Misleading Estimation of Backwardness Through Niti Aayog Sdg Index: A Study to Find Loopholes and Construction of Alternative Index With the Help of Artificial Intelligence

Abstract

UNDP Rio +20 summit in 2012 evolved a set of indicators to realise the targets of SDGs within a deadline. Measurement of the performances under these goals has followed the methodology as developed by UNDP which is nothing but the simple average of performances of the indicators under different domains. This work concludes that this methodology to measure the goal-wise as well as the composite performances is suffering from major shortcomings and proposes an alternative using the ideas of artificial intelligence. Here it is accepted that the indicators under different goals are inter-related and hence constructing index through simple average is misleading. Moreover the methodologies under the existing indices have failed to assign weights to different indicators.

This work is based on secondary data and the goal-wise indices have been determined through normalised sigmoid functions. These goal-wise indices are plotted on a radar and the area of the radar is treated as measure under composite SDG performance. The whole work is presented through an artificial neural network.

Observed that the goal-wise index as developed and tested here has shown that the UNDP as well as NITI Aayog index has delivered exaggerated values of goal-wise as well as composite performances.

Keywords: SDG Index, Sigmoidal Activation Function, Artificial Neural Network

JEL Classification: C 630, O 150