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date: 16 June 2021

Demographic Transition in India: Insights Into Population Growth, Composition, and Its Major Driversfree

Demographic Transition in India: Insights Into Population Growth, Composition, and Its Major Driversfree

  • Usha RamUsha RamInternational Institute for Population Sciences, Department of Public Health and Mortality Studies
  •  and Faujdar RamFaujdar RamPopulation Council of India and International Institute for Population Sciences

Summary

Globally, countries have followed demographic transition theory and transitioned from high levels of fertility and mortality to lower levels. These changes have resulted in the improved health and well-being of people in the form of extended longevity and considerable improvements in survival at all ages, specifically among children and through lower fertility, which empowers women. India, the second most populous country after China, covers 2.4% of the global surface area and holds 18% of the world’s population. The United Nations 2019 medium variant population estimates revealed that India would surpass China in the year 2030 and would maintain the first rank after 2030. The population of India would peak at 1.65 billion in 2061 and would begin to decline thereafter and reach 1.44 billion in the year 2100. Thus, India’s experience will pose significant challenges for the global community, which has expressed its concern about India’s rising population size and persistent higher fertility and mortality levels. India is a country of wide socioeconomic and demographic diversity across its states. The four large states of Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan accounted for 37% of the country’s total population in 2011 and continue to exhibit above replacement fertility (that is, the total fertility rate, TFR, of greater than 2.1 children per woman) and higher mortality levels and thus have great potential for future population growth. For example, nationally, the life expectancy at birth in India is below 70 years (lagging by more than 3 years when compared to the world average), but the states of Uttar Pradesh and Rajasthan have an average life expectancy of around 65–66 years.

The spatial distribution of India’s population would have a more significant influence on its future political and economic scenario. The population growth rate in Kerala may turn negative around 2036, in Andhra Pradesh (including the newly created state of Telangana) around 2041, and in Karnataka and Tamil Nadu around 2046. Conversely, Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan would have 764 million people in 2061 (45% of the national total) by the time India’s population reaches around 1.65 billion. Nationally, the total fertility rate declined from about 6.5 in early 1960 to 2.3 children per woman in 2016, a result of the massive efforts to improve comprehensive maternal and child health programs and nationwide implementation of the national health mission with a greater focus on social determinants of health. However, childhood mortality rates continue to be unacceptably high in Uttar Pradesh, Bihar, Rajasthan, and Madhya Pradesh (for every 1,000 live births, 43 to 55 children die in these states before celebrating their 5th birthday). Intertwined programmatic interventions that focus on female education and child survival are essential to yield desired fertility and mortality in several states that have experienced higher levels. These changes would be crucial for India to stabilize its population before reaching 1.65 billion. India’s demographic journey through the path of the classical demographic transition suggests that India is very close to achieving replacement fertility.

Subjects

  • Biostatistics and Data
  • Global Health
  • Sexual & Reproductive Health

Background

India is one of the oldest civilizations and has a vibrant cultural heritage coupled with remarkable diversity. The Mughals ruled the country from 1526 to 1761, and were mainly located north of Vindhyanchal. India was a British colony from 1612 until 1947, when the country attained its independence and became a sovereign nation. The British occupied all of present-day India after defeating Tipu Sultan in Mysuru and Marathas in Maharashtra. The British East India Company governed India and controlled trade throughout the region, except for Goa, which the Portuguese controlled in 1510–1961, and Pondicherry, which the French controlled in 1673–1693 and again in 1699–1962.

India has conducted a regular decadal census since 1881 that measures population size and composition as well as decadal growth at the national and subnational levels (including states, districts, and tehsils). At the dawn of Indian independence, there were about 345 million Indians. The year 1951 witnessed the first census of an independent India, recording a total population of 361 million and a moderate annual exponential growth rate of 1.25% during 1941–1951. From a population growth perspective, the year 1951 became a turning point because it indicated a population explosion since it multiplied threefold by 2001.

According to a United Nations (UN, 2019) report, India constituted 17.7% of the total world population, and was second only to China, whose share was 18.5%. The same estimates revealed that India would not only surpass China in the year 2030 with its share of 17.6% (and China’s would decrease to 17.1%) but it would also maintain the first rank after 2030. The report further indicated that Africa’s share would rise to 25.6% in 2050 and 39.4% in 2100. In contrast, the percentage share of Asia would decline from 59.5% in 2020 to 43.4% in 2100. By 2100, India would attain the first rank as far as the share of a single country is concerned. Nonetheless, its relative share would decline to 16.8% in 2050 and 13.3% in 2100. It is thus essential to examine the dynamics of population growth, its potential, and future drivers of population growth of India.

The rapid population growth caused by a comparatively quick decline in mortality and persisting higher fertility levels has been the cause of concern in most developing counties, including India. The 1961 census of India revealed an annual exponential national growth rate close to 2% during 1951–1961. The concerns were raised about the population growth and its rising size, both nationally and globally. The demographics of India—population size, growth rate, fertility, mortality, and so on—continue to occupy significant space discussions concerning its impact on various global health and developmental indicators. Alarmed at burgeoning numbers, and a view to accelerating a rapid decline in fertility levels, many developing countries, especially in Southeast Asia, launched official family planning programs in the mid-1960s. In the 1970s and 1980s, most witnessed a strong commitment by leaders to reduce fertility levels. As a result, they experienced one of the fastest transitions in levels of fertility (Pathak & Ram, 1981; Srinivasan & Pathak, 1981). Although India launched an official family planning program in the early 1950s, the real inputs for the program were recorded from the 1960s, when the program became method-mixed and target oriented. Post-independence, upon the advice of several researchers (Chitre, 1964; Gopalaswamy, 1962; Laxmi, 1964), the Indian government implemented its official family planning program in 1952 that promoted sterilization on a large scale. This was considered as the most cost-effective and impactful approach by the government given resource constraints. However, Agarwala (1964) disagreed with this and criticized the program. Recently, Srinivasan (2006) also opined that the continuous focus on sterilization (female) has dominated the Indian national family planning program. In the mid-1960s, government expanded the basket of methods for the clients and included IUD into the program. This, nonetheless failed due to several side effects on the users (Pujari et al., 1967).

The well-known linguistic, economic, and social-cultural diversity of India and its century-old demographic diversity across geographies have expanded, especially since independence. Several states in India, including Andhra Pradesh, Karnataka, Kerala, and Tamil Nadu in the southern region, have moved much faster in achieving the national goal of the replacement fertility. The onset of fertility transition in these southern states occurred when the social and development indicators such as female literacy rates, per capita income, mortality and so on were rather poorer. At the same time, Hindi-speaking states in the northern region, including Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh, continue to experience high levels of fertility as well as mortality. Nationally, fertility levels in India have fallen, and by 2000 Indian women were having an average of about 3.3 children. A significant portion of this decline came from the states in the southern region, where female literacy rates were higher, and women enjoyed greater autonomy than the women in the rest of India. While the southern states of Kerala and Tamil Nadu attained replacement-level fertility long ago, the giant northern states of Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan continue to reproduce at a prodigious rate (Krishnamoorthy, 1997; Rajan, 1994; Seal & Talwar, 1994). It is important to note that the prevailing social and economic conditions in the southern states at the time of onset of fertility transitions varied considerably. The doctrine of demographic-transition theory advocates indicates that a rise in per capita income, industrialization, and urbanization subsequently leads to reduced levels of fertility and mortality in populations. However, this did not happen in Kerala. Fertility and mortality levels in Kerala were not accompanied by the concurrent improvements in the levels of per capita income, industrialization, and urbanization (Zachariah, 1983).

Until the end of the 20th century, family welfare programs and policies in India focused on lowering fertility rates because the authorities visualized that the persisting higher fertility rates would further add to the built-in growth momentum of its population age composition. The UN’s (1987) population projections revealed that the population momentum alone would add substantially to growing numbers in India. Visaria and Visaria (1994) warned that the ultimate population size of India would be enormous if the country failed to put a brake on the fertility rate and achieved the replacement levels before 2016. It would thus be useful to elaborate on the demographic transition in India and identify gaps to provide future directions for the program to enable positive changes in matters of population growth, thereby improving the lives and well-being of its people. The national scenario masks the diversity across states. Thus, achieving the goals may be less feasible without any understanding of the issues at the subnational level. This article documents the demographic transition of India at the national and subnational levels and examines various drivers of the transition.

Data

The data for the present research come from several sources. The world population for the past and future years comes from the UN’s (2019) World Population Prospects. The time-series data for India on population size, growth rates, and age distribution at the national and state levels come from Indian government censuses conducted between 1881 and 2011. The Government of India’s National Commission on Population (NCP, 2019) projections provides the numbers for the period 2021–2036. The indicators of fertility (total fertility rates) and mortality (infant mortality rate, under-5 mortality rate, and life expectancy at birth come) are from various rounds of the Indian government’s Sample Registration System (SRS). The data for multiple years is available in the annual statistical reports published by the Registrar General of India (2020). The information on contraceptive use and marriage comes from the National Family Health Surveys (International Institute for Population Sciences [IIPS], 1993; IIPS & ICF, 2017; IIPS & ORC-Macro, 2000, 2007). Figures and tables presented throughout the article give detailed data from these sources.

Demographic Transition Theory: A Brief Description

The demographers Warren Thompson (1929) and Adolphe Landry circa 1934 (Landry, 1987), described the classical demography/population transition. However, Frank W. Notestein (1945), an American demographer proposed a precise framework and presented a systematic formulation of the theory in its real sense According to the demographic transition theory, most countries will go through a process of population change from the stage of high birth and death rates (pretransition stage 1) to the last stage of lowest birth and death rates (stage 4). In other words, countries move from the lowest pretransition stage 1 (sometimes negative growth rate) to the highest growth rate (stages 2 and 3) before reaching stage 4, when the growth rate is extremely low (occasionally negative) and the country has attained below-replacement fertility. According to the theory, the demographic transition of a nation can be described with the help of the growth rates if the country has regular censuses over a reasonably long period. In his critical exploration of the demographic transition, Kirk (1996) stated that

the timing of the decline in countries with Non-European tradition conformed to the forecast by the original authors of the theory, without exception, fall in mortality preceded the decline in the levels of fertility . . . In general, the transition period was shorter in Non-European countries than the countries inhabited by Europeans. (p. 383)

Further, the non-European countries are transitioning with a lower level of socioeconomic development (Cleland & Wilson, 1987).

Several researchers (Kaa, 1987, 2002; Lesthaeghe, 2011, 2014; Lesthaeghe & Surkyn, 2004) have referred to a second demographic transition (SDT). The SDT is a period of continued fertility decline much below-replacement fertility. The most critical factors related to this continued decline are increase in nonmarriage, individual autonomy, self-actualization, rising symmetry in sex roles, advancing female education, and economic independence of women (for details, see Lesthaeghe, 2014). Nevertheless, the postulate of SDT based on the experiences of European countries may not hold in developing countries (Cleland, 2001; Dyson, 2010). The SDT, nevertheless, is much more challenging than the original demographic transition because the countries face declining population sizes, shrinking working population, and graying population. To an extent, replacement migration could help these nations overcome these emerging challenges. Coleman (2006), using the emergence of migrants as the dominant community in some geographies compared to the natives, advocated the concept of the third demographic transition (TDT), which emphasizes the drastic change in population composition. However, the idea of TDT could be a reflection of the adjustment for the shrinking labor force that arises out of SDT, and it does not fit into the purview of demographic transition theory per se.

Results

This section discusses changes in population size, growth and its age-sex composition over time to understand India’s population transition. This is followed by a detailed exploration of the crucial factors that led to population transition. For this, we have considered four major drivers of population change that include fertility, mortality, family planning and changes in marriage pattern. Changes in fertility levels have been studied using total fertility rate. The changes in mortality have been studied using three indicators of infant mortality, under-5 mortality and expectation of life at birth. The changes in contraceptive use is examined with the help of contraceptive prevalence rate. Finally, changes in marriage pattern is examined with the help of percentage of women aged 20–24 years who were married before reaching age 18 years and women aged 30–34 years who remained single.

Population Size, Growth, and Age Structure

The UN (2019) estimated a total of 7,795 million people globally in 2020. They suggested that this number would surpass 10 billion by the turn of the 21st century (Table 1). In 2020, about 60% of the people live in Asia and a little over 17% live in Africa. By 2100, Asia would be home to 43% of the global people and Africa to 39%. The share of European countries is estimated to reduce from 9.6% in 2020 to less than 6% in 2100. While a similar pattern is predicted for the countries in Latin America and the Caribbean and the North American regions, the share of Oceania remains unchanged. China’s population, was about 19% of the global population in 2020, would reduce to less than 10% by 2100. In India the share would decrease from less than 18% in 2020 to slightly over 13% in 2100.

Table 1. Population Size and Share of the Population of World Regions, China, and India, 2020–2100

Total Population (Million) and Share (%) as of July 1

2020

2030

2050

2075

2100

Share of world regions in the world population

Africa

1,341 (17.2)

1,688 (19.7)

2,489 (25.6)

3,499 (33.1)

4,280 (39.4)

Asia

4,641 (59.5)

4,974 (58.2)

5,290 (54.3)

5,143 (48.6)

4,719 (43.4)

Europe

748 (9.6)

741 (8.7)

710 (7.3)

657 (6.2)

630 (5.8)

Latin America & the Caribbean

654 (8.4)

706 (8.3)

762 (7.8)

750 (7.1)

680 (6.3)

North America

369 (4.7)

391 (4.6)

425 (4.4)

461 (4.4)

491 (4.5)

Oceania

43 (0.6)

48 (0.6)

57 (0.6)

67 (0.6)

75 (0.7)

World

7,795 (100.0)

8,548 (100.0)

9,735 (100.0)

10,577 (100.0)

10,875 (100.0)

Share of China and India in the world population

China

1,439 (18.5)

1,464 (17.1)

1,402 (14.4)

1,222 (11.6)

1,065 (9.8)

India

1,380 (17.7)

1,504 (17.6)

1,639 (16.8)

1,609 (15.2)

1,450 (13.3)

Source: UN (2019).

The indirect estimates of crude birth and death rates for India are for the period 1901–1961. After 1971, the SRS, which was established in the late 1960s, started to provide the crude birth rate (CBR) and crude death rate (CDR) for India and bigger states annually. The most recent SRS estimates are available for the year 2017. At the beginning of the 20th century, India had very high levels of crude birth and death rates (48 births/deaths per 1,000 persons; Figure 1), which persisted until 2021. The death rates started to decline around 1930 and reached 16 deaths per 1,000 persons in 1971. The CBR, too, began to fall at a much slower pace. While the CBR was 36 births per 1,000 persons in the early 1970s, the CDR was 16 deaths per 1,000 persons. This declining trend continues, and the gap between the two rates is narrowing over time. The CBR was 20 per 1,000 persons in 2017 as compared to the CDR of 6 per 1,000 persons.

Figure 1. Crude birth rate (CBR) and crude death rate (CDR) for India, 1901–2017.

Source: For the period 1901–1961, data is from Mukherji (1976). For the period 1971–2017, data is from the annual statistical report of the Sample Registration System of India for the respective years.

At the beginning of the 20th century, India had 238 million people. The results of the first census of the new millennium revealed that India had crossed the one billion mark by the end of the 20th century as the 2001 census enumerated a total of 1,029 million Indians (Table 2). The country annually added 16.1 million people in the 1980s and 18.2 million in the 1990s. While the world population increased threefold (from 2 to 6 billion) during the last century, it grew five times in India. The 15th census of India conducted in 2011 enumerated a total of 1,210 million Indians. The population of India grew with a decadal growth rate of about 17.5% during 2001–2011, resulting in an annual exponential growth rate of 1.62% (a decline from 1.96% observed during 1991–2001). Despite a substantial reduction in the growth rate during 2001–2011, India added nearly 181 million people. The UN’s 2019 projections indicated a similar addition during 2011–2021, before the country experienced a drastic decline in the subsequent decades.

Figure 2. Estimated and observed exponential annual population growth rate (%) during 1901–2011 and 2021–2101, respectively, for India.

Source: UN (2019).

Indian annual population growth peaked at 2.22% during 1961–1971 (Table 1 and Figure 2) and stayed around 2% for the next four decades until 2001. This period may be referred to as the second stage (population explosion stage) of demographic transition for India, during which the country added approximately 590 million people. Between 2001 and 2011 India experienced a substantial decline in its population growth rate (from 1.95% in 1991–2001 to 1.62% in 2001–2011). The UN’s 2019 assessment suggested that as far as the population size as concerned, India would surpass China in the next 7–8 years and would continue to increase until the year 2061 when its population size would reach 1,650 million. India may experience a decline in its total population after 2061 and count 1,444 million people in the year 2101. Thus, India would add another 440 million people to its 2011 population size before achieving stabilization. In other words, India is likely to enter the fourth stage (near-zero growth rate) in the next 50 years or so. For India, the third stage of the demographic transition may fall between 2011 and 2051. The momentum inbuilt in the age structure of the population would mostly lead to its growth.

Table 2. Population Size, Intercensal Change (Absolute and Percentage), and Exponential Annual Growth Rate, India, 1901–2001

Census Year

Population (Millions)

Intercensal Population Change/Growth

Absolute Change (Millions)

% change

Exponential Annual Growth Rate (%)

Population observed

1901

238.4

1911

252.1

13.7

5.7

0.56

1921

251.3

−0.8

−0.3

−0.03

1931

279.0

27.7

11.0

1.05

1941

318.7

39.7

14.2

1.33

1951

361.1

42.4

13.3

1.25

1961

439.2

78.1

21.6

1.96

1971

548.2

109.0

24.8

2.22

1981

683.3

135.1

24.6

2.20

1991

846.4

163.1

23.9

2.14

2001

1,028.7

182.3

21.5

1.95

2011

1,210.2

181.5

17.6

1.62

Population estimated

2021

1,393.0

182.8

15.1

1.41

2031

1,513.7

120.7

8.7

0.83

2041

1,598.3

84.6

5.6

0.54

2051

1,641.2

42.9

2.7

0.26

2061

1,650.3

9.1

0.6

0.06

2071

1,626.4

−23.9

−1.4

−0.15

2081

1,576.1

−50.3

−3.1

−0.31

2091

1,512.4

−63.7

−4.0

−0.41

2101

1,443.5

−68.9

−4.6

−0.47

Source: Registrar General of India (n.d.-a); Population estimated from UN (2019).

Examination of the current growth rate in specific states of India, especially for the larger Indian states (in terms of population size), helps to locate growth potentials. Table 3 gives population size for 2001 and 2011, the two recent censuses of India, absolute change and state share in the total national change during 2001–2011, and the exponential population growth rate observed during 2001–2011 for 20 large states of India. The four states of Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan deserve particular attention. With a population increase of 33.6 million, Uttar Pradesh contributed the most significant growth to the total national change of 182.2 million during 2001–2011, followed by Bihar at 21.1 million and Maharashtra at 15.5 million. Kerala recorded the lowest annual exponential growth rate of 0.48%, followed by Andhra Pradesh (1.04%), Punjab (1.30%), and Odisha (1.31%). Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh together added 446 million (43%) of the total national addition and each state had an annual growth rate of 2% or more. These states are likely to make significant contributions to Indian population growth in the future because the fertility and mortality rates in these states are comparatively high and the decline in these rates has been much slower than that of other states. The most recent projections of the Government of India (NCP, 2019) indicated that by the year 2036 there would be a total of 596 million Indians, and half of them would come from these four states.

Table 3. Population Size, Intercensal Change (Absolute and Percentage), and Exponential Annual Growth Rate for Selected States of India, 2001–2011

State Name

Population (Million)

Change During 2001 –2011

Exponential Annual Growth Rate (%)

2001

2011

Absolute Change

State Share (%)a

Uttar Pradesh

166.2

199.8

33.6

18.7

1.84

Maharashtra

96.9

112.4

15.5

8.2

1.48

Bihar

83.0

104.1

21.1

11.5

2.27

West Bengal

80.2

91.3

11.1

6.0

1.30

Andhra Pradeshb

76.2

84.6

8.4

4.9

1.04

Madhya Pradesh

60.3

72.6

12.3

7.1

1.85

Tamil Nadu

62.4

72.1

9.7

5.5

1.45

Rajasthan

56.5

68.5

12.0

6.6

1.93

Karnataka

52.9

61.1

8.2

4.4

1.45

Gujarat

50.7

60.4

9.7

4.9

1.76

Odisha

36.8

42.0

5.2

2.7

1.31

Kerala

31.8

33.4

1.6

0.5

0.48

Jharkhand

26.9

33.0

6.1

3.3

2.02

Assam

26.7

31.2

4.5

2.2

1.58

Punjab

24.4

27.7

3.3

2.2

1.30

Chhattisgarh

20.8

25.5

4.7

2.7

2.04

Haryana

21.1

25.4

4.3

2.2

1.81

Jammu & Kashmir

10.1

12.5

2.4

1.6

2.12

Uttarakhand

8.5

10.1

1.6

1.1

1.72

Himachal Pradesh

6.1

6.9

0.8

0.5

1.22

Remaining states & Union Territories (UTs)

30.2

36.3

6.1

3.3

1.80

India

1,028.7

1,210.9

182.2

100.0

1.63

a Sum of states may not match to India due to rounding of the numbers.

b Undivided including Telangana.

Table 4 gives a future population scenario in the 13 large states of India subdivided into three groups based on the attainment of the replacement level of fertility. These 13 states together cover nearly 80% of the national total. Group 1 consists of four states—Rajasthan, Uttar Pradesh, Bihar, and Madhya Pradesh—that have yet to attain replacement fertility. Group 2 and Group 3 consist of the states that have recently reached replacement fertility and a long time ago, respectively. The four large states in Group 1 have enormous potential for growth, and during 2026–2036 their combined growth rate is projected to be close to 1% (0.83%). Bihar is an outlier even within this group, with a growth rate of 1.16% annually. Group 2 states would have a growth rate of around 0.37% and Group 3 of about 0.20%. These findings indicate that a major part of India’s population growth potential lies in the four states of Group 1.

Table 4. Population Size and Year of the Attainment of Replacement Fertility in 13 Large States of India Stratified by Level of Total Fertility Rate, 2011–2036

Groups / States

Population 2011 (million)a

Projected Populationb (million) in the Year

Year Attained Replacement Fertility

Annual exponential growth rate (%) during 2026–2036

2016

2021

2026

2031

2036

Group 1: States currently having above replacement-level fertility

Rajasthan

68.5

74.2

79.3

83.6

87.2

90.6

0.80

Uttar Pradeshc

199.8

216.1

230.9

242.9

252.0

259.0

0.64

Bihard

104.1

114.2

123.1

132.3

141.0

148.6

1.16

Madhya Pradeshe

72.6

78.8

84.5

89.7

94.1

97.8

0.86

Group 1 total

445.1

483.3

517.8

548.4

574.3

595.9

0.83

Share of India (%)

36.8

37.5

38.0

38.5

38.9

39.2

-

Group 2: States that have attained replacement fertility since 2005

West Bengal

91.3

95.1

98.1

100.5

102.2

102.9

2005

0.24

Punjab

27.7

29.1

30.3

31.3

32.1

32.7

2005

0.44

Odisha

42.0

43.1

44.0

44.7

45.0

45.0

2012

0.07

Maharashtra

112.4

118.7

124.4

129.3

133.5

136.8

2006

0.56

Group 2 total

273.4

286.1

296.9

305.8

312.7

317.4

0.37

Share of India (%)

22.6

22.2

21.8

21.5

21.2

20.9

-

Group 3: States attained replacement fertility before two decades

Andhra Pradesh

49.6

51.4

52.8

53.7

54.2

54.3

2,004

0.11

Karnataka

61.1

64.2

66.8

69.0

70.7

71.9

2,006

0.41

Kerala

33.4

34.6

35.5

36.2

36.7

36.9

1,988

0.19

Tamil Nadu

72.1

74.6

76.4

77.5

78.1

78.1

1,993

0.08

Telangana

35.0

36.5

37.7

38.6

39.2

39.5

2,004

0.23

Group 3 total

251.2

261.3

269.2

275.1

278.8

280.7

0.20

Share of India (%)

20.7

20.3

19.8

19.3

18.9

18.5

-

Subtotal all three groups

Absolute population size

969.7

1,030.7

1,084.0

1,129.3

1,165.8

1,194.0

0.56

Share in national total (%)

80.1

79.9

79.6

79.3

79.0

78.6

-

India

1,210.9

1,290.2

1,361.3

1,423.4

1,475.5

1,518.3

0.65

a 2011 population data from the census of India.

b Projected population for the period 2016–2036 is from NCP (2019).

c Undivided including Uttarakhand.

d Undivided including Jharkhand.

e Undivided including Chattisgarh.

Source: NCP (2019).

Population Age-Sex Composition

The population age-sex composition of a country narrates historical experiences, including wars, epidemics, famines, and so on. Population age distribution and the female to male ratio are indicative of fertility and mortality levels and the social status of the women in the populations. Along with the demographic transition in India described earlier, there has been an inevitable change in the age-sex structure—that is, the decline in mortality followed by fertility has resulted in changes to the population’s age structure. Several studies have debated and discussed the role of these changes in economic growth. Sex composition (population sex ratio overall and, more important, at birth) reflects the status of women in the society. Globally, the population sex ratio (males per 1,000 females) is favorable to the female gender. An overall sex ratio of 1,030–1,050 females per 1,000 males is standard under the natural conditions. The situation is slightly different in India.

Table 5 gives the sex ratio overall and for children younger than 5 years of age for India for a period of 120 years (1881–2011) along with the absolute change in them. For India, the overall sex ratio was close to normal until around 1931. It started to rise gradually in favor of males after that. The 1991 census of India revealed a higher overall sex ratio nationally: 1,078 males per 1,000 females. However, the scenario is different for the child sex ratio. Female children marginally outnumbered male children until 1941 as the sex ratio was in favor of the female children (960–995 male children per 1,000 female children below age 5). However, the scenario reversed when the 1951 census results were declared as the child sex ratio turned in favor of male children (1,008 male children per 1,000 female children) and has deepened over the years with the widening female-male children gap. The child sex ratio in India increased from 1,022 in 1981 to 1,047 in 1991 and further to 1,071 in 2001 and 1,082 in 2011 male children per 1,000 female children. Nationally, during the periods 1981–1991 and 1991–2001, the child sex ratio increased astonishingly by 25 and 24 units, respectively. The distorted child sex ratio in India as well as in neighboring countries in the region has been a matter of concern and point of debate and investigations among policy makers and researchers. Many have cited widespread gender-based discrimination (neglect) in the form of son preference, lower autonomy to the women, and so on as the leading cause of this distortion. These practices result in sex-selective abortions and gender-specific mortality differentials (Bongaarts, 2013; Bongaarts & Guilmoto, 2015; Guilmoto et al., 2018; Jha et al., 2011; Kashyap, 2019; Ram & Ram, 2018).

Table 5. Sex Ratio (Males per 1,000 Females) of the Total Population and Children Younger Than 5 Years of Age, India, 1881–2011

Year

Overall (All Ages)

Children Younger Than 5 Years

Sex Ratio

Intercensal Absolute Change

Sex Ratio

Intercensal Absolute Change

1881

1,038

965

1891

1,038

0

960

−5

1901

1,029

−9

969

9

1911

1,038

9

967

−2

1921

1,047

9

962

−5

1931

1,053

6

964

2

1941

1,058

5

995

31

1951

1,056

−2

1,008

13

1961

1,063

7

1,008

0

1971

1,075

12

1,021

13

1981

1,070

−5

1,022

1

1991

1,078

8

1,047

25

2001

1,072

−6

1,071

24

2011

1,060

−12

1,082

11

Notes: The sex ratio for the years 1881 and 1891 was calculated using data from Mukherji (1976). The sex ratio for children younger than 5 years of age was calculated using data from a C-series in the respective census of India.

Source: Registrar General of India (n.d.-b).

A few studies have estimated a decrease in girls due to the practice of sex-selective abortions in India and found that these practices are not universal across geographies. Instead, they vary considerably in subregions of India (Jha et al., 2011; Ram & Ram, 2018). Table 6 presents the sex ratio for selected states in India for the period 1991–2011 and the change in it. Regardless of the year, Kerala is the only state that has an overall sex ratio lower than 1,000 (i.e., females exceeding the male population). In addition, the male-female gap has widened over the past two decades by almost 43 units. Punjab and Haryana have the most skewed overall sex ratio, varying between 1,117 and 1,162 males per 1,000 females. The overall sex ratio has been in favor of males in the remaining states. However, the gaps in sex ratio seemingly have bridged over time. While the decline was sharp in the states of Uttar Pradesh, West Bengal, and Assam, it has remained mostly similar in Madhya Pradesh and Maharashtra. Similar to the overall sex ratio, Haryana and Punjab had a highly skewed child sex ratio, varying between 1,128 and 1,144, respectively, in 1991 and 1,190 and 1,169 in 2011. In 2011, Gujarat (1,110), Rajasthan (1,120), and Maharashtra (1,117) also showed a child sex ratio skewed in favor of male children. Other states also showed a considerable deficit of female children. Haryana topped the list as the child sex ratio increased by 62 units in favor of males during 1991–2011. The corresponding increase was by 59 units in Maharashtra, 50 units in Rajasthan, 44 units in Gujarat, 42 units in Madhya Pradesh, and 30–39 units in Andhra Pradesh, Bihar, Odisha, and Uttar Pradesh. Kerala was the only state where the child sex ratio improved in favor of female children by 16 units between the 1991 and 2011 censuses.

Table 6. Sex Ratio (Males per 1,000 Females) of the Total Population and Children Younger Than 5 Years of Age for India and Selected States, 1991–2011

State

Overall (All Ages)

Absolute Change: 1991–2011

Children Younger Than 5 YEARS

Absolute Change: 1991–2011

1991

2001

2011

1991

2001

2011

Andhra Pradesha

1,029

1,022

1,007

−22

1,023

1,042

1,061

38

Assam

1,084

1,070

1,044

−40

1,023

1,047

1,036

13

Bihar

1,098

1,088

1,089

−9

1,025

1,090

1,063

38

Gujarat

1,070

1,086

1,088

18

1,066

1,164

1,110

44

Haryana

1,156

1,162

1,138

−18

1,128

1,236

1,190

62

Karnataka

1,042

1,037

1,028

−14

1,040

1,063

1,048

8

Kerala

965

945

922

−43

1,051

1,028

1,035

−16

Madhya Pradesh

1,074

1,088

1,074

0

1,036

1,080

1,077

41

Maharashtra

1,071

1,084

1,076

5

1,058

1,115

1,117

59

Odisha

1,030

1,028

1,022

−8

1,028

1,060

1,058

30

Punjab

1,134

1,142

1,117

−17

1,144

1,286

1,169

25

Rajasthan

1,099

1,086

1,077

−22

1,070

1,123

1,120

50

Tamil Nadu

1,027

1,013

1,004

−23

1,052

1,047

1,059

7

Uttar Pradesh

1,138

1,114

1,096

−42

1,059

1,113

1,098

39

West Bengal

1,090

1,071

1,053

−37

1,029

1,043

1,043

14

Note: Sex ratio from respective censuses of India (Table C-6 of 1991 and C-14 of 2001 and 2011).

a Undivided including Telangana.

Almost half of the districts in the country in 2011 had a deficit of girl children. The practice of neglect of the female child resulting in sex-selective abortion and excess female mortality is universal (Guilmoto et al., 2018; Ram & Ram, 2018). A more recent analysis for India by Kashyap (2019) indicated the dominance of prenatal factors (sex-selective abortion) compared to excess female mortality (postnatal factor). Table 7 presents the sex ratio at birth (SRB) for India and selected states. The data suggest that the SRB is favorable to male children for India nationally and subnationally. Punjab and Haryana, followed by Rajasthan, Uttar Pradesh, Gujarat, and Bihar, had a highly disturbing SRB in 1999. For every 100 female births, Punjab and Haryana recorded 125 to 126 male births each, the other states recorded 112 to 118 male births. The male-female imbalance at birth has continued over time, although with a sign toward bridging the gaps. At the national level, the SRB has mostly remained unchanged at 112 male children for every 100 female children. Nonetheless, the imbalance has widened in Andhra Pradesh, Assam, and Haryana, suggesting that the efforts to address this have failed to yield desirable results. The study by Jha et al. (2011) demonstrated that the practices are more prevalent among affluent and educated people.

Table 7. Sex Ratio at Birth (Male Births Per 1,000 Female Births) and Absolute Change in Sex Ratio at Birth in India and Selected States, 1999–2016

State

Sex Ratio at Birth in the Year

Absolute Change: 1999–2016

1999

2004

2009

2013

2016

Andhra Pradesha

104

109

109

109

109

−5

Assam

102

110

108

109

109

−7

Biharb

112

116

110

110

111

1

Gujarat

118

118

111

110

117

1

Haryana

125

121

118

115

120

5

Karnataka

106

109

106

105

108

−2

Kerala

108

110

104

103

105

3

Madhya Pradeshc

110

110

109

108

109

1

Maharashtra

110

115

112

112

114

−4

Orissa

108

107

107

105

107

1

Punjab

126

125

120

115

113

13

Rajasthan

114

119

114

112

117

−3

Tamil Nadu

107

106

108

109

110

−3

Uttar Pradeshd

115

116

115

115

114

1

West Bengal

105

108

107

105

106

−1

India

111

114

110

110

112

1

a Undivided including Telangana for the years 1999, 2004, 2009, and 2013.

b Undivided including Jharkhand for the year 1999.

c Undivided including Chhattisgarh for the year 1999.

d Undivided including Uttarakhand for the years 1999, 2004, and 2009.

Source: Sex ratio from the annual statistical report of the Sample Registration System of India.

Table 8 presents age distribution by sex and dependency ratios (child, old age, and overall) for the period 1981–2011 (census of India) and 2036 for India (NCP, 2019). Figures 3A and 3B present age-sex population pyramids. The results in Table 8 suggest a visible change in the age structure over the decades. Nationally, the share of children below age 15 in the total population declined to from about 40% in 1981 to 31% in 2011. The NCP (2019) projections indicated that the share would decrease to 20% by 2036. The percentage of people aged 60 years and older increased to 9% in 2011 and is estimated to reach 15% in 2036 (over 227 million). The changes in the dependency ratios for children and older people also confirm a transition in the age structure. While the child dependency ratio in India declined from 73% in 1981 and to 51% in 2011, the dependency ratio for older people increased marginally from 12% to 14%. The official population projections suggest that in 2036 the child dependency ratio would further decline to 30% and the dependency ratio for older people would increase to 23% nationally. In 2001, India had about 587 million people in the working ages, between 15 and 59 years. Those aged 15–34 years accounted for nearly 60% (349 million). The number of people in the working ages of 15–59 years and 15–34 years increased to 733 million and 425 million, respectively, in the year 2011. Population projections suggest that in 2036, while the number of people of working age would increase to almost 989 million, young labor would reach 464 million. Such changes would impact future economic development and would call on the government to initiate innovative strategies to take care of the older population. Besides, a sharp rise in the labor force demands generation of more employment.

Table 8. Share of the Male and Female Population Out of the Total Population by Age Groups and Dependency Ratios (for Children, Older People, and Overall), India, 1981–2011 and 2036

Age Group (in Years)

Share of the Population (%) Out of a Total Population of India for the Year

1981a

1991a

2001a

2011a

Projected 2036b

Male

Female

Male

Female

Male

Female

Male

Female

Male

Female

Children below 15 years of age

0–4

6.4

6.2

6.3

6.0

5.6

5.2

4.9

4.5

3.3

3.0

5–9

7.3

6.8

6.9

6.5

6.5

6.0

5.5

5.0

3.5

3.1

10–14

6.8

6.1

6.2

5.6

6.4

5.8

5.8

5.3

3.7

3.3

Subtotal (male + female): <15

39.6

37.5

35.4

30.9

19.8

Working-age population

15–19

5.1

4.5

5.1

4.4

5.3

4.5

5.3

4.7

3.9

3.5

20–24

4.4

4.3

4.5

4.4

4.5

4.2

4.8

4.5

4.0

3.6

25–29

3.9

3.8

4.1

4.2

4.1

4.1

4.3

4.2

4.0

3.7

30–34

3.2

3.1

3.6

3.4

3.6

3.6

3.7

3.6

4.2

3.9

35–39

3.0

2.9

3.3

3.0

3.5

3.4

3.6

3.5

4.3

3.9

40–44

2.7

2.4

2.7

2.4

2.9

2.5

3.1

2.9

4.0

3.7

45–49

2.3

2.1

2.3

2.1

2.4

2.2

2.7

2.5

3.5

3.4

50–54

2.1

1.7

2.0

1.7

1.9

1.6

2.1

1.9

3.1

3.1

55–59

1.3

1.2

1.3

1.3

1.3

1.4

1.6

1.6

2.6

2.8

Subtotal (male + female): 15–59

53.9

55.7

57.1

60.5

65.1

Older population (aged 60 years or older)

3.3

3.2

3.5

3.3

3.7

3.8

4.2

4.4

7.1

7.9

Subtotal (male + female):60

6.5

6.8

7.5

8.6

15.0

All age population (million)

683.3 (100.0)

846.4 (100.0)

1,028.7 (100.0)

1,210.2 (100.0)

1,518.3 (100.0)

Dependency ratioc (both sexes)

Child (<15 years)

73.3

67.2

62.1

51.0

30.4

Old age (60 years)

12.0

12.2

13.1

14.2

23.1

Overall (<15 +60 years)

85.3

79.4

75.2

65.2

53.5

a Population is taken from the censuses of India 1981, 1991, 2001, and 2011.

b Projected population for 2036 is from NCP (2019).

c Dependency ratio from author calculations. The child dependency ratio is defined as the number of children below 15 years of age per 100 persons in the working ages of 15–59 years. The old-age dependency ratio is defined as the number of persons aged 60 years or older per 100 persons in the working ages of 15–59 years. The overall dependency ratio is defined as the number of children below 15 years of age and persons aged 60 years or older per 100 persons in the working ages of 15–59 years.

Figure 3A. Age-sex population pyramids of India, 1991.

Data from the census of India, 1991.

Figure 3B. Age-sex population pyramids of India, 2036.

Source: NCP (2019).

Major Drivers of Population Growth

Three drivers impact the population growth rate and are responsible for demographic transition: fertility, mortality, and international migration. Generally speaking, international migration has a limited role, as its volume is small. Thus, it is mainly the changes in fertility and mortality levels in a population that lead to demographic transition. This section discusses fertility and mortality transition in India and specific programmatic interventions responsible for the change in the fertility and mortality levels. India lacks good quality civil registration data on births and deaths (Ram et al., 2020; Yadav & Ram, 2019). Until the early 1970s, the estimated fertility and mortality for India and its states came from indirect methods that used census data stratified by age and sex. In the early 1970s, the Registrar General of India launched an annual nationwide system of collecting data on fertility and mortality (known as the sample registration system; SRS), which provides invaluable data for India and its states, especially for the bigger states. For the most part, the present research used fertility and mortality data from the SRS.

Fertility

Figure 4 presents the total fertility rate (TFR) for India spanning over nearly 150 years (Ram et al., 1995). The TFR gives the number of children a woman would have at the end of the reproductive period, assuming that she experiences the prevailing age patterns of fertility. The data suggests that the TFR in India virtually remained unchanged at around 6.3 children per woman from 1871–1881 until 1951–1961 (standard deviation = 0.27). There has been little fluctuation in the TFR, which is mainly attributed to the variations in the quality of age-sex data in different censuses (Mukherji, 1976). Coale’s (1986) proposition of survival strategy postulates that a TFR of less than six for the expectation of life at birth (eoo) of 20–25 years could lead to a zero or negative population growth. Thus, under a high mortality regime, maintaining a TFR of 6 and above was an excellent strategy to ensure moderate positive population growth. The decline in the TFR during the period 1896–1901 might have been the result of the famines of 1896–1997 and 1899–1901, which were among the worst ever experienced in history and affected substantial sections of the population (Dyson, 1991).

The fertility transition in India most likely began during the late 1960s. Since the inception of fertility transition, the TFR in India declined by 19% to about 1.1 fewer children per woman during the first decade (1966–1971 to 1976–1981). The 1960s witnessed a substantial change in the family planning program in India, which became target-oriented and included the introduction of intrauterine devices to the official program in 1965. The initial inherent demand for family planning and a persistently higher level of fertility may have been the reason for a relatively faster fertility decline during the first decade following the onset of the demographic transition. In the next decade (1976–1981 to 1986–1991), although the decrease in fertility continued, its pace slowed down. The decline in TFR slowed down notably in the subsequent decade of 1976–1981 to 1986–1991 when the reduction was only about 15%. The coercive approach adopted during the emergency period (1975–1977) was mainly responsible for this reduction in several states, more specifically in the larger Hindi-speaking states of Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh. This in turn accelerated the decline in TFR. Between 1986–1991 and 1996–2001, the TFR declined by 19% (from about 4 children to 3.2 children per woman). During 1996–2001, the TFR in India declined by about 14%. The mid-1990s saw a paradigm shift in the national family planning program as the country revamped the program from a target-oriented to target-free regime. This paradigm shift resulted in an initial decline/stagnation in the family planning performance in the country.

Figure 4. Total fertility rate, India, 1871–2018.

Source: Up to 1971, data from Ram, Namboodiri, and Ram (1995). For post 1971, data from annual statistical reports of the Sample Registration System for respective years.

Nationally, the TFR almost halved in the 30 years between 1986 and 2016 from 4.2 to 2.3 children per woman (Table 9). Many states in India showed a similar trend. Rural India also experienced a decline in the TFR from 4.5 in 1986 to 2.5 in 2016. However, urban India had already achieved replacement fertility in 2006. Of the states included in this analysis, eight states have already attained replacement or below-replacement fertility. The lagging states are Bihar Madhya Pradesh, Rajasthan, and Uttar Pradesh, where TFR continues to be close to 3 children per woman. As noted, these are the states that are or could be center for India population growth in the coming years. The urban areas in several states attained replacement or below-replacement fertility in 2016: the urban areas had a TFR of as low as 1.3 children per woman in West Bengal, 1.4 in Odisha, 1.5 in Andhra Pradesh, and 1.6 in Karnataka and Tamil Nadu. Further, the rural areas of Andhra Pradesh, Karnataka, Kerala, Maharashtra, Punjab, Tamil Nadu, and West Bengal had a TFR that varied between 1.7 and 1.9 children per woman in 2016.

Table 9. Total Fertility Rate for Combined, Rural, and Urban Areas and the Ratio of Rural to Urban Rate for India and Selected States, 1986–2016

Country/States

Total Fertility Rate

Change (%) 1986–2016

1986

1996

2006

2016

Combined areas

Andhra Pradesha

3.8

2.5

2.0

1.7

−55.3

Assam

4.0

3.2

2.7

2.3

−42.5

Biharb

5.2

4.5

4.2

3.3

−36.5

Gujarat

3.8

3.0

2.7

2.2

−42.1

Haryana

4.4

3.5

2.7

2.3

−47.7

Karnataka

3.5

2.6

2.1

1.8

−48.6

Kerala

2.3

1.8

1.7

1.8

−21.7

Madhya Pradeshc

4.9

4.1

3.5

2.8

−42.9

Maharashtra

3.6

2.8

2.1

1.8

−50.0

Orissa

4.2

3.1

2.5

2.0

−52.4

Punjab

3.4

2.8

2.1

1.7

−50.0

Rajasthan

5.0

4.2

3.5

2.7

−46.0

Tamil Nadu

2.7

2.1

1.7

1.6

−40.7

Uttar Pradeshd

5.4

4.9

4.2

3.1

−42.6

West Bengal

3.6

2.6

2.0

1.6

−55.6

India

4.2

3.4

2.8

2.3

−55.3

Rural areas

Andhra Pradesha

4.1

2.7

2.1

1.7

−58.5

Assam

4.2

3.4

3.0

2.4

−42.9

Biharb

5.3

4.6

4.3

3.4

−35.8

Gujarat

4.0

3.2

3.0

2.5

−37.5

Haryana

4.8

3.8

2.9

2.4

−50.0

Karnataka

3.7

2.8

2.3

1.9

−48.6

Kerala

2.3

1.8

1.7

1.8

−21.7

Madhya Pradeshc

5.4

4.4

3.9

3.1

−42.6

Maharashtra

4.0

3.2

2.3

1.9

−52.5

Orissa

4.3

3.3

2.6

2.1

−51.2

Punjab

3.6

3.0

2.1

1.7

−52.8

Rajasthan

5.3

4.5

3.8

2.8

−47.2

Tamil Nadu

2.8

2.2

1.8

1.7

−39.3

Uttar Pradeshd

5.8

5.1

4.4

3.4

−41.4

West Bengal

4.2

2.9

2.2

1.7

−59.5

India

4.5

3.7

3.1

2.5

−44.4

Urban areas

Andhra Pradesha

3.1

2.1

1.6

1.5

−51.6

Assam

2.5

2.1

1.6

1.6

−36.0

Biharb

4.2

3.2

3.0

2.5

−40.5

Gujarat

3.3

2.6

2.3

1.9

−42.4

Haryana

3.3

2.7

2.4

2.0

−39.4

Karnataka

2.9

2.1

1.7

1.6

−44.8

Kerala

2.2

1.8

1.7

1.8

−18.2

Madhya Pradeshc

3.5

2.5

2.4

2.1

−40.0

Maharashtra

3.0

2.4

1.8

1.6

−46.7

Orissa

3.1

2.3

1.7

1.4

−54.8

Punjab

3.1

2.2

1.9

1.6

−48.4

Rajasthan

3.8

3.0

2.7

2.3

−39.5

Tamil Nadu

2.4

1.8

1.6

1.6

−33.3

Uttar Pradeshd

4.0

3.7

3.2

2.4

−40.0

West Bengal

2.3

1.8

1.3

1.3

−43.5

India

3.1

2.4

2.0

1.8

−41.9

Ratio: Rural to urban

Andhra Pradesha

1.3

1.3

1.3

1.1

−14.3

Assam

1.7

1.6

1.9

1.5

−10.7

Biharb

1.3

1.4

1.4

1.4

7.8

Gujarat

1.2

1.2

1.3

1.3

8.6

Haryana

1.5

1.4

1.2

1.2

−17.5

Karnataka

1.3

1.3

1.4

1.2

−6.9

Kerala

1.0

1.0

1.0

1.0

−4.3

Madhya Pradeshc

1.5

1.8

1.6

1.5

−4.3

Maharashtra

1.3

1.3

1.3

1.2

−10.9

Orissa

1.4

1.4

1.5

1.5

8.1

Punjab

1.2

1.4

1.1

1.1

−8.5

Rajasthan

1.4

1.5

1.4

1.2

−12.7

Tamil Nadu

1.2

1.2

1.1

1.1

−8.9

Uttar Pradeshd

1.5

1.4

1.4

1.4

−2.3

West Bengal

1.8

1.6

1.7

1.3

−28.4

India

1.5

1.5

1.6

1.4

−4.3

a Undivided including Telangana for the years 1986, 1996, and 2006.

b Undivided including Jharkhand for the years 1986 and 1996.

c Undivided including Chhattisgarh for the years 1986 and 1996.

d Undivided including Uttarakhand for the years 1986, 1996, and 2006.

Source: Total fertility rate from the annual statistical report of the Sample Registration System of India.

Improved child survival and concurrent expansion of female education have led to fertility decline in developing countries like India (Davis, 1963; Dyson, 2010). We have already discussed geographic diversity in the TFR and transition. In Table 10, we present the levels of TFR by education for India and selected states. In 1992–1993, the TFR for India was 4.3 per woman for women who had completed fewer than 5 years of schooling (including nonliterate) compared to 3.3 for those who had 10 or more years of schooling; a difference of one child. By 2015–2016, the TFR declined to 2.9 per woman and 1.8 for the respective groups. Over time there is no convergence in the level of fertility in lower and higher education groups as TFR declined by 45.5% among those who had 10 or more years of schooling compared to 32.6% among those who had fewer than 5 years of schooling. Nationally, around 22% of women aged 15–49 had completed 10 or more years of schooling in 1992–1993. The share of these women increased to about 60% in 2015–2016. Although TFR is higher for less educated people in India, their share in total women aged 15–49 has been reducing rapidly due to the expansion of education. The rise in education has a significant impact on delay in age at marriage.

A similar trend is observed at the state level as well. In 2015–2016, with the exception of Bihar (TFR = 2.3), women who had 10 or more years of schooling had reached the replacement level of fertility. The lowest being in Punjab (TFR = 1.4) and the highest in Uttar Pradesh (TFR = 2.0). Women with 5–9 years of schooling in many states except Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh either reached replacement or below-replacement level fertility or are very close to achieving it. The four larger states (Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh) have lower child survival and limited outreach of female education. In 2015–2016, Kerala had 95% of women aged 15–49 with 10 or more years of schooling, which was 44% in Bihar (including Jharkhand), 46% in Rajasthan, 52% in Madhya Pradesh (including Chhattisgarh), and 53% in Uttar Pradesh (including Uttarakhand).

Table 10. Total Fertility Rate by the Educational Status of the Women, India and Selected States, 1992–2016

State/India

1992–1993

1998–1999

2005–2006

2015–2016

<5 Years

5–9 Years

≥10 Years

<5 Years

5–9 Years

≥10 Years

<5 Years

5–9 Years

≥10 Years

<5 Years

5–9 Years

≥10 years

Andhra Pradesha

3.1

2.6

2.8

1.8

1.9

2.5

2.0

1.8

1.8

1.8

1.8

1.8

Assam

5.2

3.7

3.5

2.7

2.3

2.1

3.2

2.1

1.3

1.8

1.8

1.8

Biharb

4.5

4.0

3.0

3.4

2.7

2.8

4.5

3.2

2.4

2.3

2.3

2.3

Gujarat

4.1

2.9

2.8

3.1

2.6

2.3

3.2

2.4

1.7

1.5

1.5

1.5

Haryana

4.9

3.4

3.6

3.1

2.7

2.7

3.3

2.5

2.3

1.6

1.6

1.6

Karnataka

3.9

3.4

2.8

2.1

2.1

2.4

2.3

2.1

2.1

1.8

1.8

1.8

Kerala

2.9

3.0

2.7

2.3

2.4

2.5

2.2

2.1

2.0

1.6

1.6

1.6

Madhya Pradeshc

4.4

3.7

2.9

3.2

2.7

2.5

3.7

2.8

1.9

1.9

1.9

1.9

Maharashtra

3.7

3.5

2.8

2.6

2.5

2.6

2.6

2.3

1.8

1.7

1.7

1.7

Orissa

3.7

4.0

2.5

2.8

2.7

2.4

2.9

2.0

1.9

1.7

1.7

1.7

Punjab

3.7

3.5

3.0

3.3

2.8

2.4

2.8

2.1

1.6

1.4

1.4

1.4

Rajasthan

4.1

3.0

3.1

3.7

2.6

2.5

3.7

2.5

1.8

1.8

1.8

1.8

Tamil Nadu

3.4

2.7

2.6

2.3

2.5

2.5

2.0

2.0

1.8

1.7

1.7

1.7

Uttar Pradeshd

5.7

4.5

3.1

4.1

3.2

3.0

4.5

3.3

2.4

2.0

2.0

2.0

West Bengal

3.9

3.0

2.3

2.4

1.9

1.9

2.8

1.9

1.4

1.6

1.6

1.6

India

4.3

3.5

3.3

3.0

2.5

2.6

3.4

2.4

1.9

1.8

1.8

1.8

a Undivided including Telangana (1992–1993, 1998–1999, and 2005–2006).

b Undivided including Jharkhand (1992–1993 and 1998–1999).

c Undivided including Chhattisgarh (1992–1993 and 1998–1999).

d Undivided including Uttarakhand (1992–1003 and 1998–1999).

Source: International Institute for Population Sciences (1993); International Institute for Population Sciences & ICF (2017); International Institute for Population Sciences & ORC-Macro (2000); International Institute for Population Sciences & ORC-Macro (2007).

Mortality

The mortality data has information on three key indicators: infant mortality rate (IMR), under-5 mortality rate (U5MR), and expectation of life at birth (LEB; eoo). The data comes from the SRS for India and covers about 25 years (1990–2016). The year 1990 is chosen as a base since it benchmarks the Millennium Development Goals (MDG) base year, and the year 2016 benchmarks the base year of the recently declared Sustainable Development Goals (SDGs). The MDG goal for U5MR for India was to attain a U5MR of 42 deaths of children aged below 5 years per 1,000 live births by the year 2015. The corresponding goal for the IMR was 37 infant deaths per 1,000 live births. Under the SDG, the goals are 21 and 15, respectively, for the year 2030.

At the beginning of the 20th century, in India, a newborn baby had an average life expectancy of 21–23 years (Davis, 1951; Mukherji, 1976). The SRS life table available for the period 2013–2017 revealed that a newborn baby in India would live an average of more than 69 years, which is considerably lower than in other countries globally and in the South Asian region. Nonetheless, this is a significant improvement from just about 20 years to close to 70 years, and an essential aspect of this improvement relates to IMR. At the national level, the IMR was 80 infant deaths per 1,000 live births in 1990, which declined to 68 in 2000 (12 points in 10 years; see Table 11). The first decade of the 21st century unfolded a significant decline in the IMR for India— 47 infant deaths per 1,000 live births in 2010 and 34 per 1,000 in 2016. Mortality decline in India and its states may have been due to improvements in access to health services and also an incremental increase in access to improved drinking water and sanitation. Similar to the global evidence (Fink et al., 2011), the National Family Health Survey (NFHS) data for 1992–1993 and 2015–2016 revealed a quantum jump in access to sanitation facilities (IIPS, 1993; IIPS & ICF, 2017).

The acceleration, especially after 2005, may be due to the Janani Suraksha Yojana program implemented under the National Health Mission (erstwhile known as the National Rural Health Mission). The program provided a cash incentive of Rs. 1400 to women who delivered their babies in a health facility (Stephen et al., 2010). However, compliance varies considerably across India’s states. In the year 1990, Kerala had the lowest IMR (17 infant deaths per 1,000 live births), whereas it was higher in Odisha (122), followed by Madhya Pradesh (111) and Uttar Pradesh (99). By 2016, IMR declined significantly in all states. While Kerala continued to occupy the first place with the lowest IMR, Madhya Pradesh replaced Odisha with an IMR of 47 deaths per 1,000 live births. The states, on the whole, have succeeded in reducing the IMR; however, the usual lagging states of Assam, Bihar, Madhya Pradesh, Rajasthan, Uttar Pradesh, and Odisha continue to have higher IMRs.

Table 11. Infant Mortality Rate and Percentage Change in the Rate in India and Selected States, 1990–2016

Country/State

Infant Mortality Rate in the Year

Change (%) During:

1990

1995

2000

2005

2010

2015

2016

1990–2005

2005–2016

1990–2016

Andhra Pradesha

70

67

65

57

46

37

34

18.6

40.4

51.4

Assam

76

77

65

57

58

47

44

25.0

22.8

42.1

Biharb

75

73

62

61

48

42

38

18.7

37.7

49.3

Gujarat

72

62

62

54

44

33

30

25.0

44.4

58.3

Haryana

69

69

67

60

48

36

33

13.0

45.0

52.2

Karnataka

70

62

57

50

38

28

24

28.6

52.0

65.7

Kerala

17

15

14

14

13

12

10

17.6

28.6

41.2

Madhya Pradeshc

111

99

88

76

62

50

47

31.5

38.2

57.7

Maharashtra

58

55

48

36

28

21

19

37.9

47.2

67.2

Orissa

122

103

95

75

61

46

44

38.5

41.3

63.9

Punjab

61

54

52

44

34

23

21

27.9

52.3

65.6

Rajasthan

79

85

80

67

55

43

41

15.2

38.8

48.1

Tamil Nadu

59

54

51

37

24

19

17

37.3

54.1

71.2

Uttar Pradeshd

99

86

83

73

61

46

43

26.3

41.1

56.6

West Bengal

63

58

51

38

31

26

25

39.7

34.2

60.3

India

80

74

68

58

47

37

34

27.5

41.4

57.5

a Undivided including Telangana for the years 1990, 1995, 2005, and 2010

b Undivided including Jharkhand for the years 1990 and 1995.

c Undivided including Chhattisgarh for the years 1990 and 1995.

d Undivided including Uttarakhand for the years 1990 and 1995.

Source: Infant mortality rates from the annual statistical report of the Sample Registration System of India.

Table 12 presents the gender-specific U5MRs for India and states for 1990–2016. An average of 114 children per 1,000 live births died in India in 1990 before celebrating their 5th birthday, which declined to 39 in 2016; a two-thirds decline in 26 years. During the same period, the U5MR declined from 119 to 37 for male children and from 132 to 41 for female children. Similar to IMR, the U5MR fell relatively faster in the last 16 years in India (2000–2016) when compared with the corresponding change during 1990–2000. Once again, there are vast differences across states of India in U5MR as well; the lagging states continue to have significantly higher levels of childhood mortality. In 2016 Kerala had the lowest U5MR (11), and the Madhya Pradesh had the highest (55), followed by Assam (52) and Odisha (50). The improvement in child survival in India brought a sense of security for the families to go for smaller families and contributed to the lowering of the TFR. An important point to note here is that regardless of the period studied, the U5MR in India has exceeded for female children compared to the male children. Surprisingly, most states have revealed a gender gap in childhood mortality. A study by Ram et al. (2013, 2014) documented wide disparities in the levels of under-5 mortalities in districts of India.

Table 12. Gender-Specific Under-5 Mortality Rate and Percentage Change, India and Selected States, 1990–2016

Country/State

Under-5 Mortality Rate in the Year

Change During (%)

1990

1995

2000

2005

2010

2015

2016

1990–2005

2005–2016

1990–2016

Both sexes combined

Andhra Pradesha

89

83

73

66

49

39

37

25.8

43.9

58.4

Assam

115

109

90

85

84

62

52

26.1

38.8

54.8

Biharb

111

112

88

76

66

48

43

31.5

43.4

61.3

Gujarat

104

83

82

68

57

39

33

34.6

51.5

68.3

Haryana

92

94

83

68

56

43

37

26.1

45.6

59.8

Karnataka

94

81

70

57

47

31

29

39.4

49.1

69.1

Kerala

23

20

18

18

15

13

11

21.7

38.9

52.2

Madhya Pradeshc

161

139

115

102

84

62

55

36.6

46.1

65.8

Maharashtra

79

73

56

44

33

24

21

44.3

52.3

73.4

Orissa

165

141

116

97

80

56

50

41.2

48.5

69.7

Punjab

82

69

60

54

43

27

24

34.1

55.6

70.7

Rajasthan

119

120

108

88

69

50

45

26.1

48.9

62.2

Tamil Nadu

78

68

57

43

28

20

19

44.9

55.8

75.6

Uttar Pradeshd

148

126

117

102

82

51

47

31.1

53.9

68.2

West Bengal

93

85

67

50

38

30

27

46.2

46.0

71.0

India

114

105

89

77

61

43

39

32.5

49.4

65.8

Male children

Andhra Pradesha

97

84

74

70

46

37

36

27.8

48.6

62.9

Assam

143

117

105

92

79

58

48

35.7

47.8

66.4

Biharb

117

110

84

75

60

43

35

35.9

53.3

70.1

Gujarat

110

88

77

71

52

38

34

35.5

52.1

69.1

Haryana

93

89

78

69

51

41

34

25.8

50.7

63.4

Karnataka

110

81

76

61

43

31

26

44.5

57.4

76.4

Kerala

30

19

18

16

14

12

10

46.7

37.5

66.7

Madhya Pradeshc

177

150

123

109

79

63

58

38.4

46.8

67.2

Maharashtra

83

69

55

40

31

21

20

51.8

50.0

75.9

Orissa

154

135

114

97

76

56

49

37.0

49.5

68.2

Punjab

76

64

60

52

38

27

24

31.6

53.8

68.4

Rajasthan

144

116

104

93

60

44

42

35.4

54.8

70.8

Tamil Nadu

84

58

53

47

26

20

19

44.0

59.6

77.4

Uttar Pradeshd

155

123

110

99

71

49

46

36.1

53.5

70.3

West Bengal

97

85

70

51

37

28

27

47.4

47.1

72.2

India

119

102

87

75

55

40

37

37.0

50.7

68.9

Female children

Andhra Pradesha

96

75

73

67

51

42

38

30.2

43.3

60.4

Assam

140

123

110

91

87

66

57

35.0

37.4

59.3

Biharb

139

128

98

85

68

54

51

38.8

40.0

63.3

Gujarat

123

101

86

79

60

41

33

35.8

58.2

73.2

Haryana

115

117

107

87

59

46

42

24.3

51.7

63.5

Karnataka

105

83

72

62

47

32

31

41.0

50.0

70.5

Kerala

25

17

15

15

16

14

12

40.0

20.0

52.0

Madhya Pradeshc

194

158

140

118

85

61

52

39.2

55.9

73.2

Maharashtra

80

68

60

46

35

26

23

42.5

50.0

71.3

Orissa

159

131

114

98

79

55

51

38.4

48.0

67.9

Punjab

92

84

82

64

48

26

25

30.4

60.9

72.8

Rajasthan

172

133

118

103

79

56

49

40.1

52.4

71.5

Tamil Nadu

90

62

55

44

28

21

19

51.1

56.8

78.9

Uttar Pradeshd

189

142

131

119

87

53

49

37.0

58.8

74.1

West Bengal

97

86

63

49

38

31

28

49.5

42.9

71.1

India

132

113

96

82

64

45

41

37.9

50.0

68.9

a Undivided including Telangana for the years 1990, 1995, 2005, and 2010.

b Undivided including Jharkhand for the years 1990, 1995, 2005, and 2010.

c Undivided including Chhattisgarh for the years 1990, 1995, 2005, and 2010.

d Undivided including Uttarakhand for the years 1990, 1995, 2005, and 2010.

Source: Author calculations based on data from SRS Based Life Tables for 1988–1992, 1993–1997, 1998–2002, and 2003–2007. Data for 2015 and 2016 from the annual statistical report of the Sample Registration System of India for the respective years.

We now examine levels of life expectancy at birth (LEB). Table 13 presents the relevant data for India and its states for both sexes combined as well as separately. The LEB for India was nearly 49 years during 1970–1975, which increased to about 58 years in 1986–1990, an increase of 9 years in 16 years resulting in an annual improvement of approximately 0.6 years. By 1996–2000, the LEB in India increased to 62 years and further to 69 years in 2013–2017. Up until the 1980s, nationally, Indian males lived longer than the Indian females (Ram & Ram, 1997). Data on gender-specific LEB since 1993 indicates that in India, females now live longer than males, and the gap was by 2 years in 2013–2017. The gender gap indeed widened in the mid-1990s when male LEB was at 60.4 years and females at 61.8 years. But at the same time, gender gaps in mortality have also widened for adolescents (to the female disadvantage), an anomaly indicating the downside of using only LEB for exploring gender disparity.

Table 13. Gender-Specific Life Expectancy at Birth and Changes in the Life Expectancy, India and Selected States, 1986–2017

Country/State

Both Sexes

Change (%) 1986/1990 to 2013/2017

Males

Change (%) 1986/1990 to 2013/2017

Females

Change (%) 1986/1990 to 2013/2017

1986–1990

1996–2000

2006–2010

2013–2017

1986–1990

1996–2000

2006–2010

2013–2017

1986–1990

1996–2000

2006–2010

2013–2017

Andhra Pradesha

59.1

62.7

65.8

69.7

−17.9

58.2

61.1

63.5

68.3

−14.8

60.4

64.4

68.2

71.2

–17.9

Assam

53.6

57.4

61.9

66.2

−23.5

53.6

57.3

61.0

65.4

−18.0

54.2

57.7

63.2

67.3

–24.2

Biharb

54.9

60.5

65.8

68.9

−25.5

55.7

61.1

65.5

69.2

−19.5

53.6

59.9

66.2

68.6

–28.0

Gujarat

57.7

64.4

66.8

69.7

−20.8

57.0

63.0

64.9

67.6

−15.7

58.8

65.8

69.0

72.0

–22.4

Haryana

62.2

64.4

67.0

69.7

−12.1

62.2

64.1

64.9

67.6

−8.0

62.2

64.7

69.5

72.3

–16.2

Karnataka

61.1

64.5

67.2

69.2

−13.3

60.4

62.6

64.9

67.7

−10.8

62.6

66.7

69.7

70.8

–13.1

Kerala

69.5

71.6

74.2

75.2

−8.2

66.8

68.7

71.5

72.5

−7.9

72.3

74.7

76.9

77.8

–7.6

Madhya Pradeshc

53.0

57.1

62.4

66.0

−24.5

53.7

56.6

61.1

64.2

−16.4

53.0

57.6

63.8

67.6

–27.5

Maharashtra

62.6

65.9

69.9

72.5

−15.8

61.2

64.2

67.9

71.2

−14.0

63.5

67.8

71.9

73.9

–16.4

Orissa

54.4

58.3

63.0

68.4

−25.7

54.6

57.8

62.2

67.1

−18.6

54.0

58.8

63.9

69.9

–29.4

Punjab

65.2

66.5

69.3

72.4

−11.0

64.7

65.5

67.4

71.0

−8.9

66.9

67.7

71.6

74.0

–10.6

Rajasthan

55.2

62.1

66.5

68.5

−24.1

55.2

61.0

64.7

66.3

−16.7

56.2

63.5

68.3

70.9

–26.2

Tamil Nadu

60.5

64.8

68.9

71.7

−18.5

60.0

63.5

67.1

69.9

−14.2

60.6

66.2

70.9

73.7

–21.6

Uttar Pradeshd

53.4

59.2

62.7

65.0

−21.7

54.2

59.6

61.8

64.3

−15.7

52.5

58.7

63.7

65.6

–25.0

West Bengal

60.8

64.3

69.0

71.2

−17.1

60.2

63.0

67.4

70.4

−14.5

61.2

65.7

71.0

72.2

–18.0

INDIA

59.1

62.7

65.8

69.7

−17.9

57.7

61.2

64.6

67.8

−14.9

58.1

62.7

67.7

70.4

–21.2

$$ authors calculation using SRS gender-specific life tables.

a Undivided including Telangana.

b Undivided including Jharkhand for 1986–1990 and 1996–2000.

c Undivided including Chhattisgarh for 1986–1990 and 1996–2000.

d Undivided including Uttarakhand for 1986–1990, 1996–2000, and 2006–2010

Source: From Life tables of the Sample Registration System (SRS) of India.

Family Planning and Unmet Need

India acquired the status of being the first nation globally to launch an official family planning program in 1952. However, the real push to the program came through in the 1960s when the program adopted a target-specific approach. The federal authorities in India assigned targets to the states, which were allocated to districts and further to the individual health workers at the lowest level of service provision. These targets became extremely volatile over the years, and the authorities announced disincentives and incentives to the users and the service providers based on performance (Pachauri, 2014). This period was accompanied by the emergency period (1975–1977) in India, when the program became extremely coercive. This act of the government damaged the program to a great extent and impacted the northern Hindi-speaking belt where fertility levels were higher. Although the success in fertility reduction in India is not comparable to that of other Asian countries, its achievements are by no means modest. In the initial phase, the program success was mostly monitored and evaluated using service statistics with the help of the number of acceptors and births averted as a result of family planning acceptance. Family planning surveys conducted in the 1970s and 1980s (ORG, 1972, 1982, 1990) complemented monitoring and evaluating efforts. After 1990, India launched nationwide surveys (see IIPS, 1993; IIPS & ICF, 2017; IIPS & ORC-Macro 2000, 2007). Tables 14, 15, and 16 give selected family planning indicators for India.

There has been a continuous rise in the percentage of married women using modern contraception in India. For example, just over 10% of married Indian women in 1970 used modern contraception (ORG, 1972). This percentage increased to 42.8% in 1998–1999 and to 48.5% in 2005–2006 (Table 14). India’s contraceptive prevalence rates (CPRs) are presented for the period between 1992–1993 to 2015–2016 in Table 13. At the national level, overall CPR has increased from a little over 36% in the early 1990s to close to 48% in 2015–2016, which translates to an increase of 12 units over the 23 years (an annual increase of 1.4%). The 2017 NFHS indicated that modern method CPR had marginally decreased from 48.5% in 2005–2006 to 47.8% in 2015–2016 (IIPS & ICF, 2017; IIPS & ORC-Macro, 2007). The decline in CPR of the modern method is substantial in many states, including Bihar, Gujarat, Karnataka, Kerala, Madhya Pradesh, and Tamil Nadu. This has raised debates among policy makers and researchers because these states have concurrently exhibited a significant decline in TFR levels. There is some research evidence that has indicated doubt about the estimated CPR for the period 2015–2016. A study by Jayachandran and Stover (2018) expressed concern over the quality of contraceptive data collected in the 2017 NFHS. The modern limiting method CPR showed an increase of five units (from 31% to a little over 36%) and there was a twofold rise in the modern spacing method CPR (from about 6% to over 11%) during the same period. Interestingly, CPR for traditional methods also increased, from 4% to almost 6% (IIPS & ICF, 2017).

The levels of CPR, as well as the pace of change in it, varied considerably across Indian states included in the analysis. Generally, the states in the southern and western regions revealed higher levels of CPR compared to those in the northern and eastern regions of India. While the CPR rose over time, Gujarat and Kerala had a marginal decline in the overall CPR. Assam, Odisha, and West Bengal (all three in the eastern region) and Uttar Pradesh in the northern part had higher CPR of the traditional method (abstinence and withdrawal/rhythm) compared to the remaining states. While the CPR for traditional methods declined in Assam and West Bengal, it increased from 1%–2% in 1992–1993 to over 12%–14% in 2015–2016 in Odisha and Uttar Pradesh. The use of traditional methods is higher among women who live in urban areas, who were more educated and resided in economically better-off households. The patterns of CPR are somewhat similar for the modern limiting and spacing methods across states, as seen for all methods combined. Nonetheless, a few states, such as Assam, Haryana, Odisha, Uttar Pradesh, and West Bengal, have shown a tremendous rise in the CPR for modern spacing methods.

Table 14. Contraceptive Prevalence Rate for Modem Limiting, Modern Spacing Methods and Traditional Methods of Family Planning and Percentage Change in Them, India and Selected States, 1992–2016

Country/State

1992–1993

1998–1999

2005–2006

2015–2016

Change (%): 1992–2016

1992–1993

1998–1999

2005–2006

2015–2016

Change (%): 1992–2016

Modern methods only (Overall)

Traditional methods only

Andhra Pradesha

47.0

58.9

67.0

69.4

47.7

0.4

1.8

0.6

0.6

50.0

Assam

19.9

26.6

27.0

37.0

85.9

23.1

16.7

29.5

15.4

-33.3

Biharb

21.6

22.4

28.9

23.3

7.9

1.5

1.6

5.2

0.8

–46.7

Gujarat

46.9

53.3

56.5

43.1

–8.1

2.4

5.6

10.1

3.8

58.3

Haryana

44.3

53.2

58.3

59.4

34.1

5.3

8.9

5.0

4.5

–15.1

Karnataka

47.3

56.5

62.5

51.3

8.5

1.8

1.7

1.1

0.5

–72.2

Kerala

54.4

56.1

57.9

50.3

–7.5

8.9

7.6

10.7

2.8

–68.5

Madhya Pradeshc

35.5

42.6

52.8

49.6

39.7

1.0

1.4

3.2

1.9

90.0

Maharashtra

52.5

59.9

64.9

62.5

19.0

1.2

1.0

1.9

2.3

91.7

Orissa

34.6

40.3

44.7

45.4

31.2

1.6

5.6

6.1

12.1

656.3

Punjab

51.3

53.8

56.1

66.3

29.2

7.4

12.4

7.2

9.5

28.4

Rajasthan

30.9

38.1

44.4

53.5

73.1

0.8

1.9

2.8

6.2

675.0

Tamil Nadu

45.2

50.3

60.0

52.6

16.4

4.6

1.8

1.4

0.6

–87.0

Uttar Pradeshd

18.5

22.0

29.3

31.7

71.4

1.3

5.7

14.3

13.9

969.2

West Bengal

37.3

47.3

49.9

57.0

52.8

20.1

18.5

21.3

14.2

–29.4

India

36.3

42.8

48.5

47.8

31.7

4.3

5.0

7.8

5.9

37.2

Modern methods only (limiting)

Modern methods only (spacing)

Andhra Pradesha

45.2

52.2

65.8

63.5

40.5

1.8

4.3

1.2

1.2

–33.3

Assam

14.6

16.8

13.2

9.6

–34.2

5.3

9.9

13.8

27.4

417.0

Biharb

18.6

20.1

24.4

20.7

11.3

2.9

2.2

4.5

2.5

–13.8

Gujarat

41.0

45.3

43.5

33.7

–17.8

5.9

8.1

12.9

9.4

59.3

Haryana

34.7

40.8

38.9

38.6

11.2

9.6

12.4

19.4

20.6

114.6

Karnataka

42.5

52.2

57.6

48.6

14.4

4.8

4.4

5.0

2.6

–45.8

Kerala

48.3

51.0

49.7

45.9

–5.0

6.1

5.1

8.2

4.4

–27.9

Madhya Pradeshc

31.5

38.0

45.5

42.7

35.6

4.0

4.6

7.2

6.8

70.0

Maharashtra

46.1

52.2

53.2

51.1

10.8

6.4

7.7

11.7

11.4

78.1

Orissa

31.6

35.6

34.1

28.4

–10.1

3.0

4.7

10.5

16.8

460.0

Punjab

34.0

30.9

32.0

38.1

12.1

17.3

23.0

24.0

28.3

63.6

Rajasthan

27.7

32.3

35.0

40.9

47.7

3.3

5.8

9.4

12.5

278.8

Tamil Nadu

39.5

45.9

55.4

49.4

25.1

5.7

4.3

4.6

3.1

–45.6

Uttar Pradeshd

13.1

15.6

17.4

17.4

32.8

5.5

6.4

11.8

14.2

158.2

West Bengal

30.6

33.8

32.9

29.4

–3.9

6.7

13.5

17.0

27.4

309.0

India

30.8

36.0

38.3

36.3

17.9

5.5

6.8

10.2

11.4

107.3

a Undivided including Telangana (1992–1993).

b Undivided including Jharkhand (1992–1993 and 1998–1999).

c Undivided including Chhattisgarh (1992–1993).

d Undivided including Uttarakhand (1992–1993 and 1998–1999).

Source: International Institute for Population Sciences (1993); International Institute for Population Sciences & ICF (2017); International Institute for Population Sciences & ORC-Macro (2000); International Institute for Population Sciences & ORC-Macro (2007).

Tables 15 and 16 provide data on the future demand for family planning as assessed using the information on unmet need for family planning over 25 years. Nationally, the unmet need for family planning declined by nearly 37% in two and a half decades; the unmet need of almost 20% in 1992–1993 to about 13% in 2015–2016 (Table 15). The unmet need for modern spacing methods had halved in the country from nearly 12% to 6% during the same period. However, the unmet need for family planning seemingly has remained unchanged since 2010, as the decline was by only one percentage point (from 14% to 13% for all methods and from 6.1% to 5.6% for spacing methods). Gujarat and Kerala were the only states where the total unmet need for family planning increased over time. In the remaining states, the change in the total unmet need has followed the national pattern. While the total unmet need remained nearly unchanged in Haryana, Karnataka, Madhya Pradesh, Maharashtra, and Tamil Nadu, it increased only marginally in Andhra Pradesh, Assam, and Wes Bengal. The unmet need doubled in Gujarat and increased substantially in Kerala.

In contrast, the unmet need declined in Bihar, Odisha, Rajasthan, and Uttar Pradesh during the same period. In case of unmet need for spacing methods, the data indicated substantial decline over the period for all states except Kerala, where unmet need for spacing methods rose from 6% to 8% in the last decade. A on-going investigation of NFHS data by Ram et al. (in press) showed that unmet need increased mainly due to the rise in the unmet need among the nonusers.

Table 15. Total Unmet Need for Family Planning, Unmet Need for Spacing, and Percentage Change, India and Selected States, 1992–2016

Country/State

Total Unmet Need: Limiting and Spacing Methods

Change (%) 1992–2016

Unmet Need for Spacing Methods Only

Change (%) 1992–2016

1992–1993

1998–1999

2005–2006

2015–2016

1992–1993

1998–1999

2005–2006

2015–2016

Andhra Pradesha

11.8

7.8

4.8

5.8

−50.8

7.9

5.1

2.8

3.4

–57.0

Assam

22.7

17.1

12.3

14.1

−37.9

11.8

6.9

3.6

5.8

–50.8

Biharb

25.9

25.4

24.0

20.5

−20.8

15.8

12.6

10.6

9.3

–41.1

Gujarat

13.3

8.8

8.3

17.0

27.8

8.1

5

3.9

6.6

–18.5

Haryana

16.6

7.6

9.5

9.3

−44.0

9.3

2.9

3.0

3.8

–59.1

Karnataka

18.8

11.6

10.1

10.4

−44.7

12.6

8.2

5.6

6.0

–52.4

Kerala

12.1

11.8

9.8

13.7

13.2

8.1

6.9

6.1

8.3

2.5

Madhya Pradeshc

22.4

16.6

11.8

11.9

−46.9

15.1

8.8

5.4

5.6

–62.9

Maharashtra

14.8

13

10.0

9.7

−34.5

8.8

8

5.3

4.3

–51.1

Orissa

24.3

15.7

16.1

13.6

−44.0

14.8

8.7

6.5

4.7

–68.2

Punjab

13.2

7.4

9.0

6.2

−53.0

6.8

2.8

2.8

2.3

–66.2

Rajasthan

21.2

17.9

15.7

12.3

−42.0

12.7

8.7

7.3

5.7

–55.1

Tamil Nadu

14.8

13

10.3

10.1

−31.8

8.5

6.6

4.1

4.8

–43.5

Uttar Pradeshd

30.7

25.6

22.6

17.9

−41.7

17.8

11.7

8.8

6.7

–62.4

West Bengal

17.4

12.1

9.5

7.5

−56.9

9.5

6.2

4.3

3.0

–68.4

India

20.3

16.1

13.9

12.9

−36.5

12.2

8.3

6.1

5.6

–54.1

a Undivided including Telangana (1992–1993, 1998–1999, and 2005–2006).

b Undivided including Jharkhand (1992–1993 and 1998–1999).

c Undivided including Chhattisgarh (1992–1993 and 1998–1999).

d Undivided including Uttarakhand (1992–1993 and 1998–1999).

Source: International Institute for Population Sciences (1993); International Institute for Population Sciences & ICF (2017); International Institute for Population Sciences & ORC-Macro (2000); International Institute for Population Sciences & ORC-Macro (2007).

There are 46 million married women aged 15–49 in India who have expressed an unmet need for modern contraception, of whom 14 million prefer limiting methods and 18 million prefer spacing methods. The remaining 14 million couples, who used traditional methods, are considered to have an unmet need for modern methods of contraception in the NFHS for 2015–2016 (IIPS & ICF, 2017). It is important to note that all of the nonusers having unmet need will not convert into the users for various reasons as unmet need is highly unlikely to attain a zero value. The current unmet need of 18.7% may best reduce to 4%–5%, as observed in some states (as well as other countries in the neighborhood). In other words, 35 million couples actually can be converted to users. Nonuse of contraception could be due to sterility (primary and secondary), which varies considerably across India’s states, especially after age 30 (Ram, 2010). In other words, the potential pool of available users will include fewer people, around 28–30 million. Table 16 presents the share of current users and couples with unmet needs in the states of India in the national totals. The 14 states included consist of 88% of all users in India, and nearly 84% of the couples with unmet need belonged to these 14 states. Almost 47% of the couples with unmet need come from Bihar (13%), Madhya Pradesh (5%), Rajasthan (7%), and Uttar Pradesh (21%). This share is likely to rise because the demand for contraception in other states has almost reached a saturation point. The geographic allocation of unmet need creates a challenging situation because program strength and social development in these states are inadequate and of poor quality.

Table 16. State Share of the Users of Modern Methods of Family Planning and State Share of Couples Having Total Unmet Need for Family Planning (Limiting and Spacing Combined) in the National Totals, 1992–2016

Country/State

State Share of Modern Method Users

State Share of Couples With Unmet Need

1992–1993

1998–1999

2005–2006

2015–2016

1992–1993

1998–1999

2005–2006

2015–2016

Andhra Pradesha

11.4

11.6

10.9

9.8

5.2

4.1

2.7

3.3

Assam

1.4

1.6

1.4

2.0

3.0

2.7

2.2

2.8

Biharb

5.1

4.3

4.9

3.9

11.0

13.0

13.8

13.1

Gujarat

6.4

6.4

6.0

4.7

3.3

2.8

3.1

3.2

Haryana

2.3

2.5

2.5

2.6

1.6

1.0

1.4

1.5

Karnataka

6.8

6.9

6.8

5.7

4.9

3.8

3.8

4.2

Kerala

4.9

4.3

3.8

3.0

2.0

2.4

2.2

2.2

Madhya Pradeshc

7.8

7.2

6.9

5.8

8.9

7.5

5.5

5.2

Maharashtra

13.7

13.1

12.6

12.5

7.0

7.6

6.7

7.4

Orissa

3.5

3.3

3.2

3.2

4.5

3.5

4.0

4.2

Punjab

3.2

2.9

2.6

3.1

1.5

1.1

1.5

1.6

Rajasthan

4.6

4.9

5.2

6.6

5.8

6.2

6.3

7.3

Tamil Nadu

8.4

7.7

8.0

7.1

5.0

5.3

4.8

5.0

Uttar Pradeshd

8.2

7.9

9.5

9.8

24.8

24.6

24.2

20.7

West Bengal

8.2

8.9

8.4

10.0

6.9

6.1

5.5

5.2

Subtotal

96.0

93.5

92.7

89.9

95.5

91.7

87.7

86.7

Remaining states/Union Territories (UTs)

4.0

6.5

7.3

10.1

4.5

8.3

12.3

13.3

India

100.0

100.0

100.0

100.0

100.0

100.0

100.0

100.0

a Undivided including Telangana (1992–1993, 1998–1999, and 2005–2006).

b Undivided including Jharkhand (1992–1993 and 1998–1999).

c Undivided including Chhattisgarh (1992–1993 and 1998–1999).

d Undivided including Uttarakhand (1992–1993 and 1998–1999).

Source: International Institute for Population Sciences (1993); International Institute for Population Sciences & ICF (2017); International Institute for Population Sciences & ORC-Macro (2000); International Institute for Population Sciences & ORC-Macro (2007).

Marriage

A very dark side of Indian culture has been the practice of child marriage, which was rampant in the 20th century. The Hindu scripture advocated marriage for a girl before puberty (onset of menstruation). However, girls who married early remained in the parental home until “Gauna” (Kapadia, 1966), which was generally performed at the age when the girl attains physical maturity (onset of menstruation). The Sarda Act enacted in 1929, followed by the Child Marriage Restraint Act of 1978 in India, defined the minimum legal age for marriage as 18 years for girls and 21 years for boys. Early marriage has a multidimensional effect on the lives of the females in India throughout their life course, from deprivation of education, skill development, health care access, and so on. At the macro level, the marriage pattern of a population has a significant effect on fertility and mortality (especially child mortality) levels. Marriage is one of the proximate determinants of fertility besides family planning use. The female age at marriage in India is rising, but rather slowly. The singulate mean age at marriage in India was 15.9 years in 1961, which increased to 18.3 years in 1981 and 20.8 years in 2011, an increase of about five years in five decades. In the 1990s, nearly half of the women aged 20–24 in India were married before age 18 years. This percentage reduced to about 45% in 2005–2006.

The institution of marriage in India almost remained universal. Close to 97% of the Indian women aged 30–34 years in 2011 were married (Table 17). The percentage of these women varied marginally across states. Only two states (Kerala and Odisha) had 5% of the women aged 30–34 years who were single. The percentage of single women aged 30–34 years was 4% in Karnataka and West Bengal. Data from the 2015–2016 survey indicated that about one-quarter of women aged 20–24 years were married before they were 18 years (in absolute terms, 14.5 million women married below age 18). There is a great deal of variation across the states. Around 42% of women aged 20–24 years were married before age 18 in West Bengal, followed by 40% in Bihar, 31–33% in Rajasthan, Madhya Pradesh and Andhra Pradesh, and 23–26% in Gujarat, Karnataka, and Maharashtra.

Table 17. Percentage of Women Ages 20–24 Married Before Age 18 and Percentage of Single Women Ages 30–34, India and Selected States, 2015–2016

Country/State

Women Married Before 18 Years

% Single Women Ages 30–34 Years

Percent

Number (in Thousands)

% Share in State in National Total

Andhra Pradesh

33.6

761.7

5.2

2.6

Assam

33.1

499.1

3.4

7.5

Bihar

39.7

1,763.9

12.1

1.1

Gujarat

24.0

684.8

4.7

2.9

Haryana

18.7

236.2

1.6

1.5

Karnataka

23.6

669.9

4.6

4.1

Kerala

7.8

100.9

0.7

5.0

Madhya Pradesh

31.1

1,088.8

7.5

1.9

Maharashtra

25.9

1,351.9

9.3

3.8

Orissa

21.7

424.7

2.9

5.3

Punjab

7.5

95.9

0.7

3.4

Rajasthan

31.5

1,080.8

7.4

0.9

Tamil Nadu

16.3

495.6

3.4

3.9

Uttar Pradesh

19.3

1,965.3

13.5

2.1

West Bengal

41.8

1,812.9

12.5

4.3

Subtotal

13,032.5

89.7

Rest of India

18.7

1,503.9

10.3

India

25.3

14,536.4

100.0

3.3

Source: Authors’ calculation based on data from NCP (2019) and IIPS and ICF (2017). Percent of single women data from Census of India, 2011.

Concluding Remarks

Although India holds a national treasure in its decadal censuses that have been continuously reported since 1881, the country has failed to develop and strengthen its civil registration system for births and deaths. A significant constraint faced by Indian policy makers is a lack of data with regard to its socioeconomic and demographic scenario, including fertility and mortality. This shortcoming became apparent in several policies and programs that lacked evidence-based decisions to improve the health and well-being of the population. These experiences motivated the authorities in India, and nearly two decades after the country attained independence, the Government of India initiated the sample registration system SRS in an effort to replace the civil registration system and fill the data void. In the early 1990s, the government’s focus on health and well-being led to the publication of the first National Family Health Survey in 2017. The data from these surveys has helped policy makers and researchers to gain insight into the demographic changes in India, nationally and subnationally.

India is the second-most populous country in the world. The international community has expressed concerns about the rising population size and high growth rate in India, which has received unprecedented attention in almost all platforms. Between 1961 and 2001, India’s population grew at an average rate of about 2%, and the size of the population in absolute terms exceeded one billion in 2001. During 2001–2011, the population growth slowed down substantially. However, India continued to add an average of 18 million people annually to its already large base, leading to a total national population of 1.21 billion in 2011. An assessment by the UN (2019) indicated that India’s population would peak at 1.65 billion in 2061 and would begin to decline after that and reach 1.44 billion in the year 2100. The four large states in India (Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan) continue to reveal high levels of fertility and mortality (especially during early childhood), and have great potential for future population growth. The spatial distribution of India’s population will have a significant influence on its future political and economic scenario. Kerala state may experience a negative population growth rate around 2036. The undivided Andhra Pradesh (including the newly created state of Telangana) may experience the same around 2041 and Karnataka and Tamil Nadu around 2046. Four states of Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan would have 764 million people in 2061 (45% of the national total) by the time India’s population reaches around 1.65 billion (Verma, 2018).

Changes in fertility and mortality are the two most important demographic factors contributing to population growth in India. The total fertility rate (TFR) in India declined from about 6.5 children per woman in the early 1960s to 2.3 children per woman in 2016 (a reduction of 4.2 children per woman in fewer than six decades). India is concerned about relatively high TFR in Bihar (3.3 children per woman), Uttar Pradesh (3.1 children per woman), Madhya Pradesh (2.8 children per woman), and Rajasthan (2.7 children per woman). The states have exhibited a higher unmet need for contraception and a weak public health-care delivery system. Childhood mortality in India has declined substantially, especially after the 1990s (114 in 1990 to 39 children deaths per 1,000 live births in 2016). This remarkable improvement is the result of massive efforts to improve comprehensive maternal and child health programs and nationwide implementation of the national health mission. The latter focused attention on improving the maternal and child health indicators in the country. Despite this, childhood mortality continues to be unacceptably high in Uttar Pradesh (47 children deaths per 1,000 live births), Bihar (43 children deaths per 1,000 live births), Rajasthan (45 children deaths per 1,000 live births), and Madhya Pradesh (55 children deaths per 1,000 live births). Besides, more considerable attention to improving access to public health-care services would promote contraception use immensely by way of reducing unmet needs and, in turn, reduce child mortality.

Figure 5. Future prospects of the demographic transition for India, 1950–2100.

Source: Crude birth rate (CBR) and crude death rate (CDR) for India are taken from UN (2019).

A great deal of scientific evidence suggests that the intertwined programmatic interventions focusing on female education and child survival are essential. Such efforts, notably in the four large states of Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan, would go a long way to reduce unmet need for contraception and enhance contraception use giving a big push to reducing fertility in the future. This would be crucial for India to stabilize its population before reaching 1.65 billion. India’s demographic journey through the path of the classical demographic transition suggests that the country is very close to achieving replacement fertility. Figure 5 outlines the future path of India’s transition according to the UN’s (2019) assessment. Although India may achieve replacement level fertility very soon (around 2023), the population will continue to grow until 2060 due to population momentum. Only after this, India may experience a negative growth rate; that is, the crude death rate will exceed the crude birth rate.

Further Reading

  • Caldwell, J. (1980). Mass education as a determinant of the timing of fertility decline. Population and Development Review, 6(2), 225–255.
  • Cleland, J., & Rodriguez, G. (1988). The effect of parental education on marital fertility in developing countries. Population Studies, 42, 419–442.
  • Cochrane, S. H. (1979). Fertility and education: What do we really know? Johns Hopkins University Press.
  • Dreze, J., & Murthi, M. (2001). Fertility, education, and development: Evidence from India. Population and Development Review, 27(1), 33–63.
  • Lutz, W., & Kebede, E. (2016). Education and health: Redrawing the Preston curve. Population and Development Review, 44(2), 343–361.
  • Pamuk, E. R., Fuchs, R., & Lutz, W. (2011). Comparing relative effects of education and economic resources on infant mortality in developing countries. Population and Development Review, 37(4), 637–664.
  • Preston, S. H. (1975). The changing relation between mortality and level of economic development. Population Studies, 29(2), 231–248.
  • Preston, S. H. (1980). Causes and consequences of mortality declines in less developed countries during the twentieth century. In R. A. Easterlin (Ed.), Population and economic change in developing countries (pp. 289–360). University of Chicago Press.
  • Samir, K. C., Wurzer, M., Speringer, M., & Lutz, W. (2018). Future population and human capital in heterogeneous India. Proceedings of the National Academy of Sciences, 115(33), 8328–8333.

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