Testing Inference in Inflated Beta Regressions under Model Misspecification

We consider testing inference in inflated beta regressions subject to model misspecification. In particular, quasi-z tests based on sandwich covariance matrix estimators are described and their finite sample behavior is investigated via Monte Carlo simulations. The numerical evidence shows that quasi-z testing inference can be considerably more accurate than inference made through the usual z tests, especially when there is model misspecification. Interval estimation is also considered. We also present an empirical application that uses real (not simulated) data.     

Odd-Burr generalized family of distributions with some applications

We study a new family of continuous distributions with two extra shape parameters called the Burr generalized family of distributions. We investigate the shapes of the density and hazard rate function. We derive explicit expressions for some of its mathematical quantities. The estimation of the model parameters is performed by maximum likelihood. We prove the flexibility of the new family by means of applications to two real data sets. Furthermore, we propose a new extended regression model based on the logarithm of the Burr generalized distribution. This model can be very useful to the analysis of real data and provide more realistic fits than other special regression models.     

The Decomposition of Economic Outcomes as a Result of Changes in Brazil’s Male Age–Education Structure

In Brazil, the age and education compositions of the male labor force is changing with great regional variation. Based on Demographic Census microdata, results indicate that cohort size has a negative impact on earnings, but this effect is decreasing over time. In this study we consider the impact on earnings by age and education, as well as estimated income inequality reduction and racial differentials. Fertility decline and improve regarding educational attainment had a significant influence on the decline of income inequality in the country. Moreover, the non-white population has been experiencing less success in relation to educational achievement, compared to the white population.     

Personality and Subjective Well-Being: What Hides Behind Global Analyses?

The relation between personality and subjective well-being (SWB) remains involved in a considerable ambiguity and the numerous studies conducted have neglected an approach at a more detailed level of analysis. This study explores the idea that neuroticism, extraversion and conscientiousness facets predict differentially each SWB component. A battery of self-report questionnaires was used to assess personality and SWB in 398 teachers of primary and high schools. Findings of a cross-sectional study showed that neuroticism, extraversion and conscientiousness facets contributed to significantly explain the variance in positive affect, negative affect and life satisfaction. Moreover, these facets predicted differentially each of the three SWB components. At same time, this study corroborates two important premises: the specificity of facets as discrete traits and the independence of the three SWB components.     

Insurance demand and social comparison: An experimental analysis

This paper analyzes whether social comparison can explain the low take-up of disaster insurance usually reported in field studies. We argue that risks in the case of disasters are highly correlated between subjects whereas risks for which high insurance take-up can be observed (e.g. extended warranties or cell phone insurance) are typically idiosyncratic. We set up a simple model with social reference points and show that in the presence of inequality aversion social comparison makes insurance indeed less attractive if risks are correlated. In addition we conducted a simple experiment which confirms these theoretical results. The average willingness to pay for insurance is significantly higher for idiosyncratic than for correlated risks.     

Linking gender,class and race in Brazil1

This article examines the socio‐historical effects of the intersection of representations of gender, race, and the nation which determine black Brazilian women's subaltern social position and inform their self‐representations. This intersection is presented from two perspectives: analysis of quantitative data and a case study. Through the examination of socio‐economic data on black women participation in the labour market, it show that, when compared with white women and black men, black women are unequally concentrated in certain low paid jobs in the service industry, such as for instance domestic work. Through a brief case study, basically interviews with women from different colours and social classes — i.e. domestic workers and their employers — it shows how the particularities of domestic work inform black women's race and gender self‐definitions.     

Bayesian compartmental model for an infectious disease with dynamic states of infection

Population-level proportions of individuals that fall at different points in the spectrum [of disease severity], from asymptomatic infection to severe disease, are often difficult to observe, but estimating these quantities can provide information about the nature and severity of the disease in a particular population. Logistic and multinomial regression techniques are often applied to infectious disease modeling of large populations and are suited to identifying variables associated with a particular disease or disease state. However, they are less appropriate for estimating infection state prevalence over time because they do not naturally accommodate known disease dynamics like duration of time an individual is infectious, heterogeneity in the risk of acquiring infection, and patterns of seasonality. We propose a Bayesian compartmental model to estimate latent infection state prevalence over time that easily incorporates known disease dynamics. We demonstrate how and why a stochastic compartmental model is a better approach for determining infection state proportions than multinomial regression is by using a novel method for estimating Bayes factors for models with high-dimensional parameter spaces. We provide an example using visceral leishmaniasis in Brazil and present an empirically-adjusted reproductive number for the infection.