Spatial patterns of rural poverty: an exploratory analysis in the São Francisco River Basin, Brazil

Abstract

This paper uses recently released município-level data on rural poverty in Brazil to identify and analyze spatial patterns of rural poverty in the São Francisco River Basin (SFRB). Moran's I statistics are generated and used to test for spatial autocorrelation, and to prepare cluster maps that locate rural poverty 'hot spots' and 'cold spots'. Our results indicate that poverty reduction policies in the SFRB should take into account the spatial distribution of poverty. Not only is poverty in the SFRB clustered spatially, but the bulk of the basin's poor resides in municípios that comprise the poverty 'hot spots' we identified. These clusters did not correspond to state-level boundaries (the political delineations often used to measure poverty and to manage poverty reduction programs), so scope may exist for geographically refocusing poverty reduction efforts to make them more efficient. Maybe more importantly the results set the stage for the use of spatial econometrics for a future multivariate analysis of rural poverty in the basin.

Water, food and livelihoods in river basins

“…Analysis of flows shows the potential impact of water abstraction by large farmers in the São Francisco on their poorer smallholder neighbours (Manetta et al, this issue). There is a wide disparity between wealthier farmers, who are able to respond to supermarketization, and poorer smallholder farmers, who cannot (Torres et al 2007). A scheme to divert more of the waters of the São Francisco to support farmers in the drier northern parts of the basin has been proposed off and on for years, but was shelved after a severe drought in 2001.…”

“…If the water consumed by irrigation can benefit other sectors, this can lead to a common gain, effectively substituting food security by income security. For example, a major report (ANA et al 2004, p. 264) indicates problems in the lower São Francisco Basin and its coastal zone caused by alterations in basin hydrology, but it is difficult to evaluate these problems without referring to the major reductions in poverty that have accompanied these changes (Torres et al 2007). By analysing food poverty in Ecuador, Farrow et al (2005) determined that the relationship of irrigation with food security could be both positive and negative in different areas, depending on whether irrigation also provided salaried employment.…”

Spatial Patterns of Local Income Inequalities

“…Global Moran's I statistic does not exactly show us the settlements characterized with a high (low) income level, but rather suggests that the spatial pattern of the analyzed variable is not random -there is more similarity by location than would be expected if the pattern was random [40]. Therefore in the next section we concentrate on the analysis of Gi* statistic and LISA index.…”

“…The main question addressed in this section is whether the observed pattern of income inequalities in Romania is similar to other spatial patterns or not. If there is a tendency, for example, that NUTS 5 spatial units with low (high) income level tend to be surrounded by NUTS 5 units with low (high) income level, or vice-versa, this would indicate a positive spatial autocorrelation [40]. The GeoDa software allows us to build a Moran Scatter plot with the calculation of Moran's I [32] (Eq.…”

O brilho das cidades: efeitos da urbanização sobre a pobreza rural no Brasil

“…O impacto da diminuição da pobreza a partir do mercado de trabalho é recorrentemente tratado nesta literatura (LEWIS, 1954;HARRIS;TO-DARO, 1970;LIPTON, 1980;MURGAI, 2010). No caso brasileiro, Martine (1976) discute as condições dos migrantes e não migrantes da região de destino na década de 1980 e aponta que, em alguns casos, há uma aparente mobilidade social da população migrante, Sem o devido tratamento da correlação espacial na disposição dos indivíduos em situação de pobreza, perde-se as relações de vizinhança, comumente tratadas na literatura de econometria espacial (TORRES et al, 2011;ARBIA, 2014;ANSELIN;GALLO;JAYET, 2008).…”