Inter- State Variations in Productivity trends and Technological Change in Indian industry

The present study entitled ‘Inter-State Variations in Manufacturing Productivity and Technological Changes in India’ covers a period of 38 years from l960 tol998-99. The study is mainly based on ASI data. The study starts with a discussion of the major facilitating factors of industrialization, namely, historical forces, public policy and infrastructure facilities. These are discussed in greater details in the context of our discussion on Perrox’s (1998) ‘growth pole’ and ‘development pole’, Hirschman’s (1958) ‘industrial centers’ and Myrdal’s ‘spread effect’ Most of the existing literature more or less agrees that the process of industrialization has not been unifonn in all Indian states. There has been a decline in inter-state industrial disparities over time. This aspect is dealt at some length in the third chapter. An important element that deserves detailed attention is the intra-regional differences in industrialisation. Regional industrialisation implies the emergence of a few focal points and industrial regions. Calcutta, Bombay and Madras were the initial focal points. Later other centers like Bangalore, Amritsar, Ahemedabad etc. emerged as nodal points in other states. All major states account for focal points. The analysis made in the third chapter shows that industrial activities generally converge to one or two focal points and industrial regions have emerged out of the focal points in almost all states. One of the general features of these complexes and regions is that they approximately accommodate 50 to 75 percent of the total industrial units and workers in the state. Such convergence is seen hands in glow with urbanization. It was further seen that intra-regional industrial disparity comes down in industrial states like Maharashtra, Gujarat and Uttar Pradesh.

Bivariate distributions with weighted marginals and reliablity modelling

In this paper, a family of bivariate distributions whose marginals are weighted distributions in the original variables is studied. The relationship between the failure rates of the derived and original models are obtained. These relationships are used to provide some characterizations of specific bivariate models