The microdata is a sub-set of the ACS sample. As a subset, one would expect that it would not produce the same results as shown in the summary tables. However, I would at least expect it to not vary significantly and to be within the margins of error of the summary table. However, that is not what I am finding when I use the data.census.gov microdata tool. Is this an issue with the microdata tool since it is beta or something else causing the large discrepancies? For example, I was comparing the 2022 ACS 1-Year class of worker data for Virginia using summary table S2408 and a microdata query of the same data, and it shows significantly different numbers for each class of worker as you will see if you enter the following URL's.
S2408 Table: https://data.census.gov/table/ACSST1Y2022.S2408?q=class%20of%20worker%20Virginia
Microdata: https://data.census.gov/mdat/#/search?ds=ACSPUMS1Y2022&rv=COW&nv=ucgid&wt=PWGTP&g=0400000US51
I did not compare numbers. My only comment is on your microdata setup. I did not see you account for labor force status. The tabular data stipulates "employed". Your micro did not. Maybe try adding an employed labor force status element to your micro setup....?
Dear Jill,
You need to use PUMS variables ESR and COW in combination as follows:
Note the universe for S2408 is the Employed Civilian population 16 years old and over
# ESR: Employment status recode# b [0].N/A (less than 16 years old)# 1 .Civilian employed, at work# 2 .Civilian employed, with a job but not at work# 3 .Unemployed# 4 .Armed forces, at work# 5 .Armed forces, with a job but not at work# 6 .Not in labor force
# for S2408 Employed ESR== 1 or 2
# COW Character 1# Class of worker# b [0].Not in universe (less than 16 years old/NILF who last worked# .more than 5 years ago or never worked)# 1 .Employee of a private for-profit company or business, or of an# .individual, for wages, salary, or commissions# 2 .Employee of a private not-for-profit, tax-exempt, or# .charitable organization# 3 .Local government employee (city, county, etc.)# 4 .State government employee# 5 .Federal government employee# 6 .Self-employed in own not incorporated business, professional# .practice, or farm# 7 .Self-employed in own incorporated business, professional# .practice or farm# 8 .Working without pay in family business or farm# 9 .Unemployed
For S2408# COW == 2"Civilian employed population 16 years and over : Private not-for-profit wage and salary workers"# COW == 5"Civilian employed population 16 years and over : Federal government workers";# COW == 3"Civilian employed population 16 years and over : Local government workers";# COW == 1 or 7"Civilian employed population 16 years and over : Private for-profit wage and salary workers"
# state=51 (Virginia)
# 2022 1 year PUMS and subject tables## Deviation (MoE)# S2408# Local government workers 347176 (9635) PUMS=351504 (-347175)=4329# Federal government workers 354980 (9837) PUMS=358209 (-354980)=3229# Private not-for-profit wage and salary workers 359054 (10818) PUMS=359002 (-359054) = -52# Private For-profit wage and salary workers 2839546 (22977) PUMS=2840360 -2839546=814
You can review the other categories (e.g. state workers and family workers/self employed not incorporated
The difference between the PUMS estimate and S2408 value/estimate falls within the S2408 MoE
Best
Dave Dorer
Dave,
I am new to using the PUMS data. This is immensely helpful! The ESR is what I was unfamiliar with, but now I got it working and can move forward with additional analysis. I will be sure to keep an eye on the table universe going forward.
Thank You!
Jill Kaneff
I'm curious about what stat software you use. Also I don't have details on your project and the entity supporting your analysis. But I have a foundation that does pro bono consulting for 501(c) (3) and government entities. (dorerfoundation.org). Dave Dorer
I just "googled" you and it appears that you work for
NVRC which is a Commonwealth of Virginia entity and would be eligible to receive services from DCSF. I did some work using ACS data with Erik Beecroft at the Virginia JLARC.
Best Dave