India birth rate for 2025 is 16.55, a 1.19% decline from 2024.

<ul style='margin-top:20px;'>

<li>India birth rate for 2024 was <strong>16.75</strong>, a <strong>3.74% increase</strong> from 2023.</li>
<li>India birth rate for 2023 was <strong>16.15</strong>, a <strong>1.16% decline</strong> from 2022.</li>
<li>India birth rate for 2022 was <strong>16.34</strong>, a <strong>0.94% decline</strong> from 2021.</li>
</ul>Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.

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.

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.

In 2023, the total fertility rate in India remained nearly unchanged at around 1.98 children per woman. Yet 2023 saw the lowest fertility rate in India with 1.98 children per woman. The total fertility rate is the average number of children that a woman of childbearing age (generally considered 15 to 44 years) is expected to have throughout her reproductive years. Unlike birth rates, which are based on the actual number of live births in a given population, fertility rates are estimates (similar to life expectancy) that apply to a hypothetical woman, as they assume that current patterns in age-specific fertility will remain constant throughout her reproductive years.Find more statistics on other topics about India with key insights such as life expectancy of men at birth, death rate, and life expectancy of women at birth.

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.

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: 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.

The average for 2022 based on 195 countries was 18.38 births per 1000 people. The highest value was in Niger: 45.03 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 2022. Below is a chart for all countries where data are available.

India death rate for 2025 is 7.53, a 0.76% increase from 2024.

<ul style='margin-top:20px;'>

<li>India death rate for 2024 was <strong>7.47</strong>, a <strong>0.77% increase</strong> from 2023.</li>
<li>India death rate for 2023 was <strong>7.42</strong>, a <strong>0.49% increase</strong> from 2022.</li>
<li>India death rate for 2022 was <strong>7.38</strong>, a <strong>0.49% increase</strong> from 2021.</li>
</ul>Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.

Vital Statistics: Birth Rate: per 1000 Population: Maharashtra data was reported at 15.000 NA in 2020. This records a decrease from the previous number of 15.300 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Maharashtra data is updated yearly, averaging 17.600 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 22.500 NA in 1998 and a record low of 15.000 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: Maharashtra 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 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: 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 death rate in India remained nearly unchanged at around 6.61 deaths per 1,000 inhabitants. The crude death rate is the annual number of deaths in a given population, expressed per 1,000 people. When looked at in unison with the crude birth rate, the rate of natural increase can be determined.Find more statistics on other topics about India with key insights such as life expectancy of women at birth, total fertility rate, and crude birth rate.

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: Death Rate: per 1000 Population: Karnataka data was reported at 6.200 NA in 2020. This stayed constant from the previous number of 6.200 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Karnataka data is updated yearly, averaging 7.100 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 7.900 NA in 1998 and a record low of 6.200 NA in 2020. Vital Statistics: Death Rate: per 1000 Population: Karnataka 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.GAH003: Vital Statistics: Death Rate: by States.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

According to the results of a survey on registered births in India between November 2013 and May 2014, Jain and Buddhist households had the highest share of registered births at over 90 percent. While Muslim households had a birth rate of about 69 percent and Hindu households saw a birth rate of almost 72 percent.

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.

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 the 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, with Africa's population forecast to increase from 1.4 billion in 2022 to over 3.9 billion by 2100.

The statistic shows the total population of India from 2019 to 2029. In 2023, the estimated total population in India amounted to approximately 1.43 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.