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Chapter 2
Gender Preference, Fertility Choices and Government Policy
in India
2.1 Introduction
If we consider the theory of fertility transition, many developing countries are still in the
early to mid stages of transition where observed fertility levels exceed desired family
sizes. Evidence of such phenomenon in developing countries can usually be found in high
levels of unwanted fertility, gender preferences and child replacement effects (Bongaarts,
2000). It is often established that couples in developing countries tend to have higher
fertility either because of poor access to contraceptives, or because they have a taste
preference for a certain gender composition in their existing children—or simply because
high infant mortality rates force couples to have a higher fertility rate in order to maintain
desired family size.
India has been especially affected by these problems. While the infant mortality rate has
gone down, it is still higher than many other developing countries and thus one can
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imagine that part of the reason for high fertility is the need to replace deceased children.
However, despite much effort, the impact of high child mortality on reproductive
behavior is not fully understood. Several studies have noted that couples in India tend to
have a strong preference for sons over daughters. In an effort to have sons, many couples
either continue to have children until the desired number of sons has been reached, or get
abortions done. Studies have also found that there are thousands of women in India who
would prefer to postpone or avoid pregnancy but do not use contraceptives. These women
have an "unmet need" for contraception and it‟s plausible that part of their high fertility is
unwanted.
In this paper I examine the impact of a shift in the government policy in India to reduce
fertility levels in 1997, when the government moved from policies encouraging family
planning and female sterilization to policies affecting demand for larger families. The
highly centralized, target-based and inflexible policies in the pre 1997 period with heavy
reliance on female sterilization did not address the high fertility problem appropriately.
By providing monetary and other incentives to couples opting for sterilization, these
policies ended up in targeting older women who already had a large enough family size.
Thus even though some improvements in national averages were seen, fertility reduction
stalled after some time because the taste preferences of couples remained in favor of large
family sizes with a higher concentration of boys. In contrast, the policies since 1997 have
been aimed at reducing fertility through better awareness of the benefits of smaller family
sizes. These policies target all women in reproductive age groups and thus a reduction in
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fertility is achieved by improving the overall maternal and child health, creating
awareness and demand for smaller family sizes and improving access of modern
contraceptive methods.
The rest of the paper is organized as follows. In section 2.2 I discuss the related literature
on gender bias in India and key factors resulting in such behavior. I then discuss the
policy environment in India before and after 1997. Section 2.3 describes the data used for
estimation. Section 2.4 briefly describes the original data through key summary statistics.
In section 2.5 I describe the methodology used to estimate various models, sample
selection, creation of variables and summary statistics on them. The main results are
described in section 2.6, with a conclusion in section 2.7.
2.2 Related Literatures/ Motivation
2.2.1 Preferences for Son
Several studies have noted that couples in India tend to have a strong preference for sons
over daughters (Arnold et al, 1998). In an effort to have sons, many couples either
continue to have children until the desired number of sons has been reached, or get
abortions done. In either case, this may have an adverse effect on not only general
maternal and child health in the country but also on fertility rates. Arnold et al, (1998)
conclude that son preference fundamentally affects fertility behavior and child mortality
and girls with older sisters are often at the highest risk of mortality. Previous studies have
found that a large number of social, cultural and economic considerations are at the root
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of such a preference pattern among couples (Friedman et al, 1994). Studies have
identified that there is a greater economic utility from having a son. In the joint Hindu
family system, the eldest son is expected to take care of his parents once they become
old, although there is some evidence that sons are no longer a dependable source of oldage support (Bardhan 1988). A daughter on the other hand, gets married into another
household and is morally obligated to assume responsibility for caring for her parents-inlaw. Upon marriage, the son brings a daughter-in-law home to his house who not only
helps around the house but also brings monetary gains to the household in terms of
dowry. A daughter on the other hand is an economic burden in the sense that huge sums
of dowry need to be given to marry her off.
According to traditional Hindu religion, women are not allowed to attend cremations. It is
believed that a human being will abode heaven and attain moksha (salvation) only if
his/her last rites are performed by his/her son. However, according to the religion, it is
also important to have a daughter because parents can earn religious merit by selflessly
giving her away in marriage. This suggests that the observed son preference may be the
result of a complex interplay of socio-economic factors. A strong preference for son may
hinder the attainment of ideal fertility rates in the country if couples continue to have
children in order to reach their ideal sex-composition among their children. Coupled with
the fact that the average age at marriage for women is low in India, this may also be one
of the reasons for poorer maternal and child health in India. Existing studies however do
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not exhibit a consistently strong effect of son preference on fertility (Mutharayappa, et.
al, 1997)
2.2.2 Different Policy Regimes
Population growth has long been a concern of the Government of India. More than 50
years ago, India became the first country in the developing world to initiate a statesponsored family planning program with the goal of lowering fertility and slowing down
the population growth rate. Since then, fertility levels have declined throughout the
country, with total fertility rate decreasing from 5.89 births per woman in 1971 to about
3.4 births per woman in 1994 and 2.9 births in 2004. This is still high compared to the
replacement level fertility rate of 2.1. The annual percentage growth in population though
decreased over the decades is still high at 1.93. The contraceptive prevalence rate (CPR)
has also increased from about 10 percent in the early 1970s to around 45 percent in the
mid-1990s.
Since its inception in 1951, the national family planning program has been dominated by
demographic goals. However, the government adopted and promoted highly methodspecific family planning targets in the 60s and 70s, wherein the central government gave
to each state method-specific contraceptive targets, which were based on calculations to
achieve replacement fertility level by a certain year. These targets were then distributed
to the districts by the state governments, and the district administrator or health officers
passed them on to the Primary Health Care Center (PHC). In turn, each paramedic
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working under the PHC was given a target number of new acceptors for each method. In
most states the targets for family planning were also given to non-health staff, such as
staff of revenue, education and rural development. The district administrator and the
state level technocrats and bureaucrats reviewed performance of health care organization,
at each level, based on the achievement of the targets.
Under the target-oriented planning and monitoring system introduced in 1966, the
prevailing idea in dealing with overpopulation was that drastic measures needed to be
taken. Education regarding temporary methods of contraception was neglected in favor
of encouraging permanent sterilization. Government agencies would have sterilization
quotas to fill among the employees, and the inability to meet them was sometimes met
with withheld salaries, withdrawal of annual increments or transfer to undesirable posts.
Workers were often rewarded with a radio or television if they successfully convinced
enough people to opt for the surgery. As a result, providers often over-reported the use of
reversible contraceptives or coerced couples into accepting sterilization in order to meet
program expectations. Financial incentives were also given to couples opting for
sterilization, including monetary award and better chances of loan approval. At its worst,
the target-oriented approach became highly coercive during the national emergency of
1975-77. It is alleged that almost 11 million sterilizations were performed in that period.
The program focused primarily on sterilization, mostly ignoring client choice and
limiting availability to a narrow range of services. This approach was criticized by
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extending the argument that people in developing countries have large families because
they want to and not because they don‟t have access to birth control. It was argued that
efforts should be made to increase the demand for a smaller family size rather than
increasing the supply of certain birth control measures. Some women‟s organization also
viewed this policy as a violation of human rights (Karkal M, 1998). Additionally, the
poor quality of care offered by contraceptive providers was considered indicative of the
government‟s lack of respect for women‟s health
It seemed that in its early years of inception, the national focus on sterilization created an
“all or nothing” mentality among Indians towards birth control especially since the
awareness of other, temporary methods of contraception was limited (Pathak et al. 1998).
The National Family Health Survey conducted in 1992-93 shows that of all contraceptive
use at the time, 67% was by female sterilization (compared to 9% male sterilization). The
prominence of female sterilization indicates another flaw in the India population control
strategies. By targeting women instead of men, the government inadvertently opted for
the more hazardous means of birth control. The surgical procedure in women is more
difficult and the rate of failure is high, not to mention the danger to the patient,
sometimes resulting in death.
Thus in the regime of semi-forced, safety-negligent policies, we get what is likely to be a
fairly accurate explanation of why family planning efforts failed to curb rampant
population growth in India in the 60s, 70s and 80s. When the only option available to
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many couples is one that is irreversible, not to mention potentially life-threatening,
couples would probably be inclined to either no contraceptives at all, or sterilization after
enough number of children are born. Such policies no doubt lowered TFR in the short run
but were ineffective in improving maternal and child health, reducing gender inequalities
and promoting smaller family size as a conscious choice among couples. In short, the
program as implemented was shortsighted, insensitive to the needs of clients, and
discouraged community involvement.
In 1992, the Indian government published its eighth five year plan in which it enlisted
several factors leading to poor realization of its family welfare goals. The key deterrent of
these goals was the target-based approach and centralized planning followed until then.
That same year, the government launched the Child Survival and Safe Motherhood
Program to enhance the health of women and children and further reduce maternal and
child mortality. The family Welfare Program continued to emphasize family planning
services and the child survival components of the new program—especially the
expansion of the child immunization services—was implemented earlier than the safe
motherhood components. Therefore, the overall national program still offered little to
improve the quality or availability of reproductive health services to women
The government report acknowledged other problems as well: Both initial and on-the-job
training of service providers had been poor; information and education efforts had been
ineffective, presenting family planning as a means to contain population growth rather
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than as a way to improve a family's economic and social status by limiting births; the
infrastructure for extension services in some of the more populous regions was lacking,
the program had few resources for new initiatives or for strengthening health care
services; and the government program allowed for little active involvement of the
community.
As awareness of the existing program's weaknesses was growing, evidence emerged that
data from assessments of the program did not correspond with data from populationbased surveys: Contraceptive use rates calculated from program statistics were
inconsistent with observed fertility levels, and official program-based protection rates
were significantly higher than survey-based contraceptive prevalence rates.
In April 1996, the Indian government decided to abolish method-specific family planning
targets throughout the country. In October 1997, India reoriented the national program
and radically shifted its approach to more broadly address health and family limitation
needs. The new approach, called the Reproductive and Child Health (RCH), involves a
more comprehensive set of reproductive and child health services and a focus on client
choice, service quality, gender issues and underserved groups, including adolescents,
postmenopausal women and men. The goals of the RCH program included removing all
targets; phasing out incentive payments to both providers and acceptors of family
planning methods; increasing utilization of existing facilities rather than creating new
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structures; and using the voluntary and private sectors to increase access to services and
fill gaps left by public-sector providers.
The target-free approach of 1996 meant that centrally determined targets would no longer
be the “driving force” behind the program. Instead, the community's service needs would
determine the program's priorities. With the new approach, planning was to be
decentralized and responsibilities were to reside at the level of the primary health centers:
Targets would be set by local health workers, "in consultation with the community at the
grassroots level."
2.3 Data Description
The reproductive and child health interventions provided by the Government of India are
expected to provide quality services and achieve multiple objectives. The GOI desired to
re-orient the program and strengthen the services at the out-reach level. The new scheme
required decentralization of planning, monitoring and evaluation of services at a lower
level i.e., district.
The rapid household survey was conducted with these objectives in mind. The need to
generate district level data was felt so as to design the course of the new approach. The
World Bank provided financial support for carrying out this survey. The GOI decided to
undertake Rapid Household Survey of 50 percent of the districts of the country every
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year. The first phase of this survey was conducted in 1998, the second phase in 1999 and
another round of survey was done in 2001-02. During the second phase, the importance
of collecting information on the household possession of durable assets was recognized
and survey questions were included in the questionnaire. In this survey we only use the
2001-02 dataset, mainly because the districts covered in 2001-02 are not the same as
those covered in 1999 and the survey in 2001-02 was more comprehensive than that done
in 1999.
In every district, 1100 households and all the eligible women (15-44 years) in these
households were covered. The data was collected by using uniform questionnaires,
sample design and field procedures. The survey thus provided comparable data for all
districts covered in any particular year. The main objective of the survey was to estimate
the service coverage of the following:
1.
Ante Natal Care (ANC) and Immunization services
2.
Extent of safe deliveries
3.
Contraceptive prevalence
4.
Unmet need for family planning
5.
Awareness about RTI/STI and HIV/AIDS
6.
Utilization of government health services and user's satisfaction
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A total of 247,018 women aged 15-44 years were surveyed in 289 districts. Information
on pregnancies borne up to the survey date was obtained from these women. After
excluding women for which inconsistent data was reported/recorded we are left with
244,834 cases. These women report whether or not they ever became pregnant, the year
of each birth, their age at that time, whether the birth was a single or multiple birth, sex of
the child, whether the child died and if yes, the year and month of the child‟s death. Thus
we are able to observe the complete birth history of each woman in our sample. This
becomes our original dataset with unit of observation being a woman. The next section
summarizes the main characteristics of this dataset.
2.4. Descriptive Statistics
Age of the woman at the time of survey:
Women aged 15-44 are included in this survey. About 75% of the women report their age
to be between 15 and 35. Thus, most women covered in this survey are still fertile.
However, when plotting the frequencies of reported age (Figure1), we find a bias in
reporting age in multiples of 2 and 5 years. This is expected as most women in this
sample hail from rural areas with little or no education, and no existing records of their
birth. Thus age of the woman is a very noisy variable in this sample.
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Year of birth of the child:
The earliest child bearing year recorded in this sample is 1970 and goes up to 2003. Since
most women were not observed for the whole of 2002 and 2003, we exclude these years
from our sample and only use the birth activity of each woman up to 2001. Again this
variable is very noisy (Figure 2), with rounding bias in multiples of 2 and 5 years.
However, there does seem a general upward time trend for at least the first and second
birth.
Age of the woman at the time of birth:
Surprisingly, this variable is very smooth and our guess is that since this variable is
correlated with the two very noisy variables described above, the net effect is to produce
such smoothness (figure 3). The mean age of the mother at the time of first birth is only
20 years while that at the time of second birth is only 22 years. An average woman in this
sample is susceptible to child birth every 2 years after the age of 18 (Table 2).
Number of Births:
In this sample, 89% women had at least one birth, 73% had at least 2 births, 50% had at
least 3 births, 30% had at least 4 births and only 16% had at least 5 births.
Other summary statistics:
Rural women are generally less educated and at a higher risk of early marriage in India.
We thus present other characteristics of women differentiated by residence type (Table
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1). We see that almost 69% of women in our sample come from rural areas. Only 43% of
rural women can read and write while 71% of urban women can read and write. The
average number of schooling years of a rural woman is 7.5 while that of an urban woman
is 9.5. About 68% of rural women‟s husband can read and write while 85% of urban
women‟s husband can read and write. The mean age at effective marriage (the age at
which a woman starts living with her husband) is not much different for rural and urban
women. In fact, it differs by only a year and shows that women tend to start families very
early on in life, as early as when they are 17 and 18 years old.
2.5. Methodology
In this paper I examine the fertility choices made by Indian women. Various factors such
as age of the woman, education status, whether the woman comes from an urban or rural
setting etc could affect these choices. Certain states in India are known to be poor
performing in terms of health and social indicators, and thus women coming from these
states would tend to exhibit different fertility choices as compared to women from other
states. These states are Bihar, Rajasthan, Uttar Pradesh and Madhya Pradesh. The World
Bank came up with an interesting acronym to refer to these states, viz., BIMARU, which
means “sickly” in Hindi. In the Indian setting, another factor affecting fertility choice
would be gender preferences in the composition of a woman‟s existing children. Thus, a
woman with one girl and one boy might make fertility decisions quite different from
another woman with two boys. There can be several other factors affecting fertility
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decisions, but we will mainly consider urban-rural, BIMARU-Other, and parity
differentials. Similarly, the various government policies adopted should explain part of
the fertility choices of women.
2.5.1 Sample Selection
In the Indian context, early marriage and birth are not uncommon phenomenon. In fact, in
our data, some women start childbearing as early as at the age of 10. Although modeling
fertility choices of these women is important, such an analysis is restricted by lack of
enough observations. Thus we consider a woman to reach her reproductive cycle at the
age of 13. I assume that in each 12 month period following the age of 13, each woman in
the sample makes a decision to have a child or not. There is an issue of age at effective
marriage. Since women are asked the age at which they started living with their husband,
it may be argued that not all women get married as early as 12-13 and thus one must
consider the age at effective marriage as the date when the woman starts making her
reproductive decisions. The problem with following this rule is that many times women
get married very early but continue to stay with their parents. During that time, even
though a woman may not have actually started living with her husband, she may still be
exposed to the risk of becoming pregnant if she has conjugal relationship with her
husband. Thus this variable may be very noisy and not appropriate for modeling
purposes.
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Thus each period after the age of 13, a woman makes a decision whether or not to have a
birth in the following 12 month period. If she is successful, then the woman exits the
sample. If she is not, then she remains in the sample for another 12 month period at the
end of which she is successful or not. Using this strategy, we create sub-samples of
women making decision to have their first, second, third, fourth or fifth birth. In the
model of first birth, women enter the sample at 13 and exit when they have their first
birth. If the woman has never had a child then she enters at age 13 but exits only in the
interview year. Thus each observation in our sample is now a one-year record rather than
observation, since each woman may remain in the sample for more than one period.
Collecting information in this manner, we get a new data for the birth 1 model with
1,942,876 records.
The datasets for models of birth two, three, four and five are similarly created. The only
difference is that obviously in these models a woman enters at a later age than the first
model. For example, the earliest age at which a woman can make her second birth
decision is when she is 14 years old. For births three, four and five, the entry ages are
respectively 15, 16 and 17. Also, the woman enters the second birth model only 12
months after her first birth. For example, having had one child, a woman can either
choose to have another child or not. If she chooses to have another child, then she enters
the sample 12 months after the age at which she had her first child and exit at the age at
which she had a second child. On the other hand if she chose not to have another child
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until the time of survey then she enters the sample 12 months after the age at which she
had her first child and remains in the sample until the age at the time of survey. The new
datasets thus created have 559,507 records for birth 2, 586,428 records for birth 3,
464,753 records for birth 4 and 277,899 records for birth 5.
2.5.2 Creation of variables
Dummies for whether previous children are alive:
A dummy variable is included for whether or not each of the existing children is alive at
the time the woman is sitting around in a sample. For example, if a woman had her first
birth at 14 and second at 20, then she enters the decision making for birth 2 when she is
15 and remains in the sample until she is 20. These dummies tell us for each of the 5
years she was in the sample, her first child was alive or not. Looking at the descriptive
statistics, we find that for about 92-94% of the time, a woman‟s previous child is alive.
Dummies for whether previous children are boys:
We include dummies for various combinations of gender compositions that a woman
might have in her existing children. We see that of the women who‟ve had only one
child, almost 54% have had a boy. In women with two existing children, 31% have two
boys and in women with three existing children, 14.4% have three boys. We also include
an interaction term of the boy dummy and time trend, to generate the time profile of
women who have had boys in the past.
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Time since last birth:
For each of the year a woman is active in any particular sample, I calculate the number of
years that have elapsed since her last birth. If a woman waited more than four years
before having another child, I group them together as women waiting for five or more
years since last birth. Almost 18% of women who have one existing child have another
birth within one year. Generally, it is professed that there should be at least a gap of three
years between two consecutive children for better maternal and child health. However in
our sample, only 40% women wait more than three years before a second birth. Spacing
between two children becomes worse for higher order births, with 38% women waiting
three or more years before a fourth birth and 37% women waiting that long before a fifth
birth.
Policy dummy and time trend variables:
We first test our model to see if including a time dummy for each year has more
information than simply including a time trend and conclude that fitted probabilities seem
to be linear in time. These results are presented in the next section. Thus we include a
time trend in our model. The new population policy of the government of India became
effective in 1997. We thus include a policy dummy which is one for each year from 1998
onwards. We keep a one year lag in switching the policy dummy on so to allow for the
propagation of the new policy. Finally, we interact the policy dummy with years since the
policy became effective to see if gains from the new policy are accruing overtime.
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Other variables:
I include age dummies, a dummy for whether the woman belongs to rural area and
another dummy for whether the woman belongs to the states of Bihar, Madhya Pradesh,
Rajasthan or Uttar Pradesh (BIMARU).
2.5.3 Model
I use a probit model to estimate the likelihood of having a birth, controlling for the age of
the woman, the gender of her previous children, whether they are alive or not, time since
last birth, time trend and policy variables. For example, the probability of having a
second birth conditional on a first birth will be as follows:
Prob(Birth 2=yes| birth 1=yes) = a1*(child 1 alive?) + a2*(first child boy?) + a3*(2
years since birth 1) + a4*(3 years since birth 1) + a5*(4 years since birth
1) + a6*(5 or more years since birth 1) + b1*(rural) + b2*(BIMARU) +
c1*(time) + c2*(Dummy for 1998) + c3*(D98*time) + d1*(age14) +
d2*(age15) + ……+ d29*(age42)
I run various specifications of the above model, specifically including interactions of the
policy variable with different variables in my model to see if the policy benefited certain
types of women more than others. Then using the coefficients from the basic models, I
evaluate the fitted probabilities of various births with respect to age of the woman. At the
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time the data was collected (2002), the 1998 policy had only been in effect for four years,
but the effect of the policy will take place over the lifetime of the mothers, a much longer
period of time. I use the coefficients from the above models, assume that the last year is
the ultimate distribution on first, second, third and beyond births, and then simulate the
distributions of births for a mother reaching age 13 in the last year of my sample. I do
this with and without the D98 policy variable turned on. This is the long run impact of
the policy, which will be much greater than the short run impact.
Time clusters Vs. Women Clusters:
There was an issue of whether the appropriate clustering variable would be year of birth
or the mother. Since the new policy is time specific, it can be argued that the appropriate
cluster that minimizes the variability within each cluster and maximizes the variability
between clusters is years and not the mother. I estimated the models with both types of
clusters and find that the Z scores are smaller with the time clusters. This is as predicted
since we have fewer clusters of time than of mothers. Also, we only have data for a few
years after the implementation of the policy and thus the full impact of it may not be
captured in the estimates. As a comparison, I presented some models with women
clusters in appendix A.
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2.6. Results
I first estimated all five models with time dummies to allow for full variation in time
effects. Table 2.3 reports the coefficients on just the year dummies, while suppressing the
coefficients on all other variables. We see that the likelihood of first birth does not get
affected over time, presumably because each woman cares to have at least one or two
children. In all subsequent births, the effect of the time coefficients is just scalar
transformation and relatively constant over time, except after the inception of the policy
when the likelihood of second and higher order birth goes down. This can also be seen in
figure 2.4 where I plot these time coefficients and in figure 2.5 where I plot the
conditional or fitted probabilities for a woman aged 25 years over time. Tables 2.4 and
2.5 each compares the time dummy model with a time trend model for births 1 and 2. The
first model in each table uses year dummies; while the second model captures the effects
of time using linear time trend variables in each table. A likelihood ratio test rejects the
null hypothesis that the time dummies are all in a linear trend, however, that is to be
expected with such a large dataset. Thus all subsequent models capture the effect of time
using linear time trends instead of a dummy for each year, which enables me to capture
the policy impact using a few parameters.
It also permits me to examine possible
interactions of key variables.
Table 2.6 gives regression results from the basic model described above. Coefficients on
mother‟s age dummies have been suppressed. Several points can be noted in this table.
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Firstly, couples seem to care about the result of their immediately preceding childbearing
experience. In each of the models, the fact that the immediately preceding child is alive
reduces the probability of another birth by a larger value than the life status of other
children. Looking at gender compositions, having a first child boy does not affect the
probability of second birth: the coefficient on the first boy is statistically significant but
has a small impact on the Z score (-.065). As we move to higher order births, we see that
the effect of having boys is more pronounced. The coefficients also display a nonmonotonic effect with families that have a mixture of boys and girls having fewer future
births than those with all boys or all girls. The largest decline on future births seems to
occur for women with 2 boys.
Coming from either a rural area or a BIMARU state significantly increases the chances of
going for additional births. It is also more likely that these women will go for higher
order births as well. In general women like to space their children 2-3 years apart. If too
much time has elapsed since the last birth, this would imply either that the woman is old
and has already completed her fertility choices or is young but has some reproductive
health issues. Birth 2 model does not perform so well because most women want to go
for a 2nd child. Therefore, rural women do not look different from urban and women from
BIMARU states do not look different from those from other states.
Looking at the time trend and the policy variables, we see that over time, the probability
of first and second births has remained essentially constant up until 1997, the probability
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of additional births decreased very slightly in each year without any apparent differential
effect on third, fourth or fifth order births. It may be noted that clustering on time seems
to be producing lower numbers for the standard errors making the policy dummy
statistically insignificant in all five models. This is as expected since we only have 20-30
clusters at the most for each model with such rich data. The standard errors are also
getting affected by the fact that very little time elapsed since the introduction of the new
policy in our sample. As a comparison, I presented the same results after clustering on
women in table A.1 in appendix A. the coefficients on time dummies and policy time
interaction are statistically significant in this table In the model of first births, the policy
dummy was positive and the time trend since the policy change was positive: the
improved prenatal care and information seems to have increased, not decreased the
number of first births. For the second birth, the policy effect was also small, with a
modest decline observed after two years. The dramatic impact of the policy is observed
for the third and higher births. There was a significant decrease in the probability of third
and higher births, on the order of .07 or .08 per year. If this trend persists beyond the
sample period observed here, this will have a profound effect on the birth pattern and
average family size in India.
The results from the 1997 policy reforms emphasizing demand side incentives can be
contrasted with the female sterilization policy which affected mostly older women who
had already made their fertility choices and thus had larger families anyway. After the
inception of policy, there is a significant reduction in the likelihood of giving another
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birth within 2 years of last birth. This indicates that the policy has been effective in
encouraging couples to space their first and second birth at least 3 years apart.
Table 2.7 presents the same regression as table 2.6 but with the addition of another
interaction—the interaction of boy dummy with time to compare the behavior of mothers
that have boys overtime to those who don‟t have boys. We see that having boys does not
affect the likelihood of second birth over time—presumably since most women care to
have at least two children. However, for each subsequent birth, mothers who already have
boys are more likely to go for another child in general, but less likely to go for another
child overtime. This tells us that women who are lucky to have first few male children
would in general be more likely to try and repeat the experience .Overtime the gender
preference bias does not seem to reduce with fertility decisions being strongly guided by
the number of „boy births‟. As a comparison of the standard errors, table A.2 in appendix
A reports results on the same regressions but with women clusters.
In tables 2.8a and 2.8b I report Z scores calculated by each of three types of error
structures. In general most of our coefficients are losing power because of bootstrapping.
Table 2.9 examines alternative interactions between the policy variables and other
covariates in the model. Because of colinearity, I did not include all of the interactions in
one model, but instead included one set of interactions at a time.
The results are
suggestive, not conclusive, about which interactions are most important for understanding
the impact of the policy reforms.
25
The first model shown in Table 2.9 is the same as the first in Table 2.6. In model 2, I
included the interaction of policy dummy with the dummies describing time since the last
birth. We see that except for birth 2, after the introduction of the policy there was a
significant reduction in the likelihood of another birth within two years following the last
birth. This indicates that the policy has been successful in encouraging women to increase
the spacing between two children from 2 years to the desired 3 year norm. Similarly,
coefficients of the policy gender dummy interaction tell us that since the inception of the
policy, there has been a significant reduction in the likelihood of more births if a woman
has 1-2 boys already. It appears that the policy reforms worsened rather than improved
the gender preference for boys. Results from models 4 and 5 for each birth indicate that
the reforms were not particularly effective in reducing the likelihood of additional births
in rural areas and BIMARU states. The pattern suggests that second and births and
subsequent births may have been increased rather than reduced.
Figures 2.6 through 2.10 use the estimated model to generate within sample and outside
of sample predictions for a hypothetical average woman who ages in each year given the
parameters that would be in effect in that year. Hence the same woman is assumed to
instantaneously make all of the decision that would normally occur at ages 13, 14, 15,
17,…, 42. These simulations are useful for seeing what the long run implications would
be from any delays of births and reduced fertility. The 1996 and 2002 simulations use
actual parameters for those years, while the predictions for 2008 extrapolate to reflect
patterns if the trends observed from 1998 to 2002 continued for six more years. We see
26
that an average woman is more likely to have first and second births. This is plausible if
the maternal and child health aspect of the new policy improved the maternal health of
women. The probability of third and higher order births goes down with time, and by
2008 the probability that a woman would have more than 3 births is less than half of the
1998 value. The expected number of children declines significantly, as the number of
mothers with five or more children drops to less than 10 percent of their 1996 values.
Thus the new policy should be effective over time in reducing total fertility rates through
smaller family sizes. Figures 2.11, 2.12 and 2.13 show that the policies seem to have
reduced the probabilities of births in general with relatively larger reduction for families
with more boys. In figure 2.14 I compare the likelihood of third birth for a woman who
has two girls to one that has two boys for 1996 and then compare it with 2008. Compared
to the significant reduction in likelihood of another birth for the mother with two boys,
the average mother with two girls seems to have no change in likelihood of third birth.
Hence, the 1997 national policy appears not to have reduced the significant problem of
gender bias in Indian couples.
2.7. Conclusions
In this paper we model the probability of various order birth decisions that a woman
could make, controlling for mothers‟ age, time and other covariates. Predicted
probabilities of various order births are calculated across time and for mothers of
different ages. Finally, model coefficients are used to predict birth decisions of a
hypothetical mother in various years. We find modest reduction in conditional birth
27
probabilities as time elapses after the introduction of the new policy. We also find that
with the introduction of the new policy, an average mother has improved likelihood of
first and second birth and reduced likelihood of having more than two births. If this trend
were to continue, by 2008 most mothers would choose to have 2-3 children. Finally, the
policy has not been effective in reducing the “son preference” bias as mothers with
existing sons are more likely to wait for another child than mothers with no sons.
Tables and Figures:
Table 2.1: Summary Statistics By region of Residence
Rural
Urban
167,695
77,139
% rural women
68.6
31.4
% women can read and write
42.8
71.3
Avg # of schooling years
% women's husband can read and write
Average # of husband' schooling years
7.5
68.1
9.2
9.5
85.6
10.6
Age at effective marriage
17.3
18.6
Number of Observations
Table 2.2: Other Descriptive Statistics
Variable
Number of Observations
Mean
244,834
Mean age of
Mean age of
Mean age of
Mean age of
Mean age of
Mean age of
25 years
20 years
22 years
24 years
26 years
28 years
woman at the time of survey
woman at first Birth
woman at second Birth
woman at third Birth
woman at fourth Birth
woman at fifth Birth
Average spacing between 1st and 2nd child
Average spacing between 2nd and 3rd child
Average spacing between 3rd and 4th child
Average spacing between 4th and 5th child
2.65 years
2.61 years
2.53 years
2.49 years
28
Table 2.3: Probit Regression Estimates of the Probability of Various Births with
Time Dummies, time clusters
Variable
Birth 1
Coeff
Z
-1.011
1972
-1.233
1973
-1.465
1974
-1.406
1975
-1.506
1976
-1.382
1977
-1.307
1978
-1.466
1979
-1.447
1980
-1.280
1981
-1.403
1982
-1.200
1983
-1.406
1984
-1.237
1985
-1.365
1986
-1.308
1987
-1.266
1988
-1.325
1989
-1.335
1990
-1.226
1991
-1.352
1992
-1.227
1993
-1.396
1994
-1.326
1995
-1.341
1996
-1.301
1997
-1.274
1998
-1.278
1999
-1.270
2000
-1.194
2001
-1.050
Constant
-1.644
Obs
1942876
Log-Likelihood -574117
Psuedo-R2
0.1397
1971
-1.00
-1.27
-1.52
-1.46
-1.57
-1.44
-1.36
-1.53
-1.51
-1.34
-1.46
-1.25
-1.47
-1.29
-1.42
-1.37
-1.32
-1.38
-1.39
-1.28
-1.41
-1.28
-1.46
-1.38
-1.40
-1.36
-1.33
-1.33
-1.33
-1.25
-1.10
-1.71
Birth 2
Coeff
Z
-0.156
-0.111
-0.140
-0.111
-0.081
-0.095
-0.118
-0.006
-0.147
0.048
-0.100
0.099
-0.026
0.023
0.019
-0.010
-0.009
0.118
-0.069
0.084
-0.056
0.022
-0.023
0.011
0.003
0.020
-0.062
-0.079
-0.026
-0.773
559507
-312457
0.0948
-20.39
-10.88
-12.60
-8.77
-6.70
-7.88
-9.42
-0.51
-11.77
3.68
-7.65
7.20
-1.91
1.63
1.32
-0.67
-0.62
7.85
-4.62
5.35
-3.61
1.33
-1.40
0.65
0.19
1.15
-3.53
-4.32
-1.42
-9.14
Birth 3
Coeff
Z
0.195
0.100
0.191
0.220
0.252
0.145
0.342
0.253
0.434
0.311
0.411
0.358
0.335
0.314
0.436
0.304
0.412
0.283
0.365
0.296
0.358
0.313
0.322
0.233
0.177
0.209
-0.825
586428
-246424
0.1559
25.73
8.81
15.83
18.72
20.41
11.12
26.73
19.41
33.81
23.69
29.79
25.28
23.31
21.34
28.79
20.25
26.01
18.32
22.81
18.06
21.57
18.34
18.73
13.37
9.84
11.48
-3.21
Birth 4
Coeff
Z
-0.050
-0.118
0.055
-0.110
0.046
-0.025
0.128
0.014
0.077
0.029
0.079
0.022
0.170
-0.034
0.139
0.002
0.091
0.035
0.094
0.071
0.059
-0.036
-0.053
-0.027
-0.003
464753
-159243
0.1799
-4.78
-9.39
3.75
-6.63
2.47
-1.29
6.22
0.66
3.34
1.17
3.06
0.79
5.89
-1.11
4.35
0.05
2.62
0.95
2.47
1.79
1.44
-0.84
-1.20
-0.59
-0.02
Birth 5
Coeff
Z
0.370
0.229
0.209
0.171
0.308
0.217
0.313
0.258
0.273
0.230
0.364
0.177
0.322
0.209
0.273
0.265
0.317
0.272
0.304
0.159
0.186
0.193
-0.145
277899
-91639
0.1686
25.92
14.52
10.71
7.70
13.15
8.67
11.82
8.86
8.79
6.93
10.26
4.75
8.09
5.01
6.19
5.74
6.57
5.39
5.81
2.92
3.29
3.28
-0.47
29
Table 2.4: Probit Regression Estimates of the Probability of Birth 1, with
and without Time Dummies, time clusters
Variable
BIMARU States
Rural Women
Time Trend
1997 Policy Dummy
D98*Time Trend
Constant
Time Dummies:
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Obs
Log-Likelihood
Wald-Chi2
Psuedo-R2
Coeff
Z values
Coeff
Z values
0.137
0.144
12.03
16.51
-1.644
-1.71
0.137
0.145
0.003
-0.095
0.071
-9.285
12.10
16.63
1.54
-2.03
4.38
-2.27
-1.011
-1.233
-1.465
-1.406
-1.506
-1.382
-1.307
-1.466
-1.447
-1.280
-1.403
-1.200
-1.406
-1.237
-1.365
-1.308
-1.266
-1.325
-1.335
-1.226
-1.352
-1.227
-1.396
-1.326
-1.341
-1.301
-1.274
-1.278
-1.270
-1.194
-1.050
1942876
-574117.3
119177.79
0.1397
-1.00
-1.27
-1.52
-1.46
-1.57
-1.44
-1.36
-1.53
-1.51
-1.34
-1.46
-1.25
-1.47
-1.29
-1.42
-1.37
-1.32
-1.38
-1.39
-1.28
-1.41
-1.28
-1.46
-1.38
-1.40
-1.36
-1.33
-1.33
-1.33
-1.25
-1.10
1942876
-575263.1
118103.26
0.1379
30
Table 2.5: Probit Regression Estimates of the Probability of Birth 2
given Birth 1, with and without Time Dummies
Variable
Child 1 Alive?
Child 1 Boy?
BIMARU States
Rural Women
2 years since last birth
3 years since last birth
4 years since last birth
5 or more years since last birth
Time Trend
1997 Policy Dummy
D98*Time Trend
Constant
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
No. Of Observations
Log-Likelihood
Wald-Chi2
Psuedo-R2
Coefficient Z values Coefficient Z values
-0.437
-41.21
-0.436
-40.9
-0.065
-16.97
-0.065
-16.97
0.081
6.09
0.081
6.09
0.037
2.57
0.037
2.61
0.913
64.31
0.919
60.07
0.907
124.43
0.909
113.06
0.810
63.65
0.813
62.99
0.500
34.43
0.504
32.87
0.003
1.36
-0.017
-0.37
-0.018
-1.17
-0.780
-9.00
-6.508
-1.54
-0.155
-20.72
-0.110
-10.73
-0.138
-12.11
-0.109
-8.49
-0.078
-6.34
-0.092
-7.39
-0.114
-8.96
-0.003
-0.23
-0.143
-11.21
0.052
3.98
-0.096
-7.29
0.104
7.68
-0.021
-1.56
0.029
2.10
0.025
1.81
-0.004
-0.26
-0.003
-0.19
0.125
9.05
-0.062
-4.55
0.091
6.52
-0.048
-3.51
0.030
2.10
-0.015
-1.03
0.020
1.38
0.012
0.86
0.029
2.04
-0.053
-3.63
-0.069
-4.67
-0.016
-1.08
559507
559507
-312457.93
-312918.27
56811.39
55930.22
0.0948
0.0935
31
Table 2.6: Selected Coefficients on Probit Regression of the Probability of various
order Births with time trend, year clusters and Bootstrapped Standard Errors,
without Boy Time Interaction
Birth 1
Birth 2
Variable
Coeff
Z
Coeff
Z
Child 1 Alive?
-0.436 -38.93
Child 2 Alive?
Child 3 Alive?
Child 4 Alive?
1 Boy?
-0.065 -16.16
2 Boys?
3 Boys?
4 Boys?
BIMARU States
0.137 14.32 0.081
6.90
Rural Women
0.145 17.94 0.037
2.91
2 years since last birth
0.919 68.93
3 years since last birth
0.909 117.32
4 years since last birth
0.813 70.45
5 or more years
0.504 36.76
Time Trend
0.003
1.66
0.003
1.24
1998 Policy Dummy
-0.095 -1.04 -0.017 -0.20
D98*Time Trend
0.071
2.32 -0.018 -0.62
Constant
-9.285 -2.43 -6.297 -1.36
No. Of Observations
1942876
559507
Log-Likelihood
-575263
-312920
Wald-Chi2
250983
1070000
Psuedo-R2
0.1379
0.0935
Birth 3
Coeff
Z
-0.210 -12.27
-0.406 -26.44
Birth 4
Coeff
Z
-0.215 -23.31
-0.200 -21.80
-0.395 -39.63
-0.211
-0.320
-17.89
-19.70
-0.257
-0.503
-0.445
-22.00
-32.36
-31.33
0.259
0.121
0.947
0.862
0.677
0.202
-0.007
-0.012
-0.041
14.386
586428
-246861
2570000
0.1544
13.77
8.02
44.23
56.73
49.16
10.61
-2.64
-0.16
-1.47
2.57
0.302
0.116
0.874
0.696
0.446
-0.121
-0.008
-0.019
-0.031
16.578
464753
-159504
4190000
0.1785
26.40
10.22
38.58
38.10
16.34
-3.51
-3.19
-0.27
-1.21
3.19
Birth 5
Coeff
Z
-0.123 -14.97
-0.141 -17.61
-0.167 -27.79
-0.409 -33.34
-0.289 -17.95
-0.504 -35.13
-0.517 -34.43
-0.431 -18.67
0.311 31.67
0.124
9.52
0.815 36.05
0.585 33.43
0.312 13.80
-0.289 -11.74
-0.007 -2.78
0.011
0.11
-0.034 -0.93
13.518 2.81
277899
-91755
3130000
0.1676
32
Table 2.7: Selected Coefficients on Probit Regression of the Probability of various
order Births with time trend, year clusters and bootstrapped standard errors
Birth 1
Birth 2
Variable
Coeff
Z
Coeff
Z
Child 1 Alive?
-0.436 -37.67
Child 2 Alive?
Child 3 Alive?
Child 4 Alive?
1 Boy?
-1.483 -1.41
2 Boys?
3 Boys?
4 Boys?
Boy1*Time
0.001
1.35
Boy2*Time
Boy3*Time
Boy4*Time
BIMARU States
0.137 14.32 0.081
5.76
Rural Women
0.145 17.94 0.037
2.72
2 years since last birth
0.919 79.06
3 years since last birth
0.909 133.51
4 years since last birth
0.813 62.05
5 or more years
0.504 39.05
Time Trend
0.003
1.66 0.002
0.98
1998 Policy Dummy
-0.095 -1.04 -0.017 -0.27
D98*Time Trend
0.071
2.32 -0.018 -0.79
Constant
-9.285 -2.43 -5.734 -1.14
No. Of Observations
1942876
559507
Log-Likelihood
-575263
-312918
Wald-Chi2
250983
.
Psuedo-R2
0.1379
0.0935
Birth 3
Birth 4
Coeff
Z
Coeff
Z
-0.211 -13.80 -0.215 -21.59
-0.406 -29.48 -0.200 -21.94
-0.396 -33.31
19.083 14.15 17.437
27.522 17.03 31.307
24.173
4.19
7.79
5.30
-0.010 -14.31 -0.009
-0.014 -17.25 -0.016
-0.012
-4.26
-7.93
-5.41
0.259
0.121
0.947
0.861
0.677
0.204
0.001
-0.012
-0.041
-2.921
586428
-246762
.
0.1548
16.89
8.71
55.06
54.40
59.14
10.25
0.58
-0.17
-1.68
-0.70
0.303
0.117
0.873
0.696
0.446
-0.120
0.003
-0.019
-0.031
-4.954
464753
-159450
.
0.1788
22.96
10.87
40.41
46.27
22.56
-4.14
0.78
-0.29
-1.38
-0.77
Birth 5
Coeff
Z
-0.124 -15.70
-0.140 -17.12
-0.167 -26.66
-0.409 -30.08
14.907 2.96
21.223 4.30
17.655 3.79
20.798 2.52
-0.008 -3.02
-0.011 -4.41
-0.009 -3.91
-0.011 -2.58
0.311 32.96
0.124
9.40
0.815 34.82
0.584 36.54
0.312 15.48
-0.288 -12.57
0.002
0.67
0.011
0.11
-0.034 -0.94
-4.021 -0.64
277899
-91747
.
0.1676
33
Table 2.8a: Bootstrap vs. Standard Z Scores from the Model on Likelihood of Birth
1, 2 and 3 with Time Clusters
Birth 1
Variable
Coeff
Child 1 Alive?
Child 2 Alive?
1 Boy?
2 Boys?
Boy1*time
Boy2*time
BIMARU States
0.137
Rural Women
0.145
2 years since last birth
3 years since last birth
4 years since last birth
5 or more years
Time Trend
0.003
1998 Policy Dummy
-0.095
D98*Time Trend
0.071
Constant
-9.285
Observations
1942876
Birth 2
Birth 3
Z
Z
Z
Z
(Probit) Z (BS) (OLS) Coeff (Probit) Z (BS) (OLS)
12.10 14.32 12.45
16.63 17.94 13.05
1.54
-2.03
4.38
-2.27
1.66 1.12
-1.04 -2.01
2.32 4.06
-2.43 -1.17
Coeff
-0.436 -40.97 -37.67 -41.32 -0.211
-0.406
-1.483 -0.88 -1.41 -1.38 19.083
27.522
0.001
0.85
1.35 1.34 -0.010
-0.014
0.081
6.08
5.76 6.26 0.259
0.037
2.61
2.72 2.94 0.121
0.919 60.05 79.06 48.16 0.947
0.909 112.92 133.51 88.91 0.861
0.813 63.04 62.05 49.83 0.677
0.504 32.87 39.05 33.78 0.204
0.002
1.20
0.98 1.01 0.001
-0.017 -0.37 -0.27 -0.42 -0.012
-0.018 -1.17 -0.79 -1.18 -0.041
-5.734 -1.39 -1.14 -0.83 -2.921
559507
586428
Z
Z
(Probit) Z (BS) (OLS)
-12.03
-26.00
14.77
17.18
-14.93
-17.39
14.90
8.34
43.46
49.83
46.14
8.17
0.49
-0.30
-2.93
-0.59
-13.80
-29.48
14.15
17.03
-14.31
-17.25
16.89
8.71
55.06
54.40
59.14
10.25
0.58
-0.17
-1.68
-0.70
-14.61
-34.83
3.39
2.75
-3.48
-2.85
18.94
9.52
24.98
27.65
26.30
18.71
-1.93
-0.53
-2.05
2.13
Table 2.8b: Bootstrap vs. Standard Z Scores from the Model on Likelihood of Birth
4 and 5 with Time Clusters
Variable
Child 1 Alive?
Child 2 Alive?
Child 3 Alive?
Child 4 Alive?
1 Boy?
2 Boys?
3 Boys?
4 Boys?
Boy1*Time
Boy2*Time
Boy3*Time
Boy4*Time
BIMARU States
Rural Women
2 years since last birth
3 years since last birth
4 years since last birth
5 or more years
Time Trend
1998 Policy Dummy
D98*Time Trend
Constant
No. Of Observations
Coeff
-0.215
-0.200
-0.396
Birth 4
Z (Probit) Z (BS)
-24.12
-21.59
-18.81
-21.94
-37.81
-33.31
Z (OLS)
-24.88
-26.75
-22.85
17.437
31.307
24.173
4.12
7.69
5.85
4.19
7.79
5.30
0.92
-0.75
-0.49
-0.009
-0.016
-0.012
-4.18
-7.82
-5.96
-4.26
-7.93
-5.41
-0.98
0.67
0.40
0.303
0.117
0.873
0.696
0.446
-0.120
0.003
-0.019
-0.031
-4.954
464753
24.01
9.16
34.12
41.52
18.16
-3.51
0.97
-0.47
-2.26
-0.96
22.96
10.87
40.41
46.27
22.56
-4.14
0.78
-0.29
-1.38
-0.77
19.23
10.19
21.70
29.69
19.30
5.11
-3.05
-0.57
-1.36
3.34
Birth 5
Coeff Z (Probit) Z (BS)
-0.124
-13.24
-15.70
-0.140
-18.14
-17.12
-0.167
-24.92
-26.66
-0.409
-29.19
-30.08
14.907
2.69
2.96
21.223
3.88
4.30
17.655
3.50
3.79
20.798
2.67
2.52
-0.008
-2.75
-3.02
-0.011
-3.97
-4.41
-0.009
-3.61
-3.91
-0.011
-2.73
-2.58
0.311
28.94
32.96
0.124
8.68
9.40
0.815
32.35
34.82
0.584
32.77
36.54
0.312
13.12
15.48
-0.288
-10.81
-12.57
0.002
0.62
0.67
0.011
0.19
0.11
-0.034
-1.89
-0.94
-4.021
-0.60
-0.64
277899
Z (OLS)
-13.07
-17.93
-17.00
-13.03
-0.02
-1.51
-2.14
-0.16
-0.03
1.45
2.07
0.10
17.97
10.46
20.67
32.93
15.91
-1.71
-2.75
0.08
-1.38
2.97
34
Table 2.9: Test of significance of selected variables interacted with policy variable,
Time Clusters, Bootstrapped Standard Errors
Model 1:
Model 2:
Model 3:
Model 4:
Model 5:
Variable
Trend
Time trend
D98
D98*Time Trend
Time since last birth
D98 policy dummy
D98*Time Trend
D98*2 years
D98*3 years
D98*4 years
D98*5 or more years
Gender Composition
D98 policy dummy
D98*Time Trend
D98*1 boy
D98*2 boys
D98*3 boys
D98*4 boys
BIMARU States
D98 policy dummy
D98*Time Trend
BIMARU Dummy
D98*BIMARU
Rural Women
D98 policy dummy
D98*Time Trend
Rural Dummy
D98*Rural
Birth 1
Coeff
Z
Birth 2
Coeff
Z
Birth 3
Coeff
Z
Birth 4
Coeff
Z
Birth 5
Coeff
Z
0.003
-0.095
0.071
0.003
-0.017
-0.018
1.24
-0.20
-0.62
-0.007
-0.012
-0.041
-0.008
-0.019
-0.031
-0.007
0.011
-0.034
-2.78
0.11
-0.93
0.063
-0.018
-0.143
-0.048
-0.024
-0.154
0.86
-0.73
-6.18
-2.25
-1.09
-6.68
0.152
1.44
0.118
1.22 0.085
-0.038 -1.23 -0.030 -1.03 -0.035
-0.232 -12.70 -0.231 -11.50 -0.193
-0.163 -5.60 -0.106 -2.75 0.022
-0.157 -6.27 -0.066 -1.16 0.049
-0.247 -5.93 -0.203 -3.43 -0.115
0.77
-0.84
-7.21
0.66
1.01
-2.23
-0.012
-0.018
-0.009
-0.16
-0.65
-1.38
0.063
-0.041
-0.080
-0.126
0.79
-1.58
-5.13
-6.65
0.080
-0.031
-0.091
-0.136
-0.118
0.94
-1.06
-5.64
-7.08
-7.23
0.091
-0.034
-0.084
-0.087
-0.080
-0.120
1.05
-0.99
-3.83
-4.79
-3.76
-3.73
1.66
-1.04
2.32
-2.64
-0.16
-1.47
-3.19
-0.27
-1.21
-0.063
0.072
0.154
-0.099
-0.69
2.24
17.87
-4.51
-0.045
-0.018
0.062
0.087
-0.57
-0.66
4.45
2.86
-0.054
-0.041
0.227
0.127
-0.77
-1.64
10.29
4.07
-0.037
-0.031
0.290
0.044
-0.53 0.008
-1.30 -0.034
15.58 0.308
2.11 0.007
0.07
-0.71
17.70
0.30
-0.065
0.071
0.152
-0.045
-0.84
2.58
18.02
-2.07
-0.054
-0.018
0.025
0.056
-0.81
-0.72
1.45
2.58
-0.054 -0.65
-0.041 -1.32
0.106
5.21
0.0597 2.46
-0.054
-0.031
0.104
0.048
-0.85
-1.11
6.69
1.76
-0.13
-0.79
6.13
1.63
-0.013
-0.034
0.115
0.032
35
Figure 2.1: Age of the Woman at the Time of Survey
7
6
% Frequency
% Frequency
5
4
3
2
1
0
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
Age
Figure 2.2: Year of Various Order Births
9
8
Birth 1
Birth 2
7
Birth 3
Birth 4
Birth 5
5
4
3
2
1
Year of Birth
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
1973
1972
1971
0
1970
% Frequency
6
36
Figure 2.3: Age of Woman at the Time of Various Births
35000
30000
Birth 1
Birth 2
25000
Number of Women
Birth 3
Birth 4
Birth 5
20000
15000
10000
5000
0
13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
Age
Figure 2.4: Time Coefficients from Models of Various Order Birth likelihood
0.500
Coefficient Estimate
0.000
Birth1
Birth2
-0.500
Birth3
Birth4
Birth5
-1.000
-1.500
-2.000
Year
37
Figure 2.5: Predicted Probabilities of Birth 1, 2 and 3 for Age 25 Women Using
Year Dummies
0.4
0.35
Conditional Probabilities
0.3
0.25
0.2
0.15
0.1
Birth1
0.05
Birth2
Birth3
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
1973
1972
1971
0
Figure 2.6: Density Function For Birth 1 in 1996, 2002 & 2008, By Age of Woman
0.20
0.18
Prob of Birth 1(96)
0.16
Prob of Birth 1(02)
Prob of Birth 1(08)
Probability of Birth 1
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
Age of Woman
38
Figure 2.7: Density Function of Birth 2 for 1996,2002 & 2008, By Age of Mother
0.16
0.14
Prob of Birth 2(96)
Prob of Birth 2(02)
0.12
Probability of Birth 2
Prob of Birth 2(08)
0.10
0.08
0.06
0.04
0.02
0.00
14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
Age of Mother
Figure 2.8: Density Function of Birth 3 for 1996, 2002 & 2008, By Age of Mother
0.09
0.08
0.07
Prob of Birth 3(96)
Prob of Birth 3(02)
Probability of Birth 3
0.06
Prob of Birth 3(08)
0.05
0.04
0.03
0.02
0.01
0.00
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
Age of Woman
39
Figure 2.9: Density Function of Birth 4 for 1996, 2002 & 2008, By Age of Mother
0.06
0.05
Prob of Birth 4(96)
Probability of Birth 4
0.04
Prob of Birth 4(02)
Prob of Birth 4(08)
0.03
0.02
0.01
0.00
16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
Age of Woman
Figure 2.10: Density Function of Birth 5 for 1996, 2002 & 2008, By Age of Mother
0.04
0.04
0.03
Prob of Birth 5(96)
Probability of Birth 5
Prob of Birth 5(02)
0.03
Prob of Birth 5(08)
0.02
0.02
0.01
0.01
0.00
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
Age of Woman
33
34
35
36
37
38
39
40
41
42
40
Figure 2.11: Density Function of Woman with No Boys & 2 Previous Children for
1996, 2002 & 2008
0.1
0.09
0.08
Prob (no Boys, 1996)
Prob (no Boys, 2002)
Probability of Birth
0.07
Prob (no Boys, 2008)
0.06
0.05
0.04
0.03
0.02
0.01
0
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
Age of Woman
Figure 2.12: Density Function of Woman with 1 Boy amongst 2 Previous Children
for 1996, 2002 & 2008
0.09
0.08
0.07
Prob (1 Boy, 1996)
Prob (1 Boy, 2002)
Probability of Birth
0.06
Prob (1 Boy, 2008)
0.05
0.04
0.03
0.02
0.01
0
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
Age of Woman
41
Figure 2.13: Density Function of Woman with Both Previous Children as Boys for
1996, 2002 & 2008
0.09
0.08
0.07
Prob (2 Boys, 1996)
Prob (2 Boys, 2002)
0.06
Probability of Birth
Prob (2 Boys, 2008)
0.05
0.04
0.03
0.02
0.01
0
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Age of Woman
Figure 2.14: Density Function of Woman with Both Previous Children as Boys Vs.
Girls for 1996 & 2008
0.1
0.09
0.08
Prob (2 Girls, 1996)
Prob (2 Girls, 2008)
Probability of Birth
0.07
Prob (2 Boys, 1996)
Prob (2 Boys, 2008)
0.06
0.05
0.04
0.03
0.02
0.01
0
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42