By Arpita Banerjee, Pravat Kumar Kuri
This publication highlights the advance disparities in India and considers 3 advanced components of improvement – monetary health, human development and agricultural improvement – over a interval of 40 years because the Seventies. the newness of the ebook lies in is its wealthy analytical starting place and using subtle statistical and monetary instruments to figure out the motives of socioeconomic disparity among Indian states. The developments of inequality, polarization and disparity are highlighted with reference to source of revenue, human improvement symptoms and agricultural creation and productiveness. The ebook additionally identifies the criteria underlying divergence in fiscal and social task in India and offers coverage feedback for bringing approximately extra balanced and inclusive improvement in India.
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Additional resources for Development Disparities in India: An Enquiry into Convergence
3 Methodological Issues 27 The formula for population weighted Lorenz consistent Generalised Entropy measures are m y GE (2) = f ( yi )[( i )α − 1], α ≠ 0,1 ∑ µ i =1 GE (1) = m yi yi ∑ f ( y )( µ ) log( µ ), α = 1 i i =1 m µ i =1 yi GE (0) = ∑ f ( yi ) log( ), α = 0 Where yi = the value of composite indices for state I, f(yi) is the population share of state i in total population, y − = mean value of the indices. (Cowell 1995; Shorrocks 1980; Ferodov 2002). 3. Measures of Polarization It has been argued in many studies that the standard measures of inequality is merely a scalar numerical representation of the interpersonal difference from the global mean but it ignores the clustering around the local means (Noorbaksh 2003).
The single cross-section estimator Ordinary Least Square (OLS) gives consistent estimates as long as the state-specific individual effect is captured by random disturbance term and assumed to be uncorrelated with explanatory variables. g. minimum distance (MD) approach, LSDV approach (Islam 1995), first difference GMM (Arellano and Bond 1991), system of GMM (Blundell and Bond 1998; Easterly and Levine 2001; Hoeffler 2002; Wooldridge 2002). When controlling for unobserved state-specific effects, the most important issue, which would arise is whether the technological effect is treated to be ‘fixed’ or ‘random’.
In fact, there is an abundance of literature on the issues of regional convergence of economic growth with diverse findings in India. The studies by Cashin and Sahay (1996) and Dholakia (1985, 1994) show absolute β-convergence in per capita income of Indian states, whereas studies by Marjit and Mitra (1996), Ghosh et al. (1998), Raman (1997), Dasgupta et al. (2000), Rao et al. (1999), Aiyer (2001) and Ahluwalia (2000) show absolute divergence in per capita income of Indian states. On the other hand, studies by Nagaraj et al.
Development Disparities in India: An Enquiry into Convergence by Arpita Banerjee, Pravat Kumar Kuri