Demographic indicators of development

Detail

1. Why demography matters for development

• Income and HDI tell us how well people live. Demographic indicators tell us about the people themselves: how many there are, how fast their numbers grow, their age, their sex, and where they live.
• These numbers affect development in three ways:
• Resources per person: fast population growth spreads land, jobs and schools across more people.
• Workers vs dependants: the age structure decides how many earners support each non-earner.
• Gender justice: a skewed sex ratio shows discrimination that income data cannot show.

2. The comparison table: India, China, Pakistan

Class 11 NCERT, Table 8.1 (2021–23, from WDI 2024):

Indicator	India	China	Pakistan
Population (million)	1,428	1,411	240
Annual population growth (%)	0.81	−0.10	1.96
Density (per km²)	473	150	300
Sex ratio (F per 1,000 M)	930	898	948
Fertility rate (TFR)	2.0	1.2	3.4
Urbanisation (%)	36	65	38

• India became the world's most populous country in 2023 (UN World Population Prospects).
• NCERT: one in every six people in the world is Indian and another one is Chinese.

3. Population growth rate

• Population growth rate is the yearly % change in population. It depends on births, deaths and net migration.

• Formula: Growth rate (%) = [(Births − Deaths) + (Immigrants − Emigrants)] ÷ Population at start × 100

• Worked example: a town starts the year with 1,000 people. There are 20 births, 8 deaths, and 2 more people leave than arrive.

• Growth = (20 − 8 − 2) ÷ 1,000 × 100 = 1%.

• Rule of 70 (a quick way to find how long a population takes to double): doubling time ≈ 70 ÷ growth rate.

• Pakistan: 70 ÷ 1.96 ≈ 36 years.
• India: 70 ÷ 0.81 ≈ 86 years.

• China: the rate is negative, so its population is shrinking and will never double at this rate.

• Reading the table (2021–23): Pakistan's growth is highest (1.96%). India's has slowed below 1% (0.81%). China's has turned negative (−0.10%).

4. Population density

• Population density is the number of people per square kilometre (km²).
• Formula: Density = Total population ÷ Land area (km²)

• Example: 30 lakh people on 1,000 km² → 3,000,000 ÷ 1,000 = 3,000 per km².

• China is the largest of the three in area but has the lowest density (150). India has the highest (473).

• High density means more pressure on land, water, housing and forests.

5. China's one-child norm: a case study

• Introduced in the late 1970s (1979-80). It cut population growth sharply.
• Side effect 1: a low sex ratio.
• Families preferred sons, and they were allowed only one child.
• So many families used sex-selective abortion (ending a pregnancy because the baby is a girl).

• China's sex ratio is now the lowest of the three: 898 (2021–23).

• Side effect 2: rapid ageing.

• Fewer babies were born year after year.
• So older people now form a bigger share than young people.

• Fewer workers must support more elderly people.

• Response: a two-child policy (2016), then a three-child policy (2021). Its population growth is still negative (−0.10%).

• Lesson: forcing fertility down quickly creates problems with gender and ageing that last for decades.

6. Sex ratio and son preference

• Sex ratio: India counts females per 1,000 males. The international convention is males per 100 females. Read exam questions carefully.

• Conversion example: India's 930 F per 1,000 M → (1,000 ÷ 930) × 100 ≈ 107.5 males per 100 females.

• Child sex ratio (CSR): females per 1,000 males in the 0–6 age group. It shows recent discrimination.

• Sex ratio at birth (SRB): number of female births per 1,000 male births [4].
• Son preference (valuing boys more than girls) is found in all three countries. NCERT gives it as the cause of their low sex ratios.

India's numbers

• Census 2011: overall sex ratio 943. Child sex ratio (0–6) was 918.
• NFHS-5 (2019-21): overall sex ratio 1,020. This is the first national survey with more women than men.
• Rural 1,037, urban 985 (NFHS-5, 2019-21) [2][3].
• Why above 1,000? Women live longer than men. Also, many men migrate out for work, and some are away when the survey is done.

• But the bias at birth remains: the NFHS-5 sex ratio at birth (births in the last 5 years) was only 929.

• SRS data (Sample Registration System): the Office of the Registrar General, India runs this survey all year round in randomly chosen villages and urban blocks. It gives yearly estimates of birth, death and infant mortality rates for India and each state [4].

• SRB improved from 899 (2014) to 913 (2021) [4].

• SRB was 904 in 2017-19, against 899 in 2016-18 [4].

• Economic Survey 2017-18:

• About 63 million "missing women": women who should be alive but are not, because of sex selection and neglect.
• About 21 million "unwanted girls": girls born to parents who wanted a boy.
• Son "meta-preference": parents keep having children until they get a son. So even families with no sex selection end up with more girls in large families and more boys in small ones.

Policy responses

• PCPNDT Act, 1994 (Pre-Conception and Pre-Natal Diagnostic Techniques Act): bans finding out the sex of a baby before birth.
• Beti Bachao Beti Padhao (January 2015): a scheme to stop sex selection and support girls' survival and education. Sex ratio at birth is one of the measures used to track its progress [2][3].

7. Fertility

• Total Fertility Rate (TFR): the average number of children a woman would have over her reproductive years (ages 15–49), if current age-specific birth rates stayed the same.
• Age-specific fertility rate (ASFR) = births to women of an age group ÷ number of women in that group (usually per 1,000 women).
• TFR = Sum of ASFRs across 5-year age groups × 5 (when ASFR is per woman).

• Worked example: ASFRs (births per woman per year) for the seven 5-year groups (15–19 … 45–49) are 0.02, 0.12, 0.12, 0.06, 0.03, 0.01, 0.00.

  • Sum = 0.36 → TFR = 0.36 × 5 = 1.8 (below replacement).

• Replacement level fertility: the TFR at which each generation exactly replaces itself, ignoring migration. It is about 2.1.

• Why 2.1 and not 2.0? Some children die before they grow up and have children, and slightly more boys than girls are born.

• It is higher where child deaths are high.

• India's TFR:

• NFHS-5 (2019-21): 2.0, below replacement. It was 2.2 in NFHS-4 (2015-16) [2][3].
• SRS: 2.0 in 2021, down from 2.3 in 2014 [4].
• All but five states/UTs are at or below 2.1 (NFHS-5).

• The five above 2.1: Bihar (~3.0), Meghalaya, Uttar Pradesh, Jharkhand, Manipur.

• North–south divide:

• Southern states reached low fertility early → they are ageing now.
• Northern states still have higher fertility → they have young populations.

• This affects where labour moves, federal transfers, and seat delimitation debates.

• Cross-country (2021–23): China 1.2 (far below replacement), India 2.0, Pakistan 3.4 (well above).

8. Urbanisation

• Urbanisation: the rising share of people living in urban areas (towns and cities).

• Formula: Urbanisation (%) = Urban population ÷ Total population × 100

• India: 31.1% (Census 2011), about 36% now (2021–23). China: about 65%. Pakistan: about 38%.

• Good side: cities give more jobs, higher productivity and better services.
• Bad side (NCERT): unplanned urbanisation adds to pollution, congestion and slums.

9. Demographic transition toolkit

a) Dependency ratio

• Dependency ratio = (Population aged 0–14 + Population aged 65+) ÷ Population aged 15–64 × 100
• Child dependency ratio = 0–14 ÷ 15–64 × 100

• Old-age dependency ratio = 65+ ÷ 15–64 × 100

• Worked example: a country of 100 people has 25 children (0–14), 7 elderly (65+) and 68 working-age people (15–64).

• Total = (25 + 7) ÷ 68 × 100 ≈ 47. So every 100 workers support about 47 dependants.

• Child = 25 ÷ 68 × 100 ≈ 37. Old-age = 7 ÷ 68 × 100 ≈ 10.

• As fertility falls, the child ratio falls first. Later, as people live longer, the old-age ratio rises.

b) Demographic window of opportunity

• A period of a few decades when the working-age share is at its highest and the dependency ratio at its lowest.
• UNFPA puts India's window at about 2005–2055. Treat this as an estimate.
• The window only helps if workers are healthy, skilled and employed. Using it (the demographic dividend) is covered in human-capital.

c) Population pyramid

• A bar graph of population by age group (vertical) and sex (left for males, right for females).
• Its shape shows the stage of demographic transition (the move from high birth and death rates to low ones):
• Expansive: wide base, high births. Example: Pakistan.
• Constrictive: narrowing base, falling births. Example: India now.
• Stationary: nearly straight sides, low births and deaths. Example: ageing societies such as China.

d) Ageing population

• Ageing population: a rising share of older people, caused by low fertility and longer lives.
• China's greying after the one-child norm is the classic case.
• UNFPA India Ageing Report 2023: released by UNFPA India together with IIPS (International Institute for Population Sciences, Mumbai) [5].
• The 60+ share rises from about 10% (2022) to 20.8% by 2050, about 347 million people [5][6]. (NCERT scaffold: "about 21% by 2050".)

• The 60+ population is set to more than double, from 100 million (2011) to 230 million (2036) [5].

• Kerala and Tamil Nadu are ageing well ahead of Bihar and Uttar Pradesh.

• What ageing means for policy: more demand for pensions, geriatric healthcare and care work, and fewer workers paying taxes.

Cross-references: demographic dividend → human-capital. The China vs Pakistan strategy comparison → india-china-pakistan.

Prelims Hooks

• India's sex ratio = females per 1,000 males. The international convention = males per 100 females.
• NFHS-5 (2019-21): overall sex ratio 1,020 (rural 1,037, urban 985), but sex ratio at birth 929. A high overall ratio does NOT mean the bias at birth has ended.
• TFR covers women aged 15–49. Replacement level ≈ 2.1. India's TFR is 2.0 (NFHS-5 2019-21; SRS 2021). It was 2.2 in NFHS-4.
• The five states/UTs with TFR above 2.1 in NFHS-5: Bihar, Meghalaya, UP, Jharkhand, Manipur.
• Dependency ratio = (0–14 + 65+) ÷ (15–64) × 100. Trap: working age is 15–64, not 15–59.
• SRS is run by the Office of the Registrar General, India (Ministry of Home Affairs), not by MoSPI or NFHS.
• Table 8.1 (2021–23): China has negative growth (−0.10%), the lowest sex ratio (898), the lowest TFR (1.2), the highest urbanisation (65%) and the lowest density (150), even though it is the largest in area.
• PCPNDT Act, 1994 bans sex determination before birth. Beti Bachao Beti Padhao was launched in January 2015.
• China's population policies: one-child (1979-80) → two-child (2016) → three-child (2021).
• India Ageing Report 2023 (UNFPA + IIPS): 60+ share reaches 20.8% (~347 million) by 2050.

Mains Points

• Dividend or disaster: India's dependency ratio is falling, which opens a window (about 2005–2055). But the dividend depends on jobs, skills and women's work. Without them, a young population can mean unemployment and unrest. (GS-III: growth, employment.)
• Two Indias in one country: the south has below-replacement TFR and is ageing, while the north (Bihar ~3.0, UP) is still young. This shapes labour migration, Finance Commission transfers that use population as a criterion, and the delimitation debate. Policy must differ by region: elderly care in the south, education and family planning in the north. (GS-II/III.)
• Coercion vs development: China's one-child norm cut growth but left a sex-ratio imbalance and early ageing. India reached replacement-level fertility (TFR 2.0) mainly through education, health and choice. So "development is the best contraceptive" is a stronger model than coercion. (GS-II: welfare policy.)
• Gender bias that income cannot show: an overall sex ratio of 1,020 hides an SRB of 929. There are ~63 million missing women and a son meta-preference (Economic Survey 2017-18). Laws (PCPNDT 1994) and schemes (BBBP 2015) must be backed by stronger enforcement and by changing social norms, such as women's property rights and old-age security that does not depend on sons. SRB has improved from 899 (2014) to 913 (2021). (GS-I/II.)