A State Level Analysis of the Status of Social Sector in India

Nayak, Purusottam and Mishra, Sudhanshu K (2014): A State Level Analysis of the Status of Social Sector in India.

Abstract

Social sector with the objective to satisfy the welfare needs of the people and to correct the imbalances in the economy claims a sizeable proportion of the public expenditure and has emerged as a significant sector. This paper in this regard is a state level analysis on the growth of public expenditure vis-à-vis status of social sector in India using secondary data for the period from 1990-91 to 2012-13. Status of social sector has been ascertained through construction of composite indices based on available important techniques using 12 indicators variables on health and education. The findings reveal that in India, especially after the year 2000-01, the allocation of resources on the social sector has gained momentum. It is observed that population-wise smaller states such as Mizoram, Sikkim, Meghalaya, Himachal Pradesh, Goa, Puducherry and Uttarakhand with more development-oriented attitude have achieved a good progress on social sector. On the other hand, states such as Punjab, Kerala, Jharkhand, Bihar, W. Bengal, Nagaland, Tamil Nadu and Tripura are at the lower end. To further improve the status of social sector in different states/UTs it is important that the public expenditure on this sector keeps its pace undaunted, but, perhaps, it is more important that fiscal and financial management is streamlined and its governance is improved.

• Item Type: MPRA Paper
• Language: English
• Keywords: Social Sector, Public Expenditure, Composite Index, Education, Health, India
• Subjects: O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development; O - Economic Development, Innovation, Technological Change, and Growth > O2 - Development Planning and Policy

References

Blomqvist, N. (1950). On a measure of dependence between two random variables. Annals of Mathematical Statistics 21(4): 593-600.

Bradley, C. (1985). The Absolute Correlation. The Mathematical Gazette 69(447):12-17.

Croux, C. and Dehon, C. (2010). Influence functions of the Spearman and Kendall correlation measures. Statistical Methods and Applications 19(4):497-515.

Hampel, F. R., Ronchetti, E.M., Rousseeuw, P.J. and W. A. Stahel, W.A. (1986). Robust Statistics: The Approach Based on Influence Functions, Wiley, New York.

Mishra, S.K. (2009a). A note on positive semi-definiteness of some non-Pearsonian correlation matrices. SSRN: http://ssrn.com/abstract=1419362.

Mishra, S.K. (2009b). On construction of robust composite indices by linear aggregation. IUP Journal of Computational Mathematics 2(3):24-44. Pre-print copy available at MPRA: http://mpra.ub.uni-muenchen.de/9232/1/MPRA_paper_9232.pdf.

Mishra, S.K. (2013). Global optimization of some difficult benchmark functions by host-parasite coevolutionary algorithm. Economics Bulletin 33(1):1-18.

Mishra, S.K. (2014). What Happens If in the principal component analysis the Pearsonian is replaced by the Brownian coefficient of correlation? SSRN: http://ssrn.com/abstract=2443362.

Nayak, P. and Mishra, S.K. (2012). Efficiency of Pena’s P2 distance in construction of human development indices. SSRN: http://ssrn.com/abstract=2066567.

Shevlyakov, G.L. (1997). On Robust Estimation of a Correlation Coefficient. Journal of Mathematical Sciences 83(3):434-438.

Somarriba, N. and Pena, B. (2009). Synthetic indicators of quality of life in Europe. Social Indicators Research 94(1):115–133.

Székely, G.J. and Rizzo, M.L. (2009). Brownian distance covariance. The Annals of Applied Statistics 3(4):1236-1265.