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  • 2020-2024  (23)
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  • 1
    Language: English
    Pages: 1 Online-Ressource (circa 49 Seiten) , Illustrationen
    Series Statement: Policy research working paper 9383
    Series Statement: World Bank E-Library Archive
    Series Statement: Policy research working paper
    Parallel Title: Erscheint auch als Masaki, Takaaki Small Area Estimation of Non-Monetary Poverty with Geospatial Data
    Keywords: Graue Literatur
    Abstract: This paper uses data from Sri Lanka and Tanzania to evaluate the benefits of combining household surveys with geographically comprehensive geospatial indicators to generate small area estimates of non-monetary poverty. The preferred estimates are generated by utilizing subarea-level geospatial indicators in a household-level empirical best predictor mixed model with a normalized welfare measure. Mean squared errors are estimated using a parametric bootstrap procedure. The resulting estimates are highly correlated with non-monetary poverty calculated from the full census in both countries, and the gain in precision is comparable to increasing the size of the sample by a factor of three in Sri Lanka and five in Tanzania. The empirical best predictor model moderately underestimates uncertainty, but coverage rates are similar to standard survey-based estimates that assume independent outcomes across clusters. A variety of checks, including adding noise to the welfare measure and model-based and design-based simulations, confirm that the main results are robust. The results demonstrate that combining household survey data with subarea-level geospatial indicators can greatly increase the precision of survey estimates of non-monetary poverty at comparatively low cost
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  • 2
    Language: English
    Pages: 1 Online-Ressource (circa 54 Seiten) , Illustrationen
    Series Statement: Policy research working paper 9510
    Series Statement: Policy research working paper
    Parallel Title: Erscheint auch als Print Version: Khamis, Melanie The Early Labor Market Impacts of COVID-19 in Developing Countries: Evidence from High-Frequency Phone Surveys
    Keywords: COVID-19 ; Employment ; High-frequency phone survey ; Developing countries ; Graue Literatur
    Abstract: The economic crisis caused by the COVID-19 pandemic has sharply reduced mobility and economic activity, disrupting the lives of people around the globe. This paper presents estimates on the early impact of the crisis on labor markets in 39 countries based on high-frequency phone survey data collected between April and July 2020. Workers in these countries experienced severe labor market disruptions following the COVID-19 outbreak. Based on simple averages across countries, 34 percent of the respondents reported stopping work, 20 percent of wage workers reported lack of payment for work performed, 9 percent reported job changes due to the pandemic, and 62 percent reported income loss in their household. Stopping work was more prevalent in the industrial and service sectors than in agriculture. Measures of work stoppage and income loss in the high-frequency phone survey are generally consistent with gross domestic product growth projections in Latin America and the Caribbean but not in Sub-Saharan Africa. This suggests that the survey data contribute new and important information on economic impacts in low-income countries
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  • 3
    Online Resource
    Online Resource
    Winnipeg : University of Manitoba Press
    ISBN: 9780887558696
    Language: English
    Pages: 1 online resource (493 pages)
    Series Statement: Perceptions on Truth and Reconciliation 4
    Parallel Title: Erscheint auch als
    DDC: 971.00497
    Keywords: Electronic books ; Kanada ; Kanada Commission on Aboriginal Peoples ; Indigenes Volk ; Minderheitenpolitik ; Ethnische Beziehungen ; Geschichte
    Abstract: "Sharing the Land, Sharing a Future" looks to both the past and the future as it examines the foundational work of the Royal Commission on Aboriginal Peoples (RCAP) and the legacy of its 1996 report. It assesses the Commission's influence on subsequent milestones in Indigenous-Canada relations and considers our prospects for a constructive future.
    Note: Description based on publisher supplied metadata and other sources
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  • 4
    Language: English
    Note: Vol. 2 ed. by Cora J. Voyageur
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  • 5
    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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  • 6
    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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  • 7
    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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  • 8
    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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  • 9
    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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  • 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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