Urban migration twist in NITI Aayog's multidimensional poverty index

May have led to an increase in poverty in 24 districts in 10 states and UTs

While the multidimensional poverty index (MPI), released by the NITI Aayog last week, showed a sharp decline in poverty between 2015-16 and 2019-21 at national level, urban migration seems to have led to its increase in 24 districts in 10 states and Union Territories during the same period.

Five of the 11 districts in Delhi (Central, North, West, Southwest and New Delhi) saw an increase in multidimensional poverty, followed by five in Punjab (Bathinda, Faridkot, Jalandhar, Ludhiana, Rupnagar), four in Kerala (Kasargod, Kottayam, Kozhikode, Palakkad), and two each in Haryana (Ambala, Yamuna-nagar), Tamil Nadu (Chennai, Dindigul), and Sikkim (Mangan, Namchi). Poverty increased also in one district each in Arunachal Pradesh, Meghalaya, Himachal Pradesh, and Chhattisgarh during the period.

Himanshu, assistant professor, Jawaharlal Nehru University, said the increase in the ratio of multidimensionally poor people living in these districts could be due to the continuous influx of migrants from the interior regions of the country.

“Since towns and cities in India face a continuous influx of people from interior regions, and as most of the districts that saw an increase are towns, providing them with basic facilities becomes a task for the government. This should bring our attention to urban poverty, which continues to get scant attention from policymakers,” he added. 

Between the National Family Health Survey-4 (NFHS-4) and NFHS-5, West Khasi hills district in Meghalaya saw the largest increase in the share of multidimensionally poor population by 12.9 percentage points to 52.5 per cent from 39.6 per cent previously, followed by Bijapur, North Delhi, Faridkot and Bathinda. 

R Ramkumar, professor, Tata Institute of Social Sciences (TISS), however, said the MPI should be interpreted as a deprivation index and not a poverty index, as it had a large number of variables and components, which make it difficult to interpret. 

“No single reason can be attributed to the rise of multidimensional poverty in these districts, which are spread far and wide and that too on the basis of indicators as diverse as nutrition, housing, or financial inclusion. While migration can somewhat explain these trends, more than that the performance and delivery of government schemes in these areas should be scrutinised, as a lot of indicators taken for computing MPI are input indicators,” he added.

The National MPI captures overlapping deprivations in health, education, and living standards represented by the 12 indicators aligned with the Sustainable Development Goals. The index complements income poverty measurements because it measures and compares deprivations directly. The earlier poverty estimates predominantly relied on expenditure as the sole indicator.

The MPI report noted of the 112 aspirational districts, barring Bijapur in Chhattisgarh, the others saw a decline in the share of people living in multidimensional poverty. 

The MPI showed 135 million people in India escaped multidimensional poverty between 2015-16 and 2019-21 as the share of multidimensionally poor in India declined sharply to 14.96 per cent from 24.85 per cent in the corresponding period. 

Based on the NFHS-5 data, the report provided multidimensional poverty estimates for all the states and Union Territories, 36 in total, and 707 Administrative Districts and showed the highest reduction in the proportion of multidimensionally poor was in Uttar Pradesh, where 34.3 million people escaped multidimensional poverty, followed by Bihar (22.5 million), Madhya Pradesh (13.57 million), Rajasthan (10.8 million), and West Bengal (9.26 million).