Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • MPI Ethno. Forsch.  (6)
  • Newhouse, David  (6)
  • Washington, D.C : The World Bank  (6)
  • Cham : Springer International Publishing AG
  • Poverty Reduction  (6)
  • 1
    Online Resource
    Online Resource
    Small Area Estimation of Poverty and Wealth using Geospatial Data : What have we Learned so Far? (2023)
    Washington, D.C : The World Bank
    Details
    Language: English
    Pages: 1 Online-Ressource (30 pages)
    Parallel Title: Erscheint auch als Newhouse, David Small Area Estimation of Poverty and Wealth using Geospatial Data: What have we Learned so Far?
    Keywords: Cell Phone Data ; Convolutional Neural Networks ; Development Patterns and Poverty ; Geospacial Data ; Living Standards ; Poverty and Wealth Data Prediction ; Poverty Diagnostics ; Poverty Mapping ; Poverty Reduction ; Satellite Data ; Small Area Estimation
    Abstract: This paper offers a nontechnical 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 predicts poverty and wealth accurately across space, when evaluated against census data, and their use in model-based estimates improve the accuracy and efficiency of direct survey estimates. Although the evidence is scant, models based on interpretable features appear to predict at least as well as 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. Small area estimates using geospatial data can improve the design of social assistance programs, particularly when the existing targeting system is poorly designed
    DOI: 10.1596/1813-9450-10512
    URL: Deutschlandweit zugänglich
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Malnourished but not Destitute : The Spatial Interplay between Nutrition and Poverty in Madagascar (2023)
    Washington, D.C : The World Bank
    Details
    Language: English
    Pages: 1 Online-Ressource (24 pages)
    Parallel Title: Erscheint auch als Matekenya, Dunstan Malnourished but not Destitute: The Spatial Interplay between Nutrition and Poverty in Madagascar
    Keywords: Agriculture ; Development Patterns and Poverty ; Equity and Development ; Food Insecurity ; Food Security ; Hidden Hunger ; International Economics and Trade ; Malnutrition ; Poverty ; Poverty Reduction ; Small Area Estimation ; Sustainable Development Goals
    Abstract: Hidden hunger, or micronutrient deficiencies, is a serious public health issue affecting approximately 2 billion people worldwide. Identifying areas with high prevalence of hidden hunger is crucial for targeted interventions and effective resource allocation. However, conventional methods such as nutritional assessments and dietary surveys are expensive and time-consuming, rendering them unsustainable for developing countries. This study proposes an alternative approach to estimating the prevalence of hidden hunger at the commune level in Madagascar by combining data from the household budget survey and the Demographic and Health Survey. The study employs small area estimation techniques to borrow strength from the recent census and produce precise and accurate estimates at the lowest administrative level. The findings reveal that 17.9 percent of stunted children reside in non-poor households, highlighting the ineffectiveness of using poverty levels as a targeting tool for identifying stunted children. The findings also show that 21.3 percent of non-stunted children live in impoverished households, reinforcing Sen's argument that malnutrition is not solely a product of destitution. These findings emphasize the need for tailored food security interventions designed for specific geographical areas with clustered needs rather than employing uniform nutrition policies. The study concludes by outlining policies that are appropriate for addressing various categories of hidden hunger
    DOI: 10.1596/1813-9450-10627
    URL: Deutschlandweit zugänglich
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    Improving Estimates of Mean Welfare and Uncertainty in Developing Countries (2023)
    Washington, D.C : The World Bank
    Details
    Language: English
    Pages: 1 Online-Ressource (57 pages)
    Parallel Title: Erscheint auch als Merfeld, Joshua D Improving Estimates of Mean Welfare and Uncertainty in Developing Countries
    Keywords: Development Policy ; Geospacial Data ; Household Census Data ; Machine Learning ; Macroeconomics and Economic Growth ; Poverty Reduction ; Prediction of Poverty ; Prediction of Wealth ; Welfare
    Abstract: Reliable estimates of economic welfare for small areas are valuable inputs into the design and evaluation of development policies. This paper compares the accuracy of point estimates and confidence intervals for small area estimates of wealth and poverty derived from four different prediction methods: linear mixed models, Cubist regression, extreme gradient boosting, and boosted regression forests. The evaluation draws samples from unit-level household census data from four developing countries, combines them with publicly and globally available geospatial indicators to generate small area estimates, and evaluates these estimates against aggregates calculated using the full census. Predictions of wealth are evaluated in four countries and poverty in one. All three machine learning methods outperform the traditional linear mixed model, with extreme gradient boosting and boosted regression forests generally outperforming the other alternatives. The proposed residual bootstrap procedure reliably estimates confidence intervals for the machine learning estimators, with estimated coverage rates across simulations falling between 94 and 97 percent. These results demonstrate that predictions obtained using tree-based gradient boosting with a random effect block bootstrap generate more accurate point and uncertainty estimates than prevailing methods for generating small area welfare estimates
    DOI: 10.1596/1813-9450-10348
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    Small Area Estimation of Monetary Poverty in Mexico using Satellite Imagery and Machine Learning (2022)
    Washington, D.C : The World Bank
    Details
    Language: English
    Pages: 1 Online-Ressource (72 pages)
    Parallel Title: Erscheint auch als Newhouse, David Small Area Estimation of Monetary Poverty in Mexico using Satellite Imagery and Machine Learning
    Keywords: Inequality ; Information and Communication Technologies ; Machine Learning ; Poverty ; Poverty Assessment ; Poverty Eradication ; Poverty Mapping ; Poverty Reduction ; Poverty, Environment and Development ; Satellite Data ; Small Area Estimation ; Sustainable Development Goals
    Abstract: Estimates of poverty are an important input into policy formulation in developing countries. The accurate measurement of poverty rates is therefore a first-order problem for development policy. This paper shows that combining satellite imagery with household surveys can improve the precision and accuracy of estimated poverty rates in Mexican municipalities, a level at which the survey is not considered representative. It also shows that a household-level model outperforms other common small area estimation methods. However, poverty estimates in 2015 derived from geospatial data remain less accurate than 2010 estimates derived from household census data. These results indicate that the incorporation of household survey data and widely available satellite imagery can improve on existing poverty estimates in developing countries when census data are old or when patterns of poverty are changing rapidly, even for small subgroups
    DOI: 10.1596/1813-9450-10175
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 5
    Online Resource
    Online Resource
    April 2022 Update to the Poverty and Inequality Platform (PIP) : What's New (2022)
    Washington, D.C : The World Bank
    Details
    Language: English
    Pages: 1 Online-Ressource
    Series Statement: Other papers
    Keywords: Development Patterns and Poverty ; Equity and Development ; Inequality ; Poverty ; Poverty Reduction
    Abstract: The April 2022 update to the newly launched Poverty and Inequality Platform (PIP) involves several changes to the data underlying the global poverty estimates. Some welfare aggregates have been changed for improved harmonization, and the CPI, national accounts, and population input data have been updated. This document explains these changes in detail and the reasoning behind them. Moreover, a large number of new country-years have been added, bringing the total number of surveys to more than 2,000. These include new harmonized surveys for countries in West Africa, new imputed poverty estimates for Nigeria, and recent 2020 household survey data for several countries. Global poverty estimates are now reported up to 2018 and earlier years have been revised
    DOI: 10.1596/37479
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 6
    Online Resource
    Online Resource
    Nowcasting Global Poverty (2021)
    Washington, D.C : The World Bank
    Details
    Language: English
    Pages: 1 Online-Ressource (44 pages)
    Parallel Title: Erscheint auch als Print Version: Mahler, Daniel Gerszon Nowcasting Global Poverty
    Keywords: Inequality ; Machine Learning ; Nowcasting ; Poverty ; Poverty Lines ; Poverty Measurement ; Poverty Monitoring and Analysis ; Poverty Reduction
    Abstract: This paper evaluates different methods for nowcasting country-level poverty rates, including methods that apply statistical learning to large-scale country-level data obtained from the World Development Indicators and Google Earth Engine. The methods are evaluated by withholding measured poverty rates and determining how accurately the methods predict the held-out data. A simple approach that scales the last observed welfare distribution by a fraction of real GDP per capita growth-a method that departs slightly from current World Bank practice-performs nearly as well as models using statistical learning on 1,000+ variables. This GDP-based approach outperforms all models that predict poverty rates directly, even when the last survey is up to five years old. The results indicate that in this context, the additional complexity introduced by applying statistical learning techniques to a large set of variables yields only marginal improvements in accuracy
    DOI: 10.1596/1813-9450-9860
    URL: Deutschlandweit zugänglich
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...