Mother's education and effect of son preference on fertility in Matlab,Bangladesh

This analysis follows earlier research that hypothesized and substantiated that, in a society with strong son preference, its effect on fertility would be conditional on the level of contraceptive use. Present analysis of the prospective fertility experience of 22,819 women of reproductive age during 3.5 years in Matlab, Bangladesh, shows that this effect is higher among mothers with postprimary schooling versus those with primary or no education. The higher effect conforms with the known positive relationship of contraceptive use with maternal schooling. However, this increase when contrasted with the idea that education promotes modern values, including gender equality, suggests that education in Matlab, with its traditional slant, is not resistant to son preference. In a poor, traditional society with low status for women, schooling alone is not enough to motivate women to abandon low esteem for daughters though schooling promotes child survival. But if preference for smaller family size increases, promoted by education including such modern values as gender equality, then sex preference, although it cannot be completely removed, will have minimal effect on fertility as in most developed countries.Abbreviations DSS demographic surveillance system - ICDDR,B International Centre for Diarrhoeal Disease Research, Bangladesh - MCH-FP maternal/child health and family planning - SPEF sex preference effect on fertility     

A generalized bivariate Bernoulli model with covariate dependence

Dependence in outcome variables may pose formidable difficulty in analyzing data in longitudinal studies. In the past, most of the studies made attempts to address this problem using the marginal models. However, using the marginal models alone, it is difficult to specify the measures of dependence in outcomes due to association between outcomes as well as between outcomes and explanatory variables. In this paper, a generalized approach is demonstrated using both the conditional and marginal models. This model uses link functions to test for dependence in outcome variables. The estimation and test procedures are illustrated with an application to the mobility index data from the Health and Retirement Survey and also simulations are performed for correlated binary data generated from the bivariate Bernoulli distributions. The results indicate the usefulness of the proposed method.     

Pathological Gambling and Motor Impulsivity: A Systematic Review with Meta-Analysis

Motor impulsivity, which is an impairment in withholding and cancelling inappropriate responses, may account for the inability for pathological gamblers (PGs) to inhibit their urges to gamble. The aim of this systematic review was to perform a quantitative and qualitative synthesis of existing studies in order to assess whether PGs without comorbid substance use disorder have elevated motor impulsivity, relative to healthy controls. An exhaustive literature search led to the identification of 20 studies which met inclusion criteria. A meta-analysis was then conducted on the following measures: stop signal reaction time from the stop signal task; commission errors, omission errors, and Go reaction time from the Go/No-Go task; and the motor impulsiveness subscale of the Barratt Impulsiveness Scale (BIS-Motor). The results revealed a moderate to large mean effect size of stop signal reaction time, small to moderate mean effect sizes for commission errors, omission errors and Go reaction time, and a large mean effect size for the BIS-Motor. Significant heterogeneity in effect sizes was observed on most behavioural measures, but not for the BIS-Motor or omission errors on the Go/No-Go task. Overall, these results suggest that motor impulsivity may be one of the features of PG psychopathology, accounting for their poor inhibitory control over gambling behaviours. Moreover, other deficits in sustained attention, or more generally in executive/cognitive control, may be present in PGs. We discuss the implications, limitations of existing research, and suggested avenues for future studies, particularly the need to acknowledge heterogeneity amongst PGs and amongst different behavioural measures.     

Local Box–Cox transformation on time-varying parametric models for smoothing estimation of conditional CDF with longitudinal data

Nonparametric estimation and inferences of conditional distribution functions with longitudinal data have important applications in biomedical studies, such as epidemiological studies and longitudinal clinical trials. Estimation approaches without any structural assumptions may lead to inadequate and numerically unstable estimators in practice. We propose in this paper a nonparametric approach based on time-varying parametric models for estimating the conditional distribution functions with a longitudinal sample. Our model assumes that the conditional distribution of the outcome variable at each given time point can be approximated by a parametric model after local Box–Cox transformation. Our estimation is based on a two-step smoothing method, in which we first obtain the raw estimators of the conditional distribution functions at a set of disjoint time points, and then compute the final estimators at any time by smoothing the raw estimators. Applications of our two-step estimation method have been demonstrated through a large epidemiological study of childhood growth and blood pressure. Finite sample properties of our procedures are investigated through a simulation study. Application and simulation results show that smoothing estimation from time-variant parametric models outperforms the existing kernel smoothing estimator by producing narrower pointwise bootstrap confidence band and smaller root mean squared error.     

Analyzing dependence in incidence of diabetes and heart problem using generalized bivariate geometric models with covariates

For analyzing incidence data on diabetes and health problems, the bivariate geometric probability distribution is a natural choice but remained unexplored largely due to lack of models linking covariates with the probabilities of bivariate incidence of correlated outcomes. In this paper, bivariate geometric models are proposed for two correlated incidence outcomes. The extended generalized linear models are developed to take into account covariate dependence of the bivariate probabilities of correlated incidence outcomes for diabetes and heart diseases for the elderly population. The estimation and test procedures are illustrated using the Health and Retirement Study data. Two models are shown in this paper, one based on conditional-marginal approach and the other one based on the joint probability distribution with an association parameter. The joint model with association parameter appears to be a very good choice for analyzing the covariate dependence of the joint incidence of diabetes and heart diseases. Bootstrapping is performed to measure the accuracy of estimates and the results indicate very small bias.     

Estimation and tests for a longitudinal regression model based on the Markov chain

In this paper, the dependence of transition probabilities on covariates and a test procedure for covariate dependent Markov models are examined. The nonparametric test for the role of waiting time proposed by Jones and Crowley [M. Jones, J. Crowley, Nonparametric tests of the Markov model for survival data Biometrika 79 (3) (1992) 513–522] has been extended here to transitions and reverse transitions. The limitation of the Jones and Crowley method is that it does not take account of other covariates that might have association with the probabilities of transition. A simple test procedure is proposed that can be employed for testing: (i) the significance of association between covariates and transition probabilities, and (ii) the impact of waiting time on the transition probabilities. The procedure is illustrated using panel data on hospitalization of the elderly population in the USA from the Health and Retirement Survey (HRS).     

Seasonal Patterns of Reproduction in Matlab,Bangladesh

In Bangladesh twice as many births occur in December as in July. This paper examines the seasonal patterns of the risks of conception, fetal loss and return of menses post partum in a longitudinal study of 2,300 women in 14 villages of Matlab, Bangladesh. Life tables were estimated for each month of entry event and then ‘period’ life tables were constructed with the risks for a given calendar month. Confirming the results of earlier studies, risks of resumption of menses were higher in November and December, regardless of time elapsed since the last birth. Similarly, there are increased conception risks in the period from February to April for all fecundable women. The time of lowest fetal loss and stillbirth risks is in the cool season, though this variation makes only a minor contribution to the overall seasonality of births. The pattern of fecundability estimated from data on coital frequency did not match the pattern estimated from reported conceptions; these discrepancies imply possible seasonal changes in other parameters of fecundability besides intercourse.     

Growth Effects of Remittances in Bangladesh: Is there a U‐shaped Relationship?

This article shows that the effect of remittances on economic growth involves a U‐shaped pattern, which is negative initially but later becomes positive. The analysis differs significantly from earlier studies in that it examines important methodological issues on the specification and estimation of the long‐run growth effects of remittances by estimating their impact on total factor productivity (TFP) rather than on the growth rate of GDP, using time series data from Bangladesh. The use of single‐equation cointegration methods shows that remittances’ effect on long‐run growth in Bangladesh is negative and falling until the remittances‐to‐GDP ratio is roughly eight per cent. The benefits of remittances receipts outweigh their costs and their net effects start to become positive when the ratio exceeds 14 per cent.