Values Of Total Fertility Rate Research Articles

Fertility Trends and Population Policy in the Canton of Sarajevo

The demography of Bosnia and Herzegovina fits into the Southeast-European trends with sub-replacement fertility and high emigration rates. At the national level, natural decline started in 2007, while migration is influenced by the war and its aftermath, as well as several economic and political push factors (low wages, corruption, high unemployment, etc.), and also has a serious impact on birth and death rates. On the other hand, demographic trends in the Canton of Sarajevo differ from the national pattern. It reached sub-replacement fertility more than a decade later (2019), and migration still has a positive balance: besides significant emigration rates, immigration to the Canton, especially from the other parts of Bosnia and Herzegovina, is higher. Since migration involves younger generations, it is also a factor behind the more favourable fertility rates, and can be evaluated as a consequence of better socio-economic development in the capital and its gravitational area. In the Canton, there have been several factors that contributed to the decline of birth rates in the past decades. Changing functions of family and children in the society, increasing women's employment, educational level and career aspirations, growing individualism and rationality, the changing social climate in relation to children, higher personal standards, and other socio-psychological factors should be mentioned. Birth rates were also (positively) influenced by other factors. One of the most important points is that the Canton has certainly one of the best support policies in Bosnia and Herzegovina including financial support and allowances for newborn children, subsidies in preschool education, primary and secondary schools as well as scholarships for pupils and students. Positive economic trends such as new constructions of residential buildings, business, and shopping centres, as well as an even stronger concentration of attractive institutions and activities should be emphasised. In 2021, the total fertility rate accounted for 1.38 in the Canton, while it has shown a great variation among the municipalities. The municipality with the lowest total fertility rate (Novi Grad) had the highest total number of live births in the Canton, while the municipality with the lowest total number of live births (Trnovo) was the only one with total fertility rate values above the replacement level. Due to its demographic, economic and political significance, the Canton of Sarajevo is a key area for population revitalisation in Bosnia and Herzegovina. Therefore, local measures have national significance, and can act as a model for the rest of the country in terms of population policy as well.

Spatial variability of total fertility rate and crude birth rate in a low-fertility country: Patterns and trends in regional and local scale heterogeneity across Italy, 2002–2018

Fertility is a key process shaping long-term population dynamics. Distinctive fertility trends have characterized demographic transitions, exhibiting sequential periods of spatial convergence and divergence. This descriptive study investigates the spatiotemporal evolution of Total Fertility Rate (TFR) and Crude Birth Rate (CBR) at different geographical scales in Italy between 2002 and 2018. Descriptive statistics of the TFR and CBR values across geographical scales were computed and the associated maps were prepared for the most detailed spatial levels available; specifically, down to the municipality level. Spatial analysis at the provincial and municipality level was based on both global and local Moran's indexes. Southern Italy, a mostly disadvantaged region, was characterized by relatively stable fertility patterns; fertility then decreased following an opposite trend with respect to economic conditions. The reverse relationship was observed in Northern Italy. As such, economic expansion and recession impacted fertility more intensively in Northern Italy.

POPULATION CONTROL POLICIES IN THE SPECIAL REGION OF YOGYAKARTA AFTER TOTAL FERTILITY RATE INCREASED BASED ON IDHS 2017

This study has two main objectives. The first is to explain the achievement of the value of Total Fertility Rate (TFR) in the Special Region of Yogyakarta (DIY). The second is to explain the population control policy after the increase in TFR in DIY results of the 2017 IDHS. The population control policy taken by the DIY government comes from the Grand Design of Population Development document published by the People's Welfare Bureau. The method used in this study is library research. The analyst uses qualitative descriptive. The results showed that according to IDHS data, there had been fluctuations in the value of TFR during 1991-2017. In the last ten years, the pattern of TFR values in DIY has tended to increase. Population control is necessary to prevent this tendency. The policies taken by the government in the future in terms of population control in DIY include suppressing population growth rates, maturing marital age, controlling the value of TFR, and increasing the prevalence of contraception. The DIY government will implement at least this policy until 2035.

Population Pyramids Yield Accurate Estimates of Total Fertility Rates.

The primary fertility index for a population, the total fertility rate (TFR), cannot be calculated for many areas and periods because it requires disaggregation of births by mother's age. Here we discuss a flexible framework for estimating TFR using inputs as minimal as a population pyramid. We develop five variants, each with increasing complexity and data requirements. We test accuracy across a diverse set of data sources that comprise more than 2,400 fertility schedules with known TFR values, including the Human Fertility Database, Demographic and Health Surveys, U.S. counties, and nonhuman species. We show that even the simplest and least accurate variant has a median error of only 0.09 births per woman over 2,400 fertility schedules, suggesting accurate TFR estimation over a wide range of demographic conditions. We anticipate that this framework will extend fertility analysis to new subpopulations, periods, geographies, and even species. To demonstrate the framework's utility in new applications, we produce subnational estimates of African fertility levels, reconstruct historical European TFRs for periods up to 150 years before the collection of detailed birth records, and estimate TFR for the United States conditional on race and household income.

Estimation of total fertility rate and birth averted due to contraception: regression approach

Fertility plays an important role in any demographic transition and total fertility rate (TFR) is one of the basic measurements of fertility. Absences of complete and reliable data, a large number of indirect techniques have been developed to estimate the demographic parameters with incomplete data. Some of these techniques are based on utilizing the data from stable population theory while others are based on the regression technique in which the parameters are estimated through regression equations between the dependent variable which is the TFR and the independent variables which is the socio economic well as demographic variables. The first method is based on the relationship between the Total fertility rate (TFR) and contraceptive prevalence rate for all women. By using this modified estimate of TFR, demographic analysis can easily calculate the birth averted for different regions as well as states also. In fact, provide reasonable estimates of births averted due to contraceptive use by national populations. The variables are CPR that about 51.2 percent variation in TFR can be explained by the first regression approach. The second is based on the relationship between total fertility rate (TFR) and Additive combination of CPR and proportion of currently married females having open birth interval. Using new predictor variable, the improved model explained about 55percent of the variation in TFR. The findings reveal that the values of TFR calculated by the present method are quite close to the observed values of the TFR without involving much computational complexities at different background characteristics. Estimates of births averted and the percent change in births in the absence of contraception, based on the two methods, are fairly consistent.

Fair Plan 9: Engineering Human Population to Help Safeguard Earth’s Climate

Our Fair Plan to Safeguard Earth’s Climate reduces the emission of greenhouse gases to zero over the 80-year time period 2020 to 2100. To accomplish this, humanity must reduce its carbon intensity—the amount of CO2 emitted per unit of energy—and its energy intensity—the amount of energy needed to generate a unit of Gross World Product. As shown in our Fair Plan 8 paper, reducing the future growth of the human population can also contribute to the reduction in greenhouse-gas emissions. Here, we explore this further. We project the historical decrease in Total Fertility Rate (TFR) across the 21st century toward its logistical asymptotic Reference value of 2.04 Births Per Woman (BPW). We then engineer the asymptotic TFR beginning in 2020 to 1.95, 1.85, 1.75, 1.65 & 1.55 BPW. We project the population across the 21st century for the Reference and engineered TFRs. We do so using the results of Basten, Lutz and Scherbov (2013) for the population evolution across the 21st century for 8 constant TFR values (=2.50, 2.25, 2.00, 1.75, 1.50, 1.25, 1.00 & 0.75 BPW). We find that purposefully engineering the asymptotic TFR can significantly contribute to achieving the reduction in greenhouse-gas emissions needed to transition to our Fair Plan to Safeguard Earth’s Climate.

Estimation of Total Fertility Rate and Birth Averted due to Contraception: Regression Approach

Fertility plays an important role in any demographic transition and total fertility rate (TFR) is one of the basic measurements of fertility. Due to non-availability of complete and reliable data, a large number of indirect techniques have been developed to estimate the demographic parameters with incomplete data. Some of these techniques are based on utilizing the data from stable population theory while others are based on the regression technique in which the parameters are estimated through regression equations between the dependent variable which is the TFR and the independent variables which is the socio economic well as demographic variables. In the present paper an indirect method has been proposed to estimate the TFR using regression analysis. In these types of analysis the most serious problem is the choice of predictor variable. If the choice of predictor variable is good then it gives the better estimate for the dependent variable (TFR). Using new predictor variable (proposed in this paper), the improved model explained about 85 percent of the variation in TFR. The findings reveal that the values of TFR calculated by the present method are quite close to the observed values of the TFR without involving much computational complexities at state level for different background characteristics. By using this modified estimate of TFR, the demographers can easily calculate the birth averted for different regions as well as states also.