2 resultados para mixed multinominal logit model
em Universidade Federal do Rio Grande do Norte(UFRN)
Resumo:
Family farming has been considered as the new axis of rural development in the country, the focus of several public policies, especially the National Program for Strengthening Family Agriculture - PRONAF and Food Purchase Program - PAA. PRONAF was created with the aim of providing credit to farmers, while the PAA to support family farmers through the purchase of its production. In this context, the objective of this study is to analyze the correspondence of these two public policies for family farming, in the Territories of Citizenship of the state of Rio Grande do Norte, between the years 2008 to 2010. In the methodology, the analysis was performed by comparing the distributions of the two programs in the territories of citizenship status. There were also statistical tests of differences in proportions, and Spearman correlations, and estimated a logit regression model, in order to measure the probability of a farmer participating in the PAA is associated with one of the modes of PRONAF. The data used were obtained from the National and Supply - CONAB at the Institute of Technical Assistance and Rural Extension - EMATER, and the Ministry of Agrarian Development - MDA. Among the key findings was noted that policies were associated with a direct, but low in the districts of the Territories of Citizenship. And that, in the years 2008 and 2009, only in the territories of Mato Grande, Alto Oeste and Seridó, the actions of PAA and PRONAF had direct and significant correlations. It was found that in most of the territories, policies are performed randomly, ie that both have no correlation to each other. The estimates of the logit model showed that the chance of a family farmer, the PAA participant, receive credits PRONAF A, is higher in the territory of Mato Grande, and would have a chance to fall in PRONAF B in all areas surveyed. Moreover, farmers in the territories of the Assu-Mossoró, Sertão of Apodi, Seridó and Alto Oeste, participating in the PAA would be more likely to receive credits PRONAF C, reflecting thus the family farm more consolidated these territories
Resumo:
The study aims to answer the following question: what are the different profiles of infant mortality, according to demographic, socioeconomic, infrastructure and health care, for the micro-regions at the Northeast of Brazil? Thus, the main objective is to analyze the profiles or typologies associated mortality levels sociodemographic conditions of the micro-regions, in the year 2010. To this end, the databases of birth and death certificates of SIM and SINASC (DATASUS/MS), were taken from the 2010 population Census microdata and from SIDRA/IBGE. As a methodology, a weighted multiple linear regression model was used in the analysis in order to find the most significant variables in the explanation child mortality for the year 2010. Also a cluster analysis was performed, seeking evidence, initially, of homogeneous groups of micro-regions, from of the significant variables. The logit of the infant mortality rate was used as dependent variable, while variables such as demographic, socioeconomic, infrastructure and health care in the micro-regions were taken as the independent variables of the model. The Bayesian estimation technique was applied to the database of births and deaths, due to the inconvenient fact of underreporting and random fluctuations of small quantities in small areas. The techniques of Spatial Statistics were used to determine the spatial behavior of the distribution of rates from thematic maps. In conclusion, we used the method GoM (Grade of Membership), to find typologies of mortality, associated with the selected variables by micro-regions, in order to respond the main question of the study. The results points out to the formation of three profiles: Profile 1, high infant mortality and unfavorable social conditions; Profile 2, low infant mortality, with a median social conditions of life; and Profile 3, median and high infant mortality social conditions. With this classification, it was found that, out of 188 micro-regions, 20 (10%) fits the extreme profile 1, 59 (31.4%) was characterized in the extreme profile 2, 34 (18.1%) was characterized in the extreme profile 3 and only 9 (4.8%) was classified as amorphous profile. The other micro-regions framed up in the profiles mixed. Such profiles suggest the need for different interventions in terms of public policies aimed to reducing child mortality in the region