How to define "percent males in management" from ACS5?

Sandra Tilmon over 2 years ago

Hello, I am recreating an index for my thesis, and the index used an ACS variable they describe as "percent males in management."

I see 'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER' with several management positions, from "Chief executives" to "Sales managers", etc.

How would you best recreate the definition of "percent males in management"?

The article in reference is: Messer LC, Laraia BA, Kaufman JS, et al. The development of a standardized neighborhood deprivation index. J Urban Health. 2006;83(6):1041-1062. doi:10.1007/s11524-006-9094-x

Many thanks,

Sandra

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Dave Stinchcomb over 2 years ago +2

Hi Sandra -

The original paper used Census 2000 data from the old long form survey. It is challenging to convert the occupational measures available with that survey to those available in ACS. You might…

Sandra Tilmon over 2 years ago

So far I am looking at

'B24114_002E', 'B24114_003E', 'B24114_005E', 'B24114_006E', 'B24114_007E', 'B24114_008E', 'B24114_009E', 'B24114_010E',

  'B24114_011E', 'B24114_012E', 'B24114_013E', 'B24114_014E', 'B24114_015E', 'B24114_016E', 'B24114_017E', 'B24114_018E', 'B24114_019E', 'B24114_020E',     'B24114_021E', 'B24114_022E', 'B24114_023E', 'B24114_024E', 'B24114_025E', 'B24114_026E', 'B24114_027E', 'B24114_028E', 'B24114_030E',   'B24114_031E', 'B24114_032E', 'B24114_033E', 'B24114_034E', 'B24114_035E'

with 'B24114_001E' as the denominator.

by census tract.

However, I'm coming up with "None" using the API for ACS5 for 2018 for Cook County, IL:

cookmgmt = censusdata.download('acs5', 2018, censusdata.censusgeo([('state', '17'), ('county', '031'), ('tract', '*')]),

                             ['B24114_001E', 'B24114_002E', 'B24114_003E', 'B24114_005E', 'B24114_006E', 'B24114_007E', 'B24114_008E', 'B24114_009E', 'B24114_010E',

                              'B24114_011E', 'B24114_012E', 'B24114_013E', 'B24114_014E', 'B24114_015E', 'B24114_016E', 'B24114_017E', 'B24114_018E', 'B24114_019E', 'B24114_020E',

                              'B24114_021E', 'B24114_022E', 'B24114_023E', 'B24114_024E', 'B24114_025E', 'B24114_026E', 'B24114_027E', 'B24114_028E', 'B24114_030E',

                              'B24114_031E', 'B24114_032E', 'B24114_033E', 'B24114_034E', 'B24114_035E'])
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Glenn Rice over 2 years ago

I don't have a direct answer for you, but you might try contacting one of the authors of this WSJ article with accompanying visualization:

https://graphics.wsj.com/gender-pay-gap/
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Glenn Rice over 2 years ago

You also might look at the Current Population Survey and Labor Force Characteristics from the BLS, e.g.

https://www.bls.gov/cps/lfcharacteristics.htm
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Dave Stinchcomb over 2 years ago

Hi Sandra -

The original paper used Census 2000 data from the old long form survey. It is challenging to convert the occupational measures available with that survey to those available in ACS. You might take a look at more recent papers that calculate a Krieger or Yost SES index. This 2014 paper by Yu et al. might be a good starting point:

Using a composite index of socioeconomic status to investigate health disparities while protecting the confidentiality of cancer registry data - PubMed (nih.gov)

I think they use table C24010 to compute a "percent working class" variable:

Working Class: ((C24010e20 + C24010e24 + C24010e25 + C24010e26 + C24010e27 + C24010e30 + C24010e34 + C24010e56 + C24010e60 + C24010e61 + C24010e62 + C24010e63 + C24010e66 + C24010e70)) / C24010e01) * 100

The flip side of this might be considered "percent white collar." You might select difference occupational categories for "percent management.
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