Error margins for 5-year ACS estimates

Hello:

I work for a news organization that wants to write about some of the 5-year ACS demographics, especially poverty and income, for local towns in our area. There are 340 municipalities that we cover. If we look at the main estimates for 2013-17, and compare the numbers with a non-overlapping time period such as 2006-10, we see some striking changes in income and poverty, but the margins of error give us pause.

We believe our readers would be interested in changes in income and poverty in their towns, and we can say that the top-line numbers are estimates, and are subject to margins of error. But let’s take a town in our region, Bellmawr, N.J., which has about 11,500 people. Poverty rose from 10 percent in 2006-10 to 20 percent in 2013-17, or an increase of 10 percentage points. With the error margins, however, that change could be as small as +2 percentage points to +19 on the high end.

So would you stay away from these numbers, publish them with the margins of error, aggregate towns into large sub-county geographies, or do something else? Of course, we would be doing on-the-ground reporting as well.


Thanks,

John

Parents
  • Just a couple of comments.
    1. Well, margins of error are usually larger for smaller entities, so unfortunately you are likely to get large MoEs with smaller towns.
    2. You wrote "aggregate towns into large sub-county geographies". Aren't towns and cities the largest subcounty geographies? I thought counties were divided into MCDs (minor civil divisions), which are towns and cities.
Reply
  • Just a couple of comments.
    1. Well, margins of error are usually larger for smaller entities, so unfortunately you are likely to get large MoEs with smaller towns.
    2. You wrote "aggregate towns into large sub-county geographies". Aren't towns and cities the largest subcounty geographies? I thought counties were divided into MCDs (minor civil divisions), which are towns and cities.
Children
  • Gene:

    On your second comment, I should have written "aggregate towns into CUSTOM sub-county geographies." I'm considering taking groups of MCDs and creating custom geographies, such as "Lower Bucks County" or "Gloucester County river towns."

    John
  • The first thing I would do is smooth the data for public consumption. Round median income to '00s (e.g. $76,300 rather than $76.342). Do NOT use tenths of percentages.

    I also agree with your idea of aggregating MCDs into useful larger geographic areas. It you can get them to areas of, say, 100K or more, the MOEs should go down considerably. It's useful to create such geography and use it consistently over a number of years, so the public gets used to it.

    Finally, I would NOT publish the MOEs. They'll just confuse the readers. MOEs are our responsibility to worry about and we can't shove that onto the end users of the data.

    Patty Becker