Seasonal variations in conception and birth have been frequently reported within the global human population. Although births occur throughout the year, certain months record consistently higher frequencies than others. Seasonal variation of conception/birth is a complex phenomenon; numerous factors like climate change, geographical conditions, and environmental factors (temperature, rainfall, and humidity) may affect the risk of conception. This study examined the seasonality of conception in two Indian states, Uttar Pradesh and Kerala, using directional data analysis techniques. Two general patterns were observed: the excess conception for Uttar Pradesh is in November, which is the start of winter in Uttar Pradesh, while in Kerala, excess conception is observed in August, which is the start of the monsoon season. Seasonality of conception in major socio-economic groups (Religion, Cast, Wealth, and Residence) is also observed. This study used the NFHS dataset for the analysis from 1990 to 2018.
This study employs a dynamic panel threshold regression technique to explore the potential threshold effect in the relationship between determinants of life expectancy. Analyzing a sample of 50 member nations of the Organisation of Islamic Cooperation (OIC) from 2000 to 2021, the findings reveal that the total years of schooling have a substantial and non-linear impact on life expectancy. Specifically, the threshold effect of years of schooling is positive and more pronounced in high-income OIC countries compared to low-income ones. These results suggest that policymakers in low-income OIC nations need to carefully manage their educational initiatives to improve education without adversely affecting life expectancy. Balancing the expansion of educational opportunities to maintain or increase life expectancy should be a top priority for policymakers in these countries. Achieving these objectives requires a carefully designed policy approach.
This paper establishes a rigorous foundation for population policy by applying intertemporal optimization theory to derive the optimal trade-off between population growth (or decline) and age-structure fluctuations caused by fertility rate changes. The model is based on the McKendrick-von Foerster partial differential equation, with an objective functional representing the discounted adaptation costs associated with the net reproduction rate. To enhance analytical tractability, we assume concentrated vitality rates. Our findings suggest that, over short-time horizons, an initial under- or overshooting of the net reproduction rate is optimal, while over longer horizons, an oscillating net reproduction rate emerges as the best trajectory. Numerical simulations using a stylized population structure illustrate how variations in the net reproduction rate influence the total population dynamics and age-group distributions over time. This study highlights the critical role of optimization in designing adaptive and sustainable population policies.
A new non-randomized response model is proposed. The proposed model, referred to as the alternating parallel non-randomized response model, is built on relaxing one of the assumptions of the parallel non-randomized response model. The proposed model considers the case where the probability of one of the non-sensitive questions in the parallel model is unknown. An estimator of the proportion of the sensitive attribute is derived based on the proposed model using the maximum likelihood estimation method. An efficiency comparison between the proposed model and a variant of the parallel model is then carried out and the combinations of the parameters for which the proposed model is more efficient are determined. A main advantage of the alternating parallel model is that it outperforms the variant of the parallel model when the probability of belonging to the sensitive group is small.
The second birth interval (SBI), defined as the time between a woman's first and second childbirth is a key indicator of fertility behavior, maternal health, and family planning practices. Using data from the National Family Health Survey, this study examines state-wise SBI patterns in Uttar Pradesh, Bihar, Tamil Nadu, and Kerala among women married at age 20 years or older with at least 12 years of marital duration. SBI was constructed from reported dates of first and second live births, and the sample restrictions of age and marital duration were applied to minimize truncation and censoring effects. Descriptive analysis explores variations in SBI across socio-demographic characteristics. The adequacy of the exponential distribution in modeling SBI was assessed using chi-square goodness-of-fit tests. The results indicate that the exponential model fits the observed SBI distributions well in Uttar Pradesh and Bihar suggest an approximately constant conception rate in these states. In contrast, Tamil Nadu and Kerala exhibits significantly longer and more variable SBIs, and the exponential model shows a statistically significant lack of fit, reflecting a preference for extended birth spacing. These findings highlight substantial regional heterogeneity in fertility behavior and underscore the need for state-specific modeling approaches and policy interventions when analyzing birth spacing and fertility dynamics in India.
We develop a degenerate nonautonomous first-order hyperbolic partial differential equation model to characterize the dynamics of a size-structured population exhibiting both active and quiescent states. Our model extends the classical one-state size-structured population model. We establish the well-posedness of the model through a comparison principle. We analyze the long-term behavior of the solution by employing the upper-lower solution method. More precisely, we derive conditions on the model parameters that determine whether the population persists or goes extinct. We also numerically explore how these parameters affect the persistence of the population. We demonstrate both analytically and numerically that the two-state model can approximate the classical one-state model in several special cases. Furthermore, in these cases, we find that the autonomous counterparts of both models have approximately equal basic reproduction numbers.
Here, we propose an efficient exponential type estimator of population mean of a quantitative sensitive study variable using scrambled response model in Ranked Set Sampling ($RSS$RSS) in case of missing data. It is assumed that information on non-sensitive auxiliary variable is known. The expressions of biases and Mean Square Errors ($MSE$MSE) of the proposed and other estimators are derived upto first order approximation. The conditions are derived under which proposed estimator will become more efficient as compared to other estimators. To validate the theoretical results, we perform a thorough simulation study. The effects of variance of scrambling variable, responding probabilities and varying correlation coefficient between study and auxiliary variable on $MSE$MSE s of the proposed and other estimators have been studied in the simulation process. The analysis clearly signifies the superior performance of our proposed estimator as compared to other estimators in the context of Ranked Set Sampling (RSS).
We examine how the effective size of the population affects the time for evolution. The analysis is based on an established evolutionary model that accounts for natural selection, and it shows that the mean number of rounds of guessing letters needed to change the complete genome to its desirable form is asymptotically a function of L and K. Here, L is the number of letters of the genomic "word," where each letter has been chosen from an alphabet of K letters. We extend this model so that the mean number of rounds is also a function of the effective population size. Using the same numerical values of L and K as in the original formulation, we find that there has been sufficient time for evolution, which is in line with previous analyses. The exception in our model is for cases with very small effective population sizes.
Due to various demographic, socio-economic, and environmental factors, population fluctuations occur across different spatial scales. Understanding these changes is crucial for effective spatial management. For decades, demographers have used population forecasting techniques to improve comprehension and enhance management strategies. However, traditional models have often neglected the spatial dimension of population dynamics. An innovative GIS-based modeling framework integrating the Space-Time Pattern Mining (STPM) tool with extrapolative forecasting techniques is introduced to generate short-term, municipal-level population forecasts for Serbia by 2037. Using historical population data from 1991 to 2022, three forecasting models (Curve Fit, Exponential Smoothing, and Forest-based) are employed to forecast population trends over a 15-year period (2023-2037). The most accurate model for each municipality is identified using the Evaluating Forecasts by Location method. The results indicate that Serbia will experience further population decline, characterized by a decrease in medium- and large-sized municipalities and a simultaneous increase in small-sized ones, continuing the long-term depopulation trend. The proposed framework demonstrates the potential of geospatial analyses to enhance demographic forecasting by combining spatial and temporal dimensions within a unified analytical structure.
A new mathematically exact method of population attributable fraction (PAF) decomposition free of limitations inherent in existing approaches was developed and compared to existing methods based on the Miettinen, Norton, and Niedhammer-Chastang formulae. The developed approach is applicable for two broadly used study designs involving either a single or multiple data sources to measure the predictors and outcomes of interest. The approach was applied to Medicare data to estimate six disease-specific contributions to the overall PAF of Alzheimer's disease risk: stroke (7.2%), hypertension (6.5%), diabetes (4.4%), renal disease (0.9%), traumatic brain injury (0.9%), and depression (9.6%). We found that the approximation based on the Norton formula was the best approach among the methods utilized prior to the development of our approach. However, the quality of such approximations and the respective biases should be re-estimated on a case-by-case basis. An extension of the approach to health disparities was proposed and discussed.
This research was carried out to identify the variables that influenced, in the long and short term, the birth rate in Mexico for the period 1991-2023. The tested variables were the years of education, the unemployment rate, and the Gross Domestic Product (GDP) per capita, as proxies of schooling, unemployment, and income, respectively. An econometric methodology called Augmented Autoregressive Distributed Lag (A-ARDL) model was utilized, owing to its robustness over the standard ARDL model. The model was estimated and tested with and without the presence of structural breaks, due to three events of external origins to the Mexican economy: the financial crisis at the end of 2008, the fall in oil prices in 2015, and the onset of the COVID-19 pandemic in Mexico. The results suggest that women's schooling is the main factor influencing the birth rate, and it has short- and long-term effects on this rate. The income variable does not affect the birth rate; it is not statistically significant. Also, the unemployment rate was discarded because its incorporation did not allow cointegration among the variables under study. Moreover, the econometric analysis confirms that the three events of external origins had effects on the dynamics of births in Mexico. In this sense, it is concluded that an A-ARDL model, with structural breaks, allows us to model the behavior of the birth rate in Mexico for the period 1991-2023.
We have studied the properties of a class of difference-cum-exponential estimators under the modified correlated measurement error (MCME) model. Empirical and simulation studies are carried out in R software to demonstrate the performance of the proposed class of estimators under the MCME model over the usual unbiased estimator, the difference estimators under the correlated measurement error model. Appropriate recommendations have been made based on empirical and simulation results.
Uttar Pradesh, India's most populous state, continues to face significant socio-economic disparities across its 75 districts. Despite its importance, the state struggles with slow economic growth, poor infrastructure, low literacy, and high infant and maternal mortality. This study evaluates district-wise development over 3 periods-2000-01, 2010-11, and 2022-23 using a composite index based on 21 socio-economic indicators through the Wroclaw Taxonomy method. K-means clustering further classified districts into 5 categories: Highly Developed, Developed, Developing, Less Developed, and Least Developed. The results reveal a growing divide between advanced and lagging districts, with some showing progress, while many remain stagnant or decline. These findings highlight the urgent need for targeted policy interventions, improved governance, and strategic investments tailored to regional needs. The approach supports the achievement of Sustainable Development Goals (SDGs), particularly those focused on reducing inequality, enhancing education, and promoting inclusive economic growth through evidence-based decision-making.
We use formal models to assess the effects of ancillary processes that drive the global obesity epidemic, genetic heritability, gene-environment interactions, assortative mating and fertility differentials. We address a central question in the literature on obesity, namely, whether assortative mating by body size is a key determinant of time trends of the obesity phenotype. We compare the effect of assortative mating and fertility differentials to that of gene variants and gene-environment interactions. We find that changes in assortative mating have only a small direct influence on obesity trends, though they moderate the effects of fertility differentials. Contrary to mainstream views, assortative mating is neither likely to have had a significant influence in the past nor to have one on future obesity trends. We show that the effects of genes and gene-environment interactions are more important than those of assortative mating but less so than those of fertility differentials.
Many demographers use samples of micro-records from population censuses for direct statistical analysis. If the sample lacks representativity, the resulting analysis may lead to invalid conclusions. It is necessary to evaluate the sample's representativity before conducting any analytical analysis. Data comparison and sampling variance are the two main methods of assessing the representativity of samples. Studies show that the selection of sampling method should be on the basis of the needs of the researcher; under stratified double sampling, the linear estimator and ratio estimator estimating the numbers of men and women can be combined into a synthetic estimator; using actual data, the calculated sampling variance is relatively small, and the confidence intervals include parameter values, so the representativity of the sub-samples is high. A complete theoretical framework for evaluating the sample's representativity of the census micro-records is established. It helps demographers master the method of evaluating sample representativity and draw correct conclusions from demographic analysis. The proposed sample representativity evaluation methods are not only suitable for the micro-records of population census but also for those of agricultural and economic censuses.
