Increasing inter-state disparities across India has received some attention and the question of convergence versus divergence across states is often debated. Did the reform decade of the Nineties accentuate inter-state disparities' And what can be done to ensure that trickle-down benefits of growth percolate to all regions and some backward areas are not bypassed' India is not sui generis in raising such questions. The divergence between north-west China and the southern coastal provinces has probably received much more attention.
Divergence or convergence is of course a function of the variable used to measure it. The two most commonly used indicators are growth in per capita state domestic product or the poverty ratio (head count ratio). On the former, the growth records of Gujarat or Maharashtra in the Nineties are hardly comparable to the growth records of Orissa or undivided Bihar and Uttar Pradesh. If growth in per capita SDP is the yardstick, then inter-state disparities existed in the Eighties as well. But the conclusion that these disparities increased in the Nineties also seems fairly robust.
Similarly, on poverty ratios, Goa, Punjab or Haryana are hardly comparable to Orissa or undivided Bihar. Arguably, the post-1991 reforms shifted the focus of policy change from the Centre to the states. In a federal country, most factor markets are either on the state or concurrent lists. Hence, the agenda of second-generation reforms includes items that require changes at state level. Agriculture, social infrastructure and most areas of physical infrastructure are cases in point. If reform inclinations vary across states, it logically follows that so must growth rates. And this is compounded by the phenomenon that many faster growing states tend to have lower rates of population growth. Sick public sector units also tended to be concentrated in certain geographical regions and these are not the regions that have exhibited high (private-sector-driven) employment growth in the Nineties.
In discussing inter-state disparities, the East versus the West or the North versus the South dichotomy is often emphasized. This East versus the West dichotomy, with a vertical line drawn through Kanpur, has some validity, subject to the caveat that undivided Uttar Pradesh is an extremely heterogeneous state, with the eastern parts somewhat different from the western, Noida being an obvious example. The North versus the South dichotomy, with a horizontal line drawn through the Vindhyas, also has some validity, with growth rates in Tamil Nadu, Karnataka and Andhra Pradesh picking up in the Nineties.
But like all generalizations, these hide as much as they reveal. The identification of poverty and backwardness with the BIMARU (Bihar, Madhya Pradesh, Rajasthan and Uttar Pradesh) states, with a pun on the word “bimar”, is a bit more robust. However, Rajasthan clearly no longer belongs to the classic BIMARU fold and Madhya Pradesh also seems to be climbing out. Conversely, Orissa exhibits many BIMARU characteristics. And how do Uttaranchal, Jharkhand and Chattisgarh fit into the BIMARU identification'
More important, why is one interested in identifying pockets of deprivation' While not negating the possibility of differential relative growth-rates across the country or attempting to neutralize it, the answer must be that one is interested in policy interventions that ensure uniform access to market opportunities across the country, quite apart from the socio-economic tensions that marginalization leads to. Liberalization does not mean an abdication of the state’s responsibility. Instead, it should be interpreted as a refocusing of state intervention, with an emphasis on social (education, health) and physical (power, water) infrastructure, or even governance.
The tension over better-performing states effectively cross-subsidizing states that have relatively inferior track records needs to be viewed in this light. However, development or deprivation does not necessarily follow state boundaries, which are at best, administratively determined. Unfortunately, policy intervention in India is too centralized.
This does not only mean overwhelming emphasis on the Centre in Centre-state relations, as all states invariably argue, but it also means negligible emphasis on decentralization and devolution within states, a point that states rarely make. Hence, while policy intervention is indeed needed to tackle problems of poverty and deprivation, identification of backward regions needs to go beyond state boundaries. Laveesh Bhandari and I have just undertaken such an exercise at the district-level and the book has been published on 23rd August by Konark Publishers.
How does one decide whether a district is backward' What criteria should one use' Poverty ratios provide an obvious indicator and indeed poverty ratios have earlier been used by the planning commission to identify backward districts. However, it is possible to go beyond poverty ratios and the identification in our exercise is pegged to the millenium development goals. This makes it more broad-based than earlier attempts. The international development goals (or the millenium development goals) were accepted as targets to be attained by 2015. It is a separate matter that most developing countries, including India, are way behind target. There are eight millenium development goals, some specific, others less so.
(1) Halve, between 1990 and 2015, the proportion of people whose income is less than $ 1 a day. As a further objective, halve, between 1990 and 2015, the proportion of people who suffer from hunger, measured as prevalence of underweight children or the proportion of population below minimum levels of dietary energy consumption. (2) Ensure that, by 2015, children everywhere, boys and girls alike, are able to complete a full course of primary schooling. (3) Eliminate gender disparity in primary and secondary education preferably by 2005 and in all levels of education no later than 2015. (4) Reduce by two-thirds, between 1990 and 2015, the under-five mortality rate and obvious indicators are the under-five mortality rate, the infant mortality rate and the proportion of one-year-old children immunized against measles. (5) Reduce by three-quarters, between 1990 and 2015, the maternal mortality ratio. (6) Halt by 2015 and begin to reverse the spread of HIV/AIDS. As an additional goal, halt by 2015 and begin to reverse the incidence of malaria and other major diseases. (7) Integrate the principles of sustainable development into country policies and programs and reverse the loss of environmental resources. In addition, halve, by 2015, the proportion of people without sustainable access to safe drinking water. (8) Develop an open, rule-based, predictable, non-discriminatory trading and financial system.
The first five goals are more specific, with quantifiable targets, than the last three. But even on the first five, data availability at the district-level is often a constraint. Hence, data on under-five mortality rates or maternal mortality rates are less than satisfactory. The identification in our exercise is therefore based on six indicators — income poverty (poverty ratios), hunger, infant mortality rate, immunization, literacy rate and enrollment ratios. In addition, gender disparity is highlighted through female and male literacy rates, but not actually used in the process of identifying backwardness. Data used are estimates for 2001.
Poverty ratios show backward districts not only in undivided BIMARU states, but also in Gujarat, Maharashtra, Karnataka, Tamil Nadu, Andhra Pradesh, Orissa, West Bengal and the Northeast. Hunger (defined in National Sample Survey terms) exhibits a similar spatial distribution, but is less universal than poverty and is also more concentrated towards the East and the Northeast. Other than a few neighbouring districts of Karnataka and Andhra Pradesh and the North-east, the infant mortality rate identifies undivided BIMARU and Orissa. Lack of immunization is geographically a more serious problem, with a clear North versus South divide. Low literacy rates are spread throughout the country and expectedly, this is also mirrored in gross enrollment rates.
I will have more to say on backward districts in the next article.