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.     

Bayesian analysis of a multiple-recapture model

In the Bayesian analysis of a multiple-recapture census, different diffuse prior distributions can lead to markedly different inferences about the population size N. Through consideration of the Fisher information matrix it is shown that the number of captures in each sample typically provides little information about N. This suggests that if there is no prior information about capture probabilities, then knowledge of just the sample sizes and not the number of recaptures should leave the distribution of Nunchanged. A prior model that has this property is identified and the posterior distribution is examined. In particular, asymptotic estimates of the posterior mean and variance are derived. Differences between Bayesian and classical point and interval estimators are illustrated through examples.     

Adaptable Careers: Maximizing Less and Exploring More

Today, young adults are expected to decide between educational, vocational, and job options and to make the best choice possible. Career literatures emphasize the importance of young adults' career decision making but also acknowledge the problems related to making these decisions. The authors argue that career counselors could support clients' intuitive processing of career information and help their clients to develop a positive and flexible view of the self and the environment while diminishing concerns about accountability for and irreversibility of career decisions. The authors argue that career adaptability rather than decision making should become the focal concept of career theory and practice.     

A note on cost-return calculations and decisions to migrate

In the notions of the aggregative cost-benefit migration models, this paper questions the assumption that prospective migrants behave as if they are not only concerned with, but are able to calculate expected pecuniary and non-pecuniary returns to re-location. Results of a survey which evaluates the role of cost-return calculations in decisions to migrate suggest that there may be population sub-groups who either do not care to make cost-returns calculations to migration, or who are unable to do so.     

On evaluating how well a biomarker can predict treatment response with survival data

One of the objectives of personalized medicine is to take treatment decisions based on a biomarker measurement. Therefore, it is often interesting to evaluate how well a biomarker can predict the response to a treatment. To do so, a popular methodology consists of using a regression model and testing for an interaction between treatment assignment and biomarker. However, the existence of an interaction is not sufficient for a biomarker to be predictive. It is only necessary. Hence, the use of the marker‐by‐treatment predictiveness curve has been recommended. In addition to evaluate how well a single continuous biomarker predicts treatment response, it can further help to define an optimal threshold. This curve displays the risk of a binary outcome as a function of the quantiles of the biomarker, for each treatment group. Methods that assume a binary outcome or rely on a proportional hazard model for a time‐to‐event outcome have been proposed to estimate this curve. In this work, we propose some extensions for censored data. They rely on a time‐dependent logistic model, and we propose to estimate this model via inverse probability of censoring weighting. We present simulations results and three applications to prostate cancer, liver cirrhosis, and lung cancer data. They suggest that a large number of events need to be observed to define a threshold with sufficient accuracy for clinical usefulness. They also illustrate that when the treatment effect varies with the time horizon which defines the outcome, then the optimal threshold also depends on this time horizon.     

Using Panel Data to Understand the Dynamics of Human Behavior in Response to Flooding

Insights into the dynamics of human behavior in response to flooding are urgently needed for the development of effective integrated flood risk management strategies, and for integrating human behavior in flood risk modeling. However, our understanding of the dynamics of risk perceptions, attitudes, individual recovery processes, as well as adaptive (i.e., risk reducing) intention and behavior are currently limited because of the predominant use of cross-sectional surveys in the flood risk domain. Here, we present the results from one of the first panel surveys in the flood risk domain covering a relatively long period of time (i.e., four years after a damaging event), three survey waves, and a wide range of topics relevant to the role of citizens in integrated flood risk management. The panel data, consisting of 227 individuals affected by the 2013 flood in Germany, were analyzed using repeated-measures ANOVA and latent class growth analysis (LCGA) to utilize the unique temporal dimension of the data set. Results show that attitudes, such as the respondents’ perceived responsibility within flood risk management, remain fairly stable over time. Changes are observed partly for risk perceptions and mainly for individual recovery and intentions to undertake risk-reducing measures. LCGA reveal heterogeneous recovery and adaptation trajectories that need to be taken into account in policies supporting individual recovery and stimulating societal preparedness. More panel studies in the flood risk domain are needed to gain better insights into the dynamics of individual recovery, risk-reducing behavior, and associated risk and protective factors.     

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.     

Testing Hypotheses in Nonparametric Models of Production

Data envelopment analysis (DEA) and free disposal hull (FDH) estimators are widely used to estimate efficiency of production. Practitioners use DEA estimators far more frequently than FDH estimators, implicitly assuming that production sets are convex. Moreover, use of the constant returns to scale (CRS) version of the DEA estimator requires an assumption of CRS. Although bootstrap methods have been developed for making inference about the efficiencies of individual units, until now no methods exist for making consistent inference about differences in mean efficiency across groups of producers or for testing hypotheses about model structure such as returns to scale or convexity of the production set. We use central limit theorem results from our previous work to develop additional theoretical results permitting consistent tests of model structure and provide Monte Carlo evidence on the performance of the tests in terms of size and power. In addition, the variable returns to scale version of the DEA estimator is proved to attain the faster convergence rate of the CRS-DEA estimator under CRS. Using a sample of U.S. commercial banks, we test and reject convexity of the production set, calling into question results from numerous banking studies that have imposed convexity assumptions. Supplementary materials for this article are available online.