O Potencial Distributivo do Imposto de Rendapessoa Física (IRPF)

This text argues for higher Personal Income Tax levels. We show that, for all countries for which tax information is available, Brazil is the one in which Personal Income Tax collection as a percentage of the gross tax burden is the lowest. Personal Income Taxes account for about 6% of the Gross Tax Burden, slightly more than 2% of GDP, and slightly more than 4% of family income (according to the PNAD household survey). We show that this is due both to the fact that tax brackets are so high so as to exempt 85% of income earners from paying any income tax and the fact that our highest tax bracket is only 27.5%, which is lower than the maximum tax bracket of almost all countries for which tax information is available. Using Household and Expenditure Surveys, we estimate the Personal Income Tax Concentration Coefficients at between 89 and 92, which show a very progressive tax schedule. We also estimate that families who live on self employment and business income evade or avoid 80% of their personal income tax liabilities but that families who live off employment income evade or avoid only 20%. Finally, we analyze the impact of a series of theoretical changes in Personal Income Tax rules and conclude that they would approximately double Personal Income Tax collection. If the additional revenue were compensated by a reduction in a regressive tax, such as Contribuição para o Financiamento da Seguridade Social (Cofins), so as to hold the total Tax Burden constant, the result would be a 2,3 point fall in the Gini coefficient

As complicações associadas à gravidez e os benefícios da prática de atividade física: uma revisão narrativa / Complications associated with pregnancy and the benefits of physical activity: a narrative review

Durante a gravidez, ocorrem diversas transformações que potenciam o aparecimento de diversas complicações, como por exemplo dor lombar, incontinência urinária e diástase abdominal. O objetivo deste trabalho foi realizar uma revisão narrativa da literatura relativa ao papel que a atividade física desempenha na gravidez, com especial foco na prevenção e no tratamento das diversas complicações associadas. De acordo com os estudos científicos atuais, as várias complicações decorrentes da gravidez podem ser minimizadas ou até mesmo revertidas, recorrendo à realização de atividade física de componente aeróbica, com a presença de fortalecimento muscular e de uma forma regular. A atividade física tem um grande impacto na promoção da qualidade de vida e bem-estar desta população, atuando sobretudo na prevenção de diversas complicações associadas à gravidez.

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Equivalence scales and consumer demand

Tese (doutorado)—Universidade de Brasília, Faculdade de Economia, Administração, Contabilidade e Ciência da Informação e Documentação, Programa de Pós-Graduação em Economia da Universidade de Brasília, 2012.Esta tese é composta de duas partes. Na primeira parte, analisam-se as origens e as consequências de uma característica muito comum em pesquisas de orçamentos familiares: a existência de um elevado percentual de domicílios que não possuem despesas com determinados produtos. Tais gastos nulos são de suma importância para a estimação de curvas de Engel, já que tratamentos inadequados destas observações podem resultar em estimativas viesadas e inconsistentes. Assim, primeiramente derivam-se os principais modelos desenvolvidos para lidar com este tipo de informação. Em seguida, utilizam-se alguns destes modelos para estimar os fatores que influenciam as despesas com alimentação fora do domicílio. Na segunda parte o problema consiste em comparar o bem-estar de domicílios com diferentes composições demográficas. Dado que a função de utilidade é definida apenas para indivíduos, mas diversos bens e recursos são compartilhados dentro do domicílio, análises de bem-estar entre indivíduos devem se basear em um modelo de bem-estar para o domicílio. A partir desse modelo é possível definir um índice, o custo relativo entre dois domicílios com diferentes composições demográficas situados em um mesmo nível de bem-estar. Este índice denomina-se escala de equivalência, e permite transformar a renda (ou despesa) nominal de domicílios heterogêneos em uma medida comparável de bem-estar entre indivíduos homogêneos. Como o bem-estar de um domicílio pode ser definido de diversas formas, segue que diferentes modelos, apesar de utilizarem a mesma evidência empírica, podem obter valores distintos para as escalas de equivalência. Assim, essa parte da tese é constituída de três capítulos: no primeiro analisam-se os efeitos das escalas de equivalência sobre as medidas de desigualdade e pobreza; nos dois capítulos seguintes apresentam-se diferentes modelos de escalas de equivalência desenvolvidos dentro da abordagem de preferência revelada e da abordagem subjetiva. Em ambos os capítulos estes modelos são estimados, testados à luz de seus pressupostos e discutidos. __________________________________________________________________________________ ABSTRACTThis thesis is composed of two parts. The first part analyzes the origins and consequences of a very common feature in household budget surveys: the existence of a high percentage of households that do not have expenses with a product. Such zero expenses are extremely important for the estimation of Engel curves, since inadequate treatment of these observations can result in biased and inconsistent estimates. So, first we derive the main models developed to deal with this kind of information. Then, we use some of these models to estimate the factors that influence the demand of food away from home. In the second part, we study the problem of how to compare the welfare of households with different demographic compositions. Since the utility function is defined only for individuals, but many goods and resources are shared within the household, analyzes of well-being among individuals need to be based in a household model of well-being. From this model, it is possible to define an index, the relative cost between two households in the same level of well- being, but with different demographic compositions. This index is called equivalence scale, and it transforms the nominal income (or expenditure) of heterogeneous households in a comparable measure of well-being among homogeneous individuals. Since the well-being of a household can be defined in many ways, it follows that different models, despite using the same empirical evidence, obtain different values for the equivalence scales. Thus, this part of the thesis consists of three chapters: the first analyzes the effects of equivalence scales on several measures of income inequality and poverty; the next two show different models of equivalence scales developed within the revealed preference approach and the subjective approach. In both chapters these models are estimated and tested in the light of their assumptions and a discussion is made from the results