Umfang:
1 Online-Ressource (24 Seiten)
Serie:
World Bank E-Library Archive
Inhalt:
The goals of ending extreme poverty by 2030 and working toward a more equal distribution of income are prominent in international development and agreed upon in the United Nations' Sustainable Development Goals 1 and 10. Using data from 164 countries comprising 97 percent of the world's population, this paper simulates a set of scenarios for global poverty from 2018 to 2030 under different assumptions about growth and inequality. This allows for quantifying the interdependence of the poverty and inequality goals. The paper uses different assumptions about growth incidence curves to model changes in inequality and relies on the Model-based Recursive Partitioning machine-learning algorithm to model how growth in GDP is passed through to growth as observed in household surveys. When holding within-country inequality unchanged and letting GDP per capita grow according to International Monetary Fund forecasts, the simulations suggest that the number of extreme poor (living below USD 1.90/day) will remain above 550 million in 2030, resulting in a global extreme poverty rate of 6.5 percent. If the Gini index in each country decreases by 1 percent per year, the global poverty rate could reduce to around 5.4 percent in 2030, equivalent to 100 million fewer people living in extreme poverty. Reducing each country's Gini index by 1 percent per year has a larger impact on global poverty than increasing each country's annual growth 1 percentage point above the forecasts, suggesting an important role for inequality on the path to eliminating extreme poverty
Weitere Ausg.:
Erscheint auch als Druck-Ausgabe Lakner, Christoph How Much Does Reducing Inequality Matter for Global Poverty? Washington, D.C : The World Bank, 2019
Sprache:
Englisch
DOI:
10.1596/1813-9450-8869
URL:
Volltext
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