Quality of life in Dimapur (India)

This study deals with several issues relating to an investigation into conceptualization, definition, measurement, spatial and community-wise distribution, asymmetry, inequality and a few other related aspects of quality of life in a commercial township of a developing tribal-abundant state located in a less developed, hilly and frontier region of India. Unlike many studies on the assessment of quality of life at a macro level wherein certain gross indicators (mortality rates, per capita income or literacy rate, etc. at the national or the regional level) have conventionally been used to measure QOL, in this investigation we measure QOL by means of 135 micro-level indicators - at the household and his neighbourhood level. Four facets of QOL are visualized - those related to housing, economic aspects and high consumption, which are singularly dominant and positive. They together contribute over 97 percent to the Composite Index of QOL. The fourth facet relates to accessibility. It contributes but only a little to the Index. The distribution of sample households according to the value of the Composite Index of QOL is asymmetric around the mean value. Overall, the sample households are closer to the Hell point and farther from the Bliss point. Asymmetry is the least in sector 3 followed by sector 4, and the most in sector 5 followed by the sectors 2 and 1. Average Quality of Life improves as one moves away from the core of the city, attains it peak at the medial sector and sharply declines afterwards. There are a number of destitute households in the sample. Most of them are in the rural outskirts of Dimapur, but scarcely a few in the sectors 2 and 3, where average quality of life is better. Perhaps, a residence in sector 2 or 3 is economically inaccessible to them. Construction of the facet indices as well as the composite index based on full matrix of inter-correlation among the indicators of QOL yields better results than if the indices are constructed by using block-diagonal partial information. A perusal of the table containing loadings of pooled object variables (indicators) suggests that about one-fourth of the loadings (absolute value) are less than 0.10. Exclusion of such variables from the object set would not affect the composite index of QOL adversely, but only add to the parsimony. However, retaining them does not have any undesirable affect. We avoid pruning them out. An advice to exclude such 'weaklings' from the set of object variables in order to enhance the explanatory power of the index is rather usual. We hold that such an advice is naive and its practice illusive. An inference based on partial information can never outperform the inference based on full information