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Share of the Working Age Population That Has Work Is Up, but Those Looking for Work Is Down: Data
According to the PLFS data, the unemployment rate among graduates in the age group of 15 years and above has declined. However, the same survey says that a larger number of individuals have opted for self-employment over salaried jobs, raising concerns over the unemployment situation.
#unemployment #PLFS #CMIE #labour #economy #UnionGovt #india
According to the PLFS data, the unemployment rate among graduates in the age group of 15 years and above has declined. However, the same survey says that a larger number of individuals have opted for self-employment over salaried jobs, raising concerns over the unemployment situation.
Over 40% of India’s graduates under 25 unable to find jobs, says report
There is large variation in the rate of unemployment even within the higher educated group. The unemployment rate falls from over 40 percent for educated youth under 25 years of age to less than five percent for graduates who are 35 years and above, a report titled State of Working India 2023 shows.
#unemployment #covid19 #PLFS #labour #labor #agriculture #jobs #industry #economy #india
There is large variation in the rate of unemployment even within the higher educated group. The unemployment rate falls from over 40 percent for educated youth under 25 years of age to less than five percent for graduates who are 35 years and above, a report titled State of Working India 2023 shows
Weighty evidence? Poverty estimation with missing data
https://www.ideasforindia.in/topics/poverty-inequality/weighty-evidence-poverty-estimation-with-missing-data.html
#consumer_expenditure surveys #National_Statistical Office #Consumer_Pyramid Household Surveys #representation #National_Family Health Survey #CPHS #NFHS #Periodic_Labour Force Survey #PLFS #re-weighting #maximum_entropy #bias #poor_households #datasets #statistics #monthly_per capita consumption ex
Attempts have been made to estimate poverty in India with biased survey data, by adjusting household weights to remove the bias. Based on simulation exercises with artificially contaminated household surveys, Drèze and Somanchi illustrate the limitations of this method. Its ability to correct poverty estimates varies wildly, depending on the nature of the underlying bias, which may be hard to guess – there lies the rub. When the bias changes over time, estimating poverty trends becomes truly pro
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