Climate-related migration in rural Bangladesh: a behavioural model

Research into the climate change and migration nexus has often focussed solely on how people move in response to the impacts of variability and change in climate. This notion often ignores the nature of migration as a tried and tested livelihood choice amid a variety of socio-economic and environmental opportunities and limitations. This paper closely looks at the behavioural aspects of migration decision-making in Bangladesh in the context of changes in its economy, and, increasingly, exposure to the impacts of climate variability and change. We find that villagers in areas particularly affected by increasing climatic stresses and shocks are diversifying their traditional livelihood strategies by migrating. Environmental factors, including climatic stresses and shocks, often make such shifts even more necessary. Although the migrants’ primary motivation is better income, in effect, migration becomes an effective form of adaptation. Based on a qualitative study in three geographically distinct places of Bangladesh, we propose that migration is a socially acceptable behaviour that occurs in the context of perceived environmental change and climate variability. Migration decisions are mediated by a set of ‘behavioural factors’ that assesses the efficacy of different responses to opportunities and challenges, their socio-cultural acceptance and the ability to respond successfully. This understanding has policy relevance for climate change adaptation, in terms of both how migrants are perceived and how their movements are planned for.     

Recalibration of the Grunow–Finke Assessment Tool to Improve Performance in Detecting Unnatural Epidemics

Successful identification of unnatural epidemics relies on a sensitive risk assessment tool designed for the differentiation between unnatural and natural epidemics. The Grunow–Finke tool (GFT), which has been the most widely used, however, has low sensitivity in such differentiation. We aimed to recalibrate the GFT and improve the performance in detection of unnatural epidemics. The comparator was the original GFT and its application in 11 historical outbreaks, including eight confirmed unnatural outbreaks and three natural outbreaks. Three steps were involved: (i) removing criteria, (ii) changing weighting factors, and (iii) adding and refining criteria. We created a series of alternative models to examine the changes on the parameter likelihood of unnatural outbreaks until we found a model that correctly identified all the unnatural outbreaks and natural ones. Finally, the recalibrated GFT was tested and validated with data from an unnatural and natural outbreak, respectively. A total of 238 models were tested. Through the removal of criteria, increasing or decreasing weighting factors of other criteria, adding a new criterion titled “special insights,” and setting a new threshold for likelihood, we increased the sensitivity of the GFT from 38% to 100%, and retained the specificity at 100% in detecting unnatural epidemics. Using test data from an unnatural and a natural outbreak, the recalibrated GFT correctly classified their etiology. The recalibrated GFT could be integrated into routine outbreak investigation by public health institutions and agencies responsible for biosecurity.     

That’s the ticket: explicit lottery randomisation and learning in Tullock contests

Theory and Decision - Most laboratory experiments studying Tullock contest games find that bids significantly exceed the risk-neutral equilibrium predictions. We test the generalisability of these...     

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).