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Thanks to this story, there is renewed interest in understanding the truly alarming state of #UnemploymentInIndia.
I have been writing on #unemployment consistently. Here's a THREAD of some of the recent explainers I wrote for @IndianExpress (1/n)
indianexpress.com/article/busine…
This is a good starting point to understand the issue at hand and to gain some conceptual clarity.
The data in all my pieces is either from #NSSO or @_CMIE
indianexpress.com/article/explai…
Here's how we compare with our Asian peers.
indianexpress.com/article/explai…
Read 15 tweets
This hunger index has little or nothing to do with hunger. Read blogs at dravirmani.blogspot.com/p/blog-page_25… to find out why:
2/hung When I last researched it >10 yrs ago, hunger was <2% of popukstion, poverty was 25-30% of pop & "child malnutrition", caused by dirty water & open sewers, was 35-40%. Hunger index mixes such things together to produce a meaningless index which has little to do with hunger
3hung (*population ): Leftist (intellectual elite)member of Planning commission, vetoed my internal talk on #poverty #elimination, forced removal of tables+analysis of #Hunger in Plan chapter, and subsequently forced the #NSSO to remove questions on #hunger from new surveys🙈🙉🙊
Read 3 tweets
The 2017-18 #NSSO survey results are 5-10 Standard Deviation above NSSO average for 50yrs(to 2011-12). No other data source(1 has examined) shows such outliers for 2016-19 relative to <2013-14. If there's any validity to NSSO based comparison, we should expect sweep agnst BJP!
2/ The probability of a data point more than 5 SD (standard deviation) from the mean is 1 in 4 million! The probability of sweep against the BJP may actually be higher than this because of other factors (besides unemployment rate) 😏😉🤓
2/n For the non mathematicallly inclined : mathsisfun.com/data/standard-… , en.wikipedia.org/wiki/Standard_…
Read 7 tweets

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