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RESEARCH ARTICLE
Fertility transition and socioeconomic development in
districts of India, 2001–2016
Sayantani Chatterjee* and Sanjay K. Mohanty
International Institute for Population Sciences, Mumbai, India
*Corresponding author. Email: 612sayantani@gmail.com
(Received 21 October 2019; revised 23 October 2020; accepted 23 October 2020)
Abstract
The fertility–development relationship is bi-directional, context-specific, multi-phased and inconsistent
over time. Indian districts provide an ideal setting to study this association due to their size, diversity
and disparity in socioeconomic development. The objective of this study was to understand the association
of fertility and socioeconomic development among the 640 districts of India. Data were drawn from mul-
tiple sources: Censuses of India 2001 and 2011; DLHS-2; NFHS-4; and other published sources. A district-
level data file for Total Fertility Rate (TFR) and a set of developmental indices were prepared for the 640
districts for 2001 and 2016. Computation of a composite index (District Development Index, DDI),
Ordinary Least Squares, Two Stage Least Squares and panel regressions were employed. By 2016, almost
half of all Indian districts had attained below-replacement fertility, and 15% had a TFR of above 3.0. The
DDI of India increased from 0.399 in 2001 to 0.511 by 2016 and showed large variations across districts.
The correlation coefficient between TFR and DDI was –0.658 in 2001 and –0.640 in 2016. Districts with a
DDI of between 0.3 and 0.6 in 2001 had experienced a fertility decline of more than 20%. The fertility–
development relationship was found to be strongly negative, convex and consistent over time, but the level
of association varied regionally. For any given level of DDI, fertility in 2016 was lower than in 2001; and the
association was stronger in districts with a DDI below 0.45. The negative convex association between the
two was prominent in the northern, central and eastern regions and the curves were flatter in the west,
south and north-east. The increasing number of districts with low fertility and low development draws
much attention. Some outlying districts in the north-eastern states had high TFR and high DDI
(>0.6). Based on the findings, a multi-layered strategy in districts with low socioeconomic development
is recommended. Additional investment in education, child health, employment generation and provision-
ing of contraceptives would improve the human development to achieve India’s demographic goals.
Keywords: Fertility; Fertility–development relationship; Socioeconomic development
Introduction
Fertility transition and socioeconomic development are concomitant across and within countries.
Fertility reduction is mainly driven by four factors: the pace of social and economic development,
the pace of change in economic aspiration and expectation, the pace of provision of birth control
services and the pace of reduction of the moral and social cost of birth control (Casterline, 2001).
Globally, more than half of countries have reached the replacement level of fertility and have made
significant improvement in key domains of human development. Global TFR declined from 3.3 in
1990 to 2.4 in 2018 (World Bank, 2020). Life expectancy at birth increased from 65.4 years in 1990
to 72.6 years by 2018 and the Gross National Income per capita (constant 2017 PPP $) increased
from US$9833 in 1990 to US$16,550 by 2017 during the same period (World Bank, 2020). The
average years of schooling for those aged 15 years and above increased from 6.14 to 8.4 years
© The Author(s) 2021. Published by Cambridge University Press.
Journal of Biosocial Science (2021), page 1 of 19
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(Barro & Lee, 2013; World Bank, 2020). The global composite index of human development
(Human Development Index, HDI) moved from 0.598 in 1990 to 0.731 by 2018 (UNDP, 2019).
The relationship between fertility and development has been a long-standing issue among
demographers, economists, sociologists and other social scientists. A large body of literature
has examined the association between fertility and socioeconomic development at varying geo-
graphical and individual units. The general pattern of the association between socioeconomic
development and fertility is negative (Shin, 1977; Bongaarts & Watkins, 1996;Poston,2000;
Potter et al.,2002;Bryant,2007; Brinker & Amonker, 2013;Ryabov,2015). A number of studies
have also found weak associations between socioeconomic development and fertility across varying
geographical settings (Coale & Watkins, 1986; Cleland & Wilson, 1987). While development con-
tinues to promote fertility reduction at low and medium levels of HDI, at advanced levels the rela-
tionship between fertility and development is weak (Wilson & Airey, 1999; Myrskylä et al.,2009;Fox
et al.,2019). A convex relationship between Gross Domestic Product (GDP) per capita and fertility
has been observed among Organization for Economic and Co-operation and Development (OECD)
countries (Luci-Greulich & Thévenon, 2014). Recent evidence also suggests a positive association
between fertility and development at high levels of development (Myrskylä et al.,2009; Furuoka,
2009;Myrskyläet al.,2011; Esping-Andersen & Billari, 2015; Goldscheider et al.,2015).
The fertility–development relationship appears to be bi-directional, context-specific, multi-
phased and often inconsistent over time (Boudon, 1983; Bulatao and Lee, 1983; Galloway
et al.,1994; Hirschman, 1994; Bongaarts & Watkins, 1996; Mason, 1997; Drèze & Murthi,
2001; Potter et al.,2002; Bryant, 2007; Harttgen & Vollmer, 2014; Ryabov, 2015; Fox et al.,
2019). Low fertility is conducive to the process of development. A certain level of socioeconomic
development is necessary for the onset of fertility transition and a sustained decline in fertility over
time (Pathak & Murthy, 1984; United Nations, 1995; Bongaarts & Watkins, 1996; Bryant, 2007).
Socioeconomic change modifies the incentives to have children, stimulates new ideas about child-
bearing and allows woman to achieve better access to contraceptive methods. On the other hand,
high fertility leads to lower per capita income, lower savings and investment, low educational
attainment and slow economic growth in many developing countries. While many countries
in sub-Saharan Africa have had a high level of fertility that has inhibited their level of socioeco-
nomic development, many countries in Europe, America, Australia, New Zealand and parts of
Asia are facing an increase in old-age dependency, with increased health care spending and pen-
sion costs, which adversely affect their economic growth. A few studies have also suggested that
the development scores among countries commencing fertility transition have fallen over time
(Bongaarts, 2002). The timing of the onset of fertility decline and the pace of fertility transition
varies across and within countries, and some may have witnessed the weakening of the association
between fertility and development over time (Bongaarts & Watkins, 1996).
In India, fertility transition began in the 1970s and fertility approached replacement level by
2016. The TFR in India declined from 5.2 in 1971 to 3.2 in 2000 and to 2.3 by 2016 (ORGI, 2016).
In 2016, 18 out of the 29 states of India had reached the replacement level of fertility (ORGI, 2016).
The HDI of India moved from 0.428 in 2001 to 0.728 in 2018 (UNDP, 2019). Of all the states and
union territories, thirteen now have a high level of human development (UNDP, 2019). Since the
launch of the National Health Mission in 2005, India has recorded faster improvement in socio-
economic development and a rapid decline in fertility level. There has been a substantial improve-
ment in the overall health of the population: an increase in life expectancy, reduction in maternal
and under-five mortality and improvement in maternal health care. The institutional delivery rate
increased from 39% in 2005 to 79% in 2016. The infant mortality rate declined from 134 per 1000
live births in 1971 to 37 in 2016. Educational progress has also been noteworthy, with the mean
years of schooling increasing from 1.9 years in 2005–06 to 4.4 years in 2016 (IIPS & ICF, 2017).
The country achieved over 6% growth in GDP and reduced the poverty level by half in the last
decade (from 37.2% in 2004–05 to 21.9% by 2011–12) (Planning Commission of India, 2014).
However, these national estimates mask enormous disparities at the sub-national level.
2 Sayantani Chatterjee and Sanjay K. Mohanty
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Fertility transition and socioeconomic development in India exhibit varying patterns across
states. The developed states of India, such as Kerala and Tamil Nadu, and the union territories
of Delhi, Pondicherry and Chandigarh, attained the below-replacement level of fertility over a
decade ago, and some of the less-developed states, such as Odisha and Andhra Pradesh, also
approached below-replacement level fertility. States such as Uttar Pradesh and Bihar continue
to have low socioeconomic development and high fertility, and account for around a quarter
of India’s total population. Though regular estimates of fertility and development indicators
for the states of India are available, there is limited information for the districts of India.
Indian districts are at varying stages of fertility transition and provide an ideal setting to study
the fertility and development relationship due to their size, diversity and disparity in the socio-
economic development. These are the ultimate administrative units of decentralized planning.
Districts serve as the bridge between state and household, are culturally homogeneous and still
show considerable variations in their demographic features. With growing availability of viable
and reliable data based on socioeconomic development at the district level, the number of
district-level analyses has been growing. Prior studies have largely been confined to examining
geographical variations in fertility levels (Bhat, 1996; ORGI, 1997; Das & Mohanty, 2012;
Kumar & Sathyanarayana, 2012; Guilmoto, 2000,2005,2016; Guilmoto & Rajan, 2002,2013).
A few studies assessed the association of fertility and socioeconomic development and with other
proximate determinants at the district level (Malhotra et al.,1995; Murthi et al.,1995; Drèze &
Murthi, 2001; Bhattacharya, 2006; Mohanty et al.,2016a; Singh et al.,2017). The majority of these
used limited variables and are now at least two decades old. Many examined the fertility–
development association at a time when fertility levels were quite high across districts and socio-
economic development was low. To the authors’knowledge, there have been only limited studies
focusing on the temporal patterns of socioeconomic development and fertility relationship among
Indian districts. This study therefore aimed to provide a comprehensive assessment of fertility and
development association across Indian districts, with a specific focus on the association between
fertility change and socioeconomic development.
Methods
Data
A district-level data file was prepared by estimating and compiling various indicators from dif-
ferent sources at two points in time: 2001 and 2016. The main data sources were: 1) the 2001 and
2011 Censuses of India (Census of India, 2001,2011); 2) the District Level Household and Facility
Survey (DLHS)-2, 2002–04; and 3) the National Family Health Survey (NFHS)-4, 2015–16. In
addition, data from other published sources were used (Planning Commission, Government of
Uttar Pradesh, 2008; Mohanty & Rajbhar, 2014; YASHADA, 2014; Mohanty et al.,2016b;
Bora & Saikia, 2018; Chatterjee & Mishra, 2019). A detailed description of the data sources used
in analysis is presented in Table 1. The unit of analysis was ‘district’. The 2001 Census of India
reported 593 districts, and the 2011 Census of India 640 districts. The NFHS-4 was also conducted
using the Census of India 2011 frame and covered all 640 districts. In this study, analysis was done
for all 640 districts for which Census of India 2011 and NFHS-4 information was available. To
make the number of districts uniform at 640 for 2001 and 2016, the estimates of parent districts
were used from which new districts were carved out between 2001 and 2016.
Outcome variable
The outcome variable was Total Fertility Rate (TFR). The TFR for 2016 was obtained using the
direct method from the total number of births in the last 5 years preceding NFHS-4 (Schoumaker,
2013). The TFR for 2016 at the state level was validated using the 2016 Sample Registration System
Journal of Biosocial Science 3
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(SRS). The state-level correlation coefficient between the estimated TFR from NFHS-4 and that
from the 2016 SRS was 0.923. The estimates of TFR for 2001 and 2011 for districts were taken
from an earlier publication (Mohanty & Rajbhar, 2014).
Independent variables
A composite index was created based on a set of independent variables in three dimensions of
health, social and economic. These included under-five mortality, percentage of under-5 children
underweight, school life expectancy, mean years of schooling, percentage of urban population,
female work force participation, monthly per capita expenditure and percentage of agricultural
labourers. The composite index was conceptualized analogous to the Human Development
Index (HDI), which is a widely used summary measure of socioeconomic development. The pro-
portion of scheduled caste and scheduled tribe populations and proportion of Muslim population
in each district were also used in the regression models as controlling variables. A regional dummy
Table 1. Variables, data sources and descriptive statistics of variables used in the analyses for 640 districts of India, 2001–
2016
Dimension Variable Data source and year Minimum Maximum Weight
Mean
2001
Mean
2016
Health Percentage of children
underweight under age
5 years
DLHS-2 (2002–04);
NFHS-4 (2016)
1.8 80 1/6 45 32.7
Under-five mortality rate
(per 1000)
Census of India (2001);
Estimates from Bora &
Saikia (2018) from
NFHS-4, 2016
3.1 266 1/6 99 49
Social School life expectancy
in years (6–24 years)
Census of India, 2001,
Estimates from
Chatterjee & Mishra
(2019) from Census, 2011
3.7 15.1 1/12 9.5 11.7
Mean years of schooling
(person aged 7years)
DLHS-2 (2002–04);
Estimates from
Chatterjee & Mishra
(2019) from NFHS-4, 2016
1.6 10 1/12 5.1 6.1
Percentage urban Census of India 2001 and
2011
0 100 1/12 23.8 26.3
Female work participa-
tion rate
Census of India 2001 and
2011
4.7 64 1/12 28.9 28.3
Economic Wealth Index Census of India 2001 and
2011
0.039 0.746 1/9 0.280 0.310
Monthly per capita con-
sumption expenditure
(INR)
Estimates derived from
68th round consumption
data, National Sample
Survey (NSS) 2011–12
(Mohanty et al.,2016b)
246 4184 1/9 700 1582
Percentage of agricul-
tural labourers
Census of India 2001 and
2011
0 67.5 1/9 23.2 27
DDI is a summary measure of average achievement in the key dimens ions of human development: health, social and economic. The upper
limit of percentage of children underweight was fixed at 80%. Five districts with more than 80% underweight children in 2001 were truncated
at 80. The under-five mortality rate for Thrissur district of Kerala was replaced with 7 (state-level value) as it was estimated as 0 for 2016. The
MPCE was available for 623 districts. DDI for districts with missing data on MPCE or/and underweight (DLHS-2) children was computed using
all other variables.
4 Sayantani Chatterjee and Sanjay K. Mohanty
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was created based on the geographical regional locations of the states: Uttar Pradesh-Bihar, North,
Central, West, South, East and North-East (Figure 1).
The definitions of the indicators included in the analysis were as follows. The TFR is the num-
ber of children who would be born to a hypothetical woman if she were to bear children according
to a current schedule of age-specific fertility rate and survive until the end of childbearing age. The
‘under-five mortality rate’is the probability that a newborn baby will die before reaching age 5, if
subject to current age-specific mortality rates; the ‘prevalence of underweight children under 5
years’has been defined as the percentage of children whose weights are less than two standard
deviations below the median weight for age in the international reference population. ‘School life
expectancy’is the number of years of schooling that a child is expected to receive, assuming that
the probability of his/her being enrolled in school at any particular future age is equal to the cur-
rent enrolment ratio at that age. ‘Mean years of schooling’is the average number of completed
years of education that a person aged 25 years and above had received. The ‘Wealth Index’is a
composite measure of a household’s living standard calculated based on the household’s owner-
ship of selected assets. ‘Monthly per capita expenditure’is the per capita spending on various food
and non-food items that a household incurs in a month.
Analysis
A composite index of socioeconomic development, henceforth referred to as the District
Development Index (DDI), was computed based on the set of nine variables (listed in
Table 1). Each of the dimensions was normalized to a unitary range between 0 and 1 using
Figure 1. Regions of India, 2001–2016.
Journal of Biosocial Science 5
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the maximum and minimum limits on each metric, also called ‘goalposts’. Where variables posi-
tively affected socioeconomic development, they were normalized using the formula:
XiXmin
=Xmax Xmin
(1)
If variables negatively affected development (e.g. underweight children, under-five mortality
and share of agricultural labourers) they were normalized using the formula:
Xmax Xi
=Xmax Xmin
(2)
where X
i
is the ith value, X
max
is the maximum value and X
min
is the minimum value of each
variable. The maximum and minimum values are the highest and lowest values observed among
the 640 districts over the period 2001 to 2016. Following normalization, equal weights to the
dimensions and equal weights to the variables within the dimensions were assigned similar to
the Alkire and Foster (AF) method, which is used to estimate multidimensional poverty
(Alkire & Foster, 2011). In general, assigning equal weights to dimensions is preferred when
the chosen dimensions are of relatively equal importance. Atkinson et al. (2002) put forward that
equal weighting has an intuitive appeal as ‘the interpretation of the set of indicators is greatly eased
where the individual components have degrees of importance that, while not necessarily exact
equal, are not grossly different’. The DDI ranges between 0 and 1. A value close to 0 implies a
low level of development, whereas a value approaching 1 implies a higher level of development.
Ordinary Least Squares (OLS) regression
The cross-sectional analysis is appropriate for the initial assessment of the degree to which the
relationship between fertility and development holds and how it shifts over time. In equation (4),
the term DDI2was used to capture the non-linear association between fertility and development.
Two sets of regression were estimated using OLS. The models are as follows:
TFRiab1DDIib3Mib4SiΣJ1YjRiJ εi... (3)
TFRiab1DDIib2DDI2ib3Mib4SiΣJ1YjRiJ εi... (4)
where TFR
i
is the total fertility rate in district i, DDI
i
is the district development index for district i
and R
i,j
is a set of dummy (0,1) variables, J–1 for each district i, where R
i,j
takes the value 1 if
district is in region jand 0 otherwise, M
i
is the proportion of Muslims in district i,S
i
is the pro-
portion of scheduled tribes and castes in district i,α,β
1
–β
4
and the Y
j
(j=1, ::: J–1) are param-
eters to be estimated. ϵ
i
is a random normally distributed error term.
Two Stage Least Squares regression
The OLS regression may not provide unbiased estimates due to endogeneity between fertility and
development. In such cases, Two Stage Least Squares (2SLS) is preferred (Kennedy, 2003; Greene,
2012). The percentage of households with a toilet facility was used as an instrumental variable.
This is strongly associated with developmental variables (e.g. under-five mortality) but not with
fertility. Having access to drinking water or having a toilet facility at home are commonly used as
instruments (Drèze & Murthi, 2001). Wu-Hausman tests were performed to check for endoge-
neity. The 2SLS equation was:
TFRiab1
d
DDIiβ3Miβ4SiΣJ1YjRiJ νi... (5)
d
DDIiµδ1Hi. . . (6)
where M
i
is the proportion of Muslims in district i,S
i
is the proportion of scheduled tribes and castes in
district i,H
i
is the proportion of households having a toilet facility, and α,μ,β
1
–β
4
,δ
1
and Y
j
(j=1, :::
J–1) are parameters to be estimated. ϵ
i
and ν
i
are random normally distributed error terms.
6 Sayantani Chatterjee and Sanjay K. Mohanty
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Panel regression
District-level estimates for the years 2001 and 2016 were used in the analyses. Considering the
districts as subjects with varying observations over time, the data can be considered to be a panel
where individual-specific heterogeneity can be taken care of. Panel data are well suited to studying
the dynamics of changes or transition. By combining data in two dimensions, panel data provide
more data variation, less collinearity and more degrees of freedom. Following the Hausman test, a
random-effects model was used for panel data regression. Here, the district-specific effects were
modelled as an additional, time-invariant error term for each district, which was estimated by the
Generalized Least Squares (GLS) method. Unlike a fixed-effects model, it does not preclude the
inclusion of time-invariant variables such as regions. Random-effects models further assume that
the district-specific random error is uncorrelated with the other independent variables.
Let there be districts i=1, :::,Nnested within regions j=1, :::,Jand time periods t=1, 2; TFR
i,t
be the total fertility rate in district iin time period t; DDI
i,t
be the development index for district i
in time period t;R
i,j
be a set of dummy (0,1) variables, J–1 for each district i,whereR
i,j
takes the value
1 if the district is in region jand 0 otherwise; and T
i
be a dummy variable equal to 0 if the year is 2001
and equal to 1 if the year is 2016. Here,ν
i
is an error term which is specific to each district but
constant over the two time periods (the ‘average’error for each district); ϵ
it
is the difference between
the actual TFR in a district in time period tand the predicted value adjusted for the average error;
and ϵ
it
measures the amount of change over time in the TFR in each district. Then:
TFRi;tαβ1DDIi;tβ2Mi;tβ3Si;tΣJ1gjRi;jβ4Tiviεit (7)
where M
i,t
is the proportion of Muslims in district iat time t,S
i,t
is the proportion of scheduled
tribes and castes in district iat time t, and α,β
1
::: β
4
and the γ
j
(j=1, ::: J–1) are the parameters
to be estimated.
Results
Table 1presents the data sources, goalpost and weights used to compute the DDI. Table 2presents
the descriptive statistics of TFR and DDI for the different regions of India in 2001 and 2016.
Validity and reliability of TFR and DDI
In 2016, the correlation coefficient of the estimated TFR from NFHS-4 and that of SRS for the
major states of India was 0.923. Of the states and union territories, the estimated TFR was lowest
in Goa in 2001 and 2011 (1.8 in 2001 and 1.5 in 2011) and in Sikkim in 2016 (1.1). Of the major
states, Bihar consistently had the highest TFR over time (4.9 in 2001, 4.1 in 2011 and 3.6 in 2016).
As an external validation, the DDI was validated with the HDI for two different states of India,
Uttar Pradesh and Maharashtra. The states were chosen as illustrative of high- and low-developed
states. The HDI values were available in external sources (Planning Commission, Government of
Uttar Pradesh, 2008; YASHADA, 2014). Uttar Pradesh is a less-developed state with the highest
number of districts (71) and Maharashtra is a developed state with 35 districts. The correlation
coefficient between the DDI for 2001 and the HDI for 2005 for Uttar Pradesh was 0.810; and that
between the DDI for 2016 and the HDI for 2011 for Maharashtra was 0.902. The alpha reliability
value for DDI and HDI was 0.898 in 2001 and 0.858 in 2016, suggesting that DDI captured the
state of human development reasonably well. Districts with low DDI also had low HDI, while
districts with high DDI were the districts with high HDI in each of the states. Gautam
Buddha Nagar in Uttar Pradesh ranked highest in both DDI and HDI, followed by the districts
of Lucknow and Kanpur Nagar. Conversely, Shrawasti in Uttar Pradesh ranked lowest in both
DDI and HDI. The district of Mumbai had the highest DDI and HDI followed by Pune and
Thane in Maharashtra. Nandurbar in Maharashtra had the lowest rank in both the HDI and DDI.
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Fertility transition in districts of India, 2001–2016
Figure 2presents the estimated TFR in the districts of India for the years 2001, 2011 and 2016. The
TFR of all India declined from 3.2 in 2001 to 2.3 in 2016. In 2001, the highest TFR was noted in the
district of Kishanganj (5.4), followed by Araria (5.4) and Katihar (5.3) (all in Bihar) and lowest in
Kolkata (1.5) in West Bengal, followed by Erode (1.7) in Tamil Nadu in 2001. By 2016, Mewat
district (5.8) in Haryana had the highest TFR, followed by Jaintia Hills (4.7) in Meghalaya and
Shrawasti (4.6) in Uttar Pradesh. The lowest TFR in 2016 was observed in the southern district of
Sikkim (1.0), followed by West (1.1) and North (1.2) districts in Sikkim.
In 2001, 70 of the 640 districts (11%) had below-replacement level fertility, compared with 203
(32%) in 2011 and 297 (46%) in 2016. By 2016, about 23% of the districts had TFR below 1.8 of
which 7% had very low fertility (TFR<1.5) and 15% had high fertility (TFR>3.0). The share of
districts with a TFR of 4declined from 29% in 2001 to 2% in 2016.
India’s fertility trend is the outcome of distinct regional fertility trajectories. States such as
Kerala, Tamil Nadu and Andhra Pradesh in the south, and West Bengal and Sikkim in the east,
maintained below-replacement level fertility after 2001. Furthermore, low-fertility states com-
prised the northern states of Himachal Pradesh and Punjab; the eastern states of West Bengal
and Odisha; and the vast majority of the southern states and union territories, including
Kerala, Tamil Nadu, Karnataka, Andhra Pradesh and Pondicherry. High fertility (TFR >3.0)
was largely limited to areas of Rajasthan, Uttar Pradesh, Bihar, Jharkhand, Madhya Pradesh
and the north-eastern states in the study period.
As the fertility transition progressed, a decline in the dispersion of TFR was observed. The
standard deviation of TFR was 0.953 in 2001 and 0.680 in 2016 (Table 2). The distribution of
TFR was negatively skewed over time. The transition of TFR from high variance to a declining var-
iance suggests that the fertility transition is underway in India. However, the patterns vary widely by
state. In the case of Uttar Pradesh, the standard deviation of TFR increased from 0.382 in 2001 to
0.412 in 2011 and to 0.528 in 2016. Similar patterns were also observed in the high-fertility state of
Bihar, where the standard deviation of TFR increased from 0.308 to 0.384 between 2001 and 2011,
and further to 0.477 by 2016. This suggests that the districts of Uttar Pradesh and Bihar are at an
early stage of fertility transition. In one of the low-fertility states of India, West Bengal, the standard
deviation of TFR had declined consistently –from 0.595 in 2001 to 0.390 by 2016. In Kerala, the
standard deviation of TFR declined from 0.246 in 2001 to 0.164 by 2016. This indicates that the
fertility levels in the districts of West Bengal and Kerala are converging.
Table 2. Descriptive statistics for TFR and DDI and their correlation coefficients among regions of India, 2001–2016
Regions/India No. districts
TFR DDI Correlation coefficient
2001 2016 2001 2016 2001 2016
India 640 3.30 (0.953) 2.33 (0.680) 0.399 (0.096) 0.511 (0.107) −0.658*** −0.640***
UP Bihar 109 4.59 (0.384) 3.16 (0.602) 0.318 (0.067) 0.415 (0.073) −0.789*** −0.815***
North 131 3.20 (0.786) 2.13 (0.584) 0.476 (0.085) 0.583 (0.089) −0.717*** −0.655***
Central 68 3.76 (0.468) 2.45 (0.409) 0.355 (0.054) 0.444 (0.063) −0.639*** −0.499***
West 66 2.70 (0.423) 2.04 (0.311) 0.456 (0.080) 0.538 (0.084) −0.636*** −0.625***
South 107 2.16 (0.325) 1.77 (0.224) 0.462 (0.076) 0.587 (0.093) −0.517*** −0.545***
East 73 3.17 (0.742) 2.27 (0.533) 0.323 (0.078) 0.414 (0.077) −0.572*** −0.662***
North-east 86 3.42 (0.732) 2.45 (0.747) 0.444 (0.077) 0.557 (0.084) −0.311 −0.236
DDI is a summary measure of average achievement in key dimensions of human development: health, social and economic. Figures in
parentheses indicate standard deviation of estimates.
***p<0.01; **p<0.05; *p<0.1.
8 Sayantani Chatterjee and Sanjay K. Mohanty
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Patterns of socioeconomic development in districts of India, 2001–2016
Figure 3presents the estimated DDI for 2001 and 2016 across Indian districts. At the national
level, the DDI increased from 0.399 in 2001 to 0.512 in 2016. In 2001, Kishanganj (0.159) in
Bihar experienced the lowest DDI, followed by Sheohar (0.162) and Araria (0.174) (all in
Bihar). These districts also had high fertility levels in all the time periods. On the other hand,
the DDI was highest in New Delhi (0.710), followed by East Delhi (0.696) and South West
Delhi (0.683). In 2016, the DDI was lowest in Sitamarhi (0.292), Sheohar (0.293) and Katihar
(0.293) –all districts of Bihar. The district of Chennai (0.830) in Tamil Nadu was found to have
Figure 2. Total Fertility Rate in districts of India for a) 2001, b) 2011 and c) 2016.
Journal of Biosocial Science 9
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the highest DDI in 2016, followed by Chandigarh (0.809) and New Delhi (0.806). The spatial dis-
tribution of DDI in 2001 indicated that the low DDI (below 0.35) mostly centred around the
northern, central and eastern states encompassing parts of Rajasthan, Uttar Pradesh, Madhya
Pradesh, Bihar, Jharkhand, Odisha and West Bengal. The DDI was high (above 0.65) in parts
of Delhi, Pondicherry, Chandigarh, Telangana, Tamil Nadu and Maharashtra. In 2016, low
DDI (below 0.35) was mostly found in the states of Rajasthan, Uttar Pradesh, Bihar,
Jharkhand and Odisha. The DDI was above 0.65 in Tamil Nadu, Pondicherry, Karnataka and
Kerala in the south, Delhi, Chandigarh, Himachal Pradesh and Punjab in the north, Goa and
Maharashtra in the west and parts of the north-east.
In 2001, 173 of the 640 (27.0%) districts of India had a DDI below 0.35, and this declined to 23
(3.6%) districts in 2016. Two-fifth of districts had DDI values between 0.35 and 0.45 in 2001. By
2016, 33% had DDI value between 0.45 and 0.55. The DDI curve shifted rightwards over time. At
the aggregate level, the standard deviation of DDI increased from 0.096 in 2001 to 0.107 in 2016.
However, the changes in variance in DDI across most of the regions were not considerable. The
increase in DDI was observed to be highest in districts with DDI values between 0.3 and 0.5 in
2001. The change in standard deviation of DDI over time was highest in the southern region
(0.076 in 2001 and 0.093 in 2016) followed by the north-eastern region (0.077 in 2001 and
0.084 in 2016). Thus, socioeconomic development has not been uniform across the districts of
each region and is diverging.
Association between fertility and development in districts of India, 2001–2016
Table 3presents the share of districts by levels of DDI and TFR in 2001 and 2016. The share of the
districts with a DDI below 0.45 and TFR above 3.0 almost halved –from 70% in 2001 to 39% in
2016. In 2001, of the districts with a DDI in the range 0.45–0.55, 32 (20%) had below-replacement
fertility. By 2016 the corresponding number had risen to 95 (46%). Of the districts with a DDI
below 0.45, only 4% had below-replacement fertility in 2001, while 10% did in 2016. In 2001,
Figure 3. District Development Index (DDI) in India for a) 2001, b) 2016.
10 Sayantani Chatterjee and Sanjay K. Mohanty
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about one-third of the districts with a DDI between 0.45 and 0.55 had a TFR of above 3.0.
By 2016, this had declined to 6%. The number of districts with a DDI of more than 0.55 and
below-replacement fertility increased from 20 (35%) in 2001 to 182 (79%) by 2016. Although
16% of the districts with high DDI had high fertility in 2001 (DDI of more than 0.55 and
TFR of more than 3.0), this reduced to 4% in 2016.
The correlation coefficient between TFR and DDI in the districts of India was –0.658 in 2001
and –0.640 in 2016 (Table 2). The correlation coefficients between TFR and DDI across the dif-
ferent regions revealed that their association, though remaining significant in both periods, had
weakened in the north, central and western regions. The correlation coefficient between TFR and
DDI was high in districts with DDI values less than 0.45 over time. It increased from –0.186 to –
0.302 in districts with DDI values between 0.45 and 0.55, and remained lower in districts with DDI
values above 0.55 compared with the rest. The correlation coefficient between TFR and DDI was
highest in the districts of UP-Bihar featuring high TFR and moderately low DDI. The correlation
coefficient between TFR and DDI was moderate (between –0.4 and –0.6) in the central, western,
southern and eastern regions (Table 2).
Figure 4presents the estimated TFR at varying levels of DDI among the districts of India dur-
ing 2001–2016. With a DDI below 0.35, the mean TFR declined from 4.2 in 2001 to 3.9 in 2016.
Similarly, with a DDI above 0.65, the TFR declined from 2.2 in 2001 to 1.8 in 2016. The majority of
the districts that exhibited an increase of 20–40% in DDI showed a reduction in TFR of 20–40%.
Figure 5presents the association of TFR and DDI in the districts of India in 2001 and 2016. The
scatterplot showed a convex shape of TFR–DDI curve, implying that the rate of decline in fertility
with increase in development slowed as development progressed. However, the negative and non-
linear association between TFR and DDI remained over time. The curve shifted downwards and
towards the right, suggesting an increase in the overall level of DDI and reduction in TFR. For any
given level of DDI, fertility was lower in 2016 than in 2001. However, the rate of fertility decline
over time was more prominent in districts with DDI values lying between 0.3 and 0.6. As DDI
reached the threshold level (0.35 or more), 10% of districts in 2001, and almost half of all districts
in 2016, approached below-replacement fertility. In addition, many districts still had TFRs close
to, or even above 3.0 at DDI values above 0.6 over time.
The association between DDI and TFR among districts and across regions showed varying pat-
terns. The general pattern held true across regions but the magnitude varied. The convex associa-
tion between TFR and DDI was more prominent in the north, central and eastern regions.
Furthermore, the effect of the rate of fertility reduction for any given level of DDI was greatest
in UP-Bihar and the central region. The patterns of the TFR curves for most of the districts in
these regions revealed that fertility declined, although the improvement in DDI over time was
rather slow. However, very few of these districts had below-replacement level fertility, with
DDI lying between 0.4 and 0.6. The non-linear convex association between fertility and develop-
ment was also observed in districts in the north and eastern regions. These regions included low-
and high-fertility states. In the north, the districts of Himachal Pradesh, Punjab, Jammu and
Kashmir and other union territories had low TFRs and high DDI scores. On the other hand,
Table 3. Share of districts by levels of DDI and TFR in India, 2001–2016
TFR in 2001 TFR in 2016
<2.1 2.1–3.0 >3.0 <2.1 2.1–3.0 >3.0
DDI <0.45 4.3 (18) 26.2 (110) 69.5 (292) 10.0 (20) 51.0 (102) 39.0 (78)
DDI 0.45–0.55 19.6 (32) 49.1 (80) 31.3 (51) 45.5 (95) 48.3 (101) 6.2 (13)
DDI >0.55 35.1 (20) 49.1 (28) 15.8 (9) 78.8 (182) 17.3 (40) 3.9 (9)
DDI is a summary measure of average achievement in key dimensions of human development: health, social and economic. Figures in
parentheses indicate the number of districts. Percentages are the row percentages.
Journal of Biosocial Science 11
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the majority of the districts of Rajasthan and Haryana had persistently lower levels of DDI and
high fertility. A small number of the districts belonging to West Bengal and Odisha in the east had
low fertility rates and low DDI scores. Additionally, the curve was flatter in the south and western
regions, which continued to have low fertility levels in 2001 and better DDI scores. Also, the curve
was flatter in districts in the north-east experiencing lesser fertility reduction over time. There
were some residual districts for which the TFR remained well above replacement level, even with
DDI values of 0.6 or more, but these were concentrated in certain areas of India, notably the
north-east.
The highest TFR decline (more than 2 units or more than 20%) between 2001 and 2016
occurred in twelve of the 640 districts, with DDI values ranging between 0.3 and 0.6
(Figure 6): Banswara in Rajasthan; Tirap, Upper Siang, Changlang, East Siang, Lower
Subansiri in Arunachal Pradesh, West and South Garo Hills in Meghalaya; and Mahrajganj,
Lalitpur, Ambedkar Nagar and Azamgarh in Uttar Pradesh. The decline in TFR by 2.0 units dur-
ing 2001 and 2016 was mostly observed across districts showing a wide range of DDI values, rang-
ing between 0.2 and 0.6. The decline was less pronounced for districts with DDIs lower than 0.2 or
above 0.6 in 2001. Concurrently, 25 districts with varying TFR levels in 2016 belonging to the
states of Himachal Pradesh, Nagaland, Manipur and Tamil Nadu showed an increase in TFR dur-
ing 2001–2016. The DDI values of these districts broadly ranged between 0.4 and 0.6 in 2001.
Multivariate results
Table 4presents the results of cross-sectional and panel regressions with TFR as the dependent
variable. Columns (1) and (3) present the cross-sectional results using OLS for 2001 and 2016,
respectively. Owing to the bi-directional association between fertility and socioeconomic devel-
opment, the coefficients estimated by OLS could be biased. Hence, the percentage of households
having a toilet facility was used as an instrumental variable, as the share of households having a
toilet facility is associated with DDI but not with TFR. The Yu-Hausman statistics for 2001 and
2016 suggested that DDI indeed introduced endogeneity in the analysis.
The 2SLS coefficients are presented in Columns (2) and (4) for the individual cross-sections.
The explanatory variables accounted for about three-quarters of the variation in fertility across
districts in 2001, and a little less in 2016. The DDI coefficient was negative and significant for
both 2001 and 2016. The coefficients of the share of Muslim and SC/ST populations were posi-
tively significant for both time points. The smaller coefficient of DDI in these columns suggested a
decline in association between TFR and DDI from 2001 to 2016.
The OLS and 2SLS results based on panel data accounting for district-specific effects are pre-
sented in Columns (5) and (6). Both models broadly corroborated the cross-sectional findings.
The coefficients of Generalized Least Squares with the random effect (GLS-RE) model using
4.17
3.25
2.75
2.43 2.16
3.85
2.78
2.22 2.01 1.75
Below 0.35 0.35-0.45 0.45-0.55 0.55-0.65 Above 0.65
Total Fertility Rate
District Develo
p
ment Index
2001 2015-16
Figure 4. Mean TFR at varying levels
of DDI in districts of India, 2001 and
2016.
12 Sayantani Chatterjee and Sanjay K. Mohanty
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(a) India (b) North
(c) Central
(e) South
(g) North-east (h) UP-Bihar
(f) East
(d) West
1
2
3
4
5
6
0.2 0.4 0.6 0.8
District Development Index
TFR 2001 TFR 2016
Predicted TFR 2001 Predicted TFR 2016
1
2
3
4
5
6
0.2 0.4 0.6 0.8
District Development Index
TFR 2001 TFR 2016
Predicted TFR 2001 Predicted TFR 2016
1
2
3
4
5
6
0.2 0.4 0.6 0.8
District Development Index
TFR 2001 TFR 2016
Predicted TFR 2001 Predicted TFR 2016
1
2
3
4
5
6
0.2 0.4 0.6 0.8
District Development Index
TFR 2001 TFR 2016
Predicted TFR 2001 Predicted TFR 2016
1
2
3
4
5
6
0.2 0.4 0.6 0.8
District Development Index
TFR 2001 TFR 2016
Predicted TFR 2001 Predicted TFR 2016
1
2
3
4
5
6
0.2 0.4 0.6 0.8
District Development Index
TFR 2001 TFR 2016
Predicted TFR 2001 Pre dicted TFR 2016
1
2
3
4
5
6
0.2 0.4 0.6 0.8
District Development Index
TFR 2001 TFR 2016
Predicted TFR 2001 Predicted TFR 2016
1
2
3
4
5
6
0.2 0.4 0.6 0.8
District Development Index
TFR 2001 TFR 2016
Predicted TFR 2001 Predicted TFR 2016
Figure 5. Scatterplot of TFR and DDI in districts for all India and its regions, 2001–2016.
Journal of Biosocial Science 13
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2SLS in Column (6) controlling for endogeneity suggested that an increase of 0.1 in the DDI cor-
responded to a decline of 0.41 in the TFR across the districts of India (Column (6)). Like the cross-
sections, the shares of Muslim and SC/ST were positive and significantly associated with TFR. Time
had a negative impact on fertility. For the panel model, the value of rho, i.e. the intra-class correlation
coefficient, implied that 31.5% of the variation was due to differences across the panels.
The individual cross-sectional and panel data, assuming a quadratic association between TFR
and DDI, are presented in the Columns (7–9) of Table 4. The R2statistics for 2001 and 2016 in
Columns (7) and (8) were 0.769 and 0.654, respectively. Broadly, the coefficient of DDI was neg-
ative and that of DDI2was positive, which confirmed a convex association between development
and fertility. In fact, the significant coefficients indicated that the negative correlation between
DDI and fertility was positive at a certain level of development, with a clear minimum point
in the pattern between the two variables. In the panel setting, the coefficients of DDI and
DDI2in Column (9) were both significant and larger compared with the cross-sections. The coef-
ficients of cross-sectional and panel OLS revealed that the controlling variables, i.e. Muslim pop-
ulation and SC/ST population, were statistically significant and positively associated with TFR.
Time was found to be strongly associated with TFR decline, as shown in Column (9). The
intra-class correlation coefficient suggested that 33.3% of the variation in fertility was due to dif-
ferences across the panels. Furthermore, the Chow tests for panel models (3), (4) and (9) were
statistically significant changes, suggesting that the relationship between the fertility and socioeco-
nomic development had indeed attenuated (data not shown). In summary, region exerted a strong
influence on fertility, even after controlling for other factors. Compared with the high-fertility
states of Uttar Pradesh and Bihar, fertility was distinctly lower in all other regions of India.
Discussion
Fertility transition in India is of global significance, not merely because of its large population size
but also due to its regional diversity and variations in levels of socioeconomic development and
fertility levels. The overall advancement in socioeconomic development in the last two decades has
been closely associated with the ongoing fertility transition in India. Studies exploring the association
between development and fertility in Indian districts have been limited, largely due to the dearth of
reliable data at the district level. The present study aimed at understanding the association between
development and fertility patterns in the districts of India over a period of 15 years. Although the
selection of indicators was impeded by the availability of data at the district level, the chosen indicators
captured the key aspects of human development –health, social factors and wealth dimensions.
–3
–2
–1
0
1
District Development Inde x, 2001
bandwidth = .8
0.2 0.4 0.6 0.8
Figure 6. Scatterplot of reduction in TFR
between 2001 and 2016, and DDI, 2001 in dis-
tricts of India.
14 Sayantani Chatterjee and Sanjay K. Mohanty
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Table 4. Cross-sectional and panel results of Ordinary Least Squares (OLS) and Two Stage Least Squares Regression (2SLS-RE) analyses with TFR as dependent variable
Variable
2001 2016 Panel 2001–2016 2001 2016 Panel 2001–2016
OLS 2SLS-RE OLS 2SLS-RE OLS GLS-RE OLS
(1) (2) (3) (4) (5) (6) (7) (8) (9)
DDI −4.12*** −5.24*** −3.15*** −4.26*** −3.20*** −4.12*** −8.42*** −15.99*** −13.16***
DDI2———— — —4.81*** 11.55*** 9.72***
Muslim (%) 0.004*** 0.001*** 0.003*** 0.002** 0.004*** 0.004*** 0.004*** 0.003*** 0.004***
SC & ST (%) 0.008*** 0.001*** 0.006*** 0.005*** 0.007*** 0.008*** 0.007*** 0.006*** 0.007***
Region
UP-Bihar (Ref.)
North −0.781*** −0.597*** −0.550*** −0.355*** −0.735*** −0.781*** −0.718*** −0.359*** −0.781***
Central −0.745*** −0.689*** −0.709*** −0.668*** −0.748*** −0.745*** −0.696*** −0.611*** −0.745***
West −1.31*** −1.16*** −0.734*** −0.601*** −1.08*** −1.31*** −1.24*** −0.536*** −1.31***
South −1.82*** −1.66*** −0.841*** −0.653*** −1.39*** −1.82*** −1.75*** −0.665*** −1.82***
East −1.38*** −1.33*** −0.905*** −0.861*** −1.16*** −1.38*** −1.3*** −0.835*** −1.38***
North-east −0.902*** −0.732*** −0.472*** −0.281*** −0.747*** −0.902*** −0.830*** −0.265*** −0.902***
Year
2001 (Ref.) ———— — — —— —
2016 ————−0.656*** −0.236* ——−0.585***
Constant 5.67*** 6.07*** 4.28*** 4.78*** 5.16*** 2.00* 6.53*** 7.57*** 7.41***
R20.766 0.758 0.605 0.729 −0.769 0.654
Wald χ2————3655.86 3595.66 ——4273.50
Fstatistic 228.67 214.53 107.23 100.8 373.16 209.39 118.96 —
Wu-Hausman Fstatistic —16.91 —27.02 —20.87 —— —
Partial R2—0.457 —0.494 —0.464 —— —
sigma_u ————0.268 ——— 0.263
sigma_e ————0.395 0.555 —— 0.373
rho ————0.315 ——— 0.332
R2Within ————0.731 0.7222 —— 0.769
R2Between ————0.753 0.753 —— 0.772
R2Overall ————0.746 0.743 —— 0.772
DDI is a summary measure of average achievement in key dimensions of human development: health, social and economic.
***p<0.01; **p<0.05; *p<0.1.
Journal of Biosocial Science 15
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The first key finding of the study was that fertility levels had a converging pattern across Indian
districts over the period 2001 to 2016. By 2016, almost one in five districts was a low-fertility district
(TFR below 1.8) and about half had reached below-replacement level fertility. Most of the high-fertility
districts of India belonged to the states of Rajasthan, Uttar Pradesh, Bihar and parts of the north-east.
Second, most districts had recorded an overall improvement in the level of socioeconomic develop-
ment, but their patterns of development remained similar over time. The low-development districts
were mainly concentrated in the states of Rajasthan, Uttar Pradesh, Bihar, Jharkhand and Odisha, with
little change in their development over time. The highest increase in DDI was observed among the
districts with DDI values ranging between 0.3 and 0.5 in 2001. Third, the association between socio-
economic development and TFR was negative and convex over time. The association between devel-
opment and fertility had shifted downwards at any given level of DDI in almost all districts, but by
varying degrees. However, the association between fertility and development was most prominent for
districts with DDI values less than 0.45. This pattern of association is in line with several previous
studies (Potter et al.,2002;Bryant,2007; Ryabov et al.,2015;Foxet al.,2019). Many districts that
have not yet shown substantial fertility decline have experienced a relatively lower thrust in socioeco-
nomic development. By and large, districts with DDI levels between 0.3 and 0.6 in 2001 experienced
more than 20% fertility decline over time. Such reductions were less evident in districts with low or
very high DDIs in 2001. At DDI levels beyond 0.35, districts experienced below-replacement level
fertility. Fourth, the regional patterns of fertility and development associations are worth mentioning.
While the negative convex association was prominent in the northern, central and eastern regions, the
curves were flatter in the western, southern and north-eastern regions. For any given level of DDI,
fertility was lower in 2016 than in 2001, and this effect was greatest in Uttar Pradesh, Bihar and
the central region, which experienced a slower fertility decline along with a sluggish improvement
in socioeconomic development. At the same time there were some residual districts, concentrated
mostly in the north-east, for which fertility levels lay well above the below-replacement level of fertility
and where DDI values were 0.6 or more. In certain districts of India, the association between fertility
and development seemed to have enervated. Fifth, the multivariate results showed that the macro-level
relationship between fertility and development was negative, significant and robust. However, the asso-
ciation diminished over time. In addition, time was a key determinant of fertility decline. Nonetheless,
it can be argued that the association had not weakened as such. Instead, development had ground to a
halt, following which fertility decline also slowed down.
Thus, with increasing levels of socioeconomic development and the slowing down of the pace
of improvement of socioeconomic development, the association between fertility and develop-
ment seems to wither. A plausible explanation for the downward shift in fertility is the diffusion
of new ideas favouring birth control and preference for small family size globally (Bongaarts &
Watkins, 1996; Bryant, 2007). There has been an increase in the number of Indian districts with
better developmental conditions and low fertility. However, there still remain several districts with
low socioeconomic development and moderately high fertility levels. However, the emergence of
increasing numbers of districts with reduced fertility levels and low levels of socioeconomic devel-
opment draws much attention. This suggests that factors other than the components of DDI are
causing fertility to fall. Furthermore, it suggests that poverty in these districts is not a barrier to
fertility, but that it is people’s choice to adapt to the small family norms with limited resources.
One socioeconomic theory that could explain the fertility reductions in such districts is that
restricted fertility acts as a way of enhancing children’s chances of social mobility by increasing
investment in their health and education (Polybius, 1997; Chu, 1998), as well as the diffusion of
new ideas about childbearing. Earlier studies pointed towards the existence of a moving threshold
for the onset of fertility decline. As time passes, countries begin their fertility declines with pro-
gressively lower scores of development owing to a type of cultural contagion (Bongaarts &
Watkins, 1996; Bongaarts, 2002). Furthermore, fertility might be more responsive to socioeco-
nomic change in the middle stages of the transition than in the primary stages, when diffusion
processes play a larger part (Bongaarts, 2002). Thus, the diffusion of newer ideas of small family
16 Sayantani Chatterjee and Sanjay K. Mohanty
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norms and uptake of contraception deserve a prominent place alongside socioeconomic causes
when trying to explain differentials in contemporary fertility transitions among Indian districts.
However, Bryant (2007) showed that fertility declines in countries with low development scores
can also be accommodated by socioeconomic theories. He argued that socioeconomic theories
predict only a moderately strong relationship between fertility and development indicators
and demonstrated that their association is stronger and more stable than anticipated.
Based on the study findings, some policy implications can be put forward. Apart from greater
investment in the health and educational sectors in India, a multi-layered strategy could be
adopted whereby districts are prioritized by their levels of socioeconomic development and fer-
tility. First of all, districts with high fertility and low socioeconomic development should be given
the topmost precedence. For such districts, as well as for districts with moderate fertility levels and
socioeconomic development, other than developmental planning and proper execution, expand-
ing investment in educational attainment, child survival and child health, labour-force generation
and provision of contraceptive use could be productive. For districts with low fertility and better
developmental conditions, the fast-growing aged population should be prioritized by enhancing
social security measures and investing more in adult health, especially for the elderly. For a hand-
ful of districts with high fertility levels and high socioeconomic development, programmes should
be directed at the promotion of contraceptive uptake and increasing age at marriage indirectly
through the promotion of educational attainment and job creation. It has to be kept in mind that
high fertility could be a personal choice. There should be multi-prong strategies to enhance edu-
cational attainment among the population and to improve overall child and adult health in dis-
tricts with low socioeconomic development and fast reducing fertility levels.
The study has a few limitations. A limited number of outcome variables were considered due to
data constraints. Second, the estimates for newly created districts were assumed to be the same as
those of the parent districts from which they had been segregated. Finally, the dearth of suitable
instruments restricted the analyses to ignore the quadratic nature of DDI–TFR.
Acknowledgments. The authors thank the editor and reviewers for their very detailed and insightful suggestions on earlier
versions of the paper. The data used in this paper are available online at the public domains https://dhsprogram.com/what-we-
do/survey/survey-display-355.cfm,http://www.censusindia.gov.in/ and http://rchiips.org/PRCH-2.html.
Funding. This research received no specific grant from any funding agency, commercial entity or not-for-profit organization.
Conflicts of Interest. The authors declare that there have no competing interests.
Ethical Approval. The authors assert that all procedures contributing to this work comply with the ethical standards of the
relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as
revised in 2008.
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Cite this article: Chatterjee S and Mohanty SK. Fertility transition and socioeconomic development in districts of India, 2001–
2016. Journal of Biosocial Science.https://doi.org/10.1017/S0021932020000735
Journal of Biosocial Science 19
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