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  • 2020-2024  (19)
  • Washington, D.C : The World Bank  (19)
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  • 1
    Online Resource
    Online Resource
    From Middle Class to Poverty : The Unequal Impacts of the COVID-19 Pandemic on Developing Countries (2023)
    Washington, D.C : The World Bank
    Details
    Language: English
    Pages: 1 Online-Ressource (36 pages)
    Parallel Title: Erscheint auch als Viollaz, Mariana From Middle Class to Poverty: The Unequal Impacts of the COVID-19 Pandemic on Developing Countries
    Keywords: COVID-19 Job Loss ; Distributional Impact ; Economic Insecurity ; Economic Shocks ; Household Economic Welfare ; Household Poverty ; Labor Market ; Macroeconomics and Economic Growth ; Microsimulation
    Abstract: This study combines pre-COVID-19 household surveys with 2020 macro data to simulate changes in household economic welfare and poverty rates through job losses, labor income changes, and non-labor (remittance) income changes during 2020 in Brazil, Sri Lanka, the Philippines, South Africa, and Turkiye. It first presents an in-depth analysis of employment elasticities projections-a critical input in microsimulations-for 15 developing countries. In 11 of the 15 countries, employment estimates for 2020 based on elasticities were within 5 percent of the actual employment level, but in four countries, where the labor markets were more disrupted by the pandemic, the projections considerably underestimated job losses due to the crisis. The study then presents the simulation results for the five countries, which show declines in per capita household income or consumption across the distribution, a decline in the middle class, and increased poverty, but no other clear pattern of impacts across the different quintiles. Finally, data from Brazil indicate that the simulation underestimated the magnitude of the shock throughout the distribution, especially for the wealthy, because it underestimated declines in earnings
    DOI: 10.1596/1813-9450-10304
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    How did Urban Household Enterprises in Sub-Saharan Africa Fare during COVID-19? : Evidence from High-Frequency Phone Surveys (2023)
    Washington, D.C : The World Bank
    Details
    Language: English
    Pages: 1 Online-Ressource (47 pages)
    Parallel Title: Erscheint auch als Cunningham, Wendy How did Urban Household Enterprises in Sub-Saharan Africa Fare during COVID-19? Evidence from High-Frequency Phone Surveys
    Keywords: COVID-19 Pandemic ; Health, Nutrition and Population ; Income Loss ; Income Shock ; Informal Enterprise ; Informality ; Urban Household Enterprise
    Abstract: While the impact of COVID-19 on Sub-Saharan African labor markets is well documented, there is suggestive evidence that urban households may have fared particularly poorly. This paper uses data from high-frequency phone surveys in 27 Sub-Saharan African countries to investigate which kinds of urban household enterprises were most affected, what coping strategies were utilized, and heterogeneity by sociodemographic characteristics in the short and medium run. Using linear probability models, the paper finds that households that relied on income from non-farm enterprises were hit particularly hard during the early stage of the crisis, with 20-26 percent reporting income declines, and women experiencing even greater losses. Few coping strategies were utilized in the short run to counterbalance the loss of enterprise income. As the crisis progressed, wage employment recovered more quickly than self-employment, with faster gains for non-farm household enterprises, less poor households, and those headed by males and adults. Women, adults, and non-poor self-employed household heads were more successful at leveraging external sources of support early in the pandemic, but these supports largely dropped off by August 2020. These results demonstrate the vulnerability of non-farm household enterprises in urban Sub-Saharan Africa to the COVID-19 shock and highlight the need to expand publicly and privately financed coping mechanisms, particularly for women, youth, and poor household heads who are self-employed
    DOI: 10.1596/1813-9450-10360
    URL: lizenzpflichtig
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    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
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    Online Resource
    MPI Ethno. Forsch.
  • 4
    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
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    Online Resource
    MPI Ethno. Forsch.
  • 5
    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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    Online Resource
    MPI Ethno. Forsch.
  • 6
    Online Resource
    Online Resource
    The Early Labor Market Impacts of COVID-19 in Developing Countries : Evidence from High-Frequency Phone Surveys (2021)
    Washington, D.C : The World Bank
    Details
    Language: English
    Pages: 1 Online-Ressource
    Series Statement: Other papers
    Abstract: The economic crisis caused by the Coronavirus (COVID-19) pandemic sharply reduced mobility and economic activity, disrupting the lives of people around the globe. This brief presents estimates on the crisis' impact on labor markets in thirty-nine countries based on high-frequency phone survey (HFPS) data collected between April and July 2020. Workers in these countries experienced severe labor market disruptions following the Coronavirus (COVID-19) outbreak. Thirty-four percent of respondents reported stopping work, twenty percent of wage workers reported lack of payment for work performed, nine percent reported job changes due to the pandemic, and sixty-two percent reported income loss in their household. Measures of work stoppage and income loss in the HFPS are generally consistent with gross domestic products (GDP) growth projections in Latin America and the Caribbean but not in Sub-Saharan Africa, indicating that the phone survey data contributes valuable new information about the impacts of the crisis. Ensuring availability of such critical data in the future will require investments into statistical and physical infrastructure as well as human capital to set up Emergency Observatories, which can rapidly deploy phone surveys to inform decision makers
    DOI: 10.1596/35044
    URL: Deutschlandweit zugänglich
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    Online Resource
    MPI Ethno. Forsch.
  • 7
    Online Resource
    Online Resource
    March 2021 PovcalNet Update : What's New (2021)
    Washington, D.C : The World Bank
    Details
    Language: English
    Pages: 1 Online-Ressource
    Series Statement: Other Poverty Study
    Abstract: The March 2021 update to PovcalNet 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. In addition to the changes listed here, a large number of new country-years have been added, resulting in a total number of surveys of more than 1,900. Moreover, this update includes important revisions to the historical survey data and for the first time, poverty estimates based on imputed consumption data
    DOI: 10.1596/35340
    URL: Deutschlandweit zugänglich
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    MPI Ethno. Forsch.
  • 8
    Online Resource
    Online Resource
    March 2020 PovcalNet Update : What's New (2020)
    Washington, D.C : The World Bank
    Details
    Language: English
    Pages: 1 Online-Ressource
    Series Statement: World Bank E-Library Archive
    Series Statement: Other Poverty Study
    Abstract: The March 2020 update to PovcalNet involves several changes to the data underlying the global poverty estimates. Some welfare aggregates have been changed for improved harmonization, and some of the CPI, national accounts, and population input data have been revised. This document explains these changes in detail and the reasoning behind them. In addition to the changes listed here, a large number of new country-years have been added, bringing the total number of surveys to more than 1,900
    DOI: 10.1596/33496
    URL: Deutschlandweit zugänglich
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    MPI Ethno. Forsch.
  • 9
    Online Resource
    Online Resource
    How Well Can Real-Time Indicators Track the Economic Impacts of a Crisis like COVID-19? (2022)
    Washington, D.C : The World Bank
    Details
    Language: English
    Pages: 1 Online-Ressource (42 pages)
    Parallel Title: Erscheint auch als Ten, Gi Khan How Well Can Real-Time Indicators Track the Economic Impacts of a Crisis like COVID-19?
    Keywords: Aggregated Data Analysis ; Annual GDP Variation Data ; Big Data ; Corporate Data and Reporting ; COVID-19 Real-Time Data ; Economic Conditions and Volatility ; Economic Cost of Covid ; Economic Forecasting ; Economic Indicators From Big Data ; GDP Impact Estimation ; Google Mobility Data ; Google Search Term Analysis ; ICT Data and Statistics ; Information and Communication Technologies ; Macroeconomics and Economic Growth ; Pandemic Air Quality Improvement ; Private Sector Development
    Abstract: This paper presents evidence on the extent to which a set of real-time indicators tracked changes in gross domestic product across 142 countries in 2020. The real-time indicators include Google mobility, Google search trends, food price information, nitrogen dioxide, and nighttime lights. Google mobility and staple food prices both declined sharply in March and April, followed by a rapid recovery that returned to baseline levels by July and August. Mobility and staple food prices fell less in low-income countries. Nitrogen dioxide levels show a similar pattern, with a steep fall and rapid recovery in high-income and upper-middle-income countries but not in low-income and lower-middle-income countries. In April and May, Google search terms reflecting economic distress and religiosity spiked in some regions but not others. Data on nighttime lights show no clear drop in March outside East Asia. Linear models selected using the Least Absolute Shrinkage and Selection Operator explain about a third of the variation in annual gross domestic product growth rates across 72 countries. In a smaller subset of higher income countries, real-time indicators explain about 40 percent of the variation in quarterly gross domestic product growth. Overall, mobility and food price data, as well as pollution data in more developed countries, appeared to be best at capturing the widespread economic disruption experienced during the summer of 2020. The results indicate that these real-time indicators can track a substantial percentage of both annual and quarterly changes in gross domestic product
    DOI: 10.1596/1813-9450-10080
    URL: lizenzpflichtig
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  • 10
    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.
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