The following abbreviations are used in the indicator titles:
Indicator code: E998006.T
The average number of children that would be born per woman if all women lived to the end of their childbearing years and bore children according to a given set of age- specific fertility rates. It is computed by summing the age-specific fertility rates for all ages and multiplying by the interval into which the ages are grouped. Data usually provided by the CSOs. Reports of WB, UNDP and country statistical yearbooks are used as a source for the HFA database._
collected annually.
2011, 2012 and 2013 data are estimations.
http://www.fzs.ba/
Statistical Institute of Republic of Srpska; http:// www.rzs.rs.ba
Number of livebirths per 1000 women of fertility age (15-49 years).
Data revised in December 2014.
Break in time series:
2014: According to the Census 2014 data, the total number of the population significantly reduced
(difference, compared to the previous equals about 760,000). National Statistics Office (NSO) of
Georgia revised the number of the population, population structure, etc. based on the census 2014.
This caused a sharp increase of the indicators.
1, Reihe 1.1.
http://www.destatis.de or http://www.gbe-bund.de
Coverage: The total fertility rate indicates how many children a woman would have during her life,
if her reproductive behaviour would be like that of all women between 15 and 49 years in each
considered year.
Estimation method: The total fertility rate is calculated as summarized birth rate. For this purpose
all births are sorted by the age of the mothers. Then, for each age the proportion of women of that
age, who have had a baby this year, is calculated. The result is age-specific birth rates for the
age of 15 years, 16 years etc. up to 49 years. These average values of the different ages are added.
The result is the summarized birth rate.
Break in time series: The population numbers prior to 2011 are taken from the Update of the
Population based on earlier censuses (Former Federal Republic of Germany 1987, German Democratic
Republic 1990). Starting from 2011 the population numbers are based on the Federal Census 2011
(census data as of 27 November 2015). Therefore, for the years from 2011 onwards differences to
previous publications of population related numbers are possible.
provisional.
November 2013: Total fertility rate figures for 2001-2011 have been recalculated using the new
number of live births and population.
calculated using resident population (Maltese and foreign).
www.statistica.md.
Coverage: Since 1997, the information is presented without the data for Transnistria and Bender
municipality.
Coverage: from 1998 onwards data do not cover Kosovo and Metohija Province that is under the interim
civilian and military administration of the UN.
http://www.ine.es/jaxi/menu.do?type=pcaxis&path=/t20/p318/&file=inebase&L=1
These data series have been reviewed once final population figures are available for the 2002-2011
period, provided by the 2001-2011 Intercensal Population Estimates.
www.scb.se (Online Statistical Database; table: PR0101_2015M12_DI_06-07_EN, table: BE0701AA)
1751-2012:
http://www.scb.se/sv_/Hitta-statistik/Statistik-efter-amne/Befolkning/Befolkningens-sammansattning/B
efolkningsstatistik/25788/25795/
2013-2014:
http://www.statistikdatabasen.scb.se/pxweb/sv/ssd/START__BE__BE0701/FruktsamhetSumNy/table/tableView
Layout1/?rxid=2a165fc6-0859-4a73-b696-2db1f4bd45e0
http://www.bfs.admin.ch/bfs/portal/fr/index/infothek/erhebungen__quellen/blank/blank/espop/01.html
http://www.bfs.admin.ch/bfs/portal/en/index/infothek/erhebungen__quellen/blank/blank/statpop/01.html
http://www.bfs.admin.ch/bfs/portal/fr/index/infothek/erhebungen__quellen/blank/blank/bevnat/01.html
Break in time series: In 2010, the synthesis statistic for calculation of the population of
residence (ESPOP) has been replaced by a statistic based on registers (STATPOP). From 2010: New
definition of the permanent resident population, which also includes asylum seekers with a total
length of stay of at least 12 months.
Method: In 2001, online application of Central Population Management System (MERNIS) has been
launched and with this application birth statistics have been collecting from this database. Since
MERNIS has a dynamic structure some of birth statistics are being updated regularly. Also before
2009, birth statistics were given by birthplace but after 2009 (incl. 2009) birth statistics have
started to be gathered by mother?s residence place.
2005 onwards ? Data not available
Understanding the Total Fertility Rate
The Total Fertility Rate (TFR) is a demographic measure that estimates the average number of children a woman would have over her reproductive lifetime. This indicator is crucial for understanding population growth and demographic changes within a society. By analyzing TFR, policymakers and researchers can gauge the potential for population increase or decline, influencing decisions in healthcare, education, and economic planning. The TFR provides insights into the reproductive behavior of a population, making it a fundamental statistic for social and economic analysis.
How is the Total Fertility Rate Calculated?
The calculation of the Total Fertility Rate involves summing the age-specific fertility rates across all reproductive ages, typically from 15 to 49 years. Each age-specific rate is the number of births per 1,000 women of that particular age, within a given year. The formula for TFR is expressed as the total of these rates divided by 1,000, which normalizes the figure to represent the average number of children per woman. This method ensures that the TFR provides a comprehensive snapshot of fertility trends, unaffected by the age structure of the population, thereby allowing for consistent comparisons over time and across different regions.
The Significance of the Total Fertility Rate
The Total Fertility Rate is more than a mere statistic; it is a vital indicator that affects national planning and policy. A country's TFR influences its approach to resource allocation in critical areas such as healthcare, education, and labor markets. For instance, a high TFR suggests a growing young population, necessitating more investments in schools and pediatric health services. Conversely, a low TFR might indicate an aging population, increasing the need for elderly care services and pension schemes. Understanding these dynamics helps governments anticipate and manage demographic shifts effectively.
Strengths and Limitations of the Total Fertility Rate
While the Total Fertility Rate is an invaluable tool in demographic analysis, it comes with its own set of strengths and limitations that must be considered.
Strengths
The primary advantage of the TFR is its ability to provide a standardized measure of fertility that is comparable across different geographical and temporal contexts. This comparability makes it an essential tool for international organizations and governments to assess and compare the reproductive behavior across countries. Furthermore, TFR is instrumental in predicting future population growth, aiding in the planning and implementation of various public policies and economic strategies.
Limitations
However, the TFR is not without its drawbacks. One significant limitation is its sensitivity to changes in societal norms and conditions. Shifts in attitudes towards marriage, contraception, and career prioritization can dramatically alter fertility rates, sometimes making TFR a less reliable predictor of long-term demographic trends. Additionally, the TFR does not account for the impact of immigration and emigration on a population's reproductive patterns, which can skew the data in countries with high levels of migration. Lastly, TFR assumes that the current fertility rates will persist throughout a woman's reproductive life, which may not be accurate given the rapid changes in societal and economic conditions.
In conclusion, while the Total Fertility Rate is a powerful tool for understanding demographic trends, it must be used judiciously, considering its strengths and limitations. By doing so, policymakers and researchers can better harness this metric to forecast and respond to the dynamic changes in population structures worldwide.