Fertility transition in india: An application of Bongaarts model

It is evident from many studies that fertility in India has been steadily declining. A Total Fertility Rate (TFR) of 3.4 children per women was observed for the period of 1990 to 1992, as per the National Family Health Survey 1 (NFHS 1) report [NFHS 1 Report].The TFR declined to 2.9 in 1996 to 1998, i.e. during NFHS 2 [NFHS 2 Report]. It further declined to 2.7 during 2005 to 2006 (NFHS 3) [NFHS 3 Report]. It almost reached the replacement level in 2015 to 2016 having TFR 2.2 as per NFHS 4 report [NFHS 4 Report]. Therefore, it is necessary to understand the mechanism through which socioeconomic and cultural factors affect fertility in order to know the causes of fertility decline. The proximate determinants of fertility are the link between the socioeconomic, cultural and the biological behavioral factors. Proximate determinants has a direct infl uence on fertility. If a proximate determinant changes, then fertility necessarily changes (assuming other proximate determinants remain constant). Fertility may or may not change in case of a change in socioeconomic determinant. Socioeconomic determinants infl uence the proximate determinants which again infl uence fertility [1]. About the four major states included in the paper:


Introduction
It is evident from many studies that fertility in India has been steadily declining. A Total Fertility Rate (TFR) of 3.4 children per women was observed for the period of 1990 to 1992, as per the National Family Health Survey 1 (NFHS 1) report [ 4 Report]. Therefore, it is necessary to understand the mechanism through which socioeconomic and cultural factors affect fertility in order to know the causes of fertility decline.
The proximate determinants of fertility are the link between the socioeconomic, cultural and the biological behavioral factors.
Proximate determinants has a direct infl uence on fertility. If a proximate determinant changes, then fertility necessarily changes (assuming other proximate determinants remain constant). Fertility may or may not change in case of a change in socioeconomic determinant. Socioeconomic determinants infl uence the proximate determinants which again infl uence fertility [1]. About the four major states included in the paper: Punjab: Punjab is the northern state forming the border between India and Pakistan. According to Census 2011, the total population of the state is approximately 27 million and the decadal growth rate is 13.89 percent. There are 22 districts in Punjab [2]. Bihar: Bihar is an entirely land -locked eastern state. The

Abstract
Fertility in India has been steadily declining. It almost reached the replacement level in 2015 to 2016 and evidence shows that proximate determinants has a direct infl uence on fertility. The study aims at calculating the proximate determinants of fertility in India for the period from 2005 to 06 and 2015 to 16 as well as determine the most signifi cant proximate determinant of fertility in India. It also examines the rural -urban fertility differentials for the year 2015 to 2016. The proximate determinants of fertility for few selected states in India has been calculated from 2015 to 2016. The study of the proximate determinants can help expanding clinical and community based contraceptive distribution, promoting breastfeeding, increasing age at marriage and reduce unintended pregnancies. The study is based on data obtained from National Family and Health Survey Round 3 and National Family and Health Survey Round 4. Bivariate analysis had been done to analyze the distribution of currently married women at age of 15 to 49 by biological and behavioral characteristics as well as decomposition analysis had been used to fi nd the contribution of each indices. It is revealed from the study that an increase in use of contraception has led to decline in Total Fertility Rate (TFR) over the decade. The knowledge of contraception is almost universal in India. Even after a slight decline in proportion married, there had been a considerably high contribution of the proportion married towards increasing Total Fertility Rate (TFR). It is evident from the decomposition that only the increase in use of contraception has a positive impact on declining fertility.

Data and methodology
Data source: The study was based on secondary data source and the data was obtained from two rounds of National Family and Health Survey (NFHS) i.e. NFHS 3 (to 2006) and NFHS 4 [3]. As it is a secondary survey, the data is available free for

Estimation of index of contraception (Cc)
Where, Cc= Index of contraception = Proportion of women using contraception = Average use of effectiveness of contraception The coeffi cient 1.08 represents an adjustment for the fact that women do not use contraception of they know they are sterile.
Further, proportion of women using contraception and average use of effectiveness of contraception can be calculated as: Women using conteaception  Total married women (15-49) u  The use of effectiveness of contraception was used as given by World Health Organization (WHO). The number of currently married women using a particular type of contraception was calculated in STATA by crosstabulating current method of contraception with the current marital status.

Methods of contraception Effectiveness
The number of women using contraception was calculated by subtracting the number of women not using any method from the total number of currently married women.

Estimation of index of abortion (Ca)
T F R (obs.) T F R (obs.)+A Ca  Step -1: Decomposition of TFR over time Step -2: Proportional change for each proximate determinants Proportional change in TFR (Pf)

Estimation of indices for some selected states
Four major states were selected randomly according to their region. The selected states along with their regions are:

Biological and behavioral characteristics infl uencing the proximate determinants of fertility
The Table 1

Rural -Urban differential in the model
Urban bias is an often cited characteristics of state socialist regimes [4]. Urban areas are mostly focused on wealth generation. In consequence, rural areas are at a relative disadvantage due to lack of resources, infrastructure etc. The issue of rural or urban residence is constantly important in terms of differentials in population growth, socioeconomic status and public health [4]. The indices of Bongaarts' Proximate determinants calculated for 2015 to 2016 by

Calculation of indices for some selected states of India
The calculation of indices for some selected states of India is presented in Table 5. The proportion married is highest in Bihar (east), followed by Maharashtra (west), Kerala (south), lastly Punjab (north). Similarly, the use of contraception was also highest in Bihar, followed by Kerala, Maharashtra and Punjab.
However, the postpartum infecundability is highest in Punjab.
Kerala and Maharashtra have equal postpartum infecundability.
Bihar has the lowest postpartum infecundability among the four. The abortion is highest in Kerala, followed by Bihar.
Punjab and Maharashtra have an index of approximately 1, which suggests the negligible abortion in the region. Bihar has the highest TFR followed by Maharashtra, Punjab and Kerala.
Bihar and Kerala represents two contrasting level of development within India.
TFR in developing countries are the result of decline in marital fertility or delay in age at childbearing or both.
It was evident from the decomposition that only the increase in use of contraception has a positive impact on declining fertility. All other indexes, i.e. marriage, abortion and span of breastfeeding has led to increase in fertility level. However, the use of contraception has compensated for all increase and has a tremendous decline in TFR over the decade. Most of the fertility decline is happening in most of regions of developing world, the major contributor being illiterate women. This change was seen due to increase in prevalence of contraception among women without or less education [7]. In Ethiopia, of the four proximate determinants of fertility, postpartum insusceptibility contributed the highest fertility [8][9][10][11][12][13][14][15][16].
This study also focuses on the rural -urban differentials of the model where it is evident that out of the four indices, three indices are higher for rural area. Only the fourth indices, i.e. postpartum infecundability, is higher for urban area.

Conclusion
An increase in deliberate marital fertility control is seen when a population moves through the transition from natural to controlled fertility. This control is due to rise in contraceptive use, but induced abortion also plays a major role in many societies. Due to control of marital fertility, there is a transition in marriage and postpartum infecundability as well.
To examine the changes in measures of fertility, one cannot rely on time trends in individual population.
However, a comparative analysis of a population in a two different time periods can help.

Limitations of the study
The study also has some of its own limitations. Original model of Bongaarts was used rather than the revised model due to unavailability of pre-marital and extra-marital data.
Abortion index was calculated through the TFR formula

Discussion
This paper presents a simple but comprehensive model for the relationship between the proximate determinants and fertility as given by Bongaarts. The article by Leela Visaria [5]estimates the values of proximate determinants of fertility for major states by using NFHS (1992 to 1993) data and demonstrated the interstate variations after examining evidences. The study found that in all India level, the fertility rate has declined at a very faster rate than it was expected.
According to a study by Nath and Mazumder [6], the declining