How did we calculate the Economic Vulnerability Score?
We devoured a ton of economic data from many sources and isolated the most important economic factors based on how COVID-19 was unfolding. Our differentiating consideration was that we were trying to gauge only the economic impact and not the health impact of these factors. We are not epidemiologists and wanted to play to our strengths.
This is how we calculate the Economic Vulnerability Score:
- We isolated 18 of the the most pertinent indicators based on availability and prevailing stream of research / thinking.
- We bucketed them into four overarching buckets and weighted each metric on perceived importance towards economic vulnerability. Importance was based on prevailing evidence, logic and reason (“Importance”).
- We assigned a confidence level (1% to 100%) to each metric based on how good and recent the data was (“Confidence”).
- Multiplying Importance and Confidence gave us our metric multiplier, or effective weight (“Multiplier”).
- We then assigned a “least vulnerable” and “most vulnerable” value to each metric based on evidence, logic and reason.
- We normalized (range-scaled) the metrics to remove any differences in the size of the metrics.
- We then multiplied the normalized metrics with the multiplier to give us an independent score for each state.
- We then calculated the worst possible score and took that as the denominator to calculate the vulnerability score as a %.
OUR sOURCES OF INFORMATION
We scoured the world wide web for information and were lucky to happen upon good sources (each source is linked):
- COVID-19 cases (confirmed, active, recovered, deceased, tested, zones) – covid19india.org, these guys are just incredible. Worth writing an entire blogpost on the movement they have created. They have cleaned, condensed and API’d their entire data set and their dashboard. Truly open-source.
- Sectors Partially / Fully Closed due to COVID-19
- Workers by Sector (2017-18)
- Population & Demographics (in addition to the 2011 Census, we also took 2019 projections from the Aadhar website)
- # of Migrants (2011) – unfortunately, that’s the only recent data available
- Quality of Migrant Policies Index (IMPEX 2019) – for states that does not have this index, the nationwide average was assumed.
- # of Casual Workers & Household Help (2017-2018)
- # of Casual Workers Who Earn Less Than Rs 7.5k / Month (2015-2016)
- GSDP by State
- Fiscal Deficit
- # of Micro, Small & Medium Enterprises (MSMEs) (2018-2019)
- Central and State Government Initiatives (Dvara has done an amazing job putting this together)
- Unemployment Data (CMIE – 2020)
- Fundraisers, essentials by State and City (plus we collected some data manually as well, and sourced some from GiveIndia)
- Ladakh: Since Ladakh became a Union Territory only in 2019, we have combined it with Jammu & Kashmir to align historical data.
Our methodology is not perfect, and is based on a lot of assumptions. But, our goal is to provide a high-level, evidence-based logical take is actionable. We believe in full transparency, so if you want more information on how we calculated the score, please do not hesitate to contact us. We will share everything. Any questions / concerns / additions / mistakes, do not hesitate to contact us.