Hi all,
New here! I've been lurking for a couple of months as I try to get myself familiarized with the quirks of working with ACS data. But I have run into a problem with some perplexing estimates in some key variables used in my research, and I am hoping someone here might have some insight.
Specifically, my research team and I are interested in the impact of county-level, race-specific characteristics on county child welfare performance indicators. Race-specific unemployment rates are expected to explain some of the variability in county performance. The problem we are running into is that the rates drawn from S2301 contain a substantial percentage of extreme estimates for county-level black rates (0s and, to a far lesser extent, 100s). See the first table below. I'm guessing this is a coverage issue, as 90% of the cases have margins of error above 23%.
I suppose my question here is three-fold:
- Am I correct to assume that many of these extreme values are due to limited coverage?
- If so, are there established best practices when using the ACS estimates to distinguish between valid and problematic estimates? I want to avoid recoding valid 0's as missing just as much as I want to avoid including problematic estimates.
- If the ACS data are simply not ideal for obtaining race-specific estimates, does anyone happen to know if there other data sources that would provide county level estimates by race?
Thanks so much for your help (and your patience!).
~ Miranda
black_unemprate — black unemployment rate (age 16+) (s2301_c04_011e)
| Valid Obs | Freq. | Percent | Valid | ***. | | 0 | 2306 | 17.86 | 19.86 | 19.86 | | .2 | 1 | 0.01 | 0.01 | 19.87 | | .3 | 3 | 0.02 | 0.03 | 19.90 | | : | : | : | : | : | | 98.2 | 1 | 0.01 | 0.01 | 98.31 | | 100 | 196 | 1.52 | 1.69 | 100.00 | | Total | 11609 | 89.89 | 100.00 | | | Missing | | | | | | . | 1306 | 10.11 | | | | Total | 12915 | 100.00 | | | |
Example of Raw Data
| year | state | county | name | pop16plus_uer | pop16plus_m | white_uer | white_m | black_uer | black_m | hisp_uer | hisp_m | nhwhite_uer | nhwhite_m |
| 2011 | 38 | 101 | Ward County, North Dakota | 2.9 | 0.7 | 2.6 | 0.7 | 0 | 4.2 | 10.3 | 9.4 | 2.5 | 0.7 |
| 2011 | 51 | 113 | Madison County, Virginia | 5.9 | 2.2 | 6.9 | 2.5 | 0 | 4.9 | 0 | 24.1 | 6.9 | 2.5 |
| 2012 | 27 | 105 | Nobles County, Minnesota | 5.8 | 1.4 | 5.9 | 1.6 | 0 | 4.9 | 14.2 | 5.4 | 3.9 | 1 |
| 2011 | 27 | 105 | Nobles County, Minnesota | 4.6 | 1.1 | 4.4 | 1.1 | 0 | 5.8 | 10.4 | 4.9 | 3.2 | 0.9 |
| 2014 | 8 | 37 | Eagle County, Colorado | 5 | 1.4 | 4.7 | 1.4 | 0 | 6 | 8.3 | 3.4 | 3.9 | 1.4 |
| 2011 | 21 | 173 | Montgomery County, Kentucky | 8.8 | 1.8 | 9.1 | 1.9 | 0 | 6.4 | 0 | 15.3 | 9.2 | 1.9 |
| 2012 | 40 | 137 | Stephens County, Oklahoma | 7.3 | 1.2 | 7.5 | 1.4 | 0 | 6.6 | 6.3 | 4.7 | 7.5 | 1.4 |
| 2013 | 48 | 435 | Sutton County, Texas | 1.9 | 1.5 | 2.1 | 1.6 | 0 | 66.4 | 0 | 3.3 | 5.1 | 3.9 |
| 2011 | 19 | 59 | Dickinson County, Iowa | 4.8 | 1.4 | 5 | 1.4 | 0 | 15.3 | 0 | 26.6 | 5 | 1.4 |
| 2013 | 21 | 203 | Rockcastle County, Kentucky | 12 | 2.5 | 11.9 | 2.4 | 0 | 100 | 18.2 | 51.6 | 11.9 | 2.4 |
| 2011 | 31 | 67 | Gage County, Nebraska | 6.7 | 1.6 | 6.5 | 1.6 | 0 | 100 | 32.3 | 13.8 | 6.2 | 1.6 |
| 2011 | 27 | 41 | Douglas County, Minnesota | 5.6 | 0.9 | 5.3 | 0.8 | 0 | 58.2 | 0.6 | 1.8 | 5.3 | 0.8 |
| 2012 | 56 | 33 | Sheridan County, Wyoming | 3.5 | 1 | 3.3 | 1 | 0 | 61.8 | 7.1 | 7.3 | 3.3 | 1 |