0.099
−0.030
−0.080
0.025***
0.320***
0.036
0.001
−0.011
0.021*
0.037
−0.618***
−0.191*
−0.148*
−0.104
−5.402
−2.673
βm
0.0
0.0
−0.092**
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
βk − βm
2.085
8.515
7.651
6.027
0.502
15.163
5.954
4.504
2.901
1.826
1.913
3.728
0.208
0.015
0.063
0.056
xk
1.384
2.902
1.619
2.905
0.503
13.669
5.133
4.000
1.579
3.495
0.291
2.721
0.007
0.031
0.086
0.055
xm
0.701
5.613
6.032
3.122
−0.001
1.494
0.821
0.504
1.322
−1.669
1.622
1.007
0.201
−0.016
−0.023
0.001
( xk − xm )
0.0
0.0
−0.704
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
(β k − β m ) ′ xk
0.278
0.135
0.597
−0.094
0.000
0.037
0.263
0.018
0.001
0.018
0.034
0.037
−0.124
0.003
0.003
−0.000
β m ′ ( xk − xm )
0.0
0.0
−0.149
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
(β k − β m ) ′ xm
0.278
0.135
0.042
−0.094
0.000
0.037
0.263
0.018
0.001
0.018
0.034
0.037
−0.124
0.003
0.003
−0.000
β k ′ ( xk − xm )
Notes: Per capita expenditure is instrumented, using a household wealth index. Significance is based on robust standard errors, clustered at the commune level. Sample size is 860.
Significance at the 10%, 5% and 1% levels is denoted by *, ** and ***, respectively.
PPVT, Peabody Picture Vocabulary Test.
Log (per capita exp.)
Father’s education
Mother’s education
Log (educ. expend.)
Girl
Child’s age
Years of schooling
Hours in school/day
Hours spent studying/day
Hours spent working/day
Extra math class (last 6 months)
Height-for-age z-score
Hearing problem
Understands what parents say
Long-term health problem
Serious illness/injury (last 4 years)
Average const. (segregated)
Average const. (mixed)
Variables
Table 6 Regression Estimates for PPVT Test, Older Cohort
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Asia & the Pacific Policy Studies
mixed communes than do Kinh students, a loss
of about 1.1 SDs. Yet caution is in order for the
results pertaining to ethnic minorities living in
a segregated commune because they are based
on a single commune.
Next, consider the decompositions for the
Kinh–ethnic minority gaps in the test scores of
the older cohort (last four columns of Table 5).
The difference in (log of) per capita expenditure explains about 0.2 SDs of the gap. Differences in fathers’ and mothers’ education
together explain another 0.3 SDs. The fact that
ethnic minority girls do much better than boys
actually increases the gap by about 0.15 SDs.
Turn next to the time-related variables. First,
although the fact that Kinh children have
somewhat more years of schooling explains
about 0.2–0.3 SDs of the gap, the larger impact
of years of schooling for ethnic minority children ‘unexplains’ 0.6–0.7 SDs of that gap. On
the other hand, about 0.1 SDs of the gap are
explained by the fact that Kinh children spend
about 30 more minutes per day in school than
do ethnic minority children, and the fact that
they work 1.7 fewer hours per day explains
another 0.1 SDs of that gap. Finally, better
nutrition (height-for-age) among Kinh children explained another 0.1 SDs. None of the
other components of the decomposition is very
large, and so none of them has noticeable
explanatory power.
Table 6 shows the regression and decomposition results for PPVT scores of the older
cohort. For both groups of children, household
per capita expenditure and fathers’ education
(but not mothers’ education for Kinh) have
strong impacts on learning. Girls do slightly
worse, but child age and years of schooling
have significantly positive impacts on the
PPVT score. Only one of the health variables,
a hearing problem, has a large and significantly negative effect, which is intuitive since
hearing problems are presumably more important for reading skills than for mathematics
skills.
The average constant terms show a 0.7 SD
difference between Kinh and ethnic minority
children who live in mixed communes, but
show no difference between Kinh children
living in segregated communes and those in
September 2015
mixed communes. However, there is a very
large gap between ethnic minority children
living in segregated communes and those
living in mixed communes; the former have
test scores almost 2.7 SDs lower. Again,
caution is needed as the sample contains only
one segregated ethnic minority commune.
Finally, turn to the decomposition results.
Kinh children’s test scores are 0.3 SDs higher
than ethnic minority children because, on
average, they live in wealthier households. The
positive impact of father’s education, in conjunction with the higher levels of that variable
among Kinh children, explains another 0.1
SDs. However, given the much higher impact
of mother’s education among ethnic minority
children, overall, that variable ‘unexplains’
about 0.1 SDs of the test score gap. The significant impact of years of schooling, combined with a higher value for that variable for
the Kinh children, explains almost 0.3 SDs of
the gap. Finally, Kinh parents are more likely
to report that their children have a hearing
problem, which ‘unexplains’ about 0.1 SDs of
that gap.
7. Conclusions
Ethnic minority children in Vietnam have
much lower mathematics and reading test
scores than do ethnic Kinh children, both
among a group of 5-year-old children and
among another group of 12-year-old children
tested in 2006. Given the importance of education in determining adults’ socio-economic
success, and the generally lower incomes of
ethnic minorities in Vietnam (Baulch et al.
2004), this suggests that today’s ethnic minority children will be poorer than today’s Kinh
children when they reach adulthood. A major
policy challenge for the Vietnamese government, and for donor agencies active in
Vietnam, is to understand the causes of these
disparities and then to formulate policies that
can reduce them. This article has investigated
the causes of these disparities, using the Round
2 Young Lives data from Vietnam. Although
the findings raise many questions, suggesting
the need for further research, some conclusions can be drawn based on these findings.
© 2015 The Authors. Asia and the Pacific Policy Studies
published by Crawford School of Public Policy at The Australian National University and Wiley Publishing Asia Pty Ltd
Glewwe et al.: Young Lives Data Analysis
A first, rather obvious but perhaps overlooked, finding is that these disparities are
already very large even before children start
primary school, as was seen with the younger
cohort data. It is possible that pre-school and
even nursery factors play a role in generating
these disparities, but it is difficult to pin this
down with the data available. Indeed, time
spent in nurseries and pre-schools had no
explanatory power in determining the younger
cohort’s test scores, so the problem may lie
elsewhere.
Second, language may play an important
role. Tables 1 and 4 show that, in general,
Vietnamese-speaking ethnic minority children
had much higher scores than their nonVietnamese-speaking counterparts. Yet when
comparisons are limited to children living in
‘mixed’ communes, these differences were
smaller, which suggests that it is really differences between communes, not language itself,
that matter. Perhaps one benefit of living in an
ethnically mixed commune is that ethnic
minority children interact with other children
in Vietnamese, and so obtain much more facility in that language at an early age. Note that
all tests were administered in whatever language the children wanted to take them in, so
the poor performance of ethnic minority children on these tests is not simply due to being
forced to take the test in Vietnamese. Clearly,
much more research needs to be done on the
role of mother tongue, and the mother tongue
of children’s peers, to understand more fully
why ethnic minority children fall behind.
Third, the Blinder–Oaxaca decompositions
offer at least a partial explanation of the Kinh–
ethnic minority gap in test scores. For the
younger cohort, the role played by household
expenditure is puzzling because although
513
ethnic minority children live in poorer households, they are more successful in ‘converting’
what little income they have into higher test
scores. In contrast, for the older cohort there is
no ambiguity; Kinh and ethnic minority children are equally capable at ‘converting’ household income into higher test scores, and the
higher per capita expenditure of Kinh households explains about 0.2–0.3 SDs of the gap
(of 1.3–1.5 SDs) in test scores. Parental education also plays a role, usually explaining
about 0.3 SDs of the gap for both the younger
and the older cohorts (the one exception being
the impact of mother’s education on the older
cohort’s PPVT scores, which is difficult to
interpret). None of the other variables offered
much explanatory power for explaining the
gap among the younger cohort. Among the
older cohort, more time spent in school, less
time spent working, and better nutritional
status each explain about 0.1 SDs of the gap in
the mathematics score, and more years of
schooling among Kinh children explains about
0.3 SDs the gap for the PPVT score.
Further progress on understanding the
causes of the Kinh–ethnic minority gap in
learning may require different data than those
analysed in this article. Qualitative research
along the lines of World Bank (2009) and
Vietnam Academy of Social Sciences (2009)
could be quite useful, and more quantitative
analysis using a dataset with a larger number
of ethnic minority students and more detailed
school data could be desirable. The 2006
Vietnam Household Living Standards Survey
is a promising dataset for further analysis of
this learning gap.
July 2015.
© 2015 The Authors. Asia and the Pacific Policy Studies
published by Crawford School of Public Policy at The Australian National University and Wiley Publishing Asia Pty Ltd
514
Asia & the Pacific Policy Studies
September 2015
Appendix 1
Table A1 Comparison of Young Lives Data to Nationally Representative Data
Younger cohort
Variable
Enrolled in school (%)
Ethnic minority (%)
Mother’s years of schooling
Father’s years of schooling
Height-for-age z-score (2002 VNHS)
Stunted in 2002 (HAZ ⬍ −2) (%)
Family ownership of durable goods (%):
Motorbike
TV
Refrigerator
Urban (%)
Electricity (%)
Water (%):
Borewell
Piped
Other well
Rain water
Other
Toilet (%):
Flush
Latrine
Field/pond
Other/none
Older cohort
Young Lives
data†
2006 VHLSS
data‡
Young Lives
data
2006 VHLSS
data
N/A
14.4
6.9
7.6
−0.53
10.0
N/A
20.1
6.8
7.6
−0.70
13.2
96.6
12.8
6.9
7.8
−1.42
27.8
94.0
18.3
7.0
7.8
−1.43
27.2
61.7
81.0
19.9
20.6
94.4
53.9
80.3
19.5
23.7
93.9
64.6
86.4
20.5
20.6
95.1
53.5
84.8
19.2
21.5
94.7
30.1
13.8
36.2
11.6
8.3
22.3
22.4
29.5
11.1
14.7
30.7
14.0
32.4
12.9
9.9
20.6
17.8
34.6
12.5
14.5
32.6
24.0
35.5
7.9
31.1
16.3
24.2
28.4
34.7
26.1
31.9
7.4
28.6
19.7
23.8
27.9
†Data refer to round 2, when the younger cohort children were about 5 years old and the older cohort were about 12 years
old, except height-for-age, which is from round 1.
‡Vietnam Household Living Standards Survey. These data are for 5-year olds (younger cohort) or 12-year olds (older
cohort) children. The height-for-age data are from the Vietnam Health Survey (VNHS), which was conducted in 2002.
© 2015 The Authors. Asia and the Pacific Policy Studies
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Glewwe et al.: Young Lives Data Analysis
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Table A2 Communes in Young Lives Survey for Vietnam
Province
District
Commune
(pseudonym)
Code
Phú Yên
Tuy Hòa
Tuy An
So’n Hòa
Sông Câ`u
So’n Hòa
Sông Câ`u
Bình D̄a·i
Bình D̄a·i
Châu Thành
Châu Thành
Bình D̄a·i
Bình D̄a·i
Châu Thành
Châu Thành
Bát Sát
Bă´c Hà
Ba? o Thă´ng
Ba? o Thă´ng
Bát Sát
Bă´c Hà
Ba? o Thă´ng
Chu Se
Tam Ky
Van Lan 1
Tuy Duc 1
Van Lan 2
Tuy Duc 2
Ha Tinh 1
Ha Tinh 2
Ly Hoa 1
Duc Lap 1
Ben Hai 1
Ben Hai 2
Ly Hoa 2
Duc Lap 2
Lang Ho 1
Krong Buk 1
Play Kep
Gian Son 1
Lang Ho 2
Krong Buk 2
Gian Son 2
VN001
VN002
VN003
VN004
VN005
VN006
VN007
VN008
VN009
VN010
VN011
VN012
VN013
VN014
VN015
VN016
VN017
VN018
VN019
VN020
VN021
Văn Giang
Văn Giang
Phù Cù’
Phù Cù’
Phù Cù’
Phù Cù’
Thanh Khê
Ha? i Châu
Thanh Khê
Thanh Khê
Thanh Khê
Liên Chiểu
Liên Chiểu
Na Hang
Ha Giang
Phu Thong 1
Cao Ky 1
Cao Ky 2
Phu Thong 2
Dai Tu
Pho Lu
Van Ban 1
Van Ban 2
Van Ban 3
Hania Lo 1
Hania Lo 2
VN022
VN023
VN024
VN025
VN026
VN027
VN028
VN029
VN032
VN033
VN034
VN035
VN036
Bê´n Tre
Lào Cai
Hu’ng
Yên
D̄à Nă˜ng
Number of
primary schools
Number of
satellite schools
Poverty
rate (%)
2 (1 and 2)
2 (1 and 2)
1
1
1
1
1
1
2 (A and B)
1
1
1
1
2 (A and B)
1
2 (A and B)
4(1,2,3 and 4)
2 (1 and 2)
1
1
3(1,2 and 3)
1
0
0
2
1
7
1
0
0
0
0
0
0
0
0
6
7
0
0
5
2
0
0
24.3
23.7
27.0
8.8
27.0
8.8
13.8
5.9
14.2
6.9
13.8
5.9
14.2
6.9
79.5
29.0
22.0
27.8
79.5
29.0
27.8
7.7
1
1
1
2 (A and B)
1
1
1
1
3
1
3(2 (AandB))
2
0
0
0
0
0
0
0
0
0
0
0
0
7.7
26.6
24.7
24.7
26.6
2.0
6.3
3.5
3.5
3.5
6.3
6.3
Note: Commune VN030 was split into two communes between 2002 and 2006; the new communes are called VN035 and
VN036. Similarly, VN031 was split into three communes: VN032, VN033 and VN034.
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