This post introduces a new public FSFN dashboard on permanency timing in Florida’s child welfare system. If you want to just play with the dashboard, you can find it here. All but one of the graphics in this post come from the dashboard.

Every year, in legislatures across the country, well-meaning people propose bills to speed up permanency for foster kids. Permanency is a psychological concept focused on attachment, belonging, and community. Those are hard to legislate, so people focus instead on procedural definitions. The legal meaning of permanency is to close the court case and get the state out of a family’s life for as long as possible. In the process, hopefully leaving the child better than the system found them.

That closure could happen by returning a child home to a parent, placing the child in a guardianship, or having the child adopted. It could also mean a child aging out. The end result is largely the same to the state: one less case on its docket, and varying ongoing financial obligations depending on the way the child exited care. The path a case takes can change a child’s life.

Current law prefers the two most labor-intensive forms of permanency (reunification and adoption) over the procedurally easier ones (guardianship and aging out). Current law also sets timeframes and conditions on when certain permanency goals can be achieved: reunification should be accomplished in 12 months, guardianships cannot be entered until a child has lived in a placement for 6 months, APPLA (the “plan to age out” goal) should be accepted after age 16, and adoptions require the termination of a parent’s rights and appeal periods, which — on paper at least — would require about 18 months.

The question for this post is not what the right policies should be. I want to know how things are operating under the current policies. How are we doing? Using the public Florida Safe Families Network placement database, we can trace the permanency path of individual kids down to the day.

Part #1: How kids exit the system

The chart below shows the approximately 184,000 children who exited foster care in Florida from January 2008 to April 2019. I’ve consolidated some of the categories for easier reading. You can see that around 49% of children were reunified, while 22% entered guardianships, 22% were adopted, and 7% aged out. The remaining <1% had their cases dismissed, administratively transfered or closed, or they passed away.

A handful of cases had no exit code marked and are omitted here.

Slightly over 21,000 kids (13%) statewide exited and reentered care, but only 2,900 kids (2%) did so more than once and only 405 (<1%) did so more than twice. Of the failed parental reunifications, approximately 35% successfully reunified later. Of the failed adoptions, the number was the same: 35% were later reunified with the adoptive family successfully.

In the next chart, you can see the final placement setting for each exit type (e.g., all children who were reunified out of a relative placement or who aged out from a group home). Reunifications and adoptions happen almost evenly between relatives and non-relative; guardianships come almost entirely from relative placements. Very few kids age out of a relative placement.

There’s another way to look at this data. You can stack the boxes by the type of custodian the child exited to and see the actual face of foster care: relatives. Non-relatives and foster parents, while important for every child who needs them, only account for a fraction of the total exits. The vast majority — nearly 80% — of kids went back home to their parents or to relatives. Slightly under 11% of kids were adopted by non-relatives. That’s only four points higher than the 7% who aged out. Foster care is temporary care. Forget all the saving children rhetoric — the real slogan of DCF should be “We’re here when you need us. But we really hope you don’t.”

Part #2: When kids exit the system

With the exception of aging out and death, a child’s exit from foster care happens via judicial action, usually at a court hearing. The timing of those hearings therefore matters. Using the placement database, we can look at the permanency question differently and calculate exactly when each child exited the system down to the exact number of days they had been in out-of-home care. For simplicity, we’ll look at months instead.

The chart below shows the number of kids exiting the system by the number of months they spent in care. I calculate the month by dividing the number of days in care by 30 and rounding down to the nearest whole number. That means Month 0 is 0 to 29 days, Month 1 is 30 to 59 days, etc.

If you know the dependency procedure timeline, this shape makes perfect sense:

• Cases should go to trial in the first four months, but most parents take pleas at their arraignments, which is 28 days after the removal. That means a sizeable percentage of kids exit care before the time has passed for a trial in month 3. In fact, the highest single month for exits is the first month of a case, Month 0. You might wonder if those kids needed to be removed in the first place.
• The first judicial review must happen within 3 months of the disposition or within 6 months of the shelter. The curve spikes sharply in Month 6 — presumably at that first judicial review. A quarter of kids exited by the first JR at month 6.
• The permanency hearing must be heard within twelve months of the shelter and there’s another spike. Half of kids exited care by the end of the twelfth month.
• The rest is an exponential decay curve as cases close out with no set timelines in the second year. Almost all, 95% of children, exited care by month 50.

To make more sense of the exit chart, we can break it down by exit type. The chart below shows when exits occurred for Reunifications, Guardianships, Adoptions, and Age Outs, Other, and Deaths. The patterns are very distinctive. This is the chart that made me do this whole post.

Let’s start with Reunifications (the blue-green-ish line). Reunifications were at their highest monthly rate at the beginning of cases. There could be lots of reasons for this. It could be that case management and parents were able to help remedy the circumstances that necessitated the removal where the CPIs did not (or could not in the time required for an investigation). It could be that lawyers or judges looked at the facts and decided that a removal was not legally justified. Whatever the cause, 25% of reunifications occurred in those 3 months.

The Reunification line flattens out around Month 3 and hovers through the case plan phase until Month 12. It then enters a period of exponential decay. About 25% of reunifications took longer than 12 months. There were over 26,000 children reunified after 12 months in care from 2008 to 2019.

The red Guardianship curve is similar but different in important ways. By statute, a case cannot close in guardianship until a child has been placed with that custodian for 6 months (meaning some of those placements may not be strictly legal). Not surprisingly, the chart above shows that guardianships spike to their highest point in Month 6. They bump up again slightly in Month 12, and then enter a period of exponential decay similar to Reunifications. Over 20,000 kids entered guardianships after being in care for a year.

Next, the blue Adoption line is different from either of the previous two. It is one of the few bell curves in child welfare: adoptions peaked in month 24 and were spread out evenly around that point. Adoptions overtook reunifications and guardianships as the primary exit type around Month 17.

Why so late? Because, unlike guardianships, adoptions require termination of parental rights. Unless a parent surrenders or defaults at trial, that means an entire failed dependency proceeding (approximately one year), an entire successful TPR proceeding (approximately six months), and then an entire adoption proceeding (which can also take months, especially if there are competing adoptions or complex subsidy issues). Insert appeals between all of those, and the curve makes perfect sense. Adoptions have a lot of moving parts.

I’m saving the Administrative Closures and Deaths for another day. That leaves the orange Age-out curve that is always present beneath the others. Kids whose parents cannot get them back, whose relatives will not take them, and who do not fit the profile that strangers want to adopt sit in care until they exit naturally. These kids experience the most placement instability of all of the groups. Some of them were orphaned by TPRs that never led to permanency. Others spent significant time in institutions, medical programs, or jail.

There is one more group worth talking about that is not separated on the graph by line-color but by time. That long tail of kids that reached permanency after four years is notable. There are reunifications, adoptions, and guardianships all through that period. They are proof that we should never give up completely on any of the goals.

Part #3: The system is changing over time

Most permanency tweaking is aimed at speeding up adoptions, the most procedurally complex of the exits. That’s a hard curve to move. One, adoptions have a lot of variability, so it’s hard to even know how to speed them up across the board. Two, they also have a lot of interdependence with the other goals: it’s difficult to know how putting more resources and emphasis toward adoption would affect the other, larger exit types. And lastly, even if we somehow halved the time to adoption for all kids from a median of 24 to 12 months, that would only reduce the system-wide time to permanency by about 3 months. There’s more variation than that between years, placement types, and agencies.

In fact, inside the decade average hides a different trend: permanency time has been going up across the board. The chart below shows the median number of months to each type of exit by the discharge year. The out-of-home care population size is in the background for context. Adoptions were at their fastest in 2012 at 22 months — just when the system was regearing for an expansion. All three exit types have been slowing since 2014 as the system grew larger. Overall, the median time to exit is 2 months slower than 2015.

Part #4: It matters who adopts

There is another way to speed up adoptions: find a relative. The charts below show the curves for adoptions by relatives and non-relatives. Adoptions by relatives are, on average, a staggering 6.7 months faster. That is, however, largely because relatives tend to adopt in the first 4 years or never. New non-relatives come into a child’s life all the time, and eventually one clicks, pushing the blue line out farther to the right.

Limiting the curve to the first 48 months (below), the Relatives still adopt about 3 months faster than the Non-Relatives.

It’s notable that the relative curve (above) is heavier at the front end. You can see it more clearly in the next graph, which shows the Relative to Non-Relative ratio over the months of a case. Relatives outpace Non-Relatives by up to 5x in the first months of a case. Why? Probably surrenders.

After 24 months, however, Non-Relatives begin to outpace Relatives. These are mostly failed case plan and contested TPR cases. Remember that only 11% of cases ended in a Non-Relative adoption and half of those happen in under 24 months. That means we’re looking at 5% of all cases. We could do a lot more to prepare parents, children, and custodians to work together in these situations.

There are slight differences even among the Relative adoptions. I’m not showing the graphs here, but grandparents adopt, on average, 1.5 to 2 months faster than aunts, uncles, or cousins. (The other family relations don’t show any significant differences.) Grandparents adopt a whopping 8.6 months faster than non-relatives — again in part because they adopt in the first few years or not at all.

Part #5: It also matters who takes custody for reunifications

Placement setting made a difference in reunifications as well. The median time to exit was slightly faster for non-relatives over relatives — 7 months, instead of 8 for relative placements. Institutions exited kids much faster, likely because institutions are time-limited placements, kids in them tend to be older, and there may be behavioral or other issues that make non-relatives less willing to accept the child. What was different was the shape of the curves indicating when those exits happened.

The chart below shows reunifications from each of the placement settings. (This only considers the child’s last placement setting — the child could have been in multiple other placements before exiting care.) You can see the so-called “Short Stays” in the blue line: over 6,000 kids were reunified from foster care in under 30 days. A smaller, but notable, effect is seen in group homes and institutions. Relatives, on the other hand, do not show the same sharp short-stay pattern.

What the relatives and non-relative curves share is the drop-off at Month 12. This is likely a result of the permanency hearing where judges are asked to make a major decision on the case: attempt reunification or go to TPR. The same drop isn’t seen in group homes and institutions, suggesting those (usually older) kids aren’t presenting the same choices. They may already be in APPLA.

Part #6: There are also differences in the CBCs (and circuits)

The database tells us which lead agency made each placement, so we can also tell differences in how CBCs exit kids from care. It’s important to note that courts are the ultimate deciders of when kids exit care. The database doesn’t tell us which judge is assigned to a case, so we are left with comparisons by CBC.

The first difference was surprising to me: different CBCs have drastically different rates of reunification and adoption. The chart below plots each CBC’s rate of reunification against its rate of adoption. The linear relationship is expected: a higher percentage of one means a lower percentage of the other. (Guardianships and aging out aren’t included here, so they account for some of the other differences).

What struck me was the significant difference in how kids exit each agency. Agencies in the top-left quadrant use adoptions at higher rates: over 28% of their exits were to adoption, and under 42% of their exits were to reunification. The lowest reunification rate was nearly 30%, and the highest adoption rate was over one third. These agencies tend to be a little smaller and in the northern regions of the state (though not exclusively). In contrast, agencies in the bottom-right quadrant use reunifications at higher rates.

The only (active) agency squarely in the top-right is Families First Network. It appears to be using adoption and reunification at higher than average rates (at the expense of guardianships and aging out). Essentially no agency is found in the bottom left quadrant.

We might expect CBCs that focus more on adoptions to have slower median times to permanency. We’ll see they don’t.

The next chart breaks down the CBCs by size (throughput, actually — the number of children who exited care during the time period) and median months to permanency. There’s no direct correlation. CBCs can be any combination of big, small, fast, or slow. Eckerd runs two big agencies in neighboring counties and one is much faster than the other. ChildNet in Broward is two months slower than its sibling ChildNet Palm Beach.

It is also notable that smaller does not always mean faster, but bigger almost always means slower.

The next chart looks at how adoption rates relate to the median time to exit. There is no significant linear correlation. Instead, it’s a parabola. Big agencies tend to be in the middle, and smaller agencies tend to take the extremes. Those extremes are Family Support Services, which handles a high percentage of adoptions very quickly, and CBC of Brevard, which handles fewer adoptions but still moves cases quickly.

Two agencies stand out as extremes on the chart above in terms of size to adoption ratio: Family Support Services in the bottom right and ChildNet (Broward) in the top left. How can FSS be so fast and do so many adoptions, while ChildNet (Broward) is so slow and does so few? We can look at their curves for a clue.

The chart below shows FSS’s adoption curves. The orange line specifically shows adoptions from relative placements. The far left of it looks more like the guardianship line at other agencies. FSS (or its courts system) is somehow getting relatives to adopt in the first six months of cases. That means parents are surrendering without even doing a full case plan. The first judicial review is at 6 months and full adoptions are happening at significant rates before that. The median number of months for a relative adoption by FSS is 14 months compared to 24 months statewide. That’s insanely fast. I’m not convinced it’s a good thing. (Note: the trend seems to have ended after 2015, which suggests a policy or personnel change.)

On the other hand, ChildNet (Broward), has a fairly normal time for relative adoptions at 24 months. It’s the non-relative adoptions that take longer: 29 months. ChildNet’s non-relative reunifications also take longer, so there appears to be something going on with foster placements — either at ChildNet or in the judiciary. The institution and group home numbers are also high, so it might be worth exploring whether the use of congregate placementis is contributing to delayed permanency.

The median adoption times across the state are very, very different. Below is a map of adoptions by county of last placement. You can see the streak of green counties starting in Duval and working down to Levy. Maybe someone who works in those areas can explain it, but those are the fastest adoption counties over the last decade.

Meanwhile, reunifications also show significant differences. The median time to reunify can take up to 50% longer in adjacent counties. For example the median time in Lake County is 5 months, while in Polk it’s 8 months. Two different CBCs and court circuits, very different experiences.

Part #7: What does it all mean?

I started by saying that this post isn’t about what we should do, and it isn’t. There are still some policy nuggets worth mining in the numbers. Here are the top five.

1. It’s not good that we don’t have a statewide advocacy group for parents and relatives. They make up 81% of the exits in foster care, and their concerns for the kids and the system are valid. Parents get attorneys in their cases, but that’s not the same as an advocacy group to shape policy. Why aren’t they being organized and heard?
2. A year is a long time in the life of a child? Not if the alternative is needlessly losing their parents and family forever. There are very few child welfare slogans that rile me up like that one, because it is almost always uttered before offering some policy change that would risk cutting off the 30,000 kids who got reunified or placed in a guardianship after 12 months. It’s also misleading: it lumps all kids together — some want to go home and are willing to wait, others aren’t; and it doesn’t recognize that different cases have different barriers and different communities have different resources. We should spend more time examining the differences around Florida that lead to vastly different permanency times before we set increasingly restrictive statewide mandates on parents who are not given even playing fields.
3. There are lots of kids we could probably avoid removing if we tried. The idea of short-stayers is nothing new. We see above that over 6,000 kids were removed to foster care and returned in under 30 days in Florida. That seems like needless trauma. This is worth talking more about.
4. Adoptions are actually pretty okay. I’m comfortable with the fact that adoptions are a bell curve and that they take about 2 years. Those kids are generally really young and in super-stable, loving homes. We can continue to speed up the paperwork and administrative delays on the post-TPR backend without robbing parents of the chance to reunify at the front. We could also speed up some cases by closing them in Permanent Guardianships with visitation rights instead.
5. We need a real plan in the second year. On the other hand, I’m not comfortable with the fact that reunifications and guardianships just fizzle out in the second year. This year a bill was proposed that had language requiring permanency hearings every 60 days after the first year. (The language got dropped later.) It didn’t require any programmatic changes, resource investments or even evaluations to determine what steps were needed to help a parent be successful. It would have just forced everyone into court every two months until the judge got tired and set the case for TPR. I get the motivation, but we need a meaningful Year Two Plan.

This post is already too long. There are a lot of conversations we can have around this type of information, especially about the differences that families experience by geography. All of the diagrams and charts in it are from the Plotting Permanency Tableau. You can filter down to the CBC and county level of many of the graphs. As always, if you find any errors or something that doesn’t look right, please let me know.

A video of a child being forcibly removed from his mother has been in the news lately. It’s brutal to watch. A group of police officers and security guards yank at the one-year-old while another swings a taser wildly around the room at anyone who gets too close. The woman is on the floor. Her sin is apparently trespassing, i.e,. sitting on the floor instead of standing when there were no seats available in the four-hour line. The charges are later dropped because she was trespassing at a government office (to extend daycare for her child) where people are actually allowed to be, and people sit on the floor in airports all the time and nobody rips their kids from them. The harm’s already done to the child, though. Nobody in the crowd intervenes — they don’t want to get arrested, shot, or lose their place in line — but they document it for the world to see.

(For added absurdity and likely thanks to the word “baby” in the title, the video I watched was preambled with an ad for Zales’s Enchanted Disney collaboration wherein a rich-looking white lady finds a diamond ring on a table, puts it on, and imagines she’s become a Disney Princess®. She has no idea whose ring it even is, but it’s hers now (hey she deserves it). Nobody arrests her for theft and wrestles her baby out of her arms. She does not spend a few nights in jail for having the audacity of self-worth. She, in fact, lives happily ever after.)

Children are removed from their parents every day, and it frequently looks just like that video. It is traumatic for everyone (especially the child) and is supposed to be only for very exigent reasons. In Florida, we know that anywhere from 1,000 to 1,500 kids enter the foster care system each month. The video got me thinking about when those removals happen.

I had a lot of assumptions. I’ve heard that removals go up around the holidays, and that they go down in the summers when kids are home. I felt sure that fewer removals would happen on the weekends, but also that there should be no reason for that because kids would be in more danger when not in school. Removals are supposed to happen when they are unavoidable, not when they are convenient to the investigator.

And I thought: how many kids are actually removed on Christmas? That would be horrible. 

As part of a project we are working on, our office came into possession of the entire Florida DCF placement database (anonymized and unforgivably massive). This database includes the removal dates and details of 280,839 kids going back to some who entered care in the 1980s. Looking only at the removals from January 1, 2007 to December 31, 2017, we have data on 156,357 kids. Some of those kids came into care multiple times, so the time period covers 181,799 removals. (Caveat: as with all real-world records, the data is only as good as it is. Given the large numbers here, it is reasonable to assume that errors are evenly spread out and not biased in any given direction.)

How many of those removals were on a holiday?

Since we have the dates for all the removals, it isn’t hard to count them. On a normal, non-holiday day, the average number of removals is about 46. For Christmas, the average is six. For Thanksgiving, it’s eight. Only 67 kids were removed on Christmas Day in Florida from 2007-2017.

There are a few takeaways there. First, holidays seem to suppress removal numbers, at least on the day of the holiday itself. The only holiday that appears above average is Columbus Day; but, with year-to-year variations, it — along with President’s Day and New Year’s Eve — is not statistically different than a normal non-holiday. There is no holiday where more kids are removed than average (though there are periods of the year when removals are up, discussed below).

So what about the theory that more kids are removed around the holidays? If you thought (like I did) that there would be a giant spike in removals before or after, say, Christmas — well, there isn’t. In fact, removals bump up slightly and then start dropping off around the week before Christmas (a.k.a. now). The slight bump isn’t enough to call a correction. A glance at DCF’s dashboard on investigations shows that Decembers are usually high points for closing investigations during the year, so these numbers are even more pronounced. Removals don’t pick up again until January when school is back in session.

Here is the year-round chart. This confirms that removals go down in the Summer and rise again in the new school year. If you thought sentimentality was what kept DCF from removing kids on Christmas, think again. You see similar holiday drops on the 4th of July and Veterans Day. The dips would be more pronounced for MLK Day, Labor Day and Memorial Day, except those holidays don’t happen on the same calendar day each year. Kids don’t get removed on holidays because investigators are on vacation.

So that’s the question: if only 15 kids had to be removed on the 4th of July, why did 48 have to be removed on the 7th of July? 

What about the weekends?

On average only 14 kids per day were removed on Saturdays and Sundays. The highest day of the week for removals was Thursday — probably because court is held one day after a removal and nobody wants to go to court on a Saturday, for sure. At 14 removals per day, the weekend was on par with the 4th of July.

And, while we’re at it, what about time of day? Kids get removed during business hours. The later in the day, the higher the number of removals.  Below you can see three distinct peaks in the following hours: 9:00am, 1:00pm, and 5:00pm. Only 416 kids in this dataset were removed in the 6:00am hour. Maybe children are safer before dawn? (Those 5,580 kids removed at midnight include entries without a valid date — i.e., “00:00:00”. Don’t read into that spike.) I’m not including a graph because it’s messy, but if you look at the whole week the highest rates of removals happened during the 5:00pm hour on Thursdays. No surprise.

So removals happen all the time, except holidays, weekends, and usually not outside of business hours. And they especially happen when investigators first get to work, after lunch, and right before they go home for the day. Something feels very wrong about that. It’s as if “risk” is also a product of convenience, which is not how child protection is supposed to work.

If the averages hold, around six (and up to twelve) kids will be removed on Christmas Day this year. Let those removals be necessary and kind.

The First DCA published statistics on its caseloads and decisions. But notably (as appellate judges like to say), the length of time they take to resolve cases was not reported. It motivated me to update the How Long do Appeals Take tableau.

The answer? Probably 120 to 170 days for a Dependency case, 260 to 576 days for a Criminal case, and 345 to 603 days for a Civil case.

You can explore each district and case type at the tableau here.

In July of this year, the Florida foster care system did something unseen since February 2014: it shrunk. For the first time in over 50 months, the year-over-year (YOY) change in out-of-home care numbers went down by 45 children. By August it was down 118, and the reports out this month for October show a contraction of 165.

While a reduction of 165 kids does not seem like much in a system with over 24,000 children in it, the slowing actually started in January 2016, when the system was growing at a staggering YOY rate of 2,540 kids.  Just the month before held a YOY increase of 2,683 — the fastest growth since data is available in 2003.

There’s no official definition of a contraction period, or any way to tell if one is real or a blip. I actually sat on this post for a few months to make sure the trend was stable — we’ve had hurricanes, elections, resignations, and other unusual events recently, so I wanted to let those pass.

In reality, though, changes from positive to negative OOHC growth (expansions to contractions) do not happen quickly and appear largely driven by intentional policies and not outside events. The expansion under Secretary Hadi in 2004-2006 lasted 19 months and ended abruptly with the Secretary’s resignation from office in December 2006. The subsequent contraction during the Butterworth and Sheldon administrations lasted 50 months and never wavered until three months into Secretary Wilkins’ term. That change of direction occurred in March 2011, right in the middle of the public hearings and media frenzy on the Barahona case, though the contraction had been slowing since 2009 and was well on the way to reversing course even without the public outrage to speed it along. (That is, media frenzy tends to reinforce — not set — existing child welfare policy positions.)

Oddly, Secretary Wilkins’ DCF changed its expansionary course by August 2012 and entered a contraction period that continued sharply until the month that he resigned in July 2013. (I’ve never heard a good explanation for that period.) The tide immediately turned back toward expansion, continuing through Interim Secretary Jacobo and halfway through Secretary Carroll’s tenure. Growth peaked in December 2015 and then precipitously fell, flattened, and then fell again. (Note that steady growth is still growth — the chart above shows change. The charts below show the actual counts.)

Even though the system as a whole tends to move in unison, not every geographic area shifts course at the same time. The current contraction has been driven largely by sharp decreases in OOHC in three circuits — 17 (Broward), 11 (Miami), and 18 (Brevard) — which shrunk a total of 670 children over the previous year in October.  The top three growth circuits — 1 (Pensacola), 7 (DeLand), 9 (Orange/Osceola) — only grew by 127 kids in all.

Decreases were clustered largely, but not exclusively, in the southern regions. Here are the changes by county.

The contractions appear driven largely by reductions in removals.  (I’ve chosen to use seasonal trends below to make the changes over time more clear. The actual numbers for removals and discharges have large but regular oscillations month to month due to seasonal effects like summer and national adoption day. The raw numbers are much harder to read.)

You can see the same decreases in removals statewide. Here’s the statewide seasonal trend graph.

Here are the seasonal trend graphs for all circuits. If you notice anything interesting or know why any of these charts look the way they do, let me know.

Circuit 20

Circuit 19

Circuit 18

Circuit 17

Circuit 16

Circuit 15

Circuit 14

Circuit 13

Circuit 12

Circuit 11

Circuit 10

Circuit 9

Circuit 8

Circuit 7

Circuit 6

Circuit 5

Circuit 4

Circuit 3

Circuit 2

Circuit 1

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:

• Racial Disparity by Case Stage
• Racial Disparity by Placement Type

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.

Our office has been handling more appeals lately, and I am learning the rhythm of the process a little better each day. Appeals seem to go like this: (1) you lose or win at trial and feel really emotional about it, (2) you file your appeal or get noticed that someone filed one on you, and (3) you wait until you don’t feel anything at all anymore. Somewhere in there you file a brief. Then you wait some more and file other briefs. Sometimes a court reporter loses your transcripts and tells you your trial never happened. That can rouse some feelings, but they pass. Because mostly you just wait.

And while you’re waiting, everyone is constantly asking you how much longer they’ll have to wait. I haven’t yet mastered delivering earnest but vague statements of reassurance, such as “waiting is good because it means you haven’t lost yet.” I’ve heard that’s what appellate lawyers do. The people waiting don’t think waiting is good, because it means they haven’t won yet either.

I wanted a real answer to the question how much longer? I looked all over the internet. There were reports (cited below) on dependency and TPR apppeals from 2010 and 2015, but no follow-ups or ongoing data on whether those reforms were successful. There were also lengthy reports on trial court clearance statistics. There was nothing (that I could find) on the district courts. So I decided to create something.

But first, an answer to How long do I have to wait on my appeal?

Probably at least 122 days for a dependency or TPR case.

Probably at least 293 days for anything else. 

Probably a little longer if your case is in the Second DCA.

There. Quit asking.

I put it all in a tableau so you can play with it.

The details are really interesting, if you’re into numbers. I put it all into a tableau, a quick version of which should appear here:

A full version with more stats is available here. (You can also use the link if the embedded tableau above didn’t show.) The full version breaks things down by DCA, case type, wins and losses, and originating divisions. I will commit to updating it for a few months to test for stability. I can’t promise after that.

The process – also, why didn’t this already exist?

My plan was basically to dive in, coming up for air every now and then to run the same “florida district court statistics” google search to see if I missed something. If anyone wants to recreate (or check) my work, here’s how it went.

Step 1 to finding an answer was to see what information I even had access to. All of the DCAs report their opinions on their websites. Three of them use a searchable system that creates spreadsheets by month. Two publish weekly text lists that you have to go through on your own.  All of the DCAs use an online docket system that has a very convenient URL interface for going right to the case you want, unless that case is a dependency case.

Step 2 was figuring out how many cases they’re even putting out. My curiosity knows no bounds, but my actual time to spend on this was limited to a week or so.  The answer was about 200 cases per month per DCA. That wasn’t bad.  I planned to do a 10% sample of three months anyway, so 60 cases per DCA felt reasonable.

Step 3 was dealing with the fact that dependency cases are restricted from the public, so they are not available on the online docket system. Instead, I had to look them up on Westlaw and pull out their appellate case numbers and the outcomes. Fortunately, all of the DCAs use a linear case numbering system (for example 15-001 was filed earlier than 15-055 in the year 2015). Once I had case numbers and filing dates of known cases, I could interpolate the dependency filing dates to within a few days. That was good enough for these purposes.

Step 4 was pulling all of the data on 459 cases and punching it into a spreadsheet. I then crunched some probabilities, ran some ANOVAs, generated a few survival reports, and made some tableaus based on what was statistically relevant. Some people have other hobbies, I guess.

What I could tell you about appellate cases would not fill a book

The sample size of three months was enough to get a big picture number, but not enough to do a lot of fine parsing of the data. As I add months in the future, maybe things will stand out. In the meantime, here is what I can say with a reasonable amount of confidence.

More people won than I expected, but still not that many. About 11% of the cases were “wins.” I defined win very broadly to include anything that wasn’t a straight affirmance or dismissal of a petition.

The DCAs were surprisingly similar.  I was concerned that a 10% sample would result in garbage. It didn’t. All of the samples were roughly normal. The 1st, 3rd, 4th, and 5th all had numbers that were statistically indistinguishable. (A bigger dataset may eventually tease them apart, but this one didn’t.) Only the 2nd DCA stood out as statistically higher than the rest. For example, the 2nd DCA processed half of its cases in 282 days (+/- 18), while the statewide average was 208 days (+/- 11).

Below is a survival graph. Imagine the top left corner as the starting line, and each district racing to the bottom. The cumulative survival of 1.0 equals 100% of cases still open (“surviving”), and 0.4 would equal 40% of cases still open. The first to the bottom (measured in days across the bottom) is the fastest. As you can see below, four of the DCAs reach the bottom at about the same time.  The 2nd DCA stands out as statistically different, in large part because it was slower off the line and struggled with its last 20% of cases compared to other districts.

There wasn’t much variation among the types of cases, except for dependency. The average of 208 days also applied to case types, but dependency stood out as significantly faster. It took the DCAs only 121 days (+/- 2) to process half of their dependency cases. Civil and criminal were indistinguishable in this dataset, though more info later may tease them out as well. There weren’t enough probate, worker’s comp, family, or administrative appeals to say much about them individually yet.

You can see below that dependency cases resolved much faster than anything else. Civil, criminal, and family are pretty consistent in the middle. (Civil starts out slower, but eventually catches up to criminal.) The jaggy curves are probate and worker’s comp cases, which only had a few examples of each.

There was no measurable difference between writs and appeals. Again, a larger dataset may tease out a difference, but the line for writs and appeals were indistinguishable in this one.

Dependency “wins” follow the curve, but exaggerate it a little.  Again again, there aren’t that many dependency wins either. But in this dataset at least, they tended to come out faster at first, then move closer to the win curve above after a case has already taken about 150 days. This is a slight exaggeration of the full win curve above, which also flips somewhere around 150 days.

You can’t predict a win based solely on amount of time open.  Again, I want to stress that there are very few wins in general (11%) and they are scattered across the timeline. Knowing that an appeal has been open for 600 days doesn’t tell you much about its eventual outcome because the last 10% of the “loss” line accounts for far more cases than the last 10% of the “win” line.

Even though wins are a little faster or slower as a group, you can only know that after you know the outcome of the case. I ran the numbers — if you only know how many days the appeal took, you can predict a win with 5% accuracy. Adding in the DCA, appeal type, and division only gets you to 11% accuracy. That’s worse than guessing.

The takeaway

The good news is that this data supports a claim that the Court’s previous efforts (below) to speed up dependency appeals actually worked. Only time will tell if that is a stable finding or if I just happened to look at a particularly fast few months.  Stay tuned.

More info on the appellate courts

Recommendations for Dependency and Termination of Parental Rights Appeals (Florida Supreme Court – Commission on District and Trial Court Performance & Accountability – April 2016)

Dependency and Termination of Parental Rights Appeals (Florida Supreme Court – Commission on District and Trial Court Performance & Accountability – June 2014)

A Review of the Florida District Courts of Appeal Boundaries and Workload (OPPAGA – February 2017)

Examining the Work of State Courts: An analysis of 2010 state court caseloads (Court Statistics Project – December 2012)

Clearance Rate Dashboards (flcourts.org – September 2009 to present)