India was one among the 193 United Nations member states to adopt the Sustainable Development Goals (SDGs) in September 2015. It has been making sincere efforts to achieve these goals. The SDG India Index: Baseline Report 2018, released to the public in December 2018 by NITI Aayog, is a useful comparative account of how well different States and Union Territories have performed so far in their efforts to achieve these goals.
In this effort, it has not been possible to establish suitable indicators for three of the 17 goals, including climate action (SDG-13). This is on account of either lack of identification of appropriate indicators or of the inability to compare different States. On the whole, 62 indicators representing 14 goals have been identified based on their measurability across States over time. A progress performance assessment has been made towards targets set by the Government of India, or the UN SDGs target for 2030, or the average of the three best-performing States. For reasons of comparability, all these indicators are normalised.
Four categories
Based on a scale of 0 to 100, the States are categorised into four groups: achievers, front runners, performers, and aspirants. Achievers are those States which have already accomplished the set target. Front runners are those States that are very close to realising them. A majority of the States are categorised as performers and some lag behind as aspirants. Although classification sounds like an appropriate thing to do, there is arbitrariness in the exercise in the sense that in a unitary range, those States with scores till the midpoint are categorised as aspirants and a cluster of States in a close range of progress are termed as performers. A few States are designated as front runners. The three front runner States — Tamil Nadu, Kerala, and Himachal Pradesh — assume values of 66, 69 and 69, respectively, as against a range of States with values between 50 and 64. With the national score being 57, almost 17 States qualify as above or equal to the national score. Plotted on a graph, there is a negatively skewed distribution of scores with a reasonable tail to the left, a fat presence in the middle, and a tapering to the right. This needs to be recognised in classification; otherwise the arbitrariness with which the classification is made somewhat hints at a purposive designation of a few States in two extremes and a major share of them in between.
The problem of averaging
Further, when one reads into the performance on various SDGs, it is found that many States fall into the aspirant category, especially for SDG-5 (gender equality), SDG-9 (industry innovation and infrastructure) and SDG-11 (sustainable cities and communities). These kinds of differences could well be emerging owing to a different number of indicators considered under different SDGs as well as their corresponding variability across the States. This is evident in the variation of scores across different goals. For instance, in case of goals 1 and 2, the range for the majority of the States is between 35 and 80. For goals 3 and 6, the range is between 25 and 100. Again, for goal 5, it ranges between 24 and 50. Given these variations across different goals, merely averaging them not only compromises on robustness but also masks the disaggregated story to a large extent. Not only does the feature of the progress performance pattern need to be recognised in such classification but also the pathway of progress in development indicators, which has a character removed from linearity. Given that this is a measure of progress towards a target, the States near the target get a value closer to one compared to those which are away from the target assuming a lower value. These values are determined in relative terms in the sense that they represent the unitary position of the States within the available scale of gap between the minimum achieved and the target. Such positioning conveys a linear distance, which does not differentiate a given distance between two States which have performed well compared with another pair of States which are far from achieving the target.
The difference in progress between the three front runner States is three points. This is perhaps not similar to the distance between the performing States of Telangana and Andhra Pradesh, which too have a three-point difference. Such comprehension of achievement is limited as regards to comparing States, let alone designating them into four categories.
What can be done?
Finally, the process of aggregation adopted to present the summary index of compliance with the targets being a simple average assumes that each of the goals as well as the corresponding set of indicators are equally important and can substitute for each other. This also overlooks the aspect of inter-dependence of various goals, although it is upfront stated in the exercise. To ensure minimum robustness of this measure, a geometric average would have served towards avoiding perfect substitutability of one goal with the other. It means achievement of progress in one goal cannot compensate for compromise in another. While this exercise serves as a report card of performance of States as regards compliance with the SDGs, its scientific adequacy is compromised with arbitrariness that presents a stereotypical pattern of performance rather than bringing out surprises.
The choice of indicators representing specific goals need not necessarily be guided by availability but also their explicit independence from one another. This may help in making a uniform set of indicators for each of the goals with proper representation without duplication. On the whole, this performance assessment may not be misleading, but it does not help us understand the relative significance of compliance in some goals that helps in compliance of the other. Thus, performance assessment of SDGs while overlooking the strict interdependence of them may not be rewarding.
U.S. Mishra and S. Irudaya Rajan are Professors, Centre for Development Studies, Kerala
