Resumo O objetivo do artigo foi o de averiguar como foi distribuído o crédito do BNDES para as 100 maiores empresas, considerando a safra 2012/2013, em moagem de cana-de-açúcar do setor sucroalcooleiro brasileiro no período de 2002 até 2018, mensurando-se ainda comparativamente a concessão de crédito do BNDES e a quantidade de moagem de cana-de-açúcar, a razão de concentração e o coeficiente de Gini. Construiu-se também, comparativamente, curvas de Lorenz. Em todos os indicadores mensurados constatou-se maior concentração e desigualdade na concessão de crédito para as maiores empresas em relação às menores. Observou-se ainda assimetria com relação ao acesso ao crédito, com os maiores tendo maior acesso em relação aos menores. As dez maiores empresas captaram crédito do BNDES. Dos cinquenta maiores, trinta e duas captaram crédito do BNDES. Do quinquagésimo primeiro ao centésimo grupo, sete grupos captaram crédito.

Abstract The goal of the paper was to investigate how BNDES credit was distributed to the 100 largest companies, considering the 2012/2013 harvest, in sugarcane crushing, in the Brazilian sugar and alcohol sector in the period from 2002 to 2018. In addition, a comparative measurement was carried out considering the granting of BNDES credit and the amount of crushed sugarcane, the concentration ratio and the Gini coefficient. Lorenz curves were also constructed comparatively. In all the indicators measured, there was greater concentration and inequality in the granting of credit to the largest companies in relation to the smaller ones. Asymmetry was also observed in relation to access to credit, with the larger companies having greater access in relation to the smaller ones. All ten largest companies have borrowed from the BNDES. Of the fifty largest, thirty-two received credit from the BNDES. From the fifty-first to the hundredth group, seven companies received credit.

ABSTRACT The objective of this study was to measure the short- and long-term elasticities of the Gross Domestic Product (GDP), productivity, prices paid to producers, milk powder imports, minimum wages, and exchange ratios in milk collection between 1999 and 2016. For the measurement, the cointegration technique for data in a balanced panel was used, contemplating five states with great representativeness in the Brazilian industrial milk collection: Minas Gerais, Paraná, Rio Grande do Sul, Goiás, and São Paulo. These states corresponded to 73.3% of all formal milk collection in 2016. Results indicated that GDP, productivity, and imports impacted long-term milk collection. Productivity and GDP were the variables with the greatest long-term impact, with elasticities of 1.395 and 1.297, respectively. Minimum wages impacted milk collection to a lesser scale, −0.802; and exchange ratio and imports showed lower coefficients, that is, 0.177 and −0.078, respectively. In the short term, minimum wages was the significant variable to explain the milk collection and presented a coefficient of −0.213. The variables with the greatest impact on formal milk collection during the period analyzed were productivity, which made the production process more efficient, and variation in the Brazilian consumer’s income, represented by the GDP variable, which increased the demand for dairy products.

Resumo: O objetivo deste trabalho foi estimar a oferta cafeeira por meio da calibração de modelos estatísticos, com variáveis econômicas e climáticas, das principais regiões produtoras do Estado de São Paulo. As regiões estudadas foram Batatais, Caconde, Cássia dos Coqueiros, Cristais Paulista, Espírito Santo do Pinhal, Marília, Mococa e Osvaldo Cruz. Foram utilizados dados de oferta cafeeira, variáveis econômicas (crédito rural, crédito rural na agricultura e valor da produção) e variáveis climáticas (temperatura do ar, precipitação pluvial, evapotranspiração potencial, deficiência e excedente hídrico) de cada região, para o período de 2000-2014. Os modelos foram calibrados com uso de técnicas de regressão linear múltipla, e todas as combinações possíveis foram testadas para a seleção das variáveis. A oferta cafeeira foi a variável dependente, e as demais, as independentes. A acurácia e a precisão dos modelos foram analisadas pelo erro percentual médio e pelo coeficiente de determinação ajustado, respectivamente. As variáveis que mais influenciam a oferta cafeeira são o valor de produção e a temperatura do ar. É possível estimar a oferta cafeeira com regressões lineares múltiplas por meio de variáveis econômicas e elementos climáticos. Os modelos mais acurados são os calibrados para estimar a oferta cafeeira das regiões de Cássia dos Coqueiros e Osvaldo Cruz.

Abstract: The objective of this work was to estimate the coffee supply by calibrating statistical models with economic and climatic variables for the main producing regions of the state of São Paulo, Brazil. The regions were Batatais, Caconde, Cássia dos Coqueiros, Cristais Paulista, Espírito Santo do Pinhal, Marília, Mococa, and Osvaldo Cruz. Data on coffee supply, economic variables (rural credit, rural agricultural credit, and production value), and climatic variables (air temperature, rainfall, potential evapotranspiration, water deficit, and water surplus) for each region, during the period from 2000-2014, were used. The models were calibrated using multiple linear regression, and all possible combinations were tested for selecting the variables. Coffee supply was the dependent variable, and the other ones were considered independent. The accuracy and precision of the models were assessed by the mean absolute percentage error and the adjusted coefficient of determination, respectively. The variables that most affect coffee supply are production value and air temperature. Coffee supply can be estimated with multiple linear regressions using economic and climatic variables. The most accurate models are those calibrated to estimate coffee supply for the regions of Cássia dos Coqueiros and Osvaldo Cruz.