The ACS has provided estimates that are consistent with the long form, but on a much more frequent basis. Every year the Census Bureau surveys a little more than 1% of the US population. 1% sounds pretty small right? Actually, we are talking about 1% of roughly 323 million people (You can check the US and World population clock here). So, The Census Bureau is surveying about 3.5 million United States housing units every single year. This is great news, because the ACS is used for all sorts of decision making. Federal agencies use the ACS to allocate federal funding. The Census Bureau estimates that the ACS is used to allocate about $400 million per year. Many private-sector organizations use ACS for marketing their products (likely generating much more than $400 million in revenue). Researchers use the ACS to evaluate the impact of governmental policies at all levels of government. While surveying 3.5 million housing units per year is great for many purposes, at certain geographical scales there are too few people being surveyed to fully understand the impacts of governmental or private-sector decisions. It might be too large a stretch to make reference to the 3-year estimates as being the Goldilocks of ACS data, but the next several sections outline why the 3-year estimates are quite important.
Size of Government
To provide for more accurate data (smaller confidence intervals if you want to nerd out), the United States Census Bureau takes 1-year, 3-year, and 5-year averages of the ACS survey. An easy way to think about this is that a 1-year average is great if you want to make estimates for governments with 65,000 or more people. Okay, so this could be everything from Southfield (73,001 in 2014) to Detroit (680,281 in 2014). A 5-year estimate is great if you want to make inferences about smaller cities with a population of 20,000 or fewer. This could include such Michigan governments as small as Alpha Village(83 in 2014) or as large as Ypsilanti (19,844 in 2014). The 3-year estimates are perfect for medium-sized communities with populations between 20,000 and 65,000. Examples of this size government include Birmingham city (20,368 in 2013) and Taylor city (62,232 in 2013). It should also be noted that many counties across the United States fall precisely within this population range.
While thinking about the appropriate size of the community is the most obvious way to consider the benefits of the 3-year and 5-year estimates, one should also consider the time frame. So, assuming you weren’t living under a rock for a few years, you probably heard (or felt economically) the great recession that hit the United States and Detroit particularly hard. If we want to understand what happened in this period relative to the pre- and post- recession among various communities, the ACS is a great source. However, if we use the 5-year ACS (2007-2011; 2008-2012; 2009-2013) we combine vastly different periods. As noted by RLS Demographics, if we have 3-year estimates we could compare the pre- recession (2005-2007) to the recession (2008-2010), and compare both to the post-recession period (2010-2013). This type of comparison is especially important if we are adopting policies to ameliorate the effects of the recession, and also want to know if they worked. The point is that we need not think about these 3-year estimates as just being appropriate to different-size cities. They are very important when considering the inclusion of different time points.
As researchers studying subpopulations (by gender, race, ethnicity, age, etc.), or politicians making decisions that will impact small communities, within small- to mid-sized geographies, the 1-year ACS will provide extremely large confidence intervals. In other words, estimates become unreliable. It’s like me telling you that there is a 51% chance I will make a basket- but I could be off by 25% in either direction. Therefore, the real likelihood of me making the shot will fall somewhere between 26% and 76%. Still, your odds, on average, of betting on me in this fictitious scenario are better than you will get at Motor City Casino, but understand that a good proportion of the time you will be wrong! Comparing Table 1 and 2 explains the importance of the three year estimates on subpopulations.
Table 1: Comparing Large and Small Counties Total Population (1- and 3-year estimates)
When we look at Table 1 and compare the labor force population for the 1-year and 3-year estimates between a large county (Wayne County) and a medium-sized county (Marquette County), there seems to be little difference in the estimates. This could suggest that we are fine with simply using the 1-year estimates. However, the real issue comes in when we analyze subpopulations (Table 2).
Table 2: Comparing Large and Small Counties Subpopulation (1- and 3-year estimates)
Table 2 makes clearer why the 3-year estimates are important when considering what happens to subpopulations. We have a significantly different sense of the labor market in 2012 when comparing the 1-year and 3-year ACS in Marquette County. Even the poverty levels in a large county like Wayne are off by 3.5%.
Great, now that we all agree (implicit because you are still reading) that we need the 1-year ACS estimate for very timely results with larger populations; the 3-year estimates for moderate-sized geographies, reasonable time-frames, and for more precise subpopulation results; and the 5-year estimates for much smaller geographies and more refined subpopulation analyses, I am here to let you know we have a PROBLEM! In 2015, the United States Congress cut the Census Bureau’s budget by $2.4 million. As a result of this cut, Census discontinued the 3-year ACS estimates.
So Why Discontinue the 3-year ACS?
It’s certainly not entirely the fault of the Census Bureau; the political attack on the ACS could be fodder for several blog posts. The irony in these attacks runs deep once one realizes that the efforts to cut the ACS have been levied by members of Congress who rely indirectly and often directly on the data for their own personal and professional uses.
Well, the most obvious answer is that it will help with the budget shortfall (here is a copy of the FY2016 budget proposal). One justification that has been given by the Bureau is that the 3-year estimates were only meant to be temporary. However, as the RLS Demographics group makes clear, if this is true it was never mentioned to the communities that have become dependent on these estimates.
A Parting Thought and a Revealing Graphic
It’s true that losing the 3-year estimates is not nearly as bad as when several members of Congress were calling for the discontinuation of the ACS in general, which would have effectively left us with no precise estimates of the current demographic and socio-economic standing of communities across the United States. Arguably, losing the 1-year or 5-year estimates would be significantly worse. Thus, while we try to see the glass as half full (by averaging the glass when it is about 1/5th full and when it is about 4/5ths full), losing the 3-year ACS does really limit our ability to derive reliable estimates for mid-size cities, subpopulations, and within useful and reasonable time-frames.
What To Do Now?
Best case scenario… Run for Congress. Win the seat. Become a ranking member and be placed on the Appropriations Committee. Pass a bill with broad bi-partisan support in both chambers that is quickly signed by the President that reinstitutes the 3-year estimates. Ensure that the Census Bureau has ample resources to carry this out in a timely manner.
Worst case scenario… Contact the Census Bureau and your member of Congress and let them know that you think the 3-year estimates are important. Then, share this blog post with your friends, family, colleagues, neighbors, and anyone who will listen through every medium possible (snail mail with the website URL at the head of the letter, call someone’s pager from a landline and let them know to check out the blog post, send the link by email, post the link on Facebook, write a short Twitter feed, or take a picture of the site and post it on Instagram). Whatever the case, spread the word!