Small area estimation of non-monetary poverty with geospatial data

Masaki, Takaaki; Newhouse, David; Silwal, Ani; Bedada, Adane; Engstrom, Ryan

doi:10.3233/sji-210902

Cited by 10 publications

(11 citation statements)

References 60 publications

(60 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main data sets used in this analysis are: the Demographic and Health Survey (DHS) from 2015 in Zimbabwe; population density, night-time light intensity for 2015 and distance to nearest Open Street Map (OSM) road obtained via WorldPop ( www.worldpop.org ); and poverty Small Area Estimations (SMEs) from the World Bank at ward level [ 50 ]. The datasets used are fully outlined in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

Estimating spatially disaggregated probability of severe COVID-19 and the impact of handwashing interventions: The case of Zimbabwe

Joseph,

Milusheva,

Sturrock

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Introduction The severity of COVID-19 disease varies substantially between individuals, with some infections being asymptomatic while others are fatal. Several risk factors have been identified that affect the progression of SARS-CoV-2 to severe COVID-19. They include age, smoking and presence of underlying comorbidities such as respiratory illness, HIV, anemia and obesity. Given that respiratory illness is one such comorbidity and is affected by hand hygiene, it is plausible that improving access to handwashing could lower the risk of severe COVID-19 among a population. In this paper, we estimate the potential impact of improved access to handwashing on the risk of respiratory illness and its knock-on impact on the risk of developing severe COVID-19 disease across Zimbabwe. Methods Spatial generalized additive models were applied to cluster level data from the 2015 Demographic and Health Survey. These models were used to generate continuous (1km resolution) estimates of risk factors for severe COVID-19, including prevalence of major comorbidities (respiratory illness, HIV without viral load suppression, anemia and obesity) and prevalence of smoking, which were aggregated to district level alongside estimates of the proportion of the population under 50 from Worldpop data. The risk of severe COVID-19 was then calculated for each district using published estimates of the relationship between comorbidities, smoking and age (under 50) and severe COVID-19. Two scenarios were then simulated to see how changing access to handwashing facilities could have knock on implications for the prevalence of severe COVID-19 in the population. Results This modeling conducted in this study shows that (1) current risk of severe disease is heterogeneous across the country, due to differences in individual characteristics and household conditions and (2) that if the quantifiable estimates on the importance of handwashing for transmission are sound, then improvements in handwashing access could lead to reductions in the risk of severe COVID-19 of up to 16% from the estimated current levels across all districts. Conclusions Taken alongside the likely impact on transmission of SARS-CoV-2 itself, as well as countless other pathogens, this result adds further support for the expansion of access to handwashing across the country. It also highlights the spatial differences in risk of severe COVID-19, and thus the opportunity for better planning to focus limited resources in high-risk areas in order to potentially reduce the number of severe cases.

show abstract

Section: Methodsmentioning

confidence: 99%

Estimating spatially disaggregated probability of severe COVID-19 and the impact of handwashing interventions: The case of Zimbabwe

Joseph,

Milusheva,

Sturrock

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Although there has been mixed success in mapping poverty only using alternative data sources, 25 alternative data sources and advanced analytics can enhance household survey-derived estimates (Masaki et al, 2022), and may be especially helpful when survey data are old 26 . Given that household surveys in forced displacement contexts are newly aligning with international statistical standards and beginning to cover refugees, IDPs, and host communities in comparable ways, there is good potential for data scientists to add value by working alongside household survey teams collecting data on the forcibly displaced.…”

Section: A New Agendamentioning

confidence: 99%

Accelerating and enhancing the generation of socioeconomic data to inform forced displacement policy and response

Brock,

Mugera

2023

Data & Policy

View full text Add to dashboard Cite

There are now an estimated 114 million forcibly displaced people worldwide, some 88% of whom are in low- and middle-income countries. For governments and international organizations to design effective policies and responses, they require comparable and accessible socioeconomic data on those affected by forced displacement, including host communities. Such data is required to understand needs, as well as interactions between complex drivers of displacement and barriers to durable solutions. However, high-quality data of this kind takes time to collect and is costly. Can the ever-increasing volume of open data and evolving innovative techniques accelerate and enhance its generation? Are there applications of alternative data sources, advanced statistics, and machine-learning that could be adapted for forced displacement settings, considering their specific legal and ethical dimensions? As a catalytic bridge between the World Bank and UNHCR, the Joint Data Center on Forced Displacement convened a workshop to answer these questions. This paper summarizes the emergent messages from the workshop and recommendations for future areas of focus and ways forward for the community of practice on socioeconomic data on forced displacement. Three recommended areas of future focus are: enhancing and optimizing household survey sampling approaches; estimating forced displacement socioeconomic indicators from alternative data sources; and amplifying data accessibility and discoverability. Three key features of the recommended approach are: strong complementarity with the existing data-collection-to-use-pipeline; data responsibility built-in and tailored to forced displacement contexts; and iterative assessment of operational relevance to ensure continuous focus on improving outcomes for those affected by forced displacement.

show abstract

“…Masaki et al [24] also consider the prediction of non-monetary poverty in Tanzania and Sri Lanka. Their study used census data from both countries to construct a non-monetary welfare index, and classified households whose index fell below a percentile threshold roughly equal to the prevailing national poverty rate as non-monetarily poor.…”

Section: Geospatial Data Predicts Poverty and Wealth Accurately Acros...mentioning

confidence: 99%

“…Census estimates CNN 0.47 Babenko et al [20] Mexico [23] Sri Lanka DS Division Non-monetary poverty Census 2012 Census EBP 0.77 Masaki et al [24] Sri Lanka DS Division Asset index Census Design-based simulation using census XGB 0.83 Merfeld and Newhouse [18] Sub-Saharan Africa Village Asset index DHS Crossvalidation CNN 0.70 Yeh et al [25] Sub-Saharan Africa Village Consumption LSMS Crossvalidation CNNTL 0.37-0.55 Jean et al [15] Sub-Saharan Africa Village Asset index DHS Crossvalidation CNNTL 0.55-0.75 Jean et al [15] Sub-Saharan Africa Village Asset index DHS Crossvalidation XGB 0.56 Chi et al [11] Tanzania District Non-monetary poverty 2018 Census EBP 0.77 Masaki et al [24] Northeastern Tanzania…”

Section: Mcs-enighmentioning

confidence: 99%

“…Simulated sample Census EBP 0.96 Masaki et al [24] Togo Canton Asset index Pooled DHS Independent survey XGB 0.84 Chi et al [11] Note: BSEM = Bayesian Structural Equation Model, EBP = Empirical Best Predictor, GPR = Gaussian process Regression, OLS = Ordinary Least Squares, RF=Random Forest, CNN = Convolutional Neural Network, CNNTL = Convolutional Neural Network with Transfer Learning, XGB = XGBoost.…”

Section: District Nonmonetary Censusmentioning

confidence: 99%

See 1 more Smart Citation

Small Area Estimation of Poverty and Wealth Using Geospatial Data: What have We Learned So Far?

Newhouse

2023

Calcutta Statistical Association Bulletin

Self Cite

View full text Add to dashboard Cite

This article offers a non-technical review of selected applications that combine survey and geospatial data to generate small area estimates of wealth or poverty. Publicly available data from satellites and phones predict poverty and wealth accurately across space, when evaluated against census data, and their use in model-based estimates improves the accuracy and efficiency of direct survey estimates. Models based on interpretable features appear to predict more accurately than estimates derived from convolutional neural networks. Estimates for sampled areas are significantly more accurate than those for non-sampled areas due to informative sampling. In general, estimates benefit from using geospatial data at the most disaggregated level possible. Tree-based machine learning methods appear to generate more accurate estimates than linear mixed models in common settings. Small area estimates using geospatial data can improve the design of social assistance programmes, particularly when the existing targeting system is poorly designed. AMS subject classification: 62P20

show abstract

Small area estimation of non-monetary poverty with geospatial data

Cited by 10 publications

References 60 publications

Estimating spatially disaggregated probability of severe COVID-19 and the impact of handwashing interventions: The case of Zimbabwe

Estimating spatially disaggregated probability of severe COVID-19 and the impact of handwashing interventions: The case of Zimbabwe

Accelerating and enhancing the generation of socioeconomic data to inform forced displacement policy and response

Small Area Estimation of Poverty and Wealth Using Geospatial Data: What have We Learned So Far?

Contact Info

Product

Resources

About