Growth and Poverty in Maharashtra

Maharashtra is among the richest states in India in terms of per capita income, yet incidence of poverty in the state remains close to the national average. The states economy grew at a faster rate than the all-India average during 1980-1 to 1992-3, but it slowed down a bit during 1993-4 to 2003-4 due to poorer performance of agriculture and industry. Agricultures contribution to GSDP has come down to 12 per cent in 2002-3, but more than 50 per cent of total workers are still engaged in this. Cropping pattern has been shifting to greater value addition non-cereal crops like fruits, vegetables, oilseeds and sugarcane. Composition of manufacturing has shifted towards more capital-intensive sectors. Communication, transport and public administration have accounted for large part of service growth. The benefits of this growth process have, however, not spread equally across social groups or regions, which partly explains prevalence of high poverty compared to other states at similar mean income. The much talked about Maharashtra Employment Guarantee Scheme (MEGS) has had limited success and its coverage across districts/divisions is not proportionate to the share of poor. Despite these developments, rural poverty has reduced from 38 per cent in 1993-4 to around 24 per cent in 1999-2000. Given current investment flows, the overall growth potential of Maharashtra does look bright for the medium run. But, distributional implications of the emerging growth pattern across sectors suggest that the poor might not benefit proportionately from the growth process. The lessons that Maharashtra provides is that growth has to be more broad-based and inclusive, and that intervention through social welfare programmes like MEGS should be designed to suit the local resource base of poorer regions for faster poverty reduction.Growth, poverty, Maharashtra

India’s Foreign Trade – An Overview

Export-Play, Important Role of any country’s business India is one among these countries that have been exporting a large number of product and raw material to other countries to earn economy wealth. India is 19th largest export economy. India’s overall, export- in 2019-20 was US $313138.5 million and total import was US$ 473995.2 million and trade balance was US $160856.7 million. The main object of the paper is to analyse the structural change in foreign trade- Under new Exim policy. The period of the study is from 2010-11 to 2019-20. The result shows that USA, UAE, Hongkong, UK, Germany, Saudi Arbia and China accounted from more than 40$ 330078.1 million in the year 2018-19 decline to US $313138.5 million in the year 2019-20. The total export from India decreased by 5.13$ 473995.2 million which China contributed by 37.76%, USA 7.52%, Saudi Ariba 3.60%, Hongkong 3.5%, UAE .38% and Germany 2.81%,. The result show that USA is most important trading partner followed by UAE an UK, Hongkong, China and other countries

How to Securely Compute the Modulo-Two Sum of Binary Sources

In secure multiparty computation, mutually distrusting users in a network want to collaborate to compute functions of data which is distributed among the users. The users should not learn any additional information about the data of others than what they may infer from their own data and the functions they are computing. Previous works have mostly considered the worst case context (i.e., without assuming any distribution for the data); Lee and Abbe (2014) is a notable exception. Here, we study the average case (i.e., we work with a distribution on the data) where correctness and privacy is only desired asymptotically. For concreteness and simplicity, we consider a secure version of the function computation problem of K\"orner and Marton (1979) where two users observe a doubly symmetric binary source with parameter p and the third user wants to compute the XOR. We show that the amount of communication and randomness resources required depends on the level of correctness desired. When zero-error and perfect privacy are required, the results of Data et al. (2014) show that it can be achieved if and only if a total rate of 1 bit is communicated between every pair of users and private randomness at the rate of 1 is used up. In contrast, we show here that, if we only want the probability of error to vanish asymptotically in block length, it can be achieved by a lower rate (binary entropy of p) for all the links and for private randomness; this also guarantees perfect privacy. We also show that no smaller rates are possible even if privacy is only required asymptotically.Comment: 6 pages, 1 figure, extended version of submission to IEEE Information Theory Workshop, 201