Abstract
Monitoring progress towards the fulfillment of the Sustainable Development Goals (SDGs) requires the assessment of potential future trends in poverty. This paper presents an econometric tool that provides a methodological framework to carry out projections of poverty rates worldwide and aims at assessing absolute poverty changes at the global level under different scenarios. The model combines country-specific historical estimates of the distribution of income, using Beta–Lorenz curves, with projections of population changes by age and education attainment level, as well as GDP projections to provide the first set of internally consistent poverty projections for all countries of the world. Making use of demographic and economic projections developed in the context of the Intergovernmental Panel on Climate Change’s Shared Socioeconomic Pathways, we create poverty paths by country up to the year 2030. The differences implied by different global scenarios span worldwide poverty rates ranging from 4.5% (around 375 million persons) to almost 6% (over 500 million persons) by the end of our projection period. The largest differences in poverty headcount and poverty rates across scenarios appear for Sub-Saharan Africa, where the projections for the most optimistic scenario imply over 300 million individuals living in extreme poverty in 2030. The results of the comparison of poverty scenarios point towards the difficulty of fulfilling the first goal of the SDGs unless further development policy efforts are enacted.
Highlights
In September 2015, 193 world leaders adopted the Sustainable Development Goals (SDGs) and called for a “data revolution” (United Nations, 2013) to enhance accountability in measuring the progress towards their fulfillment
Monitoring trends in poverty reduction and providing tools that are able to assess the effects of policies on the likelihood of fulfilling this SDG are items which are high in the agenda of priorities for the scientific community at the moment
We develop a methodological framework aimed at modeling and projecting poverty headcounts globally that builds upon the combination of new estimates of the worldwide distribution of income and macroeconomic projections of population by age and educational attainment level, as well as income per capita which have been recently developed in the context of climate change research (Lutz and KC, 2017, Crespo-Cuaresma, 2017)
Summary
In September 2015, 193 world leaders adopted the Sustainable Development Goals (SDGs) and called for a “data revolution” (United Nations, 2013) to enhance accountability in measuring the progress towards their fulfillment. We combine efforts to compute projections of global poverty assuming no changes in within-country income distributions (Ravallion, 2013) with the latest generation of average income per capita projection models, which rely on observed global trends in human capital indicators. This allows us to improve on the methods employed in the recent literature (Edward and Sumner, 2014) by providing poverty projections which are compatible with the scenarios used in long-run prediction exercises in the climate change community. An online tool based on the methodology described in this piece, the World Poverty Clock, provides an informative and user-friendly visualization platform that allows the user to understand the progress and possible challenges to the fulfillment of the SDG target concerning the worldwide eradication of extreme poverty under the business-as-usual assumptions provided by the SSP2 scenario
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
