Banca de DEFESA: CAROLINA DA SILVA

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
DISCENTE : CAROLINA DA SILVA
DATA : 29/03/2019
HORA: 10:30
LOCAL: Auditório 3, NEPSA 2
TÍTULO:

Intermunicipal Migration in Brazil: Evidences for 2000 and 2010.

PALAVRAS-CHAVES:

Intermunicipal migration; Brazil; Migratory Efficacy Index.

PÁGINAS: 64
GRANDE ÁREA: Ciências Sociais Aplicadas
ÁREA: Economia
RESUMO:

This dissertation investigates the pattern of intermunicipal migration in Brazil and explains, through the Migration Effectiveness Index (EMI), to what extent the spatial distribution of population in Brazil is associated with municipal income differentials, crime, education, GDP per capita inequality, the issue of infrastructure, the poverty rate and the human development index (HDI). For this purpose, data from the IBGE Demographic Census of 2000 and 2010 were used for the exploration of spatial agglomerations related to the potential of attraction and emission of migrants. In this way, it was possible to observe that the municipalities located in the North and Northeast regions are the main poles emitting migrants, registering strong emission areas, and the South and Southeast regions registered areas of strong attraction for migrants, for both periods in question. The empirical analysis was based on the Spatial Panel Fixed Effects SAR Model Spatial Fixed Panel Data Regression Model, and the closest K criterion was also used (10). Thus, the results showed that the variables GDP per capita, HDI, gini index, infrastructure, poverty rate and expected income play a relevant role in the intermunicipal migrations directed to Brazilian municipalities, that is, the municipalities that received the most migrants were those with higher levels of gross domestic product, better levels of municipal human development, lower income inequalities, better access to infrastructure, lower levels of poverty and better remuneration. The direct, indirect and total effects were necessary in the analysis, because they show the bias caused by not including the spatial effects in the regression model. And, through this measure of impact it can be seen that the variables: gini index, infrastructure and poverty rate presented negative values, both relative to direct effects and indirect effects. Considering the indirect effects of these variables we have the spillovers effects.

MEMBROS DA BANCA:
Presidente - 2323056 - DIEGO DE MARIA ANDRE
Interna - 1474874 - JANAINA DA SILVA ALVES
Externo à Instituição - VICTOR HUGO DE OLIVEIRA SILVA - UNIFOR