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Introduction
Introduction
I. Research
topic
One of the greatest concerns
to developmental economists and economic policy makers is the interaction
between population growth and economic development. Both have consequences for
the natural world, the political order, and human wellbeing. The key element
behind the change in population, particularly in a country like Vietnam where
the growth is so rapid, is the level and pattern of fertility. Although
mortality and migration also contribute to the size, structure and growth of
population, and are hence important areas of study in their own right,
population dynamics are strongly molded by fertility. Fertility is also
important because it is inextricably bound up with many aspects of the economic
and social milieu. A better understanding of fertility behavior may provide
insight into a wide range of social and economic, patterns and changes such as
labor force participation, income distribution and education aspirations for
children.
As we know that while the
economic growth of Vietnam is still at low level, Vietnam's population continues
to growth rapidly. This population growth is mainly as a result at the fertility
level. Yet the fertility has declined continuously, to probably more than two
children per women in 1999, though substantial differences remain between rural
and urban areas and among regions (see Nguyen Huu Minh, 2000). Fertility level
in Vietnam has changed fast in recent years. Based on data sets of census
population in 1960, 1979, 1989, Dr Dang Thu calculates that the total fertility
rate (TFR) in the North was 5.47 in the period 1946-50 and 5.36 in the period
1950-54. TFR increased rapidly during 1955-60 by 6.8, during the following ten
years it was steady and then tended to decline gradually. TFR of the whole
country is 5.78 in 1970-74, declining by 4.17 in 1985-1989. According to the
final preliminary report of census population (see Chu Thi Loan,2000), the
population of Vietnam was 76,327,919 persons in 1999. An increase of 11.9
million people in 10 years. Currently Vietnam's population is equivalent to the
population of a middle country. Average increasing population rate in the period
1989-1999 was 1.7%. It fell to 0.5% (compared with 1989). In 1998, the total
fertility rate of the whole country was 2.5 children per woman at child-bearing
age. It fell to 35% compared with the ten past years. However, there are many
differences among the regions. For example, the fertility rate of ethnic
minorities in remote areas is still at high levels. High fertility rates in
minority areas has proved to be great obstacle to the implementation of poverty
alleviation programs, socio-economic development and improvement of living
conditions.
Although important, there
have been very few researches on determinants of fertility in Vietnam. People
just want to mention it but do not really focus on explaining it. Therefore, in
the thesis, I will concentrate on pointing out the major socio-economic
determinants of fertility in Vietnam. By this study, I hope to make some
contribution to policy maker on the family planning program of reducing
fertility and promoting economic development.
II. Focus and
scope of the thesis
1. Focus of the Thesis
The research will focus on
·
Reviewing
theoretical and empirical issues concerning the fertility definitions, fertility
measurement and factors affecting fertility level.
·
Analyzing the
fertility phenomenon of Vietnam and major determinants of fertility level based
on data obtained from Vietnam Living Standard Survey (1997/1998).
·
Recommending
policies to reduce the fertility level in Vietnam.
2. Scope of
the Thesis
This thesis, will review
theoretical and empirical issues relating to factors affecting fertility. These
will be analyzed in socio-economic terms that relate to the mother's
characteristics and this 'social' aspect implies education, and culture. In
addition, the fertility differences between rural and urban, among regions of
the whole country will be analyzed in this study. Some other differences such as
socio-economic characteristic of father, biology, migration, some proximate
variables including contraception, abortion, post-partum infecundability,
spontaneous intrauterine mortality and permanent sterility ..are omitted from
this thesis.
III. The research question
As already mentioned, the
purpose of this study is to concentrate on analyzing the determinants of
fertility in Vietnam, so the main research question is:
What are the major
socio-economic
determinants of fertility in Vietnam?
To answer this main question,
the research will examine the following sub-question:
1. Is the fertility equal
across regions ?
2. What are the major
socio-economic determinants of fertility in Vietnam and how important are they?
3. What is the role of
various factors in explaining the differences or indifferences of fertility
between rural and urban/ among regions/ and ethnic minorities?
4. What are the
recommendations for the Government to undertake its policies on these factors?
iv. Structure
of the thesis
Besides the Introduction,
Appendix and Bibliography, the thesis includes four chapter:
Chapter 1: Analytical
Framework
Chapter 2: Fertility status
in Vietnam
Chapter 3: Determinants of
fertility in Vietnam: Regression and
Chapter 4: Conclusion and
Recommendation
Chapter 1: Analytical Framework
This chapter focuses on a
selection of the more influential theories of fertility.
I. Concepts and Definitions
Fertility is real fertility
ability of a population. It is different from fecundity. Fertility or
survival births is affected by fecundity, marriage ages, status available and
contraceptive use, economic development, status of women, and structural age -
sex. Fecundity is the number of children a woman would have in the
absence of all fertility-inhibiting factor.
Similar to above authors, GSO
(1997) in Vietnam refers to live - born in term of fertility which is
defined that live birth is the complete expulsion or extraction from its mother,
a product of conception, irrespective of the duration of pregnancy, which, after
such separation, breathes or shows any other evidence of life, such as beating
of the heart, pulsation of the umbilical cord, or definite movement of voluntary
muscles, whether or not the umbilical cord has been cut or the placenta is
attached; each product of such a birth is considered live birth.
II.
Measurement of fertility
1. Macro
measures of fertility
The "macro" measures which
refer to the fertility of population or a sub-group and the "micro" measures
which cover individuals.
Aggregate measures of
fertility are either period - specific or cohort-specific. The period-specific
are cross-sectional measures which show the current or perhaps past, fertility
level of the population under study. These measures are defined over a specific
period of time( including crude birth rate, child-woman ratio, general fertility
rate, age specific fertility rates, total fertility rate, gross reproductive
rate, and net reproductive rate.), typically of a one year or a five year
duration. In contrast, cohort fertility measures represent the fertility
experience of an actual birth or marriage cohort as it moves longitudinally
through time. In practice, cohort measures are rarely used because of the large
amount of data required for calculating such measures.
2 Micro measures of fertility
Micro fertility behavior is
commonly studied in terms of the cumulative fertility and in terms of current
fertility. The most frequently used variable for cumulative fertility is the
number of children ever born.
2.1. Cumulative fertility
or Children ever born (CEB)
CEB is the cumulative number
of live births a woman has had. Although cumulative fertility can be an
appropriate dependent variable for some analytical purposes and easiest
fertility measure on which to collect data. However, as in the case of the
aggregate measures of cumulative fertility, underreporting can result from age
misreporting and omission of children who have died or have left the household.
Therefore, measures of recent fertility, such as the number of children born in
the previous five years, whether the respondent is currently pregnant, or
whether she has given birth in the previous year, are often more useful for
policy analysis.
2.2. Recent fertility
This indicator not only does
better reflect fertility behavior at the present time, it also avoids the
misreporting problems associated with cumulative measures. It is perhaps more
frequently possible to use a measure of fertility which covers the past year
because it is common in demographic surveys to collect information on live
births by the age of mother during the previous 12-month period. This
information is widely used for calculating aggregate annual fertility levels and
for monitoring changes therein over time.
Another category of fertility
measures used in micro-level research consists of various measures of desired
fertility or family size preferences.
2.3. Desire family size
(DFS)
DFS is defined as the number
of existing children plus the number of additional children desired by parents.
However, DFS and additional children desired are also beset by both measurement
and theoretical problems. Whether or not to include these cases in the analysis
and if so, how to include them, and if not, how to deal with the potential
resulting bias, are difficult questions.
III.
Theoretical Models of fertility determinants
The economic theory of
fertility, there are two broad strands of influence. One such strand can be
found in the works of Becker, Mincer and Willis that is the household "demand"
model. The other strand is seen through the works of Easterlin that is synthesis
model. What is the work's of each strand. We can analyze as follows:
1. The household "demand" model
1.1. The economic theory
of fertility before Becker
This theory stems from the
views of Leibenstein and Okun. According to Leibenstein, he said that our
central notion is that people behave in the same way as they would if they
applied rough calculations to the problem of determining the number of birth
they desire. And such calculations would depend on balancing the satisfactions
or utilities to be derived from an additional birth as against the "cost". He
pointed out that we have to distinguish among three types of cost.
Firstly:
The utility to be derived
from the child as "a consumption", as a source of personal pleasure to the
parents; secondly: The utility to be derived from the child as a
productive agent, that is the child may be expected to enter the labor force and
contribute to family income; finally: The utility derived from the
prospective child as a potential source of security, either in old age or
otherwise;
In the Lebenstein model
fertility he pointed out that fertility fell as per capita income rose for two
main reasons. First, as per capita income increases there are associated changes
in the structure of economic activities which tend to reduce the value of
children to their parents. Leibenstein suggests that the main causes of this
decline are the decreasing utility of children of a given order in providing
old-age security to their parents and the decreasing potential of children
contributing to family income through work activities. On the other hand,
Lebenstein views the costs of children of a given order as increasing as per
capita income increases because "the style in which a child is maintained
depends on the position and income of parents" (Leibenstein (1957), p.161)
Leibenstein also thought that the indirect costs of children of o given order
would rise because he considered "opportunities activities as likely to grow as
income increase." (Leibenstein (1957), p.162). That are views which were widely
accepted at the time of his writing and contribute to useful things for many
researchers of fertility afterwards.
1.2. Becker's Economic
theory of fertility
Modern economist's modeling
of fertility behavior began with two related insights: that "production" - of
meals, healthy children and so on, i, e. of the whole range of goods and
services not normally traded in the market - occurs in the household as well as
the factory; and that an important input to the production (and consumption) of
household-produced commodities is the scarce time of households as optimizing
decisions representing economic choices, made fertility, an apparently
non-economic area of human behavior, a valid subject for study by economists.
In this household model,
children, or more correctly child services, are a normal good; with rising
income parents will, all other things the same, want more child services. How
then does the model explain the association of high and rising income with low
and falling fertility? In two possible ways. First is a price effect. A
principal component of the cost of children is the time parents devote to
childbearing and childrearing. In higher income societies, wages and thus
parents' opportunity cost of time are relatively higher. Insofar as children are
more time intensive than other consumption commodities, their relative price
rises with rising wages.
A second explanation for the
association of rising income levels with falling fertility is the possibility
that parents will substitute "quality" of children for high numbers of
"quantity" in the production (rearing) and consumption (enjoying) of children -
for example, preferring a few well-educated children to many who are not
educated..
2. The synthesis model
A second approach of
economists in the so-called "synthesis" model of fertility (Easterlin(1978),
Easterlin. Pollak and Waachter (1980)). Like the household demand model, the
synthesis model posits a utility maximizing household which faces a set of
market and shadow prices, a particular household production demand-oriented
analysis with demographers' modeling of the supply of children, thus
"synthesizing" economic and demographic approaches. It also incorporates some
aspects of sociologists' emphasis on the endogeneity of tastes (as opposed to
the economists' emphasis on utility maximization constrained only by prices and
income), thus "synthesizing" economic and sociological approaches.
In this model, variables
which affect supply are based on the proximate determinants of fertility from
the Bonggarts (1978,1982) model. This model states that fertility is function of
sexual exposure, deliberate fertility control, and fecundability (the
possibility to become pregnant)
Although both models have
many useful things for research on fertility in recent year, they also have
their limits, generally well recognized by their designers, both conceptually
and as tools for empirical work. Their limitations are as follows:
Ø
The assumption
of utility maximization. Although it is true in the larger field of economics,
this assumption has been challenged. The critique is that an increasing degree
of rationality in society as a whole is ignored as a possible explainer of
fertility decline. For example, a decline in the influence of religion could
reduce fertility even in the absence of price changes.
Ø
The "family"
utility function. In these models, a single utility function is assumed to
embody the preferences of husband and wife for number (and quality) of children.
Yet there is evidence that husband and wife do not always agree
Although the theoretical work
has not well-established theory, and has often not yielded specific, testable
hypotheses, some common themes have emerged which have provided general
guidelines concerning the variables to be examined in empirical studies of
fertility. The following section will provide some factors that affect
fertility.
IV. Empirical research on determinants of fertility.
Existing studies often show
that factors including culture, religion, age, education level, income,
occupation, status of women, and infrastructure have a strong effect on
fertility. Although there are many factors affecting fertility, only those
mentioned above will be attributed in this study.
1. Culture and religion
Religions beliefs and
cultural pattern usually have strong associations with the roles that women and
men play in society. As Cleland 1983 said that polygamy is one of the factor of
traditional lifestyle that typically inhibits the number of children born per
women. Differences in religion also have an impact on fertility. As a religious
group, the Muslims have the lowest fertility (4.17) and those with "other"
religions (mostly Zione, a local religion) have the highest (6.33). (Ceccato,
2000, p9).
2. Age
Age is one of the most
important variables characteristic individual participants in the reproductive
process and thus is included in some form in almost all studies of fertility.
Many researches also find that age of the women has a positive effect on
fertility. According to Desai 1996, when age of the woman increases by 1, the
number of children ever born will increase 0.170 children (that is the case of
Vietnam). In addition, the relationship between age and fertility in Cote
d'Ivoire is the same as Vietnam. If age of the woman increases 1, the number of
children ever born will increase 0.4296 children (Martha Ainsworth, 1989).
3. Income
Empirical studies have
reached mixed conclusions concerning the relationship between income and
fertility. If the analysis is restricted to the developing countries, income and
fertility are sometimes positively related. For example, In Vietnam, the
relationship between expenditure per adult (proxy for income) is positive. There
results show quite clearly that in rural Vietnam the poor have fewer children
than the better-off (Jaikishan Desai, 1995) and according to Ainsworth (1989),
the positive relationship between income and fertility is also seen in Cote
d'Ivoire. However, if one examines the relationship at the aggregate level for a
cross-section of all countries, including industrialized countries, the expected
negative relationship seems to hold such as Japan, Germany... This is suggestive
of a non-linear relationship at the aggregate level, with fertility initially
increasing with higher positive relationship between income and fertility
posited above, but the difficulty of empirically isolating this theoretical
concept has led to varied results and no firm conclusions (Ghazi M. Frarooq and
Deborah S. DeGraff, 1988,p37).
4. Education
In comparisons of couples in
20 developing countries, lower fertility is almost always exhibited when the
wife has secondary education (Rodrigue and Cleland, 1982). Summaries of
household multivariate studies show that the effect of wife's education is
negative more often, and more likely to be statistically significant. In
addition, there is evidence the negative effect does not appear until higher
levels of education are reached. Some primary education, in fact, appears to
increase rather than decrease fertility. (in Sierra Leone) (Cohanrane, 1983,
p607). This positive effect of education at low levels is especially marked in
rural areas (Hermalin and Mason, 1980) and in less urbanized and poorer
developing countries, where per capita in come is below US$500 (Cochrance,
1983,p578) It generally does not appears in more advanced developing countries.
However, data analysis of 'the survey in changes of family and fertility 1990'
(Nguyen Thi Van Anh, 1993), indicates that education level does not affect
desire for more children significantly in Vietnam and according to Nguyen Minh
Thang, Charles Hirschman, Nguyen Huu Minh, 1995 said that education level is not
a deciding factor in fertility decisions because the impact of factors that
strongly affect fertility, such as education level of women, has been reduced
over time. In addition, the results in Cote d'Ivoire show that schooling has a
negative effect on fertility (Ainsworth, 1989).
5. Occupation
Studies in developed
countries indicate a negative relationship between female work hours and
fertility. But studies in less developed countries have not always indicated a
negative relationship and in the empirical work is presented by McCabe and
Rosenzweig who obtain strong positive association between female wage and
fertility
6. Infrastructure
The empirical
evidence in Vietnam suggested that fertility of people who have electricity or
have a radio/television is lower than for women who do not have electricity or
who do not have a radio/television. Moreover, fertility rates of groups of
people with a high fertility history have experienced a distinct drop in
fertility rate with the introduction of television/radio to their area (Nguyen
Minh Thang, 2000). Another study showed that Communes have a road passing
through have lower fertility.
Chapter II: Fertility status
in Vietnam
The thesis make extensive use
of the VLSS 1997-1998, the extremely rich datasets for analyzing
The children ever born by
current fertility (including the children were born since 1993 up to 1998
) will be used to analysis in this thesis because this indicator not only does
better reflect fertility behavior at the present time, it also avoids the
misreporting problems associated with cumulative measures.
I. Characteristics of fertility in Vietnam
1. Fertility trends
The fertility level in
Vietnam has been declining since in the 1970s (Jones, 1982: Alman et al 1991,
Banister, 1993, Nguyen Van Phai et al, 1996, Nguyen Minh Thang, 1999).
Especially, it declines rapidly from 1993. We can see as follows in Table 1.
Table 1.
Total fertility in the period of 1969-1999 in Vietnam.
Unit: number of children
|
Period |
1969-1974 |
1974-1979 |
1986-1987 |
1987-1988 |
1988-1989 |
1991-1992 |
1992-1993 |
1993-1994 |
1992-1996 |
1997-1998* |
|
TFR |
6.1 |
4.8 |
4.2 |
4.0 |
3.8 |
3.9 |
3.5 |
3.1 |
2.7 |
2.6 |
Sources: ESCAP; population
census 1979, 1989, 1999: VNDHS 88, VNDHS 94, VNDHS 97, *author's calculation
based on VLSS 1997-98
It is difficult to exactly
compare data from different sources as the nature of the questions vary from
survey to survey. Also, the technique of estimating fertility rates is different
in each survey. As Table 1 shows, the fertility rate in Vietnam has decreased
rapidly from 6.1 children in the 1969-1974 period to 4.8 children in the
1974-1979 period to 4.2 children in the 1986-1987 period to 4.0 children in the
1987-1988 period to 3.8 children in the 1988-1989 to 3.9 children in the
1991-1992 period to 3.5 children in the 1992-1993 period to 3.1 children in the
1993-1994 to 2.7 children in the 1992-1996 period and to 2.6 children in the
1997-1998 in the period.
2. Features
of fertility in Vietnam
As I presented above, because
TFR, ASFR ....indexes are difficult to calculated and to understand so that in
this study the CEB index will be chosen to analyze the fertility differences in
Vietnam.
2.1. Deferential
fertility among regions
The general fertility patters
and trends can be analyzed for the whole country and different sub-groups of the
population. Table 2 presents the number of children ever born of a woman of the
nation as a whole and its seven geographical regions.
Table 2.
The number of CEB for the 5 years preceding the survey by Region,
1997-98
Unit: person
|
Regions |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
Total |
|
CEB |
3.13 |
2.65 |
3.30 |
3.30 |
4.01 |
2.93 |
3.22 |
3.06 |
Source: Author's
calculation based on 1997-1998 VLSS
It has been shown that the
highest fertility levels are observed for the Central Highlands (region 5). In
Central Highlands, a woman has average of 4.01 children while a woman in Vietnam
has average of 3.10 children. However, the Red River Delta and the Southeast
have the lowest marital fertility. Why are there different among regions?
Almost two-third of
Vietnamese state that they are Buddhists. In this section we divide religion
into 3 groups like Buddhists, Christians and none both of them. There are
significant differences among religions. According to the Vietnam living
standard survey in 1997-98, which did obtain fertility rates by religion for
Christian, children ever born per a woman age from 15-49 have 4.10; for
Buddhists 3.02 and the other 2.91 (Author's calculation based on VLSSs 1997-98).
From mentioned above, we can see that religion also impact on fertility, for
Christians, number of children born is higher than that of the rest of the
religion.
2.2. Deferential
fertility among Ethnic structures
Vietnam has 54 ethnic groups.
The Kinh (Vietnamese) group accounts for 80 percent of the population. The Kinh
live largely in the river deltas and cities. Other ethnic groups like Nung,
Muong, E de, Gia Rai, and Thai live in highlands, mountains and plateaus. In
this thesis, It is considered two groups that are Kinh and non-Kinh group.The
fertility level of each ethnic group is quite different, the number of children
ever born of Kinh is 2.75 children per woman while that of non-Kinh group is
3.37 children per woman( Author's calculation based on 1997-98 VLSS).
II. Socio-economic development and Fertility
1. Women's education
For women, education has a
great effect on knowledge, attitude and practice in the field of family planning
and reproductive health. Table 3 represents the number of children ever born of
married women by education.
Table 3.
Relationship between Fertility with women's education
Unit: number of children
|
Education |
No schooling |
Primary |
Lower-secondary |
Upper-secondary |
University |
Total |
|
Children ever born |
4.61 |
3.42 |
2.75 |
2.25 |
1.87 |
3.05 |
Source: Author's
estimation based on 1997-98 VLSS
We can see that the
relationship between women's education and fertility is inversely associated. If
a woman has no schooling, she will have 4.61 children increasing by 1.51 times
compared to CEB in entire country in the same period. In contrast, with women's
education at university or higher, the number of children ever born will be
reduced significantly, it declines by 1.63 times compared to CEB in a whole
country. The higher education of women is, the lower fertility level is.
2. Women's
occupation and fertility
Fertility level across kinds
of occupation is also observed in Figure 1. As one expects, "White collar
workers" had the lowest number of children ever born per women (2.13 children
per a woman), "Agriculture" had the highest number of children ever born per
women (3.27). Looking at the Figure 1 we can see that if women's job is
Agriculture, the number of children ever born is 3.27 children. It increases by
1.17 times compared to that one of women having no professional job, 1.54 times
compared to that one of "white collar workers", 1.1 times compared to that one
of "sales workers", and 1.36 times compared to that one of the "Blue collar
workers". It is quite suitable for Vietnamese case. This relation can be
presented more detail in among regions and between rural and urban.
Figure 1: Fertility differences by women's occupation

Figure 1.
Fertility differences by women's occupation
Source: Author's
calculation based on VLSS 1997-98.
3. Income
and fertility
For the convenience of
analysis, we divided 2444 observation into five quintiles and each quintile
contains 20 percent of observations according to their income. The first
quintile includes 20 percent of the lowest income level and the fifth one
contains 20 percent of the highest income level
Table 4 presents the results of fertility level by quintile. As expected,
households of the lowest income level have the children higher than that of the
rest of the quintile. The average number of children per a woman in the lowest
income quintile - quintile 1 is 3.75 persons while this figure in quintile 2-the
second lowest income quintile is less than 1.18 times compared to that one of
quintile 1. The number of children in quintile 5 - the highest income quintile
is 1.65 times lower that one of quintile is 1 and 1.41 times lower than quintile
2 and lowest compared to that the whole sample.
Table 4.
Fertility changes between rural and urban by quintile
Unit: people
|
Quintile |
Rural |
Urban |
Total |
|
Quintile 1 |
3.75 |
3.78 |
3.76 |
|
Quintile 2 |
3.19 |
3.26 |
3.20 |
|
Quintile 3 |
2.52 |
2.43 |
2.51 |
|
Quintile 4 |
2.51 |
2.16 |
2.41 |
|
Quintile 5 |
2.76 |
1.97 |
2.27 |
|
Total` |
3.19 |
2.37 |
3.06 |
Source: Author's
calculation based on 1997-98 VLSS
Chapter III: Determinants of
fertility in Vietnam
1. Methodology
The thesis is interested in
qualitative determinants of fertility level. The decompositions start with
standard regression function, which was widely used to analyze determinants of
fertility (Cochrane 1979, T.P, Schultz 1974, 1981 and T.W. Schultz, 1974 and
Ainsworth et al 1996). The empirical model of fertility determinants regresses a
measure of cumulative fertility children ever born to each women - on a set of
independent variables that are assumed to be exogenous to fertility decision but
that influence either demand for or supply of children. This reduced-form model
of determinants can be written as
Y =
a
+bX
+u
Where: Y is the cumulative
fertility children ever born,
a
is intercept,
b
is a vector of coefficient, u is a random disturbance term reflecting unobserved
characteristics distributed with mean Zero and constant variance, X is vector of
individual characteristics. The model for CEB and posited signs can be
summarized as:
Y = f (x1+,
x2-, x3- , x4-,
x5+, x6-, x7-,
x8- , x9-, x10-,
x11+)
where x1 is the
age of women; x2 is education level; x3 is occupation of
women; x4 is income; x5 is religion of women; x6
is the woman's ethnic group; x7 is region variable; x8 is
area of residence such as urban or rural; x9 is marital status; x10
indicates accessing to electricity; x11 is frequency of cutting
electricity.
The regression model will be
estimated by single - equation regression techniques assumption that explanatory
variables are exogenous. The analysis here will be mainly estimated by OLS.
However, OLS technique does not reflect the family with two children or less
they want to have more children while probit
technique needs the demand for that purpose. Therefore, I will use both of them
to estimate the children ever born or probability of having more children in
order to compare between the two techniques. So that in this my thesis I will
run some regression models. The selection of variables and their expected signs
are used for equation regression that will be explained more detail in table 6:
II. Estimated results and Explanation
1. Ordinary Linear Squared model
1.1 Fertility model in
the whole country
The following discussion
outlines general trends in the parameter estimates. Initially, the regression
model was estimated with explanatory variables as mentioned above. However, when
this was done the coefficients on linear term for some regional variable s, for
tribe variable and one of the job variable were not statistically significant at
the 10 percent significance level. Therefore some of that variables were omitted
and the regression model was re-estimated. In this study, I decide to omit the
tribe variable for all regressions in later because it is not only statistically
significant but also unexpected sign.
Examining first the regional
location variables as Table 5 shows. Relative to Northern Uplands (the omitted
regional dummy), the average number of children ever born in The Central
Highlands, North Central Coast, and Central Coast are significantly higher. At
the 10 percent significance level, the coefficients on linear term for Red River
Delta, North Central coast, Central Coast and the Southeast are not
statistically significant. However, as expected, the sight of Red River delta,
Central Coast, Southeast and Mekong areas variables with fertility negative
while in other parts of the country is positive. There results are interesting
for some main reasons. First, they show that discussions of fertility in Vietnam
should not be cast simply in terms of North-South differences. Even though the
Northern Uplands, the North-Central Coast and the Red River Delta have similar
political pasts, the economic conditions in the Northern Upland and
North-Central region are quite different from those in the Red River Delta.
Second, these results indicate that the highest fertility observed in the
Central Highlands, as discussed in chapter two, is related to lower education
and living standard.
Considering next the
educational variables on the fertility level. Table 5 also shows that the
education level of the married women has a statistically significant effect on
the fertility. As expected, the relationship between education and fertility is
negative. Particularly, the number of children ever born of a woman having
primary-education was 0.840 lower than the benchmark of no-education. This
figure decreased with higher education attainment 1.208 for lower -secondary,
1.533 for upper-secondary and 1.931 for university and higher. In generally this
is quite true for the most of the nations in the world and particularly in
Vietnam. This result is interpreted as follows:
There are a variety of
hypotheses about why expanding educational opportunities are inversely
associated with fertility (Cochrance 1979, and Casterlin 1983).First, because
women are unlikely to marry while they are still in school, schooling is
associated better-educated women and better-educated populations are likely to
have a greater knowledge of contraception and the ability to practice it more
effectively. Second, for women with advanced education, particularly university
degrees, childbearing involves large opportunity costs in the form of foregone
income. Third, within a budget constraint there is always a trade - off between
quantity and quality when it comes to children; better-educated women may opt
for fewer children with high-quality lives, choosing, in a manner of speaking,
to reproduce themselves socially rather than simply physiologically. Finally,
and perhaps most importantly, mandatory school attendance, expanding educational
expectations in the younger generation, and rising educational norms increase
the cost of children and reduce the value of child labor for the entire, not
only the educated, population. Thus, in this last view, expanding educational
opportunities affect the fertility decisions of all couples.
Regression results also shows
that women’s job is negatively associated with the number of children ever born.
However, only three kinds of occupations were statistically significant. The
number of children ever born of all occupation were significantly lower than the
no-job benchmark. The average number of children ever born of a woman having
white-collar occupation decreased 0.521 lower than the benchmark of no-job. This
figure decreased with the other occupation 0.246 for sales occupation, 0.273 for
blue-collar workers and 0.150 for the agricultural workers. This negative
relationship can be explained as follows:
As in the case of education
attainment. Potential causal linkages between work and fertility include the
increased opportunity cost of women's time, the incompatibility of child-care
and work, exposure to smaller family size norms and different attitudes towards
women's role, greater access to information particularly concerning family
planning, delayed marriage, and reduction in breastfeeding. Although women
having agricultural sector is also negatively associated with the number of
children ever born (comparison with women's no-job), this result is not
statistically significant and the same as the descriptive section. It may be
resulted from multivariate analysis that lead to sample bias due to merging
many variables. Therefore, our expectation is not satisfied.
The next variable, age of
women has significantly positive effects on the number of children ever born.
Holding all other explanatory constant, if the women has an additional year of
age, the average number of children ever born would increase by approximately
0.181 children. This understandable that age plays an important role in
fertility outcomes due to its association with menarche, marriage, widowhood,
divorce, frequency of intercouse, fecundity and menopause.
Expenditure impacting on
fertility is seen as Table 5 (proxy for income). In general, fertility is
negative associated with per capita expenditure in the whole country, it happens
as expected. The result in model shows that the average number of children ever
born by 0.0001children (controlling for all other explanatory variables). As Huw
Jones (1984) said that 'the rich get richer and the poor get children'. This
result indicates that there are trade off between quantity and quality. A rise
in income was said to increase aspirations for social advancement, which may be
though of an increased desire for other goods that compete with children for
family resources. This desire would act to reduce the number of children people
would have. In addition, rises in income and economic development imply such as
changes as increase in education, shifts from rural to urban employment and life
expectancy, and increased availability of contraceptive information. Each of
these phenomena is by itself related to lower fertility. We also see that rise
in income can lead to reducing in the roles of children. High income implies the
rich family so that they do not need supporting of children for old-age, illness
or disabilities...both of these possibilities are likely to reduce fertility.
Marital status of women
variables are showed that there is negatively associated with fertility level as
expected. The average number of children ever born of widowed women was 0.613
children lower than the benchmark of without widowed and separated women, 1.409
children for separated or divorced women. It can be explained as follows:
Marital status is intended to represent exposure to sexual intercouse as
discussion above. In Vietnam, remarriage proportion of widowed and separated or
divorced women is very low. Moreover, in recent years there are many divorces or
separates of the couples. Therefore, the reducing in fertility is not avoidable.
Another variable, that is
religious variables, variables in this dummy group are Buddhist with coefficient
0.184 and Christian with 0.487. This coefficients are highly statistically
significant. One can argue that "Christian" has more number of children ever
born than "Buddhist" and both of them has more number of children ever born than
benchmark of none-both of them. The difference in fertility level of
"Christian", "Buddhist" and none-both of them can, therefore, mainly be
explained by the difference in custom. Moreover, fertility is equated with
virtue and spiritual approval, while reproductive failure and self-imposed
fertility restriction are seen as punishment and evil respectively.
The last, but not least
important, variable in this dummy group is areas of residence with coefficient
-0.341. This coefficient is also highly significant. Indicating that "women in
urban" has less children born than "women in rural". It is understandable that
because women in urban has access to better education, a wider spectrum of work
opportunities a more improved public health environment, and generally more
avenues for self-improvement and social mobility. They also face higher coasts
in raising children so that fertility of urban tends to be substantially lower
than rural fertility.
Table 5: Regression results for CEB
|
Variable |
The Whole |
Rural Area |
|
Coefficients |
P |
Coefficients |
P |
|
Red River Delta |
-0.126 |
0.291 |
-0.071 |
0.541 |
|
North Central Coast |
0.276 |
0.083 |
0.279 |
0.009 |
|
South Central Coast |
0.003 |
0.989 |
-0.026 |
0.838 |
|
Central Highlands |
0.406 |
0.010 |
0.459 |
0.006 |
|
South East |
-0.071 |
0.601 |
0.099 |
0.553 |
|
Mekong Delta |
-0.258 |
0.087 |
-0.450 |
0.000 |
|
Years of schooling |
|
|
|
|
|
Primary-school |
-0.840 |
0.000 |
-0.709 |
0.000 |
|
Lower-secondary |
-1.208 |
0.000 |
-1.048 |
0.000 |
|
Upper-secondary |
-1.533 |
0.000 |
-1.374 |
0.000 |
|
University or higher |
-1.931 |
0.000 |
-2.112 |
0.000 |
|
Occupation |
|
|
|
|
|
White-collar workers |
-0.521 |
0.000 |
-0.554 |
0.019 |
|
Sales workers |
-0.246 |
0.065 |
-0.261 |
0.029 |
|
Blue-collar workers |
-0.273 |
0.095 |
-0.199 |
0.024 |
|
Agricultural workers |
-0.150 |
0.294 |
-0.233 |
0.017 |
|
Age |
0.181 |
0.000 |
0.197 |
0.000 |
|
Religions |
|
|
|
|
|
Buddhist |
0.184 |
0.040 |
0.161 |
0.069 |
|
Christian |
0.487 |
0.007 |
0.295 |
0.002 |
|
Marital Status |
|
|
|
|
|
Widowed |
-0.613 |
0.023 |
-0.612 |
0.014 |
|
Separated or divorced |
-1.409 |
0.000 |
-1.617 |
0.000 |
|
Income |
-0.0001 |
0.000 |
-0.0001 |
0.000 |
|
Urban |
-0.341 |
0.000 |
|
|
|
Electricity |
|
|
-0.005 |
0.000 |
|
Frequency of cut electricity |
|
|
0.076 |
0.024 |
|
Intercept |
-1.426 |
|
-1.426 |
0.000 |
|
R2 |
0.561 |
|
0.561 |
|
|
F(21, 164) |
54.53 |
|
54.53 |
|
Dependent variable is Children ever born after
1992
Source: Author's
calculations based on data of Vietnam Living standard survey 1997-98
1.2. Children ever born
Model in Rural area.
Turning to examine the
regression results from the children ever born model in rural area. The model
replicated the approach used in the children ever born model in the whole
country. The dependent variables is still number of children ever born. The
independent variables are the same as used in the whole country of children ever
born model. However, in the model of rural area, community variables is added .
Table 5 summarizes the
regression for children ever born in rural area. Firstly we will look at the
region variables, the Red River Delta, the South Central Coast and South East do
not appear to be statistically significant and sight of the South East in rural
is different with that of the whole country. The coefficients for regional
variables in Red River Delta, Northern Central Coast, Central Highlands
estimated here higher than those estimated in the whole country, in the other
hand, the coefficients for regional variables in South Central Coast and Mekong
areas estimated here decreasing in fertility is stronger than those of the whole
country. It can be explained that some regions has discrimination.
Effects of women's education
on fertility level estimated in the children ever born in rural area model
nearly the same as those estimated in the whole country model. With the women's
education level in primary, lower, upper, the decreasing in the average number
of children ever born in rural area is weaker than those of the whole country
while in level of university this figure is stronger than urban area. It
reflects that in rural area the number of women having university education less
than those of urban area or the whole country so that they usually have high
position in the society or work opportunities are more than the urban after
graduating from university.
Occupation variables are
negatively associated with the number of children ever born as the same those
estimated in the whole country model. However, in the model of rural area, the
blue-collar worker variable is not statistically significant while the
agricultural worker variable is statistically significant. It is recognized that
the women having the blue collar occupation accounts for a small proportion
while agricultural occupation dominated. In addition, the recent years, having
many newly agricultural implements substituted for manual working and happened
the lack of land in many area which lead to abundance of labor and reducing in
the value of children. Therefore, many couples do not want to bear more
children. This results is also different from the analysis of chapter 2, due to
this multivariate analysis.
As Table 5 also shows that
the age, marital status (widowed, separated or divorced women), religion, and
expenditure (proxy for income) coefficients in the rural model is nearly the
same as those of the whole country model.
The next variable that is
community variable such as having electricity or frequency of cutting
electricity in a community. The regression give us a coefficient of -0.005 for
having electricity variable, implying a negative relationship between the number
of children ever born and the having electricity in a community. This negative
relationship can be explained as follows: Because having electricity is one of
the index that reflects a good condition of living in an area while the
frequency of cutting electricity is positively associated with children ever
born. Holding all other explanatory variables the same, an additional frequency
of cutting electricity, the average number of children ever born increased by
0.076 children.
2. Probit model
2.1. Model of desiring
more children in the whole country
Table 7 presents the probit
model of the probability of having more children in Vietnam 1997-98. Rather than
reporting the parameter estimates", Which are difficult to interpret on their
own. The marginal effects indicate the more children and respective explanatory
variable. For dummy variables, the marginal effect is calculated as the change
in the dependent variable associated with a move from a value of 0 for the
dummy, to 1.
Examining first the region
variables. Table 7 indicates that the probability of having more children in
1998 is 13 percent point lower at the 10 percent significance level if women
live a Red River Delta and is 17 percent point lower at that significance level
if women live a Mekong area. But this relationship is not monotonic, women
residing in all other regions, except the South East is likely to have more
children at the 10 percent significance level.
The more variable important
factor is education variables. This education level of women has strong negative
relationship with fertility. Relative to the no-education (the omitted
educational dummy), women in all other education level are less likely to have
more children at the 10 percent significant.
Although all occupation of
women variables are negatively associated with fertility, they do not appear to
statistically significant at the 10 percent significance level.
Next variables, age of women,
an additional year of age of the women is associated with a 6 percent point
higher probability of fertility for that women. In addition, religions of women
has also positive relationship with fertility. If religion of women is Buddhist,
it will be likely to have more children (with a marginal effect of 0.072 and
0.097 for Christian). This results show that there are the impacting of religion
on fertility level.
Considering next the sets of
variables such as marital status of women, tribe, income, and area of residence
(urban) except tribe and widowed women variables, appear to be statistically
significant at the 10 percent significance level and reduce the probability of
having mare children. However, relationship between income of women and
fertility is not strong. If income increases by 1 unit, the probability of not
having more children is not significant (with marginal effect of 0.000047).
In Summary, the probit model
has indicated that a woman having less than 3 children is more likely associated
with higher education level, stable job, living in area with good socioeconomic
condition.
|
Table 6: Variables Definitions
and Summary Statistics
Number of observations 2444
|
|
Variables |
Mean |
Std. Dev |
Definition |
|
Dependent variable |
|
|
|
|
CBE |
3.109 |
1.930 |
The number of children ever
born |
|
CB2 |
0.523 |
0.500 |
Dummy variable, =1 if woman
had 3 or more children and
0 if she had 2 or less |
|
Regional Characteristics |
|
|
|
|
Red River Delta |
0.126 |
0.332 |
Dummy variable, =1 if woman
resides in Red River Delta |
|
North Central Coast |
0.286 |
0.700 |
Dummy variable, =1 if woman
resides in North Central Coast |
|
South Central Coast |
0.394 |
1.014 |
Dummy variable, =1 if woman
resides in South Central Coast |
|
Central Highlands |
0.550 |
1.378 |
Dummy variable, =1 if woman
resides in Central Highlands |
|
South East |
0.743 |
1.778 |
Dummy variable, =1 if woman
resides in South East |
|
Mekong Delta |
0.977 |
2.222 |
Dummy variable, =1 if woman
resides in Mekong Delta |
Education
|
|
|
|
|
Primary education |
3.443 |
5.102 |
Dummy variable, =1 if woman
has Primary education |
|
Lower secondary education |
4.944 |
5.908z |
Dummy variable, =1 if woman
has lower secondary education |
|
Upper secondary education |
1.888 |
4.582 |
Dummy variable, =1 if woman
has upper secondary education |
|
University or higher |
0.223 |
1.755 |
Dummy variable, =1 if woman
has university or higher |
|
Occupation |
|
|
|
|
White collar worker |
0.452 |
2.193 |
Dummy variable, =1 if woman
has white collar worker |
|
Sellers |
2.371 |
4.779 |
Dummy variable, =1 if woman
has sellers |
|
Blue collar workers |
0.854 |
3.222 |
Dummy variable, =1 if woman
has blue collar workers |
|
Agriculture |
9.739 |
6.443 |
Dummy variable, =1 if woman
has agricultural sector |
|
Age |
32.950 |
6.465 |
Year of age of woman (from
15-49) |
|
Tribe |
|
|
|
|
Minority |
8.700 |
4.474 |
Dummy variable, =1 if woman
has minority ethnic |
|
Religions |
|
|
|
|
Buddhist |
1.301 |
3.732 |
Dummy variable, =1 if woman
has Buddhist |
|
Christian |
1.841 |
4.107 |
Dummy variable, =1 if woman
has Christian |
|
Marital status |
|
|
|
|
Widowed |
0.137 |
1.277 |
Dummy variable, =1 if widowed
- woman |
|
Separated |
0.234 |
0.729 |
Dummy variable, =1 if
separated - woman |
|
Income |
2308.302 |
1859.6 |
|
|
Urban98 |
0.196 |
0.397 |
Dummy variable, =1 if woman
lives in Urban areas |
Source: Author's calculations
based on data of Vietnam Living standards Survey 1997-1998
Table 7:
Probability of bearing children per woman in the whole country
Number of obs 2444
Prob >chi2 0.000
Pseudo R2 0.3653
|
More children's preference |
dF/dx |
Standard error |
P>|z| |
x-bar |
|
Red River Delta* |
-0.126 |
0.047 |
0.008 |
0.126 |
|
North Central Coast* |
0.087 |
0.044 |
0.048 |
0.143 |
|
South Central Coast* |
0.094 |
0.049 |
0.072 |
0.131 |
|
Central Highlands* |
0.225 |
0.056 |
0.000 |
0.137 |
|
South East* |
0.028 |
0.059 |
0.639 |
0.149 |
|
Mekong Delta* |
-0.165 |
0.046 |
0.000 |
0.163 |
|
Years of schooling |
|
|
|
|
|
Primary-school* |
-0.133 |
0.049 |
0.004 |
0.313 |
|
Lower-secondary* |
-0.213 |
0.046 |
0.000 |
0.412 |
|
Upper-secondary* |
-0.383 |
0.046 |
0.000 |
0.145 |
|
University or higher* |
-0.451 |
0.063 |
0.000 |
0.016 |
|
Occupation |
|
|
|
|
|
White-collar workers* |
-0.154 |
0.078 |
0.086 |
0.035 |
|
Sales workers* |
-0.028 |
0.045 |
0.541 |
0.167 |
|
Blue-collar workers* |
-0.036 |
0.063 |
0.557 |
0.056 |
|
Agricultural workers* |
0.065 |
0.039 |
0.097 |
0.589 |
|
Age |
0.0595 |
0.003 |
0.000 |
32.950 |
|
Religions |
|
|
|
|
|
Buddhist* |
0.072 |
0.035 |
0.036 |
0.167 |
|
Christian* |
0.097 |
0.041 |
0.024 |
0.108 |
|
Marital Status |
|
|
|
|
|
Widowed* |
-0.106 |
0.135 |
0.357 |
0.011 |
|
Separated or divorced* |
-0.225 |
0.027 |
0.000 |
0.018 |
|
Income |
-0.000047 |
9.93e-06 |
0.000 |
2308.3 |
|
Urban areas |
-0.262 |
0.038 |
0.000 |
0.196 |
|
Obs. P |
0.523 |
|
|
|
|
Pred. P |
0.546 |
(at x bar) |
|
|
Note: (*) dF/dx is for discrete change of dummy
variable from 0 to 1
Source: Author's calculations based on data of
Vietnam Living standard survey 1997-98
Z and P|z|
are the test of the underlying coefficient being 0
2.2. Model of desiring
more children in the Rural area.
This model also replicated
the approach used in the whole country model. However, these is a difference
between 2 models. In rural area model, community variables (accessing to
electricity and frequency of cutting electricity) are added as discussion above.
Table 8 summarizes the
regression results for the model of rural area. Firstly we will look at the
region variables. In the Red River Delta, South East and Mekong area are
inversely related to fertility (desiring more children) (with a respectively
marginal effect -0.062, -0.001, and -0.261). It is the same model of the whole
country, that relationship is also not monotonic.
Although effects of education
on having more children are estimated those estimated in the model of the whole
country. The marginal effect of education in rural is lower than that of the
whole country.
Like the whole country model,
all of the women's occupation variables are not statistically significant at the
10 percent significance level, access to while-collar worker, sales, and
agriculture reduce the probability that a woman have more children. But this
relationship is also not monotonic, if the women are blue-collar workers, the
probability of having more children increase again that is an exception case.
Next some variables such as
marital status of women, tribe, income and religion of women are estimated in
this model nearly the same estimated variable in the whole country model by both
sight and the statistically significant at the 10 percent of those variable.
The last, but not least
important, variables in this community group is electricity using and frequency
of cutting electricity. Accessing to electricity variable reduces the
probability of having more children at the 10 percent significant level (a
marginal effect of 0.002) while frequency of cutting electricity is inversely
related to accessing to electricity, it does not appear to be significant at the
10 percent significance level and increases the probability of having more
children.
Table 8:
Probability of bearing children in rural area (Probit Estimates)
Number of obs 1996
Prob >chi2 0.000
Pseudo R2
0.3609
|
More children's preference |
dF/dx |
Std.Err |
P>|z| |
x-bar |
|
Red River Delta* |
-0.062 |
0.052 |
0.227 |
0.112 |
|
North Central Coast* |
0.079 |
0.044 |
0.080 |
0.160 |
|
South Central Coast* |
0.011 |
0.057 |
0.849 |
0.118 |
|
Central Highlands* |
0.093 |
0.069 |
0.193 |
0.171 |
|
South East* |
-0.001 |
0.073 |
0.990 |
0.114 |
|
Mekong Delta* |
-0.261 |
0.056 |
0.000 |
0.162 |
|
Years of schooling |
|
|
|
|
|
Primary-school* |
-0.079 |
0.056 |
0.122 |
0.327 |
|
Lower-secondary* |
-0.141 |
0.050 |
0.005 |
0.422 |
|
Upper-secondary* |
-0.264 |
0.064 |
0.000 |
0.110 |
|
University or higher* |
-0.361 |
0.179 |
0.072 |
0.005 |
|
Occupation |
|
|
|
|
|
White-collar workers* |
-0.113 |
0.103 |
0.262 |
0.198 |
|
Sales workers* |
-0.004 |
0.055 |
0.941 |
0.119 |
|
Blue-collar workers* |
0.074 |
0.068 |
0.304 |
0.041 |
|
Agricultural workers* |
0.033 |
0.042 |
0.434 |
0.700 |
|
Age |
0.060 |
0.003 |
0.000 |
32.863 |
|
Religions |
|
|
|
|
|
Buddhist* |
0.087 |
0.038 |
0.028 |
0.1485 |
|
Christian* |
0.128 |
0.039 |
0.002 |
0.118 |
|
Marital Status |
|
|
|
|
|
Widowed* |
-0.179 |
0.139 |
0.191 |
0.014 |
|
Separated or divorced* |
-0.301 |
0.038 |
0.000 |
0.016 |
|
Income |
-0.0000682 |
0.000039 |
0.000 |
1864.94 |
|
Electricity |
-0.002 |
0.001 |
0.000 |
58.633 |
|
Frequency of cut electricity |
0.014 |
0.0150 |
0.364 |
|
|
Obs. P |
0.581 |
|
|
|
|
Pred.P |
0.645 |
|
|
|
Note: (*) dF/dx is for discrete change of dummy
variable from 0 to 1
Source: Author's calculations based on data of
Vietnam Living standard survey 1997-98
Z and P|z|
are the test of the underlying coefficient being 0
Chapter 4: Conclusion and
recommendation
Basing on the analysis of
data and information obtained from a Vietnam Living Standard Survey, several
findings and policy recommendation have been drawn from the previous chapters.
Ø
The analysis
finds that an increase in the wife's education of the couples is important in
reducing the number of children ever born or desired completed family size in
Vietnam. Therefore we must continue to invest in the education of women.
Education is the cornerstone to improving the status of women, both for the
skill set it provides for gaining entry to the labor force. Moreover, education
is one of the strongest determinants of fertility level of any socioeconomic
variables in the data set. At least, mass education for women has to be
implemented in the whole country, especially in Northern Uplands, Central Coast
and Central Highland. Because the basis literacy skills that are conferred bring
the concept of the written word into the realm of consciousness and enable the
individual at the very least, to hear the message about family planning or
health practices. In addition, with higher levels of education, the woman's
attitudes toward marriage and childbearing become more important, yet her
behavior will also be affected by the norms of the social groups to which she
belongs. No individual's behavior can ever be divorced from the social,
economic, and cultural context in which she is situated, but as educational
attainment rises, the individual moves away from community based childbearing
norms toward a more individual set of beliefs and behaviors and, perhaps, into
social networks with lower fertility norms.
Ø
The finding
that the poor belonged to mainly in rural areas and women working in non-modern
areas, especially in agriculture sector and women without job have more children
than those of modern areas.
Thus, the government should be made to increase the opportunities for female
employment in off-farm activities in the formal labor market. Because if farm
women get good opportunities to work in off-farm or non-farm activities outside
their own farm, the value of their time will be increased which also increase
the cost of their children. Thus, they would prefer to have fewer children than
before.
Ø
In this
thesis finds that the number of children ever born or "more than two child
family" in the Northern Uplands, North Central Coast, and Central highlands
tended to be higher than in Red River Delta and Mekong areas.
Therefore, the policy measures to support the development of such regions (such
as provision of credit, technical assistance, and investing in education) are
necessary. In addition, Government or local officials should introduce broad
social programs to raise status of women, which is essential to prevent early
marriage, and the use of incentives and disincentives to encourage preferences
for smaller families.
This study shows that
cultural and religious factors also impact on fertility level. Therefore, all of
the policy measures listed above should be applied.
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