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 total fertility rate of the world has dropped from around 5 children per woman in 1950, to 2.2 children per woman in 2025, which means that women today are having fewer than half the number of children that women did 75 years ago. Replacement level fertility This change has come as a result of the global demographic transition, and is influenced by factors such as the significant reduction in infant and child mortality, reduced number of child marriages, increased educational and vocational opportunities for women, and the increased efficacy and availability of contraception. While this change has become synonymous with societal progress, it does have wide-reaching demographic impact - if the global average falls below replacement level (roughly 2.1 children per woman), as is expected to happen in the 2050s, then this will lead to long-term population decline on a global scale. Regional variations When broken down by continent, Africa is the only region with a fertility rate above the global average, and, alongside Oceania, it is the only region with a fertility rate above replacement level. Until the 1980s, the average woman in Africa could expect to have 6-7 children over the course of their lifetime, and there are still several countries in Africa where women can still expect to have 5 or more children in 2025. Historically, Europe has had the lowest fertility rates in the world over the past century, falling below replacement level in 1975. Europe's population has grown through a combination of migration and increasing life expectancy, however even high immigration rates could not prevent its population from going into decline in 2021.

Crude birth rates, age-specific fertility rates and total fertility rates (live births), 2000 to most recent year.

Between 2010 and 2023, the fertility rate in Sweden decreased steadily, dropping to 1.5. The fertility rate is defined as the number of children that would be born or are likely to be born to a woman if she lives to the end of her reproductive years. A similar decline was visible for the crude birth rate, which in 2022 was down at 10 births per 1,000 inhabitants.

More immigrants than emigrants

Despite the decreasing fertility- and crude birth rate in Sweden, the population in Sweden continues to grow. More babies are born each year than people dying, which contributes to a growing population. However, the major reason behind the continued population growth is the positive inflow of immigrants. Few people are leaving the country, while many more migrants are arriving in Sweden.

Fertility rate in Europe

Even though the fertility rate in the country decreased over the last 10 years, Sweden had a higher fertility rate than many other countries in Europe in 2023. The Faroe Islands had the highest fertility rate, whereas Andorra had the lowest.

The outbreak of the COVID-19 in early 2020 and the recurring epidemic in later years have disturbed China’s economy. Moreover, China’s demographic dividend has been disappearing due to its fastest aging population and declining birth rate. The birth rates in eastern provinces of China are much lower than those of the western provinces. Considering the impacts of the COVID-19 and aging population, this paper focused on the relationship between birth rate and the disposable income and tried to find effective measures to raise China’s birth rate. We discovered through regression analysis that the link between per capita disposable income and birth rate is initially "reverse J" and later "inverted J", indicating that per capita disposable income will influence the birth rate. Women’s employment rate and educational level are negatively correlated with the birth rate. To raise the fertility rate in China, it is necessary to increase the marriage rate and the willingness to have children by raising the per capita disposable income and introducing effective tax relief policies.

In the United Kingdom, the crude birth rate in 1800 was 37 live births per thousand people, meaning that 3.7 percent of the population had been born in that year. From 1800 until 1830, the crude birth rate jumped between 35 and 45, before plateauing between 35 and 37 until the 1880s. From 1880 until the Second World War, the crude birth rate dropped to just under fifteen births per one thousand people, with the only increase coming directly after World War One. After WWII, the United Kingdom experienced a baby boom, as many soldiers returned home and the economy recovered, however this boom stopped in the late 1960s and the crude birth rate went into decline again. From the late 1970s until today, the crude birth rate has remained between eleven and fourteen, and is expected to be 11.5 in 2020.

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).

Live births and stillbirths annual summary statistics, by sex, age of mother, whether within marriage or civil partnership, percentage of non-UK-born mothers, birth rates and births by month and mothers' area of usual residence.

In the United States, the crude birth rate in 1800 was 48.3 live births per thousand people, meaning that 4.8 percent of the population had been born in that year. Between 1815 and 1825 the crude birth rate jumped from 46.5 to 54.7 (possibly due to Florida becoming a part of the US, but this is unclear), but from this point until the Second World War the crude birth rate dropped gradually, reaching 19.2 in 1935. Through the 1940s, 50s and 60s the US experienced it's baby boom, and the birth rate reached 24.1 in 1955, before dropping again until 1980. From the 1980s until today the birth rate's decline has slowed, and is expected to reach twelve in 2020, meaning that just over 1 percent of the population will be born in 2020.

About this dataset

Context

The dataset tabulates the data for the Texas population pyramid, which represents the Texas population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Texas, is 32.4.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Texas, is 19.0.
• Total dependency ratio for Texas is 51.4.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Texas is 5.3.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Texas population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Texas for the selected age group is shown in the following column.
• Population (Female): The female population in the Texas for the selected age group is shown in the following column.
• Total Population: The total population of the Texas for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Texas Population by Age. You can refer the same here

BackgroundAge-related decline in fertility is widely recognized. However, a quantitative evaluation of changes in oocyte quality and quantity remains insufficient. Therefore, developing a mathematical model to quantitatively predict live birth rates affected by these changes is essential for supporting decision-making in assisted reproductive technology.MethodsIn this retrospective cohort study, we developed a mathematical model to predict live birth rates based on oocyte quality and quantity using IVF treatment data from our clinic over an 8-year period. In the first stage, medically meaningful model functions were selected, and curve fitting was performed using weighted nonlinear least-squares regression to quantify age-related changes in oocyte quality and quantity. For oocyte quality, a comparative analysis was conducted on our clinical data and other large-scale datasets, modeling the live birth rate per single vitrified-warmed blastocyst transfer (SVBT) in correlation with the euploidy rate. For oocyte quantity, the distributions of anti-Müllerian hormone levels, antral follicle count, mature oocyte count, and transferable embryo count were analyzed by two-dimensional weighted nonlinear least-squares regression. In the second stage, logistic regression was applied to analyze live birth rates per SVBT and oocyte pick-up, incorporating multiple explanatory variables.ResultsThe adjusted R-squared values for the curve fitting results were above 0.9, indicating high fitting accuracy. In oocyte quality evaluation, all datasets showed that the values declined to half their peak by the age of 40 years. With respect to oocyte quantity, complete distribution characteristics were successfully modeled, enabling calculations at any percentile value. Logistic regression analysis incorporating blastocyst grade and culture duration as explanatory variables allowed for embryo selection based on a single indicator (i.e., the live birth rate). In the predictive model for live birth rate per oocyte pick-up, which included age, AMH levels, and number of retrieval cycles as explanatory variables, logistic regression analysis showed an AUC of 0.84 and an accuracy of 76.4%, demonstrating high predictive performance.ConclusionMathematical models of age-dependent oocyte quality and quantity were successfully developed. These models were integrated to construct a multi-variable predictive tool for estimating live birth rates, offering valuable insights for reproductive decision-making.

Nigeria: The number of crude births per 1000 people, per year: The latest value from 2023 is 32.95 births per 1000 people, a decline from 33.19 births per 1000 people in 2022. In comparison, the world average is 17.86 births per 1000 people, based on data from 196 countries. Historically, the average for Nigeria from 1960 to 2023 is 43.95 births per 1000 people. The minimum value, 32.95 births per 1000 people, was reached in 2023 while the maximum of 49.25 births per 1000 people was recorded in 1978.

Many places around the world have experienced population growth in the past decade and even population decline due to the COVID pandemic. According to worldometer’s current statistics the global population continues to thrive reaching a little over 8 billion and still growing. Although, Kazakhstan only ranks 64 we can see that they have a decent 1.21 percent yearly change with the net change being about 225,000 to the total of 19 million. When we look at their 2021 stats from Our World in Data for birth rates and death rates per 1,000 people, we can see that they are still a growing population as the birth rate (21.54) is double the death rate (10.23). Birthrates measure the number of births in a population by using a percentage or a ratio per 1,000 people and Death rates measure using the same methods (Marston, Knox, Liverman, Del Casino, Robbins, 2019, p. 39). Not only does this contribute to the growing population, but groups of people who weren’t living there whose ethnicity is from Kazakhstan are moving back into their home country. Ethnicity is defined as a “state of belonging to a social group that has a common national or cultural tradition; socially created system of rules about who belongs to a particular group” (Marston, Knox, Liverman, Del Casino, Robbins, 2019, p. 36). Population growth isn’t necessarily a bad thing as long as it is sustainable, but for Kazakhstan population growth can be dangerous as generally they have been struggling with basic economic rights and are being directed to the northern region.

This map service, derived from World Bank data, shows various characteristics of the Health topic. The World Bank Group provides financing, state-of-the-art analysis, and policy advice to help countries expand access to quality, affordable health care; protects people from falling into poverty or worsening poverty due to illness; and promotes investments in all sectors that form the foundation of healthy societies.Age Dependency Ratio: Age dependency ratio is the ratio of dependents--people younger than 15 or older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population. Data from 1960 – 2012.Age Dependency Ratio Old: Age dependency ratio, old, is the ratio of older dependents--people older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population. Data from 1960 – 2012.Birth/Death Rate: Crude birth/death rate indicates the number of births/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. Data spans from 1960 – 2008.Total Fertility: Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with current age-specific fertility rates. Data shown is for 1960 - 2008.Population Growth: Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage.
Population is based on the de facto definition of population, which
counts all residents regardless of legal status or citizenship--except
for refugees not permanently settled in the country of asylum, who are
generally considered part of the population of the country of origin. Data spans from 1960 – 2009.Life Expectancy: Life expectancy at birth indicates the number of years a newborn infant
would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. Data spans from 1960 – 2008.Population Female: Female population is the percentage of the population that is female. Population is based on the de facto definition of population. Data from 1960 – 2009.For more information, please visit: World Bank Open Data. _Other International User Community content that may interest you World Bank World Bank Age World Bank Health

In 2023, there were around *** live births per 1,000 inhabitants in Japan, down from about *** in the previous year. The total number of live births in the nation amounted to approximately ******* in 2023. Japan’s super aging society Directly after the end of WWII, the live birth rate in Japan was over ** per 1,000 of population. The rate has constantly dropped in the last decades after the second baby boom (between 1971 and 1974). Meanwhile, the life expectancy of the Japanese people has increased, reaching about **** years for women and **** years for men in 2022. Due to the combination of both factors, Japan has developed into one of the most rapidly aging societies in the world. Almost ** percent of Japan’s population is currently aged 65 years and older, falling into the “super-aged nation” defined by international institutions and organizations.  Decreasing number of marriages In Japan, the number of births outside of marriage is small. The Japanese government, therefore, considers the decreasing number of marriages as the driving factor behind the country’s fertility decline. As of 2023, the number of marriages per 1,000 Japanese citizens was ***, less than half compared to that in the early *****. The average age of first marriage has also risen for both men and women. This trend can be partially attributed to the increasing number of employed and therefore financially and socially independent women in the past two decades. The employment rate of women in Japan exceeded ** percent for the first time in history in ****.

Rapid population growth in developing countries in the middle of the 20th century led to fears of a population explosion and motivated the inception of what effectively became a global population-control program. The initiative, propelled in its beginnings by intellectual elites in the United States, Sweden, and some developing countries, mobilized resources to enact policies aimed at reducing fertility by widening contraception provision and changing family-size norms. In the following five decades, fertility rates fell dramatically, with a majority of countries converging to a fertility rate just above two children per woman, despite large cross-country differences in economic variables such as GDP per capita, education levels, urbanization, and female labor force participation. The fast decline in fertility rates in developing economies stands in sharp contrast with the gradual decline experienced earlier by more mature economies. In this paper, we argue that population-control policies likely played a central role in the global decline in fertility rates in recent decades and can explain some patterns of that fertility decline that are not well accounted for by other socioeconomic factors.

About this dataset

Context

The dataset tabulates the data for the Watford City, ND population pyramid, which represents the Watford City population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Watford City, ND, is 38.0.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Watford City, ND, is 9.8.
• Total dependency ratio for Watford City, ND is 47.8.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Watford City, ND is 10.2.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Watford City population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Watford City for the selected age group is shown in the following column.
• Population (Female): The female population in the Watford City for the selected age group is shown in the following column.
• Total Population: The total population of the Watford City for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Watford City Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Indiana population pyramid, which represents the Indiana population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Indiana, is 30.0.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Indiana, is 25.5.
• Total dependency ratio for Indiana is 55.4.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Indiana is 3.9.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Indiana population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Indiana for the selected age group is shown in the following column.
• Population (Female): The female population in the Indiana for the selected age group is shown in the following column.
• Total Population: The total population of the Indiana for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Indiana Population by Age. You can refer the same here

This is the population and household status by township in Seocheon-gun, Chungcheongnam-do in June 2025. It provides the population, number of households, number of men, number of women, sex ratio, and population per household by township. Seocheon-gun is one of the nine areas with a population decline in Chungcheongnam-do. (Based on: June 30, 2025) Since there are many data, not all data can be displayed in Excel, Notepad, etc., so please use analysis software. For more detailed population statistics and status, please check the notice on the Seocheon-gun Office website. https://www.seocheon.go.kr/

This map service, derived from World Bank data, shows various characteristics of the Health topic. The World Bank Group provides financing, state-of-the-art analysis, and policy advice to help countries expand access to quality, affordable health care; protects people from falling into poverty or worsening poverty due to illness; and promotes investments in all sectors that form the foundation of healthy societies.Age Dependency Ratio: Age dependency ratio is the ratio of dependents--people younger than 15 or older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population. Data from 1960 – 2012.Age Dependency Ratio Old: Age dependency ratio, old, is the ratio of older dependents--people older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population. Data from 1960 – 2012.Birth/Death Rate: Crude birth/death rate indicates the number of births/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. Data spans from 1960 – 2008.Total Fertility: Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with current age-specific fertility rates. Data shown is for 1960 - 2008.Population Growth: Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage.
Population is based on the de facto definition of population, which
counts all residents regardless of legal status or citizenship--except
for refugees not permanently settled in the country of asylum, who are
generally considered part of the population of the country of origin. Data spans from 1960 – 2009.Life Expectancy: Life expectancy at birth indicates the number of years a newborn infant
would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. Data spans from 1960 – 2008.Population Female: Female population is the percentage of the population that is female. Population is based on the de facto definition of population. Data from 1960 – 2009.For more information, please visit: World Bank Open Data. _Other International User Community content that may interest you World Bank World Bank Age World Bank Health