Linking month-to-month respondents

I am working with the CPS data from multiple years (2017-2021) and am trying to validate the sex, race, and age of respondents month-to-month. The respondent does not need to be in the sample for all 8 MISH. I am just trying to make sure I am comparing the same person from one month to the other (eg. January to February). This means that my data includes individuals who have a different number of completed MISH (some will only be in the panel twice, while others can be in it all 8 times).

I found the Validation Code for Long Files in the CPS Summer Workshop material and tried to use it for my validation. However, I noticed that the code really only works when I have data from a set number of months. For example, if I use my data and run the validation check for sex I end up with a sex_total_match variable with values ranging from 1-8. Since I do not have a set number of MISH I am observing, this means that any value 2 to 7 can be valid or invalid and so I would not be able to use the all_match code at the end of the do file. I found that if I used egen sex_total_match=min(sex_match), by(cpsidp) instead of “total” I end up with the correct variable. However, I was wondering if there was a better way to go about validating my month-to-month links when I do not have a set number of MISH I am observing.

The validation code we provide as part of the workshop requires that a respondent appears in all timepoints being linked. To use the code, you will need to specify the number of expected observations based on the linkage type you’re working with. For full-panel links, this number is 8. For adjacent-year links or adjacent-month links, this number is 2. This code assumes presence in the survey at all linked time points. If you’re interested in respondents that did not complete the panel and want to validate records with partial panel completion based on demographic variables, you will need to write your own validation code. With long formatted data, you might try something like this:

egen sex_min = min(sex), by(cpsidp)
egen sex_max = max(sex), by(cpsidp)
gen match = 0
replace match = 1 if sex_min = sex_max

Repeat the same for RACE. With AGE you will want to give a 1-year buffer for those under 84 and a 5-year buffer for those 84 and above. You can then generate an all_match variable that is equal to 1 if all of the variables match.