Does Poverty Trap Rural Malagasy Households?

Thomas, Anne-Claire; Gaspart, Frédéric

doi:10.1016/j.worlddev.2014.11.012

Cited by 18 publications

(17 citation statements)

References 38 publications

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“…To answer the question on where pocket poverty is more apparent, data indicates that poverty is evident in rural areas as proven by this and many other studies (Christiaensen & Todo, 2013;Thomas & Gaspart, 2015). A lot had been done to transform rural areas with infrastructure development and up to date facilities to enhance the standard of living of those in the rural areas (Nair & Sagaran, 2015).…”

Section: Discussionmentioning

confidence: 90%

“…They found a positive relationship between rural area and poverty and a negative relationship between poverty and urbanisation and stated that migration out of agriculture into the missing middle (rural nonfarm economy and secondary towns) yields more inclusive growth patterns and faster poverty reduction than agglomeration in megacities. Thomas and Gaspart (2015) investigated the persistently high poverty rates in rural Madagascar. They found that households in rural areas were more likely to be poor than urban areas and high poverty persistence rates observed in rural Malagasy household sample arise from both state dependence-creating poverty traps and adverse household characteristics making them more likely to be poor.…”

Section: Literature Reviewmentioning

confidence: 99%

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Pockets of poverty in the northern states of Malaysia

et al. 2018

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Despite Malaysia's tremendous economic growth and remarkable achievement in poverty reduction for the nation, pockets of poverty remain in certain regions. Pockets of poverty could be analysed using micro data when macro data is unable to provide a clear understanding on the existence of poverty. This study assesses the pockets of poverty in the Northern States of Malaysia. Northern States of Malaysia consist of Perlis, Kedah, Penang and Perak. The objectives of this paper are twofold: to identify pockets of poverty in the Northern States of Malaysia using household income data and to determine the factors that affect these pockets of poverty. Pockets of poverty in this region are investigated among strata, ethnic groups, income level and education. For this purpose, the National Poverty Line Income (PLI) is determined to specify the poor. Logistic probability function is estimated to assess factors that influenced poverty in the Northern States of Malaysia. This study uses the Household Income Survey (HIS) data for 2009 and 2012. From data analysis, it could be concluded that pockets of poverty exist among elderly, women, Bumiputera and those living in the rural areas. Logistic probability analysis indicates that variables that are statistically significant in influencing poverty are age, gender, location, education level, Chinese (ethnic group) and married (marital status). These findings are vital to develop appropriate policy orientation in targeting the correct group in solving poverty.

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Section: Discussionmentioning

confidence: 90%

Section: Literature Reviewmentioning

confidence: 99%

Pockets of poverty in the northern states of Malaysia

et al. 2018

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“…In effect, those territories trapped in different poverty dimensions have similar characteristics: their high rurality [33][34][35][36], their distance from the administrative and commercial capitals [37], and structural factors such as difficult access to their territories [38]. In addition, the poverty intensity, the education deprivation gap, and the deepest entrapment occurs in those territories where the majority of the population is indigenous [38,39].…”

Section: Poverty Trapsmentioning

confidence: 99%

Poverty Traps in the Municipalities of Ecuador: Empirical Evidence

2018

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The objective of this research is to identify from a spatial and temporal perspective the territories that are located in a “poverty trap” scenario. This is a scenario that does not allow overcoming the conditions and determinants that gave rise to this precarious situation, creating a vicious circle where the conditions of poverty endure through time. The methodology applied is an exploratory analysis of spatial dependence through Moran’s scatterplot and local indicators of spatial association (LISA) maps to visualize the spatial clusters of poverty. The database used is that of the population and housing censuses of 1990, 2001, and 2010 at the cantonal level. The results determine that 73 cantons were in a poverty trap over the period 1990–2001, while from 2001–2010, there were 75 cantons in this situation, which were located mainly in the provinces of Esmeraldas, Manabí, and Loja.

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“…A variety of modeling approaches have been deployed to detect and understand poverty, such as linking remote sensing and machine learning (Blumenstock et al 2015, Jean et al 2016 Ecology and Society 25(4): 3 https://www.ecologyandsociety.org/vol25/iss4/art3/ Watmough et al 2019), and statistical models (Carter et al 2007, Cinner et al 2011, Thomas and Gaspart 2015. These methods are important and have contributed to identifying factors influencing poverty for policy interventions, however, they may not be able to capture the nonlinearity of livelihood trajectories, or fail to factor how household heterogeneity and decision making lead to alternative outcomes (Thomas andGaspart 2015, Grêt-Regamey et al 2019). Systems simulation tools, which allow researchers to uncover the underlying relationships and complexities of smallholder livelihood systems, are needed to better understand the failures or successes of poverty-alleviation interventions and quantify the effects of household heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

Pathways out of poverty through the lens of development resilience: an agent-based simulation

Dou¹,

Deadman²,

Berbés-Blázquez³

et al. 2020

E&S

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Poverty alleviation for smallholders must consider the increasingly varied and intertwined impacts of climate change and globalization. This calls for a resilience perspective that includes eradication of poverty and resilience enhancement under extreme events and shocks. Applying the framework of development resilience, we constructed an agent-based model based on small farming households in the Amazon Delta region in Brazil, and we used it to identify pathways out of poverty and sources for resilience among these households. The model allows us to explore the nonlinearity and heterogeneous nature of the smallholder livelihood systems, including how different household characteristics and livelihood strategies contribute to divergent livelihood outcomes. Using a unique yet simple tracking method, we were able to show the stochastic dynamics of individual household livelihoods in the face of various shocks, and how these households move in and out of different states of poverty over time (i.e., extremely poor, chronic poor, and nonpoor). By comparing traits of households that ended up in different states, we showed the need for targeted interventions for alternative livelihood strategies and key resources improvement. Different from conventional poverty alleviation programs, our findings emphasize empowering smallholders with different livelihood options. This has practical implications in terms of identifying leverage points in smallholder livelihood systems (e.g., livelihood strategy, land resources) that government and other agencies can use to intervene more effectively for households to become prosperous.

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Does Poverty Trap Rural Malagasy Households?

Cited by 18 publications

References 38 publications

Pockets of poverty in the northern states of Malaysia

Pockets of poverty in the northern states of Malaysia

Poverty Traps in the Municipalities of Ecuador: Empirical Evidence

Pathways out of poverty through the lens of development resilience: an agent-based simulation

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