The Statistical Modeling Of The Fertility Of Chinese Women

This article is concerned with the statistical modeling of children ever born (CEB) fertility data. It is shown that in a low fertility population, such as China, the use of linear regression approaches to model CEB is statistically inappropriate because the distribution of the CEB variable is often heavily skewed with a long right tail. For five sub-groups of Chinese women, their fertility is modeled using Poisson, negative binomial, and ordinary least squares (OLS) regression models. It is shown that in almost all instances there would have been major errors of statistical inference had the interpretations of the results been based only on the results of the linear regression models

The Multinomial Regression Modeling of the Cause-of-Death Mortality of the Oldest Old in the U.S.

The statistical modeling of the causes of death of the oldest old (persons aged 80 and over) in the U.S. in 2001 was conducted in this article. Data were analyzed using a multinomial logistic regression model (MNLM) because multiple causes of death are coded on death certificates and the codes are nominal. The percentage distribution of the 10 major causes of death among the oldest old was first examined; we next estimated a multinomial logistic regression equation to predict the likelihood of elders dying of one of the causes of death compared to dying of an “other cause.” The independent variables used in the equation were age, sex, race, Hispanic origin, marital status, education, and metropolitan/non-metropolitan residence. Our analysis provides insights into the cause of death structure and dynamics of the oldest old in the U.S., demonstrates that MNLM is an appropriate statistical model when the dependent variable has nominal outcomes, and shows the statistical interpretation for complex results provided by MNLM

Deaths Exceed Births in Most of Europe, But Not in the United States

In this brief, authors Kenneth Johnson, Layton Fields, and Dudley Poston, Jr. present important new findings about the diminishing number of births compared to deaths in Europe and the United States from their recent article in Population and Development Review. Their research focuses on the prevalence and dynamics of natural decrease in subareas of Europe and the United States in the first decade of the twenty-first century using counties (United States) or county-equivalents (Europe). The authors report that 58 percent of the 1,391 counties of Europe had more deaths than births during that period compared to just 28 percent of the 3,137 U.S. counties. Natural decrease is more widespread in Europe because its population is older, fertility rates are lower, and there are fewer women of child-bearing age. Natural decrease is a major policy concern because it drains the demographic resilience from a region, diminishing its economic viability and competitiveness. The implications of the recent European immigrant surge for natural decrease are uncertain, but the authors’ analysis suggests that natural decrease is likely to remain widespread in Europe for the foreseeable future

Using Zero-inflated Count Regression Models To Estimate The Fertility Of U. S. Women

In the modeling of count variables there is sometimes a preponderance of zero counts. This article concerns the estimation of Poisson regression models (PRM) and negative binomial regression models (NBRM) to predict the average number of children ever born (CEB) to women in the U.S. The PRM and NBRM will often under-predict zeros because they do not consider zero counts of women who are not trying to have children. The fertility of U.S. white and Mexican-origin women show that zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models perform better in many respects than the Poisson and negative binomial models. Zero-inflated Poisson and negative binomial regression models are statistically appropriate for the modeling of fertility in low fertility populations, especially when there is a preponderance of women in the society with no children

Cultural Inheritance and Fertility Outcomes: An Analysis from Evolutionary and Interdisciplinary Perspectives

Taking evolutionary and interdisciplinary perspectives, this study views the reproductive result as an evolutionary outcome that may be affected by parental characteristics through cultural inheritance. We hypothesize that inheriting more cultural traits from parents leads to a greater resemblance between fertility outcomes of the offspring and their parents. In societies that experience a demographic transition, a greater resemblance can be indicated by a higher level of fertility of the offspring and a sooner transition from union formation to childbearing. We operationalize inheriting cultural traits from parents as reporting a religious affiliation the same as those of their parents. Through analyzing data from the National Survey of Family Growth (NSFG) Cycle 6, our results show that inheriting the same religious traits from parents does have an effect on one’s fertility. In particular, women who reported the same religious affiliations as those of their parents reported a greater number of children. They tend to have births inside, rather than outside, of marriage. Inside marriage, they are also more likely to give births sooner, rather than later. These findings support our hypotheses and help to build a theoretical framework that explains the changes in fertility outcomes from an interdisciplinary perspective

Patterns and Variation in the Sex Ratio at Birth in the Republic of Korea

In this paper, we examine sex ratio patterns at birth (SRB) among sub-areas of South Korea during the 1990s. We report higher than biologically normal SRBs at varying levels of geography, for three years in the 1990s. These high SRBs are mainly due to prenatal sex identification followed by female-specific induced abortion. This strategy has been implemented in the Republic of Korea following the country's dramatic reduction in fertility. Higher than biologically normal sex ratios at birth carry important implications for the society's males and females, particularly a few decades after their births, when young people begin to exercise marriage options. We estimate that approximately 25 years after 1990, around 2015, approximately 10 to 13 percent of marriage-age males in South Korea will be unsuccessful in their courtship pursuits. In 2015, there could be as many as 400,000 South Korean men of marriageable ages unable to find wives. We explore implications of this unbalanced marriage market for South Korea and its excess male population

Developments in demography in the 21st century

This book introduces demographic applications which employ current demographic concepts and theories and cutting-edge methods and findings, all of which have and will continue to have an impact in the broad area of social demography. Through providing an introduction to new and current developments in demography, methodological and statistical issues, data issues, issues of health, aging and mortality, and issues in social demography, this book gives new insights into data, substantive issues, and methodological approaches that will assist readers in their use of demography in their research. At the same time it shows demographers, sociologists, economists, statisticians, methodologists, planners, and marketers how they may learn and improve upon the quality and relevance of their demographic investigations now and in the future

Avoiding Inferential Errors in Public Health Research: The Statistical Modelling of Physical Activity Behavior

Background: A review of the health behavior literature on the statistical modeling of days of physical activity (PA) indicates that in many instances linear regression models have been used. It is inappropriate statistically to model a count dependent variable such as days of physical activity with Ordinary Least Squares (OLS). Many count variables have skewed distributions, and, also, have a preponderance of zeroes. Count variables should not be treated as continuous and unbounded. If OLS is used, estimations of the regression will frequently turn out to be inefficient, inconsistent and biased, and such outcomes could well have incorrect impacts on health programs and policies.Methods: We considered three statistical methods for modelling the distribution of days of PA data for respondents in the 2013 Health Information Trends Survey (HINTS). The three regression models analyzed were: Ordinary Least Squares (OLS), Negative Binomial (NBRM), and Zero-inflated Negative Binomial (ZINB). We used the exact same predictor variables in the three models. Our results illustrate the differences in the results.Results: Our analyses of the PA data demonstrated that the ZINB model fits the observed PA data better than either the OLS or the NBRM models. The coefficients and standard errors differed in the zero-inflated count models from the other models. For instance, the ZINB coefficient for the association between income and PA behavior was not statistically significant (p>0.05), whereas in the NBRM and in the OLS models, it was statistically significant (p<0.05).Conclusions: The inappropriate use of regression models could well lead to wrong statistical inferences. Our analyses of the number of days of moderate PA demonstrated that the ZINB count model fits the observed PA data much better than the OLS model and the NBRM

Texas Business Review, March 1973

The Business Situation in Texas; Texas Construction Rising Housing Costs; Texas Population in 1970: 6. The Changing Status of WomenBureau of Business Researc