What Works for Africa's Poorest

Lawson, Edited by David; Ado-Kofie, Lawrence; Hulme, David

doi:10.3362/9781780448435

Cited by 3 publications

(3 citation statements)

References 79 publications

(116 reference statements)

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“…For example, rainfall effects differ by the level of education analysed, geographical location and welfare levels of a household. It is therefore perhaps more appropriate to embed policy responses within adaptive social protection frameworks for countries, frameworks that are increasingly based on national social assistance registries (Lawson et al, 2017(Lawson et al, , 2020. Such an approach is perhaps further to be advocated when we consider that households facing negative rainfall shocks also face a combination of other types of exogenous shocksuch as Covid-19.…”

Section: Discussionmentioning

confidence: 99%

No Rain No Gain – How Reduced Rainfall Impacts on Children’s School Attendance in Uganda

Lawson

Agamile

2020

SSRN Journal

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Section: Discussionmentioning

confidence: 99%

No Rain No Gain – How Reduced Rainfall Impacts on Children’s School Attendance in Uganda

Lawson

Agamile

2020

SSRN Journal

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“…The PKH program utilises targeting criteria and conditions for determining eligibility for cash transfers. However, the application of this targeting procedure has resulted in the stigmatisation of potential beneficiaries (Lawson et al, 2017).…”

Section: The Implications Of Individual People's Attitudes To Governm...mentioning

confidence: 99%

Moving beyond stereotype: A qualitative study of long‐standing recipients of the Indonesian conditional cash transfers (CCT/PKH)

Habibullah¹,

Yuda

Setiawan³

et al. 2023

Soc Policy Adm

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This qualitative study explores the experiences and attitudes of welfare recipients towards Indonesia's conditional cash transfer (CCT/PKH) program. The specific objectives of our paper include examining the program's impact on impoverished families, their response to stereotyping, and the implications of stereotyping for government policy among Indonesian low‐income families. We conducted twenty in‐depth interviews with key stakeholders (program professionals, community leaders, program facilitators) and end‐users (beneficiaries). Despite the attached stereotype, our findings show that not all beneficiaries with improved socioeconomic conditions wish to leave the program. In many cases, this occurs as beneficiaries use their CCT/PKH accounts as collateral to borrow from unregulated lenders and repay their debts. A few instances where we found self‐voluntary withdrawal from the program were only caused by the shame of receiving stickers attached to their homes – not because of their social‐economic status improvement – leading to difficulty affording their necessities afterwards. Overall, the finding that individuals, whether eligible or not, are not ashamed to claim PKH, despite the stigma attached to the program, contrasts with studies on welfare stigma, claiming that stigma can prevent individuals from claiming their welfare rights.

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“…Individuals and households with common characteristics sometimes cluster together either by choice or due to social, economic, geographical or political forces [31]. The assumption of spatial autocorrelation in wealth may be valid, as poverty tends to concentrate in mountainous regions, arid land, land-locked areas, and levels-off closer to the national/financial capitals, bodies of water and coastal areas [32]. In recent years however, the emergence of secondary cities in many LMICs may have led to certain degree of within-country redistribution of the population, economic opportunities and wealth [33].…”

Section: Introductionmentioning

confidence: 99%

Comparison of spatial interpolation methods to create high-resolution poverty maps for low- and middle-income countries

Wong

Brady

Campbell

et al. 2018

J. R. Soc. Interface.

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High-resolution poverty maps are important tools for promoting equitable and sustainable development. In settings without data at every location, we can use spatial interpolation (SI) to create such maps using sample-based surveys and additional covariates. In the model-based geostatistics (MBG) framework for SI, it is typically assumed that the similarity of two areas is inversely related to their distance between one another. Applications of spline interpolation take a contrasting approach that an area's absolute location and its characteristics are more important for prediction than distance to/characteristics of other locations. This study compares prediction accuracy of the MBG approach with spline interpolation as part of a generalized additive model (GAM) for four low- and middle-income countries. We also identify any potentially generalizable data characteristics influencing comparative accuracy. We found spatially scattered pockets of wealth in Malawi and Tanzania (corresponding to the major cities), and overarching spatial gradients in Kenya and Nigeria. Spline interpolation/GAM performed better than MBG for Malawi, Nigeria and Tanzania, but marginally worse in Kenya. We conclude that the spatial patterns of wealth and other covariates should be carefully accounted for when choosing the best SI approach. This is particularly pertinent as different methods capture geographical variation differently.

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What Works for Africa's Poorest

Cited by 3 publications

References 79 publications

No Rain No Gain – How Reduced Rainfall Impacts on Children’s School Attendance in Uganda

No Rain No Gain – How Reduced Rainfall Impacts on Children’s School Attendance in Uganda

Moving beyond stereotype: A qualitative study of long‐standing recipients of the Indonesian conditional cash transfers (CCT/PKH)

Comparison of spatial interpolation methods to create high-resolution poverty maps for low- and middle-income countries

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