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  • 1
    Language: English
    Pages: 1 Online-Ressource (49 pages)
    Parallel Title: Erscheint auch als Atamanov, Aziz New Evidence on Inequality of Opportunity in Sub-Saharan Africa: More Unequal than we Thought
    Keywords: Circumstances ; Consumpton Inequality ; Equity and Development ; Inequality ; Inequality of Opportunity ; Poverty Reduction ; Social Development
    Abstract: Unequal access to economic opportunity for individuals with different innate characteristics, such as ethnicity or parents' socioeconomic status, is often seen as both morally undesirable and bad for economic growth. This paper estimates inequality of opportunity, or the share of inequality explained by birth characteristics, across 18 countries in Sub-Saharan Africa. For many countries, this is the first time inequality of opportunity is measured. The paper uses nationally representative household survey data harmonized to allow for cross-country comparisons. Using consumption per capita as the outcome, the findings show that inequality of opportunity in Sub-Saharan Africa is stark and more pronounced than previously estimated. On average, inherited circumstances explain more than half of inequality in the region. Estimates range from 40 to 60 percent in most countries and reach 74 percent in South Africa. The findings show that birthplace, parents' education, and ethnicity tend to be the most significant contributors, but there is large variation in the importance of circumstances across countries. This represents the most comprehensive estimate of inequality of opportunity to date in the poorest and one of the most unequal regions in the world, and it underscores the pressing need for policy makers to intensify their efforts to address inequality of opportunity to foster societies that are more equitable and unlock the full potential for growth in the region
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  • 2
    Online Resource
    Online Resource
    Washington, D.C : The World Bank
    Language: English
    Pages: 1 Online-Ressource (45 pages)
    Parallel Title: Erscheint auch als Wollburg, Philip The Climate Implications of Ending Global Poverty
    Keywords: Climate Change ; Climate Change Economics ; Co2 Emission Goals ; Environment and Poverty ; Greenhouse Gas Emissions ; Inequality ; Macroeconomics and Economic Growth ; Poverty ; Poverty and Climate Ambitions
    Abstract: Previous studies have explored potential conflicts between ending poverty and limiting global warming, by focusing on the carbon emissions of the world's poorest. This paper instead focuses on economic growth as the driver of poverty alleviation and estimates the emissions associated with the growth needed to eradicate poverty. With this framing, eradicating poverty requires not only increasing the consumption of poor people, but also the consumption of non-poor people in poor countries. Even in this more pessimistic framing, the global emissions increase associated with eradicating extreme poverty is small, at 2.37 gigatonnes of equivalent carbon dioxide in 2050, or 4.9 percent of 2019 global emissions. These additional emissions would not materially affect the global climate change challenge: global emissions would need to be reduced by 2.08 gigatonnes of equivalent carbon dioxide per year, instead of the 2.0 gigatonnes of equivalent carbon dioxide per year needed in the absence of any extreme poverty eradication. Lower inequality, higher energy efficiency, and decarbonization of energy can significantly ease this trade-off: assuming the best historical performance in all countries, the additional emissions for poverty eradication are reduced by 90 percent. Therefore, the need to eradicate extreme poverty cannot be used as a justification for reducing the world's climate ambitions. When trade-offs exist, the eradication of extreme poverty can be prioritized with negligible emissions implications. The estimated emissions of eradicating poverty are 15.3 percent of 2019 emissions with the lower-middle-income poverty line at USD 3.65 per day and or 45.7 percent of 2019 emissions with the USD 6.85 upper-middle-income poverty line. The challenge to align the world's development and climate objectives is not in reconciling extreme poverty alleviation with climate objectives but in providing middle-income standards of living in a sustainable manner
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  • 3
    Language: English
    Pages: 1 Online-Ressource (77 pages)
    Parallel Title: Erscheint auch als Jolliffe, Dean Mitchell Assessing the Impact of the 2017 PPPs on the International Poverty Line and Global Poverty
    Keywords: Geographic Distribution Of Poverty ; Global Poverty ; Inequality ; Inflation ; International Trade and Trade Rules ; National Poverty Rate ; Poverty and Equity ; Poverty Impact Evaluation ; Poverty Lines ; Poverty Monitoring and Analysis ; Poverty Reduction ; Public-Private Partnership
    Abstract: Purchasing power parities (PPPs) are used to estimate the international poverty line (IPL) in a common currency and account for relative price differences across countries when measuring global poverty. This paper assesses the impact of the 2017 PPPs on the nominal value of the IPL and global poverty. The analysis indicates that updating the USD 1.90 IPL in 2011 PPP dollars to 2017 PPP dollars results in an IPL of approximately USD 2.15-a finding that is robust to various methods and assumptions. Based on an IPL of USD 2.15, the global extreme poverty rate in 2017 falls from 9.3 to 9.1 percent, reducing the count of people who are poor by 16 million. This is a modest change compared with previous updates of PPP data. The paper also assesses the methodological stability between the 2011 and 2017 PPPs, scrutinizes large changes at the country level, and analyzes higher poverty lines with the 2017 PPPs
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  • 4
    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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