Hi Friends,

Even as I launch this today ( my 80th Birthday ), I realize that there is yet so much to say and do. There is just no time to look back, no time to wonder,"Will anyone read these pages?"

With regards,
Hemen Parekh
27 June 2013

Now as I approach my 90th birthday ( 27 June 2023 ) , I invite you to visit my Digital Avatar ( www.hemenparekh.ai ) – and continue chatting with me , even when I am no more here physically

Monday 7 May 2018

#UnEmployment #JobsData #EPFO #DigitalIndia


Jobs  Data : How to automate Compilation


Economic Times ( 06 May 2018 ) carries following article :









Summary :


*  CMIE’s ESTIMATE of JOBS


   2016-17…………………….406.7  million
   2017-18…………………… 406     million




*  GHOSH & GHOSH  STUDY


    FY  17……………………….. 45.4 lakh additions to payroll across 190 industries
    FY  18……………………… ..36.8 lakh




*  EPFO  Data


    Dec-Feb ( FY 18 ) ……………. 9.7 lakh
    Apr-Feb……………………………   46.5 lakh additions to payroll

    Sep-Feb  (  FY  18  )…………  31.1 lakh jobs created of which 18-25 age group category
                                             has 22 lakh






This divergence is not surprising !



It is much like having 5 wall clocks in your drawing room, each needing occasional winding



You may rest assured that , quite likely , all will show a different  “ time “ – even if slightly different from the others



And just because you have 5 clocks to depend upon, you become careless in timely winding !  You feel , you can always count on at least one to be working and showing correct time



Except that you don’t know which one !



So , expect this controversy ( as to which Job Data is correct ) to continue , even after 5 years



Now , the Chinese are very obsessed about “ Skilling / Re-Skilling “ their manpower



So , they are unlikely to be satisfied with just data about their “ Unemployment Rate





They would want unassailable data about :



#   Total number of employees ( category / region / industry )


#   Employment Density ( Industry wise / Region wise / Skill wise etc )


#   Net Employment Growth Rate ( weekly - monthly / Industry-wise )


#   Co-relation with no of persons graduating at various levels


#   Data about those Unemployed ( " Graduating " less " Employed " )


#   Overtime Statistics ( Use / Abuse )


#   Work-hour Analysis ( Ave hours / week - month )


#   Wage / Salary Rates ( Yuan per hour ) - Industry wise / Region wise


#   Compliance with labour laws / tax laws / Apprentice Act etc


#   Job Market Forecasts through BIG DATA ANALYTIC ( region / industry )


#   Demographic  Profiles of employees  ( Rural to Urban migration )


#   Per Capita Income Growth for persons  ( MOM / YOY )


#   Changing composition between Blue Collar and White Collar employees




So , expect them to implement , in the next 6 months :


From  BAD  to  MAD  [  01  June  2016   ]



Even as our EXPERTS continue to spar on TV channels in 2022  !



08  May  2018







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