Impact of Global Crises on Poverty: Measurement, Trends, and Challenges

Mustafa Yagci

20 Mar, 2023

Introduction

While poverty is generally measured using a monetary basis, it is widely acknowledged that poverty is characterized by multiple dimensions beyond income poverty. This blog post compares the various multidimensional measures of poverty and finds that some of the gains in poverty eradication in the previous decades are likely lost due to the COVID-19 pandemic and other adverse shocks to the global economy. While access to the latest poverty data remains a challenge, evidence shows that Sub-Saharan Africa and South Asia host the largest number of poor people. Rural and children’s poverty levels are also higher in these regions than the average. The progress on addressing learning poverty has been stalled in these regions due to the COVID-19 pandemic.


Multidimensional Poverty Index (MPI)

The MPI developed by the OPHI and UNDP captures poverty in terms of three dimensions (health, education, and living standards), comprised of 10 indicators (nutrition, child mortality, years of schooling, school attendance, cooking fuel, sanitation, drinking water, electricity, housing, and assets) (Figure 1). These indicators characterize poverty in terms of multiple deprivations. Every year, the global MPI is published to assess the extent of poverty around the world.


Figure 1: Global MPI Dimensions and Indicators


The MPI assesses poverty at the individual level. Using this measure, a person is identified as poor when they are deprived in at least a third of the (weighted) indicators. The MPI also provides information on the extent and intensity of poverty using the percentage of deprivations they are experiencing.

Poverty is multi-faceted and cannot be measured solely by monetary poverty. Moreover, income poverty and other poverty dimensions do not necessarily correlate. Figure 2 shows that for countries with available multidimensional poverty, severe poverty, and monetary poverty data, each data point paints varying pictures. That is, the USD 1.90/day measure does not always match the measure of multidimensional poverty.


Figure 2: Multidimensional poverty vs monetary poverty

Source: 2022 Global MPI Databank


The 2022 global MPI is calculated by using microdata from household surveys across 111 countries, covering 6.1 billion people. One of the main challenges for global poverty estimations is that survey data are collected infrequently and most of the surveys correspond to pre-COVID-19 experiences. Because the COVID-19 shock altered trajectories, it is imperative to adjust and update estimates to reflect the state of poverty post-COVID-19. Notwithstanding the absence of more recent microdata, simulations on the existing data reveal that COVID-19 may have set progress in reducing MPI back by 3 to 10 years.

The main findings of the 2022 global MPI report can be summarized as follows.

  • Across 111 countries, 19.1% of people, or about 1.2 billion people live in acute multidimensional poverty. Almost half of these people are under the age of 18.
  • Sub-Saharan Africa hosts the largest number of multidimensionally poor people (nearly 579 million), followed by South Asia (385 million).
  • About 470.1 million people are deprived of both nutrition and sanitation, potentially making them more vulnerable to infectious diseases.
  • About 593.3 million people are simultaneously deprived of cooking fuel and electricity, adversely affecting living standards.
  • A poor person in South Asia is more likely to be deprived of nutrition, cooking fuel, sanitation, and housing. A poor person in Sub-Saharan Africa faces similar deprivations along with drinking water and electricity.
  • Poverty levels for children and rural populations are higher than average poverty in many countries.


Multidimensional Poverty Measure (MPM)

Inspired and guided by the MPI approach, the World Bank supplemented its monetary poverty estimation with measures on education and access to basic infrastructure indicators. This resulted in the multidimensional poverty measure (MPM). Figure 3 illustrates the dimensions and indicators of MPM introduced by the World Bank.


Figure 3: Global MPM Dimensions and Indicators


The MPM uses data mainly from the harmonized household surveys in the Global Monitoring Database. Accordingly, the World Bank MPM treats a household as multidimensionally poor if it is deprived of indicators whose weight adds up to 1/3 or more.

The main findings of the 2022 MPM report can be summarized as follows.

  • The COVID-19 pandemic increased the global extreme poverty rate from an estimated 8.4% in 2019 to 9.3% in 2020.
  • It is projected that 7% of the world population or around 574 million people will still live in extreme poverty by 2030, because of the deceleration of poverty reduction which began even before the COVID-19 pandemic.
  • In 2019, about 47% of the world population lived in poverty, as measured by living on less than USD 6.85 a day.


Global Learning Poverty

The learning poverty indicator introduced by the UNESCO Institute for Statistics and the World Bank measures the ability of a child to read and understand a simple text by age 10. The measure combines schooling and learning indicators and estimates the share of children who have not achieved minimum reading proficiency (as measured in schools) and adjusts it by the proportion of children who are out of school (and are assumed not able to read proficiently).

In 2019, there is a 57% global learning poverty rate in low- and middle-income countries. This is forecasted to rise to 70% in 2022 due to the impact of COVID-19 on schooling–school closures and the sudden shift to online learning. Simulations of learning poverty indicate that by 2022, learning poverty rates can reach about 90% in Sub-Saharan Africa, 79% in Latin America, 78% in South Asia, 70% in the Middle East and North Africa, about 45% in East Asia and Pacific, and 14% in Europe and Central Asia (Figure 4).


Figure 4: Global Learning Poverty Rates by Region (2022, %)


The dismal forecasts on education and learning indicate the critical need to accelerate targeted intervention to improve not only school attendance in the post-pandemic world but also the quality of education received by children and youth at all educational levels. Left unaddressed, the world may face a human capital catastrophe leading to lower productivity and slow economic growth in the coming years.


Conclusion

Defining and measuring poverty around the world is a significant international development challenge. Without timely and reliable data, evidence-based policies cannot target the main obstacles in eradicating poverty. Despite the data challenges, international organizations such as the UNDP, UNESCO, World Bank, and research centers such as the OPHI have introduced novel measurements of poverty with multiple dimensions. MPI, MPM, and learning poverty measures aim to reflect the on-the-ground reality by generating nonmonetary measures of poverty.

With the impact of the COVID-19 pandemic and the ensuing shocks to the global economy, poverty rates around the world are rising. This makes achieving SDG 1 even more challenging, as poverty interventions are set back and are in competition with other national expenditures in resource allocation.

Tackling and eradicating poverty requires a long-term commitment from different segments of society. Governments, international organizations, universities, research centers, and the private sector need to form strong partnerships and act together to address the pressing challenges of poverty.

Using multidimensional measures of poverty, the related interventions require to be multidimensional as well. To eradicate poverty and achieve inclusive growth, access to education, health, and basic services such as drinking water, sanitation, and shelter needs to be improved. These do not only aim to make living better for the current generation of poor, but also for the generations that follow them.

To measure and compare poverty at the global scale, measures such as MPI, MPM, and learning poverty rely on country-level, outdated data for similar indicators. While helpful in providing a good understanding of country-level poverty comparisons worldwide, they have shortcomings with regard to providing the evidence-basis for timely targeted interventions, as evidenced during the recent crises.

In this regard, for targeted interventions to be successful and better address the demands of the poor, the measures need to be reviewed with a view to accessing more micro-level data using non-traditional sources for districts, cities, and neighborhoods in a country.

To continuously enhance multidimensional poverty measures, fostering partnerships between various stakeholders is essential. For instance, the Islamic Development Bank Group’s (IsDBG) partnership with Global Partnership for Sustainable Development Data (GPSDD) utilized satellite imagery to provide timely data for environmental protection and agricultural productivity. These forms of partnerships are required to reduce time and cost to collect reliable, accurate data. Additionally, the agglomeration of expertise in such partnerships creates synergies to efficiently address the most critical problems of the world today.


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