Aplicação da “Lei” de Kaldor-Verdoorn para a agricultura brasileira: análise para o ano de 2017 - Souza, Fábio Roberto de (2022)
Directed to the application of the Kaldor-Verdoorn "Law", to Brazilian agriculture for the year 2017, due to the active role that the agricultural sector has in the economic and social spheres of Brazil, the present study aimed to investigate the relationship between productivity of labor and the production of the national agricultural sector, aiming to analyze the performance of labor productivity in crops located in Brazilian geographic micro-regions in the year 2017, examining the structure of spatial clusters to know the sources of growth in agriculture. For this, two methodologies were used to investigate the relationship of Kaldor's second "Law", the first being the exploratory analysis of spatial data (AEDE) and the second a simple regression with the models of spatial dependence: SAR, SEM and SAC. The results originated by the exploratory analysis of spatial data (AEDE), found that both for the univariate and for the bivariate case, referring to the Kaldor-Verdoorn relationship, the highest concentrations of spatial clusters were located in the microregions that obtained high and low indexes of agricultural production and productivity. However, specifically, for the bivariate case, that is, Kaldor's “Law” relation, there was also a high concentration of spatial clusters of the Low-High type. As for the spatial independence models applied in the Kaldor-Verdoorn relationship, after performing and interpreting all the tests, the one that proved to be the most suitable for the inferences of the present study was the SAC spatial autoregressive error model. The results evidenced by this spatial model showed that for the five hundred and fifty-four geographic micro-regions, the relationship of Kaldor's second "Law", between the people occupied, or employed, in agriculture and the value of Brazilian agricultural production, presented elasticity superior to 0.65%, thus, indicating a high degree of influence of the second variable on the first.