Ease of Living Index to Empower Citizens in Aspiring for a Better Quality of Life from their City Authorities

PIB
Ease of Living Index launched by the Ministry of Housing and Urban Affairs on 13th August 2018 has attracted wide public enthusiasm and provided an opportunity to Urban Planners, Municipal Authorities and public at large a baseline data for wider public debate. It is expected that the baseline data will fulfill the demands of cross section of people in aspiring for a better quality of life form their city administration. This is a unique exercise and is based on an open and participatory assessment of cities along with physical audit of urban metrics in a transparent manner. The assessment, certainly, is more than just a ranking exercise. It marks the beginning of the creation of arobust baselinealong 78 urban metrics and seeks to drive evidence-based thinkingon urban planning and development.It has also initiated an healthy competition between the cities based on the rankings and generated acute interest, comparisons, critiques and analysis by citizens and experts in the public domain.
Process Overview
Through an international bidding process, M/s IPSOS Research Private Limited in consortium with M/s Athena Infonomics India Private Limited and Economist Intelligence Unit (EIU)were selected for assessment of liveability indices. The implementation of the assessment commenced formally on 19 January, 2018.Transparency and neutrality are critical attributes that define the success of this exercise.The assessment is open and participatory and started with a nation-wide drive to encourage cities to provide data online through a dedicated data entry portal.
Two rounds of quality control and excel-based audit were performed on the data provided by the cities and errors were identified. Every city was given an opportunity to fix the errors and update their data sheets.This was followed by a round of document-based audit by a set of independent professionals to validate the veracity of the data. This was done by comparing data from supporting documents (in the form of published plan documents, administrative reports etc.) with the information presented by cities in the data entry portal.
Finally, a physical audit was conducted for selected parameters which could be physically verified (for example, availability of passenger information systems) through a network of trained field staff.
Defining features that influence the assessment outcomes and Rankings:
1.    Indicators and Weightages
The foremost aspect that influences a city’s performance is the set of indicators that the city is being assessed on and weightages assigned to them.  In the current assessment, the physical infrastructure pillar receives the highest weightage of 45%, with several of the indicators focusing on universalization of services(Sanitation, Power, Water, Sewer,Transport, Public Services etc.Thus, cities that are observed to be doing better in terms of service coverage stand to gain significantly.
The other feature is the differential weights associated with indicators based on whether they are classified as supporting or core. A core indicator receives a weightage of 70% while a supporting indicator only receives a weightage of 30%. For example, a city that has taken significant efforts to restore ecologically sensitive areas (core indicator) within its jurisdiction stands to gain more on the theme of ‘identity and culture’ vis-à-vis its performance on an indicator such as number of cultural/sports events hosted (supporting indicator).
2.    Quantum of Data Available with Cities
Every city was invited to participate in a data collation exercise through an online data entry portal. Multiple departments participated to provide data on over 500 questions, cutting across 78 indicators. Cities that had strong systems for data generation and reporting and/or a history of planning that was evidence based (City Sanitation Plan, Mobility Plans etc.) were observed to perform better as they are simply better equipped to provide data. Cities that have inadequate systems of record keeping were observed to be at a disadvantage.
3.    Quality of Data Provided
To encourage cities to provide sound data, an incentive in the form of higher weightages has been deployed for indicators that are backed by supporting documents. Cities that could support the data provided with strong secondary documents (ex: SLIPs, DPRs, City Mobility Plans etc.) were given due weight-age in their score.
4.    Relative benchmarks
To ensure that the assessment offers a level playing field to all cities, relative benchmarks are assigned for 22 of the 78 indicators in which cities are evaluated against their comparable peers, defined by the population. Cities were classified into 4 categories namely- Group 1 – Below 0.5 Million, Group 2 – 0.5 to 1 million, Group 3- 1 to 4 Million, Group 4 – Above 4 million
For example, Karimnagar (a city that is in the ‘below 0.5 million’ category) is observed to be performing better than Hyderabad as its benchmarks on several such indicators are fundamentally different. For example, the benchmark value (best performing city’s data) for Karimnagar on surveillance density is 1.76 (number of CCTV cameras per km of road length) while for a city like Hyderabad, the benchmark is 19.2.