This statistic shows the fertility rate worldwide from 1975 to 2100. By 2100, the worldwide fertility rate is projected to be an average of 1.94 children per woman.

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.

Chart and table of the U.S. birth rate from 1950 to 2025. United Nations projections are also included through the year 2100.

Chart and table of the World birth rate from 1950 to 2025. United Nations projections are also included through the year 2100.

Population figures over a 25-year period, including births, deaths and migration by sex for regions and local authorities in England. 2018-based estimates are the latest principal projection.

This statistic shows the projected number of births in the United States from 2017 to 2060. In the year 2060, it is projected that there will be about 4.39 million births in the United States.

Germany FSO Projection: Population: High Birth Rate Based Trend data was reported at 71,236.000 Person th in 2060. This records a decrease from the previous number of 71,533.000 Person th for 2059. Germany FSO Projection: Population: High Birth Rate Based Trend data is updated yearly, averaging 78,472.000 Person th from Dec 2014 (Median) to 2060, with 47 observations. The data reached an all-time high of 81,691.000 Person th in 2019 and a record low of 71,236.000 Person th in 2060. Germany FSO Projection: Population: High Birth Rate Based Trend data remains active status in CEIC and is reported by Federal Statistics Office Germany. The data is categorized under Global Database’s Germany – Table DE.G003: Population: Projection: Federal Statistics Office Germany.

Population Projections: Gross Birth Rate per year. Annual. National.

The projected crude birth rate in India, at national level, was expected to decrease to about 13 births per thousand people by 2031 to 2035 as opposed to the national crude birth rate from 2011 to 2015 which stood at more than 20 births per thousand people. At state level, Bihar reflected the highest crude birth rate from 2011 to 2015 as well as the highest projected crude birth rate from 2031-2035. By contrast, the states with the lowest projected crude birth rates were Punjab, Tamil Nadu, and Andhra Pradesh during the same time period.

This data set contains estimated teen birth rates for age group 15–19 (expressed per 1,000 females aged 15–19) by county and year.

DEFINITIONS

Estimated teen birth rate: Model-based estimates of teen birth rates for age group 15–19 (expressed per 1,000 females aged 15–19) for a specific county and year. Estimated county teen birth rates were obtained using the methods described elsewhere (1,2,3,4). These annual county-level teen birth estimates “borrow strength” across counties and years to generate accurate estimates where data are sparse due to small population size (1,2,3,4). The inferential method uses information—including the estimated teen birth rates from neighboring counties across years and the associated explanatory variables—to provide a stable estimate of the county teen birth rate. Median teen birth rate: The middle value of the estimated teen birth rates for the age group 15–19 for counties in a state. Bayesian credible intervals: A range of values within which there is a 95% probability that the actual teen birth rate will fall, based on the observed teen births data and the model.

NOTES

Data on the number of live births for women aged 15–19 years were extracted from the National Center for Health Statistics’ (NCHS) National Vital Statistics System birth data files for 2003–2015 (5).

Population estimates were extracted from the files containing intercensal and postcensal bridged-race population estimates provided by NCHS. For each year, the July population estimates were used, with the exception of the year of the decennial census, 2010, for which the April estimates were used.

Hierarchical Bayesian space–time models were used to generate hierarchical Bayesian estimates of county teen birth rates for each year during 2003–2015 (1,2,3,4).

The Bayesian analogue of the frequentist confidence interval is defined as the Bayesian credible interval. A 100*(1-α)% Bayesian credible interval for an unknown parameter vector θ and observed data vector y is a subset C of parameter space Ф such that 1-α≤P({C│y})=∫p{θ │y}dθ, where integration is performed over the set and is replaced by summation for discrete components of θ. The probability that θ lies in C given the observed data y is at least (1- α) (6).

County borders in Alaska changed, and new counties were formed and others were merged, during 2003–2015. These changes were reflected in the population files but not in the natality files. For this reason, two counties in Alaska were collapsed so that the birth and population counts were comparable. Additionally, Kalawao County, a remote island county in Hawaii, recorded no births, and census estimates indicated a denominator of 0 (i.e., no females between the ages of 15 and 19 years residing in the county from 2003 through 2015). For this reason, Kalawao County was removed from the analysis. Also , Bedford City, Virginia, was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. For consistency, Bedford City was merged with Bedford County, Virginia, for the entire 2003–2015 period. Final analysis was conducted on 3,137 counties for each year from 2003 through 2015. County boundaries are consistent with the vintage 2005–2007 bridged-race population file geographies (7).

Taiwan TW: UCB Projection: Crude Birth Rate: per 1000 Persons data was reported at 6.000 NA in 2050. This stayed constant from the previous number of 6.000 NA for 2049. Taiwan TW: UCB Projection: Crude Birth Rate: per 1000 Persons data is updated yearly, averaging 7.900 NA from Jun 1990 (Median) to 2050, with 61 observations. The data reached an all-time high of 16.600 NA in 1990 and a record low of 5.800 NA in 2044. Taiwan TW: UCB Projection: Crude Birth Rate: per 1000 Persons data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s Taiwan – Table TW.US Census Bureau: Demographic Projection.

Introduction This report presents projections of population from 2015 to 2025 by age and sex for Illinois, Chicago and Illinois counties produced for the Certificate of Need (CON) Program. As actual future population trends are unknown, the projected numbers should not be considered a precise prediction of the future population; rather, these projections, calculated under a specific set of assumptions, indicate the levels of population that would result if our assumptions about each population component (births, deaths and net migration) hold true. The assumptions used in this report, and the details presented below, generally assume a continuation of current trends. Methodology These projections were produced using a demographic cohort-component projection model. In this model, each component of population change – birth, death and net migration – is projected separately for each five-year birth cohort and sex. The cohort – component method employs the following basic demographic balancing equation: P1 = P0 + B – D + NM Where: P1 = Population at the end of the period; P0 = Population at the beginning of the period; B = Resident births during the period; D = Resident deaths during the period; and NM = Net migration (Inmigration – Outmigration) during the period. The model roughly works as follows: for every five-year projection period, the base population, disaggregated by five-year age groups and sex, is “survived” to the next five-year period by applying the appropriate survival rates for each age and sex group; next, net migrants by age and sex are added to the survived population. The population under 5 years of age is generated by applying age specific birth rates to the survived females in childbearing age (15 to 49 years). Base Population These projections began with the July 1, 2010 population estimates by age and sex produced by the U.S. Census Bureau. The most recent census population of April 1, 2010 was the base for July 1, 2010 population estimates. Special Populations In 19 counties, the college dormitory population or adult inmates in correctional facilities accounted for 5 percent or more of the total population of the county; these counties were considered as special counties. There were six college dorm counties (Champaign, Coles, DeKalb, Jackson, McDonough and McLean) and 13 correctional facilities counties (Bond, Brown, Crawford, Fayette, Fulton, Jefferson, Johnson, Lawrence, Lee, Logan, Montgomery, Perry and Randolph) that qualified as special counties. When projecting the population, these special populations were first subtracted from the base populations for each special county; then they were added back to the projected population to produce the total population projections by age and sex. The base special population by age and sex from the 2010 population census was used for this purpose with the assumption that this population will remain the same throughout each projection period. Mortality Future deaths were projected by applying age and sex specific survival rates to each age and sex specific base population. The assumptions on survival rates were developed on the basis of trends of mortality rates in the individual life tables constructed for each level of geography for 1989-1991, 1999-2001 and 2009-2011. The application of five-year survival rates provides a projection of the number of persons from the initial population expected to be alive in five years. Resident deaths data by age and sex from 1989 to 2011 were provided by the Illinois Center for Health Statistics (ICHS), Illinois Department of Public Health. Fertility Total fertility rates (TFRs) were first computed for each county. For most counties, the projected 2015 TFRs were computed as the average of the 2000 and 2010 TFRs. 2010 or 2015 rates were retained for 2020 projections, depending on the birth trend of each county. The age-specific birth rates (ASBR) were next computed for each county by multiplying the 2010 ASBR by each projected TFR. Total births were then projected for each county by applying age-specific birth rates to the projected female population of reproductive ages (15 to 49 years). The total births were broken down by sex, using an assumed sex-ratio at birth. These births were survived five years applying assumed survival ratios to get the projected population for the age group 0-4. For the special counties, special populations by age and sex were taken out before computing age-specific birth rates. The resident birth data used to compute age-specific birth rates for 1989-1991, 1999-2001 and 2009-2011 came from ICHS. Births to females younger than 15 years of age were added to those of the 15-19 age group and births to women older than 49 years of age were added to the 45-49 age group. Net Migration Migration is the major component of population change in Illinois, Chicago and Illinois counties. The state is experiencing a significant loss of population through internal (domestic migration within the U.S.) net migration. Unlike data on births and deaths, migration data based on administrative records are not available on a regular basis. Most data on migration are collected through surveys or indirectly from administrative records (IRS individual tax returns). For this report, net migration trends have been reviewed using data from different sources and methods (such as residual method) from the University of Wisconsin, Madison, Illinois Department of Public Health, individual exemptions data from the Internal Revenue Service, and survey data from the U.S. Census Bureau. On the basis of knowledge gained through this review and of levels of net migration from different sources, assumptions have been made that Illinois will have annual net migrants of -40, 000, -35,000 and -30,000 during 2010-2015, 2015-2020 and 2020-2025, respectively. These figures have been distributed among the counties, using age and sex distribution of net migrants during 1995-2000. The 2000 population census was the last decennial census, which included the question “Where did you live five years ago?” The age and sex distribution of the net migrants was derived, using answers to this question. The net migration for Chicago has been derived independently, using census survival method for 1990-2000 and 2000-2010 under the assumption that the annual net migration for Chicago will be -40,000, -30,000 and -25,000 for 2010-2015, 2015-2020 and 2020-2025, respectively. The age and sex distribution from the 2000-2010 net migration was used to distribute the net migrants for the projection periods. Conclusion These projections were prepared for use by the Certificate of Need (CON) Program; they are produced using evidence-based techniques, reasonable assumptions and the best available input data. However, as assumptions of future demographic trends may contain errors, the resulting projections are unlikely to be free of errors. In general, projections of small areas are less reliable than those for larger areas, and the farther in the future projections are made, the less reliable they may become. When possible, these projections should be regularly reviewed and updated, using more recent birth, death and migration data.

Chart and table of the Canada birth rate from 1950 to 2025. United Nations projections are also included through the year 2100.

Ecuador EC: Crude Birth Rate: per 1000 Persons data was reported at 11.300 NA in 2050. This records a decrease from the previous number of 11.400 NA for 2049. Ecuador EC: Crude Birth Rate: per 1000 Persons data is updated yearly, averaging 17.000 NA from Jun 1990 (Median) to 2050, with 61 observations. The data reached an all-time high of 30.400 NA in 1990 and a record low of 11.300 NA in 2050. Ecuador EC: Crude Birth Rate: per 1000 Persons data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s Ecuador – Table EC.US Census Bureau: Demographic Projection.

Population Projections: Gross Birth Rate per year. Annual. Provinces.

This table shows the average number of children per woman, the percentage of women by number and the average age of the mother at the birth of her first child. All figures are according to the birth generation of the woman. The figures refer to the population of the Netherlands.

Data available from: generation of Birth of 1935

Status of the figures: The figures based on the number of births observed up to and including 2016 are definitive. This means that the figures up to and including the 1966 birth generation are entirely based on observations. Figures from birth generation 1967 have been complemented by future expectations of fertility rates from the CBS Population Forecast 2017-2060. The figures from birth generation 2002 are based entirely on forecast figures.

Amendments as of 16 December 2020: This table has been discontinued. See paragraph 3 for the successor to this table.

Amendments as of 19 December 2017: None, this is a new table in which the previous forecast has been adjusted on the basis of the observations now available. The forecast period now runs from 2017 to 2060. This new table incorporates the figures related to birth observations up to and including 2016.

When will there be new figures? The frequency of appearance of this table is one-off. In December 2020, a new table with the (prognosis of the) key birth rates will be published.

Chart and table of the Asia birth rate from 1950 to 2025. United Nations projections are also included through the year 2100.

Today, globally, women of childbearing age have an average of approximately 2.2 children over the course of their lifetime. In pre-industrial times, most women could expect to have somewhere between five and ten live births throughout their lifetime; however, the demographic transition then sees fertility rates fall significantly. Looking ahead, it is believed that the global fertility rate will fall below replacement level in the 2050s, which will eventually lead to population decline when life expectancy plateaus. Recent decades Between the 1950s and 1970s, the global fertility rate was roughly five children per woman - this was partly due to the post-WWII baby boom in many countries, on top of already-high rates in less-developed countries. The drop around 1960 can be attributed to China's "Great Leap Forward", where famine and disease in the world's most populous country saw the global fertility rate drop by roughly 0.5 children per woman. Between the 1970s and today, fertility rates fell consistently, although the rate of decline noticeably slowed as the baby boomer generation then began having their own children. Replacement level fertility Replacement level fertility, i.e. the number of children born per woman that a population needs for long-term stability, is approximately 2.1 children per woman. Populations may continue to grow naturally despite below-replacement level fertility, due to reduced mortality and increased life expectancy, however, these will plateau with time and then population decline will occur. It is believed that the global fertility rate will drop below replacement level in the mid-2050s, although improvements in healthcare and living standards will see population growth continue into the 2080s when the global population will then start falling.

The data shows the year-wise estimated birth rates, death rates, infant mortality rates by residence by rural, urban and total for the states and union territories of India over the time period of seven years from 2009 to 2015.

Note: Infant Mortality Rate for smaller States & Union Territories are based on three-years period 2013-15.

Chart and table of the Africa fertility rate from 1950 to 2025. United Nations projections are also included through the year 2100.