Theoretical Economics Letters, 2013, 3, 328-339
Published Online December 2013 (
Open Access TEL
Health and Household Income in Vietnam
Cuong Tat Do1*, Anh Ngoc Thi Ngo2
1Division of Public Policy Analysis, Institution of Economics,
National Academy of Administration and Politics, Hanoi, Vietnam
2Division of Economic Management, Institution of Economics,
National Academy of Administration and Politics, Hanoi, Vietnam
Email: *,
Received September 29, 2013; revised October 29, 2013; accepted November 6, 2013
Copyright © 2013 Cuong Tat Do, Anh Ngoc Thi Ngo. This is an open access article distributed under the Creative Commons Attri-
bution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited. In accordance of the Creative Commons Attribution License all Copyrights © 2013 are reserved for SCIRP and the owner of
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This study provides empirical evidence regarding to the relationship between household income and individual health,
as well as the correlation between health and education at provincial level. We apply the concept of human health capi-
tal theory into models which treat health as a form of human capital in income process and education progress. We em-
ploy two datasets, one is Vietnam Household Living Standard Survey wave in 2002, 2004 and 2006, and the other is the
dataset for provincial level in the year 1999, 2002 and 2004, in order to make two panels. Constructing panels allow us
to exploit “within” variation in health, income and education to figure out the possible unobservable biased estimates of
the impact of health on income and education on health in a short period of panel data. Household income is signifi-
cantly affected by individual health and life expectancy is considerably influenced by education. These findings could
be seen as evidence for policy makers in health and education policy in the context of development planning.
Keywords: Household Income; Individual Health; Life Expectancy; Illiterate Rate; Vietnam; VHLSS
1. Introduction
Since the mid of 1980s, Vietnam has experienced a trans-
formation from central planning economy to market-
oriented economy. This process has been demonstrated
by a significant growth of income per capita and the re-
duction of poverty [1]. In nearly three decades, Viet-
namese economy has become a member of middle in-
come league from a low developed country1. There are a
number of factors that can be seen as critical determi-
nants of rapid growth. Researchers usually claim rapid
growth as a result of employing effectively conventional
factors such as physical capital, educational human capi-
tal, trade liberalization, technology and technical diffu-
sion. To my best knowledge, however, there is a lack of
study on the role of health human capital, which is usu-
ally shown as a critical determinant of labor’s productiv-
ity, on agricultural households’ income.
This paper investigates the determinant of individual
health capital on households’ income in the production
process. The theoretical framework of the paper is based
on the several prominent theories [2-4]. Additionally,
empirical tasks have been done with Vietnamese House-
holds Living Standard Survey (here after VHLSS) where
the relationship between individual health capital and
agricultural households’ income can be tested, and the
panel data are at provincial level.
The development of estimation of the relationship be-
tween health and income has been transformed consid-
erably. The relationship was claimed from the income to
health, which means that richer people will invest more
in health, from health to income, which means that peo-
ple or nation with better health will have higher income.
According to [4], there are four pathways where health
can contribute to income. Firstly, people who have better
health will work longer hours, and because they have
good health, they are more productive and gain more
income. Secondly, healthier people will have more in-
centive to invest on their knowledge and skill which re-
sult in better training and education. Thirdly, in the life
cycle, greater longevity will reduce saving rate in the
*Corresponding author.
1The average growth rate of income per capita from 1990 to 2010 is
about 6.5%, and the fraction of poverty households has been reduced
from nearly 60% in 1993 to 14% in 2008 [1].
C. T. DO, A. N. T. NGO 329
productive life period, so financial market will gain more
investment. Finally, healthier population will lead to a
low mortality rate of children and then reduce fertility
rate. Additionally, healthier population will also lead to
longer working age of population, and this is a major de-
terminant of economic growth and per capita income. The
first and the second points are the main hypotheses, about
which this study wants to test for the case of Vietnam.
This study concerns more about agricultural house-
holds and farmers, rather than people who are living in
urban areas or working in non-agricultural sector, be-
cause farmers are working based heavily on their physi-
cal functional status and strength. Moreover, when a
farmer gets sick, it may be one day or longer, and he
does not have any payment for sick-leave like laborers
who are active in non-agricultural sector. This situation
could be also seen as a weak point of public policy when
the government does not provide any health insurance to
this part of their citizens.
In addition, microeconomic theory suggests that pro-
ductivity of laborer tends to converge to the real wage at
the equilibrium of labor market. This assumption, actu-
ally, doesn’t always hold in the context of urban area,
where sometimes the wage rate of laborer is not driven
by demand and supply sides. For example, non-market
factors such as relationship with government officials or
private connection are crucial elements of having good
earning [5,6]. On the other hand, agricultural laborers are
usually self-employment, and then their productivities in
the long-term and short-term are depending significantly
on their health status. For that reason, the relationship
between human health status and productivity in urban
areas does not have much attention among researchers.
Instead, many researchers focus on the relationship be-
tween health and productivity in rural and agricultural
areas, which will be summarized in the next section.
The next section is a summary of the literature on the
relationship between human health capital and agricul-
tural productivity. Section 3 presents a detail description
on the dataset which is used for this study and Section 4
shows the empirical results. The last section will provide
a discussion about the major findings, implications and
limitation of the study.
2. Literature Review
2.1. The Relationship between Health and
The correlation of health and income has been attracted
many researchers. There is abundant evidence regarding
the analyzing this relationship at individual and aggre-
gate levels [7-12]. Particularly, for rural areas, farmers’
incomes are responding considerably to their health. A
woman in Tra Vinh province of Vietnam said “Poor peo-
ple cannot improve their health status because they live
day by day, and if they get sick they are in trouble be-
cause they have to borrow money and pay interest” [12, p.
82]. The relationship between health and income is quite
complex due to the complexity of sources of income. Liu
et al. (2008) [5] divide income of Chinese households
into two parts: 1) one calls market earning, which in-
cludes wages income, home gardening income, farming
income, livestock income, business income and income
from subsidies and benefits; 2) and the others are
non-market earnings, which includes income from food
coupon, housing subsidy, income from other subsidies,
income from other sources and income from childcare
subsidy. The way of classifying income of those re-
searchers is quite similar with the way of [12] when he
analyses the relationship between health and income of
Vietnamese households. According to him, incomes of
farmers come from two sources: 1) earned income,
which includes wage income, income from agricultural
and family business; and 2) unearned income, which
includes gift, remittances and pension. The results be-
tween the two researches are quite identical. Individual
health has influenced considerably on rural households’
income. Additionally, the endogeneity of health variables
regarding to estimation the effect of health on income has
been concerned by scholars [5,9].
2.2. The Effects of Health on Productivity
Measuring the contribution of health capital to labor pro-
ductivity is attracted a plenty of researchers. They have
shown many ways of calculating this relationship. In a
series of papers from 1992 to 1994, Robert Fogel meas-
ures the effect of workers’ height movement to individu-
als’ standard of living in the long-term. Adults in four
countries, United States of America (USA), Brazil, Côte
d’Ivoire and Vietnam were studied and they found that
the differences between the heights of adults in these
countries could explain for the diverse of income of them.
In addition, this information could be extended for ana-
lyzing the wealth and health status differences between
four countries.
At household level, many research point out the corre-
lation between income and health status. [13] showed
that the poorest household will have the worst health
status. They found that in Brazil “a 1 percent increase in
height is associated with an almost 8 percent increase in
wages”, while in USA a 1 percent increase in height has
led to 1 percent raise in salaries. According to these au-
thors, taller men are combined with higher educational
attainment in both countries. Moreover, the better health
status worker tends to do more efficient, longer hour and
productive than the worker who have lower health status.
Incorporating with the heights of people, [14] found that
shorter men have a larger part than taller men in unem-
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ployment. For example, “over 10 percent of men who are
154 cm tall were not working at the date of the survey,
but among those who are about 167 cm tall, the fraction
is only 5 percent”. The similar results are applied for
women as well.
In contrast, the association connecting ill health and
wage has been reported with both sides. In an experi-
mental study in Tanzania, [15] found that workers have
suffered from schitosomiasis have lower productivity
than other workers who do not have this disease, while C.
Gateff et al. (1971) (cited in [6]) found that no different
between workers who are infected schitosomiasis and
who are not infected in an investigational research in
Cameroon. The different between two experiments sug-
gests that we may not apply this method for analyzing
contributions of human health capital to labors’ produc-
tivity due to the endogeneity of health. Another disad-
vantage of experimental study is the result does not have
great power of generalizing due to small and bias sample.
Therefore, it is said that non-experimental methods can
be a good choice.
In non-experimental studies, researchers are often use
data from household survey, and then they have only
used self-reported data from respondents. This way of
thinking has a main downside which is health contains
measurement error discussed above. Similar with experi-
mental research, non-experimental investigations have
mixed results. [16] affirm that Ivorian males show that ill
health workers have lower wage than good health work-
ers, while [17] conclude that in Indonesia’s rural area,
farmers’ productivity has not been affected by ill health
farmers. The case of Indonesia can explain that if a
farmer cannot work, they will ask their neighbors or rela-
tives help them. The situation in Indonesia is quite simi-
lar with the case of Vietnamese agriculture sector.
2.3. Economic Effects of Health
Studying economic effects of health has differed in the
level of aggregation. Some concentrate on individual
health status, whereas the others use data at country level.
Researchers employ lot methods of measuring the eco-
nomic effects of health, but these methods can be catego-
rized in: anthropometric variables, survival and mortality
variables, morbidity variables, general health and func-
tional status variables [5,18,19].
The effect of health on income is varied due to the
choice of independent variables. Therefore, we cannot
capture economic effects of health on income productiv-
ity by only one variable or function. [5] provide a short
list of selecting health variable from micro level to macro
level. At micro level, households’ income and individual
health are the two major variables in their model, while
at macro level income per capita and aggregated health
variable are the two main variables. Almost studies of
this relationship show us a positive relationship between
income productivity or income per capita and health.
Better health, better nutrition intake will robust the pro-
ductivity of laborers and healthier people will willing to
participate longer to labor market in some Asian coun-
A common methodological concern among these stu-
dies is the endogeneity of health in measuring the rate of
return to income productivity or income per capita. In
order to figure out this issue, intellectuals often employ
instrumental variables regression as their major work-
horse. In the case of lacking instrumental variables, fixed
effect model could be seen as a second best selection
3. Data and Model
3.1. Data
VHLSS is an ongoing survey which is focused on living
standard of Vietnamese people. The four waves are con-
ducted in 2002, 2004, 2006 and 2008 respectively. In the
four surveys, there are number of households whose may
appear in the four waves or at least two waves, but due to
different purpose of each wave the specific questionnaire
on health in 2006 does not appear all in these surveys.
The survey in 2006 contained rich source of data related
to health indicators. However, in order to compare be-
tween years we employ only variables which appear in
three dataset, so unfortunately the rich health indicators
in VHLSS 2006 could not be employed in this study. The
Table 1 reports some basic on the VHLSS 2002, 2004
and 2006.
These datasets are used to estimate Equation (1).
Dataset at provincial level is constructed from a rich
source of data which contains statistical data for 61
provinces in 1999 and 2002, 64 provinces in 2004 (there
some provinces have been divided to two provinces). In
the dataset, life expectancy for male and female have
been provided with information about income per capita
and percentage of public spending on health in each
province. Therefore, this dataset provides a suitable data
for testing second statement of [21] in the context of
transition and developing country like Vietnam. Addi-
tionally, this dataset is used to estimate Equations (2) and
3.2. Models
3.2.1. Individual Health—Household Income Model
In order to analyze the first point, our empirical model
employs an income production function. Human health
capital stock is measured at individual level, while in-
come is observed at household level2. Households in the
2This approach has been employed by [5] for China Health and Nutri-
tion survey.
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model are agricultural households3. Additionally, to ana-
lyze the second point, our empirical model will be based
on regression analysis with a linear function which will
be discussed further in the next paragraphs. Data for this
goal is at provincial level rather than the individual level
because the dependent variable is life expectancy. Life
expectancy cannot calculate from VHLSS 2002, 2004
and 2006 where these datasets does not include informa-
tion about mortality rate at different ages, so we cannot
build up life table at individual level. Fortunately, Gen-
eral Statistical Office provides a very good dataset which
includes income per capita and life expectancy at provin-
cial level beside other important variables. Therefore, in
order to test second point of [21], we can run a regression
based on provided provincial data. The following part
will provide a description of models that the paper em-
ploys to test first and second points.
The first model is individual level model where de-
pendent variable is households’ income (sources of
household’s income is presented in Table 2). Thus, the
basic model can be expressed as linear function with de-
pendent variable is natural logarithm of households’ in-
come as follows:
itit it
ijtjt ij
ln YlnLandH
 
 
where, : Household i at the time t; : Individual
in the household i at the time ; it : Income of
household at the time t; : Endowment
wealth of household at the time ;
: Average
health status of household measured by health expen-
diture per member in the household at the time ;
i t
: a vector of controlled variables;
: unobserved
effect; ij
: iid. error term.
In fact, individual health variables may respond con-
siderably to households’ permanent income rather than
temporary income or vice versa. In the case, unobserved
individual and household characteristics, such as family
background or individual fundamental health gift, might
be the cause of endogeneity of individual health variables
Table 1. Information of VHLSS 2002, 2004 and 2006.
2002 2004 2006
Field work time: 05-11/2002 05-11/2004 05-11/2006
Households to be interviewed in design: 30,000 9000 9000
Households actually appeared in the VHLSS 2006 29,530 9189 9189
Number of communes in household datasets 2901 3061 3063
Number of district in household datasets 607 630 630
Number of provinces in household datasets 61 64 64
Number of provinces in commune datasets 61 64 64
Source: Author’s calculation.
Table 2. Income components.
Earning from agricultural activities in the last 12 months
First component of agricultural household income
Earning from non-agricultural activities in the last 12 months
Oversea and domestic remittance from people who are not household members
Pension, one-time sickness and job loss allowance
Social welfare allowance
Income from insurance
Interest of savings and the like
Income from leasing
Total annual
income Second component of agricultural household income
Income from charity organizations, associations and firms
Souce: Summary from questionnaire of VHLSS in 2002, 2004 and 2006. r
3The reason for selecting this type of households is that agricultural labourers are self-employed and they rely considerably on their health status.
When they have got illnesses, they do not have any other source of income. Therefore, the relationship between health and income might stronger
than people whose are living in the urban areas.
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in income function [6]. Using cross-sectional dataset may
lead to a bias estimated coefficient of individual health
Providentially, very few of these factors have changed
overtime and most of them is driven by fixed observables
). Employing three VHLSS datasets in a fixed effect
model, the unobserved variables at individual level could
be figured out. Within/fixed effect allows us to control
these effects. However, in interpreting model, it needs an
explanation with care because the model only catches
these effects of individual health status on households’
income in the short-run period of time. Actually, in the
short-run period of time, the estimation might have bias
coefficient of individual health due to the reflection of
household income shock on individual health shock. To
my best knowledge, the effects of individual health vari-
ables on income are usually dominated by the first com-
ponent of household income that describe in Table 2.
There are several important points. Firstly, the labor
market in Vietnam is new and far from perfect market
and rural households have a very small amount of in-
come from market wages. Secondly, it is very difficult to
separate individual contribution to agricultural household
income in the case of Vietnam. This is quite similar with
other situation in developing countries (for other example
please sees [5]. In the survey, income part, respondents
did not report their income for each member of their
family. Therefore, in this paper, we calculate agricultural
households income based on their report on their family
sources of income from agricultural works and non-agri-
cultural work. For non-agricultural work, extra earnings
from market from trade and working seasonally in urban
areas in the leisure after harvest time have been em-
ployed to calculate agricultural household incomes. Then,
we divide total households income by total working hour
per year in order to get average income per hour per
worker. Next, agricultural households’ incomes have
been deflated by using region and year specific deflators.
Thirdly, health status is measured by the number of days
that farmers cannot work due to illness or injury and the
amount money is spent for treatments or health equip-
ments. Finally, our model aims to explain the relation-
ship between individual health and income per capita, so
this model does not equal to model explains the correla-
tion between individual health and individual income.
Thus, our individual health-household income model,
with specific for Vietnam agricultural households and
farmers, is an evidence for estimating the relationship
between individual health and individual income produc-
3.2.2. Life Expectancy—Illiterate Model at Provincial
In order to analyze the relationship between health and
education at provincial level, we use data at provincial
level with life expectancy variable is proxied for health
and the rate of illiterate people as a proxy for education.
The rationale of this relationship is that when people live
longer, which is reflected by an increasing of life expec-
tancy, they will have more incentive to learn, so the rate
of adults who illiterate will be decreased. Additionally, to
analyze the different respond between male and female
health on education, the baseline and expand models will
be estimated by gender. The baseline model is presented
as follows:
ijtijt it ijt
Life Illiterate
 (2)
where, : province i in the time t, i1, 64i
; : gender with indicates
specifies male; ijt : General life ex-
pectancy of male/female of province at the time ;
: the percentage of illiterate of male/female of
province at the time ;
: constant;
: coefficient;
: the unobservable factors of province at the time
, these factors may be cultural background, social co-
hesion or other;
: error term.
The expand model will employ some variables such as
natural logarithm of income per capita at provincial level,
the percentage of provincial budget spending for health
in total provincial budget and two dummy variables in-
dicate the province achieving high and medium value of
human development index (here after HDI). Income per
capita and public spending on health present the specific
wellbeing of each province. Positive relationship be-
tween them and health means that higher income and
public spending on health will lead to the better health
for people. Additionally, in order to test the robust effects
of high and medium HDI provinces, the model employs
two dummy variables call high and medium. High and
medium will have value 1 if the province belongs to high
or medium HDI group respectively and 0 for others. The
expand model, thus, is presented as follows:
ijtijt it
itit it ijt
LifeIlliterateln ln
Spending Z
 
 
where the additional variables:
ln ln
: natural logarithm income per capita of prov-
ince in the time ; it : Public spending,
measured by percentage in total public expenditure, for
health of province in the time ; it
: Controlled
variables which include some geographical characteris-
tics of the province i and other indicator variables.
There are several important points. Firstly, in Vietnam
provinces have partially depended on central government,
but the political system in Vietnam is not similar with the
federal system of the USA. Secondly, it is very difficult
to calculate life expectancy at provincial level in the long
period of time because the statistical data collection in
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Vietnam is still weak and incomplete. Thirdly, it is diffi-
cult to calculate average years of schooling at provincial
level due to the lacking of available and reliable data.
Our model employs the rate of adult illiterate as a proxy
for education because when people are healthier they will
have more incentive to learn more. Particularly, in Viet-
nam, after the Independence War, adults have lost their
incentive to study more because the country was in the
reconstruction stages, so the illiterate rate was very high.
Then, when their economic status is better than before, if
they feel their health is good enough they will learn more.
Therefore, the percentage of illiterate at provincial level
could be seen as a signal of the relationship between
health and education. Finally, this model aims to explain
the relationship between life expectancy and education at
provincial level. Please keep in mind that Vietnam is still
low developed country and the fraction of rural people in
total population is higher than 65%, so we might employ
average life expectancy to stand for rural people and not
to lose the general meaning. Therefore, the model life
expectancy—illiterate model at provincial level could be
seen as an evidence for estimation the relationship be-
tween life expectancy and education.
4. Empirical Findings
4.1. Descriptive Statistics
The basic descriptive statistics at household level is pro-
vided in the Table 3. Obviously, there is a considerable
variation of average health expenditure and agricultural
income. In order to catch the actual effects of age on the
relationship between health and income, individuals whose
age from 20 to over 65 have been selected and formed in
a panel dataset. The proportion of individuals, whose age
from 30 to 64, is close to 75%. This fraction is consistent
with the overall population distribution of adults in Viet-
The significant increase in agricultural households’ in-
come in the sample has reflected the remarkable increase
of economic growth in Vietnam during the study period,
from nearly 1 million per year in 2002 to 1.3 million in
2006. Associate with the increase of income, health ex-
penditure of agricultural households has been increased
considerably from 182,000 per year in 2002 to 351,600
in 2006. Actually, the rate of increasing in health expen-
diture is higher than the rate of income growth, 24.5%
compare to 9.1% annual.
In fact, VHLSSs are not a perfect panel dataset. Due to
the purpose of each wave, the numbers of households
and places have been selected independently. However,
there is a small amount of repeated households in the
three waves. To estimate the fixed effects at the individ-
ual level, it is necessary to have a panel data with the
repeated value of individuals in a specific time period.
Table 3. Descriptive statistic for household leve l.
Variables MeanStandard
Natural logarithm of household income 9.2211.003
Health Status
Natural logarithm of household health
expenditure per member 5.7811.381
Wealth Endowment
Natural logarithm of household agricultural land 8.8681.399
Years of schooling 6.7703.283
No school 0.0570.233
Elementary 0.3220.467
Secondary 0.4950.500
High school 0.1240.330
Married Status
Married 0.8750.330
Red River Delta 0.2340.423
North East 0.2050.404
North West 0.0620.242
North Central Coast 0.1560.363
South Central Coast 0.0910.288
Central Highlands 0.0650.246
South East 0.0410.199
Age Group
Age 1 (20 - 29) 0.0480.214
Age 2 (30 - 39) 0.2570.437
Age 3 (40 - 49) 0.3260.326
Age 4 (50 - 64) 0.2520.434
Age 5 (65+) 0.1150.319
The basic descriptive statistics at provincial level is
provided in Table 4. Income per capita, measured by
USD, has been increased significantly from approxi-
mately 290 USD per year in 1999 to roughly 500 USD in
2004. The increase is also similar with the trend of in-
creasing household income in the period of 2002 to 2006
above, but due to lack of data for agricultural income at
provincial level there is a difficult to link between health-
income relationship at household and provincial levels.
The proportions of public spending on health in total
available budget are quite similar with the fraction of
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Table 4. Descriptive statistic for provincial level.
Variables 1999 2002 2004
Percentage of public spending on health (%) 4.61 (1.58) 4.68 (1.07) 4.37 (1.04)
Annual personal income (USD) 289.68 (303.31) 368.80 (478.43) 498.65 (798.71)
Male life expectancy 66.42 (4.04) 67.53 (3.62) 67.85 (3.40)
Female life expectancy 72.90 (3.88) 73.24 (3.29) 73.57 (3.15)
Male illiterate rate (%) 7.54 (6.55) 6.77 (5.97) 7.02 (6.71)
Female illiterate rate (%) 15.74 (10.43) 13.12 (9.58) 12.98 (10.68)
Source: Author’s calculation. Note: Numbers in parentheses are standard deviation, while these others are means.
Table 5. Overall sample regression.
household spending on health. This characteristic allows
us to believe that there is an available link between pub-
lic and private health expenditure. However, in order to
prove the existence of this connection, we need more
detail data.
Independent variables OLS Fixed e ffects
Natural logarithm of
health expenditure 0.028* (0.011) 0.060* (0.013)
Natural logarithm of
agricultural land 0.309* (0.012) 0.248* (0.012)
Years of schooling 0.028* (0.005) -
Married status 0.121* (0.045) -
Age 1 (20 - 29) 0.212* (0.079) 1.682* (0.209)
Age 2 (30 - 39) 0.011 (0.052) 1.216* (0.176)
Age 3 (40 - 49) 0.194* (0.050) 0.768* (0.156)
Age 4 (50 - 64) 0.201* (0.050) 0.335* (0.131)
Red River Delta 0.786* (0.051) -
North East 0.794* (0.051) -
North West 0.845* (0.068) -
North Central Coast 0.975* (0.054) -
South Central Coast 0.949* (0.061) -
Central Highlands 0.512* (0.067) -
South East 0.501* (0.077) -
Time effect 0.204* (0.020) -
Constant 6.199* (0.133) 7.409* (0.204)
Adjusted-R2 0.4647
ρ 0.578
F test that all 0
(rejected) 1.99
The increasing of income per capita at provincial level
is associated with the increasing of life expectancy. Ad-
ditionally, there is a negative relation between male and
female life expectancy and male and female illiterate rate
at provincial level from 1999 to 2004, respectively.
To measure the fixed effects of provincial conditions
to people health at provincial level, a panel data at the
level has been constructed by using several sources of
data. However, due to the lacking of available and reli-
able data, a panel with only three years periods is con-
structed. Thus, our analysis may be restricted and very
risky if we apply the conclusion of this period to other
period of time.
4.2. Individual Healthhousehold Income Model
We begin with estimates over the all sample, which are
provided in Table 5 for household sample. Initially, sim-
ple ordinary least squares estimates (here after OLS),
standard errors have been adjusted for household level,
are consistent with our first major hypothesis: individual
health is strongly connected with household income. The
elasticity of individual health expenditure on agricultural
households is significant at 1%. The positive sign of
health coefficient implies that the increasing in health
will lead to an increasing in income. Particularly, 1% in-
crease in health expenditure might increase 2.8% house-
hold income.
As can be seen from Table 5, both OLS and fixed ef-
fects regressions provide a statistical significant estima-
tion the elasticity of individual health expenditure on
agricultural household income. However, in this circum-
stance, fixed effects estimation has more reliable coeffi-
cient than OLS approach. By its assumptions, OLS ap-
proaches individual health expenditure as an exogenous
variable. In order to explore the relationship between
Note: *is statistical significant at 1%. Source: Author’s estimation.
individual health expenditure and household income,
fixed-effects estimation has been employed because this
regression technique can figure out the effect of unob-
served factors. From the Table 5, it is clear that our first
major hypothesis does still hold. Additionally, the elas-
ticity of individual health expenditure here is robust, 1%
C. T. DO, A. N. T. NGO 335
increasing in health expenditure will lead to 6% increas-
ing in income. The estimation of ρ suggests that just over
50% of variation in agricultural household income is
related to the inter-household differences in the income
growth. In this case, OLS results are inconsistent because
the rejection at 1% of the null hypothesis test which in-
dicates the constant terms are equal to all unit. In short,
the first major hypothesis has been hold under both OLS
and fixed effects estimations, but our conclusion is based
on the result of fixed effects estimation because the
OLS’s outcome might not reliable.
In order to find more about the relationship between
individual health and household income, we do fixed-
effect regression for each age group. This task can pro-
vide further information about the relationship among the
groups. The results will be reported in the discussion
4.3. Life ExpectancyEducation Model at
Provincial Level
We begin with estimates the baseline model for male and
female life expectancy, presented in Table 6. To begin
with, OLS regressions have been run for male and female
separately. The signs of coefficients in both estimations
are negative which imply that the decreasing of male and
female illiterate rate will lead to an increasing in male
and female life expectancy at provincial level in Vietnam.
Male illiterate rate coefficient is 40% higher than the fe-
male coefficient. Based on empirical result, 1% decreases
in male and female illiterate rate will lead to an increase
of male and female life expectancy about roughly 0.4 and
0.2 year old respectively.
In order to have a more comprehensive understand
about the effect of other unobserved factors such as en-
vironment or cultural background, fixed-effects approach
has been applied. The result of fixed-effects estimation
for male is considerably robust, while the outcome for
female changes slightly. The effects of male illiterate on
male life expectancy decrease from 0.363 in OLS ap-
proach to 0.904 in fixed effects approach (nearly 150%),
while this effect for female increase from 0.215 in OLS
strategy to 0.188 in fixed-effects strategy (only 12.5%),
detail result is provided in Table 6. This result implies
the different gender respond to life expectancy and the
different in gender unobserved factors.
The F test for both regressions in fixed-effects strategy
is statistical significant at 1%. This implies that the coef-
ficients in OLS approach are biased and the results of
OLS estimations are inconsistent. The ρ value in column
2 and 4 mean that just over 95% and roughly 90% of
variation of male and female life-expectancy can be ex-
plained by inter-provinces differences in male and female
life expectancy respectively. In order to have more in-
formation about the unobserved effects, Hausman and
Taylor instrumental variables (here after HT/IV) regres-
sion has been employed in the next sections. HT/IV re-
gression is selected because it allows time-variant and
time-invariant variables appear together in the same es-
timation. Under HT/IV approach, it is expected that the
effect of male and female illiterate will be robust.
Additionally, in the expand model (Equation (3)), sev-
eral variables have been added. This might lead to a bet-
ter estimation when error term is smaller. In fact, how-
ever, added variables are stand for some characteristics
of the provinces. Particularly, the fraction of public spen-
ding on health may well present for the economic per-
formance of the province where rich province will have
larger fund for public health expenditure.
5. Discussion
5.1. Individual HealthHousehold Income Model
This part reports empirical estimates the relationship be-
Table 6. Baseline model at provincial level.
Dependent variable is male life expectancy Dependent variable is female life expectancy
OLS Fixed effects OLS Fixed effects
Male illiterate rate 0.363* (0.033) 0.904* (0.142)
Female illiterate rate 0.215* (0.018) 0.188* (0.041)
Constant 69.866* (0.320) 73.714* (1.013) 76.251* (0.327) 75.865* (0.585)
R2 0.3865 0.3898 0.4103 0.4135
ρ 0.9527 0.8920
F test 19.05 Rejected 23.00 Rejected
ote: Numbers in parentheses are standard error. * is statistical significant at 1%. Source: Author’s estimation.
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tween agricultural households’ income and individual
health capital using the unique sample from Vietnam.
There are several interesting results from empirical esti-
mates. For overall sample, better individual health capital
has resulted in an enhanced agricultural households’ in-
come. Beside the effect of wealth endowment, presented
by agricultural land, the contribution of human health
capital to income is considerable in the context of Viet-
nam agriculture. Empirical findings, presented in Tabl e 7,
regarding to the relationship between individual health
capital and households income support the first major
hypothesis. This implies better health labor will work
longer hour than normal health labor and then gain
higher income. The positive elasticity of log health ex-
penditure to log agricultural households’ income through
several regressions suggests the proof of the hypothesis.
Additionally, individual health capital has been valued
differently among age groups. Older farmers do not value
their health as well as young farmers, Table 8. In the five
age groups, farmers who age is between 30 and 39 value
their health much more important than the other groups.
Interestingly, the coefficient starts with 0.071, and then
reaches the highest value at 0.106 and decrease to the
lowest value in the last age group. Correspondingly, the
coefficient of log of agricultural land is increasing sig-
nificantly through age groups. This situation is quite
suitable with Vietnam cultural background. Old farmers
usually value their health lower than their assets4. How-
ever, this result cannot apply for all farmers in Vietnam
because the size of sample of this study is not large
On the other hand, there is a modest evidence of the
relationship between individual health and agricultural
households’ income. The health coefficients are only
statistical significant for age group 2 and 3. Almost F
tests for fixed-effects estimations for five age groups
imply that there are significant individual effects and in
this case pooled estimation might inappropriate. The
value of ρ indicates that the 41.4% and 54.6% variation
of agricultural households’ income is due to the inter-
family characteristics for age group 2 and 3 respectively.
The larger economic return on health investment appears
in age group 2 and 3. This means at young age farmers
might invest more on their health due to the expectation
that better health will gain more income. In addition,
farmers in age group 2 and 3 are also the most productive
farmers because they gain enough knowledge about their
job and their living experience is good enough for earn-
ing more money. This result also confirm as an evidence
for proving the first major hypothesis. The most produc-
tive farmers invest more on their health in order to not
only maintain their ability to earn money but also to en-
hance their income by working longer hours.
There are two readily apparent reasons for these find-
ings. Firstly, farmers are working based on their health
and they will not have any income when they get sick, so
they have invested in health. Spending on health of
farmers has been constrained by their income. Under-
standing about the relationship between health and in-
come need to have adequate knowledge. To acquire more
knowledge, farmers need to have more financial resour-
Table 7. Baseline fixed effects model of the relationship
between he alth and income .
Independent variables Fixed effects
Log of health expenditure 0.073*** (0.013)
Log of agricultural land 0.287*** (0.012)
Constant 6.261*** (0.126)
ρ 0.478
F test (rejected at 1%) 2.46
*** is statistical significant at 1%. Source: Author’s estimation.
Table 8. Relationship betwee n health-income by age groups.
Fixed-effects mod el
Age 1 (20 - 29) Age 2 (30 - 39) Age 3 (40 - 49) Age 4 (50 - 64) Age 5 (65+)
Log of health expenditure 0.071 (0.08) 0.10*** (0.03) 0.08*** (0.02) 0.04 (0.02) 0.02 (0.04)
Log of agricultural land 0.19** (0.09) 0.26*** (0.03) 0.24*** (0.02) 0.27*** (0.02) 0.34*** (0.04)
Constant 6.53*** (0.79) 6.18*** (0.31) 6.72*** (0.23) 6.70*** (0.26) 6.21*** (0.41)
ρ 0.410 0.414 0.546 0.573 0.597
F test 1.07 accepted 1.41 rejected at 1%2.24 rejected at 1%2.43 rejected at 1% 2.86 rejected at 1%
** and *** are statistical significant at 5% and 1% respectively. Source: Author’s estimation.
4Old farmers usually think that they live long enough and do not want to spend their earning on health care. Instead, they save their money for their
next generation. Additionally, they have spent almost their life time to gain current assets and they know how hard to get assets in their life. Therefore
they tend to hold their assets rather than to focus on their health. This way of thinking could be seen as constrain for developing health insurance for
farmers in Vietnam recently.
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C. T. DO, A. N. T. NGO 337
ces. Only young farmers have enough ability to earn
more money and learn more knowledge, while old farm-
ers do not have enough time and ability to study more.
As a consequence, investing decision in health of young
farmers is a high priority, while for old farmers invest-
ment in health is not high priority. Alternatively, health
shock has affected considerably to low income house-
holds, especially agricultural households. Farmers do not
have any support from the government to buy health in-
surance and their incomes do not enough to sign a con-
tract with health insurance providers. Hence, farmers
need to save their income in order to have enough money
to invest in health and reduce the harm of health shock.
5.2. Life ExpectancyEducation Model at
Provincial Level
This part presents the empirical result of the relationship
between life expectancy and education at provincial level,
presented in Tables 9 and 10. Education is proxied by
the percentage of male and female illiterate in total
population at provincial level. There are a several inter-
esting findings. Firstly, the effect of male illiterate on
male life expectancy is negative and statistical significant
at 5% and 1% for the both regressions, while this effect
for female is only significant at 5% and 10% for only
second regression (Table 9). However, all coefficient of
male and female illiterate have negative sign (Tables 9
and 10). It means that if illiterate rate is reduced, life ex-
pectancy will be increased. The result supports the sec-
ond major hypothesis that when people health is better,
they will tend to learn more. Secondly, men education
has higher effect than women on life expectancy. If male
illiterate rate reduces 1%, male life expectancy will rise
roughly 0.5-year-old, while if female illiterate rate re-
duces 1%, female life expectancy will increase approxi-
mately 0.15-year-old (Tables 9 and 10). This result re-
flects the situation that men are willing to gain more new
knowledge and further education than women. It may
true because in Vietnam men are usually breadwinner in
family and women usually take major part in doing
housework and taking care the children. So far, we can-
not conclude that there is an inequality between men and
women in terms of opportunity to have more education.
Alternatively, the value of ρ in all models for both gen-
ders indicates that almost variation in male and female
life expectancy is related to provincial effect. The F tests
in these regressions imply that there are significant pro-
vincial level effects, so pooled OLS would be inappro-
priate. Finally, when income per capita associated with
environmental conditions, measured by the distance from
centre of province to the 17th parallel, is added in the
model, the value of ρ is increased slightly. This implies
that the variation in male and female life expectancy
have been explained more and provincial unobserved
effects are included properly in the model.
We uncover a little evidence about the relationship
between the fraction of public health expenditure in the
total provincial budget and male and female life expec-
Table 9. Effect of male illiterate rate on male life expectancy.
Fixed effects HT/IV
(I) (II) (I) (II)
Male illiterate 0.38** (0.162) 0.482*** (0.157)0.405** (0.158) 0.639*** (0.136)
Fraction of public spending on health 0.521* (0.267) 0.536** (0.258)0.482* (0.264) 0.515* (0.269)
Effect of high human development index provinces 0.736** (0.294) 0.742** (0.284) 0.696** (0.288) 0.665** (0.293)
Effect of medium human development index provinces 0.578* (0.296) 0.625** (0.286) 0.522* (0.291) 0.552* (0.297)
Natural log of income per capita 2.616*** (0.462) 2.381*** (0.424)
Natural log of income per capita × distance to 17th parallel 0.386*** (0.066) 0.289*** (0.048)
Controlled for high human development index provincesNo No Yes Yes
Controlled for medium human development index provincesNo No Yes Yes
Constant 54.568*** (3.608) 55.682*** (3.444)57.960*** (5.093) 65.150*** (4.058)
ρ 0.872 0.918 0.856 0.902
F test that all 0
12.29 15.43
Note: Numbers in parentheses are standard errors. *, ** and *** are statistical significant at 10%, 5% and 1% respectively. Source: Author’s estimation.
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Table 10. Effect of female illiterate rate on female life expectancy.
Fixed effects HT/IV
(I) (II) (I) (II)
Female illiterate 0.132 (0.086) 0.153* (0.087) 0.046 (0 .061) 0.117** (0.049)
Fraction of public spending on health 0.312 (0.236) 0.307 (0.235)
Effect of high human development index provinces 0.012 (0.121) 0.025 (0.119) 0.263 (0.254) 0.323 (0.254)
Effect of medium human development index provinces 0.0027 (0.122) 0.037 (0.122) 0.288 (0.259) 0.283 (0.260)
Natural log of income per capita 2.752** (1.099) 1.452*** (0 .453)
Natural log of income per capita × distance to 17th parallel 0.305** (0.153) 0.124*** (0.042)
Controlled for high human development index provincesNo No Yes Yes
Controlled for medium human development index provincesNo No Yes Yes
Year effect Yes Yes No No
Constant 60.368*** (5.579) 64.569*** (5.115) 60.262*** (4.561) 66.007*** (3.216)
R2 0.5577 0.4424
ρ 0.863 0.894 0.811 0.836
F test that all 0
13.40 16.39
Note: Numbers in parentheses are standard errors. *, ** and *** are statistical significant at 10%, 5% and 1% respectively. Source: Author’s estimation.
tancy. Interestingly, the more spending on health care by
provincial government, the less life expectancy people
have. In here, we do not have enough information about
the quality and performance of public spending on health,
but the empirical result implies that there should present
some issues about this fraction of spending. Provincial
governments should reduce their fraction of public
spending on health. In addition, this result might raise a
question about the way of spending on health care of
provincial governments. It is quite similar with the cur-
rent situation that some governments in rich countries
have to reform their health care sector and the need to
reform the way of spending money on health care of the
government in Vietnam as well.
Based on the empirical results in this part, the second
major hypothesis has been hold. Better health people
tend to study more and then the percentage of male and
female adult illiterate should be reduced. The result, ac-
tually, is more robust in male population than female side.
This situation brings us to the conflict in terms of logic.
Women live longer than men due to the data on life ex-
pectancy. Therefore, women should have more education
than men, but the effect of illiterate rate on life expec-
tancy of women is smaller than men. This situation may
be explained by the dynamic of studying more of women
are lower than men due to the foundation of cultural
background. We cannot prove this conclusion right now
because of lacking data, so it could be seen a suggestion
for further research.
6. Conclusions
This paper tries to test the two famous hypotheses raised
by [21]. The empirical results show that the two hy-
potheses are held with the data. The first hypothesis is
proved considerably by data at household level, while the
second hypothesis is supported partly by data at provin-
cial level. Unfortunately, we cannot have rich enough
sources of data which allow us to do two tests together in
the same dataset. As a consequence, the link between the
two hypotheses is weak.
At household level, individual health status has con-
tributed noticeably to the agricultural households. The
empirical result confirms that the elasticity of individual
health expenditure on household income is quite large
and cannot be rejected by the data. This implies that bet-
ter health farmers will have higher productivity and then
more income. The young farmers have valued their
health much more carefully than the old farmers. Addi-
tionally, farmers at younger age tend to invest more in
health in order to maintain their ability of working in the
longer hour and then earning more money, while the
other groups of age value their health less important than
farmers in the age between 30 and 49. The first major
hypothesis is consistent with the empirical result of these
age groups, hence.
At provincial level, the relationship between health
and education is presented by the correlation between life
expectancy and adult illiterate rate by gender, which be
seen as an evidence for the second major hypothesis.
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Based on the empirical result, the second hypothesis
doesn’t hold considerably in men and women sample.
Better health men workers are willing to learn more
knowledge. Women live longer than men but have less
dynamic to gain more knowledge due to the effect of
culture in Vietnam. We cannot prove this relation here
because of lack of data. Thus, this topic could be seen as
a further research of this study.
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