Fertility related methods

In this paper, we present a method for reconstructing and smoothing fertility trends by combining birth histories from multiple surveys. Data used in the paper come from World fertility surveys and Demographic and Health surveys . This method usesPoisson regression and restricted cubic splines to produce detailed long term fertility trends. In the first part, we present the method and illustrate its application by combining several fertility surveys in Colombia. Next, simulated birth histories are used to validate the method in a variety of situations. Finally, the method is applied to several countries, from various parts of the world, with varying numbers of surveys and with different data quality problems. Finally, these trends are compared with published fertility trends.

For the new round of IIASA/Oxford education projections, we present a new fertility scenario making process that attempts to overcome limitations of regular assumption making. Firstly, we include a large number of international experts to feed into the scenario making process by answering a questionnaire to identify main drivers of fertility, and secondly, a group of meta-expert evaluates results from the questionnaire. The construction of the scenarios consists of a three staged modeling approach. First, we estimate a model, using a country’s level and decrease of fertility during the past five-year period and compare it to countries with similar characteristics since 1970. Second, we estimate expected decrease of fertility by employing information, gathered from the fertility questionnaire. And third, numerical point estimates, supplied by the meta-experts, are utilized to estimate future fertility decline. Combining the information from three qualitatively very different sources, we are able to provide a new set of fertility assumptions to feed into the IIASA/Oxford education projections. This paper discusses a new assumption making approach for countries in today’s high fertility world and compares the differences in methods and results to the Bayesian projection methodology introduced by the United Nations.

The poor reproductive health among women is the outcome of prevailing unintended fertility due to lack of family planning practices at the time of need. The present study aims to address this issue in the State of Odisha, India. Odisha with strong concentration of tribal population has moderate fertility but very high infant, child and maternal mortality in India. The paper examines the variation in unmet need among women of the said population. It uses data from three rounds of India’s the National Family Survey (NFHS-1, NFHS-2 & NFHS-3). Multinomial logistic regression (m-log) models and GIS have been used to analyze the data. The study reveals that the programme should look into the unmet need of women who are young, lower parity, illiterate, non-working, lower standard of living and poor inter-spouse communication about family planning in order to address the unmet need for family planning, maternal and child health among the population.

According to census data,the TFR in China was 1.18 in 2010,which activated the discussion of census data quality and the real fertility level in China.It is necessary to figure out how credible are recent census data,especially the fertility data,which are the fundamental index of population and important factor closely related to the public policies.This paper focuses on assessing the fertility data quality in China’s recent censuses and methods of improving it. The household registration data and educational statistics,which are independent of census system,were used to research whether there was underreporting in census data and improve the quality of it.The results suggested there was underreporting in census data, especially in the low-age groups.The data quality of educational statistics is the best because the number of children in the educational statistics has no close relationship with the interest of local governments and reporters,which is quite different from census data.So we combined the tendency of census data and the fertility level of education statistical data to correct the missing reports in the census data both in direct and indirect method to make improvements for the quality of census data,especially the low-age group data.Finally,the estimated TFR after emendation is listed at the end of this paper.

Existing studies of fertility concentration have focused on nonparametric measures such as `have half', `half have', and the well-known Gini index. Here, we review the literature and discuss the nature of fertility variation, as well as the behavioural patterns leading to over- and underdispersion respectively and their theoretical implications. We demonstrate that commonly used concentration measures are less informative than is generally believed, and cannot be used to study changes in fertility variation in the presence of changes in the level of fertility, or to draw inferences about behavioural heterogeneity or change. Instead, we focus on the statistical measure of dispersion (i.e. the variance-to-mean ratio), and present a collection of empirical results on patterns of over- and underdispersion in relation to the overall level of fertility, and discuss their significance. The finding that, especially when fertility is low, instances of under-dispersion are common, which are difficult to model with the statistical toolkit customary in demographic analysis, motivates an introduction to alternative count models recently revived in the statistical literature and an examination of their ability to fit both over- and under-dispersed distributions of complete parity.

A software application developed by Djarot can be used to measure as well as to project contraceptive prevalence rate and total modified combined unmet need for contraception especially in the field more easily. According to conceptual framework, there are two types of unmet need for contraception. These are Westoff's manifest unmet need and Palmore's latent unmet need. When these types combined called combined unmet need for contraception. Currently married women in reproductive ages (acronym as MWRAs) can be divided into two mayor divisions, MWRAs using contraception or called contraceptive prevalence rate (acronym as CPR) and MWRAs not using contraception consists of fecund and infecund women. MWRAs' fecund not using contraception divided into pregnant or amenorrheic and not pregnant or amenorrheic that divided as unmet need for spacing and unmet need for limiting. Sum of unmet need for spacing and limiting is total unmet need. Next, based on tracks of manifest and latent unmet need, it is known total manifest unmet need and total latent unmet need. Furthermore, if total manifest unmet need added to total latent unmet need then gotten total combined unmet need. Because of data information collected from regularly recording and reporting systems.there are a three types of modifications.

Unintended pregnancy is an important demographic measure used to justify and assess the success of family planning programs. There are three commonly used techniques to measure unwanted and mistimed pregnancies, all of which rely on the simplifying assumption that fertility preferences are relatively stable. Accumulated research from a variety of contexts, however, indicates that key measures of fertility preference, including ideal family size, the desire for another child, and the desired timing of next pregnancy, change frequently over time, particularly as women’s life circumstances evolve. This paper uses seven waves of panel data, each spaced four months apart, from young women in southern Malawi to examine the prevalence of change in fertility preferences and the implications for measures of unwanted and mistimed pregnancy. Specifically, we estimate the prevalence of unwanted and unintended pregnancy using seven different measures, three of which allow for preferences to change over time. The paper concludes with a discussion of the differences in estimates and the possible implications for measures of unintendedness in other contexts.