Skip to main content
PMC home page
Springer Nature - PMC COVID-19 Collection logoLink to Springer Nature - PMC COVID-19 Collection
. 2022 Mar 2;20(3):559–585. doi: 10.1007/s10888-021-09510-w

How much does reducing inequality matter for global poverty?

Christoph Lakner 1,✉, Daniel Gerszon Mahler 1, Mario Negre 1,2, Espen Beer Prydz 1,3
PMCID: PMC8890464  PMID: 35261573

Abstract

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% 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/day) will remain above 600 million in 2030, resulting in a global extreme poverty rate of 7.4%. If the Gini index in each country decreases by 1% per year, the global poverty rate could reduce to around 6.3% in 2030, equivalent to 89 million fewer people living in extreme poverty. Reducing each country’s Gini index by 1% 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% in all countries, then more than 90 million may have been driven into extreme poverty in 2020.

Keywords: Poverty, Inequality, Inclusive growth, COVID-19, SDGs, Simulation, Machine-learning

Acknowledgements

The authors wish to thank R. Andrés Castañeda, Shaohua Chen, Francisco Ferreira, La-Bhus Fah Jirasavetakul, Dean Joliffe, Aart Kraay, Peter Lanjouw, Christian Meyer, Prem Sangraula, Umar Serajuddin, and Renos Vakis, as well as two anonymous referees and the editor for helpful comments and suggestions. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. We gratefully acknowledge financial support from the UK government through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Programme, as well as the EFO No. 1340 (Measuring Poverty in a Changing World), and the Strategic Research Program (TF018888) for earlier versions of this paper.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. Anand S, Segal P. What do we know about global income inequality? J. Econ. Lit. 2008;46(1):57–94. doi: 10.1257/jel.46.1.57. [DOI] [Google Scholar]
  2. Atamanov, A., Jolliffe, D.M., Lakner, C., Prydz, E.B. Purchasing power parities used in global poverty measurement. World Bank Global Poverty Monitoring Technical Note, no. 5 (2018)
  3. Atamanov, A., Lakner, C., Mahler, D.G., Tetteh Baah, S., Yang, J.: The effect of new PPP estimates on global poverty: A first look. World Bank Global Poverty Monitoring Technical Note, no. 12 (2020)
  4. Birdsall N, Lustig N, Meyer CJ. The strugglers: the new poor in Latin America? World Dev. 2014;60:132–146. doi: 10.1016/j.worlddev.2014.03.019. [DOI] [Google Scholar]
  5. Breiman, L., Friedman, J., Stone, C., Olshen, R.: Classification and regression trees. Taylor & Francis, Belmont (1984)
  6. Castaneda Aguilar, R.A., Lakner, C., Prydz, E.B., Soler Lopez, J.A., Wu, R., Zhao, Q.: Estimating global poverty in Stata: The PovcalNet Command. World Bank Global Poverty Monitoring Technical Note, no. 9 (2019a)
  7. Castaneda Aguilar, R.A., Mahler, D.G., Newhouse, D.: Nowcasting global poverty. Paper presented at the Special IARIW-World Bank Conference. New Approaches to Defining and Measuring Poverty in a Growing World, Washington, DC, November 7–8 (2019b)
  8. Chandy, L., Ledlie, N., Penciakova, V.: The final countdown: Prospects for ending extreme poverty by 2030. Global Views Policy Paper 2013-04, The Brookings Institution, Washington DC (2013)
  9. Chen, S., Ravallion, M.: The developing world is poorer than we thought, but no less successful in the fight against poverty. Q. J. Econ. 125(4), 1577–1625 (2010)
  10. Corral, P., Irwin, A., Krishnan, N., Mahler, D.G., Vishwanath, T.: Fragility and conflict: On the front lines of the fight against poverty. World Bank, Washington, DC (2020)
  11. Deaton A. Measuring poverty in a growing world (or measuring growth in a poor world) Rev. Econ. Stat. 2005;87(1):1–19. doi: 10.1162/0034653053327612. [DOI] [Google Scholar]
  12. Dhongde S, Minoiu C. Global poverty estimates: a sensitivity analysis. World Dev. 2013;44:1–13. doi: 10.1016/j.worlddev.2012.12.010. [DOI] [Google Scholar]
  13. Edward P, Sumner A. Estimating the Scale and Geography of Global Poverty Now and in the Future: How Much Difference Do Method and Assumptions Make? World Dev. 2014;58:67–82. doi: 10.1016/j.worlddev.2013.12.009. [DOI] [Google Scholar]
  14. Ferreira, F., Leite, P.: Policy options for meeting the Millennium Development Goals in Brazil: Can micro-simulations help? Econ. J. Latin Am. Caribbean Econ. Assoc. 3(2), 235–280 (2003)
  15. Ferreira, F., Ravallion, M.: Poverty and inequality: the global context. In: Nolan, B., Salverda, W., Smeeding, T. (eds.) Oxford Handbook of Economic Inequality. Oxford University Press, Oxford (2009)
  16. Ferreira F, Chen S, Dabalen A, Dikhanov Y, Hamadeh N, Jolliffe D, Narayan A, Prydz EB, Revenga A, Sangraula P, Serajuddin U, Yoshida N. A global count of the extreme poor in 2012: Data issues, methodology and initial results. J. Econ. Inequal. 2016;14(2):141–172. doi: 10.1007/s10888-016-9326-6. [DOI] [Google Scholar]
  17. Foster, J., Greer, J., Thorbecke, E.: A class of decomposable poverty measures. Econometrica. 52(3), 485–97 (1984)
  18. Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, Berlin (2009)
  19. Hellebrandt, T., Mauro, P.: The Future of worldwide income distribution. Working Paper Series 15 – 7, Peterson Institute for International Economics (2015)
  20. Higgins, M., Williamson, J.G. Explaining inequality the world round: Cohort size, kuznets curves, and openness. Southeast Asian Stud. 40(3) (2002)
  21. Hillebrand, E.: The global distribution of income in 2050. World Dev. 36(5), 727–40 (2008)
  22. Hoy, C., Samman, E.: What if growth had been as good for the poor as everyone else? Report (May). Overseas Development Institute, London (2015)
  23. International Monetary Fund: Fiscal policy and income inequality. IMF Policy Paper, Washington (2014)
  24. Jolliffe D, Prydz. E. Estimating international poverty lines from comparable national thresholds. J. Econ. Inequal. 2016;14(2):185–198. doi: 10.1007/s10888-016-9327-5. [DOI] [Google Scholar]
  25. Kakwani N. Poverty and economic growth with application to Côte d’Ivoire. Rev. Income Wealth. 1993;39(2):121–139. doi: 10.1111/j.1475-4991.1993.tb00443.x. [DOI] [Google Scholar]
  26. Karver, J., Kenny, C., Sumner, A.: MDGs 2.0: What goals, targets and timeframe? CGD Working Paper, Center for Global Development: Washington DC (2012)
  27. Kuznets S. Economic growth and income inequality. Am. Econ. Rev. 1955;45(1):1–28. [Google Scholar]
  28. Laborde, D., Martin, W., Vos, R.: Poverty and food insecurity could grow dramatically as COVID-19 spreads. IFPRI Blog. Available at https://www.ifpri.org/blog/poverty-and-food-insecurity-could-grow-dramatically-covid-19-spreads (2020)
  29. Lakner, C., Mahler, D.G., Nguyen, M.C., Azevedo, J.P., Chen, S., Jolliffe, D.M., Prydz, E.B., Sangraula, P.: Consumer price indices used in global poverty measurement. World Bank Global Poverty Monitoring Technical Note, no. 4 (2018)
  30. Lakner, C., Milanovic, B.: Global income distribution: from the fall of the Berlin Wall to the great recession. World Bank Econ. Rev. 30(2), 203–232 (2016)
  31. Lakner, C., Negre, M., Prydz, E.B.: Twinning the goals: how can promoting shared prosperity help to reduce global poverty? Policy Research Working Paper Series 7106, The World Bank (2014)
  32. Minoiu C, Reddy S. Kernel density estimation on grouped data: the case of poverty assessment. J. Econ. Inequal. 2014;12(2):163–189. doi: 10.1007/s10888-012-9220-9. [DOI] [Google Scholar]
  33. Ncube, M., Brixiova, Z., Bicaba, Z.: Can dreams come true? Eliminating extreme poverty in Africa by 2030. IZA Discussion Paper Series No. 8120 (2014)
  34. Pinkovskiy M, Sala-i-Martin X. Lights, Camera… Income! Illuminating the national accounts-household surveys debate. Q. J. Econ. 2016;131(2):579–631. doi: 10.1093/qje/qjw003. [DOI] [Google Scholar]
  35. Prydz, E.B., Jolliffe, D.M., Lakner, C., Mahler, D.G., Sangraula, P.: National accounts data used in global poverty measurement. World Bank Global Poverty Monitoring Technical Note, no. 8 (2019)
  36. Ravallion M. Growth, inequality and poverty: looking beyond averages. World Dev. 2001;29(11):1803–1815. doi: 10.1016/S0305-750X(01)00072-9. [DOI] [Google Scholar]
  37. Ravallion M. Measuring aggregate welfare in developing countries: How well do national accounts and surveys agree? Rev. Econ. Stat. 2003;85(3):645–652. doi: 10.1162/003465303322369786. [DOI] [Google Scholar]
  38. Ravallion, M.: How long will it take to lift one billion people out of poverty? World Bank Res. Obs. 28(2), 139--158 (2013)
  39. Ravallion M. Are the world’s poorest being left behind? J. Econ. Growth. 2016;21:139–164. doi: 10.1007/s10887-016-9126-7. [DOI] [Google Scholar]
  40. Ravallion, M. SDG1: The last 3%. Center for Global Development Working Paper No. 527 (2020)
  41. Ravallion M, Chen S. Measuring pro-poor growth. Econ. Lett. 2003;78(1):93–99. doi: 10.1016/S0165-1765(02)00205-7. [DOI] [Google Scholar]
  42. Rodrik D. The past, present, and future of economic growth. Challenge. 2014;57(3):5–39. doi: 10.2753/0577-5132570301. [DOI] [Google Scholar]
  43. Shorrocks, A., Wan., G.: Ungrouping income distributions. Working paper 2008/16, UNUWIDER (2008)
  44. Sumner, A., Hoy, C., Ortiz-Juarez, E. Estimates of the impact of COVID-19 on global poverty. UNU-WIDER Working Paper 43 (2020)
  45. United Nations: Open working group proposal for sustainable development goals. United Nations, New York (2014)
  46. World Bank: Prosperity for All/Ending extreme poverty: A Note for the World Bank Group Spring Meetings 2014. Washington DC (2014)
  47. World Bank: A measured approach to ending poverty and boosting shared prosperity: Concepts, data, and the twin goals. Policy Research Report, The World Bank, Washington DC (2015)
  48. World Bank: Poverty and Shared Prosperity 2016: Taking on Inequality. The World Bank, Washington, DC (2016)
  49. World Bank: Poverty and Shared Prosperity 2018: Piecing together the poverty puzzle. The World Bank, Washington DC (2018)
  50. World Bank: Comparability over time at the country level for international poverty measures. Data catalog, Washington DC (2019)
  51. World Bank. Global Shared Prosperity Database 2020. https://datacatalog.worldbank.org/dataset/global-database-shared-prosperity. Accessed June 1, 2020.
  52. Yoshida, N., Uematsu, H., Sobrado, C.E.: Is extreme poverty going to end? An analytical framework to evaluate progress in ending extreme poverty. Policy Research Working Paper 6740, The World Bank, Washington DC (2014)
  53. Zeileis A, Hothorn T, Hornik K. Model-based recursive partitioning. J. Comput. Graph. Stat. 2008;17(2):492–514. doi: 10.1198/106186008X319331. [DOI] [Google Scholar]

Articles from Journal of Economic Inequality are provided here courtesy of Nature Publishing Group

RESOURCES