ISSN (Print) - 0012-9976 | ISSN (Online) - 2349-8846

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Urban Bias in PDS

Urban Bias in PDS M H Suryanarayana DISTRIBUTION of foodgrains through the public distribution system (PDS) is widely believed to be concentrated in urban areas or, in other words, PDS is believed to have an urban bias,1 Most of the existing studies are based on official data on offtake of foodgrains for the PDS and are somewhat outdated. However, a recent analysis |Dev and Suryanarayana (DS hereafter) 1991) of the data [National Sample Survey Organisation 1990] on purchases from the PDS vis-a-vis open market from the National Sample Survey (NSS) for the year 1986-1987 revealed a different picture. Examining the PDS quantity purchased per market dependent population in rural and urban areas, inter alia, the study concluded that the question of bias in the PDS is no longer one of rural versus urban at the all - India level and for most of the stales, Howes and Jha (HJ hereafter) (1992) contest our methodology and arrive at the opposite conclusion that the PDS is urban biased. Providing the rationale for the chosen criteria and hence our conclusions, this note points to the shortcomings of IlTs criticisms. Mus response, though belated, is pertinent since these methodological issues have a bearing on the policy debate on the proposed revamping of the PDS, PDS is an important instrument of ensuring food security through the market mechanism. Food security has to do with both availability of food and the ability to acquire it. PDS aims at ensuring food availability at reasonable prices and hence can guard against food insecurity caused by instability in food prices and production. The ability to acquire food, on the other hand, concerns policies towards employment generation and poverty alleviation. Therefore, the appropriateness of policy packages for ensuring food security in a given sector depends upon the size and composition of the population. It is in this light that the PDS and the question of bias, if any, has to be evaluated using measures normalized with respect to appropriate variables.

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