Real-Time Placement Odds Can Smooth the School Choice Process
Some families going through the school placement process overestimate their chances of getting into their top choices, and fail to match at any school as a result. A new study co-authored by Yale SOM’s Seth Zimmerman finds that a software platform that provides warnings about the placement odds at top schools can dramatically reduce non-placements.
Since the early 2000s, many school districts around the world have adopted an approach called deferred acceptance to place K–12 students in their preferred schools. The math behind this system is at once complex and elegant, but for families, it’s meant to be simple: they are instructed to list the schools they like in order of preference.
While deferred acceptance is widely acknowledged to be a major improvement over the methods that preceded it, the system rests on a key assumption: that families know all of the schools available to them and are submitting complete lists of the ones they like. But in practice, many don’t, according to new research from Seth Zimmerman of Yale SOM.
It turns out that many families know relatively little about their choices and list only a handful of schools, even when there are other good options available to them, resulting in easily avoidable non-placements. Very often, the study found, families overestimate their chances of getting into their top choices and don’t think to expend the time and effort it takes to learn about other schools. The result is that the families are disappointed by the result, and the school district fails to fill open seats in desirable schools.
Happily, Zimmerman and his co-authors—Felipe Arteaga of the University of California, Berkeley, and Adam J. Kapor and Christopher A. Neilson of Princeton University—identified a solution: a smart matching platform that delivers personalized warnings to applicants at high risk of non-placement. About a fifth of applicants who received these warnings added schools to their lists, decreasing their risk of non-placement by 58%.
Simple pop-up messages within the application platform “have profound effects on the applications people submit,” Zimmerman says. “It’s one of the most effective light-touch policy interventions that I’ve ever seen evaluated. They reduce their risks of non-placements by half, and they enroll in better schools.”
Zimmerman and his colleagues conducted their research in two school systems: Chile’s national system and the New Haven school district, which adopted deferred acceptance in 2016 and 2019, respectively. They started in Chile, where policymakers were struggling to understand why students weren’t receiving placements, despite available seats in desirable schools.
The researchers surveyed about 49,000 Chilean families who participated in the choice system to find out. In theory, families should list all schools they want to attend. “The only reason I should stop listing is if there are no other schools available or I don’t think that any other schools are good,” Zimmerman explains. But that wasn’t the case. “What they would often tell you is that the reason they stopped listing schools is because they were hard to learn about, or they just think they’re going to get in to one of the schools that’s already on their list.”
Families were wildly overconfident about their chances about getting into many of those schools, the survey data showed. Applicants whose chances of placement were close to zero estimated their placement odds at 70%. Since it’s hard for families to learn about new schools and many overestimate their chances of placement, many applicants stopped searching too soon and listed too few schools on their applications—resulting in non-placements that could likely have been avoided.
To the researchers, these survey findings suggested that what applicants most needed was a reality check about their chances. If they knew they might not get into any schools on their list, it would encourage them to take time to investigate other schools that might be a good fit.
To deliver these warnings, the researchers tested a new tool that used back-end data about all the applications in the system to deliver front-end warnings to users at high-risk of non-placement—pop-up messages or emails that said, “We have detected that many families are applying to the same schools that you are. Those schools do not have enough seats for everyone. We recommend you add more schools to increase your chance of being placed.” These warnings triggered 21.6% of applicants to add schools to their list.
Personalizing the warnings was important, the researchers discovered. Telling people to add schools to their applications without explaining why failed to change behavior, as did general and non-personalized warnings about the overall possibility of not being placed.
However, creating a system technically sophisticated enough to deliver such personalized warnings is beyond the reach of many school districts. To help bridge the gap, one of the researchers founded a nonprofit called ConsiliumBots that developed the smart matching application platform and is now implementing it in other school systems; Zimmerman serves as one of the organization’s academic advisers.
The benefits of the smart matching platform and its personalized warnings weren’t limited to Chile. When the researchers tested the system in New Haven, they found similarly significant benefits: 13.8% of applicants with risky applications added more schools to their lists, reducing their non-placement chances by 42%.
To Zimmerman, the research suggests that school districts can significantly reduce non-placement by implementing a smart matching platform. “I think every district with centralized school choice should probably be using something like this,” he says.
And for families struggling to navigate school choice, well, Zimmerman can relate—his own child is a student in New Haven public schools, so he’s experienced the uncertainty and anxiety of the application process firsthand. He has simple advice—and some words of comfort: “List the schools you like in the order you like them, and don’t feel bad if you’re confused.”