Publication: How much does reducing inequality matter for global poverty?
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2022-03-02
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2022-03-16
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The goals of ending extreme poverty by 2030 and working towards a more equal distribution of incomes are part of the United Nations’ Sustainable Development Goals. Using data from 166 countries comprising 97.5 percent of the world’s population, we simulate scenarios for global poverty from 2019 to 2030 under various assumptions about growth and inequality. We use different assumptions about growth incidence curves to model changes in inequality and rely on a machine-learning algorithm called model-based recursive partitioning 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 World Bank forecasts and historically observed growth rates, our simulations suggest that the number of extreme poor (living on less than 1.90 dollars/day) will remain above 600 million in 2030, resulting in a global extreme poverty rate of 7.4 percent. If the Gini index in each country decreases by 1 percent per year, the global poverty rate could reduce to around 6.3 percent in 2030, equivalent to 89 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 forecasts. We also study the impact of COVID-19 on poverty and find that the pandemic may have driven around 60 million people into extreme poverty in 2020. If the pandemic increased the Gini index by 2 percent in all countries, then more than 90 million may have been driven into extreme poverty in 2020.
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Publication How Much Does Reducing Inequality Matter for Global Poverty?(World Bank, Washington, DC, 2020-06-01)The goals of ending extreme poverty by 2030 and working towards a more equal distribution of incomes are part of the United Nations' Sustainable Development Goals. Using data from 166 countries comprising 97.5 percent of the world's population, we simulate scenarios for global poverty from 2019 to 2030 under various assumptions about growth and inequality. We use different assumptions about growth incidence curves to model changes in inequality, and rely on a machine-learning algorithm called model-based recursive partitioning 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 World Bank forecasts and historically observed growth rates, our simulations suggest that the number of extreme poor (living on less than 1.90 US Dollars/day) will remain above 600 million in 2030, resulting in a global extreme poverty rate of 7.4 percent. If the Gini index in each country decreases by 1 percent per year, the global poverty rate could reduce to around 6.3 percent in 2030, equivalent to 89 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 points above forecasts. We also study the impact of COVID-19 on poverty and find that the pandemic may have driven around 60 million people into extreme poverty in 2020. If the virus increased the Gini by 2 pecent in all countries, then more than 90 million may have been driven into extreme poverty in 2020.Publication How Much Does Reducing Inequality Matter for Global Poverty?(World Bank, Washington, DC, 2019-05)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 $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. 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