In 2023, the crude birth rate in live births per 1,000 inhabitants in India stood at 16.15. Between 1960 and 2023, the figure dropped by 26.75, though the decline followed an uneven course rather than a steady trajectory.

In India, the crude birth rate in 1880 was 41.5 live births per thousand people, meaning that approximately 4.2 percent of the population had been born in that year. After an initial jump from 40.9 to 46.5 births per thousand between 1885 and 1890, India's crude birth rate remained consistent at just over 45 until the middle of the twentieth century. It was during the late 1940s that India gained its independence from the British Empire, and from this point the crude birth rate has gradually decreased from over 45 births per thousand people in 1945, to below twenty today. In 2020, it is expected to be just 18 births per thousand.

In 2020, the northern state of Uttar Pradesh had the highest urban birth rate of 22.1 births per 1,000 inhabitants. It was followed by states of Bihar and Rajasthan. Among other states, Himachal Pradesh had the lowest birth rate in the urban areas that year.

Vital Statistics: Birth Rate: per 1000 Population: Uttar Pradesh data was reported at 25.100 NA in 2020. This records a decrease from the previous number of 25.400 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Uttar Pradesh data is updated yearly, averaging 28.700 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 32.800 NA in 2000 and a record low of 25.100 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: Uttar Pradesh data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH002: Vital Statistics: Birth Rate: by States.

Vital Statistics: Birth Rate: per 1000 Population: Punjab data was reported at 14.300 NA in 2020. This records a decrease from the previous number of 14.500 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Punjab data is updated yearly, averaging 17.000 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 22.400 NA in 1998 and a record low of 14.300 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: Punjab data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH002: Vital Statistics: Birth Rate: by States.

The average for 2022 based on 196 countries was 18.19 births per 1000 people. The highest value was in the Central African Republic: 45.42 births per 1000 people and the lowest value was in Hong Kong: 4.4 births per 1000 people. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

In 2023, the total fertility rate in children per woman in India was 1.98. Between 1960 and 2023, the figure dropped by 3.94, though the decline followed an uneven course rather than a steady trajectory.

The statistic displays the birth rate in India between 2009 and 2013. In 2009, the birth rate was around 19.8 births per 1,000 inhabitants, and has dropped slightly since. The fertility rate or the number of children born per woman in India can be found here.

Vital Statistics: Birth Rate: per 1000 Population: West Bengal data was reported at 14.600 NA in 2020. This records a decrease from the previous number of 14.900 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: West Bengal data is updated yearly, averaging 17.200 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 21.300 NA in 1998 and a record low of 14.600 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: West Bengal data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH002: Vital Statistics: Birth Rate: by States.

Vital Statistics: Birth Rate: per 1000 Population: Telangana data was reported at 16.400 NA in 2020. This records a decrease from the previous number of 16.700 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Telangana data is updated yearly, averaging 17.200 NA from Dec 2014 (Median) to 2020, with 7 observations. The data reached an all-time high of 18.000 NA in 2014 and a record low of 16.400 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: Telangana data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH002: Vital Statistics: Birth Rate: by States.

Vital Statistics: Birth Rate: per 1000 Population: Gujarat data was reported at 19.300 NA in 2020. This records a decrease from the previous number of 19.500 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Gujarat data is updated yearly, averaging 22.300 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 25.500 NA in 1998 and a record low of 19.300 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: Gujarat data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH002: Vital Statistics: Birth Rate: by States.

Vital Statistics: Birth Rate: per 1000 Population: Haryana data was reported at 19.900 NA in 2020. This records a decrease from the previous number of 20.100 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Haryana data is updated yearly, averaging 23.000 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 27.600 NA in 1998 and a record low of 19.900 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: Haryana data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH002: Vital Statistics: Birth Rate: by States.

In 2023, the infant mortality rate in India was at about 24.5 deaths per 1,000 live births, a significant decrease from previous years. Infant mortality as an indicatorThe infant mortality rate is the number of deaths of children under one year of age per 1,000 live births. This rate is an important key indicator for a country’s health and standard of living; a low infant mortality rate indicates a high standard of healthcare. Causes of infant mortality include premature birth, sepsis or meningitis, sudden infant death syndrome, and pneumonia. Globally, the infant mortality rate has shrunk from 63 infant deaths per 1,000 live births to 27 since 1990 and is forecast to drop to 8 infant deaths per 1,000 live births by the year 2100. India’s rural problemWith 32 infant deaths per 1,000 live births, India is neither among the countries with the highest nor among those with the lowest infant mortality rate. Its decrease indicates an increase in medical care and hygiene, as well as a decrease in female infanticide. Increasing life expectancy at birth is another indicator that shows that the living conditions of the Indian population are improving. Still, India’s inhabitants predominantly live in rural areas, where standards of living as well as access to medical care and hygiene are traditionally lower and more complicated than in cities. Public health programs are thus put in place by the government to ensure further improvement.

Vital Statistics: Birth Rate: per 1000 Population: Assam data was reported at 20.800 NA in 2020. This records a decrease from the previous number of 21.000 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Assam data is updated yearly, averaging 23.600 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 27.900 NA in 1998 and a record low of 20.800 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: Assam data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH002: Vital Statistics: Birth Rate: by States.

Niger had the highest birth rate in the world in 2024, with a birth rate of 46.6 births per 1,000 inhabitants. Angola, Benin, Mali, and Uganda followed. Except for Afghanistan, all 20 countries with the highest birth rates in the world were located in Sub-Saharan Africa. High infant mortality The reasons behind the high birth rates in many Sub-Saharan African countries are manyfold, but a major reason is that infant mortality remains high on the continent, despite decreasing steadily over the past decades, resulting in high birth rates to counter death rates. Moreover, many nations in Sub-Saharan Africa are highly reliant on small-scale farming, meaning that more hands are of importance. Additionally, polygamy is not uncommon in the region, and having many children is often seen as a symbol of status. Fastest-growing populations As the high fertility rates coincide with decreasing death rates, countries in Sub-Saharan Africa have the highest population growth rates in the world. As a result, Africa's population is forecast to increase from 1.4 billion in 2022 to over 3.9 billion by 2100.

Vital Statistics: Birth Rate: per 1000 Population: Andhra Pradesh: Urban data was reported at 15.000 NA in 2020. This records a decrease from the previous number of 15.200 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Andhra Pradesh: Urban data is updated yearly, averaging 16.700 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 21.100 NA in 1998 and a record low of 15.000 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: Andhra Pradesh: Urban data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH002: Vital Statistics: Birth Rate: by States.

In 2023, there were five countries, where the average woman of childbearing age can expect to have over six children throughout their lifetime. In fact, of the 20 countries in the world with the highest fertility rates, Afghanistan and Yemen are the only countries not found in Sub-Saharan Africa. High fertility rates in Africa With a fertility rate of 6.13 and 6.12 children per woman, Somalia and Chad were the countries with the highest fertility rate in the world. Population growth in Chad is among the highest in the world. Lack of healthcare access, as well as food instability, political instability, and climate change, are all exacerbating conditions that keep Chad's infant mortality rates high, which is generally the driver behind high fertility rates. This situation is common across much of the continent, and, although there has been considerable progress in recent decades, development in Sub-Saharan Africa is not moving as quickly as it did in other regions. Demographic transition While these countries have the highest fertility rates in the world, their rates are all on a generally downward trajectory due to a phenomenon known as the demographic transition. The third stage (of five) of this transition sees birth rates drop in response to decreased infant and child mortality, as families no longer feel the need to compensate for lost children. Eventually, fertility rates fall below replacement level (approximately 2.1 children per woman), which eventually leads to natural population decline once life expectancy plateaus. In some of the most developed countries today, low fertility rates are creating severe econoic and societal challenges as workforces are shrinking while aging populations are placin a greater burden on both public and personal resources.

The statistic shows the total population of India from 2020 to 2030. In 2024, the estimated total population in India amounted to approximately 1.44 billion people. Total population in India India currently has the second-largest population in the world and is projected to overtake top-ranking China within forty years. Its residents comprise more than one-seventh of the entire world’s population, and despite a slowly decreasing fertility rate (which still exceeds the replacement rate and keeps the median age of the population relatively low), an increasing life expectancy adds to an expanding population. In comparison with other countries whose populations are decreasing, such as Japan, India has a relatively small share of aged population, which indicates the probability of lower death rates and higher retention of the existing population. With a land mass of less than half that of the United States and a population almost four times greater, India has recognized potential problems of its growing population. Government attempts to implement family planning programs have achieved varying degrees of success. Initiatives such as sterilization programs in the 1970s have been blamed for creating general antipathy to family planning, but the combined efforts of various family planning and contraception programs have helped halve fertility rates since the 1960s. The population growth rate has correspondingly shrunk as well, but has not yet reached less than one percent growth per year. As home to thousands of ethnic groups, hundreds of languages, and numerous religions, a cohesive and broadly-supported effort to reduce population growth is difficult to create. Despite that, India is one country to watch in coming years. It is also a growing economic power; among other measures, its GDP per capita was expected to triple between 2003 and 2013 and was listed as the third-ranked country for its share of the global gross domestic product.

For most of the past two centuries, falling birth rates have been associated with societal progress. During the demographic transition, where pre-industrial societies modernize in terms of fertility and mortality, falling death rates, especially among infants and children, are the first major change. In response, as more children survive into adulthood, women have fewer children as the need to compensate for child mortality declines. This transition has happened at different times across the world and is an ongoing process, with early industrial countries being the first to transition, and Sub-Saharan African countries being the most recent to do so. Additionally, some Asian countries (particularly China through government policy) have gone through their demographic transitions at a much faster pace than those deemed more developed. Today, in countries such as Japan, Italy, and Germany, birth rates have fallen well below death rates; this is no longer considered a positive demographic trend, as it leads to natural population decline, and may create an over-aged population that could place a burden on healthcare systems.

Tiger (Panthera tigris) is an indicator species of ecological health and conservation efforts. Due to excessive poaching, the tiger was locally extinct in Panna Tiger Reserve, central India. Subsequent successful reintroduction efforts have brought the species back from the verge of extinction and have demonstrated the success of conservation translocations in response to such critical situations. To understand the demographic characteristics of the tigers reintroduced to Panna Tiger Reserve, we used an ensemble approach of different sampling techniques and direct observations from a long-term data-set spanning more than 10 years. We evaluated different demographic indicators (population status, growth rate, mean litter size, inter-birth interval, and survival probability). Since reintroduction in 2009, 18 females have recruited 120 cubs from 45 litters. This led to 59 individuals in 2021 with a growth rate of ~26%. The mean litter size was 2.66 (SE 0.1), and the inter-birth interval was 19.16 months (SE 0.5). The high survival rate of the reintroduced population (0.82±0.2) helped to achieve the success of reintroduction. We observed non-constant mortality trajectories for both sexes (higher survival probabilities for females) with a moderately higher risk of death in younger (<1 year) and older (>10 years) individuals. Our results showed the effectiveness of translocation and conservation efforts. The recovered population can be used as a founder for augmentation in other recovering tiger populations. A long-term tiger-centric management plan should be implemented in the area adjacent to Panna Tiger Reserve to conserve and secure the habitat of the entire landscape for the long-term survival of the reintroduced population in a metapopulation framework. Methods Data Collection Radio telemetry A total of 25 tigers (7 males and 18 females; Table S1) were radio-collared between March 2009 and June 2020 as a part of the long-term project entitled “Tiger Reintroduction and Recovery Programme in Panna Tiger Reserve, Madhya Pradesh.” Animals were captured and collared under the permission of the Madhya Pradesh Forest Department (MPFD Letter No./Exp./2009/1205 dated 31/8/09) following the capture rule and regulation of the Wildlife Protection Act, 1972 Section 11 (1A). Animals were tracked and immobilized, using a ‘Hellabrunn mixture’ (125 mg xylazine + 100 mg ketamine/ml) (Hafner et al., 1989) injected through a Tele-inject projector (Model 4V.31). The target individuals were chemically immobilized. The entire process took place under the supervision of a veterinarian. Tigers were fitted with Very High Frequency transmitters (15 individuals; Telonics® Inc) and VHF/ GPS/ UHF collars (10 individuals; African Wildlife Tracking® Inc and Vetronic Aerospace®). All collared tigers were monitored very intensively with UHF and satellite tools. Staff and researchers jointly monitored VHF collared individuals and tracked the animals 24 hours per day, 7 days per week for the duration of the study. Camera trapping Grid-based systematic camera trap sampling was carried out from 2012-2016 in a 4km2 grid cell size; a more intensive effort took place from 2017-2021 with a 2km2 grid cell size (Jhala et al., 2019). The entire PTR was sampled systematically by placing a pair of camera traps (531 locations) on either side of dirt roads, animal trails, or dry river beds to maximize the chances of capturing tigers on camera. Camera traps were active for at least 30 days during the winter season. In addition to the double-sided camera traps, a single-sided continuous camera trap monitoring system (CCMS) was adapted to monitor the movement of non-collared tigers throughout the year. We used a grid-based approach (same 2km2 grid cell size) for CCMS to sample throughout PTR. Simultaneously, camera traps were also placed opportunistically at vantage points, kills, and nearby den sites. Cameras were checked every 5-7 days. Individually identifiable tiger pictures, including both flanks, were updated every year. Newly captured tiger images were compared manually by using their respective unique stripe patterns. The intensive use of radio-telemetry and camera trapping helped us to document the emigration of tigers from PTR. As there are no other source populations around PTR, we did not record any immigration events during 2009-2021. Routine patrolling with elephants, camera traps, and intensive radio-telemetry helped us to quantify the IBI, initial litter size and cub survival. Analytical methods Population status and growth rate All adult and sub-adult tigers were radio-collared during the initial days after reintroduction. With a growing tiger population, all individuals were not radio-tagged; therefore, the camera trap-based survey method was adapted to understand the movement of non-collared animals. To calculate the growth rate of tigers, we used the software Vortex version 10 (Lacy & Pollak, 2014) with 100 iterations. Vortex is appropriate for modelling species with low fecundity and long life spans and is the most commonly used software in published reintroduction models (Armstrong & Reynolds, 2012). The growth rate (r) of r > 0 indicates the population grows, while r < 0 indicates a population decline. Similarly, the annual multiplicative growth rate (λ) indicates a positive population growth if λ > 1.0 (Nt+1 > Nt), while λ < 1.0 (Nt+1 < Nt) indicates a population decline. Litter size and inter-birth interval Tiger individuals were identified by their unique stripe patterns (McDougal, 1977; Karanth, 1995) on their left and right flanks. Recording and documenting actual litter size at birth for any free-ranging elusive large carnivores is difficult; therefore, we determined the litter size of the tiger at the first sighting. Once the first sight or photo captured of the female with cubs was recorded, the approximate date of birth of the cubs was estimated by deducting two months from the first appearance (Smith et al., 1987). However, for collared females, the litter size or date of birth of cubs was confirmed by the direct sighting, using radio-telemetry tracking. The IBI was calculated when the same female produced second or consecutive successful litters. We assumed the cubs were dead, if not photo captured or found to be moving with mothers for more than six months. Usually, females conceive and give birth to another litter within 4-10 months after losing all cubs of the previous litter; such instances were discarded for IBI calculations (Singh et al., 2013). Since our monitoring was intensive, we had a high detection of tigers during the study period, except for when the individuals dispersed outside the PTR. Survivorship The detection non-detection matrix was prepared by compiling camera trap, CCMS, and radio-telemetry (to ensure whether the individual was within the PTR or not) data, and data were analyzed in the Capture-Mark-Recapture (CMR) framework (Table S1); since the detection probability of an animal within its home range was not involved in our study, imperfect detection was intentionally not addressed in our analysis. We used the Cormack-Jolly-Seber (CJS; Pledger et al., 2003) method to estimate the survival rate from one sampling period to the next; the survival rate is calculated as a proportion of animals alive at time ti versus time ti+1. Survival (ϕ) and recapture probability (p) depend on marked individuals' re-observation. Sex of each tiger, an intrinsic factor, and time (extrinsic factor) were included as covariates in the model of survival rate. As males and females have different life history traits, their survival probabilities might differ (Smith, 1993). Males show a lower survival probability than females in most mammalian species (Krebs, 1972). We modelled the survival probability using the ‘marked’ package (Laake et al., 2013) in R Core Team (2022). The Akaike Information Criterion (AIC) value was calculated for every model to determine the best fit model.