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Status:Closed    Asked:Nov 07, 2016 - 11:53 AM

Why are not all the "not in the labour force" categories filled for the EMPSTAT variable?

I am using the EMPSTAT variable from the basic monthly samples of IPUMS-CPS (time period: 1997-2013) to distinguish the labour market states employment, unemployment and not in the labour force (NILF). In my (weighted) results, I do not get enough NILF people, compared to official statistics. I was therefore wondering whether there are NILF people missing from the basic monthly samples. In particular: Why are the NILF codes 30, 31, 33, 35 not filled?


Best regards


Ronald

 
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Staff Answer

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Jeff Bloem

Staff

In regards to the NILF codes, take note of the discussion on comparability of the "NILF" codes in the EMPSTAT variable over time. In particular, in the mid-1990s there was a redesign of the type of information available on persons not in the labor force. Beginning in 1995 and onward, persons not in the labor force were grouped into three categories: "retired", "disabled", and "other". This detail shouldn't change the total number of persons "not in the labor force", however. One detail to keep an eye on when trying to replicate official statistics is the method used for weighting the data in the official statistics. The WTFINL is most often used when using person-level IPUMS-CPS basic monthly data. Additoinally, we typically don't expect to exactly replicate official statistics.

 

Nov 08, 2016 - 09:13 AM

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Dear Jeff,


Thanks a lot for your answer. I was simply surprised to see that the participation rate I compute for June 2010 with IPUMS-CPS is 73% (rather than 65% in official statistics), and the unemployment rate being 6.3% (rather than over 9%). But if I understand you correctly, there are no groups of the population (such as housewives) that are systematically excluded from the IPUMS-CPS sample?


Best regards


Ronald

 

Nov 09, 2016 - 11:24 AM

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Hi Ronald:

Although we typically don't expect to exactly replicate official statistics, we do expect to be closer than what you are reporting here. There shouldn't be anyone who is systematically excluded from the IPUMS-CPS sample that isn't systematically excluded in the BLS CPS samples. I'd suggest looking over how the official statistics calculate their figures and see if your analysis matches this methodology. I know that the BLS does do some seasonal adjustments when calculating their figures. I'll aslo mention that while the persentage doesn't match the weighted count between these two data sourses is pretty close: 14.6 million in the BLS and 15 million in IPUMS-CPS (16+ population only).

Jeff

 

Nov 10, 2016 - 10:10 AM

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