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  • 2020-2024  (19)
  • 1975-1979
  • 1945-1949
  • Newhouse, David  (19)
  • Washington, D.C : The World Bank  (19)
  • Bielefeld : transcript
  • Napoli : FedOA - Federico II University Press
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  • 1
    Language: English
    Pages: 1 Online-Ressource (21 pages)
    Parallel Title: Erscheint auch als Seuyong, Feraud Tchuisseu Who did Covid-19 Hurt the Most in Sub-Saharan Africa?
    Keywords: Covid-19 ; Distributional Impacts ; Finance and Development ; Finance and Financial Sector Development ; Health Monitoring and Evaluation ; Health, Nutrition and Population
    Abstract: How did the economic crisis caused by the Covid-19 pandemic impact poor households in Sub-Saharan Africa This paper tackles this question by combining 73 High-Frequency Phone Surveys collected by national governments in 14 countries with older nationally representative surveys containing information on household consumption. In particular, it examines how outcomes differed according to predicted per capita consumption quintiles in the first wave of the survey, and in subsequent waves by households' predicted per capita consumption. The initial shock affected households throughout the predicted welfare distribution. Households in the bottom 40 percent responded by sharply increasing farming activities between May and July of 2020 and gradually increasing ownership of non-farm enterprises starting in August. This coincided with an improvement in welfare, as measured by a decline in food insecurity and distressed asset sales among these households during the second half of 2020. With respect to education, children in the bottom quintile were 15 percentage points less likely to engage in learning activities than those in the top quintile in the immediate aftermath of the crisis, and the engagement gap between the bottom 40 and top 60 widened in the summer before narrowing in the fall due to large declines in engagement among the top 60. Poorer households were slightly more likely to report receiving public assistance immediately following the shock, and this difference changed little over the course of 2020. The results highlight the widespread impacts of the crisis both on welfare and children's educational engagement, the importance of agriculture and household non-farm enterprises as safety nets for the poor, and the substantial recovery made by the poorest households in the year following the crisis
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  • 2
    Language: English
    Pages: 1 Online-Ressource (62 pages)
    Parallel Title: Erscheint auch als Cunningham, Wendy Urban Informality in Sub-Saharan Africa: Profiling Workers and Firms in an Urban Context
    Keywords: Employment and Unemployment ; Informal Sector ; Low Skilled Workers ; Self-Employment ; Social Protections and Labor ; Urban Informal Sector ; Women in The Workforce ; Work and Working Conditions
    Abstract: This paper describes the state of informal sector work in urban Sub-Saharan Africa, using household surveys from 26 countries representing 61 percent of the population of Sub-Saharan Africa and firm surveys from three countries. Five main conclusions emerge. First, the urban informal sector is large and persistent in Sub-Saharan Africa. Approximately 56 to 65 percent of urban workers are informal, half of whom are self-employed. Data from five countries suggest little systematic reduction in the prevalence of informality during the 2010s. Second, heterogeneity in the African informal sector cuts along demographic lines. Women are overrepresented in informal self-employment, men in informal wage work, and youth in unpaid employment. Third, while the urban informal workers are, on average, poorer and in less-skilled occupations than formal sector workers, the majority are not extremely poor and are in mid-skilled occupations. Fourth, informal enterprises are small and are challenged to survive and grow into job-creating firms. Few find much benefit from registration given the costs, both monetary (taxes) and transactional (information about the registration process). Fifth, access to urban public services (utilities) is weakly associated with the probability of working in an informal job, although access to mobile phones is high across all job types. If thriving urban jobs are to contribute to economic and social development in Africa, it will be crucial for policies and programs to take into consideration the heterogeneity in jobs, the profile of workers, and the urban context
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  • 3
    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
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  • 4
    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
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  • 5
    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
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  • 6
    Online Resource
    Online Resource
    Washington, D.C : The World Bank
    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
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  • 7
    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
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  • 8
    Language: English
    Pages: 1 Online-Ressource (47 pages)
    Parallel Title: Erscheint auch als Merfeld, Joshua D Combining Survey and Geospatial Data can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes
    Keywords: Data Integration ; Economic Empowerment ; Employment and Unemployment ; Gender ; Gender Monitoring and Evaluation ; Gendered Employment Data ; Geospatial Data ; Human Capital ; Labor Force Participation ; Labor Markets ; Local Employment Estimates ; Local Labor Participation ; Municipal Unemployment Results ; Small Area Estimation ; Social Capital ; Social Development ; Social Protections and Labor ; Unemployment ; Women's Labor Market Outcomes
    Abstract: Better understanding the geography of women's labor market outcomes within countries is important to inform targeted efforts to increase women's economic empowerment. This paper assesses the extent to which a method that combines simulated survey data from urban areas in Mexico with broadly available geospatial indicators from Google Earth Engine and OpenStreetMap can significantly improve estimates of labor force participation and unemployment rates. Incorporating geospatial information substantially increases the accuracy of male and female labor force participation and unemployment rates at the state level, reducing mean absolute deviation by 50 to 62 percent for labor force participation and 25 to 52 percent for unemployment. Small area estimation using a nested error conditional random effect model also greatly improves municipal estimates of labor force participation, as the mean absolute error falls by approximately half, while the mean squared error falls by almost 75 percent when holding coverage rates constant. In contrast, the results for municipal unemployment rate estimates are not reliable because values of unemployment rates are low and therefore poorly suited for linear models. The municipal results hold in repeated simulations of alternative samples. Models utilizing Basic Geo-Statistical Area (AGEB)-level auxiliary information generate more accurate predictions than area-level models specified using the same auxiliary data. Overall, integrating survey data and publicly available geospatial indicators is feasible and can greatly improve state-level estimates of male and female labor force participation and unemployment rates, as well as municipal estimates of male and female labor force participation
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  • 9
    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
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  • 10
    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
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  • 11
    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
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  • 12
    Language: English
    Pages: 1 Online-Ressource (37 pages)
    Parallel Title: Erscheint auch als Tabakis, Chrysostomos The Welfare Implications of COVID-19 for Fragile and Conflict-Affected Areas
    Keywords: Access and Equity in Basic Education ; Access of Poor To Social Services ; Agriculture ; Conflict ; Covid In Conflict-Affected Households ; COVID-19 Restriction Social Impact ; Education ; Food and Nutrition Policy ; Food Insecurity ; Food Security ; Fragility ; Health, Nutrition and Population ; Household Welfare ; Inequality ; Pandemic Social Impact ; Violence
    Abstract: Understanding the impacts of the COVID-19 pandemic on households' welfare in areas at the admin-1 level subject to fragility, conflict, and violence is important to inform programs and policies in this context. Harmonized data from high-frequency phone surveys indicate that, at the onset of the pandemic, a higher fraction of households in areas affected by fragility, conflict, and violence reported income declines and a higher fraction of respondents reported that they had stopped working since the beginning of the crisis. Households in areas affected by fragility, conflict, and violence were far less likely to report receiving government assistance than those in other areas. These findings suggest that the initial effects of the pandemic exacerbated preexisting economic gaps between areas affected by fragility, conflict, and violence and other areas, indicating that an even larger effort will be necessary in areas affected by fragility, conflict, and violence to recover from COVID-19, with implications for funding needs and policy as well as program design
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  • 13
    Language: English
    Pages: 1 Online-Ressource (63 pages)
    Parallel Title: Erscheint auch als Print Version: Kugler, Maurice How Did the COVID-19 Crisis Affect Different Types of Workers in the Developing World?
    Abstract: This paper investigates the impacts of the economic shock caused by the COVID-19 pandemic on the employment of different types of workers in developing countries. Employment outcomes are taken from a set of high-frequency phone surveys conducted by the World Bank and National Statistics Offices in 40 countries. Larger shares of female, young, less educated, and urban workers stopped working. Gender gaps in work stoppage were particularly pronounced and stemmed mainly from differences within sectors rather than differential employment patterns across sectors. Differences in work stoppage between urban and rural workers were markedly smaller than those across gender, age, and education groups. Preliminary results from 10 countries suggest that following the initial shock at the start of the pandemic, employment rates partially recovered between April and August, with greater gains for those groups that had borne the brunt of the early jobs losses. Although the high-frequency phone surveys greatly over-represent household heads and therefore overestimate employment rates, case studies in five countries suggest that they provide a reasonably accurate measure of disparities in employment levels by gender, education, and urban/rural location following the onset of the crisis, although they perform less well in capturing disparities between age groups. These results shed new light on the labor market consequences of the COVID-19 crisis in developing countries, and suggest that real-time phone surveys, despite their lack of representativeness, are a valuable source of information to measure differential employment impacts across groups during a crisis
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  • 14
    Online Resource
    Online Resource
    Washington, D.C : The World Bank
    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
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  • 15
    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
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  • 16
    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
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  • 17
    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
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  • 18
    Language: English
    Pages: 1 Online-Ressource
    Series Statement: World Bank E-Library Archive
    Series Statement: Other Poverty Study
    Abstract: The ongoing coronavirus pandemic is expected to drastically slow 2020 GDP per capita growth in Sub-Saharan Africa (SSA) by about 5 percentage points compared to pre-pandemic forecasts. This note presents results from an analysis of a comprehensive database of surveys from 45 of 48 SSA countries to examine the effects of the project fall in growth on poverty in the region. An additional 26 million people in SSA, and as much as 58 million, may fall into extreme poverty defined by the international poverty line of 1.90 US Dollars per day in 2011 PPP. The poverty rate for SSA will likely increase more than two percentage points, setting back poverty reduction in the region by about 5 years
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  • 19
    Language: English
    Pages: 1 Online-Ressource
    Series Statement: World Bank E-Library Archive
    Series Statement: Other papers
    Abstract: This note builds on previous collaboration between the World Bank Group and UNICEF to estimate the global extent of child poverty. We estimate that in 2017, 17.5 percent of children in the world (or 356 million) younger than 18 years lived on less than 1.90 Dollars PPP per day, as opposed to 7.9 percent of adults ages 18 and above. The poverty rate of children at the 3.20 Dollars and 5.50 Dollars lines were 41.5 and 66.7 percent, respectively. The number of children living in extreme poverty declined by approximately 29 million between 2013 and 2017. In 2017, Sub-Saharan Africa accounted for two thirds of extremely poor children, and South Asia another 18 percent. These estimates are based on the Global Monitoring Database (GMD) of household surveys compiled in Spring 2020 and consists of surveys from 149 countries that are also used for the official World Bank poverty estimates. Because the estimates pertain to 2017, they do not consider the adverse economic impact of the COVID-19 (coronavirus) pandemic
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