Elasticity function of a discrete random variable and its properties

Elasticity (or elasticity function) is a new concept that allows us to characterize the probability distribution of any random variable in the same way as characteristic functions and hazard and reverse hazard functions do. Initially defined for continuous variables, it was necessary to extend the definition of elasticity and study its properties in the case of discrete variables. A first attempt to define discrete elasticity is seen in Veres-Ferrer and Pavía (2014a). This paper develops this definition and makes a comparative study of its properties, relating them to the properties shown by discrete hazard and reverse hazard, as both defined in Chechile (2011). Similar to continuous elasticity, one of the most interesting properties of discrete elasticity focuses on the rate of change that this undergoes throughout its support. This paper centers on the study of the rate of change and develops a set of properties that allows us to carry out a detailed analysis. Finally, it addresses the calculation of the elasticity for the resulting variable obtained from discretizing a continuous random variable, distinguishing whether its domain is in real positives or negatives.     

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.     

Dynamic Updating of Labour Force Estimates: JARES

Abstract. In this paper a new algorithm is proposed in order to produce an automatic dynamic compound estimator of the labour force based on an interactive scheme. The proposed algorithm, JARES, is based on the probability estimator of Jaynes based on the notion of maximum entropy of a given probability distribution with a constraint on the average of an external information. The iterative scheme is based on the solution of a set of linear equations which represent the algebraic relationships between the weights and the estimates.     

Dual class shares,board of directors’ effectiveness and firm’s market value: an empirical study

This study aims to identify whether a relationship exists between the controlling shareholders’ voting power and outside directors’ effectiveness in maximizing firms’ financial performance. We analyze a panel data with 3057 observations for the 2000–2012 period using a random effects model, logit and probit regressions, and the two-stage model of Heckman in the Brazilian stock market. Our findings show that firms whose controlling shareholders use dual class shares to leverage their voting power have less independence from the board and worse financial performance and market value. Further, the percentage of outside directors tends to be ineffective in increasing the firm’s value, and in changing the firm’s chief executive officer (CEO) when (1) the controlling shareholder’s voting power is leveraged, or (2) when the CEO assumes a position on the board of directors simultaneously. We interpreted that these results are in line with the arguments in favor of the existence of a new agency cost, which is related to the undue obedience of board members to authority, such as the largest controlling shareholder or the CEO in Brazilian listed firms.     

Poverty in People with Disabilities: Indicators from the Capability Approach

People with disability are particularly exposed to poor living conditions: on one hand they have more difficulties in getting an income cause to their inabilities, on the other hand conditions of poverty increase the risk of disability. However, little rigorous quantitative research has been undertaken to measure the real impact of disability on poverty. Methods based on monetary set (income and consumption) may not reflect the real possibility to convert resources into the fulfilment of personal goals. The capability approach, developed by A. K. Sen, offers a useful framework for the evaluation and assessment of individual well-being considering both monetary and non-monetary resources. The study describes a methodology, derived from the capability approach, to estimate the additional income needed for various types of household with and without disabled members to ensure the same satisfaction with economic resources. The method has been applied to the Italian contest, where 2.6 million people with disability live, and then the results are compared with the findings from an analogous study conducted in UK. The study reveals that disabled people in Italy need an income level two times higher to enjoy the same level of economic satisfaction, while in UK 50% of extra resource are sufficient. The findings suggests that traditional evaluations based on consumption lead to an underestimation of the needs of household containing disabled members. It follows that the financial benefits envisaged by actions planned by government through current instruments may not be completely effective.     

Exposure to Traffic and Risk of Hospitalization Due to Injuries

Research on the risk of motor vehicle injuries and their relationship with the amount of travel has been only partially analyzed. The few individual exposure assessments are related to very specific subsets of the driving and traveling populations. This study analyzes the relationship between kilometers traveled and hospitalization due to motor vehicle injuries. Twelve thousand three hundred and sixty nine Spanish university graduates from the Seguimiento Universidad de Navarra multipurpose cohort study were evaluated. They had not been hospitalized due to motor vehicle injuries at baseline and were followed up to eight years. Biannual questionnaires allowed for self‐reporting of kilometers traveled in motor vehicles, together with incidence of hospitalization. Covariates in the Cox regression models included age and gender and baseline use of safety belt while driving, driving a vehicle with driver‐side airbag, driving a motorcycle, and drinking and driving. There were 49,766 participant‐years with an average yearly travel of 7,828 km per person‐year. Thirty‐six subjects reported a first hospitalization event during this time. The adjusted hazard ratio per additional kilometer traveled was 1.00005 (95% confidence interval 1.000013 to 1.000086). Even the smallest of reductions in the amount of kilometers traveled (from an average of 3,250 km per year to 1,000) has a statistically significant protective effect on the likelihood of sustaining hospitalization due to motor vehicle injury (aHR 0.9, 95% CI 0.78 to 0.98). In light of current policies aimed to reduce motorized traffic due to environmental concerns, it may be appropriate to consider the additional health benefit related to reductions in injuries.     

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.