Country's non-income-based poverty level has fallen over the past 10 years, shows new report

Country's non-income-based poverty level has fallen over the past 10 years, shows new report


For long, economists have argued among themselves whether income should be the only criterion for measuring poverty. After all, in real life a person can face multiple deprivations, say, in terms of access to education, health and living standards, among others. 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.

India's multidimensional headcount ratio (H) viz. the proportion or incidence of people (within a given population) who experience multiple deprivations has reduced from 54.7 percent to 27.5 percent during the last 10 years viz. between 2005-06 and 2015-16. This is revealed in the recently released Global MPI 2018 report, which has been co-produced by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI).

The total number of poor people, who face multiple deprivations in education, health and living standards, has dropped by 271 million in the last one decade viz. from 635.2 million to 364.2 million between 2005-06 and 2015-16. Please see table-1.  

The report on multidimensional poverty finds that the intensity of poverty (A), which measures deprivations that multidimensionally poor people face on an average, has declined from 51.07 percent to 43.9 percent between 2005-06 and 2015-16.



As a result, one could notice that the multidimensional poverty index (MPI) of the country, which is the product of multidimensional headcount ratio (H) and intensity (or breadth) of poverty (A), has shrunk from 0.279 to 0.121 between 2005-06 and 2015-16. Please check table-1 for details.
 
Readers may note that the UPA government (UPA-1 and UPA-2) was ruling at the Centre in 8 out of the 10 years (viz. between 2005-06 and 2015-16) during which multidimensional poverty reduced, as is observed by the present report.

Cross-country comparison

In comparison to India (MPI=0.121), the MPIs of Bangladesh (MPI=0.194), Bhutan (MPI=0.175), Afghanistan (MPI=0.273), Myanmar (MPI=0.176), Nepal (MPI=0.154) and Pakistan (MPI=0.228) are higher. China (MPI=0.017) and Maldives (MPI=0.007), on the other hand, have lower MPIs than India. Please check chart-1.

In terms of multidimensional headcount ratio (H), the country (H=27.51 percent) lags behind Bangladesh (H=41.07 percent), Bhutan (H=37.34 percent), Afghanistan (H=56.10 percent), Myanmar (H=38.35 percent), Nepal (H=35.25 percent) and Pakistan (H=43.88 percent), but is ahead of China (H=4.11 percent) and Maldives (1.88 percent).

In terms of intensity of poverty (A), India (A=43.90 percent) lags behind Bangladesh (A=47.33 percent), Bhutan (A=46.83 percent), Afghanistan (A=48.72 percent), Myanmar (A= 45.92 percent), and Pakistan (A=52.04 percent), but is ahead of China (H=41.38 percent), Nepal (A=43.58 percent) and Maldives (A=36.61 percent).

Chart 1: Multidimensional Poverty Index (MPI), Multidimensional Headcount Ratio (%) and Intensity of Deprivation (%) across countries in 2015-16
 

 
Source: Multidimensional poverty across countries, please click here to access the data 
 
Please click on the red icons to get data on multidimensional poverty in various South Asian countries.
 
It is interesting to observe that although the country has a lower multidimensional headcount ratio (H) than Nepal, the latter enjoys a lower intensity of poverty as compared to the former. From the chart-1, it is clear that in Nepal the average poor person is deprived in 43.58 percent of the weighted indicators (total 10 in numbers -- nutrition, child mortality, years of schooling, school attendance, sanitation, cooking fuel, drinking water, electricity, housing and assets), whereas in India the average poor person is deprived in 43.9 percent of the weighted indicators.

Please click here to get an idea about multidimensional poverty at the global level.
 
In the words of Sanjay G Reddy, although the latest data shows that the rate of decline in multidimensional poverty has been the greatest for the most deprived, huge gaps in the level of deprivations, based on religion, caste and regions, still exists. Please go through the following sections to understand why it is so.

Rural-urban dichotomy in multidimensional poverty

In rural India, multidimensional headcount ratio (H) has decreased from 68.0 percent to 36.5 percent during the last 10 years viz. between 2005-06 and 2015-16. In urban India, the same has fallen from 24.6 percent to 9.0 percent between 2005-06 and 2015-16.  

The total number of people affected by non-income poverty in rural areas has lessened by nearly 40.6 percent viz. from 547.5 million in 2005-06 to 325.1 million in 2015-16. Similarly, in urban areas, the total number of people affected by multidimensional poverty has fallen by more than 50 percent viz. from 87.7 million to 39.1 million in the same time span. Please see table-2.  


 
The intensity of poverty (A) in rural India has declined from 51.8 percent to 44.1 percent between 2005-06 and 2015-16. The same in urban areas has fallen from 46.6 percent to 42.6 percent between 2005-06 and 2015-16.

The country's MPI in rural areas has dropped from 0.352 to 0.161 between 2005-06 and 2015-16. The same in urban areas has lessened from 0.115 to 0.039 during that 10-year span.

Multidimensional poverty across states & Union Territories (UTs)


It could be observed from table-3 that the top five states/ UTs in terms of proportion of people affected by non-income poverty in 2015-16 are Bihar (52.2 percent), Jharkhand (45.8 percent), Madhya Pradesh (40.6 percent), Uttar Pradesh (40.4 percent) and Chhattisgarh (36.3 percent). The bottom five states/ UTs in terms of proportion of people affected by non-income poverty are Kerala (1.1 percent), Delhi (3.8 percent), Sikkim (4.9 percent), Goa (5.6 percent) and Punjab (6.0 percent).

The highest fall in multidimensional headcount ratio (H) between 2005-06 and 2015-16 has been noted for Arunachal Pradesh (35.7 percentage points), followed by Tripura (34.3 p.p.), Andhra Pradesh (34.1 p.p.), Chhattisgarh (33.7 p.p.) and Nagaland (33.6 p.p.).

In 2015-16, the top five states/ UTs in terms of number of people affected by non-income poverty are Uttar Pradesh (82.9 million), Bihar (60.4 million), Madhya Pradesh (34.8 million), West Bengal (25.9 million) and Rajasthan (22.9 million). The bottom five states/ UTs in terms of number of people affected by non-income poverty are Sikkim (27,000), Goa (88,000), Mizoram (1.08 lakh), Arunachal Pradesh (2.73 lakh) and Nagaland (3.70 lakh).



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 (50.3 million), followed by West Bengal (26.8 million), Andhra Pradesh (26.6 million), Maharashtra (21.6 million) and Karnataka (20.1 million).    
 
The top five states/ UTs in terms of intensity of poverty are Bihar (47.2 percent), Rajasthan & Mizoram (both 45.2 percent), Uttar Pradesh & Jharkhand (both 44.7 percent), Assam (44.6 percent) and Meghalaya (44.5 percent). The bottom five states/ UTs in terms of intensity of poverty are Goa (37.2 percent), Kerala & Himachal Pradesh (both 37.4 percent), Tamil Nadu (37.5 percent), Sikkim (38.1 percent) and Karnataka (39.8 percent).

The top five states/ UTs in terms of MPI are Bihar (MPI=0.246), Jharkhand (MPI=0.205), Uttar Pradesh & Madhya Pradesh (both MPI= 0.180), Assam (MPI=0.160) and Odisha (MPI=0.154). The bottom five states/ UTs in terms of MPI are Kerala (MPI=0.004), Delhi (MPI=0.016), Sikkim (MPI=0.019), Goa (MPI=0.021) and Punjab (MPI=0.025).

Multidimensional poverty among religious groups


Among Muslims (H=60.3 percent in 2006; H=31.1 percent in 2016), multidimensional headcount ratio is the highest, followed by the Hindus (H=54.9 percent in 2006; H=27.7 percent in 2016) and the Christians (H=38.8 percent in 2006; H=16.1 percent in 2016). The intensity of poverty is higher among Muslims (A=54.9 percent in 2006; A=46.4 percent in 2016) as compared to rest of the religions. MPI is higher among Muslims (MPI=0.331 in 2006; MPI=0.144 in 2016) as compared to rest of the religions.  

Table 4: Multidimensional Poverty across Religious Subgroups
 
Table 4 Multidimensional Poverty Across Religious Subgroups
 
Source: Multidimensional Poverty Reduction in India 2005/6-2015/16: Still a Long Way to Go but the Poorest Are Catching Up -Sabina Alkire, Christian Oldiges and Usha Kanagaratnam, September, 2018, please click here to access
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In absolute terms, MPI, A and H reduced faster for Muslims as compared to other religious groups.

Multidimensional poverty among castes


From the table 5, it could be observed that the multidimensional headcount ratio (H) among the Scheduled Castes (SCs) has reduced from 65.0 percent in 2006 to 32.9 percent in 2016 -- a drop by 32.1 percentage points. Multidimensional headcount ratio (H) among the Scheduled Tribes (STs) has fallen from 79.8 percent in 2006 to 50.0 percent in 2016 -- a fall by 29.8 percentage points. The same among the Other Backward Classes (OBCs) has decreased from 57.9 percentage in 2006 to 26.9 percent in 2016 -- a decrease by 31.0 percentage points.

Table 5: Multidimensional Poverty across Caste Groups
 
Table 5 Multidimensional Poverty across Caste Groups
 
 
Source: Multidimensional Poverty Reduction in India 2005/6-2015/16: Still a Long Way to Go but the Poorest Are Catching Up -Sabina Alkire, Christian Oldiges and Usha Kanagaratnam, September, 2018, please click here to access
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MPI has decreased the most in absolute terms for STs (-0.218), followed by SCs (-0.193) and OBCs (-0.174).

Multidimensional poverty among age-groups

From the table-6, it could be noted that multidimensional headcount ratio (H) is the highest among the age-group 0-9 years (viz. H=40.9 percent) in 2016. Similarly, MPI is the highest among the age-group 0-9 years (MPI=0.371 in 2006; MPI=0.189 in 2016).

Table 6: Multidimensional Poverty across Age Groups

Table 6 Multidimensional Poverty across Age Groups
 
Source: Multidimensional Poverty Reduction in India 2005/6-2015/16: Still a Long Way to Go but the Poorest Are Catching Up -Sabina Alkire, Christian Oldiges and Usha Kanagaratnam, September, 2018, please click here to access
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How did the country's multidimensional poverty change?

In order to understand how the country's poverty has changed, it would be useful for us to look at the change in censored headcount ratios in each of the 10 indicators. The censored headcount ratios are the proportion of people who are MPI poor and experience deprivations in each of the indicators. The latest available data shows that the highest absolute decline in censored headcounts between 2005-06 and 2015-16 has been observed for assets (-27.9 percentage points), followed by cooking fuel (-26.6 p.p.), sanitation (-25.8 p.p.), nutrition (-22.9 p.p.), housing (-21.3 p.p.) and electricity (-20.3 p.p.).

According to the background paper entitled Multidimensional Poverty Reduction in India 2005/6-2015/16: Still a Long Way to go but the Poorest are Catching Up, all censored headcount ratios decreased by at least 50 percent except for housing. For some indicators, the censored headcount ratios have even dropped by more than 70 percent.

Methodology


According to the background paper by Sabina Alkire and Selim Jahan (2018) entitled The New Global MPI 2018: Aligning with the Sustainable Development Goals, the MPI uses information from 10 indicators that are categorized in three dimensions: health, education and living standards, and which identify each person as deprived depending upon the joint achievements of household members.

The global MPI uses the cross-dimensional poverty cut-offs of one-third, identifying each person as poor if their weighted deprivations sum to one-third or more. Two other poverty cut-offs are also used: severe poverty (the percentage of people deprived in at least half of the weighted indicators) and vulnerability (the proportion of people deprived in 20 to 33 percent of weighted indicators).

If a person experience one-third of the weighted deprivations or more, s/he is identified as MPI poor. If it is half or more s/he is identified as severely poor. If it is 20 percent to just under one-third, s/he is vulnerable of falling into poverty. Finally, this information is aggregated into the MPI, which is the product of the poverty rate (or incidence of multidimensional poverty) and the average deprivation score among the poor (or intensity).

The final draft of the MPI Primer (October 2011), which has been co-written by Maria Emma Santos and Sabina Alkire, says that after determining whether a household is deprived in each indicator, the next step is to weight those deprivations and add them up. The "score" will then be used to determine whether the household is poor or not. If the sum of the household's weighted deprivation is 1/3rd or more of total possible deprivations, then it will be poor. If a household's weighted deprivations do not add up to 1/3rd of the total, then that household is considered non-poor.

It is considered as a crucial step within the identification part of the MPI. The deprivations of the non-poor households is ignored and in formal terms this means that their deprivations are censored. While calculating the headcount ratio, the non-poor household is included only in the denominator as part of the total population.

The dimensions, indicators, deprivation cut-offs, and weights of the latest global MPI is as follows:
 
MPI indicators
 
Source: Multidimensional Poverty Reduction in India 2005/6-2015/16: Still a Long Way to Go but the Poorest Are Catching Up -Sabina Alkire, Christian Oldiges and Usha Kanagaratnam, September, 2018, please click here to access
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Once the MPI has been computed and the deprivations of the non-poor have been censored, one can look at the censored headcount ratios: the proportion of people who are poor and deprived in each of the indicators. These headcount ratios differ from the raw headcount ratios in the sense that they only consider the deprivations of those that are poor, ignoring the deprivations of the non-poor (in other words, counting them as zero).

It needs to be mentioned here that multidimensional poverty in India in 2005-06 and 2015-16 is calculated using data from the National Family Health Survey-3 (NFHS-3) and National Family Health Survey-4 (NFHS-4), respectively. 
 
Please click here to access the key findings of the Global Multidimensional Poverty Index 2018 report.

References:

Global Multidimensional Poverty Index 2018, University of Oxford and UNDP, please click here to access; also click here to access
 
Multidimensional poverty across countries, please click here to access the data 

271 million fewer poor people in India, UNDP, 20 September, 2018, please click here to access

The New Global MPI 2018: Aligning with the Sustainable Development Goals (2018) -Sabina Alkire and Selim Jahan, United Nations Development Programme (UNDP), please click here to access

Multidimensional Poverty Reduction in India 2005/6-2015/16: Still a Long Way to Go but the Poorest Are Catching Up -Sabina Alkire, Christian Oldiges and Usha Kanagaratnam, September, 2018, please click here to access

MPI in India: A Case Study, please click here to access 

Change in MPI over time in India, please click here to access  

Multidimensional poverty across Indian districts, please click here to access 

Training Material for Producing National Human Development Reports: The Multidimensional Poverty Index (MPI) - Maria Emma Santos and Sabina Alkire, Final draft, October, 2011, please click here to access
 
India's BIMARU states developing but not catching up -Rukmini S, Livemint.com, 30 October, 2018, please click here to access

UNDP data on poverty shows gains are in line with Modi's slogan, not a product of it -Sanjay G Reddy, ThePrint.in, 26 October, 2018, please click here to access

India's progress against multidimensional poverty -Francine Pickup, Livemint.com, 17 October, 2018, please click here to access  
 
Image Courtesy: Himanshu Joshi



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