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NEWS ALERTS | Sustained efforts required to reduce multidimensional poverty amidst the pandemic
Sustained efforts required to reduce multidimensional poverty amidst the pandemic

Sustained efforts required to reduce multidimensional poverty amidst the pandemic

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published Published on Oct 27, 2020   modified Modified on Apr 28, 2021


Multidimensional poverty is about non-monetary poverty and is strongly associated with the challenges of achieving the Sustainable Development Goals (SDGs). Although previously defined only in monetary terms, poverty is now understood to include the lived reality of people’s experiences and the multiple deprivations they face.

India’s multidimensional headcount ratio (H) i.e. the proportion or incidence of people (within a given population) who experience multiple deprivations has reduced from 55.1 percent to 27.9 percent during the last 10 years i.e. between 2005-06 and 2015-16. This is revealed in this year’s Global MPI report, which has been co-produced by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI). Please check chart-1.

The total number of poor people, who face multiple deprivations in education, health and living standards, has dropped by around 273 million in the last one decade i.e. from 642.5 million to 369.6 million between 2005-06 and 2015-16. However, the same report has estimated that the total number of multidimensionally poor people in the country has increased by 7.9 million between 2015-16 and 2018.

On its website, the UNDP has mentioned that "[w]hile data are not yet available to measure the rise of global poverty after the pandemic, simulations based on different scenarios suggest that, if unaddressed, progress across 70 developing countries could be set back 3–10 years."

The number of multidimensionally poor people is calculated as the product of the incidence of multidimensional poverty and the population size, according to the report entitled Global Multidimensional Poverty Index 2020 -- Charting pathways out of multidimensional poverty: Achieving the SDGs.

Source: Global Multidimensional Poverty Index 2020, please click here to access
Note: Based on harmonized indicator definitions for strict comparability over time
---

Released in July this year, the report by UNDP and OPHI indicates that the multidimensional poverty index (MPI) of India, which is the product of multidimensional headcount ratio (H) and intensity (or breadth) of poverty (A), was 0.123 in 2015-16. In comparison to India, MPI values in Bangladesh (0.104) and Sri Lanka (0.011) were lower, whereas MPI values for Afghanistan (0.272), Myanmar (0.176), Nepal (0.148) and Pakistan (0.198) were higher. A lower value of MPI is socially desirable since it shows reduction in multidimensional poverty. Please see table-1 for details.

Table 1: Multidimensional Poverty Index across South Asian countries

Source: 2020 Global Multidimensional Poverty Index (MPI), Oxford Poverty & Human Development Initiative and UNDP, please click here to access

Note: D indicates data from Demographic and Health Surveys, M from Multiple Indicator Cluster Surveys, N from national surveys. Please click here for the list of national surveys
---

It should be noted that the multidimensional poverty index (MPI), which offers a valuable complement to traditional income-based poverty measures, was first introduced in the 2010 Human Development Report (HDR). The MPI looks at both the number of deprived people and the intensity of their deprivations.

The intensity of poverty (A), which measures deprivations that multidimensionally poor people face on an average, has declined from 51.3 percent to 43.9 percent between 2005-06 and 2015-16.

The MPI-T value (i.e. multidimensional poverty index estimate that is based on harmonized indicator definitions for strict comparability over time) for India has decreased from 0.283 to 0.123 between 2005-06 and 2015-16.

Please note that MPI is a measure that looks beyond income to include access to safe water, education, electricity, food and six other indicators. Please click here to access the technical notes in order to know more about the MPI and how it is calculated.

Multidimensional poverty across the states/ UTs

It can be observed from table-2 that the top five states/ UTs in terms of proportion of people affected by non-monetary poverty in 2015-16 were Bihar (52.5 percent), Jharkhand (46.5 percent), Madhya Pradesh (41.1 percent), Uttar Pradesh (40.8 percent) and Chhattisgarh (36.8 percent). The bottom five states/ UTs in terms of proportion of people affected by non-monetary poverty were Kerala (1.1 percent), Delhi (4.3 percent), Sikkim (4.9 percent), Goa (5.5 percent) and Punjab (6.1 percent).

The highest fall in multidimensional headcount ratio (H) between 2005-06 and 2015-16 has been noted for Arunachal Pradesh (35.6 percentage points), followed by Tripura (34.3 p.p.), Andhra Pradesh (33.6 p.p.), Nagaland (33.3 p.p.) and Chhattisgarh (33.2 p.p.).

In 2015-16, the top five states/ UTs in terms of number of people affected by non-monetary poverty were Uttar Pradesh (84.7 million), Bihar (61.8 million), Madhya Pradesh (35.6 million), West Bengal (26.2 million) and Rajasthan (23.6 million). The bottom five states/ UTs in terms of number of people affected by non-monetary poverty were Sikkim (27,000), Goa (89,000), Mizoram (1.10 lakh), Arunachal Pradesh (2.76 lakh) and Nagaland (3.77 lakh).

Table 2: Multidimensional Poverty across states/ UTs

Source: Global MPI data tables 2020, please click here to access
Note: Based on harmonized indicator definitions for strict comparability over time
---

In absolute terms, the highest drop in the number of people affected by multidimensional poverty between 2005-06 and 2015-16 has been noted for Uttar Pradesh (nearly 46 million), followed by Andhra Pradesh (27.5 million), West Bengal (27.2 million), Maharashtra (21.2 million) and Karnataka (about 20 million).    
 
In 2015-16, the top five states/ UTs in terms of intensity of poverty were Bihar (47.2 percent), Rajasthan (45.3 percent), Mizoram (45.2 percent), Uttar Pradesh, Jharkhand and Assam (each 44.7 percent) and Meghalaya (44.5 percent). The bottom five states/ UTs in terms of intensity of poverty were Goa (37.2 percent), Kerala (37.3 percent), Himachal Pradesh (37.4 percent), Tamil Nadu (37.5 percent) and Sikkim (38.1 percent).

In 2015-16, the top five states/ UTs in terms of MPI were Bihar (MPI=0.248), Jharkhand (MPI=0.208), Uttar Pradesh (MPI= 0.183), Madhya Pradesh (MPI= 0.182), and Assam (MPI=0.162). The bottom five states/ UTs in terms of MPI were Kerala (MPI=0.004), Delhi (MPI=0.018), Sikkim (MPI=0.019), Goa (MPI=0.020) and Punjab (MPI=0.025).

Rural-urban dichotomy in multidimensional poverty

The MPI value for rural areas exceeded MPI value for urban areas in both 2005-06 and 2015-16. The multidimensional headcount ratio in rural areas was 36.8 percent in 2015-16, whereas it was 9.2 percent in urban areas. The intensity of poverty, which measures deprivations that multidimensionally poor people face on an average, was 42.6 percent in urban areas and 44.1 percent in rural areas during 2015-16. Please see table-3.

Table 3: MPI in rural and urban India

Source: Global MPI data tables 2020, please click here to access
Note: Based on harmonized indicator definitions for strict comparability over time
---

In rural India, the multidimensional headcount ratio has decreased from 68.1 percent to 36.8 percent during the last 10 years i.e. between 2005-06 and 2015-16. In urban India, the same has fallen from 25.0 percent to 9.2 percent between 2005-06 and 2015-16.  

The total number of people affected by non-monetary poverty in rural areas has lessened by nearly 40 percent i.e. from 550.4 million in 2005-06 to 328.7 million in 2015-16. Similarly, in urban areas, the total number of people affected by multidimensional poverty has fallen by more than 55 percent i.e. from 87.2 million to 39.1 million in the same time span.

The intensity of poverty in rural India has declined from 52.0 percent to 44.1 percent between 2005-06 and 2015-16. The same in urban areas has fallen from 46.8 percent to 42.6 percent between 2005-06 and 2015-16.

The country's MPI in rural areas has dropped from 0.355 to 0.163 between 2005-06 and 2015-16. The same in urban areas has lessened from 0.117 to 0.039 during that 10-year span.

Poverty headcount ratios

Chart-2 compares the headcount ratios of the global MPI and monetary poverty measures. The height of the first bar of chart-2 shows the proportion of people who are MPI poor. The second and third bars represent the percentage of people who are poor according to the World Bank’s $1.90 a day and $3.10 a day poverty lines. The last bar represents the percentage of people who are poor according to the national income or consumption and expenditure poverty measures.

Chart-2: Headcount Ratios by Poverty Measures

Source: Global MPI Country Briefing 2020: India (South Asia), please click here to access
---

It is clear from chart-2 that a higher proportion of Indians suffer from multidimensional poverty vis-à-vis the monetary poverty that is nationally measured.

The key findings of the report entitled Global Multidimensional Poverty Index 2020 -- Charting pathways out of multidimensional poverty: Achieving the SDGs (released in July 2020) are as follows:

• Four countries—Armenia (2010–2015/2016), India (2005/2006–2015/2016), Nicaragua (2001–2011/2012) and North Macedonia (2005/2006–2011) halved their global MPI-T value (i.e. Multidimensional Poverty Index estimate that is based on harmonized indicator definitions for strict comparability over time) and did so in 5.5–10.5 years. These countries show what is possible for countries with very different initial poverty levels. They account for roughly a fifth of the world’s population, mostly because of India’s large population.

• India and Nicaragua’s time periods cover 10 and 10.5 years respectively, and during that time both countries halved their MPI-T values among children. So decisive change for children is possible but requires conscious policy efforts.

• Of the 65 countries that reduced their MPI-T value, 50 also reduced the number of people living in poverty. The largest reduction was in India, where approximately 273 million people moved out of multidimensional poverty over 10 years. In China more than 70 million people moved out of multidimensional poverty over four years, and 19 million people in Bangladesh and almost 8 million people in Indonesia did so over five years. In Pakistan almost 4 million people moved out of poverty over five years. Some smaller countries also achieved a remarkable reduction: almost 4 million in Nepal and more than 3 million in Kenya over five years.

• Three South Asian countries (Bangladesh, India and Nepal) were among the 16 fastest countries to reduce their MPI-T value.

• Ten countries account for 60 percent of unvaccinated children, and 40 percent of children unvaccinated for DTP3 live in just four countries: Nigeria, India, Pakistan and Indonesia. Populous developing countries can contribute considerably to the number of unvaccinated children despite achieving high immunization coverage, as evidenced by India’s 2.6 million under-vaccinated children and 89 percent coverage rate.

• While more than 450 million people have gained access to clean cooking fuels since 2010 in China and India as a result of liquefied petroleum gas programmes and clean air policies, the challenge in Sub-Saharan Africa, where 463 million people in rural areas are multidimensionally poor and deprived in cooking fuel, remains acute.

References

Global Multidimensional Poverty Index 2020 -- Charting pathways out of multidimensional poverty: Achieving the SDGs, United Nations Development Programme (UNDP) and Oxford Poverty and Human Development Initiative (OPHI), please click here to read more

Global MPI Country Briefing 2020: India (South Asia), please click here to access

MPI 2020 Technical Notes, UNDP and OPHI, please click here to access

News alert: Country's non-income-based poverty level has fallen over the past 10 years, shows new report, Published on Oct 30, 2018, Inclusive Media for Change, please click here to access

 

Image Courtesy: Himanshu Joshi

 


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