Hi ACS friends,
I'm trying to explain to some brilliant but non-ACS-knowledgeable coworkers and others why the recommended approach is to use *non-overlapping* periods when making comparisons over time. I've looked at the Census doc (https://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html) and all I can find is the guidance that says Do use non-overlapping datasets, Do not use overlapping datasets. I'm having a hard time finding the why. All I can come up with is this:
4-5ths of the sample is the same in adjacent datasets, e.g., 2014-2018 vs. 2015-2019. You can think of Census removing respondents from 2014, and adding respondents from 2019, but the respondents from 2015, 2016, 2017, and 2018 are the same. We should compare two completely different datasets, which means two non-overlapping periods.
I'm curious how others answer this question as well! -Diana
A colleague suggested looking at this paper (https://www.census.gov/library/working-papers/2012/adrm/rrs2012-03.html). There's a short discussion about non-overlapping periods in section 4.4 on page 9…
Here's how I'd put it to a non-expert: 5-year estimates exist because that's the time required to collect a sufficient number of survey responses (particularly in small areas like census tracts, ZCTAs, or small towns). The 5-year estimates in small areas generally already have high margins of error. Given how few responses there are in five years, looking at only one year's worth of responses would give you very unreliable data. And (as you articulated), when comparing overlapping estimates, you're effectively comparing only the non-overlapping years.For larger areas (states, CBSAs, big cities, big counties), you could just use the 1-year estimates; the reason you'd use the 5-year estimates is for their lower margins of error. By comparing overlapping years, you lose that advantage.
tl;dr: There's a reason they're 5-year estimates!