The study employs the panel data of 15 hi-tech industries over the period of 2000-2010 in order to examine the effectiveness of R&D with respect to productivity change and indentify the significant contributing factors with intensity in the Chinese hi-tech sector. The Malmquist Productivity...

Shephard’s distance functions are widely used instruments for characterizing technology and for estimating efficiency in contemporary economic theory and practice. Recently, they have been generalized by the Luenberger shortage function, or Chambers-Chung-Färe directional distance function....

This paper answers the puzzling questions that why under the similar set of economic conditions service sector in India grew while manufacturing could not and how economic reforms in 1990s accelerated the productivity growth. The paper provides a very innovative and convincing explanation. Two...

Studies on Indian manufacturing have been unable to provide consistent estimates of productivity and its growth rates. This paper performs detailed and exhaustive set of accounting exercises for the period 1970-2003 using production function, index number and envelopment analysis methods. TFP...

Based on the bibliographical data available with the RePEc (Research Papers in Economics), the Internet Documents in Economics Access Service (IDEAS) publishes every month the up-dated academic rankings of different geographic regions (countries/states in the US). This paper raises the question...

Construction of (composite) indices by the PCA is very common, but this method has a preference for highly correlated variables to the poorly correlated variables in the data set. However, poor correlation does not entail the marginal importance, since correlation coefficients among the...

In this paper we construct thirteen different types of composite indices by linear combination of indicator variables (with and without outliers/data corruption). Weights of different indicator variables are obtained by maximization of the sum of squared (and, alternatively, absolute)...

The classical canonical correlation analysis is extremely greedy to maximize the squared correlation between two sets of variables. As a result, if one of the variables in the dataset-1 is very highly correlated with another variable in the dataset-2, the canonical correlation will be very high...

Rank-ordering of individuals or objects on multiple criteria has many important practical applications. A reasonably representative composite rank ordering of multi-attribute objects/individuals or multi-dimensional points is often obtained by the Principal Component Analysis, although much...

This paper demonstrates that if we intend to optimally rank order n objects (candidates) each of which has m attributes or rank scores awarded by m evaluators, then the ordinal ranking of objects by the conventional principal component based factor scores turns out to be suboptimal. Three...