Source- Live Mint
There are two main facets to the spatial poverty trap in India. One is geographical isolation and the other is backwardness
There are many ways to identify and measure poverty. Among them, the Indicus initiative of identifying the population of India’s poor and the associated factors of poverty at a grid cell of 1 sq. km is the first of its kind. Identifying the population of the poor at such a granular level has wide policy implications. Government policies in India have a long history of addressing the issue of developing backward areas. At the national level, data shows poverty reduction initiatives have been impressive, registering a decline of almost 25 percentage points from 1993-94 to 2011-12; yet, the states that were focused on for poverty alleviation programmes, such as Bihar, Uttar Pradesh and Jharkhand, continue to lag behind. Apparently, the poverty alleviation programmes have had limited success and one of the main reasons is the lack of appropriate measures to identify the poor population in a vast land of multiple diversities. Under the government’s Backward Regions Grant Fund, meant to redress regional imbalances in development, about 250 districts were identified as backward. An amount of Rs.2,965 crore was released under the programme in 2007-08 to bridge critical gaps in local infrastructure and other development requirements that were not being adequately met through existing inflows. Poverty rates in these 250 districts continue to be high, exceeding 30% in most cases. A micro-level analysis of these 250 districts shows about 80% of the poor population is concentrated in just 30% of the grid cells. However, small pockets with a high concentration of poor population are spread out across the country. The crux lies in identifying the common characteristics in high-poverty areas for better targeting of poverty alleviation programmes. There are two main facets to the spatial poverty trap. One is geographical isolation and the other is backwardness. High poverty rates are identified in areas close to forests, rocky terrain or snow-covered areas. Inaccessibility and scant communication services coupled with erratic weather conditions trap the poor in a vicious cycle of poverty. The north-eastern part of the country and the Himalayan ranges of Jammu and Kashmir have large parts of their land inaccessible and are also less favoured for agriculture. In addition, poor farm practices lead to low yields and soil degradation, while lack of access to markets and infrastructure bottlenecks constrain the ability of poor households to improve the farming system or obtain off-farm employment. The other aspect of areas with high concentration of poverty is the sparseness of physical and social infrastructure in terms of road connectivity, educational institutions and hospital services. This is a common characteristic across all the states in the country. Clearly, development is the key to reducing poverty in these areas. Interestingly, while poverty rates are high in geographically isolated or backward areas, concentration of poor population is generally spotted near areas which have the potential for earning a living. For instance, Indicus research identifies high concentration of poor population near mining areas or in close proximity to water. In many of the rich mining areas of Jharkhand, Chhattisgarh and West Bengal, a very high concentration of poor population is identified. Influx of population into these economic zones is common and most often, it is unskilled workers on a large scale that crowd these zones. For balanced development, it is necessary that essential social sectors, such as education, health, etc., also grow in parallel with economic development, without which inequality grows. Our micro-level analysis also identifies pockets with high concentration of poor population in some of the fairly developed states such as Kerala and Tamil Nadu, where poverty rates are comparable with the developed countries of the world. Our spatial analysis shows inequality in the reach of services. Similarly, in some of our highly developed cities, such as Delhi, Mumbai and Bengaluru, economic opportunities are concentrated only in small parts of the city. In a large part of the city, the number of services reaching the population is far from adequate. An attempt at identifying where the poor live has opened a way for a far greater set of policy insights. Our concept of spatial poverty to identify pockets of poverty and measure its extent via remote sensing can be used for a more focused and targeted approach to tackle poverty.