scholarly journals Fertility Trend in Ghana

2013 ◽  
Vol 20 (2) ◽  
Author(s):  
Samuel Gaisie
Keyword(s):  
Fertility Trend

scholarly journals TREND OF SYNTHETIC PARITY PROGRESSION RATIO (SPPR) OF INDIA: EVIDENCE FROM NATIONAL FAMILY HEALTH SURVEYS

2019 ◽  
pp. 153-172
Author(s):  
Abhishek Bharti ◽  
Anup Kumar ◽  
B. P. Singh
Keyword(s):  
Total Fertility Rate ◽  
Health Surveys ◽  
Family Health ◽  
Higher Order ◽  
Parity Progression ◽  
National Family Health Survey ◽  
Parity Progression Ratio ◽  
National Family ◽  
Fertility Trend ◽  
Regional Fertility

Fertility dynamics have been studied in this paper from 1977 to 2015. Regional fertility changes are analyzed using all four rounds of National Family Health Survey (NFHS) data. Synthetic Parity Progression Ratios (SPPR) and Total fertility rate (based on PPR) are used to analyze the fertility trend. Except for first parity, there is a decline in second and higher order birth of all the six regions. Reduction of third and higher order birth is the main reason for this decline.  

scholarly journals Towards the fertility trend: unraveling the economic potential of igneous suites through whole-rock and zircon geochemistry (example from the Tapajós Mineral Province, Northern Brazil)

2022 ◽  
pp. 104643
Author(s):  
Lucas Villela Cassini ◽  
Jean-François Moyen ◽  
Gabriel Cellier ◽  
Bruna de Freitas ◽  
Caetano Juliani ◽  
...  
Keyword(s):  
Economic Potential ◽  
Fertility Trend ◽  
Northern Brazil

scholarly journals Czech family policy

2018 ◽  
pp. 457-466
Author(s):  
Biljana Stankovic
Keyword(s):  
Czech Republic ◽  
Family Policy ◽  
Population Policy ◽  
Economic Consequences ◽  
Low Fertility ◽  
Social Changes ◽  
The European Union ◽  
Cultural Conditions ◽  
The Social ◽  
Fertility Trend

The paper presents the development and transformation of the Czech population policy since the 1950s. It changed from the pronatalist, carried out at a time when the Czech Republic was part of the communist Czechoslovakia, to mostly social in the time of the transition from the 1990s, and the actualization and introduction of new measures in the last decade. The measures that were defined and implemented over a certain period of time represented the state?s response to the family and reproductive behavior of the population, most often reflected in low fertility, largely determined by the current social, economic and cultural conditions. In this sense, the period of the greatest challenges came after 1989, with the transformation of the social and political system and the great economic and social changes that followed, as well as the decline in fertility to an extremely low level. At that time, family policy excluded the pronatalist incentives and benefits and only kept social measures aimed at reducing poverty and alleviating inequalities. Since the early 2000s, new measures have been defined and implemented, motivated by the need to stop and change the declining fertility trend that reached the lowest level (TFR 1.13 in 1999), by looking at the possible negative socio-economic consequences, as well as the recommendations and directives of the European Union, member of which became Czech Republic in 2004. Since 2000, the decline in fertility stopped, TFR reached 1.43 in 2011 and according to data for 2016, it was 1.63 children per woman.

scholarly journals Estimating Trends in Fertility in Kenya from Non Birth History Data

2020 ◽  
Author(s):  
Paul Waweru Ngugi
Keyword(s):  
Health Survey ◽  
Total Fertility Rate ◽  
Demographic And Health Survey ◽  
Data Evaluation ◽  
Reference Period ◽  
Survey Period ◽  
Indirect Methods ◽  
History Data ◽  
Birth History ◽  
Fertility Trend

AbstractThis study aimed at determining the extent to which methods for estimating trends in fertility without use of birth history could be used on Kenyan surveys data by employing the own-children method (OCM) and reverse survival (RS) method in estimating fertility trend in the country. The study used data from 2015/16 Kenya Integrated Household and Budget Survey (KIHBS) and 2014 Kenya Demographic and Health Survey (KDHS). Data evaluation was done in order to obtain optimal fertility estimates. 2015/16 KIHBS data reported a Whipples index of 49.0 and 57.5 for terminal digits 0 and 5 respectively. Myer’s blended index was 2.9 and this was an indication that in general the data was accurate and therefore did not require any adjustment to improve its quality before use. Results from 2015/16 KIHBS showed that RS estimated Total Fertility Rate to be 3.5 as compared to OCM that estimated it to be 3.8. The results from 2014 KDHS dataset were consistent when using both RS and OCM. The two indirect methods can give consistent fertility estimates when the reference period is closer to the survey period but in the fourth and fifth year RS tends to systematically overstate fertility as compared to OCM. This study found out that in the absence of full birth history data, RS and OCM can reliably estimate consistent fertility estimates and trend.

International Migration in Russia: Dynamics, Policies, Forecast

2011 ◽  
pp. 35-53
Author(s):  
S. Ivanov
Keyword(s):  
Population Dynamics ◽  
Population Size ◽  
International Migration ◽  
Low Fertility ◽  
Population Projections ◽  
Future Trends ◽  
Net Migration ◽  
Fertility Trend ◽  
And Migration

Large flows of migrants into low fertility countries increase the weight of international migration in the population dynamics. The current net migration inflow to Russia is about 350 thousand people per year. Migration hypotheses of the population projections include the assumption of constancy of this level until 2050; net migration of 50 thousand people per year; zero net migration and net migration at the level of 900 thousand people per year. Migration hypotheses are combined with the hypotheses of future trends in fertility, including the low, the middle and the high variants. With low fertility and zero net migration the population by 2050 will be reduced by almost half. Stable migration combined with low fertility will not prevent depopulation of the order of almost 1/3 by 2050. If fertility trend follows the high path and migration remains constant the population size will be restored in 10 years and then will increase by 10 per cent. Тo restore the population size by 2020 with slowly increasing fertility, approximately 1, 5 million people more should immigrate into Russia every year than emigrate from it.

Fertility Trend and Pattern in a Rural Area of South Africa in the Context of HIV / AIDS

2004 ◽  
Vol 8 (2) ◽  
pp. 38 ◽  
Author(s):  
Carol S. Camlin ◽  
Michel Garenne ◽  
Tom A. Moultrie
Keyword(s):  
South Africa ◽  
Rural Area ◽  
Fertility Trend ◽  
Hiv Aids

Some causes of yield variation in an intensive spring barley experiment at Woburn, 1972–8

1983 ◽  
Vol 100 (1) ◽  
pp. 175-189 ◽  
Author(s):  
D. Hornby ◽  
D. R. Henden ◽  
J. A. Catt
Keyword(s):  
Linear Trend ◽  
Spring Barley ◽  
Linear Component ◽  
Soil P ◽  
Yield Variation ◽  
Available Water ◽  
Fertility Trend ◽  
Western Block ◽  
Significant Yield ◽  
Effect On Yield

SUMMARYAn experiment with two blocks containing phased sequences of continuous spring barley after beans or fallow was located on sandy soil over Lower Greensand on a gentle north to south (N–S) slope at Woburn Farm. Season had the greatest effect on yield with a 135% difference between the worst (1975, 1·73 t/ha) and the best (1974, 4·06 t/ha). years. N–S position was the next most important factor with average differences of 65 and 52% between the plots at the top and bottom of the site in blocks I and II respectively. The third most important factor was E–W position which gave a maximum difference of 35% in 1975.A fertility trend with a strong linear component, which was most conspicuous in drier years, followed the main slope of the experiment and was attributed to erosion (fieldwash). After 1972 as different cropping sequences were progressively introduced, yield variation due to crop sequence differences was confounded with this positional effect.Crops in the eastern block were taller by l·5–12·3% and, after adjustment for the linear trend, yield was on average 15·6% greater than in the western block. The site is astride a N–S soil boundary with Stackyard series to the east and Cottenham series to the west. The Stackyard soil has a greater available water capacity, and is subject to drought less frequently than the Cottenham soil. Using Penman's (1971) data for the Cottenham series at Woburn and estimates of profile available water for the two series elsewhere on the farm, theoretical yields were derived, which were generally greater than actual yields adjusted for the N–S linear trend (block means 1·47–4·32 t/ha), but which showed similar trends in the between-block differences. Explanations for discrepancies between theoretical and actual yields are discussed. The incidence and severity of take-all disease and differences in soil pH were always small and unlikely to have caused significant yield variations.

Field Plans: Why the Biometrician Finds them Useful

1981 ◽  
Vol 17 (3) ◽  
pp. 243-256
Author(s):  
K. Ryder
Keyword(s):  
Experimental Site ◽  
Fertility Trend ◽  
Do So

SUMMARYWhen agricultural experimenters send data to a statistics unit for analysis, they do not usually include a field plan of the experimental site unless specially requested to do so. But such a plan can be a great help to a biometrician: he can use it to check that the design used in the field was correctly described, and to arrange the plot residuals in the field layout as a way of detecting a fertility trend in the site and of relating anomalous values to particular locations on the ground. This paper gives examples of how a field plan can be used to the benefit of the experimenter.

PRACTICAL USE OF NEIGHBOR METHODS AND ALTERNATIVES IN THE ANALYSIS OF FIELD TRIALS

1987 ◽  
Vol 67 (2) ◽  
pp. 477-489 ◽  
Author(s):  
M. R. BINNS
Keyword(s):  
Field Data ◽  
Field Experiments ◽  
Field Trials ◽  
Covariance Analysis ◽  
Standard Methods ◽  
Designed Experiments ◽  
Fertility Trends ◽  
Lattice Design ◽  
Fertility Trend ◽  
Actual Field

Neighbor methods for the analysis of field experiments are described with the minimum of mathematical detail. Using actual field data, two popular methods are compared with standard blocking, row and column elimination, and covariance analysis. It is shown that simple randomized block analysis is likely to give biased treatment effects when there is a fertility trend. For large experiments, row and column analysis is likely to be inferior to covariance analysis using well-chosen covariates. Neighbor analysis can be more precise than standard methods based on well-designed experiments with appropriate blocking. Situations where neighbor methods may not work well are suggested, but since the methods are easy to use they are recommended when fertility trends are suspected and a simple detective tool is required.Key words: Neighbor analysis, covariate analysis, lattice design, fertility trends

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