Paper short abstract:

We employ a multi-system integrated assessment model to project multi-dimensional poverty. We produce global and country-level projections under three scenarios: a base case, a counterfactual, and a policy push with a 2050 horizon. SDG 1.2 is not met by 2030, or even 2050 under base case. Even with a policy push, a handful of countries fail to meet the target by 2050.

Paper long abstract:

Keywords: SDG, multidimensional poverty, human development, projection, policy intervention

Context and Motivation:

Multidimensional poverty (MP) is a shift from exclusively assessing poverty through monetary deprivation. Unlike the traditional poverty measures, MP considers a comprehensive set of well-being needs. It addresses some of the shortcomings of uni-dimensional income or consumption-based measures and is crucial for understanding the breadth and severity of poverty dynamics. The comprehensive nature of the indicator helps policy makers design more focused policy interventions. Projections of MP at a global scale and over a long time horizon are useful in designing policy interventions for sustainable development and address global long-horizon crises like the climate change.

Measurement:

Measurement of multidimensional poverty begins with identifying essential components of well-being, including income, education, health, access to basic services like water, sanitation, electricity, or communication, or ownership of minimum assets required for functioning. Poverty thresholds are established for each component, categorizing individuals as poor or non-poor based on their status relative to these thresholds. These simultaneous deprivations are then combined with appropriate weights, and an aggregate cut-off is set to identify multidimensionally poor individuals or households. Measures such as the headcount ratio and other higher-order poverty indicators are then computed, adjusting for the intensity of multi-dimensional poverty, i.e., the average of the share of poverty dimensions experienced by each household or individual (Alkire and Foster, 2011; Alkire et al., 205).

Current Projections:

Data from comprehensive household surveys, such as the Demographic and Health Surveys (DHS), have facilitated the development of multidimensional poverty indices for numerous countries over several years. Today, several international indices are available, including the Multidimensional Poverty Index (MPI) by the UNDP and Oxford University, which assesses deprivation across three dimensions: health, education, and living standards, for ten indicators (UNDP and OPHI, 2023). Recently, the World Bank introduced the Multidimensional Poverty Measure (MPM), comprising six indicators across three dimensions: monetary, education, and access to basic infrastructure (Diaz-Bonilla et al., 2023).

Alkire et al (2023) used dynamic models to project MPI for a large number of countries over a short horizon using historical MPI data to select the appropriate rate-of-change models and to calibrate the parameters for those models. Dynamic univariate models are not capable of assessing the impact of policy interventions and compare them across scenarios.

Methodology:

Our paper uses a long-horizon global integrated assessment model, International Futures system (IFs) to project MPI for the World, income groups, regions and 189 countries for an end of the century time horizon. IFs is a large-scale, integrated global modeling system designed to explore long-term patterns and trends across multiple dimensions of human development (Hughes, 2019). The model provides a comprehensive framework for analyzing and projecting a wide range of variables and indicators related to global development, including demographics, economics, energy, environment, education, health, governance, and international relations.

MP for the study is initialized with the World Bank’s MPM (Diaz-Bonilla et al., 2023). IFs model projects almost all the sub-dimensions and indicators used in MPM. We compute future MPI as a composite of the sub-dimensional projections and through an analytic approach. We check the robustness of the projections by conducting cross-comparisons.

The paper uses three scenarios: a base case, a counterfactual and a policy push. The base case in the IFs model depicts the continuation of the current development trajectory. It offers an insight into the projected path of the world if there are no major policy changes, no major shocks, or no technological game changers. In the counterfactual scenario, countries are forced to attain the SDG 1.2 of halving MP (UN 2015) within the target date. In the more realistic policy push scenario, a number of aggressive but realistic policy interventions are combined in a coordinated policy push.

Analysis and results:

We do not have data on MPM for 2015 for all countries. For countries with a starting point after 20165, we add 15 years to the starting point to determine the SDG target year. Our analysis finds that the world fails to meet the SDG 1.2 within this timeline. Income group wise, low-income and some lower-middle-income countries would fail to reach the target of halving MP even by 2050. Under the policy push scenario, many countries are able to halve MP in 15 years, most countries reach the target by 2050. However, a handful of Sub-Saharan African countries, e.g., South Sudan, CAR, Guinea-Bissau, fail to meet the target even by 2050 despite the policy push. These are our preliminary results.

Conclusion:

This paper proposes to contribute to the existing literature on multidimensional poverty by providing a long-range analysis of multi-dimensional poverty and the uncertainty around meeting important global goals like the SDG. By examining the potential impact of economic, social, and demographic changes on poverty levels across multiple dimensions, the paper informs evidence-based policy-making and contribute to efforts aimed at achieving sustainable development goals.

References:

Alkire, Sabina, et al. (2023). "On Track or Not? Projecting the Global Multidimensional Poverty Index." Journal of Development Economics. 165, 103150. doi: 10.1016/j.jdeveco.2023.103150.

Alkire, Sabina, et al. (2015). Multidimensional Poverty Measurement and Analysis. Oxford: Oxford Academic.

Alkire, Sabina, & James Foster. (2011). “Counting and multidimensional poverty measurement.” Journal of Public Economics, 95(7–8), 476-487.

Diaz-Bonilla, Carolina, et al. (2023). “November 2023 Update to the Multidimensional Poverty Measure - What’s New (English).” Global Poverty Monitoring Technical Note, No. 34. Washington, D.C.: World Bank Group.

Hughes, Barry B. (2019). International Futures: Building and Using Global Models. London: London Academic Press.

Hughes, B.B., et al. (2015). Reducing Global Poverty: Patterns of Potential Human Progress Volume 1. Abingdon: Routledge.

Moyer, J.D., et al. (2022). “How Many People is the COVID-19 Pandemic Pushing into Poverty? A Long-term Forecast to 2050 with Alternative Scenarios.” PLoS One, 17(7).

UNDP and OPHI. (2023). Global Multidimensional Poverty Index 2023: Unstacking Global Poverty – Data for High-impact Action, United Nations Development Program (UNDP), and Oxford Poverty and Human Development Initiative (OPHI), University of Oxford.

UN. 2015. Transforming Our World: The 2030 Agenda for Sustainable Development. Resolution Adopted by the General Assembly on 25 September 2015, 42809, 1-13.