Assuming you are using the weighted Census and ACS data samples, one way you can accomplish this is by downloading the following variables and using the following commands (if you are using Stata). If you are using CPS data or another data set, then the variables and Stata commands may differ from what I have outlined below.

***Variables Needed for Census and ACS data

age

empstat

school

educ (select the detailed version of this variable "educd")

If you are using Stata, you can run the following commands to get what you are looking for:

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***This will generate a dummy variable that identifies people between 18 and 24

gen age_18_24 = age>=18 & age<=24

***This will generate a dummy variable that identifies people in the LF who are not working

gen not_work_but_in_lf = empstat==2

***This will generate a dummy variable that identifies people who are not in school

gen not_in_school = school==1

***This will generate a variable that identifies people who do not have any post-secondary schooling

gen no_ps = educd<=64

***This will generate a dummy variable for people in your sample who meet all your criteria

gen count = age_18_24==1 & no_work_but_in_lf==1 & not_in_school==1 & no_ps==1

***This will generate the population estimate for the individuals in your sample that meet all your criteria which uses the weighting variable "perwt" to make a weighted sum

collapse (sum)count [pweight = perwt]

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If you want to get these figures regionally or over time, you need to add a "by" command to the collapse command at the end of your code.

An example of this would be:

collapse (sum)count [pweight = perwt], by(year)

which will give you the count by year

or

collapse (sum)count [pweight = perwt], by(statefip)

which will give you the count by state

or

collapse (sum)count [pweight = perwt], by(statefip year)

which will give you the count by state and year

If you need any further guidance, you can email me at zjrutledge@ucdavis.edu and I will be happy to help.