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ABSTRACT
Current researches have earmarked ‘Regional competitiveness’ as the ability of a region to attract and maintain firms and improve the standard of living of participants. In order to augment the level of regional competitiveness, investment in infrastructure, or more specifically, investments in economic infrastructure, which promotes economic activities, in particular, is essential. The search of the relationship between the two, i.e., regional competitiveness and economic infrastructure can lead to a better allocation of resources and subsequent augmentation of regional development planning. Hence the present research.
Accordingly, four objectives have been contemplated:
Objective 1: To delineate and subsequently select a homogeneous region for investigation of regional competitiveness; objective 2: To arrive at the indicators of critical components of economic infrastructure; objective 3: To assess the relative importance of the indicators of economic infrastructure in the variations of regional competitiveness; and, objective 4: To assess the efficiency of the districts in utilizing the economic infrastructure.
For each objective, a set of analyses have been carried out:
For objective 1, districts of India have been regionalized according to their resource base, and economic structure through k-means cluster analysis. Consequently, thirty homogeneous regions have been delineated, and one region belonging to the Indo-Gangetic plain has been selected as the study area. For objective 2, RIDIT and GRA analyses have been performed to determine the critical components of economic infrastructure from experts’ and stakeholders’ opinion survey.
Finally, indicators for the critical components of economic infrastructure have been selected through an extensive literature survey. For objective 3, relative weights of the indicators have been assessed with the application of multiple techniques; like beta coefficient and others.
Subsequently, the sensitivity of the indicators has been assessed with Artificial Neural Network.
For objective 4, assessment of the efficiency of districts in utilizing the critical components of economic infrastructure has been performed with Data Envelopment analysis. Subsequently, ANOVA with post-hoc test has been performed for different groups of districts for competitiveness and efficiency. Finally, spatial statistics like; Spatial Auto-correlation, Getis-Ord Gi hotspot analysis, and Anselin spatial cluster analysis have been performed for both competitiveness and efficiency.
As a whole, findings from each objective are:
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From objective 1; it is evident that the selected region has the lowest level of regional competitiveness and the highest level of the population among the thirty delineated regions.
From objective 2; it can be inferred that the inclusion of rural infrastructure is essential for the analysis of the competitiveness of the region, especially if the region is predominantly agriculture-based. Form objective 3; it is evident that the rural components of the infrastructure are more critical than urban components for the region. Furthermore, for all the variables, the more significant share of the total effect is contributed by common effect than unique effect. The beta coefficient is the best technique among all applied techniques for estimation of relative weight. Commonality analysis is extremely relevant for the detailed investigation of unique and shared effects among the variables. Regional competitiveness is found to be most sensitive to the variable of rural roads. From objective 4; it can be inferred that, with the increase in the level of competitiveness, efficiency in utilizing economic infrastructure increases as a cubic function.
Zero or lower value of slack indicates the need for investment in an infrastructure for a particular district. Level of competitiveness of a district is affected by its economic structure. Values of competitiveness and efficiency are statistically different for districts belonging to different states.
The result implies the criticality of ‘institutions and governance’ for the competitiveness of the region. A spatial pattern of agglomeration of sub-region of a similar level of competitiveness and efficiency is evident.
In summary, the present research has been able to depict a detailed picture of earmarking of ranges of the attractiveness of the districts based on its quality of development for capital and labor inflow within the homogeneous region. It is also imperative that the approach may be replicated for similar researches of regional competitiveness in any region of the world with modified indicators, and a case-specific understanding of regional competitiveness. In conclusion, it can be said that the patterns of non-homogeneity and interdependence within homogeneous regions in different river valley regions of the world can be analyzed and compared to advance a good research vocabulary on regional competitiveness based on economic infrastructure.
Keywords: Regional competitiveness, Economic infrastructure, Relative weight, Efficiency, Spatial statistics.
