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STREAMS: A Benchmark of Naturalistic Streaming Data for Online Continual Learning

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American Community Survey (ACS) Employment

kawine opened this issue · comments

commented

Predict whether an individual is employed, after filtering the ACS PUMS data sample to only include individuals between the ages of 16 and 90. See https://github.com/zykls/folktables. Copy the existing ACSEmployment setup in that repo, but add the RELSHIPP feature in addition to RELP, since that represents a coding change that was overlooked in the repo.

X: coded features (see dictionary here)

  • 'AGEP': person age
  • 'SCHL': schooling
  • 'MAR': marital status
  • RELP: relationship to head-of-household (old)
  • RELSHIPP: relationship to head-of-household (new)
  • 'DIS': disabled
  • 'ESP': employment status of parents
  • 'CIT': citizenship status
  • 'MIG': mobility status
  • 'MIL': military service
  • 'ANC': ancestry records
  • 'NATIVITY': nativity or foreign-born
  • 'DEAR': hearing difficulty
  • 'DEYE': vision difficulty
  • 'DREM': cognitive difficulty
  • SEX: sex
  • RAC1P: race code

Y: ESR (employment status recode)

Domains:

  • State
  • Survey year (2014 – 2018)

What horizon? Person or household? https://github.com/zykls/folktables/blob/c4fc62bd357fcf19e2d5f4643f21aa931ef9e718/folktables/acs.py#L17

Also, should we be using race code as a domain / group? Survey year seems less interesting than race code (IMO)

cc @kawine