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LAUS – Peak Year of Employment by County

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Yesterday, I posted this map of QCEW that shows the year of peak employment by county, going back to 1975. A different survey is the LAUS, which only goes back to 1990 and has a somewhat different result, in part because it only goes back 30 years versus 45 years.
  • Local Area Unemployment Statistics (LAUS)- Estimation based on a survey of households, CES, and Unemployment Insurance claims
  • Quarterly Census of Employment and Wages (QCEW) - Census of all employers liable for Unemployment Insurance (97-99% of total non-agricultural employment)
Learn more about these different surveys here: https://statistics.labor.ny.gov/lscompare.shtm

LAUS - Peak Year of Employment by County

Meet Todd, A Total Chump Who Did The Responsible Thing And Worked His Way Through College | Babylon Bee

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Meet Todd, A Total Chump Who Did The Responsible Thing And Worked His Way Through College | Babylon Bee

BALTIMORE, MD — Local PC Technician Todd Manfroy was singled out as a chump by his local community Wednesday after it was discovered he worked his way through college to avoid taking on student loan debt. As a result, the 32-year-old fool was unable to take advantage of President Biden's recent student loan forgiveness plan.

According to sources, Manfroy worked full-time as a telemarketer while taking night classes at the University of Baltimore where he studied Computer Science.

Working with Census microdata • tidycensus

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Working with Census microdata • tidycensus

Microdata is the individual-level responses to the ACS that is used to create the summary and detail tables the Census publishes. Instead of a getting one row per state from a table, we can get one row per respondent. For the American Community Survey, this data is called the Public Use Microdata Sample (PUMS).

 

Using PUMS data instead of the published tables can be a very powerful tool. It can, for instance, allow you to create custom estimates that aren’t available in pre-aggregated tables. You can also use microdata to fit models on individual-level data instead of modeling block groups or census tracts.