# Working with mixed PUMAs in 5-Year Data

**URL:** <https://forum.ipums.org/t/working-with-mixed-pumas-in-5-year-data/6188>\
**Category:** General\
**Created:** [November 12, 2024, 4:32pm UTC](https://forum.ipums.org/t/working-with-mixed-pumas-in-5-year-data/6188 "2024-11-12T16:32:14Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![Anthony\_Capote](https://avatars.discourse-cdn.com/v4/letter/a/ad7895/32.png) [@Anthony\_Capote](https://forum.ipums.org/u/Anthony_Capote)\
**Post date:** [November 12, 2024, 4:32pm UTC](https://forum.ipums.org/t/working-with-mixed-pumas-in-5-year-data/6188/1 "2024-11-12T16:32:14Z")

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I am working on some calculations for New York City — specifically around the number of non-citizens in NYC’s labor force — and using the 2022 5-Year ACS sample.

The problem I keep running into is that I cannot seem to get an accurate total count of people living in NYC because the PUMA codes changed from 2021 to 2022. No matter how I write out the R code, I keep getting a total sum of 7.4 million people living in New York, when I know I should see closer to 8.6 million.

Here is the code I have tried so far:

ACS\_NYC ← ACS2022[STATEFIP == 36 & PUMA \>= 3701 & PUMA \<= 4503] #This version just tries to catch all the possible PUMA codes across all the years

ACS\_NYC ← ACS2022[MULTYEAR \<= 2021 & STATEFIP == 36 & PUMA \>= 3701 & PUMA \<= 4114 | MULTYEAR == 2022 & STATEFIP == 36 & PUMA \>= 4103 & PUMA \<= 4503] #This version tries to sort out PUMAs base don the year the survey was taken

Both methods yield the same result. Any help you can offer is greatly appreciated. I have already run the same code with 1-year data from 2022, but I would like to use the 5-year data to assimilate this prohject with the rest of my work in other areas.

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**Author:** ![Isabel\_Pastoor](https://sea1.discourse-cdn.com/flex019/user_avatar/forum.ipums.org/isabel_pastoor/32/788_2.png) [@Isabel\_Pastoor](https://forum.ipums.org/u/Isabel_Pastoor)\
**Post date:** [November 14, 2024, 4:56pm UTC](https://forum.ipums.org/t/working-with-mixed-pumas-in-5-year-data/6188/2 "2024-11-14T16:56:42Z")

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PUMAs are population-based geographic areas that contain at least and approximately 100,000 residents. Since populations change over time, PUMAs also change over time. The 2010 PUMA definitions are used in the 2012-2021 ACS, while the 2020 PUMA definitions are used in the 2022-2031 ACS. The 2022 5-year ACS therefore includes data that use 2010 PUMA definitions, and data that use 2020 PUMA definitions. You’ll need to adjust your PUMA-based definition of New York City for the different PUMA definitions that are used in the 2022 5-year data.

Your second approach looks correct to me. When I define the NYC population as you did in your second approach, and sum the value of [PERWT](https://usa.ipums.org/usa-action/variables/PERWT#description_section) (the person level sampling weight) for all those who live in NYC in the 2022 5-year ACS, I get an estimated population of 8,615,432. Code review is beyond the scope of IPUMS User Support, but I suggest checking all of your code, paying careful attention to applying the [sampling weights](https://usa.ipums.org/usa-action/faq#ques8) and ensuring that you have used parentheses to apply or/and operators appropriately when accounting for the year-specific PUMAs.

Note that the population in a 5-year ACS sample is the estimated _average_ population across the 5-year period, and will differ from the estimated 5-year population in each of the 1-year data files individually.

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**Author:** ![Isabel\_Pastoor](https://sea1.discourse-cdn.com/flex019/user_avatar/forum.ipums.org/isabel_pastoor/32/788_2.png) [@Isabel\_Pastoor](https://forum.ipums.org/u/Isabel_Pastoor)\
**Post date:** [November 15, 2024, 3:07pm UTC](https://forum.ipums.org/t/working-with-mixed-pumas-in-5-year-data/6188/3 "2024-11-15T15:07:32Z")

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I want to add that you can also identify New York City using the IPUMS USA variable [CITY](https://usa.ipums.org/usa-action/variables/CITY#description_section). The CITY code for NYC is the same across all years, accounting for PUMA changes over time, reducing the work you would need to do to identify the city in a multi-year dataset. Also see the [CITY comparability tab](https://usa.ipums.org/usa-action/variables/CITY#comparability_section), where we provide crosswalks between PUMAs and large places, which could be helpful to confirm which PUMAs correspond to NYC.