Principal Dimensions of Regional Agricultural & Socio-economic Disparities in Haryana

Abstract

The present study is an attempt towards identification of principal agricultural and socioeconomic dimensions in Haryana using principal component analysis (PCA) and canonical correlation analysis techniques. Principal component analysis transforms the original set of variables into a smaller set of linear combinations that account for most of the variation of the original data whereas canonical correlation analysis determines pairs of canonical variates which are orthogonal linear combinations of the variables within each set that best explain the variability both within and between sets. Canonical correlation analysis also identifies and measures the strength of relationships between two vectors of variables measured on the same individuals. The study was conducted for three periods i.e. 1991-92, 2001-02 and 2011-12. The district was considered as the unit of analysis and analysis is based on 19 indicators from the agriculture sector and 9 indicators from the socioeconomic sector. The first principal component (PC) of agriculture sector represents the overall level of agriculture and livestock with 42.07, 28.71, and 28.01 per cent of the total variation in periods Original Research Article