An investigation of the spatial determinants of the local prevalence of poverty in rural Malawi

Benson, Todd; Chamberlin, Jordan; Rhinehart, Ingrid

doi:10.1016/j.foodpol.2005.09.004

Cited by 76 publications

(85 citation statements)

References 7 publications

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“…The importance of asset wealth in positively enhancing the well-being of rural households has been reported by other studies in the region (Benson, Chamberlin, and Rhinehart 2005;Bokosi 2006;Ellis and Ade Freeman 2004;Mukherjee and Benson 2003;Mussa and Pauw 2011). Generally, education and asset accumulation are crucially important in determining the well-being of the households in the rural communities of the study countries.…”

Section: Resultssupporting

confidence: 64%

“…Earlier studies have also reported similar results that better-off households are characterized by asset accumulation involving livestock and off-farm income sources (Benson, Chamberlin, and Rhinehart 2005;Ellis and Ade Freeman 2004;Mussa and Pauw 2011). On the other hand, the number of farm plots was found to be negatively related to the well-being of the households (Table 7).…”

Section: Malawisupporting

confidence: 56%

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Poverty in maize growing rural communities of southern Africa

Kassie¹,

Abate

Langyintuo

et al. 2014

Development Studies Research

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This study provides a detailed analysis of the extent and determinants of poverty in the maize-based rural economies of selected districts in southern African countries of Angola, Malawi, Mozambique, and Zambia. Poverty profiles showed that households that were absolutely poor had significantly smaller family sizes, fewer livestock, smaller farm sizes, smaller number of illiterate household members, and less number of important assets such as phones and radios. The study also details the extent and determinants of poverty in the study countries using quantile regression. The importance of maize technology use and resource allocation to the crop in determining the magnitude of poverty is an important finding as the sampled households were essentially semi-subsistent with limited market orientation. This finding also justifies the effort being exerted on the development and deployment of maize and maize-related technologies in rural communities of southern Africa.

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Section: Resultssupporting

confidence: 64%

Section: Malawisupporting

confidence: 56%

Poverty in maize growing rural communities of southern Africa

Kassie¹,

Abate

Langyintuo

et al. 2014

Development Studies Research

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show abstract

“…Simelton et al [14] observed that small droughts might have relatively large effects on maize yields in the face of inadequate adaptive capacity and vice versa because high adaptive capacity lowers vulnerability. A variety of socio-economic proxies have been suggested for use in indicator-based approaches for vulnerability assessment, including: level of education and poverty, availability of safety nets and transportation systems [68][69][70][71][72][73][74].…”

Section: Adaptive Capacity Indexmentioning

confidence: 99%

Vulnerability of Maize Yields to Droughts in Uganda

Epule

Ford

Lwasa

et al. 2017

Water

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Climate projections in Sub-Saharan Africa (SSA) forecast an increase in the intensity and frequency of droughts with implications for maize production. While studies have examined how maize might be affected at the continental level, there have been few national or sub-national studies of vulnerability. We develop a vulnerability index that combines sensitivity, exposure and adaptive capacity and that integrates agroecological, climatic and socio-economic variables to evaluate the national and spatial pattern of maize yield vulnerability to droughts in Uganda. The results show that maize yields in the north of Uganda are more vulnerable to droughts than in the south and nationally. Adaptive capacity is higher in the south of the country than in the north. Maize yields also record higher levels of sensitivity and exposure in the north of Uganda than in the south. Latitudinally, it is observed that maize yields in Uganda tend to record higher levels of vulnerability, exposure and sensitivity towards higher latitudes, while in contrast, the adaptive capacity of maize yields is higher towards the lower latitudes. In addition to lower precipitation levels in the north of the country, these observations can also be explained by poor soil quality in most of the north and socio-economic proxies, such as, higher poverty and lower literacy rates in the north of Uganda.

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“…This is particularly true in analyses related to healthcare (e.g., incidence of diseases and access to medical services e.g., [40][41][42][43]), environmental protection (e.g., [44][45][46]), the real estate market (e.g., [47,48]), poverty (e.g., [49,50]) and migration (e.g., [51][52][53][54]). The GWR tool is also used in labour market analyses (e.g., [55][56][57][58]).…”

Section: Methodsmentioning

confidence: 99%

Geographically Weighted Regression in the Analysis of Unemployment in Poland

Lewandowska-Gwarda

2018

IJGI

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Abstract:The main aim of this paper is an application of Geographically Weighted Regression (which enables the identification of the variability of regression coefficients in the geographical space) in the analysis of unemployment in Poland 2015. The study is conducted using 2015 statistical data for 380 districts (LAU 1) in Poland. The research results show that the determinants of unemployment are diverse in the geographic space and do not have a significant impact on unemployment rates in all spatial units (LAU 1). The existence of clusters of districts, characterised by the influence of the variables and a similar strength of interactions, is confirmed. Geographically Weighted Regression (GWR) proved to be an extremely effective instrument of spatial data analysis. The model had a considerably better fit with empirical data than the global model, and it enabled the drawing of detailed conclusions concerning the local determinants of unemployment in Poland.

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An investigation of the spatial determinants of the local prevalence of poverty in rural Malawi

Cited by 76 publications

References 7 publications

Poverty in maize growing rural communities of southern Africa

Poverty in maize growing rural communities of southern Africa

Vulnerability of Maize Yields to Droughts in Uganda

Geographically Weighted Regression in the Analysis of Unemployment in Poland

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