The ACLU of Florida did a fantastic (and super data-heavy) study of racial and ethnic disparities in the Miami criminal justice system called Unequal Treatment. It’s amazing and you should check it out. The study reminded me that DCF publishes its own statistics on race, but they are buried in the Trend Report excel graveyard. This weekend I decided to dig them up for folks to see.
All of the diagrams in this post are in tableaus here:
The analysis is based on data from May 2017 to April 2018.
The gist: DCF’s out-of-home care population is racially disparate. You start with the hypothesis that child abuse is equally likely across all racial populations and the system will treat everyone the same, therefore the OOHC population will mostly look like the general population. It doesn’t. Black kids are over-represented by 33.1% in OOHC. So-call “Other” kids (which are mostly mixed race and Asian kids) are over-represented by 37.4%. White kids, on the other hand, are under-represented by 15.5%. If you divide those numbers to get the ratio, you get approximately 1.59. This means non-white kids are 1.59x represented over white kids.
The differences aren’t uniform across the state. So your next hypothesis might be that whatever is causing the differences would be systemic across the state. It’s not. Racial disparity in OOHC varies greatly among the counties, with some even having a bias towards White kids. The map below shows the disparity index (i.e., the ratio of non-white to white bias in the system). Orange counties have a Non-white bias. Blue counties have a White-bias. (Counties with no statistically significant difference are shaded a neutral taupe color.)
What does a White-bias county look like? Dixie County has the out-of-home care numbers most biased toward White kids (it’s the dark blue county in the map above). The county has approximately 16,000 people, skews slightly Democrat, and has about 14.5% of its population below the poverty line. It is 77% rural and approximately 9.0% Black. It is the third-whitest county in Florida. Based on the race demographics, you would naively expect about four Black kids and 48 White kids in its OOHC population. What you get is 0 Black kids and 51 White. It’s not huge, but it is statistically significant. Compare the next example to see why.
What does a Non-white bias county look like? Miami. Miami is obviously huge and Latin — it has 2.7M people, and is 65% Hispanic (any race). It is 17.1% Black (non-Hispanic) and 15.4% White (non-Hispanic). About 51% of its population was foreign-born. It voted 63% Democratic in the 2016 elections. It’s racial disparity is extreme: Non-white kids are over-represented by 140%, while White kids are under-represented by 45%. You would expected about 1,400 white kids in foster care in Miami — you get around 775. Meanwhile, you would expect 435 Black kids, and you find about 1,050. The racial disparity index is 4.25.
Racial disparity generally increases the deeper into the system you get. Your next hypothesis may be that once kids are in the system they are treated by the same rules and same players, and should therefore have similar outcomes. No again. DCF breaks its numbers down by the stage of a case: Investigation, Verification, Removal, OOHC, Spending more than 12 months in OOHC, and Discharge from care. Racial disparity tends to rise the farther into a case you get.
The disparity index numbers go something like this. Remember that a positive number means that Non-white kids are represented that many times more than White kids. A negative number is biased towards White kids. A (*) indicates no statistically significant value.
If you look at the Statewide column, you can see that Investigations have a stronger bias than Verifications. Once a child is in care, Discharges tend to be less racially biased than Removals, which actually increases OOHC and 12+ bias over time. The pattern is on steroids for Miami where non-White kids are 4.44x more represented in the 12+ population than White kids.
What about placements? If the process itself has racial bias in it, then it may be safe to bet that placements have a similar bias. This time we assume that the breakdown of kids in a given placement type will be the same as the general OOHC numbers. It’s not. Statewide, Non-white kids are over-represented in the Runaway, Facility, and Other populations, while White kids are slightly over-represented in the Relative and Foster Care populations. The non-Relative caregiver placement did not show any statistically significant differences, possibly because it’s a smaller population and therefore requires more difference to be significant.
The expected vs. actual values for Facility placements look like this.
Breaking the data down by county makes it harder to find statistically significant values. For example, only eight counties show significant differences in their facility placement numbers.
Four counties had significant disparities in their foster home placements, and three of those were White-biased.
This isn’t to say that the other counties are perfectly balanced. When we parse the numbers down to the tiny levels of “the four kids on runaway in Dixie county” then differences have to be more pronounced to distinguish a real difference from just random noise and the techniques I’m using here aren’t very good at small numbers. This data says “we can’t see a difference with the tools we’re using,” not “there is no difference.”
We can’t tell why from this data. This is also important: this type of observational data does not show causation or even hint at underlying causes. A lot of writing has been done on systemic racism in the child welfare system, and the expert consensus is that the disproportionalities we see here are a consequence of (1) interplay between poverty and race at the individual and community level, (2) heightened governmental surveillance and intervention in non-white communities (like the ACLU report highlights), and (3) personal bias in individual decision-makers (for example the family that only wants to adopt a child of their own race or the judge who is less likely to approve the removal of a child of their own race).
Even if these effects may be undetectable in an individual case (or, more likely, they’re one of a hundred other things going on in a case), when you multiply them across tens-of-thousands of kids and decades, you can start seeing the cumulative impact. You only have to remove one more kid than you discharge each month to grow a population over time. If racial factors increase removals and suppress discharges even marginally, that can explode into real differences that must be addressed. For a full discussion see Shattered Bonds: The Color of Child Welfare by Dorothy Roberts.
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