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  • 1
    Online Resource
    Online Resource
    Europe and Central Asia Economic Update, Spring 2023 : Weak Growth, High Inflation, and a Cost-of-Living Crisis (2023)
    Washington, D.C : The World Bank
    Details
    Language: English
    Pages: 1 Online-Ressource (122 pages)
    Series Statement: Europe and Central Asia Economic Update
    Parallel Title: Erscheint auch als
    Keywords: Cost-Of-Living ; Economic Forecasts ; Growth ; Inflation ; Policy Recommendations ; Poverty ; Uncertainty ; Vulnerability
    Abstract: Economic growth slowed sharply last year in Europe and Central Asia, as Russia's invasion of Ukraine, a surge in inflation, and the sharp tightening of monetary policy and financing conditions hit private consumption, investment, and trade. The marked increase in food and energy prices boosted inflation to a pace not seen in 20 years. The burden of inflation was spread unevenly across households. The poorest households faced inflation that was more than 2 percentage points higher than the inflation faced by the richest households, with this difference exceeding 5 percentage points in some countries. Poverty and inequality rates derived from household-specific inflation rates differ from those based on the standard consumer price index (CPI) approach. These differences have important policy implications, because many programs use CPI-based inflation adjustments, which do not accurately capture changes in the cost of living of targeted populations. Output growth in the region is projected to remain little changed in 2023 but better than projected in January 2023, largely reflecting upgrades to the pace of expansion in Poland, Russia, and Turkiye
    URL: Deutschlandweit zugänglich
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    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
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    MPI Ethno. Forsch.
  • 3
    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
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    MPI Ethno. Forsch.
  • 4
    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
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    MPI Ethno. Forsch.
  • 5
    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
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    MPI Ethno. Forsch.
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