The mobility problem in Britain: new findings from the analysis of birth cohort data

Social mobility is now a matter of greater political concern in Britain than at any time previously. However, the data available for the determination of mobility trends are less adequate today than two or three decades ago. It is widely believed in political and in media circles that social mobility is in decline. But the evidence so far available from sociological research, focused on intergenerational class mobility, is not supportive of this view. We present results based on a newly‐constructed dataset covering four birth cohorts that provides improved data for the study of trends in class mobility and that also allows analyses to move from the twentieth into the twenty‐first century. These results confirm that there has been no decline in mobility, whether considered in absolute or relative terms. In the case of women, there is in fact evidence of mobility increasing. However, the better quality and extended range of our data enable us to identify other ‘mobility problems’ than the supposed decline. Among the members of successive cohorts, the experience of absolute upward mobility is becoming less common and that of absolute downward mobility more common; and class‐linked inequalities in relative chances of mobility and immobility appear wider than previously thought.     

“THE WAR FOR THE FARE”: HOW DRIVER COMPENSATION AFFECTS BUS SYSTEM PERFORMANCE

Prior to 2007, two systems of bus driver compensation coexisted in Santiago, Chile: one paid drivers per passenger transported, while the other paid a fixed wage. Per‐passenger drivers engaged in “The War for the Fare,” altering their driving patterns to compete for passengers. Examining these systems on similar routes in Santiago, we observed two key findings. Compared with the fixed‐wage system, the per‐passenger system leads to (1) 13% shorter passenger wait times, via reduced bunching of buses and (2) 67% more accidents per kilometer driven, via more aggressive driving. We discuss implications for the design of incentives in public transit. (JEL L92, M52, R41)     

To adjust or not to adjust for baseline when analyzing repeated binary responses? The case of complete data when treatment comparison at study end is of interest

The benefits of adjusting for baseline covariates are not as straightforward with repeated binary responses as with continuous response variables. Therefore, in this study, we compared different methods for analyzing repeated binary data through simulations when the outcome at the study endpoint is of interest. Methods compared included chi‐square, Fisher's exact test, covariate adjusted/unadjusted logistic regression (Adj.logit/Unadj.logit), covariate adjusted/unadjusted generalized estimating equations (Adj.GEE/Unadj.GEE), covariate adjusted/unadjusted generalized linear mixed model (Adj.GLMM/Unadj.GLMM). All these methods preserved the type I error close to the nominal level. Covariate adjusted methods improved power compared with the unadjusted methods because of the increased treatment effect estimates, especially when the correlation between the baseline and outcome was strong, even though there was an apparent increase in standard errors. Results of the Chi‐squared test were identical to those for the unadjusted logistic regression. Fisher's exact test was the most conservative test regarding the type I error rate and also with the lowest power. Without missing data, there was no gain in using a repeated measures approach over a simple logistic regression at the final time point. Analysis of results from five phase III diabetes trials of the same compound was consistent with the simulation findings. Therefore, covariate adjusted analysis is recommended for repeated binary data when the study endpoint is of interest. Copyright © 2015 John Wiley & Sons, Ltd.     

Bayesian estimation and case influence diagnostics for the zero-inflated negative binomial regression model

In recent years, there has been considerable interest in regression models based on zero-inflated distributions. These models are commonly encountered in many disciplines, such as medicine, public health, and environmental sciences, among others. The zero-inflated Poisson (ZIP) model has been typically considered for these types of problems. However, the ZIP model can fail if the non-zero counts are overdispersed in relation to the Poisson distribution, hence the zero-inflated negative binomial (ZINB) model may be more appropriate. In this paper, we present a Bayesian approach for fitting the ZINB regression model. This model considers that an observed zero may come from a point mass distribution at zero or from the negative binomial model. The likelihood function is utilized to compute not only some Bayesian model selection measures, but also to develop Bayesian case-deletion influence diagnostics based on q-divergence measures. The approach can be easily implemented using standard Bayesian software, such as WinBUGS. The performance of the proposed method is evaluated with a simulation study. Further, a real data set is analyzed, where we show that ZINB regression models seems to fit the data better than the Poisson counterpart.     

Generalized Estimation of the BLUP in Mixed-Effects Models: A Comparison with ML and REML

The Best Linear Unbiased Predictor (BLUP) in mixed models is a function of the variance components and they are estimated using maximum likelihood (ML) or restricted ML methods. Nonconvergence of BLUP would occur due to a drawback of the standard likelihood-based approaches. In such situations, ML and REML either do not provide any BLUPs or all become equal. To overcome this drawback, we provide a generalized estimate (GE) of BLUP that does not suffer from the problem of negative or zero variance components, and compare its performance against the ML and REML estimates of BLUP. Simulated and published data are used to compare BLUP.