The distinct role of group-central and group-peripheral norms in taxpaying behaviour

In recent years, a social identity approach has been used to help understand why people do or do not pay tax [see Taylor, 2003; Wenzel, M., 2002. The impact of outcome orientation and justice concerns on tax compliance: the role of taxpayers’ identity. Journal of Applied Psychology 87, 629–645; Wenzel, M., 2004. An analysis of norm processes in tax compliance. Journal of Economic Psychology 25, 213–228; Wenzel, M., 2005. Misperception of social norms about tax compliance: from theory to intervention. Journal of Economic Psychology 26, 862–883; Wenzel, M., 2007. The multiplicity of taxpayer identities and their implications for tax ethics. Law & Policy 29, 31–50]. This research, which has focused almost exclusively on national identity, indicates that the more people identify with a group, the more likely they are to adhere to its tax norms and values. However, conformity to group norms may be more nuanced than this, and depend on (a) the meaning or content of the identity in question [e.g., Turner, J.C., 1999. Some current themes in research on social identity and self-categorization theories. In: Ellemers, N., Spears, R., Doojse, B. (Eds.), Social Identity: Context, Commitment, Content. Blackwell, Oxford, pp. 6–34] and (b) whether the norms and values are central or peripheral to the content of that identity. In line with this idea, two studies explored whether the concept and act of taxpaying are more central to what it means to be a member of one's nation than of one's occupational group. Both studies confirm this expectation. Importantly, the findings also suggest that although occupational groups have different norms and values in relation to pre-tax behaviours (e.g., how to deal with extra income), these too can be peripheral to what it means to a group member. If norms are peripheral to identity content, conformity to such norms may be independent of group identification.     

Predictive factors of acculturation attitudes and social support among Asian immigrants in the USA

This study examines acculturation attitude and its relation to social support among Asian immigrants. The data were collected from 242 Korean, Indian and Filipino immigrants in the USA using the Acculturation Attitude Scale and the Social Support Index. Findings showed that Koreans had a less positive attitude toward acculturation than did Indian and Filipino participants. Acculturation attitude was positively correlated with educational level and English fluency. This study also found a negative correlation between acculturation attitude and social support. Social support from friends and English fluency were identified as significant predictor variables in determining the level of acculturation attitude. These findings not only contribute to social work education and practice, but also increase cultural sensitivity and awareness in working with these populations.     

Voting Technology,Political Responsiveness,and Infant Health: Evidence From Brazil

This paper studies the introduction of electronic voting technology in Brazilian elections. Estimates exploiting a regression discontinuity design indicate that electronic voting reduced residual (error‐ridden and uncounted) votes and promoted a large de facto enfranchisement of mainly less educated citizens. Estimates exploiting the unique pattern of the technology's phase‐in across states over time suggest that, as predicted by political economy models, it shifted government spending toward health care, which is particularly beneficial to the poor. Positive effects on both the utilization of health services (prenatal visits) and newborn health (low‐weight births) are also found for less educated mothers, but not for the more educated.     

Nonparametric Estimation of the Number of Drug Users in Hong Kong Using Repeated Multiple Lists

We update a previous approach to the estimation of the size of an open population when there are multiple lists at each time point. Our motivation is 35 years of longitudinal data on the detection of drug users by the Central Registry of Drug Abuse in Hong Kong. We develop a two‐stage smoothing spline approach. This gives a flexible and easily implemented alternative to the previous method which was based on kernel smoothing. The new method retains the property of reducing the variability of the individual estimates at each time point. We evaluate the new method by means of a simulation study that includes an examination of the effects of variable selection. The new method is then applied to data collected by the Central Registry of Drug Abuse. The parameter estimates obtained are compared with the well known Jolly–Seber estimates based on single capture methods.     

Boosting multi-state models

One important goal in multi-state modelling is to explore information about conditional transition-type-specific hazard rate functions by estimating influencing effects of explanatory variables. This may be performed using single transition-type-specific models if these covariate effects are assumed to be different across transition-types. To investigate whether this assumption holds or whether one of the effects is equal across several transition-types (cross-transition-type effect), a combined model has to be applied, for instance with the use of a stratified partial likelihood formulation. Here, prior knowledge about the underlying covariate effect mechanisms is often sparse, especially about ineffectivenesses of transition-type-specific or cross-transition-type effects. As a consequence, data-driven variable selection is an important task: a large number of estimable effects has to be taken into account if joint modelling of all transition-types is performed. A related but subsequent task is model choice: is an effect satisfactory estimated assuming linearity, or is the true underlying nature strongly deviating from linearity? This article introduces component-wise Functional Gradient Descent Boosting (short boosting) for multi-state models, an approach performing unsupervised variable selection and model choice simultaneously within a single estimation run. We demonstrate that features and advantages in the application of boosting introduced and illustrated in classical regression scenarios remain present in the transfer to multi-state models. As a consequence, boosting provides an effective means to answer questions about ineffectiveness and non-linearity of single transition-type-specific or cross-transition-type effects.     

Cox regression with missing covariate data using a modified partial likelihood method

Missing covariate values is a common problem in survival analysis. In this paper we propose a novel method for the Cox regression model that is close to maximum likelihood but avoids the use of the EM-algorithm. It exploits that the observed hazard function is multiplicative in the baseline hazard function with the idea being to profile out this function before carrying out the estimation of the parameter of interest. In this step one uses a Breslow type estimator to estimate the cumulative baseline hazard function. We focus on the situation where the observed covariates are categorical which allows us to calculate estimators without having to assume anything about the distribution of the covariates. We show that the proposed estimator is consistent and asymptotically normal, and derive a consistent estimator of the variance–covariance matrix that does not involve any choice of a perturbation parameter. Moderate sample size performance of the estimators is investigated via simulation and by application to a real data example.     

Model averaging inconcentration–QT analyses

This article describes how a frequentist model averaging approach can be used for concentration–QT analyses in the context of thorough QTc studies. Based on simulations, we have concluded that starting from three candidate model families (linear, exponential, and Emax) the model averaging approach leads to treatment effect estimates that are quite robust with respect to the control of the type I error in nearly all simulated scenarios; in particular, with the model averaging approach, the type I error appears less sensitive to model misspecification than the widely used linear model. We noticed also few differences in terms of performance between the model averaging approach and the more classical model selection approach, but we believe that, despite both can be recommended in practice, the model averaging approach can be more appealing because of some deficiencies of model selection approach pointed out in the literature. We think that a model averaging or model selection approach should be systematically considered for conducting concentration–QT analyses. Copyright © 2016 John Wiley & Sons, Ltd.     

Exponentiality Test Based on the Progressive Type II Censoring via Cumulative Entropy

In this article, we use cumulative residual Kullback-Leibler information (CRKL) and cumulative Kullback-Leibler information (CKL) to construct two goodness-of-fit test statistics for testing exponentiality with progressively Type-II censored data. The power of the proposed tests are compared with the power of goodness-of-fit test for exponentiality introduced by Balakrishnan et al. (2007 Balakrishnan, N., Habibi Rad, A., Arghami, N.R. (2007). Testing exponentiality based on Kullback-Leibler information with progressively type-II censored data. IEEE Transactions on Reliability 56(2):301–307.[Crossref], [Web of Science ®] , [Google Scholar]). We show that when the hazard function of the alternative is monotone decreasing, the test based on CRKL has higher power and when the hazard function of the alternative is non-monotone, the test based on CKL has higher power. But, when it is monotone increasing the power difference between test based on CKL and their proposed test is not so remarkable. The use of the proposed tests is shown in an illustrative example.     

Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence

Most existing reduced-form macroeconomic multivariate time series models employ elliptical disturbances, so that the forecast densities produced are symmetric. In this article, we use a copula model with asymmetric margins to produce forecast densities with the scope for severe departures from symmetry. Empirical and skew t distributions are employed for the margins, and a high-dimensional Gaussian copula is used to jointly capture cross-sectional and (multivariate) serial dependence. The copula parameter matrix is given by the correlation matrix of a latent stationary and Markov vector autoregression (VAR). We show that the likelihood can be evaluated efficiently using the unique partial correlations, and estimate the copula using Bayesian methods. We examine the forecasting performance of the model for four U.S. macroeconomic variables between 1975:Q1 and 2011:Q2 using quarterly real-time data. We find that the point and density forecasts from the copula model are competitive with those from a Bayesian VAR. During the recent recession the forecast densities exhibit substantial asymmetry, avoiding some of the pitfalls of the symmetric forecast densities from the Bayesian VAR. We show that the asymmetries in the predictive distributions of GDP growth and inflation are similar to those found in the probabilistic forecasts from the Survey of Professional Forecasters. Last, we find that unlike the linear VAR model, our fitted Gaussian copula models exhibit nonlinear dependencies between some macroeconomic variables. This article has online supplementary material.