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Fertility Decline in India: Contributions by Uneducated Women Using Contraception

Fertility Decline in India: Contributions by Uneducated Women Using Contraception

India's fertility transition is driven by major fertility declines among women who are illiterate. Consequently, the earlier emphasis on women's education and socio-economic conditions as determinants of fertility decline is shifting to research on the study of reciprocally initiated positive contributions of fertility decline to the improvement of the health of women and children. This analysis indicates that illiterate women and their children are the greatest recipients of the benefits of health and socio-economic advancement. The standardised percentages of women without education who received three antenatal care check-ups and whose children received full immunisation are sharply higher for women with two children and less than for those with more than two children. Child mortality reductions for women of lower parities are steeply higher for uneducated women compared with educated women. These cumulative benefits of low fertility, in effect, have speeded up the health improvement and socio-economic advancement of the states.

SPECIAL ARTICLE

Fertility Decline in India: Contributions by Uneducated Women Using Contraception

P Arokiasamy

India’s fertility transition is driven by major fertility declines among women who are illiterate. Consequently, the earlier emphasis on women’s education and socio-economic conditions as determinants of fertility decline is shifting to research on the study of reciprocally initiated positive contributions of fertility decline to the improvement of the health of women and children. This analysis indicates that illiterate women and their children are the greatest recipients of the benefits of health and socio-economic advancement. The standardised percentages of women without education who received three antenatal care check-ups and whose children received full immunisation are sharply higher for women with two children and less than for those with more than two children. Child mortality reductions for women of lower parities are steeply higher for uneducated women compared with educated women. These cumulative benefits of low fertility, in effect, have speeded up the health improvement and socio-economic advancement of the states.

The author is thankful to J Pradhan and R Arumugam for their assistance in data analysis.

P Arokiasamy (parokiasamy@yahoo.co.uk) is with the Department of Development Studies, International Institute for Population Sciences, Mumbai.

F
ertility transition in the states is apace since the 1990s. Data from the Sample Registration System (SRS) indicates that the crude birth rate (per 1,000 population) and total fertility rate (TFR) declined from 31 and 4.0, respectively, in 1991 to 24 and 2.6, respectively, in 2004. Eleven states of India have reached the replacement level fertility. Meanwhile, six states have TFR in the range of 2.2-2.5 and the remaining states have a TFR of more than 2.5, with the highest TFR (4.0) in Bihar and U ttar Pradesh (IIPS 2007). Based on the conventional framework dealing with socio-economic determinants of fertility, a major body of past literature demonstrated evidence of early and s ignificant fertility reduction among the educated and socioeconomically advanced women (United Nations 1973; Caldwell et al 1982; Coale and Watkins 1987; Axinn and Barber 2001; Dreze and Murthi 2001). However, emerging evidence since the 1990s points to the fact that the bulk of fertility decline in India is now occurring among women without education and this transition is being driven in a major way by the increasing contraceptive p revalence rates among the uneducated women (Bhat 2002; McNay et al 2003). Declining fertility, now under way with varying speed in every Indian state, and in most regions of the developing world, is one of the most notable demographic behavioural changes occurring in the world.

A growing volume of recent literature recognises that the fertility reduction among uneducated women in India is a complementary phenomenon to the early phase of fertility decline among educated women (see McNay et al 2003; Arokiasamy et al 2004). The emergence of such a trend in demographic behaviour is consistent with the diffusion school perspective that diffusion of fertility decline progresses faster than socio-economic development, though the two processes are described as complementary to each other (Montgomery and Casterline 1996; Lesthaege and Vanderhoeft 2001). Bongaarts and Watkins (1996) have shown evidence of systematic shift over time in the relationship between development indicators and fertility, where in diffusion process tends to play a greater role in the middle stages of transition.

The momentum in fertility transition among the illiterate women of India through widespread use of contraception is a remarkable demographic behavioural change, aided by rapidly spreading community norms of low demand for children and facilitated by various means of communication and diffusion of ideas. This momentum is critical for taking forward India’s fertility transition in view of the fact that close to 60% women are illiterate in Rajasthan and Uttar Pradesh, according to 2001 Census of India, notwithstanding a growing policy thrust to improve literacy rates in these poorer states. Consequently, the predominant emphasis of fertility research on development and health indicators as determinants of fertility decline is now steadily and decisively shifting towards the dynamics of fertility decline among uneducated women and its consequences.

A major focus of this shift is on the reciprocally initiated positive contributions of fertility decline among uneducated women. Such contributions through reverse causation include major health improvements for women and children, educational deve lopment of children and progress in economic condition of families. This is a critical threshold point of departure from the main thrust of previous studies that dealt with the assessment of the contributions of increasing female education and socio-economic conditions on fertility reduction. In that context, there has been scepticism in the past to recognise a clear process of reverse causation of fertility impact on poverty and family well-being. This, however, was followed by a more balanced argument of mutually reinforcing effect over time between fertility decline and economic performance (IUSSP 1998). Nevertheless, such perceived long-term two-way causations do not predispose the emergence of new evidence on the net positive impact of fertility decline on health and well-being that is now being widely debated and increasingly recognised. More recent studies based on revisionist perspective have confirmed a net negative impact of high population growth on economic growth and poverty reduction, while declining fertility is seen to have spurred economic growth and poverty reduction (Bloom and Williamson 1997; Kelley 2001; Kelley and Schmidt 2001).

The demographic changes in recent decades in the east Asian and Latin American countries point to such clearly recognisable evidence of impact of fertility decline towards acceleration in economic development and health improvements. For instance, the decline in TFR from more than five during 1965-70 to 1.8 children during 1995-2000 in east Asia contributed not only to the global prospects, but the conditions of livelihood and well-being among the east Asian population themselves (United Nations 2001; Knodel 1992). Bloom and Williamson (1997) estimated that demographic factors accounted for about half of east Asia’s excess growth of real gross domestic product (GDP) per capita in 1970-90.

Merrick (2002) showed that effects of demographic change on economic growth are useful to explain the past experience of different countries and regions; slow growth in Africa versus the burst of growth in east Asia. Analysing cross-sectional data of 45 developing countries, he demonstrated that had the average country reduced its birth rate by five per 1,000 throughout the 1980s, the average country poverty incidence of 18.9% in the mid-1980s would have been reduced to 12.6% between 1990 and 1995. Merrick concluded that both theory and improved and expanded empirical efforts support the likelihood that high average fertility at the country level hurts the poor, and that their own high fertility can contribute to their children’s poverty. Developed countries’ experience also suggests such reverse causational effect of fertility decline. Payne (2004) observed that the reduction in total fertility rate from 3.9 in 1960 to 1.4 children in 2000 in Canada had clearly contributed to improvements in w omen’s maternal and reproductive health. The health benefits of low f ertility to women and children including child schooling have been widely reported in micro studies as well (Bhat 2002; Schultz 2005).

1 Context and Objectives

The past demographic experience of east Asian countries in 1970-90 is now seen to be closely replicating in the Indian states beginning with the south and moving forward to west, east and other northern states. The emergence of these demographic shifts has concordance with the following long-term policy rationale of fertility control:

  • to promote health of women and children
  • accelerate poverty reduction
  • achieve faster improvement in socio-economic conditions.
  • The main arguments of well-known social and economic change theories of fertility have been that the perceived positive health, social and economic benefits as a result of quantity-quality trade-off concerning number of children motivate parents to reduce fertility (Davis 1963; Blake 1989; Becker 1981). In the Indian context, women adopting sterilisation at a relatively young age of 25-30 may realise a longer period of unencumbered activity in the household and community, but transformation of opportunities for social and economic progress has not been empirically analysed (Schultz 2005).

    The dynamics of recent demographic changes and economic progress in India clearly point to the need for a more comprehensive and systematic measurement of the potential benefits of reverse causation of fertility reduction in general, and among uneducated women, in particular. Though this has been a subject of concern in recent literature, efforts towards such an assessment have been constrained by the challenges of data analysis in order to establish causal impact of fertility decline in terms of returns in social, economic and health improvement (Birdsall et al 2001). We argue that improved quantitative methods with time series and micro level data provide a potential for credible prediction of low fertility contributions. Two specific types of assessments are possible. First, cross-sectional panel macro data can be effectively used to measure and disaggregate evidence of fertility impact on performance in economic and social indicators. Second, household level data from nationally representative demographic and health surveys are valuable for measuring health impact and social benefits of low fertility.

    Set to this context, the twofold objectives of this analysis are:

    (a) assess systematic linkages between levels of fertility reduction and the health and development improvements amongst the states of India; (b) compare women and children’s health and quality of life indicators for women using contraception (sterilised women) with two and less than two children and those with more than two children.

    This analysis seeks to explore both macro and micro level evidence of the health and development outcomes of fertility decline for the following key women and child health indicators. At the macro level, empirical evidence is investigated if Indian states with greater fertility reduction have achieved higher rates of economic growth, faster reduction in poverty and infant mortality, and faster rates of increase in literacy and women’s work participation.

    At the micro level, low fertility impact has been studied with evidence of whether “low fertility among uneducated sterilised women” compared with those with high fertility, contributed to an improvement in the health of women – maternal healthcare coverage; rise in utilisation of healthcare services for their

    july 25, 2009 vol xliv no 30

    children – immunisation coverage; greater reduction in neonatal, infant and child mortality; increase in school attendance rate of children.

    Based on this perspective, this paper demonstrates a significant volume of evidence; that in India, the reverse causation of fertility reduction among illiterate women adopting contraception has contributed immensely to the improvement of health of women and children and in raising the standards of socioeconomic conditions of their families.

    2 Methodology

    Data from the census, SRS, Centre for Monitoring Indian Economy (CMIE) are used for macro (state) level analysis. Data from three rounds of National Family Health Survey (NFHS-1992-93, 1998-99, 2005-06) are used in trend and decomposition analysis of fertility decline and contraceptive use. Data from NFHS-2 are used to assess evidence on the micro (household) level impact of fertility decline on key women and child health indicators.

    We use decomposition procedures and econometric multivariate panel data regression analyses to assess the macro level impact fertility decline on key health and development indicators. Multivariate logistic regression models are estimated to compare differences in critical women and child health indicators between low and high fertility families.

    2.1 Decomposition Analysis

    To obtain contributions of fertility reductions by illiterate and literate women and by changes in educational composition of women for major states and India, the total fertility rates for the three rounds of NFHS conducted in 1992-93, 1998-99 and 2005-06 are decomposed by women’s education. The decomposition method proposed by Kitagawa (1955) and modified by Bhat (2002) is used. The following formula is used:

    k k Fb – Fa = ΣFb,i C b,i – ΣFa,i Ca,i

    i=1 i = 1

    – Fa,1) (Cb,1 + Ca,1) k + Ca,i) k + Fa,i)

    (Fb,1(Cb,i(Fb,i

    = + Σ(Fb,i – Fa,i) + Σ(Cb,i – Ca,i)

    222

    i= 2 i= 1

    where Ca,i and Cb,i refer to proportion of women of educational class i at time a and time b, respectively.

    The three additive terms on the right-hand side of the equation give, respectively, the contributions of the above-mentioned three components of overall change in total fertility (with i=1 denoting the category of illiterate women). It may be noted that by using the average values of the two points of time as weights to the categoryspecific differences, the above decomposition formula neatly distributes the overall change into three components, without leaving anything for the residual category of “interaction” effects.

    2.2 Panel Data Regression

    A panel data regression differs from regular time-series or crosssection regression because of double subscript on its variables (Baltagi 2001). The panel regression is represented by the equation:

    yit = α+ Xitβ+ uit i = 1... ... ...N; t = 1 ... ... ...T

    with i denoting population and t denoting time. The i subscript, therefore, denotes the cross-sectional dimension, whereas t

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    denotes the time-series dimension. α is a scalar, β is Kx1, Xit is the l-th observation on K explanatory variables and uit is the error component. where, uit= μi + vit

    denotes the unobservable individual specific effect and Vit

    μi denotes the remainder disturbance.

    Three models of panel data regression have been estimated for predicting the percentage of population below poverty line (BPL), infant mortality rate and female work participation rate with their respective set of predictors. Pooled observations of cross-section data across states of India over three time periods of 1981, 1991 and 2001 – the period of major fertility decline in most states – have been used. The predictors included in the models are TFR and relevant covariates such as female literacy rate, per capita state domestic product (SDP), percentage of males employed in non-agricultural sector, percentage of urban population and sex ratio and period dummy variables for 1981-91 and 1991-2001. Variables infant mortality rate, percentage of population below poverty line (BPL) and female work p articipation rate are used as co-predictors in models in which they are not used as dependent variables. The theoretical basis of retaining these predictors in the panel regression models is discussed in results sections.

    2.3 Multivariate Logistic Regression

    The NFHS provides data on maternal healthcare utilisation, body mass index (BMI), anaemia and reproductive morbidity and on child health and nutrition indicators: these include height, weight, anaemia, healthcare utilisation and child survival. We analyse key women and child health indicators between educated and uneducated women at sterilisation that critically bring out low fertility impact. Multivariate logistic regression models are estimated using NFHS-2 (1998-99) data set for assessing micro (household) level impact of fertility decline on selected women and child health indicators. These indicators are antenatal care (ANC) coverage (at least three checks-ups) to represent women’s health and child healthcare utilisation (all vaccinations coverage) and child survival (neonatal, infant and child mortality) represent child health, besides children of 6-14 years attending school. The data for children born in the last three years prior to the survey are used for the analysis of ANC and immunisation. Data on children born in the last five years are used for child mortality analysis. Comparative analyses are performed between sterilised women with two and less than two children and those sterilised women with greater than two children and between educated and uneducated women with these selected women and child health indicators as dependent variable. The logistic regression coefficients are converted into adjusted probabilities through the following steps: Step 1: By using regression coefficient and mean values of independent variables, the probability is computed as: P0= exp (Z)/ {1+exp (Z)}, P1=1-P0 where, Z is the estimated value of response variable for all categories of each variable. Step 2: The probability P is multiplied by 100 to obtain the percentage values.

    When calculating adjusted percentages for categories of a given predictor variable, other related predictor variables such as women’s age, religion, caste, place of residence and standard of living index are held constant at their mean values. The tables consisting of adjusted probabilities in percentages are presented.

    2.4 Cox Proportional Hazard Model

    Proportional hazard model is applied to estimate neonatal, postneonatal and child mortality rates by parity and women’s education controlling for interrelated covariates. Proportional hazard model is a multivariate extension of the life table, combining the regression model with cohort life table analysis. The estimated hazard coefficients for categories of women’s parity by women’s education controlling for covariates are then converted into adjusted neonatal, post-neonatal and child mortality rates using Multiple Classification Analysis (MCA).

    3 Decomposition of Fertility Reduction

    The statewise trends in TFR by levels of women’s education among NFHS-1, NFHS-2 and NFHS-3 shows a greater amount of fertility reduction among the illiterate women compared with women having high school education and above (Table 1).

    The magnitude of overall TFR reduction vis-à-vis TFR reduction among illiterate women over the period of three rounds of NFHS surveys shows that the main contribution to the overall fertility decline in majority of the Indian states comes from illiterate women. The states that make major contributions include Bihar, Haryana, Himachal Pradesh, Punjab, Jammu and Kashmir,

    Table 1: Trends in Total Fertility Rate and Decomposition of Fertility Change by Education of Women in India, NFHS-1, NFHS-2 and NFHS-3

    M adhya Pradesh, Uttar Pradesh, Assam, West Bengal, Andhra Pradesh and Karnataka.

    Decomposition results of TFR reduction by women’s education demonstrate further evidence that 40-64% of fertility reduction between NFHS-1 and NFHS-2 is contributed by illiterate women in Bihar, Haryana, Himachal Pradesh, Madhya Pradesh, Uttar Pradesh, West Bengal and Karnataka. The corresponding contribution of fertility reduction by educated women is a low of 10% in Bihar and a high of 40% in Himachal Pradesh. In addition to the above seven states, fertility reduction between NFHS-1 and NFHS-3 for the state of Andhra Pradesh is seen through major contribution of illite rate women. In Kerala, Tamil Nadu, Maharashtra, Gujarat and Orissa, the difference in TFR by education has narrowed down due to sustained decline in TFR contributed by uneducated women in the 1980s and 1990s. Change in educational composition of women is the next largest contributor to changes in TFR. The fertility reduction among educated women accounts for the lowest amount of changes in overall TFR. Only 13% and 29% of the changes in TFR between NFHS-1 and NFHS-2, NFHS-1 and NFHS-3 are explained by fertility decline among educated women. Similar decomposition analysis by Bhat (2002) based on census data showed that 65% of India’s fertility decline during the 1990s was due to the fall in fertility among i lliterate women.

    The reduction in TFR in the Indian states is mainly because of emerging behavioural change among the uneducated women. The three rounds of NFHS surveys demonstrate further evidence of illiterate women making a massive contribution to the rise in overall contraceptive prevalence rate (Table 2, p 59). For instance, current use of contraception among all currently married women rose

    States

    Total Fertility Rate Change in TFR between NFHS-1 NFHS-2 NFHS-3 NFHS-1 NFHS-1 1992-93 1998-99 2005-06 and 2 and 3

    Percentage Contribution of Change in TFR by Education between from 41% in 1992-93, to 48% in 1998-NFHS-1 (1992-93) & NFHS-1 (1992-93) &

    99 and 56% in 2005-06. The corre-

    NFHS-2 (1998-99) NFHS-3 (2005-06) Illiterate Literate Change in Illiterate Literate Change in sponding rise in contraceptive use rate Women Women Educational Women Women Educational

    Composition Composition among currently married illiterate of Women of Women

    women is from 34% in 1992-92, to 43%

    North Bihar1 Haryana Himachal Pradesh 4 3.99 2.97 3.49 2.88 2.14 4 2.69 1.94 0.51 0.88 0.83 -1.3 1.03 41 64 43 10 24 40 49 12 17 1 83 48 0 47 55 99-30-3 in 1998-99 and 52% in 2005-06. The contraceptive prevalence rose more steeply among illiterate women com-
    Jammu and Kashmir2 3.13 2.71 2.38 0.42 0.75 24 18 58 28 47 25 pared with all currently married
    Madhya Pradesh 3.9 3.31 3.12 0.59 0.78 47 12 41 53 25 22 women and educated women during
    Punjab 2.92 2.21 1.99 0.71 0.93 37 34 29 43 50 7 the three successive rounds of NFHs.
    Rajasthan 3.63 3.78 3.21 0.42 0.42 21 21 58 21 21 58 For instance, in 1992-93, the contra
    Uttar Pradesh1 4.82 3.99 3.82 0.83 1 68 23 17 74 26 0 ceptive prevalence rate among illiter-
    East ate women (67%) rose higher than the
    Orissa West Bengal West Gujarat 2.92 2.92 2.99 2.46 2.29 2.72 2.37 2.27 2.42 0.46 0.63 0.27 0.55 0.65 0.57 23 47 14 16 13 13 54 37 73 4 38 11 51 27 46 4535 43 contraceptive prevalence rate for all women (64%) only in one state, Kerala. Six years later in 1998-99, the contra
    Maharashtra 2.86 2.52 2.11 0.34 0.75 21 13 66 32 43 25 ceptive prevalence rate among illiterate

    Southwomen rose greater than that of all Andhra Pradesh 2.59 2.25 1.79 0.34 0.8 34 -66 62 18 20

    women in five more states Haryana,

    Karnataka 2.85 2.13 2.08 0.72 0.77 56 16 28 64 13 23

    Himachal Pradesh, Punjab, M aharashtra

    Kerala 2 1.96 1.93 0.04 0.07 0.1 4 96 -12 19 93

    and Karnataka cutting across all regions

    Tamil Nadu 2.48 2.19 1.8 0.29 0.68 22 7 71 27 41 32

    of the country, besides Kerala. By

    India 3.39 2.85 2.68 0.54 0.71 41 13 46 42 29 29

    2005-06 with the addition of four more

    1 Figures for NFHS-1 and 2 refer to undivided Bihar (includes Jharkhand), Madhya Pradesh (includes Chhattisgarh) and Uttar Pradesh (includes Uttarakhand)).

    states, Orissa, Gujarat, Andhra Pradesh

    2 Figures for NFHS-1 refer to only Jammu region of Jammu and Kashmir. Source: IIPS 1995, IIPS 2000 and 2007. and Tamil Nadu, 10 out of 17 major

    july 25, 2009 vol xliv no 30

    Table 2: Trends in Percentage of Current Users of Contraceptive Methods among Currently Married Women, NFHS-1, NFHS-2 and NFHS-3

    State Illiterate Women Total (all Women)
    NFHS-1 NFHS2 NFHS-3 NFHS-1 NFHS2 NFHS-3
    1992-93 1998-99 2005-06 1992-93 1998-99 2005-06
    North
    Bihar1 17.6 20.5 29 23.1 24.5 34.1
    Haryana 47.8 63.4 66.4 49.7 62.4 63.4
    Himachal Pradesh 58 73.5 81.3 58.4 67.7 72.6
    Jammu and Kashmir2 45.7 46.2 49 49.4 49.1 52.6
    Madhya Pradesh1 33.6 41.5 55.9 36.5 44.3 55.9
    Punjab 56.7 67.9 66.4 58.7 66.7 63.3

    south Indian states of Kerala, Tamil Nadu, Karnataka and Andhra Pradesh is seen to have resulted in pronounced improvements in all of the five health and development indicators in the succeeding lag period. Evidence of striking reduction in poverty and infant mortality, rise in per capita SDP, and female literacy is seen in all the four states in correspondence with the preceding periods of fertility reduction. Among the four states, Kerala leads in all the indicators followed by Tamil Nadu, Karnataka and

    Figure 1: Trends in Fertility Decline, Poverty Reduction and Gains in Literacy (1971-2001)

    Rajasthan 29.2 37.2 44.1 31.8 40.3 47.27 100

    Literacy rate/percentage of

    6

    Uttar Pradesh1

    15.5 24 39.7 19.8 28.1 43.6

    East

    Assam 32.1 39.5 51.2 42.8 43.3 56.5

    Orissa 33.8 45.2 66.4 36.3 46.8 63.3

    Total fertility rate

    households BPL

    5

    60

    4

    3

    40

    2

    20

    1

    West Bengal 49.1 62.2 70.2 57.4 66.6 71.5

    0

    0

    1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001

    West

    Andhra Pradesh Karnataka Tamil Nadu Kerala

    Gujarat 46.3 56.8 67.1 49.3 59 66.6

    7 100

    Maharashtra 54.1 62.5 72.8 53.7 60.9 66.9

    6

    Per capita State DomesticPer capita State DomesticPercapita State DomesticLiteracy rate/percentage ofLiteracy rate/percentage of

    South

    5

    4

    3

    Product (in Rs ‘000) Product (in Rs ‘000) Product (in Rs ‘000) households BPL households BPL

    Total fertility rate Total fertility rate

    Andhra Pradesh 43.5 58.8 70.5 47 59.6 67.7
    Karnataka 45.5 58.7 69.7 49.1 58.3 63.6
    Kerala 66.7 74 72.6 63.3 63.7 68.6

    2

    Tamil Nadu 47.5 51.8 67.9 49.8 52.1 61.4

    1

    India 33.9 42.9 52.1 40.6 48.2 56.3

    0

    0

    1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001

    1 Figures for NFHS-1 and 2 refer to undivided Bihar (includes Jharkhand), Madhya Pradesh (includes

    Haryana Gujarat Pu njab West Bengal Maharashtra

    Chhattisgarh) and Uttar Pradesh (includes Uttarakhand). 2 Figures for NFHS-1 refer to only Jammu region of Jammu and Kashmir.

    7 100 Source: IIPS 1995, 2000 and 2006.

    6

    5

    4

    3

    states reached higher contraceptive prevalence rate among illiterate

    women compared with contraceptive prevalence rate for all women

    in the age group 15-49 years in the state. The contraceptive preva

    2

    1

    lence rate among illiterate women range from 64-81% compared

    0

    0

    with 61-73% among all women in these 10 states.

    1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 Uttar Pradesh Bihar Rajasthan Madhya Pradesh Orissa

    The trend in the above 10 states further suggests that the con-

    Literacy rate Percentage of households below poverty line

    traceptive prevalence rate among uneducated women is likely to

    Figure 2: Trends in Fertility Decline and Per Capita SDP Growth (1971-2001)

    surpass that of educated women in the remaining states within the next few years. In almost all the states, TFR reductions are 7 30

    6

    25

    seen with a concomitant increase in contraceptive prevalence

    Total fertility rate Total fertility rate Total fertility rate

    5

    20

    4

    15

    3

    10

    2

    1

    rate among illiterate women compared with educated women.

    Such a major shift in demographic behaviour clearly suggests

    that uneducated women perceive contraceptive use and fertility

    limitation as means to realise growing parental aspirations of

    0

    0

    1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001

    good quality education, better health of children and overall

    Andhra Pradesh Karnataka Tamil Nadu Kerala

    well-being of families. Results presented below provide ample

    7 30

    evidence of potential positive health and development contribu

    tions of fertility decline both at the macro and micro level and;

    both short-term and long-term gains are found.

    4 Macro Impact of Fertility Decline

    Figures 1, 2 and 3 (p 60) display charts with macro level compari

    6

    25

    5

    4

    3 10

    2 1 5

    0

    1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001

    Haryana Gujarat Pu njab West Bengal Maharashtra

    son of trends in TFRs vis-à-vis health and development indicators 7

    for major states of India. The data presented in these charts show

    6 5

    that states with significant fertility reduction during the respec

    4

    tive decades of 1970s, 1980s and 1990s have also correspondingly

    3

    experienced remarkable progress in poverty and infant mortality

    2

    5

    1

    reduction, literacy rate, per capita SDP growth, human develop

    0

    0

    1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001

    ment index in the following 5-10 year lag period. For instance,

    Uttar Pradesh Bihar Rajasthan Madhya Pradesh Orissa

    rapid decline of fertility during the last three decades in the four

    Per capita state domestic product

    Andhra Pradesh. The states in the next stage of demographic transition such as Maharashtra, Gujarat, Punjab, Haryana and West Bengal follow closely the south Indian states with continuing fertility decline during 1971-2001. The concomitant progress in literacy and reduction in poverty and infant mortality has been substantial in all these five states. The rise in per capita SDP in Haryana, Punjab and Maharashtra exceeded than that of south Indian states for the recent decade of 1991-2001.

    Haryana and Punjab are ahead in poverty reduction with less than 10% of BPL households in 2001. In contrast, the pace of fertility decline has been very slow in the states of Uttar Pradesh, Bihar, Rajasthan and Madhya Pradesh during the three decades of 1971-2001. The trends show a modest decline of TFR from about six children in 1971 to four to five children in 1991 but stagnating

    Figure 3: Trends in Fertility Decline and Infant Mortality Reduction SDP (1971-2001)

    160

    6

    120

    5 4 3 2 1 0 80 40 0 Total fertility rateInfant mortality rate

    It may be argued that these broad associations between fertility decline and health and human development indicators, however, cannot be taken to provide a confirmation of evidence of strong and clear one-way reverse causational impact of fertility decline. The causational direction needs to be further examined given the possibility of two-way causation that fertility decline is not only a possible cause of more rapid economic growth and social progress, but could be an outcome of the later as well (IUSSP 1998). Measuring explicit reverse causation effect of fertility decline on health and development, therefore as stated earlier, entails a more careful analysis of evidence of such gross associations.

    5 Panel Data Regression Analysis

    In assessing macro level evidence of the impact of fertility decline on health and development trends, the literature points to possible constraints of a perfect econometric model that allows for direct causation of socio-economic determinants on fertility and reverse

    causational effect of fertility on health and development. We use

    panel data regression model, an improvised econometric proce

    dure, on pooled observations of cross-sectional data across 14

    major states of India over three time periods of 1981, 1991 and

    1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001

    Andhra Pradesh Karnataka Tamil Nadu Kerala

    2001 to estimate fertility impact on three selected health and

    development indicators. Panel data regression has the advantage

    7 160

    1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 Haryana Gujarat Pu njab West Bengal Maharashtra

    of less collinearity among the variables, more efficiency with

    degrees of freedom and measuring the effects that are easily not

    detectable in pure cross-section or time-series data (Baltagi 2001).

    Panel data sets are considered more dynamic as they provide

    6 5 120

    Infant mortality rate Infant mortality rate

    Total fertility rateTotal fertility rate

    4

    80

    3

    2

    40

    1

    time-trend data over cross-sections. Panel data regression in

    00

    volves two methods of estimation – fixed and random effects. In the fixed effects model, the effects of predictor variables are estimated using ordinary least squares method. The method

    1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 1971 1981 1991 2001 Uttar Pradesh Bihar Rajasthan Madhya Pradesh Orissa

    160

    6

    suffers from greater loss of degrees of freedom and time invariant

    variables are not allowed. In the case of random effects model,

    the predictor variables effects are estimated using generalised

    least square method and Hausman’s test is used to test the statis

    5

    120

    4 3 80

    2

    40

    1 0 0

    tical significance. The random effects model is more helpful to

    capture the temporal effects of predictors including possible

    Infant mortality rate

    around four children during 1991-2001. With persistent high fertility, these states have been stagnating with meagre improvements in health and development indicators.

    From a comparative perspective, the states in south, west and east regions have had some advantage of higher literacy rate and lower infant mortality in the initial phase of 1971-81 and 1981-91 than the states in the north region. However, almost all the states had very meagre per capita SDP in the early 1980s. The real takeoff point was the acceleration in per capita SDP growth in the south, west and eastern states during 1990-91 to 2000-01, following sharp fertility declines in the 1980s and early 1990s. The historic economic reform initiatives in the 1990s coincided with this shift in demographic advantage. In contrast, the four northern states stagnated in both fertility decline and per capita SDP growth. The rise in lite racy rate and reduction in poverty and infant mortality also accelerated in the south, west and eastern states with faster fertility decline but stagnated in the northern states with slower f ertility decline.

    lagged effects during specific time periods.

    Both fixed and random effects models are estimated for three different dependent variables, (1) percentage of BPL population,

    (2) infant mortality rate, and (3) female work participation rate. Each of the three models considers six common predictors that comprise TFR, female literacy rate, sex ratio, percentage of male in non-agricultural sector, percentage of urban population, and period dummy variables for 1981-91 and 1991-2001 (Table 3, p 61). In accordance with the reverse causation theoretical framework, the TFR represents the principal predictor variable for each of the models predicting respectively, poverty, infant mortality and female work participation rate. The other covariates included in the model are female literacy rate as a well known determinant of all the above three factors and sex ratio as a measure of gender inequality. Variables like percentage of male workers in nonagricultural sector and percentage of urban population in the models represent economic progress and urbanisation. The period dummy variable is crucial to explain the temporal effects of the changes in the predictors during the period including possibly

    july 25, 2009 vol xliv no 30

    EPW

    Table 3: Panel Data Regression Analysis of Fertility Impact on Poverty, Infant Mortality and Female Work Participation Rate with State-wise Pooled Cross-Sectional Data of 1981, 1991 and 2001

    Dependent Variable

    Percentage of Households Infant Mortality Rate Female Work Below Poverty Line Participation Rate Predictor Variables Fixed Random Fixed Random Fixed Random Effects Effects Effects Effects Effects Effects

    Infant mortality rate -0.006 0.007* – – 0.008 0.006

    Female work participation rate -0.012 0.002 0.020* 0.021 – –

    Period dummy 1981-91 -0.158 -0.079 -0.720** -0.427* 0.724 0.728*

    Period dummy 1991-2001 -0.469 -0.354* -1.602** -0.771* 1.172 1.750**

    Constant 2.632* 0.382 1.908 3.156 14.823* 10.705*

    R square Within 0.89 0.86 0.90 0.88 0.60 0.57

    Significance of Chi2 (Hausman’s test)

    Number of observations 42 42 42 42 42 42

    * p<0.10; ** p<0.05 *** p<.001

    the lagged effects. Apart from the above factors, the model for poverty includes infant mortality as a health-related predictor and female work participation rate to represent women’s economic condition. Per capita SDP and female work participation rate are additional predictor variables for the model for infant mortality and per capita SDP and infant mortality rate are relevant additional predictors for the model on female work participation rate. The estimates of fixed and random effects of predictors of panel regression models for poverty, infant mortality and female work participation rate are presented in Table 3.

    The estimates of both fixed and random effects models together establish evidences of fertility impact on poverty, infant mortality and female work participation. Controlling for other relevant socio-economic covariates, the random effects model shows that fertility decline during the last three decades has had a significant contribution to the reduction in poverty and infant mortality and a rise in women’s economic participation. This evidence of reverse causational impact of fertility decline on three of the health and development factors is more credible, when it is viewed from the context of findings from a previous study that “poverty exerts no significant influence on fertility” based on district-level panel data of India for 1981 and 1991 (Drèze and Murthi 2001). Presumably, this vice versa reverse causation effect of fertility decline on poverty reduction possibly predisposes an expected positive effect of poverty on fertility between mid to final stages of fertility transition as this being the case with many Indian states. Variable percentage of males employed in non-agricultural sector, as a measure of industrial economic development also reveals modest contribution to poverty reduction.

    Economic & Political Weekly

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    july 25, 2009 vol xliv no 30

    The substantial random effect of period dummy variable 19912001 suggests an independent temporal effect, a reflection of the greater impact of fertility decline and other co-predictors on poverty reduction during this period.

    Aside from fertility impact, the evidence of significant negative effect of female literacy and per capita SDP on infant mortality from the fixed effects model confirms that they continue to mediate as important socio-economic determinants of child health improvement at the macro level. The negative associations between per capita SDP and female work participation rate suggest that states with higher and growing per capita SDP are not necessarily resulting in higher female work participation rate. The lack of significant association between level of urbanisation and reduction in poverty, infant mortality and rise in female work participation rate, points to the fact that trends in rural areas determine the net association between the dependent factor and the explanatory variables. Variable sex ratio of the population which represents gender inequality norms shows very little association with any of three dependent factors. The period dummy variables for 1981-91 and 1991-2001 reveal strong effects on infant mortality and female work participation rate in both fixed and random effects models. In particular, the period effects for 1991-2001 suggest a significant reduction in infant mortality and a rise in female work participation rate that might represent the temporal effects of the combined improvements in healthcare, the predictor variables, besides fertility decline.

    6 Household Level Impact

    In this section, we present micro level evidence of low fertility impact on selected women and child health indicators and child schooling based on logistic regression analysis of NFHS-2 data. Adjusted percentage of women receiving three ANC check-ups, children receiving full immunisation, levels of neonatal, postneonatal and child mortality and children aged 6-14 years attending school are compared between educated and uneducated women further categorised by parities of women at sterilisation. We limit our analysis to the sample of sterilised women in order to measure evidence of causal impact of fertility decline in a more exclusive form. Because the net fertility impact cannot be clearly established for women not using contraception and those using temporary methods of contraception as they are likely to have additional children. In the analysis of ANC and immunisation coverage, states with same level of health services coverage have been grouped together to carry out analysis with sufficient number of cases for categories of variables by women’s education and parity.

    The following results on key women and child health indicators reveal that low fertility among uneducated sterilised women leads to significant improvements in the health of women and children and greater school enrolment among children.

    6.1 ANC and Immunisation

    Table 4 (p 62) presents the adjusted percentage of sterilised women who received three ANC check-ups by women’s parity and education. Among both educated and uneducated categories, the adjusted percentage of sterilised women who received a minimum

    Table 4: Adjusted Percentage of Women Who Received at Least Three Antenatal Check-ups by Women’s Education and Parity at Sterilisation (NFHS 1998-99)

    Uneducated Women Educated Women States Parity 2 Parity 3 Parity 2 Parity 3 and Less and More and Less and More

    Delhi, Haryana and Punjab Himachal Pradesh and Jammu and Kashmir 55.3 30.1 67.5 56.4

    Assam, Orissa and West Bengal 55.4 52.8 76.9 58.7

    India 59.2 36.4 87.1 68.3

    Adjusted percentages are calculated from logistic regression coefficients for categories of a given predictor variable, other variablesare, held constant at their mean values. The control variables include age of women, caste, religion, standard of living index and residence.

    Table 5: Adjusted Percentage of Children Fully Immunised by Women’s Education and Parity at Sterilisation (NFHS 1998-99)

    Uneducated Women Educated Women
    States Parity 2 Parity 3 Parity 2 Parity 3
    and Less and More and Less and More
    Andhra Pradesh and Karnataka 51.8 39.6 70.3 56.6
    Goa, Tamil Nadu and Kerala 90.8 75 82.8 78.3
    Gujarat and Maharashtra 47.5 55.3 74.3 63.8
    Delhi, Haryana and Punjab Himachal Pradesh
    and Jammu and Kashmir 56.6 53.2 82.8 75.1
    Bihar, Uttar Pradesh, Rajasthan and
    Madhya Pradesh 15.8 10.3 24.8 33.6
    Assam, Orissa and West Bengal 41 33 81 44.6
    India 48.3 34.4 72.8 55.6

    The adjusted percentages are calculated from logistic regression coefficients. When calculating adjusted percentages for categories of a given predictor variable, variables are held constant at their mean values. The control variables include age of women, caste, religion, standard of living index and residence.

    of three ANC checks-ups is higher for women with two and less than two parities compared to those with greater than two parities amongst most states. For all India sample of sterilised uneducated women, the ANC coverage for women with two and less parities is 59% compared with 36% for women three parities.

    For sterilised educated women the ANC coverage is 87% for low parity women and 68% for high parity women.

    The difference in coverage of three ANC check-ups (in percetage points) between low and high parity women is substantially greater for uneducated women compared with educated women in most states, where ANC coverage is moderate to low. The net low parity impact on ANC coverage is steeply higher in north and north central states of Bihar, Haryana, Punjab and Jammu and Kashmir. In high ANC coverage states of Kerala, Tamil Nadu and Karnataka the differences are expectedly marginal due to highly equitable access to, and narrowing differentials in ANC by parity and education. The low parity effect on ANC coverage is also substantial among educated sterilised women in most of the states. In sum, the low fertility impact in raising ANC coverage is almost same as education impact on ANC coverage.

    Evidence of similar differences by women’s parity and education are demonstrated with respect to percentage of children fully immunised almost consistently in states of all regions (Table 5). The percentage of children fully immunised is consistently higher among low parity sterilised women than the high parity sterilised women regardless of their educational status in all the states.

    6.2 Child Mortality

    Table 6 compares low parity effect on reduction of neonatal, postneonatal and child mortality between educated and uneducated women. First, women’s education shows a strong effect on child mortality but this effect is greater for women with two and less than two parities consistently across all the states. Second, the low parity effect on reduction on neonatal, post-neonatal and child mortality emerges to be two to three times higher than education effect on child mortality reduction. For instance, for the total India sample of sterilised women, neonatal mortality is nearly twice higher for uneducated women than educated women. Compared with this, among uneducated sterilised women, neonatal mortality is more than three times higher (64) for women with three and more parities compared with women

    Table 6: Adjusted Neonatal, Post Neonatal and Child Mortality Rates by Mother’s Education and Parity at Sterilisation,

    (NFHS 1998-99)

    States Neonatal Mortality (0-28 Days) Uneducated Women Educated Women Post Neonatal Mortality (1-11 Months) Uneducated Women Educated Women Child Mortality (12-47 Months) Uneducated Women Educated Women neonatal mortality is similarly three
    Parity 2 and Less Parity 3 and More Parity 2 and Less Parity 3 and More Parity 2 and Less Parity 3 and More Parity 2 and Less Parity 3 and More Parity 2 and Less Parity 3 Parity 2 Parity 3 and More and Less and More times higher for women of high parity than low parity. More striking is the
    Andhra Pradesh 21 71 10 28 6 23 18 94 13 38 10 35 result that child mortality in all stages
    Bihar 5 41 11 28 6 9 18 37 3 21 3 22 is lower for uneducated sterilised
    Gujarat 21 62 15 40 16 71 13 41 8 38 women with low parity than educated
    Haryana 5 41 8 24 4 21 8 51 7 30 6 23
    Karnataka 19 55 9 29 7 18 16 52 11 37 11 24 sterilised women of high parity.
    Kerala 14 76 10 12 1 14 10 47 3 45 3 13 Clearly, in every state, the net positive
    Madhya Pradesh Maharashtra 15 19 79 57 17 5 4537 9 5 3016 8 14 71 74 9 8 37 34 4 4 39 19 returns of low fertility on child mortality reduction are the largest than returns in
    Orissa 33 76 9 30 5 15 14 64 13 51 5 31 other health indicators such as coverage
    Punjab 12 59 11 18 8 15 20 11 34 in antenatal care and immunisation.
    Rajasthan 16 59 12 39 11 28 11 64 7 37 6 43

    of two and less parities (18). Among the educated sterilised women also,

    Tamil Nadu 21 70 929 9 30 16 81 8 39 7 39 6.3 Child Schooling West Bengal 15 55 8 32 6 17 16 52 9 26 5 36

    Low fertility in general, and among un-

    Uttar Pradesh 13 71 12 39 4 23 16 62 8 42 – –

    educated women in particular, in the

    India 18 64 11 35 6 21 14 63 9 36 6 31

    Indian states have been reported to

    The adjusted neonatal, post neonatal and child mortality rates have been calculated by converting the coefficients of cox proportional hazard model. When calculating adjusted child mortality rates for categories of a given predictor variable, other variables are held r esult in both quantitative and qualitaconstant at their mean values. The control variables include age of women, caste, religion, standard of living index and residence.

    – shows Insufficient number of cases. tive improvement in child schooling

    july 25, 2009 vol xliv no 30

    Table 7: Percentage of Children Attending School (6-14 Years) by Mother’s Education and Parity at Sterilisation, NFHS (1998-99)

    States Uneducated Women Educated Women Parity 2 and Less Parity 3 and More Parity 2 and Less Parity 3 and More

    Andhra Pradesh 85.2 72.5 97.8 92.9

    Haryana 91.3 86.6 100 97.4

    Himachal Pradesh 97.5 96.5 99.2 98.7

    Madhya Pradesh 81.3 74.3 100 93.3

    West Bengal 83.1 73.3 92.9 90.2

    Uttar Pradesh 96.7 80 97.3 94.7

    India 87.3 79.3 98.4 95.6

    The adjusted percentages are calculated from logistic regression coefficients. When calculating adjusted percentages for categories of a given predictor variable, other variables are held constant at their mean value. The control variables include age of women, caste, religion, standard of living index and residence.

    (Bhat 2002; Retnakumar and Arokia samy 2006). Low fertility choice among uneducated women and the consequent positive benefits in child schooling is theorised as an outcome of quantityquality trade-off on number of children. Results presented in Table 7 reveal that the percentage of children aged 6-14 attending school is greater for both educated and uneducated women with lower parities (two and less) compared with women of higher parities (three and more). In particular, the difference in overall school attendance rate for India between women with low and high parities is steeper for uneducated women (87% and 79%) compared with educated women (98% and 96%).

    For educated women with lower parities of two and less, the school attendance rate range between 98 and 100% in most states except in Bihar (91%), West Bengal (93%) and Uttar Pradesh (97%). For educated women with higher parities, the school attendance rate ranged from 90-95% in half of the states and between 96 and 99% among the rest of the states. In contrast, the school attendance rate among children of uneducated women drops to an average of 79% for women with high parities but rises sharply to 87% among women of low parities. The impact of low fertility among uneducated on child schooling is certainly substantial.

    7 Conclusions

    In this analysis, we explored and presented evidence of fertility decline among illiterate women in India and its positive contributions to progress in health and development conditions.

    First, fertility decline among uneducated has been a major driving force for the recent acceleration in fertility transition in India. Between 1992-93 and 2005-06, more than two-fifths of reduction in TFR was contributed by illiterate women with the remaining proportion equally contributed by literate women and changes in educational composition of women. Concomitantly, the increase in contraceptive prevalence rate among uneducated women has been larger and faster among uneducated than educated women. We suggest that the choice of low fertility among uneducated is guided by quantity-quality trade-off and maximisation of benefits of health and well-being for women and children.

    Second, as proposed, this remarkable demographic behaviour change has conclusively contributed to the overall health and development of the states with significant direct health benefits for uneducated women and their children. Macro level trend analysis of the link among fertility, health and development indicators firstly shows the evidence of what economists have often

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    referred to as the potential reinforcing effects between demographic change and economic growth. However, we have tried to untangle such complementary effects to shed insights on one way causation effects with estimates from macro level panel data regression models. The results illustrate modest positive impact of fertility decline on poverty and infant mortality reduction, and a rise in female work participation rate. The larger fertility change among uneducated women has been a greatly influential factor in raising average economic and health conditions of the states.

    Third, we establish more convincing evidence of causal impact of low fertility with potential health benefits for women and their children based on micro data from the national family health surveys. The adjusted percentage of women receiving ANC and their children receiving full immunisation is significantly higher for uneducated sterilised women with two and less number of children compared with uneducated sterilised women with three and more number of children. Similar differences are found in

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