Today’s UE systems use placement algorithms grounded in extensive research on market design. These systems are designed in ways that can make the application process relatively easy for families and result in an efficient matching of students to schools. However, public misunderstandings and distrust can prevent people from interacting with UE systems in the ways that their designers intended.
Research on UE systems nationwide finds little overall change in the degree of racial and socioeconomic segregation when cities adopt UE (relative to changes in cities that never implement UE). However, since UE systems remain relatively young, the long-term effects are uncertain. Additionally, nationwide studies can mask important, city-specific effects since UE systems differ from one another in their designs and contexts.
Policymakers must decide on issues such as what type of placement algorithm to use, which students should receive priority in the placement process, whether to use a single round or multiple rounds of placement, and how the post-match enrollment process works for families seeking a new or different school. Research indicates that decisions on these issues can matter. For example, geographic priorities can make popular schools less accessible to marginalized groups.
Much of the existing research on UE systems uses families’ ranked school requests to draw inferences about which school characteristics families value. This research generally finds that families prefer schools that are closer to home and that are “higher-quality” schools, although the findings related to quality are nuanced. Studies have also indicated preferences for certain student demographic profiles and school programs. System leaders can use this demand information to make decisions about school openings, closings, and other aspects of “portfolio management” . However, both system leaders and researchers should be aware of the limitations of these studies. In particular, some families might not request certain types of schools—such as academically high-performing schools—because they confront barriers that keep them out.
Unified (or common) enrollment is a centralized approach to managing the student application and enrollment processes in settings with school choice options. In recent years, many cities and districts with choice programs—such as charter schools and intra-district choice plans—have created unified enrollment (UE) systems. These systems differ from one another but share two core features in common. First, UE systems centralize the application process. That is, families can apply to multiple schools with a single application, typically by ranking their preferred choices. Second, UE systems centralize the placement process. They use a placement algorithm to assign students to schools based on applicant requests, seat availability, and school priorities (such as sibling or geographic priority). Proponents of UE systems believe that they can improve the efficiency, fairness, and transparency of highly decentralized, choice-heavy systems. To understand how those benefits arise—and what might prevent them from arising—it helps to understand the challenges faced by decentralized systems where each school runs its own application and enrollment process.
A wave of school choice reforms in the early 2000s introduced new complexities to the school enrollment process. Typically, when students attend locally assigned public schools, the enrollment process is straightforward. It might require families to submit routine paperwork, such as residency and immunization forms, but it does not require them to submit applications or fulfill admissions requirements. Additionally, there is no risk that their children will be denied a seat because the school is full. School choice programs such as charter schools and interdistrict choice processes are different. They generally require families to apply to schools, and they need a mechanism for handling scenarios where the number of applicants exceeds the number of seats available.
By the beginning of the 2010s, studies of charter school “market share” were showing that strikingly large proportions of students in U.S. cities were enrolling in charter schools charter schools1 Moreover, families in many of these cities could choose from magnet schools, public schools outside of their school boundary, and/or voucher-funded private schools. This situation amounted to highly decentralized choice settings that presented enrollment-related challenges to families, school leaders, and system leaders alike. Families needed to collect information, submit an application to each school individually (in accordance with school-specific requirements and deadlines), and complete the enrollment process. School leaders had to manage their own application and selection processes (e.g., using random lotteries) and then plan for the coming school year without knowing how many of the students to whom they offered admission would ultimately enroll. System leaders had no way to efficiently match students to schools and had limited visibility into schools’ enrollment practices.
These decentralized enrollment settings are vulnerable to certain inefficiencies, inequities, and problematic behaviors. For example, parents might need to travel around their city during school hours to submit applications in person. Doing so can be especially challenging for those with inflexible work hours or difficulty in accessing transportation. School leaders have to make staffing decisions amid considerable uncertainty about their upcoming enrollment and funding levels. In fact, families might have incentives to collect multiple offers, and the system has no centralized mechanism to prevent this behavior or sort through families’ requests to make placements in a strategic way.
UE systems present possible solutions to these problems. They can reduce the burdens involved in applying to schools, eliminate the need for each school to manage its own enrollment process, and use algorithms designed to make school placements that efficiently match students to schools. In doing so, they have the potential to mitigate inequities in school choice, improve transparency, and help more families enroll in schools that serve their children well. However, research shows that simply installing a UE system is not enough to ensure that the system fulfills this potential. How—and for whom—a system works can fundamentally depend on policymakers’ actions and decisions.
Understudied issues
Today’s UE systems differ from one another in meaningful ways. Such differences include whether the systems are sector-specific or cross-sector systems (e.g., include both district and charter schools) and whether they operate in places with major transportation or information barriers. These differences complicate our ability to generalize findings across locales. Policymakers would benefit from having more rigorous, city-specific research that can speak to the outcomes and issues specific to their places of work.
Policy considerations
Policymakers should be clear about the goals that they have for their school choice enrollment process. If the goals are to produce large, short-term changes in enrollment patterns or student outcomes, then the literature suggests that transitioning to UE may be insufficient to achieve such goals on their own (without accompanying policies). If the goals are to make the school choice process easier for families and more efficient in matching students to schools, then adopting a UE system could be a promising approach. Regardless, policymakers should pay attention to the details of system design and the context in which these systems operate. They should also be aware that the literature on UE systems is modest but growing and that the effects of UE in other contexts might not generalize well to their own context.
Key finding #1: Research shows that UE systems can make the application process easier for families, reduce the amount of burden and uncertainty for school leaders, and match students to schools more efficiently. However, it also shows that families need support in navigating UE systems.
UE systems have two core components: a single, centralized application that families can use to apply to multiple schools and a mechanism for making school placements.
Some UE systems began with a common application and then incorporated a placement algorithm. The use of a single application offers benefits relative to school-specific applications. First, it reduces the burden and stress involved in applying to many schools individually. Doing so proved especially burdensome in places where schools used many different deadlines and application processes, including requirements to apply in person. Second, a common application might help families learn about schools that they otherwise would not have considered—and stir governments and nonprofits to provide more standardized information about schools’ offerings and performance.2 These benefits could be particularly important for helping certain families overcome barriers to school access. Such families include those who lack the job flexibility or transportation access to drop off applications during school hours. However, completing a common application can present challenges of its own, with some having equity implications. For example, many UE systems have early deadlines for their main round applications (typically, 6–12 months before the school year begins). Missing that one application deadline could severely reduce a family’s chances of obtaining a seat in a popular school.3 Nonetheless, most research emphasizes the benefits of moving to a single application for reducing administrative burden, especially for marginalized populations.
The second component of UE systems is the mechanism (algorithm) for placing students in schools. Algorithms make placements based on applicants’ ranked requests, the seat availability in schools, system priorities (e.g., geographic or sibling priority), and/or lottery numbers. Today’s placement algorithms are grounded in a rich literature on market design, including pioneering work by Alvin Roth and Lloyd Shapley that led to a Nobel Prize in Economic Sciences. This economics literature provides the theories and assumptions underlying school choice mechanisms. It also contains analyses and debates about the relative merits of different types of mechanisms, including deferred-acceptance (DA) algorithms and top-trading cycles (TTC).4 Many of today’s systems use DA algorithms with the key property of being “strategy proof.” That is, an applicant’s best strategy, selfishly, is to rank schools in the applicant’s true order of preference. The applicant cannot game the algorithm with an alternate strategy (e.g., ranking a less preferred but less popular school first). Those interested in reading more about how these algorithms work can find both detailed summaries of the theories underlying these algorithms5 and illustrations that translate the algorithms into layperson’s language.6
One author reviewed the empirical research on school choice mechanisms. He observed that the most important determinants of how a UE system operates are not necessarily the details of algorithm design (e.g., DA or TTC) but, rather, “basic issues” of policy design.7 Such issues include whether users understand how the system works and how placements are made after the initial algorithm-based matching process is complete.
There is evidence of challenges along these lines. For example, a series of interviews with New Orleans parents revealed several common myths about how the placement algorithm works.8 If applicants make decisions based on misguided beliefs about their optimal strategy, then they could undermine their chances of obtaining a desired placement.9 Other research indicates that applicants are not ranking as many schools as they should in the main round, leaving themselves vulnerable to not receiving a placement when the most options are available.10
Interestingly, misconceptions have arisen even though the optimal strategy seems relatively easy to explain: rank schools in your true order of preference (until you run out of rows in the application or schools that you would consider). It is possible that these instructions are not being understood clearly enough. It is also possible that, for some applicants, distrust, not misinformation, is the primary obstacle. .11
In summary, research on centralized enrollment shows that UE systems have considerable potential to improve the school choice enrollment process relative to highly decentralized settings. It also shows that simply installing a UE system—without accompanying supports—is not enough to fulfill that potential.. Today’s UE systems are built on theoretically elegant algorithms. However, they operate in complicated social contexts with imperfect users, and the interactions between users and systems define how these systems operate.
Key finding #2: Overall, moving from decentralized enrollment to UE seems to have only modest effects on student enrollment patterns and outcomes, at least in the short run. However, the evidence on this matter is limited, and the effects can vary across contexts and time horizons.
Compared to the vast literature on school choice mechanisms, few studies have assessed how the implementation of a UE system affects student enrollment patterns or outcomes. One contributing factor is that it is empirically challenging to identify the effects of moving to UE. This is especially true for multicity, nationwide studies. Consider some of the challenges involved in estimating causal effects.
Cities tend to implement UE systems over a multiyear period. Rather than flipping an “on” switch that moves all schools (and seats) from decentralized enrollment to UE, a city might start with a common application and later move to a placement algorithm. Additionally, more schools, sectors, or grade levels might join (or exit) the UE system over time. This possibility makes it difficult to distinguish a clear, “untreated” pre-UE period from a clear, “treated” UE period.
Most UE systems include only a subset of the sectors, schools, or grade levels that they could potentially include.12 This raises questions about how much impact a UE system could realistically have on citywide enrollment patterns or outcomes.
The types of cities that adopt a UE system might simultaneously adopt other policies—or have other conditions—that independently affect student enrollment patterns or outcomes. For example, a city that moves to UE in hopes of facilitating more choice might also invest in its student transportation system. If researchers find that students are enrolling in schools farther from home, then should that finding be attributed to the new UE system or to the improved transportation offerings? These types of issues present challenges to researchers using difference-in-differences and interrupted time series designs.
UE systems differ from one another in key respects. Such differences include which schools participate and which students receive the highest-priority access to oversubscribed schools. Even with results from a high-quality, nationwide study, it might be hard to assess how those findings generalize to a particular setting.
One study examined the country’s 100 largest school districts and identified which of those districts used a centralized school lottery (roughly half of them, with more at the high school level than at the elementary school level).13 It measured the racial, ethnic, and socioeconomic segregation of those districts in 2003—before any of them had adopted a centralized lottery—and again in 2018 once those lotteries were in place. Using a difference-in-differences strategy, the study determined that centralized lotteries probably had a null net effect on segregation. It found suggestive evidence that centralized systems might have played “a role in diminishing white flight from public school systems.” However, the authors of the study were careful to note the crudeness of their approach since cities adopted UE systems at various points over this long period of time (and other factors might have affected segregation).
Other researchers have examined city-specific enrollment trends, and their findings have been mixed. Research from Chicago indicated that the move to the GoCPS platform reduced problems associated with students receiving multiple offers but did not have strong effects on the share of students enrolling in high-performing high schools.14 In New Orleans, where individual schools entered UE in different years, schools that previously enrolled a highly disproportionate number of white students appeared to enroll more nonwhite students after they entered OneApp (due to overall enrollment increases, not a corresponding decline in white enrollment).15 The New Orleans study did not find much evidence of effects on student or school outcomes. In Washington, DC, researchers found little overall change in desegregation trends after MySchoolDC adoption, but they found evidence of increasing segregation in high- and low-income areas.16
The literature on UE systems would benefit from having more research that examines the effects of UE adoption, as well as more guidance regarding how policymakers might think about the generalizability of these studies to their local contexts.
Key finding #3: UE systems present policymakers with many decisions about system design. These decisions have implications for efficiency, equity, and transparency.
UE systems require policymakers to make an assortment of decisions about how to design these systems and their surrounding infrastructure (e.g., information and transportation offerings). Researchers have examined the implications of some of these decisions. On other matters, the evidence is less clear.
As described under key finding #1, many academic articles in economics explore the relative benefits and drawbacks of different school choice mechanisms (e.g., DA algorithms, TTC mechanisms, and “the Boston mechanism”).17 Some studies have examined the placement priorities embedded in these algorithms that determine which students are offered seats when the number of requests exceeds the number of seats available. Not only which priorities are used but also how those priorities are implemented is important.18 For example, priorities tend to be hierarchical, where one priority—or one combination of priorities—has a larger impact on placements than does another. A low-ranking priority might not have much influence on placements. Similarly, whether a priority applies to all seats or only a subset of them (a “partial” priority) can influence how much impact that priority ultimately has on placement patterns.
A study of school choice placements in New Orleans found that when Black and white applicants submitted the same first-choice request for kindergarten, white applicants were more likely to obtain a placement.19 This occurred without there being any racial priorities in the placement algorithm. Rather, white applicants benefitted from being more likely to have geographic priority at oversubscribed schools. In New York City, researchers have described barriers to access related to school admissions priorities and requirements (e.g., requirements to attend open houses).20 One study simulated the potential effects of removing academic and other criteria in New York. It showed that doing so could produce sizable declines in middle school segregation.21
Another set of questions for policymakers relates to which schools should participate—and how much say those schools should have over how they participate. Allowing individual school leaders to determine whether to participate may lead to a situation in which the most popular (and perhaps highest-performing) schools have incentives to stay out.22 Allowing individual school leaders to have a say in defining their own priorities, as some systems do, could hinder efforts to make high-performing schools accessible to marginalized groups.23
Other decisions relate to structural issues, such as how many schools families should be able to rank and how many rounds of selection will occur. Today’s UE systems differ in these characteristics. Theoretically, there are benefits to allowing families to request more schools, including for avoiding situations where applicants could request the maximum number of schools allowed and still not receive a placement in any school. In practice, the impacts depend on how families respond to having the option to request more schools. For example, applicants might not make use of additional options because they struggle to make distinctions after their top choices. Furthermore, while this issue has not been studied carefully in the school choice context, some applicants might become overwhelmed by the invitation to request a large number of options—and respond by requesting fewer of them. Regarding the number of rounds, one study found that a second round allowed applicants who were dissatisfied with their initial placement to find seats in schools that are more similar, observably, to the school that they had ranked first.24 However, policymakers must grapple with the implications of multiple rounds for their school choice calendars.25 Early deadlines can create their own challenges, with potential implications for equitable access.
This list of the decisions that policymakers confront is far from complete. For example, policymakers must decide what type of information to present and how to structure it.26 In doing so, they might, intentionally or not, nudge families toward certain schools (e.g., highly rated schools). Policymakers must also consider what additional supports families might need, which includes the possibility of specialized supports for groups with specialized needs, such as immigrant families and families of students with disabilities.
Key finding #4: UE systems offer opportunities to learn about families’ preferences for and experiences with schools—and opportunities to use what is learned to inform decision-making.
UE systems generate troves of data about families’ desires for schools. Such data come from the ranked lists of school requests that applicants submit. This type of information does not exist in traditional district settings. It also does not exist in highly decentralized school choice environments, where rankings are not made explicit and collecting application data is more challenging.
This presents opportunities for system leaders as well as researchers. System leaders can learn about the specific schools that are popular (or unpopular) with families as well as the school characteristics associated with their popularity. They can also work with researchers to learn from lottery-based evaluations of school or program performance.27
Early research on UE systems considered the potential benefits from system leaders’ use of these data to inform decision-making on matters such as what types of schools to open and where.28 Since then, however, there has been little exploration of how system leaders are using these data—or how they could use the data more effectively. The field would benefit from more qualitative research on these issues.
Several studies have examined applicants’ rank-ordered school requests to draw inferences about families’ preferences.29 These studies use various regression-based approaches to identify which school characteristics are associated with schools being ranked 1) on more people’s applications and 2) higher on those applications. Perhaps the most consistent takeaway from these studies is that families prefer schools that are closer to home. Some researchers also find preferences for certain demographic profiles or school programs. Most find evidence that applicants value school quality, although these findings are nuanced. For example, research from New York City suggests that parents value having higher-achieving peers but not having stronger value-added school performance (conditional on peer achievement).30 However, a randomized experiment in New Orleans found that families incorporated data on value-added performance when such data were sent directly to them.31 This study found especially strong effects for families of students with disabilities. Some studies find evidence suggesting that different subgroups value different indicators of school quality.
A study of Denver school requests found that parents of different racial and ethnic groups share a desire for high-performing schools.32 However, the authors of the study noted that school requests differ markedly across groups, largely due to differences in these groups’ proximity to high-performing schools. A study in Chicago made similar observations about the unequal barriers that different groups encounter in the choice system.33
These differences underscore an important limitation in the use of applicants’ school requests to make inferences about their preferences. Applicants may not request schools that they would, in fact, want for their children if they encounter barriers along the way. For example, if a parent lacks reliable transportation and the highest-performing schools are hard to reach, that parent may not request those high-performing schools. This could be the case even if school performance is the parent’s top criterion when evaluating schools. These different reasons for requesting schools can be difficult or impossible to tease apart in data, and they have implications for how we should interpret group differences in school requests.
National Alliance for Public Charter Schools. 2011. A Growing Movement: America's Largest Charter School Communities. Sixth Annual Edition.↩︎
Gross, Betheny, Michael DeArmond, and Patrick Denice. 2015. Common Enrollment, Parents, and School Choice: Early Evidence from Denver and New Orleans (Making School Choice Work Series). Center on Reinventing Public Education.↩︎
Jochim, Ashley, Michael DeArmond, Betheny Gross, and Robin Lake. 2014. How Parents Experience Public School Choice. Center for Reinventing Public Education.↩︎
Abdulkadiroğlu, Atila, and Tayfun Sönmez. 2003. School choice: A Mechanism Design Approach. American Economic Review 93(3): 729–747; Abdulkadiroğlu, Atila, and Tayfun Sönmez. 2013. Matching Markets: Theory and Practice. Advances in Economics and Econometrics 1: 3–47.↩︎
Pathak, Parag A. 2011. The Mechanism Design Approach to Student Assignment. Annu. Rev. Econ. 3(1): 513–536; Abdulkadiroğlu A., and T. Andersson. 2022. School Choice (NBER Working Paper No. 29822). National Bureau of Economic Research.↩︎
Benner, Meg, and Ulrich Boser. 2018. Expanding Access to High Quality Schools: Implementing School Choice Algorithms. Center for American Progress; Valant, Jon. 2023. How Does the New Orleans School Placement Algorithm Work, and What Is the Best Strategy for Getting a Preferred Placement? Education Research Alliance for New Orleans.↩︎
Pathak, Parag A. 2017. What Really Matters in Designing School Choice Mechanisms. Advances in Economics and Econometrics 1(12): 176–214.↩︎
Weixler, Lindsay, Olivia Carr, Alica Gerry, Monica Daniels, and Cathy Balfe. 2023. How New Orleans Families Navigate Early Childhood and Kindergarten School Choice. Brief 6: How Well Do Parents Understand the Enrollment Process? New Orleans Collaborative for Early Childhood Research.↩︎
Kapor, Adam J., Christopher A. Neilson, and Seth D. Zimmerman. 2020. Heterogeneous Beliefs and School Choice Mechanisms. American Economic Review 110(5): 1274–1315; Valent (2023).↩︎
Harris, Douglas N., Jon Valant, and Betheny Gross. 2015. The New Orleans One App. Education Next 15(4).↩︎
Weixler et al. (2023); Akchurin, Maria, and Gabriel Chouhy. 2024. Designing Better Access to Education? Unified Enrollment, School Choice, and the Limits of Algorithmic Fairness in New Orleans School Admissions. Qualitative Sociology 1–43.↩︎
Additionally, in some systems, such as that in Washington, DC, public schools participate in UE but offer only a small number of seats to families who live out of the school’s residential boundary.↩︎
Monarrez, Tomas, and Carina Chien. 2021. Dividing Lines: Racially Unequal School Boundaries in US Public School Systems (Research Report). Urban Institute.↩︎
Sartain, Lauren, Riley Lewers, and Lisa Barrow. 2023. Assigning Students to Schools in an Era of Public School Choice: Patterns in Enrollment, Applications, and Offers in Chicago. Educational Evaluation and Policy Analysis 01623737231206777.↩︎
Lincove, Jane Arnold, and Jon Valant. 2024. The Effects of Unified School Enrollment Systems on School Demographics and Outcomes: Evidence from New Orleans’ Transition to a Centralized School Lottery. American Educational Research Journal.↩︎
Saltmarsh, Jason, and Francisco Lagos. 2024. Segregation and School Composition in Washington, DC Following the Implementation of a Unified School Lottery. Policy Studies 1–27.↩︎
Pais, Joana, and Ágnes Pintér. 2008 School Choice and Information: An Experimental Study on Matching Mechanisms." Games and Economic Behavior 64(1): 303–328; Pathak (2011); Abdulkadiroğlu and Andersson (2022).↩︎
Dur, Umut, Scott Duke Kominers, Parag A. Pathak, and Tayfun Sönmez. Reserve Design: Unintended Consequences and the Demise of Boston’s Walk Zones. 2018. Journal of Political Economy 126(6): 2457–2479.↩︎
Valant, Jon, and Brigham Walker. 2023. Setting Priorities in School Choice Enrollment Systems: Who Benefits from Placement Algorithm Preferences? (Technical Report). National Center for Research on Education Access and Choice.↩︎
Sattin-Bajaj, Carolyn, and Allison Roda. 2020. Opportunity Hoarding in School Choice Contexts: The Role of Policy Design in Promoting Middle-Class Parents’ Exclusionary Behaviors. Educational Policy 34(7): 992–1035.↩︎
Idoux, Clemence. 2022. Integrating New York City Schools: The Role of Admission Criteria and Family Preferences. MIT Department of Economics.↩︎
Ekmekci, Mehmet, and M. Bumin Yenmez. 2019. Common Enrollment in School Choice. Theoretical Economics 14(4): 1237–1270.↩︎
Saltmarsh Lagos (2024).↩︎
Lincove, Jane Arnold, Jon Valant, and Joshua M. Cowen. 2018. You Can't Always Get What You Want: Capacity Constraints in a Choice-based School System. Economics of Education Review 67: 94–109.↩︎
Gross, DeArmond, and Denice (2015).↩︎
Corcoran, Sean P., and Jennifer L. Jennings. 2019. Information and School Choice. In Handbook of Research on School Choice. Routledge. 365–378.↩︎
Abdulkadiroğlu, Atila, Joshua D. Angrist, Yusuke Narita, and Parag A. Pathak. 2017. Research Design Meets Market Design: Using Centralized Assignment for Impact Evaluation. Econometrica 85(5): 1373–1432; Angrist, Joshua, Peter Hull, Parag A. Pathak, and Christopher Walters. 2024. Credible School Value-Added with Undersubscribed School Lotteries. Review of Economics and Statistics 106(1): 1–19; Weiland, Christina, Rebecca Unterman, Susan Dynarski, Rachel Abenavoli, Howard Bloom, Breno Braga, Anne-Marie Faria et al. 2024. Lottery-Based Evaluations of Early Education Programs: Opportunities and Challenges for Building the Next Generation of Evidence. AERA Open 10.↩︎
Gross, DeArmond, and Denice (2015).↩︎
Denice, Patrick, and Betheny Gross. 2016. Choice, Preferences, and Constraints: Evidence from Public School Applications in Denver. Sociology of Education 89(4): 300–320; Glazerman, Steven, and Dallas Dotter. 2017. Market Signals: Evidence on the Determinants and Consequences of School Choice from a Citywide Lottery. Educational Evaluation and Policy Analysis 39(4): 593–619; Lincove, Jane A., Joshua M. Cowen, and Jason P. Imbrogno. 2018. What's in Your Portfolio? How Parents Rank Traditional Public, Private, and Charter Schools in Post-Katrina New Orleans’ Citywide System of School Choice. Education Finance and Policy 13(2): 194–226; Abdulkadiroğlu, Atila, Parag A. Pathak, Jonathan Schellenberg, and Christopher R. Walters. 2020. Do Parents Value School Effectiveness? American Economic Review 110(5): 1502–1539; Harris, Douglas N., and Matthew F. Larsen. 2023. What Schools Do Families Want (and Why)? Evidence on Revealed Preferences from New Orleans. Educational Evaluation and Policy Analysis 45(3): 496–519.↩︎
Abdulkadiroğlu et al. (2020).↩︎
Valant, Jon, and Lindsay H. Weixler. 2022. Informing School-Choosing Families about Their Options: A Field Experiment from New Orleans. Educational Evaluation and Policy Analysis 44(4): 608–637.↩︎
Denice and Gross (2016).↩︎
Sartain, Lauren, and Lisa Barrow. 2022. The Pathway to Enrolling in a High-Performance High School: Understanding Barriers to Access. Education Finance and Policy 17(3): 379–407.↩︎
Valant, Jon (2025). "Enrollment Systems," in Live Handbook of Education Policy Research, in Douglas Harris (ed.), Association for Education Finance and Policy, viewed 04/12/2025, https://livehandbook.org/k-12-education/aspects-of-choice/enrollment-systems/.