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Socioeconomic mobility across generations is an important measure of how developed and open to equal opportunities a nation is. Understanding the mechanisms behind intergenerational mobility is essential for the implementation of effective public policies toward economic development. This study aimed to measure intergenerational mobility specifically in education, using data from the National Household Sample Survey for the year 2014, where 9 birth cohorts were utilized to obtain regression coefficients, correlation coefficients, and the decomposition of the correlation coefficient. Among the main findings, an increase in intergenerational mobility over time was observed in both the regression coefficient and the correlation coefficient. Additionally, there was limited mobility among groups such as women and non-white individuals. Regarding regions, the Northeast region experienced significant growth in mobility, no longer being the region with the highest persistence over time. As for the type of persistence, it became composed of parents and children who have the same level of education, such as high school and higher education. [ABSTRACT FROM AUTHOR]

This article aims to analyze the parental reproduction of occupations and their effects on income according to the diverse socioeconomic conditions of Brazil. Logistic regressions and wage decomposition using RIF -Recentered Influence Function—were applied to the microdata of the 2019 National Continuous Household Sample Survey. We found that sons tend to follow the occupation of fathers and daughters that of mothers; rural dwellers from traditional families and from poorer classes with low levels of education are more likely to reproduce their parents' occupation. Parental reproduction of occupations among the poor leads to lower remuneration for individuals compared to those who choose other occupations and to the maintenance of economic poverty. Impacts are greatest in the smallest quantile of wage distribution and for the poor. [ABSTRACT FROM AUTHOR]

Many models for environmental data that are observed in time and space have been proposed in the literature. The main objective of these models is usually to make predictions in time and to perform interpolations in space. Realistic predictions and interpolations are obtained when the process and its variability are well represented through a model that takes into consideration its peculiarities. In this paper, we propose a spatio-temporal model to handle observations that come from distributions with heavy tails and for which the assumption of isotropy is not realistic. As a natural choice for a heavy-tailed model, we take a Student's-t distribution. The Student's-t distribution, while being symmetric, provides greater flexibility in modeling data with kurtosis and shape different from the Gaussian distribution. We handle anisotropy through a spatial deformation method. Under this approach, the original geographic space of observations gets mapped into a new space where isotropy holds. Our main result is, therefore, an anisotropic model based on the heavy-tailed t distribution. Bayesian approach and the use of MCMC enable us to sample from the posterior distribution of the model parameters. In Sect. 2, we discuss the main properties of the proposed model. In Sect. 3, we present a simulation study, showing its superiority over the traditional isotropic Gaussian model. In Sect. 4, we show the motivation that has led us to propose the t distribution-based anisotropic model—the real dataset of evaporation coming from the Rio Grande do Sul state of Brazil. [ABSTRACT FROM AUTHOR]

Urbanization results in loss of natural habitats and, consequently, reduction of richness and abundance of specialist to the detriment of generalist species. We hypothesized that a greater richness of trypanosomatid in Didelphis albiventris would be found in fragments of urban forests in Campo Grande, Mato Grosso do Sul, Brazil, that presented a larger richness of small mammals. We used parasitological, molecular, and serological methods to detect Trypanosoma spp. infection in D. albiventris (n = 43) from forest fragments. PCR was performed with primers specific for 18S rDNA, 24Sα rDNA, mini-chromosome satellites, and mini-exon genes. IFAT was used to detect anti–Trypanosoma cruzi IgG. All hemoculture was negative. We detected trypanosomatid DNA in blood of 35% of opossum. Two opossums were seropositive for T. cruzi. The trypanosomatid species number infecting D. albiventris was higher in the areas with greater abundance, rather than richness of small mammals. We found D. albiventris parasitized by T. cruzi in single and co-infections with Leishmania spp., recently described molecular operational taxonomic unit (MOTU) named DID, and Trypanosoma lainsoni. We concluded that (i) trypanosome richness may be determined by small mammal abundance, (ii) D. albiventris confirmed to be bio-accumulators of trypanosomatids, and (iii) T. lainsoni demonstrated a higher host range than described up to the present. [ABSTRACT FROM AUTHOR]

The present study aims to analyze the wage gap between the public and private sectors in Brazil from 2008 to 2016 based on microdata from the Annual Social Information Report (Relação Anual de Informações Sociais, RAIS). The results show that public-sector wages are higher than private-sector wages in Brazil and are decreasing at federal, state, and municipal levels. These differences are greater for workers in the judiciary, and in large municipalities and metropolises. The most skilled individuals receive lower salaries in the public sector, and the public–private difference is greater among women than men. It is also possible to verify that the wage gap can be explained better by the composition of employees in each sector than by the unobserved characteristics that affect the ways in which the sectors pay. At the end of the analyzed period, there is a reduction in participation in private-sector employment and an increase in the public–private wage gap with the economic downturn in the country. [ABSTRACT FROM AUTHOR]

In this work we present a Bayesian analysis in linear regression models with spatially varying coefficients for modeling and inference in spatio-temporal processes. This kind of model is particularly appealing in situations where the effect of one or more explanatory processes on the response present substantial spatial heterogeneity. We describe for this model how to make inference about the regression coefficients and response processes under two scenarios: when the explanatory processes are known throughout the study region, and when they are known only at the sampling locations. Using a simulation experiment we investigate how parameter inference and interpolation performance are affected by some features of the data and prior distribution that is used. The proposed methodology is used to model the dataset on PM10 levels in the metropolitan region of Rio de Janeiro presented in Paez and Gamerman (2003). [ABSTRACT FROM AUTHOR]

This paper aimed to study the behavior of Brazilian productivity between 2004 and 2014 and its impact on growth in a disaggregated analysis. To this end, performance was studied by examining the measurement of total factor productivity and was also based on sectoral and regional aggregation in an attempt to verify which sectors and regions contributed most to the low aggregate productivity in Brazil, considering the few disaggregated studies on productivity. In addition, based on the estimates, a growth decomposition was performed to verify the contribution of productivity to economic growth. Additionally, econometric methodologies were used to calculate productivity in order to verify whether the results obtained were similar, using panel estimates with pooled data, fixed effects, and a dynamic panel in level and differences. Among the results, it was verified that there was higher productivity growth in services and that there was a decrease in industry. The regions that achieved better performance were the North, Northeast, and Center-West, depending on the sector analyzed. In addition, considering all the regions, only industry contributed negatively to growth, except in the Center-West. Regarding econometrics, the pooled data model presented the best results. [ABSTRACT FROM AUTHOR]